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Sample records for optimize hospital schedules

  1. Optimization Model for Capacity Management and Bed Scheduling for Hospital

    Sitepu, Suryati; Mawengkang, Herman; Husein, Ismail

    2018-01-01

    Hospital is a very important institution to provide health care for people. It is not surprising that nowadays the people’s demands for hospital is increasing.. However, due to the rising cost of healthcare services, hospitals need to consider efficiencies in order to overcome these two problems. This paper deals with an integrated strategy of staff capacity management and bed allocation planning to tackle these problems. Mathematically, the strategy can be modeled as an integer linear programming problem. We solve the model using a direct neighborhood search approach, based on the notion of superbasic variables.

  2. Anesthesiology Nurse Scheduling using Particle Swarm Optimization

    Leopoldo Altamirano

    2012-02-01

    Full Text Available In this article we present an approach designed to solve a real world problem: the Anesthesiology Nurse Scheduling Problem (ANSP at a public French hospital. The anesthesiology nurses are one of the most shared resources in the hospital and we attempt to find a fair/balanced schedule for them, taking into account a set of constraints and the nursesarsquo; stated preferences, concerning the different shifts. We propose a particle swarm optimization algorithm to solve the ANSP. Finally, we compare our technique with previous results obtained using integer programming.

  3. Optimizing Unmanned Aircraft System Scheduling

    2008-06-01

    ASC-U uses a deterministic algorithm to optimize over a given finite time horizon to obtain near-optimal UAS mission area assignments. ASC-U...the details of the algorithm . We set an upper bound on the total number of schedules that can be generated, so as not to create unsolvable ILPs. We...COL_MISSION_NAME)) If Trim( CStr (rMissions(iRow, COL_MISSION_REQUIRED))) <> "" Then If CLng(rMissions(iRow, COL_MISSION_REQUIRED)) > CLng

  4. Developing optimal nurses work schedule using integer programming

    Shahidin, Ainon Mardhiyah; Said, Mohd Syazwan Md; Said, Noor Hizwan Mohamad; Sazali, Noor Izatie Amaliena

    2017-08-01

    Time management is the art of arranging, organizing and scheduling one's time for the purpose of generating more effective work and productivity. Scheduling is the process of deciding how to commit resources between varieties of possible tasks. Thus, it is crucial for every organization to have a good work schedule for their staffs. The job of Ward nurses at hospitals runs for 24 hours every day. Therefore, nurses will be working using shift scheduling. This study is aimed to solve the nurse scheduling problem at an emergency ward of a private hospital. A 7-day work schedule for 7 consecutive weeks satisfying all the constraints set by the hospital will be developed using Integer Programming. The work schedule for the nurses obtained gives an optimal solution where all the constraints are being satisfied successfully.

  5. Schedule optimization study implementation plan

    1993-11-01

    This Implementation Plan is intended to provide a basis for improvements in the conduct of the Environmental Restoration (ER) Program at Hanford. The Plan is based on the findings of the Schedule Optimization Study (SOS) team which was convened for two weeks in September 1992 at the request of the U.S. Department of Energy (DOE) Richland Operations Office (RL). The need for the study arose out of a schedule dispute regarding the submission of the 1100-EM-1 Operable Unit (OU) Remedial Investigation/Feasibility Study (RI/FS) Work Plan. The SOS team was comprised of independent professionals from other federal agencies and the private sector experienced in environmental restoration within the federal system. The objective of the team was to examine reasons for the lengthy RI/FS process and recommend ways to expedite it. The SOS team issued their Final Report in December 1992. The report found the most serious impediments to cleanup relate to a series of management and policy issues which are within the control of the three parties managing and monitoring Hanford -- the DOE, U.S. Environmental Protection Agency (EPA), and the State of Washington Department of Ecology (Ecology). The SOS Report identified the following eight cross-cutting issues as the root of major impediments to the Hanford Site cleanup. Each of these eight issues is quoted from the SOS Report followed by a brief, general description of the proposed approach being developed

  6. Scheduling nurses’ shifts at PGI Cikini Hospital

    Nainggolan, J. C. T.; Kusumastuti, R. D.

    2018-03-01

    Hospitals play an essential role in the community by providing medical services to the public. In order to provide high quality medical services, hospitals must manage their resources (including nurses) effectively and efficiently. Scheduling of nurses’ work shifts, in particular, is crucial, and must be conducted carefully to ensure availability and fairness. This research discusses the job scheduling system for nurses in PGI Cikini Hospital, Jakarta with Goal Programming approach. The research objectives are to identify nurse scheduling criteria and find the best schedule that can meet the criteria. The model has hospital regulations (including government regulations) as hard constraints, and nurses’ preferences as soft constraints. We gather primary data (hospital regulations and nurses’ preferences) through interviews with three Head Nurses and distributing questionnaires to fifty nurses. The results show that on the best schedule, all hard constraints can be satisfied. However, only two out of four soft constraints are satisfied. Compared to current scheduling practice, the resulting schedule ensures the availability of nurses as it satisfies all hospital’s regulations and it has a higher level of fairness as it can accommodate some of the nurses’ preferences.

  7. Cloud Service Scheduling Algorithm Research and Optimization

    Hongyan Cui

    2017-01-01

    Full Text Available We propose a cloud service scheduling model that is referred to as the Task Scheduling System (TSS. In the user module, the process time of each task is in accordance with a general distribution. In the task scheduling module, we take a weighted sum of makespan and flowtime as the objective function and use an Ant Colony Optimization (ACO and a Genetic Algorithm (GA to solve the problem of cloud task scheduling. Simulation results show that the convergence speed and output performance of our Genetic Algorithm-Chaos Ant Colony Optimization (GA-CACO are optimal.

  8. Optimization of Hierarchically Scheduled Heterogeneous Embedded Systems

    Pop, Traian; Pop, Paul; Eles, Petru

    2005-01-01

    We present an approach to the analysis and optimization of heterogeneous distributed embedded systems. The systems are heterogeneous not only in terms of hardware components, but also in terms of communication protocols and scheduling policies. When several scheduling policies share a resource......, they are organized in a hierarchy. In this paper, we address design problems that are characteristic to such hierarchically scheduled systems: assignment of scheduling policies to tasks, mapping of tasks to hardware components, and the scheduling of the activities. We present algorithms for solving these problems....... Our heuristics are able to find schedulable implementations under limited resources, achieving an efficient utilization of the system. The developed algorithms are evaluated using extensive experiments and a real-life example....

  9. Optimal scheduling of coproduction with a storage

    Ravn, H.F.; Rygard, J.M.

    1993-02-01

    We consider the problem of optimal scheduling of a system with combined heat and heat (CHP) units and a heat storege. The purpose of the heat storage is to permit a partial decoupling of the variations in the demand for heat and electrical power. We formulate the problem of optimal scheduling as that of minimizing the total costs over the planning period. The heat demand from the district heating system and the ''shadow prices'' for the electrical power system are taken as externally given parameters. The resulting model is solved by dynamic programming. We describe implementation details and we give examples of result of the optimization. (au) (12 refs.)

  10. An introduction to optimal satellite range scheduling

    Vázquez Álvarez, Antonio José

    2015-01-01

    The satellite range scheduling (SRS) problem, an important operations research problem in the aerospace industry consisting of allocating tasks among satellites and Earth-bound objects, is examined in this book. SRS principles and solutions are applicable to many areas, including: Satellite communications, where tasks are communication intervals between sets of satellites and ground stations Earth observation, where tasks are observations of spots on the Earth by satellites Sensor scheduling, where tasks are observations of satellites by sensors on the Earth. This self-contained monograph begins with a structured compendium of the problem and moves on to explain the optimal approach to the solution, which includes aspects from graph theory, set theory, game theory and belief networks. This book is accessible to students, professionals and researchers in a variety of fields, including: operations research, optimization, scheduling theory, dynamic programming and game theory. Taking account of the distributed, ...

  11. Bilevel Fuzzy Chance Constrained Hospital Outpatient Appointment Scheduling Model

    Xiaoyang Zhou

    2016-01-01

    Full Text Available Hospital outpatient departments operate by selling fixed period appointments for different treatments. The challenge being faced is to improve profit by determining the mix of full time and part time doctors and allocating appointments (which involves scheduling a combination of doctors, patients, and treatments to a time period in a department optimally. In this paper, a bilevel fuzzy chance constrained model is developed to solve the hospital outpatient appointment scheduling problem based on revenue management. In the model, the hospital, the leader in the hierarchy, decides the mix of the hired full time and part time doctors to maximize the total profit; each department, the follower in the hierarchy, makes the decision of the appointment scheduling to maximize its own profit while simultaneously minimizing surplus capacity. Doctor wage and demand are considered as fuzzy variables to better describe the real-life situation. Then we use chance operator to handle the model with fuzzy parameters and equivalently transform the appointment scheduling model into a crisp model. Moreover, interactive algorithm based on satisfaction is employed to convert the bilevel programming into a single level programming, in order to make it solvable. Finally, the numerical experiments were executed to demonstrate the efficiency and effectiveness of the proposed approaches.

  12. Optimal Scheduling of Domestic Appliances via MILP

    Zdenek Bradac

    2014-12-01

    Full Text Available This paper analyzes a consumption scheduling mechanism for domestic appliances within a home area network. The aim of the proposed scheduling is to minimize the total energy price paid by the consumer and to reduce power peaks in order to achieve a balanced daily load schedule. An exact and computationally efficient mixed-integer linear programming (MILP formulation of the problem is presented. This model is verified by several problem instances. Realistic scenarios based on the real price tariffs commercially available in the Czech Republic are calculated. The results obtained by solving the optimization problem are compared with a simulation of the ripple control service currently used by many domestic consumers in the Czech Republic.

  13. Optimal scheduling using priced timed automata

    Behrmann, Gerd; Larsen, Kim Guldstrand; Rasmussen, Jacob Illum

    2005-01-01

    This contribution reports on the considerable effort made recently towards extending and applying well-established timed automata technology to optimal scheduling and planning problems. The effort of the authors in this direction has to a large extent been carried out as part of the European...... projects VHS [20] and AMETIST [16] and are available in the recently released UPPAAL CORA [12], a variant of the real-time verification tool UPPAAL [18, 5] specialized for cost-optimal reachability for the extended model of so-called priced timed automata....

  14. Optimized Treatment Schedules for Chronic Myeloid Leukemia.

    Qie He

    2016-10-01

    Full Text Available Over the past decade, several targeted therapies (e.g. imatinib, dasatinib, nilotinib have been developed to treat Chronic Myeloid Leukemia (CML. Despite an initial response to therapy, drug resistance remains a problem for some CML patients. Recent studies have shown that resistance mutations that preexist treatment can be detected in a substantial number of patients, and that this may be associated with eventual treatment failure. One proposed method to extend treatment efficacy is to use a combination of multiple targeted therapies. However, the design of such combination therapies (timing, sequence, etc. remains an open challenge. In this work we mathematically model the dynamics of CML response to combination therapy and analyze the impact of combination treatment schedules on treatment efficacy in patients with preexisting resistance. We then propose an optimization problem to find the best schedule of multiple therapies based on the evolution of CML according to our ordinary differential equation model. This resulting optimization problem is nontrivial due to the presence of ordinary different equation constraints and integer variables. Our model also incorporates drug toxicity constraints by tracking the dynamics of patient neutrophil counts in response to therapy. We determine optimal combination strategies that maximize time until treatment failure on hypothetical patients, using parameters estimated from clinical data in the literature.

  15. Algorithm comparison for schedule optimization in MR fingerprinting.

    Cohen, Ouri; Rosen, Matthew S

    2017-09-01

    In MR Fingerprinting, the flip angles and repetition times are chosen according to a pseudorandom schedule. In previous work, we have shown that maximizing the discrimination between different tissue types by optimizing the acquisition schedule allows reductions in the number of measurements required. The ideal optimization algorithm for this application remains unknown, however. In this work we examine several different optimization algorithms to determine the one best suited for optimizing MR Fingerprinting acquisition schedules. Copyright © 2017 Elsevier Inc. All rights reserved.

  16. Group Elevator Peak Scheduling Based on Robust Optimization Model

    ZHANG, J.

    2013-08-01

    Full Text Available Scheduling of Elevator Group Control System (EGCS is a typical combinatorial optimization problem. Uncertain group scheduling under peak traffic flows has become a research focus and difficulty recently. RO (Robust Optimization method is a novel and effective way to deal with uncertain scheduling problem. In this paper, a peak scheduling method based on RO model for multi-elevator system is proposed. The method is immune to the uncertainty of peak traffic flows, optimal scheduling is realized without getting exact numbers of each calling floor's waiting passengers. Specifically, energy-saving oriented multi-objective scheduling price is proposed, RO uncertain peak scheduling model is built to minimize the price. Because RO uncertain model could not be solved directly, RO uncertain model is transformed to RO certain model by elevator scheduling robust counterparts. Because solution space of elevator scheduling is enormous, to solve RO certain model in short time, ant colony solving algorithm for elevator scheduling is proposed. Based on the algorithm, optimal scheduling solutions are found quickly, and group elevators are scheduled according to the solutions. Simulation results show the method could improve scheduling performances effectively in peak pattern. Group elevators' efficient operation is realized by the RO scheduling method.

  17. Multi-agent Pareto appointment exchanging in hospital patient scheduling

    I.B. Vermeulen (Ivan); S.M. Bohte (Sander); D.J.A. Somefun (Koye); J.A. La Poutré (Han)

    2007-01-01

    htmlabstractWe present a dynamic and distributed approach to the hospital patient scheduling problem, in which patients can have multiple appointments that have to be scheduled to different resources. To efficiently solve this problem we develop a multi-agent Pareto-improvement appointment

  18. Multi-agent Pareto appointment exchanging in hospital patient scheduling

    Vermeulen, I.B.; Bohté, S.M.; Somefun, D.J.A.; Poutré, La J.A.

    2007-01-01

    We present a dynamic and distributed approach to the hospital patient scheduling problem, in which patients can have multiple appointments that have to be scheduled to different resources. To efficiently solve this problem we develop a multi-agent Pareto-improvement appointment exchanging algorithm:

  19. Parametric Optimization of Hospital Design

    Holst, Malene Kirstine; Kirkegaard, Poul Henning; Christoffersen, L.D.

    2013-01-01

    Present paper presents a parametric performancebased design model for optimizing hospital design. The design model operates with geometric input parameters defining the functional requirements of the hospital and input parameters in terms of performance objectives defining the design requirements...... and preferences of the hospital with respect to performances. The design model takes point of departure in the hospital functionalities as a set of defined parameters and rules describing the design requirements and preferences....

  20. Microcomputer-based workforce scheduling for hospital porters.

    Lin, C K

    1999-01-01

    This paper focuses on labour scheduling for hospital porters who are the major workforce providing routine cleansing of wards, transportation and messenger services. Generating an equitable monthly roster for porters while meeting the daily minimum demand is a tedious task scheduled manually by a supervisor. In considering a variety of constraints and goals, a manual schedule was usually produced in seven to ten days. To be in line with the strategic goal of scientific management of an acute care regional hospital in Hong Kong, a microcomputer-based algorithm was developed to schedule the monthly roster. The algorithm, coded in Digital Visual Fortran 5.0 Professional, could generate a monthly roster in seconds. Implementation has been carried out since September 1998 and the results proved to be useful to hospital administrators and porters. This paper discusses both the technical and human issues involved during the computerization process.

  1. Optimal Hospital Layout Design

    Holst, Malene Kirstine

    foundation. The basis of the present study lies in solving the architectural design problem in order to respond to functionalities and performances. The emphasis is the practical applicability for architects, engineers and hospital planners for assuring usability and a holistic approach of functionalities...... a correlation matrix. The correlation factor defines the framework for conceptual design, whereby the design considers functionalities and their requirements and preferences. It facilitates implementation of evidence-based design as it is prepared for ongoing update and it is based on actual data. Hence......, this contribution is a model for hospital design, where design derives as a response to the defined variables, requirements and preferences....

  2. A Gas Scheduling Optimization Model for Steel Enterprises

    Niu Honghai

    2017-01-01

    Full Text Available Regarding the scheduling problems of steel enterprises, this research designs the gas scheduling optimization model according to the rules and priorities. Considering different features and the process changes of the gas unit in the process of actual production, the calculation model of process state and gas consumption soft measurement together with the rules of scheduling optimization is proposed to provide the dispatchers with real-time gas using status of each process, then help them to timely schedule and reduce the gas volume fluctuations. In the meantime, operation forewarning and alarm functions are provided to avoid the abnormal situation in the scheduling, which has brought about very good application effect in the actual scheduling and ensures the safety of the gas pipe network system and the production stability.

  3. Optimal investment and scheduling of distributed energy resources with uncertainty in electric vehicles driving schedules

    Cardoso, Goncalo [Technical Univ. of Lisbon (Portugal); Stadler, Michael [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Center for Energy and Innovation Technologies (Austria); Bozchalui, Mohammed C. [NEC Laboratories American Inc., Irving, TX (United States); Sharma, Ratnesh [NEC Laboratories American Inc., Irving, TX (United States); Marnay, Chris [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Barbosa-Povoa, Ana [Technical Univ. of Lisbon (Portugal); Ferrao, Paulo [Technical Univ. of Lisbon (Portugal)

    2013-12-06

    The large scale penetration of electric vehicles (EVs) will introduce technical challenges to the distribution grid, but also carries the potential for vehicle-to-grid services. Namely, if available in large enough numbers, EVs can be used as a distributed energy resource (DER) and their presence can influence optimal DER investment and scheduling decisions in microgrids. In this work, a novel EV fleet aggregator model is introduced in a stochastic formulation of DER-CAM [1], an optimization tool used to address DER investment and scheduling problems. This is used to assess the impact of EV interconnections on optimal DER solutions considering uncertainty in EV driving schedules. Optimization results indicate that EVs can have a significant impact on DER investments, particularly if considering short payback periods. Furthermore, results suggest that uncertainty in driving schedules carries little significance to total energy costs, which is corroborated by results obtained using the stochastic formulation of the problem.

  4. Optimal Grid Scheduling Using Improved Artificial Bee Colony Algorithm

    T. Vigneswari; M. A. Maluk Mohamed

    2015-01-01

    Job Scheduling plays an important role for efficient utilization of grid resources available across different domains and geographical zones. Scheduling of jobs is challenging and NPcomplete. Evolutionary / Swarm Intelligence algorithms have been extensively used to address the NP problem in grid scheduling. Artificial Bee Colony (ABC) has been proposed for optimization problems based on foraging behaviour of bees. This work proposes a modified ABC algorithm, Cluster Hete...

  5. Robust Optimization for Household Load Scheduling with Uncertain Parameters

    Jidong Wang

    2018-04-01

    Full Text Available Home energy management systems (HEMS face many challenges of uncertainty, which have a great impact on the scheduling of home appliances. To handle the uncertain parameters in the household load scheduling problem, this paper uses a robust optimization method to rebuild the household load scheduling model for home energy management. The model proposed in this paper can provide the complete robust schedules for customers while considering the disturbance of uncertain parameters. The complete robust schedules can not only guarantee the customers’ comfort constraints but also cooperatively schedule the electric devices for cost minimization and load shifting. Moreover, it is available for customers to obtain multiple schedules through setting different robust levels while considering the trade-off between the comfort and economy.

  6. customer-teller scheduling system for optimizing banks service

    els Bank Teller scheduling system for optimizing a Banks customer service. The model takes into account real time .... tinuous observation by management personnel and thus results in .... relationships in a mathematical model. A mathematical ...

  7. SIMULTANEOUS SCHEDULING AND OPERATIONAL OPTIMIZATION OF MULTIPRODUCT, CYCLIC CONTINUOUS PLANTS

    A. Alle

    2002-03-01

    Full Text Available The problems of scheduling and optimization of operational conditions in multistage, multiproduct continuous plants with intermediate storage are simultaneously addressed. An MINLP model, called TSPFLOW, which is based on the TSP formulation for product sequencing, is proposed to schedule the operation of such plants. TSPFLOW yields a one-order-of-magnitude CPU time reduction as well as the solution of instances larger than those formerly reported (Pinto and Grossmann, 1994. Secondly, processing rates and yields are introduced as additional optimization variables in order to state the simultaneous problem of scheduling with operational optimization. Results show that trade-offs are very complex and that the development of a straightforward (rule of thumb method to optimally schedule the operation is less effective than the proposed approach.

  8. SIMULTANEOUS SCHEDULING AND OPERATIONAL OPTIMIZATION OF MULTIPRODUCT, CYCLIC CONTINUOUS PLANTS

    Alle A.

    2002-01-01

    Full Text Available The problems of scheduling and optimization of operational conditions in multistage, multiproduct continuous plants with intermediate storage are simultaneously addressed. An MINLP model, called TSPFLOW, which is based on the TSP formulation for product sequencing, is proposed to schedule the operation of such plants. TSPFLOW yields a one-order-of-magnitude CPU time reduction as well as the solution of instances larger than those formerly reported (Pinto and Grossmann, 1994. Secondly, processing rates and yields are introduced as additional optimization variables in order to state the simultaneous problem of scheduling with operational optimization. Results show that trade-offs are very complex and that the development of a straightforward (rule of thumb method to optimally schedule the operation is less effective than the proposed approach.

  9. Refrigerator Optimal Scheduling to Minimise the Cost of Operation

    Bálint Roland

    2016-12-01

    Full Text Available The cost optimal scheduling of a household refrigerator is presented in this work. The fundamental approach is the model predictive control methodology applied to the piecewise affine model of the refrigerator.

  10. Locomotive Schedule Optimization for Da-qin Heavy Haul Railway

    Ruiye Su

    2015-01-01

    Full Text Available The main difference between locomotive schedule of heavy haul railways and that of regular rail transportation is the number of locomotives utilized for one train. One heavy-loaded train usually has more than one locomotive, but a regular train only has one. This paper develops an optimization model for the multilocomotive scheduling problem (MLSP through analyzing the current locomotive schedule of Da-qin Railway. The objective function of our paper is to minimize the total number of utilized locomotives. The MLSP is nondeterministic polynomial (NP hard. Therefore, we convert the multilocomotive traction problem into a single-locomotive traction problem. Then, the single-locomotive traction problem (SLTP can be converted into an assignment problem. The Hungarian algorithm is applied to solve the model and obtain the optimal locomotive schedule. We use the variance of detention time of locomotives at stations to evaluate the stability of locomotive schedule. In order to evaluate the effectiveness of the proposed optimization model, case studies for 20 kt and 30 kt heavy-loaded combined trains on Da-qin Railway are both conducted. Compared to the current schedules, the optimal schedules from the proposed models can save 62 and 47 locomotives for 20 kt and 30 kt heavy-loaded combined trains, respectively. Therefore, the effectiveness of the proposed model and its solution algorithm are both valid.

  11. Optimal Scheduling of Doctors Outpatient Departments Based on Patients’ Behavior

    Zongwei Ren

    2016-01-01

    Full Text Available The low operational efficiency in the field of medical and health care has become a serious problem in China; the long time that the patients have to wait for is the main phenomenon of difficult medical service. Medical industry is service-oriented and its main purpose is to make profits, so the benefits and competitiveness of a hospital depend on patient satisfaction. This paper makes a survey on a large hospital in Harbin of China and collects relevant data and then uses the prospect theory to analyze patients’ and doctors’ behavioral characteristics and the model of patient satisfaction is established based on fuzzy theory with a triplet α/β/γ. The optimal scheduling of clinic is described as a problem with the rule of first come, first served which maximizes patient satisfaction for the main goal and minimizes operating costs for the secondary goal. And the corresponding mathematical model is established. Finally, a solution method named plant growth simulation algorithm (PGSA is presented. Then, by means of calculating of the example and comparing with genetic algorithm, the results show that the optimum can be reached; meanwhile the efficiency of the presented algorithm is better than the genetic algorithm.

  12. Optimal Time-Abstract Schedulers for CTMDPs and Markov Games

    Markus Rabe

    2010-06-01

    Full Text Available We study time-bounded reachability in continuous-time Markov decision processes for time-abstract scheduler classes. Such reachability problems play a paramount role in dependability analysis and the modelling of manufacturing and queueing systems. Consequently, their analysis has been studied intensively, and techniques for the approximation of optimal control are well understood. From a mathematical point of view, however, the question of approximation is secondary compared to the fundamental question whether or not optimal control exists. We demonstrate the existence of optimal schedulers for the time-abstract scheduler classes for all CTMDPs. Our proof is constructive: We show how to compute optimal time-abstract strategies with finite memory. It turns out that these optimal schedulers have an amazingly simple structure---they converge to an easy-to-compute memoryless scheduling policy after a finite number of steps. Finally, we show that our argument can easily be lifted to Markov games: We show that both players have a likewise simple optimal strategy in these more general structures.

  13. Optimal investment and scheduling of distributed energy resources with uncertainty in electric vehicle driving schedules

    Cardoso, G.; Stadler, M.; Bozchalui, M.C.; Sharma, R.; Marnay, C.; Barbosa-Póvoa, A.; Ferrão, P.

    2014-01-01

    The large scale penetration of electric vehicles (EVs) will introduce technical challenges to the distribution grid, but also carries the potential for vehicle-to-grid services. Namely, if available in large enough numbers, EVs can be used as a distributed energy resource (DER) and their presence can influence optimal DER investment and scheduling decisions in microgrids. In this work, a novel EV fleet aggregator model is introduced in a stochastic formulation of DER-CAM [1], an optimization tool used to address DER investment and scheduling problems. This is used to assess the impact of EV interconnections on optimal DER solutions considering uncertainty in EV driving schedules. Optimization results indicate that EVs can have a significant impact on DER investments, particularly if considering short payback periods. Furthermore, results suggest that uncertainty in driving schedules carries little significance to total energy costs, which is corroborated by results obtained using the stochastic formulation of the problem. - Highlights: • This paper introduces a new EV aggregator model in the DER-CAM model and expands it with a stochastic formulation. • The model is used to analyze the impact of EVs in DER investment decisions in a large office building. • The uncertainty in EV driving patterns is considered through scenarios based on data from a daily commute driving survey. • Results indicate that EVs have a significant impact in optimal DER decisions, particularly when looking at short payback periods. • Furthermore, results indicate that uncertainty in EV driving schedules has little impact on DER investment decisions

  14. Nurse scheduling in a hospital emergency department: A case study at a Thai university hospital

    Aussadavut Dumrongsiri

    2018-02-01

    Full Text Available Common problems of Thai nurses are low quality of life, working long hours, and a high turnover rate. The workload imbalance among nurses also worsens the turnover rate. With careful schedule planning, nurses do not have to work in consecutive shifts and can rest more. We interviewed and collected data from an emergency department at a hospital administered by a Thai university, related to objectives and constraints of monthly nurse scheduling, and actual monthly schedules. A multi-objective mathematical model was developed using the open source “OpenSolver” software in MS-Excel for nurse schedulers to freely use. We tested the model using actual data collected from the department and found that the schedules created by the model tended to provide more balanced workloads and more days off compared to the schedules created manually by a real scheduler. The model also suggested an easy policy to increase the number of nurses for future expansion.

  15. The nurse scheduling problem: a goal programming and nonlinear optimization approaches

    Hakim, L.; Bakhtiar, T.; Jaharuddin

    2017-01-01

    Nurses scheduling is an activity of allocating nurses to conduct a set of tasks at certain room at a hospital or health centre within a certain period. One of obstacles in the nurse scheduling is the lack of resources in order to fulfil the needs of the hospital. Nurse scheduling which is undertaken manually will be at risk of not fulfilling some nursing rules set by the hospital. Therefore, this study aimed to perform scheduling models that satisfy all the specific rules set by the management of Bogor State Hospital. We have developed three models to overcome the scheduling needs. Model 1 is designed to schedule nurses who are solely assigned to a certain inpatient unit and Model 2 is constructed to manage nurses who are assigned to an inpatient room as well as at Polyclinic room as conjunct nurses. As the assignment of nurses on each shift is uneven, then we propose Model 3 to minimize the variance of the workload in order to achieve equitable assignment on every shift. The first two models are formulated in goal programming framework, while the last model is in nonlinear optimization form.

  16. Enhanced OTSG economics optimizing CAPEX + OPEX + Schedule

    Armstrong, Peter [KTI Engineered to Perform (Canada)

    2011-07-01

    This presentation gives an insight into the economics involved in producing once-through steam (OTSG) generators in Canada and it offers a proposal for improving returns in the industry. The objective of this presentation was to show the importance of developing OTSG manufacturing based on the future of this technology. The growing demand for OTSG by oil companies was presented, suggesting that this is a good time to put effort into developing more efficient designs. After the background of this technology was discussed, several economical aspects of the process were addressed; for instance, reducing capital expenditure (CAPEX) and operation expenditure (OPEX), outsourcing production, and project scheduling and execution. Initiatives proposed included: reducing plot size, producing larger OTSGs, reducing downtime, using alternative fuels, forming a global manufacturing network, and avoiding order blockage. Finally, the idea of designing a portable OTSG was proposed, showing its flexibility in oil extraction projects.

  17. Optimal mechanisms for single machine scheduling

    Heydenreich, B.; Mishra, D.; Müller, R.; Uetz, Marc Jochen; Papadimitriou, C.; Zhang, S.

    2008-01-01

    We study the design of optimal mechanisms in a setting here job-agents compete for being processed by a service provider that can handle one job at a time. Each job has a processing time and incurs a waiting cost. Jobs need to be compensated for waiting. We consider two models, one where only the

  18. Electric power systems advanced forecasting techniques and optimal generation scheduling

    Catalão, João P S

    2012-01-01

    Overview of Electric Power Generation SystemsCláudio MonteiroUncertainty and Risk in Generation SchedulingRabih A. JabrShort-Term Load ForecastingAlexandre P. Alves da Silva and Vitor H. FerreiraShort-Term Electricity Price ForecastingNima AmjadyShort-Term Wind Power ForecastingGregor Giebel and Michael DenhardPrice-Based Scheduling for GencosGovinda B. Shrestha and Songbo QiaoOptimal Self-Schedule of a Hydro Producer under UncertaintyF. Javier Díaz and Javie

  19. Resource Allocation and Outpatient Appointment Scheduling Using Simulation Optimization

    Carrie Ka Yuk Lin

    2017-01-01

    Full Text Available This paper studies the real-life problems of outpatient clinics having the multiple objectives of minimizing resource overtime, patient waiting time, and waiting area congestion. In the clinic, there are several patient classes, each of which follows different treatment procedure flow paths through a multiphase and multiserver queuing system with scarce staff and limited space. We incorporate the stochastic factors for the probabilities of the patients being diverted into different flow paths, patient punctuality, arrival times, procedure duration, and the number of accompanied visitors. We present a novel two-stage simulation-based heuristic algorithm to assess various tactical and operational decisions for optimizing the multiple objectives. In stage I, we search for a resource allocation plan, and in stage II, we determine a block appointment schedule by patient class and a service discipline for the daily operational level. We also explore the effects of the separate strategies and their integration to identify the best possible combination. The computational experiments are designed on the basis of data from a study of an ophthalmology clinic in a public hospital. Results show that our approach significantly mitigates the undesirable outcomes by integrating the strategies and increasing the resource flexibility at the bottleneck procedures without adding resources.

  20. Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing

    Imam Ahmad Ashari

    2016-11-01

    Full Text Available Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization  algorithm in solving the case of course scheduling.

  1. Optimization models for flight test scheduling

    Holian, Derreck

    As threats around the world increase with nations developing new generations of warfare technology, the Unites States is keen on maintaining its position on top of the defense technology curve. This in return indicates that the U.S. military/government must research, develop, procure, and sustain new systems in the defense sector to safeguard this position. Currently, the Lockheed Martin F-35 Joint Strike Fighter (JSF) Lightning II is being developed, tested, and deployed to the U.S. military at Low Rate Initial Production (LRIP). The simultaneous act of testing and deployment is due to the contracted procurement process intended to provide a rapid Initial Operating Capability (IOC) release of the 5th Generation fighter. For this reason, many factors go into the determination of what is to be tested, in what order, and at which time due to the military requirements. A certain system or envelope of the aircraft must be assessed prior to releasing that capability into service. The objective of this praxis is to aide in the determination of what testing can be achieved on an aircraft at a point in time. Furthermore, it will define the optimum allocation of test points to aircraft and determine a prioritization of restrictions to be mitigated so that the test program can be best supported. The system described in this praxis has been deployed across the F-35 test program and testing sites. It has discovered hundreds of available test points for an aircraft to fly when it was thought none existed thus preventing an aircraft from being grounded. Additionally, it has saved hundreds of labor hours and greatly reduced the occurrence of test point reflight. Due to the proprietary nature of the JSF program, details regarding the actual test points, test plans, and all other program specific information have not been presented. Generic, representative data is used for example and proof-of-concept purposes. Apart from the data correlation algorithms, the optimization associated

  2. Optimal load scheduling in commercial and residential microgrids

    Ganji Tanha, Mohammad Mahdi

    Residential and commercial electricity customers use more than two third of the total energy consumed in the United States, representing a significant resource of demand response. Price-based demand response, which is in response to changes in electricity prices, represents the adjustments in load through optimal load scheduling (OLS). In this study, an efficient model for OLS is developed for residential and commercial microgrids which include aggregated loads in single-units and communal loads. Single unit loads which include fixed, adjustable and shiftable loads are controllable by the unit occupants. Communal loads which include pool pumps, elevators and central heating/cooling systems are shared among the units. In order to optimally schedule residential and commercial loads, a community-based optimal load scheduling (CBOLS) is proposed in this thesis. The CBOLS schedule considers hourly market prices, occupants' comfort level, and microgrid operation constraints. The CBOLS' objective in residential and commercial microgrids is the constrained minimization of the total cost of supplying the aggregator load, defined as the microgrid load minus the microgrid generation. This problem is represented by a large-scale mixed-integer optimization for supplying single-unit and communal loads. The Lagrangian relaxation methodology is used to relax the linking communal load constraint and decompose the independent single-unit functions into subproblems which can be solved in parallel. The optimal solution is acceptable if the aggregator load limit and the duality gap are within the bounds. If any of the proposed criteria is not satisfied, the Lagrangian multiplier will be updated and a new optimal load schedule will be regenerated until both constraints are satisfied. The proposed method is applied to several case studies and the results are presented for the Galvin Center load on the 16th floor of the IIT Tower in Chicago.

  3. Optimal charging schedule of an electric vehicle fleet

    Hu, Junjie; You, Shi; Østergaard, Jacob

    2011-01-01

    In this paper, we propose an approach to optimize the charging schedule of an Electric Vehicle (EV) fleet both taking into account spot price and individual EV driving requirement with the goal of minimizing charging costs. A flexible and suitable mathematic model is introduced to characterize...

  4. Optimization of the solution of the problem of scheduling theory ...

    This article describes the genetic algorithm used to solve the problem related to the scheduling theory. A large number of different methods is described in the scientific literature. The main issue that faced the problem in question is that it is necessary to search the optimal solution in a large search space for the set of ...

  5. Verification and Optimization of a PLC Control Schedule

    Brinksma, Hendrik; Mader, Angelika H.; Havelund, K.; Penix, J.; Visser, W.

    We report on the use of the SPIN model checker for both the verification of a process control program and the derivation of optimal control schedules. This work was carried out as part of a case study for the EC VHS project (Verification of Hybrid Systems), in which the program for a Programmable

  6. On using priced timed automata to achieve optimal scheduling

    Rasmussen, Jacob Illum; Larsen, Kim Guldstrand; Subramani, K.

    2006-01-01

    This contribution reports on the considerable effort made recently towards extending and applying well-established timed automata technology to optimal scheduling and planning problems. The effort of the authors in this direction has to a large extent been carried out as part of the European proj...... of so-called priced timed automata....

  7. Intelligent discrete particle swarm optimization for multiprocessor task scheduling problem

    S Sarathambekai

    2017-03-01

    Full Text Available Discrete particle swarm optimization is one of the most recently developed population-based meta-heuristic optimization algorithm in swarm intelligence that can be used in any discrete optimization problems. This article presents a discrete particle swarm optimization algorithm to efficiently schedule the tasks in the heterogeneous multiprocessor systems. All the optimization algorithms share a common algorithmic step, namely population initialization. It plays a significant role because it can affect the convergence speed and also the quality of the final solution. The random initialization is the most commonly used method in majority of the evolutionary algorithms to generate solutions in the initial population. The initial good quality solutions can facilitate the algorithm to locate the optimal solution or else it may prevent the algorithm from finding the optimal solution. Intelligence should be incorporated to generate the initial population in order to avoid the premature convergence. This article presents a discrete particle swarm optimization algorithm, which incorporates opposition-based technique to generate initial population and greedy algorithm to balance the load of the processors. Make span, flow time, and reliability cost are three different measures used to evaluate the efficiency of the proposed discrete particle swarm optimization algorithm for scheduling independent tasks in distributed systems. Computational simulations are done based on a set of benchmark instances to assess the performance of the proposed algorithm.

  8. Joint optimization of production scheduling and machine group preventive maintenance

    Xiao, Lei; Song, Sanling; Chen, Xiaohui; Coit, David W.

    2016-01-01

    Joint optimization models were developed combining group preventive maintenance of a series system and production scheduling. In this paper, we propose a joint optimization model to minimize the total cost including production cost, preventive maintenance cost, minimal repair cost for unexpected failures and tardiness cost. The total cost depends on both the production process and the machine maintenance plan associated with reliability. For the problems addressed in this research, any machine unavailability leads to system downtime. Therefore, it is important to optimize the preventive maintenance of machines because their performance impacts the collective production processing associated with all machines. Too lengthy preventive maintenance intervals may be associated with low scheduled machine maintenance cost, but may incur expensive costs for unplanned failure due to low machine reliability. Alternatively, too frequent preventive maintenance activities may achieve the desired high reliability machines, but unacceptably high scheduled maintenance cost. Additionally, product scheduling plans affect tardiness and maintenance cost. Two results are obtained when solving the problem; the optimal group preventive maintenance interval for machines, and the assignment of each job, including the corresponding start time and completion time. To solve this non-deterministic polynomial-time problem, random keys genetic algorithms are used, and a numerical example is solved to illustrate the proposed model. - Highlights: • Group preventive maintenance (PM) planning and production scheduling are jointed. • Maintenance interval and assignment of jobs are decided by minimizing total cost. • Relationships among system age, PM, job processing time are quantified. • Random keys genetic algorithms (GA) are used to solve the NP-hard problem. • Random keys GA and Particle Swarm Optimization (PSO) are compared.

  9. Optimizing donor scheduling before recruitment: An effective approach to increasing apheresis platelet collections.

    Lokhandwala, Parvez M; Shike, Hiroko; Wang, Ming; Domen, Ronald E; George, Melissa R

    2018-01-01

    Typical approach for increasing apheresis platelet collections is to recruit new donors. Here, we investigated the effectiveness of an alternative strategy: optimizing donor scheduling, prior to recruitment, at a hospital-based blood donor center. Analysis of collections, during the 89 consecutive months since opening of donor center, was performed. Linear regression and segmented time-series analyses were performed to calculate growth rates of collections and to test for statistical differences, respectively. Pre-intervention donor scheduling capacity was 39/month. In the absence of active donor recruitment, during the first 29 months, the number of collections rose gradually to 24/month (growth-rate of 0.70/month). However, between month-30 and -55, collections exhibited a plateau at 25.6 ± 3.0 (growth-rate of -0.09/month) (pcollection days/week (month-72). Consequently, the scheduling capacity increased to 130/month. Post-interventions, apheresis platelet collections between month-56 and -81 exhibited a spontaneous renewed growth at a rate of 0.62/month (pcollections. Apheresis platelet collections plateau at nearly 2/3rd of the scheduling capacity. Optimizing the scheduling capacity prior to active donor recruitment is an effective strategy to increase platelet collections at a hospital-based donor center.

  10. Optimal Scheduling of Residential Microgrids Considering Virtual Energy Storage System

    Weiliang Liu

    2018-04-01

    Full Text Available The increasingly complex residential microgrids (r-microgrid consisting of renewable generation, energy storage systems, and residential buildings require a more intelligent scheduling method. Firstly, aiming at the radiant floor heating/cooling system widely utilized in residential buildings, the mathematical relationship between the operative temperature and heating/cooling demand is established based on the equivalent thermodynamic parameters (ETP model, by which the thermal storage capacity is analyzed. Secondly, the radiant floor heating/cooling system is treated as virtual energy storage system (VESS, and an optimization model based on mixed-integer nonlinear programming (MINLP for r-microgrid scheduling is established which takes thermal comfort level and economy as the optimization objectives. Finally, the optimal scheduling results of two typical r-microgrids are analyzed. Case studies demonstrate that the proposed scheduling method can effectively employ the thermal storage capacity of radiant floor heating/cooling system, thus lowering the operating cost of the r-microgrid effectively while ensuring the thermal comfort level of users.

  11. Optimal scheduling for distribution network with redox flow battery storage

    Hosseina, Majid; Bathaee, Seyed Mohammad Taghi

    2016-01-01

    Highlights: • A novel method for optimal scheduling of storages in radial network is presented. • Peak shaving and load leveling are the main objectives. • Vanadium redox flow battery is considered as the energy storage unit. • Real data is used for simulation. - Abstract: There are many advantages to utilize storages in electric power system. Peak shaving, load leveling, load frequency control, integration of renewable, energy trading and spinning reserve are the most important of them. Batteries, especially redox flow batteries, are one of the appropriate storages for utilization in distribution network. This paper presents a novel, heuristic and practical method for optimal scheduling in distribution network with flow battery storage. This heuristic method is more suitable for scheduling and operation of distribution networks which require installation of storages. Peak shaving and load leveling is considered as the main objective in this paper. Several indices are presented in this paper for determine the place of storages and also scheduling for optimal use of energy in them. Simulations of this paper are based on real information of distribution network substation that located in Semnan, Iran.

  12. Scheduling with Bus Access Optimization for Distributed Embedded Systems

    Eles, Petru; Doboli, Alex; Pop, Paul

    2000-01-01

    of control. Our goal is to derive a worst case delay by which the system completes execution, such that this delay is as small as possible; to generate a logically and temporally deterministic schedule; and to optimize parameters of the communication protocol such that this delay is guaranteed. We have......In this paper, we concentrate on aspects related to the synthesis of distributed embedded systems consisting of programmable processors and application-specific hardware components. The approach is based on an abstract graph representation that captures, at process level, both dataflow and the flow......, generates an efficient bus access scheme as well as the schedule tables for activation of processes and communications....

  13. Tramp ship routing and scheduling with integrated bunker optimization

    Vilhelmsen, Charlotte; Lusby, Richard Martin; Larsen, Jesper

    2014-01-01

    is referred to as bunker and bunker costs constitute a significant part of the daily operating costs. There can be great variations in bunker prices across bunker ports so it is important to carefully plan bunkering for each ship. As ships operate 24 hours a day, they must refuel during operations. Therefore...... and scheduling phase and present a mixed integer programming formulation for the integrated problem of optimally routing, scheduling and bunkering a tramp fleet. Aside from the integration of bunker, this model also extends standard tramp formulations by using load dependent costs, speed and bunker consumption...

  14. An Optimal Scheduling Dispatch of a Microgrid under Risk Assessment

    Whei-Min Lin

    2018-06-01

    Full Text Available This paper presents the scheduling dispatch of a microgrid (MG, while considering renewable energy, battery storage systems, and time-of-use price. For the risk evaluation of an MG, the Value-at-Risk (VAR is calculated by using the Historical Simulation Method (HSM. By considering the various confidence levels of the VAR, a scheduling dispatch model of the MG is formulated to achieve a reasonable trade-off between the risk and cost. An Improved Bee Swarm Optimization (IBSO is proposed to solve the scheduling dispatch model of the MG. In the IBSO procedure, the Sin-wave Weight Factor (SWF and Forward-Backward Control Factor (FBCF are embedded in the bee swarm of the BSO to improve the movement behaviors of each bee, specifically, its search efficiency and accuracy. The effectiveness of the IBSO is demonstrated via a real MG case and the results are compared with other methods. In either a grid-connected scenario or a stand-alone scenario, an optimal scheduling dispatch of MGs is carried out, herein, at various confidence levels of risk. The simulation results provide more information for handling uncertain environments when analyzing the VAR of MGs.

  15. Optimal Intermittent Dose Schedules for Chemotherapy Using Genetic Algorithm

    Nadia ALAM

    2013-08-01

    Full Text Available In this paper, a design method for optimal cancer chemotherapy schedules via genetic algorithm (GA is presented. The design targets the key objective of chemotherapy to minimize the size of cancer tumor after a predefined time with keeping toxic side effects in limit. This is a difficult target to achieve using conventional clinical methods due to poor therapeutic indices of existing anti-cancer drugs. Moreover, there are clinical limitations in treatment administration to maintain continuous treatment. Besides, carefully decided rest periods are recommended to for patient’s comfort. Three intermittent drug scheduling schemes are presented in this paper where GA is used to optimize the dose quantities and timings by satisfying several treatment constraints. All three schemes are found to be effective in total elimination of cancer tumor after an agreed treatment length. The number of cancer cells is found zero at the end of the treatment for all three cases with tolerable toxicity. Finally, two of the schemes, “Fixed interval variable dose (FIVD and “Periodic dose” that are periodic in characteristic have been emphasized due to their additional simplicity in administration along with friendliness to patients. responses to the designed treatment schedules. Therefore the proposed design method is capable of planning effective, simple, patient friendly and acceptable chemotherapy schedules.

  16. Using Optimization Models for Scheduling in Enterprise Resource Planning Systems

    Frank Herrmann

    2016-03-01

    Full Text Available Companies often use specially-designed production systems and change them from time to time. They produce small batches in order to satisfy specific demands with the least tardiness. This imposes high demands on high-performance scheduling algorithms which can be rapidly adapted to changes in the production system. As a solution, this paper proposes a generic approach: solutions were obtained using a widely-used commercially-available tool for solving linear optimization models, which is available in an Enterprise Resource Planning System (in the SAP system for example or can be connected to it. In a real-world application of a flow shop with special restrictions this approach is successfully used on a standard personal computer. Thus, the main implication is that optimal scheduling with a commercially-available tool, incorporated in an Enterprise Resource Planning System, may be the best approach.

  17. Optimal scheduling in call centers with a callback option

    Legros , Benjamin; Jouini , Oualid; Koole , Ger

    2016-01-01

    International audience; We consider a call center model with a callback option, which allows to transform an inbound call into an outbound one. A delayed call, with a long anticipated waiting time, receives the option to be called back. We assume a probabilistic customer reaction to the callback offer (option). The objective of the system manager is to characterize the optimal call scheduling that minimizes the expected waiting and abandonment costs. For the single-server case, we prove that ...

  18. Optimal Results and Numerical Simulations for Flow Shop Scheduling Problems

    Tao Ren

    2012-01-01

    Full Text Available This paper considers the m-machine flow shop problem with two objectives: makespan with release dates and total quadratic completion time, respectively. For Fm|rj|Cmax, we prove the asymptotic optimality for any dense scheduling when the problem scale is large enough. For Fm‖ΣCj2, improvement strategy with local search is presented to promote the performance of the classical SPT heuristic. At the end of the paper, simulations show the effectiveness of the improvement strategy.

  19. Resource-Optimal Scheduling Using Priced Timed Automata

    Larsen, Kim Guldstrand; Rasmussen, Jacob Illum; Subramani, K.

    2004-01-01

    In this paper, we show how the simple structure of the linear programs encountered during symbolic minimum-cost reachability analysis of priced timed automata can be exploited in order to substantially improve the performance of the current algorithm. The idea is rooted in duality of linear......-80 percent performance gain. As a main application area, we show how to solve energy-optimal task graph scheduling problems using the framework of priced timed automata....

  20. Global Optimization of Nonlinear Blend-Scheduling Problems

    Pedro A. Castillo Castillo

    2017-04-01

    Full Text Available The scheduling of gasoline-blending operations is an important problem in the oil refining industry. This problem not only exhibits the combinatorial nature that is intrinsic to scheduling problems, but also non-convex nonlinear behavior, due to the blending of various materials with different quality properties. In this work, a global optimization algorithm is proposed to solve a previously published continuous-time mixed-integer nonlinear scheduling model for gasoline blending. The model includes blend recipe optimization, the distribution problem, and several important operational features and constraints. The algorithm employs piecewise McCormick relaxation (PMCR and normalized multiparametric disaggregation technique (NMDT to compute estimates of the global optimum. These techniques partition the domain of one of the variables in a bilinear term and generate convex relaxations for each partition. By increasing the number of partitions and reducing the domain of the variables, the algorithm is able to refine the estimates of the global solution. The algorithm is compared to two commercial global solvers and two heuristic methods by solving four examples from the literature. Results show that the proposed global optimization algorithm performs on par with commercial solvers but is not as fast as heuristic approaches.

  1. A Privacy-Preserving Distributed Optimal Scheduling for Interconnected Microgrids

    Nian Liu

    2016-12-01

    Full Text Available With the development of microgrids (MGs, interconnected operation of multiple MGs is becoming a promising strategy for the smart grid. In this paper, a privacy-preserving distributed optimal scheduling method is proposed for the interconnected microgrids (IMG with a battery energy storage system (BESS and renewable energy resources (RESs. The optimal scheduling problem is modeled to minimize the coalitional operation cost of the IMG, including the fuel cost of conventional distributed generators and the life loss cost of BESSs. By using the framework of the alternating direction method of multipliers (ADMM, a distributed optimal scheduling model and an iteration solution algorithm for the IMG is introduced; only the expected exchanging power (EEP of each MG is required during the iterations. Furthermore, a privacy-preserving strategy for the sharing of the EEP among MGs is designed to work with the mechanism of the distributed algorithm. According to the security analysis, the EEP can be delivered in a cooperative and privacy-preserving way. A case study and numerical results are given in terms of the convergence of the algorithm, the comparison of the costs and the implementation efficiency.

  2. Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

    Ghulam Hafeez

    2018-03-01

    Full Text Available With the rapid advancement in technology, electrical energy consumption is increasing rapidly. Especially, in the residential sector, more than 80% of electrical energy is being consumed because of consumer negligence. This brings the challenging task of maintaining the balance between the demand and supply of electric power. In this paper, we focus on the problem of load balancing via load scheduling under utility and rooftop photovoltaic (PV units to reduce electricity cost and peak to average ratio (PAR in demand-side management. For this purpose, we adopted genetic algorithm (GA, binary particle swarm optimization (BPSO, wind-driven optimization (WDO, and our proposed genetic WDO (GWDO algorithm, which is a hybrid of GA and WDO, to schedule the household load. For energy cost estimation, combined real-time pricing (RTP and inclined block rate (IBR were used. The proposed algorithm shifts load from peak consumption hours to off-peak hours based on combined pricing scheme and generation from rooftop PV units. Simulation results validate our proposed GWDO algorithm in terms of electricity cost and PAR reduction while considering all three scenarios which we have considered in this work: (1 load scheduling without renewable energy sources (RESs and energy storage system (ESS, (2 load scheduling with RESs, and (3 load scheduling with RESs and ESS. Furthermore, our proposed scheme reduced electricity cost and PAR by 22.5% and 29.1% in scenario 1, 47.7% and 30% in scenario 2, and 49.2% and 35.4% in scenario 3, respectively, as compared to unscheduled electricity consumption.

  3. Optimal scheduling of micro grids based on single objective programming

    Chen, Yue

    2018-04-01

    Faced with the growing demand for electricity and the shortage of fossil fuels, how to optimally optimize the micro-grid has become an important research topic to maximize the economic, technological and environmental benefits of the micro-grid. This paper considers the role of the battery and the micro-grid and power grid to allow the exchange of power not exceeding 150kW preconditions, the main study of the economy to load for the goal is to minimize the electricity cost (abandonment of wind), to establish an optimization model, and to solve the problem by genetic algorithm. The optimal scheduling scheme is obtained and the utilization of renewable energy and the impact of the battery involved in regulation are analyzed.

  4. Terminal Control Area Aircraft Scheduling and Trajectory Optimization Approaches

    Samà Marcella

    2017-01-01

    Full Text Available Aviation authorities are seeking optimization methods to better use the available infrastructure and better manage aircraft movements. This paper deals with the realtime scheduling of take-off and landing aircraft at a busy terminal control area and with the optimization of aircraft trajectories during the landing procedures. The first problem aims to reduce the propagation of delays, while the second problem aims to either minimize the travel time or reduce the fuel consumption. Both problems are particularly complex, since the first one is NP-hard while the second one is nonlinear and a combined solution needs to be computed in a short-time during operations. This paper proposes a framework for the lexicographic optimization of the two problems. Computational experiments are performed for the Milano Malpensa airport and show the existing gaps between the performance indicators of the two problems when different lexicographic optimization approaches are considered.

  5. A probabilistic computational framework for bridge network optimal maintenance scheduling

    Bocchini, Paolo; Frangopol, Dan M.

    2011-01-01

    This paper presents a probabilistic computational framework for the Pareto optimization of the preventive maintenance applications to bridges of a highway transportation network. The bridge characteristics are represented by their uncertain reliability index profiles. The in/out of service states of the bridges are simulated taking into account their correlation structure. Multi-objective Genetic Algorithms have been chosen as numerical tool for the solution of the optimization problem. The design variables of the optimization are the preventive maintenance schedules of all the bridges of the network. The two conflicting objectives are the minimization of the total present maintenance cost and the maximization of the network performance indicator. The final result is the Pareto front of optimal solutions among which the managers should chose, depending on engineering and economical factors. A numerical example illustrates the application of the proposed approach.

  6. Fog computing job scheduling optimization based on bees swarm

    Bitam, Salim; Zeadally, Sherali; Mellouk, Abdelhamid

    2018-04-01

    Fog computing is a new computing architecture, composed of a set of near-user edge devices called fog nodes, which collaborate together in order to perform computational services such as running applications, storing an important amount of data, and transmitting messages. Fog computing extends cloud computing by deploying digital resources at the premise of mobile users. In this new paradigm, management and operating functions, such as job scheduling aim at providing high-performance, cost-effective services requested by mobile users and executed by fog nodes. We propose a new bio-inspired optimization approach called Bees Life Algorithm (BLA) aimed at addressing the job scheduling problem in the fog computing environment. Our proposed approach is based on the optimized distribution of a set of tasks among all the fog computing nodes. The objective is to find an optimal tradeoff between CPU execution time and allocated memory required by fog computing services established by mobile users. Our empirical performance evaluation results demonstrate that the proposal outperforms the traditional particle swarm optimization and genetic algorithm in terms of CPU execution time and allocated memory.

  7. Optimal residential smart appliances scheduling considering distribution network constraints

    Yu-Ree Kim

    2016-01-01

    Full Text Available As smart appliances (SAs are more widely adopted within distribution networks, residential consumers can contribute to electricity market operations with demand response resources and reduce their electricity bill. However, if the schedules of demand response resources are determined only by the economic electricity rate signal, the schedule can be unfeasible due to the distribution network constraints. Furthermore, it is impossible for consumers to understand the complex physical characteristics and reflect them in their everyday behaviors. This paper introduces the concept of load coordinating retailer (LCR that deals with demand responsive appliances to reduce electrical consumption for the given distribution network constraints. The LCR can play the role of both conventional retailer and aggregated demand response provider for residential customers. It determines the optimal schedules for the aggregated neighboring SAs according to their types within each distribution feeder. The optimization algorithms are developed using Mixed Integer Linear Programming, and the distribution network is solved by the Newton–Raphson AC power flow.

  8. Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems

    Patan, Maciej

    2012-01-01

    Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed al...

  9. Flow shop scheduling algorithm to optimize warehouse activities

    P. Centobelli

    2016-01-01

    Full Text Available Successful flow-shop scheduling outlines a more rapid and efficient process of order fulfilment in warehouse activities. Indeed the way and the speed of order processing and, in particular, the operations concerning materials handling between the upper stocking area and a lower forward picking one must be optimized. The two activities, drops and pickings, have considerable impact on important performance parameters for Supply Chain wholesaler companies. In this paper, a new flow shop scheduling algorithm is formulated in order to process a greater number of orders by replacing the FIFO logic for the drops activities of a wholesaler company on a daily basis. The System Dynamics modelling and simulation have been used to simulate the actual scenario and the output solutions. Finally, a t-Student test validates the modelled algorithm, granting that it can be used for all wholesalers based on drop and picking activities.

  10. Routing and Scheduling Optimization Model of Sea Transportation

    barus, Mika debora br; asyrafy, Habib; nababan, Esther; mawengkang, Herman

    2018-01-01

    This paper examines the routing and scheduling optimization model of sea transportation. One of the issues discussed is about the transportation of ships carrying crude oil (tankers) which is distributed to many islands. The consideration is the cost of transportation which consists of travel costs and the cost of layover at the port. Crude oil to be distributed consists of several types. This paper develops routing and scheduling model taking into consideration some objective functions and constraints. The formulation of the mathematical model analyzed is to minimize costs based on the total distance visited by the tanker and minimize the cost of the ports. In order for the model of the problem to be more realistic and the cost calculated to be more appropriate then added a parameter that states the multiplier factor of cost increases as the charge of crude oil is filled.

  11. Research on optimizing pass schedule of tandem cold mill

    Lu, C.; Wang, J.S.; Zhao, Q.L.; Liu, X.H.; Wang, G.D.

    2000-01-01

    In this paper, research on pass schedule of tandem cold mill (TCM) is carried out. According to load (reduction, rolling force, motor power) balance, non-linear equations set with variables of inter-stand thickness is constructed. The pass schedule optimization is carried out by solving the non-linear equations set. As the traditional method, the Newton-Raphson method is used for solving the non-linear equations set. In this paper a new simple method is brought up. On basis of the monotone relations between thickness and load, the inter-stands thickness is adjusted dynamically. The solution of non-linear equations set can be converged by iterative calculation. This method can avoid the derivative calculation used by traditional method. So, this method is simple and calculation speed is high. It is suitable for on-line control. (author)

  12. A PSO approach for preventive maintenance scheduling optimization

    Pereira, C.M.N.A.; Lapa, C.M.F.; Mol, A.C.A.; Luz, A.F. da

    2009-01-01

    This work presents a Particle Swarm Optimization (PSO) approach for preventive maintenance policy optimization, focused in reliability and cost. The probabilistic model for reliability and cost evaluation is developed in such a way that flexible intervals between maintenance are allowed. As PSO is skilled for realcoded continuous spaces, a non-conventional codification has been developed in order to allow PSO to solve scheduling problems (which is discrete) with variable number of maintenance interventions. In order to evaluate the proposed methodology, the High Pressure Injection System (HPIS) of a typical 4-loop PWR has been considered. Results demonstrate ability in finding optimal solutions, for which expert knowledge had to be automatically discovered by PSO. (author)

  13. A robust optimization approach for energy generation scheduling in microgrids

    Wang, Ran; Wang, Ping; Xiao, Gaoxi

    2015-01-01

    Highlights: • A new uncertainty model is proposed for better describing unstable energy demands. • An optimization problem is formulated to minimize the cost of microgrid operations. • Robust optimization algorithms are developed to transform and solve the problem. • The proposed scheme can prominently reduce energy expenses. • Numerical results provide useful insights for future investment policy making. - Abstract: In this paper, a cost minimization problem is formulated to intelligently schedule energy generations for microgrids equipped with unstable renewable sources and combined heat and power (CHP) generators. In such systems, the fluctuant net demands (i.e., the electricity demands not balanced by renewable energies) and heat demands impose unprecedented challenges. To cope with the uncertainty nature of net demand and heat demand, a new flexible uncertainty model is developed. Specifically, we introduce reference distributions according to predictions and field measurements and then define uncertainty sets to confine net and heat demands. The model allows the net demand and heat demand distributions to fluctuate around their reference distributions. Another difficulty existing in this problem is the indeterminate electricity market prices. We develop chance constraint approximations and robust optimization approaches to firstly transform and then solve the prime problem. Numerical results based on real-world data evaluate the impacts of different parameters. It is shown that our energy generation scheduling strategy performs well and the integration of combined heat and power (CHP) generators effectively reduces the system expenditure. Our research also helps shed some illuminations on the investment policy making for microgrids.

  14. Optimization and Flight Schedules of Pioneer Routes in Papua Province

    Ronting, Y.; Adisasmita, S. A.; Hamid, S.; Hustim, M.

    2018-04-01

    The province of Papua has a very varied topography, ranging from swampy lowlands, hills, and plateaus up steep hills. The total area of land is 410,660 km2, which consists of 28 counties and one city, 389 districts and 5.420 villages. The population of Papua Province in 2017 was 3.265.202 people with an average growth of 4.21% per year. The transportation services is still low, especially in the mountainous region, which is isolated and could only be reached by an air transportation mode, causing a considerable price disparity between coastal and mountainous areas. The purpose of this paper is to develop the route optimization and pioneer flight schedules models as an airbridge. This research is conducted by collecting primary data and secondary data. Data is based on field surveys; interviews; discussions with airport authority, official government, etc; and also from various agencies. The analytical tools used to optimization flight schedule and route are analyzed by add-in solver in Microsoft Excel. The results of the analysis we can get a more optimal route so that it can save transportation costs by 7.26%.

  15. Multi-objective group scheduling optimization integrated with preventive maintenance

    Liao, Wenzhu; Zhang, Xiufang; Jiang, Min

    2017-11-01

    This article proposes a single-machine-based integration model to meet the requirements of production scheduling and preventive maintenance in group production. To describe the production for identical/similar and different jobs, this integrated model considers the learning and forgetting effects. Based on machine degradation, the deterioration effect is also considered. Moreover, perfect maintenance and minimal repair are adopted in this integrated model. The multi-objective of minimizing total completion time and maintenance cost is taken to meet the dual requirements of delivery date and cost. Finally, a genetic algorithm is developed to solve this optimization model, and the computation results demonstrate that this integrated model is effective and reliable.

  16. Resource allocation in IT projects: using schedule optimization

    Michael Chilton

    2014-01-01

    Full Text Available Resource allocation is the process of assigning resources to tasks throughout the life of a project. Despite sophisticated software packages devoted to keeping track of tasks, resources and resource assignments, it is often the case that project managers find some resources over-allocated and therefore unable to complete the assigned work in the allotted amount of time. Most scheduling software has provisions for leveling resources, but the techniques for doing so simply add time to the schedule and may cause delays in tasks that are critical to the project in meeting deadlines. This paper presents a software application that ensures that resources are properly balanced at the beginning of the project and eliminates the situation in which resources become over-allocated. It can be used in a multi-project environment and reused throughout the project as tasks, resource assignments and availability, and the project scope change. The application utilizes the bounded enumeration technique to formulate an optimal schedule for which both the task sequence and resource availability are taken into account. It is run on a database server to reduce the running time and make it a viable application for practitioners.

  17. Electromagnetic interference-aware transmission scheduling and power control for dynamic wireless access in hospital environments.

    Phunchongharn, Phond; Hossain, Ekram; Camorlinga, Sergio

    2011-11-01

    We study the multiple access problem for e-Health applications (referred to as secondary users) coexisting with medical devices (referred to as primary or protected users) in a hospital environment. In particular, we focus on transmission scheduling and power control of secondary users in multiple spatial reuse time-division multiple access (STDMA) networks. The objective is to maximize the spectrum utilization of secondary users and minimize their power consumption subject to the electromagnetic interference (EMI) constraints for active and passive medical devices and minimum throughput guarantee for secondary users. The multiple access problem is formulated as a dual objective optimization problem which is shown to be NP-complete. We propose a joint scheduling and power control algorithm based on a greedy approach to solve the problem with much lower computational complexity. To this end, an enhanced greedy algorithm is proposed to improve the performance of the greedy algorithm by finding the optimal sequence of secondary users for scheduling. Using extensive simulations, the tradeoff in performance in terms of spectrum utilization, energy consumption, and computational complexity is evaluated for both the algorithms.

  18. Microgrid optimal scheduling considering impact of high penetration wind generation

    Alanazi, Abdulaziz

    The objective of this thesis is to study the impact of high penetration wind energy in economic and reliable operation of microgrids. Wind power is variable, i.e., constantly changing, and nondispatchable, i.e., cannot be controlled by the microgrid controller. Thus an accurate forecasting of wind power is an essential task in order to study its impacts in microgrid operation. Two commonly used forecasting methods including Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) have been used in this thesis to improve the wind power forecasting. The forecasting error is calculated using a Mean Absolute Percentage Error (MAPE) and is improved using the ANN. The wind forecast is further used in the microgrid optimal scheduling problem. The microgrid optimal scheduling is performed by developing a viable model for security-constrained unit commitment (SCUC) based on mixed-integer linear programing (MILP) method. The proposed SCUC is solved for various wind penetration levels and the relationship between the total cost and the wind power penetration is found. In order to reduce microgrid power transfer fluctuations, an additional constraint is proposed and added to the SCUC formulation. The new constraint would control the time-based fluctuations. The impact of the constraint on microgrid SCUC results is tested and validated with numerical analysis. Finally, the applicability of proposed models is demonstrated through numerical simulations.

  19. WE-D-BRE-04: Modeling Optimal Concurrent Chemotherapy Schedules

    Jeong, J; Deasy, J O

    2014-01-01

    Purpose: Concurrent chemo-radiation therapy (CCRT) has become a more common cancer treatment option with a better tumor control rate for several tumor sites, including head and neck and lung cancer. In this work, possible optimal chemotherapy schedules were investigated by implementing chemotherapy cell-kill into a tumor response model of RT. Methods: The chemotherapy effect has been added into a published model (Jeong et al., PMB (2013) 58:4897), in which the tumor response to RT can be simulated with the effects of hypoxia and proliferation. Based on the two-compartment pharmacokinetic model, the temporal concentration of chemotherapy agent was estimated. Log cell-kill was assumed and the cell-kill constant was estimated from the observed increase in local control due to concurrent chemotherapy. For a simplified two cycle CCRT regime, several different starting times and intervals were simulated with conventional RT regime (2Gy/fx, 5fx/wk). The effectiveness of CCRT was evaluated in terms of reduction in radiation dose required for 50% of control to find the optimal chemotherapy schedule. Results: Assuming the typical slope of dose response curve (γ50=2), the observed 10% increase in local control rate was evaluated to be equivalent to an extra RT dose of about 4 Gy, from which the cell-kill rate of chemotherapy was derived to be about 0.35. Best response was obtained when chemotherapy was started at about 3 weeks after RT began. As the interval between two cycles decreases, the efficacy of chemotherapy increases with broader range of optimal starting times. Conclusion: The effect of chemotherapy has been implemented into the resource-conservation tumor response model to investigate CCRT. The results suggest that the concurrent chemotherapy might be more effective when delayed for about 3 weeks, due to lower tumor burden and a larger fraction of proliferating cells after reoxygenation

  20. REPNET: project scheduling and workflow optimization for Construction Projects

    Marco Alvise Bragadin

    2013-10-01

    Full Text Available Project planning and control are core processes for construction management. In practice project planning is achieved by network - based techniques like Precedence Diagramming Method (PDM.Indeed many researchers and practitioners claims that networking techniques as such do not provide a suitable model for construction projects. Construction process modeling should incorporate for specific features of resource flows through project activities. So an improved resource scheduling method for construction is developed, called REPNET, based on a precedence network plotted on a resource–space chart and presented with a flow-line chart. The heuristics of REPNET are used to carry out resource timing while optimizing processes flows and resource usage. The method has been tested on a sample project.

  1. Optimal Rules for Single Machine Scheduling with Stochastic Breakdowns

    Jinwei Gu

    2014-01-01

    Full Text Available This paper studies the problem of scheduling a set of jobs on a single machine subject to stochastic breakdowns, where jobs have to be restarted if preemptions occur because of breakdowns. The breakdown process of the machine is independent of the jobs processed on the machine. The processing times required to complete the jobs are constants if no breakdown occurs. The machine uptimes are independently and identically distributed (i.i.d. and are subject to a uniform distribution. It is proved that the Longest Processing Time first (LPT rule minimizes the expected makespan. For the large-scale problem, it is also showed that the Shortest Processing Time first (SPT rule is optimal to minimize the expected total completion times of all jobs.

  2. Seeking optimality in fruit pulping schedules: A case study*

    J.H. Van Vuuren

    2014-01-01

    Full Text Available The process of scheduling fruit pulping for the production of fruit juices is of great importance in the beverage industry. Decisions have to be made regarding available processing time, the disposal of fruit that will not be pulped before stock loss due to spoilage, the fulfilment of customer demand and an optimal financial position. Sheduling depends on the capacity of the work force, pulping machine limitations and delivery deadlines. However, the situation is often encountered where the plant manager has to decide which fruit batches (usually from stock piles of overwhelming proportions during the harvesting season are to be pulped in order to minimize losses due to fruit deterioration. Such decisions are usually done manually, based on intuition and experience. A mathematical model is presented here which constructs a pulping strategy while minimising cascading financial losses associated with fruit grade drops within the stock pile. It is shown in particular that a minimisation of fruit losses is not a good criterion for optimality, and that substantial financial gains may be accomplished when minimising financial losses in stead of fruit losses, which is currently standard practice at most fruit pulping plants.

  3. Optimal Algorithms and a PTAS for Cost-Aware Scheduling

    L. Chen; N. Megow; R. Rischke; L. Stougie (Leen); J. Verschae

    2015-01-01

    htmlabstractWe consider a natural generalization of classical scheduling problems in which using a time unit for processing a job causes some time-dependent cost which must be paid in addition to the standard scheduling cost. We study the scheduling objectives of minimizing the makespan and the

  4. Optimal Temporal Decoupling in Task Scheduling with Preferences

    Endhoven, L.; Klos, T.B.; Witteveen, C.

    2011-01-01

    Multi-agent planning and scheduling concerns finding a joint plan to achieve some set of common goals with several independent agents each aiming to find a plan or schedule for their part of the goals. To avoid conflicts in these individual plans or schedules decoupling is used. Such a decoupling

  5. Optimal scheduling for distributed hybrid system with pumped hydro storage

    Kusakana, Kanzumba

    2016-01-01

    Highlights: • Pumped hydro storage is proposed for isolated hybrid PV–Wind–Diesel systems. • Optimal control is developed to dispatch power flow economically. • A case study is conducted using the model for an isolated load. • Effects of seasons on the system’s optimal scheduling are examined through simulation. - Abstract: Photovoltaic and wind power generations are currently seen as sustainable options of in rural electrification, particularly in standalone applications. However the variable character of solar and wind resources as well as the variable load demand prevent these generation systems from being totally reliable without suitable energy storage system. Several research works have been conducted on the use of photovoltaic and wind systems in rural electrification; however most of these works have not considered other ways of storing energy except for conventional battery storage systems. In this paper, an energy dispatch model that satisfies the load demand, taking into account the intermittent nature of the solar and wind energy sources and variations in demand, is presented for a hybrid system consisting of a photovoltaic unit, a wind unit, a pumped hydro storage system and a diesel generator. The main purpose of the developed model is to minimize the hybrid system’s operation cost while optimizing the system’s power flow considering the different component’s operational constraints. The simulations have been performed using “fmincon” implemented in Matlab. The model have been applied to two test examples; the simulation results are analyzed and compared to the case where the diesel generator is used alone to supply the given load demand. The results show that using the developed control model for the proposed hybrid system, fuel saving can be achieved compared to the case where the diesel is used alone to supply the same load patters.

  6. Improving management of patients with autism spectrum disorder having scheduled surgery: optimizing practice.

    Thompson, Debbie Gearner; Tielsch-Goddard, Anna

    2014-01-01

    Surgical preparation for children with autism spectrum disorders can be a challenge to perioperative staff because of the unique individual needs and behaviors in this population. Most children with autism function best in predictable, routine environments, and being in the hospital and other health care settings can create a stressful situation. This prospective, descriptive, quality improvement project was conducted to optimize best practices for perioperative staff and better individualize the plan of care for the autistic child and his or her family. Forty-three patients with a diagnosis of autism or autistic spectrum disorder were seen over 6 months at a suburban pediatric hospital affiliated with a major urban pediatric hospital and had an upcoming scheduled surgery or procedure requiring anesthesia. Caregivers were interviewed before and after surgery to collect information to better help their child cope with their hospital visit. In an evaluation of project outcomes, data were tabulated and summarized and interview data were qualitatively coded for emerging themes to improve the perioperative process for the child. Findings showed that staff members were able to recognize potential and actual stressors and help identify individual needs of surgical patients with autism. The families were pleased and appreciative of the individual attention and focus on their child's special needs. Investigators also found increased staff interest in optimizing the surgical experience for autistic children. Copyright © 2014 National Association of Pediatric Nurse Practitioners. Published by Mosby, Inc. All rights reserved.

  7. Optimizing the Shunting Schedule of Electric Multiple Units Depot Using an Enhanced Particle Swarm Optimization Algorithm

    Jiaxi Wang

    2016-01-01

    Full Text Available The shunting schedule of electric multiple units depot (SSED is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality.

  8. Optimizing the Shunting Schedule of Electric Multiple Units Depot Using an Enhanced Particle Swarm Optimization Algorithm

    Jin, Junchen

    2016-01-01

    The shunting schedule of electric multiple units depot (SSED) is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO) algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality. PMID:27436998

  9. TRU Waste Management Program. Cost/schedule optimization analysis

    Detamore, J.A.; Raudenbush, M.H.; Wolaver, R.W.; Hastings, G.A.

    1985-10-01

    This Current Year Work Plan presents in detail a description of the activities to be performed by the Joint Integration Office Rockwell International (JIO/RI) during FY86. It breaks down the activities into two major work areas: Program Management and Program Analysis. Program Management is performed by the JIO/RI by providing technical planning and guidance for the development of advanced TRU waste management capabilities. This includes equipment/facility design, engineering, construction, and operations. These functions are integrated to allow transition from interim storage to final disposition. JIO/RI tasks include program requirements identification, long-range technical planning, budget development, program planning document preparation, task guidance development, task monitoring, task progress information gathering and reporting to DOE, interfacing with other agencies and DOE lead programs, integrating public involvement with program efforts, and preparation of reports for DOE detailing program status. Program Analysis is performed by the JIO/RI to support identification and assessment of alternatives, and development of long-term TRU waste program capabilities. These analyses include short-term analyses in response to DOE information requests, along with performing an RH Cost/Schedule Optimization report. Systems models will be developed, updated, and upgraded as needed to enhance JIO/RI's capability to evaluate the adequacy of program efforts in various fields. A TRU program data base will be maintained and updated to provide DOE with timely responses to inventory related questions

  10. Optimal OFDMA Downlink Scheduling Under a Control Signaling Cost Constraint

    Larsson, Erik G.

    2010-01-01

    This paper proposes a new algorithm for downlink scheduling in OFDMA systems. The method maximizes the throughput, taking into account the amount of signaling needed to transmit scheduling maps to the users. A combinatorial problem is formulated and solved via a dynamic programming approach reminiscent of the Viterbi algorithm. The total computational complexity of the algorithm is upper boundedby O(K^4N) where K is the number of users that are being considered for scheduling in a frame and N...

  11. Simultaneous optimization of planning and scheduling in an oil refinery

    Zondervan, E.; van Boekel, T.P.J.; Fransoo, J.C.; Haan, de A.B.; Postikopoulos, E.N.; Georgiadis, M.C.; Kokossis, A.

    2011-01-01

    In earlier work we have developed and tested a scheduling model [1] in the AIMMS software. In this follow-up contribution we will develop a planning model. Next we will identify the information flow between scheduling model and the planning model. Lastly we will integrate the two models in a

  12. Optimizing an F-16 Squadron Weekly Pilot Schedule for the Turkish Air Force

    2010-03-01

    disrupted schedules are rescheduled , minimizing the total number of changes with respect to the previous schedule’s objective function. Output...producing rosters for a nursing staff in a large general hospital (Dowsland, 1998) and afterwards Aickelin and Dowsland use an Indirect Genetic...algorithm to improve the solutions of the nurse scheduling problem which is similar to the fighter squadron pilot scheduling problem (Aickelin and

  13. OPTIMIZED PARTICLE SWARM OPTIMIZATION BASED DEADLINE CONSTRAINED TASK SCHEDULING IN HYBRID CLOUD

    Dhananjay Kumar

    2016-01-01

    Full Text Available Cloud Computing is a dominant way of sharing of computing resources that can be configured and provisioned easily. Task scheduling in Hybrid cloud is a challenge as it suffers from producing the best QoS (Quality of Service when there is a high demand. In this paper a new resource allocation algorithm, to find the best External Cloud provider when the intermediate provider’s resources aren’t enough to satisfy the customer’s demand is proposed. The proposed algorithm called Optimized Particle Swarm Optimization (OPSO combines the two metaheuristic algorithms namely Particle Swarm Optimization and Ant Colony Optimization (ACO. These metaheuristic algorithms are used for the purpose of optimization in the search space of the required solution, to find the best resource from the pool of resources and to obtain maximum profit even when the number of tasks submitted for execution is very high. This optimization is performed to allocate job requests to internal and external cloud providers to obtain maximum profit. It helps to improve the system performance by improving the CPU utilization, and handle multiple requests at the same time. The simulation result shows that an OPSO yields 0.1% - 5% profit to the intermediate cloud provider compared with standard PSO and ACO algorithms and it also increases the CPU utilization by 0.1%.

  14. PARTICAL SWARM OPTIMIZATION OF TASK SCHEDULING IN CLOUD COMPUTING

    Payal Jaglan*, Chander Diwakar

    2016-01-01

    Resource provisioning and pricing modeling in cloud computing makes it an inevitable technology both on developer and consumer end. Easy accessibility of software and freedom of hardware configuration increase its demand in IT industry. It’s ability to provide a user-friendly environment, software independence, quality, pricing index and easy accessibility of infrastructure via internet. Task scheduling plays an important role in cloud computing systems. Task scheduling in cloud computing mea...

  15. A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan

    Lian Zhigang; Gu Xingsheng; Jiao Bin

    2008-01-01

    It is well known that the flow-shop scheduling problem (FSSP) is a branch of production scheduling and is NP-hard. Now, many different approaches have been applied for permutation flow-shop scheduling to minimize makespan, but current algorithms even for moderate size problems cannot be solved to guarantee optimality. Some literatures searching PSO for continuous optimization problems are reported, but papers searching PSO for discrete scheduling problems are few. In this paper, according to the discrete characteristic of FSSP, a novel particle swarm optimization (NPSO) algorithm is presented and successfully applied to permutation flow-shop scheduling to minimize makespan. Computation experiments of seven representative instances (Taillard) based on practical data were made, and comparing the NPSO with standard GA, we obtain that the NPSO is clearly more efficacious than standard GA for FSSP to minimize makespan

  16. Sensitivity of the optimal parameter settings for a LTE packet scheduler

    Fernandez-Diaz, I.; Litjens, R.; van den Berg, C.A.; Dimitrova, D.C.; Spaey, K.

    Advanced packet scheduling schemes in 3G/3G+ mobile networks provide one or more parameters to optimise the trade-off between QoS and resource efficiency. In this paper we study the sensitivity of the optimal parameter setting for packet scheduling in LTE radio networks with respect to various

  17. Simulated Stochastic Approximation Annealing for Global Optimization With a Square-Root Cooling Schedule

    Liang, Faming; Cheng, Yichen; Lin, Guang

    2014-01-01

    cooling schedule, for example, a square-root cooling schedule, while guaranteeing the global optima to be reached when the temperature tends to zero. The new algorithm has been tested on a few benchmark optimization problems, including feed-forward neural

  18. Monitoring Strategies in Permeable Pavement Systems to Optimize Maintenance Scheduling

    As the surface in a permeable pavement system clogs and performance decreases, maintenance is required to preserve the design function. Currently, guidance is limited for scheduling maintenance on an as needed basis. Previous research has shown that surface clogging in a permea...

  19. optimal scheduling of petroleum products distribution in nigeria

    MECHANICAL ENGINEERING

    The study reveals that any variation in supply, demand and ... and storage depots for easy shipment of the products from ... The system should be robust yet simple to support routine ..... (10)Klabjan, D. Topics in airline crew scheduling and ...

  20. An optimal algorithm for preemptive on-line scheduling

    Chen, B.; Vliet, van A.; Woeginger, G.J.

    1995-01-01

    We investigate the problem of on-line scheduling jobs on m identical parallel machines where preemption is allowed. The goal is to minimize the makespan. We derive an approximation algorithm with worst-case guarantee mm/(mm - (m - 1)m) for every m 2, which increasingly tends to e/(e - 1) ˜ 1.58 as m

  1. Scheduling with Optimized Communication for Time-Triggered Embedded Systems

    Pop, Paul; Eles, Petru; Peng, Zebo

    1999-01-01

    We present an approach to process scheduling for synthesis of safety-critical distributed embedded systems.Our system model captures both the flow of data and that of control. The communication model is based on a time-triggered protocol. We take into consideration overheads due to communication...

  2. Optimal Power Scheduling for an Islanded Hybrid Microgrid

    Hernández, Adriana Carolina Luna; Aldana, Nelson Leonardo Diaz; Savaghebi, Mehdi

    2016-01-01

    model, wherein the disconnection of the load and not charging the battery when there is surplus of energy are penalized while physical constraints and requirements for a feasible deployment in the real system are considered. The proposed scheduling scheme is tested using a real-time control platform (d...

  3. Simulation and Optimization Methodologies for Military Transportation Network Routing and Scheduling and for Military Medical Services

    Rodin, Ervin Y

    2005-01-01

    The purpose of this present research was to develop a generic model and methodology for analyzing and optimizing large-scale air transportation networks including both their routing and their scheduling...

  4. Discrete bat algorithm for optimal problem of permutation flow shop scheduling.

    Luo, Qifang; Zhou, Yongquan; Xie, Jian; Ma, Mingzhi; Li, Liangliang

    2014-01-01

    A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the discrete bat algorithm to further improve the performance. Finally, the experimental results show the suitability and efficiency of the present discrete bat algorithm for optimal permutation flow shop scheduling problem.

  5. Discrete Bat Algorithm for Optimal Problem of Permutation Flow Shop Scheduling

    Luo, Qifang; Zhou, Yongquan; Xie, Jian; Ma, Mingzhi; Li, Liangliang

    2014-01-01

    A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the discrete bat algorithm to further improve the performance. Finally, the experimental results show the suitability and efficiency of the present discrete bat algorithm for optimal permutation flow shop scheduling problem. PMID:25243220

  6. Optimizing Training Event Schedules at Naval Air Station Fallon

    2018-03-01

    Time VBA Visual Basic for Applications WTI Weapons and Tactics Instructor xiii THIS PAGE INTENTIONALLY LEFT BLANK xiv Executive Summary Naval Air...emitter inventory for each site. Constraint (3.6) ensures scheduled flight events have access to an emitter, at the proper location, required for the...flight event requests and their respective requirements into a specificmacro-enabled excel worksheet (Microsoftl, 2017). A series of VBA ( VBA , 2017

  7. Preemptive Online Scheduling: Optimal Algorithms for All Speeds

    Ebenlendr, Tomáš; Jawor, W.; Sgall, Jiří

    2009-01-01

    Roč. 53, č. 4 (2009), s. 504-522 ISSN 0178-4617 R&D Projects: GA MŠk(CZ) 1M0545; GA ČR GA201/05/0124; GA AV ČR IAA1019401 Institutional research plan: CEZ:AV0Z10190503 Keywords : anline algorithms * scheduling Subject RIV: IN - Informatics, Computer Science Impact factor: 0.917, year: 2009

  8. Optimal and online preemptive scheduling on uniformly related machines

    Ebenlendr, Tomáš; Sgall, J.

    2009-01-01

    Roč. 12, č. 5 (2009), s. 517-527 ISSN 1094-6136 R&D Projects: GA MŠk(CZ) 1M0545; GA AV ČR IAA100190902; GA AV ČR IAA1019401 Institutional research plan: CEZ:AV0Z10190503 Keywords : online scheduling * preemption * uniformly related machines Subject RIV: IN - Informatics, Computer Science Impact factor: 1.265, year: 2009

  9. Military Free Fall Scheduling And Manifest Optimization Model

    2016-12-01

    zone. As interest in qualifying more personnel increased, the course expanded. By the mid-1990s, Reyes explains, a new location was required to better...Since 2005, the Chilean Professional Soccer Association has used operations research techniques to schedule professional leagues in Chile . These...a new parachute the students are using, the RA-1. The RA-1 parachute has a longer glide ratio, which means the rate of descent is slower than with

  10. An optimal dynamic interval preventive maintenance scheduling for series systems

    Gao, Yicong; Feng, Yixiong; Zhang, Zixian; Tan, Jianrong

    2015-01-01

    This paper studies preventive maintenance (PM) with dynamic interval for a multi-component system. Instead of equal interval, the time of PM period in the proposed dynamic interval model is not a fixed constant, which varies from interval-down to interval-up. It is helpful to reduce the outage loss on frequent repair parts and avoid lack of maintenance of the equipment by controlling the equipment maintenance frequency, when compared to a periodic PM scheme. According to the definition of dynamic interval, the reliability of system is analyzed from the failure mechanisms of its components and the different effects of non-periodic PM actions on the reliability of the components. Following the proposed model of reliability, a novel framework for solving the non-periodical PM schedule with dynamic interval based on the multi-objective genetic algorithm is proposed. The framework denotes the strategies include updating strategy, deleting strategy, inserting strategy and moving strategy, which is set to correct the invalid population individuals of the algorithm. The values of the dynamic interval and the selections of PM action for the components on every PM stage are determined by achieving a certain level of system availability with the minimum total PM-related cost. Finally, a typical rotary table system of NC machine tool is used as an example to describe the proposed method. - Highlights: • A non-periodic preventive maintenance scheduling model is proposed. • A framework for solving the non-periodical PM schedule problem is developed. • The interval of non-periodic PM is flexible and schedule can be better adjusted. • Dynamic interval leads to more efficient solutions than fixed interval does

  11. Optimal imaging surveillance schedules after liver-directed therapy for hepatocellular carcinoma.

    Boas, F Edward; Do, Bao; Louie, John D; Kothary, Nishita; Hwang, Gloria L; Kuo, William T; Hovsepian, David M; Kantrowitz, Mark; Sze, Daniel Y

    2015-01-01

    To optimize surveillance schedules for the detection of recurrent hepatocellular carcinoma (HCC) after liver-directed therapy. New methods have emerged that allow quantitative analysis and optimization of surveillance schedules for diseases with substantial rates of recurrence such as HCC. These methods were applied to 1,766 consecutive chemoembolization, radioembolization, and radiofrequency ablation procedures performed on 910 patients between 2006 and 2011. Computed tomography or magnetic resonance imaging performed just before repeat therapy was set as the time of "recurrence," which included residual and locally recurrent tumor as well as new liver tumors. Time-to-recurrence distribution was estimated by Kaplan-Meier method. Average diagnostic delay (time between recurrence and detection) was calculated for each proposed surveillance schedule using the time-to-recurrence distribution. An optimized surveillance schedule could then be derived to minimize the average diagnostic delay. Recurrence is 6.5 times more likely in the first year after treatment than in the second. Therefore, screening should be much more frequent in the first year. For eight time points in the first 2 years of follow-up, the optimal schedule is 2, 4, 6, 8, 11, 14, 18, and 24 months. This schedule reduces diagnostic delay compared with published schedules and is cost-effective. The calculated optimal surveillance schedules include shorter-interval follow-up when there is a higher probability of recurrence and longer-interval follow-up when there is a lower probability. Cost can be optimized for a specified acceptable diagnostic delay or diagnostic delay can be optimized within a specified acceptable cost. Copyright © 2015 SIR. Published by Elsevier Inc. All rights reserved.

  12. Optimal Scheduling of a Multi-Carrier Energy Hub Supplemented By Battery Energy Storage Systems

    Javadi, Mohammad Sadegh; Anvari-Moghaddam, Amjad; Guerrero, Josep M.

    2017-01-01

    This paper introduces a management model for optimal scheduling of a multi-carrier energy hub. In the proposed hub, three types of assets are considered: dispersed generating systems (DGs) such as micro-combined heat and power (mCHP) units, storage devices such as battery-based electrical storage...... systems (ESSs), and heating/cooling devices such as electrical heater, heat-pumps and absorption chillers. The optimal scheduling and management of the examined energy hub assets in line with electrical transactions with distribution network is modeled as a mixed-integer non-linear optimization problem....... In this regard, optimal operating points of DG units as well as ESSs are calculated based on a cost-effective strategy. Degradation cost of ESSs is also taken into consideration for short-term scheduling. Simulation results demonstrate that including well-planned energy storage options together with optimal...

  13. DAILY SCHEDULING OF SMALL HYDRO POWER PLANTS DISPATCH WITH MODIFIED PARTICLES SWARM OPTIMIZATION

    Sinvaldo Rodrigues Moreno

    2015-04-01

    Full Text Available This paper presents a new approach for short-term hydro power scheduling of reservoirs using an algorithm-based Particle Swarm Optimization (PSO. PSO is a population-based algorithm designed to find good solutions to optimization problems, its characteristics have encouraged its adoption to tackle a variety of problems in different fields. In this paper the authors consider an optimization problem related to a daily scheduling of small hydro power dispatch. The goal is construct a feasible solution that maximize the cascade electricity production, following the environmental constraints and water balance. The paper proposes an improved Particle Swarm Optimization (PSO algorithm, which takes advantage of simplicity and facility of implementation. The algorithm was successfully applied to the optimization of the daily schedule strategies of small hydro power plants, considering maximum water utilization and all constraints related to simultaneous water uses. Extensive computational tests and comparisons with other heuristics methods showed the effectiveness of the proposed approach.

  14. Scheduling algorithm for data relay satellite optical communication based on artificial intelligent optimization

    Zhao, Wei-hu; Zhao, Jing; Zhao, Shang-hong; Li, Yong-jun; Wang, Xiang; Dong, Yi; Dong, Chen

    2013-08-01

    Optical satellite communication with the advantages of broadband, large capacity and low power consuming broke the bottleneck of the traditional microwave satellite communication. The formation of the Space-based Information System with the technology of high performance optical inter-satellite communication and the realization of global seamless coverage and mobile terminal accessing are the necessary trend of the development of optical satellite communication. Considering the resources, missions and restraints of Data Relay Satellite Optical Communication System, a model of optical communication resources scheduling is established and a scheduling algorithm based on artificial intelligent optimization is put forwarded. According to the multi-relay-satellite, multi-user-satellite, multi-optical-antenna and multi-mission with several priority weights, the resources are scheduled reasonable by the operation: "Ascertain Current Mission Scheduling Time" and "Refresh Latter Mission Time-Window". The priority weight is considered as the parameter of the fitness function and the scheduling project is optimized by the Genetic Algorithm. The simulation scenarios including 3 relay satellites with 6 optical antennas, 12 user satellites and 30 missions, the simulation result reveals that the algorithm obtain satisfactory results in both efficiency and performance and resources scheduling model and the optimization algorithm are suitable in multi-relay-satellite, multi-user-satellite, and multi-optical-antenna recourses scheduling problem.

  15. Congestion game scheduling for virtual drug screening optimization

    Nikitina, Natalia; Ivashko, Evgeny; Tchernykh, Andrei

    2018-02-01

    In virtual drug screening, the chemical diversity of hits is an important factor, along with their predicted activity. Moreover, interim results are of interest for directing the further research, and their diversity is also desirable. In this paper, we consider a problem of obtaining a diverse set of virtual screening hits in a short time. To this end, we propose a mathematical model of task scheduling for virtual drug screening in high-performance computational systems as a congestion game between computational nodes to find the equilibrium solutions for best balancing the number of interim hits with their chemical diversity. The model considers the heterogeneous environment with workload uncertainty, processing time uncertainty, and limited knowledge about the input dataset structure. We perform computational experiments and evaluate the performance of the developed approach considering organic molecules database GDB-9. The used set of molecules is rich enough to demonstrate the feasibility and practicability of proposed solutions. We compare the algorithm with two known heuristics used in practice and observe that game-based scheduling outperforms them by the hit discovery rate and chemical diversity at earlier steps. Based on these results, we use a social utility metric for assessing the efficiency of our equilibrium solutions and show that they reach greatest values.

  16. Cross-layer optimized rate adaptation and scheduling for multiple-user wireless video streaming

    Ozcelebi, T.; Sunay, M.O.; Tekalp, A.M.; Civanlar, M.R.

    2007-01-01

    We present a cross-layer optimized video rate adaptation and user scheduling scheme for multi-user wireless video streaming aiming for maximum quality of service (QoS) for each user,, maximum system video throughput, and QoS fairness among users. These objectives are jointly optimized using a

  17. Geometric Distribution-Based Readers Scheduling Optimization Algorithm Using Artificial Immune System

    Litian Duan

    2016-11-01

    Full Text Available In the multiple-reader environment (MRE of radio frequency identification (RFID system, multiple readers are often scheduled to interrogate the randomized tags via operating at different time slots or frequency channels to decrease the signal interferences. Based on this, a Geometric Distribution-based Multiple-reader Scheduling Optimization Algorithm using Artificial Immune System (GD-MRSOA-AIS is proposed to fairly and optimally schedule the readers operating from the viewpoint of resource allocations. GD-MRSOA-AIS is composed of two parts, where a geometric distribution function combined with the fairness consideration is first introduced to generate the feasible scheduling schemes for reader operation. After that, artificial immune system (including immune clone, immune mutation and immune suppression quickly optimize these feasible ones as the optimal scheduling scheme to ensure that readers are fairly operating with larger effective interrogation range and lower interferences. Compared with the state-of-the-art algorithm, the simulation results indicate that GD-MRSOA-AIS could efficiently schedules the multiple readers operating with a fairer resource allocation scheme, performing in larger effective interrogation range.

  18. Improving financial performance by modeling and analysis of radiology procedure scheduling at a large community hospital.

    Lu, Lingbo; Li, Jingshan; Gisler, Paula

    2011-06-01

    Radiology tests, such as MRI, CT-scan, X-ray and ultrasound, are cost intensive and insurance pre-approvals are necessary to get reimbursement. In some cases, tests may be denied for payments by insurance companies due to lack of pre-approvals, inaccurate or missing necessary information. This can lead to substantial revenue losses for the hospital. In this paper, we present a simulation study of a centralized scheduling process for outpatient radiology tests at a large community hospital (Central Baptist Hospital in Lexington, Kentucky). Based on analysis of the central scheduling process, a simulation model of information flow in the process has been developed. Using such a model, the root causes of financial losses associated with errors and omissions in this process were identified and analyzed, and their impacts were quantified. In addition, "what-if" analysis was conducted to identify potential process improvement strategies in the form of recommendations to the hospital leadership. Such a model provides a quantitative tool for continuous improvement and process control in radiology outpatient test scheduling process to reduce financial losses associated with process error. This method of analysis is also applicable to other departments in the hospital.

  19. RSM 1.0 - A RESUPPLY SCHEDULER USING INTEGER OPTIMIZATION

    Viterna, L. A.

    1994-01-01

    RSM, Resupply Scheduling Modeler, is a fully menu-driven program that uses integer programming techniques to determine an optimum schedule for replacing components on or before the end of a fixed replacement period. Although written to analyze the electrical power system on the Space Station Freedom, RSM is quite general and can be used to model the resupply of almost any system subject to user-defined resource constraints. RSM is based on a specific form of the general linear programming problem in which all variables in the objective function and all variables in the constraints are integers. While more computationally intensive, integer programming was required for accuracy when modeling systems with small quantities of components. Input values for component life cane be real numbers, RSM converts them to integers by dividing the lifetime by the period duration, then reducing the result to the next lowest integer. For each component, there is a set of constraints that insure that it is replaced before its lifetime expires. RSM includes user-defined constraints such as transportation mass and volume limits, as well as component life, available repair crew time and assembly sequences. A weighting factor allows the program to minimize factors such as cost. The program then performs an iterative analysis, which is displayed during the processing. A message gives the first period in which resources are being exceeded on each iteration. If the scheduling problem is unfeasible, the final message will also indicate the first period in which resources were exceeded. RSM is written in APL2 for IBM PC series computers and compatibles. A stand-alone executable version of RSM is provided; however, this is a "packed" version of RSM which can only utilize the memory within the 640K DOS limit. This executable requires at least 640K of memory and DOS 3.1 or higher. Source code for an APL2/PC workspace version is also provided. This version of RSM can make full use of any

  20. Trusted Service Scheduling and Optimization Strategy Design of Service Recommendation

    Xiaona Xia

    2017-01-01

    Full Text Available More and more Web services raise the demands of personalized service recommendation; there exist some recommendation technologies, which improve the qualities of service recommendation by using service ranking and collaborative filtering. However, privacy and security are also important issues in service scheduling process; social relationships have been the key factors of interpersonal communication; service selection based on user preferences has become an inevitable trend. Starting from user demand preferences, this paper analyzes social topology and service demand information and obtains trusted social relationships; then we construct the fusion model of service historical preferences and potential ones; according to social service recommendation demands, TSRSR algorithm has completed designing. Through experiments, TSRSR algorithm is much better than the others, which can effectively improve potential preferences’ learning. Furthermore, the research results of this paper have more significance to study the security and privacy of service recommendation.

  1. HURON (HUman and Robotic Optimization Network) Multi-Agent Temporal Activity Planner/Scheduler

    Hua, Hook; Mrozinski, Joseph J.; Elfes, Alberto; Adumitroaie, Virgil; Shelton, Kacie E.; Smith, Jeffrey H.; Lincoln, William P.; Weisbin, Charles R.

    2012-01-01

    HURON solves the problem of how to optimize a plan and schedule for assigning multiple agents to a temporal sequence of actions (e.g., science tasks). Developed as a generic planning and scheduling tool, HURON has been used to optimize space mission surface operations. The tool has also been used to analyze lunar architectures for a variety of surface operational scenarios in order to maximize return on investment and productivity. These scenarios include numerous science activities performed by a diverse set of agents: humans, teleoperated rovers, and autonomous rovers. Once given a set of agents, activities, resources, resource constraints, temporal constraints, and de pendencies, HURON computes an optimal schedule that meets a specified goal (e.g., maximum productivity or minimum time), subject to the constraints. HURON performs planning and scheduling optimization as a graph search in state-space with forward progression. Each node in the graph contains a state instance. Starting with the initial node, a graph is automatically constructed with new successive nodes of each new state to explore. The optimization uses a set of pre-conditions and post-conditions to create the children states. The Python language was adopted to not only enable more agile development, but to also allow the domain experts to easily define their optimization models. A graphical user interface was also developed to facilitate real-time search information feedback and interaction by the operator in the search optimization process. The HURON package has many potential uses in the fields of Operations Research and Management Science where this technology applies to many commercial domains requiring optimization to reduce costs. For example, optimizing a fleet of transportation truck routes, aircraft flight scheduling, and other route-planning scenarios involving multiple agent task optimization would all benefit by using HURON.

  2. Scheduling home-appliances to optimize energy consumption

    Rossello Busquet, Ana

    In order to optimize the energy consumption, energy demand peaks should be avoided, and energy consumption should be smoothly distributed over time. This can be achieved by setting a maximum energy consumption per user’s household. In other words, the overall consumption of the user’s appliances...

  3. TaskMaster: a prototype graphical user interface to a schedule optimization model

    Banham, Stephen R.

    1990-01-01

    Approved for public release, distribution is unlimited This thesis investigates the use of current graphical interface techniques to build more effective computer-user interfaces to Operations Research (OR) schedule optimization models. The design is directed at the scheduling decision maker who possesses limited OR experience. The feasibility and validity of building an interface for this kind of user is demonstrated in the development of a prototype graphical user interface called TaskMa...

  4. Optimization of Multiperiod Mixed Train Schedule on High-Speed Railway

    Wenliang Zhou

    2015-01-01

    Full Text Available For providing passengers with periodic operation trains and making trains’ time distribution better fit that of passengers, the multiperiod mixed train schedule is first proposed in this paper. It makes each type of train having same origin, destination, route, and stop stations operate based on a periodic basis and allows different types of train to have various operation periods. Then a model of optimizing multiperiod mixed train schedule is built to minimize passengers generalized travel costs with the constraints of trains of same type operating periodically, safe interval requirements of trains’ departure, and arrival times, and so forth. And its heuristic algorithm is designed to optimize the multiperiod mixed train schedule beginning with generating an initial solution by scheduling all types of train type by type and then repeatedly improving their periodic schedules until the objective value cannot be reduced or the iteration number reaches its maximum. Finally, example results illustrate that the proposed model and algorithm can effectively gain a better multiperiod mixed train schedule. However, its passengers deferred times and advanced times are a little higher than these of an aperiodic train schedule.

  5. Constraint optimization model of a scheduling problem for a robotic arm in automatic systems

    Kristiansen, Ewa; Smith, Stephen F.; Kristiansen, Morten

    2014-01-01

    are characteristics of the painting process application itself. Unlike spot-welding, painting tasks require movement of the entire robot arm. In addition to minimizing intertask duration, the scheduler must strive to maximize painting quality and the problem is formulated as a multi-objective optimization problem....... The scheduling model is implemented as a stand-alone module using constraint programming, and integrated with a larger automatic system. The results of a number of simulation experiments with simple parts are reported, both to characterize the functionality of the scheduler and to illustrate the operation...... of the entire software system for automatic generation of robot programs for painting....

  6. A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm

    Liping Liu

    2018-01-01

    Full Text Available Cognitive radio is a promising technology for improving spectrum utilization, which allows cognitive users access to the licensed spectrum while primary users are absent. In this paper, we design a resource allocation framework based on graph theory for spectrum assignment in cognitive radio networks. The framework takes into account the constraints that interference for primary users and possible collision among cognitive users. Based on the proposed model, we formulate a system utility function to maximize the system benefit. Based on the proposed model and objective problem, we design an improved ant colony optimization algorithm (IACO from two aspects: first, we introduce differential evolution (DE process to accelerate convergence speed by monitoring mechanism; then we design a variable neighborhood search (VNS process to avoid the algorithm falling into the local optimal. Simulation results demonstrate that the improved algorithm achieves better performance.

  7. Optimization of time–temperature schedule for nitridation of silicon ...

    pact was optimized by kinetic study of the reaction, 3Si + 2N2 = Si3N4 at four different temperatures (1250°C,. 1300°C, 1350°C and 1400°C). ... Reaction sintered silicon nitride; nitridation; reaction kinetics. 1. Introduction. Formation of ..... cation of silica layer resulted in active oxidation of silicon at high temperature to ...

  8. Hybrid glowworm swarm optimization for task scheduling in the cloud environment

    Zhou, Jing; Dong, Shoubin

    2018-06-01

    In recent years many heuristic algorithms have been proposed to solve task scheduling problems in the cloud environment owing to their optimization capability. This article proposes a hybrid glowworm swarm optimization (HGSO) based on glowworm swarm optimization (GSO), which uses a technique of evolutionary computation, a strategy of quantum behaviour based on the principle of neighbourhood, offspring production and random walk, to achieve more efficient scheduling with reasonable scheduling costs. The proposed HGSO reduces the redundant computation and the dependence on the initialization of GSO, accelerates the convergence and more easily escapes from local optima. The conducted experiments and statistical analysis showed that in most cases the proposed HGSO algorithm outperformed previous heuristic algorithms to deal with independent tasks.

  9. Efficient operation scheduling for adsorption chillers using predictive optimization-based control methods

    Bürger, Adrian; Sawant, Parantapa; Bohlayer, Markus; Altmann-Dieses, Angelika; Braun, Marco; Diehl, Moritz

    2017-10-01

    Within this work, the benefits of using predictive control methods for the operation of Adsorption Cooling Machines (ACMs) are shown on a simulation study. Since the internal control decisions of series-manufactured ACMs often cannot be influenced, the work focuses on optimized scheduling of an ACM considering its internal functioning as well as forecasts for load and driving energy occurrence. For illustration, an assumed solar thermal climate system is introduced and a system model suitable for use within gradient-based optimization methods is developed. The results of a system simulation using a conventional scheme for ACM scheduling are compared to the results of a predictive, optimization-based scheduling approach for the same exemplary scenario of load and driving energy occurrence. The benefits of the latter approach are shown and future actions for application of these methods for system control are addressed.

  10. Optimization of scheduling system for plant watering using electric cars in agro techno park

    Oktavia Adiwijaya, Nelly; Herlambang, Yudha; Slamin

    2018-04-01

    Agro Techno Park in University of Jember is a special area used for the development of agriculture, livestock and fishery. In this plantation, the process of watering the plants is according to the frequency of each plant needs. This research develops the optimization of plant watering scheduling system using edge coloring of graph. This research was conducted in 3 stages, namely, data collection phase, analysis phase, and system development stage. The collected data was analyzed and then converted into a graph by using bipartite adjacency matrix representation. The development phase is conducted to build a web-based watering schedule optimization system. The result of this research showed that the schedule system is optimal because it can maximize the use of all electric cars to water the plants and minimize the number of idle cars.

  11. Optimal Electric Vehicle Scheduling: A Co-Optimized System and Customer Perspective

    Maigha

    Electric vehicles provide a two pronged solution to the problems faced by the electricity and transportation sectors. They provide a green, highly efficient alternative to the internal combustion engine vehicles, thus reducing our dependence on fossil fuels. Secondly, they bear the potential of supporting the grid as energy storage devices while incentivising the customers through their participation in energy markets. Despite these advantages, widespread adoption of electric vehicles faces socio-technical and economic bottleneck. This dissertation seeks to provide solutions that balance system and customer objectives under present technological capabilities. The research uses electric vehicles as controllable loads and resources. The idea is to provide the customers with required tools to make an informed decision while considering the system conditions. First, a genetic algorithm based optimal charging strategy to reduce the impact of aggregated electric vehicle load has been presented. A Monte Carlo based solution strategy studies change in the solution under different objective functions. This day-ahead scheduling is then extended to real-time coordination using a moving-horizon approach. Further, battery degradation costs have been explored with vehicle-to-grid implementations, thus accounting for customer net-revenue and vehicle utility for grid support. A Pareto front, thus obtained, provides the nexus between customer and system desired operating points. Finally, we propose a transactive business model for a smart airport parking facility. This model identifies various revenue streams and satisfaction indices that benefit the parking lot owner and the customer, thus adding value to the electric vehicle.

  12. Optimal hydro scheduling and offering strategies considering price uncertainty and risk management

    Catalão, J.P.S.; Pousinho, H.M.I.; Contreras, J.

    2012-01-01

    Hydro energy represents a priority in the energy policy of Portugal, with the aim of decreasing the dependence on fossil fuels. In this context, optimal hydro scheduling acquires added significance in moving towards a sustainable environment. A mixed-integer nonlinear programming approach is considered to enable optimal hydro scheduling for the short-term time horizon, including the effect of head on power production, start-up costs related to the units, multiple regions of operation, and constraints on discharge variation. As new contributions to the field, market uncertainty is introduced in the model via price scenarios and risk management is included using Conditional Value-at-Risk to limit profit volatility. Moreover, plant scheduling and pool offering by the hydro power producer are simultaneously considered to solve a realistic cascaded hydro system. -- Highlights: ► A mixed-integer nonlinear programming approach is considered for optimal hydro scheduling. ► Market uncertainty is introduced in the model via price scenarios. ► Risk management is included using conditional value-at-risk. ► Plant scheduling and pool offering by the hydro power producer are simultaneously considered. ► A realistic cascaded hydro system is solved.

  13. An improved ant colony optimization algorithm with fault tolerance for job scheduling in grid computing systems.

    Hajara Idris

    Full Text Available The Grid scheduler, schedules user jobs on the best available resource in terms of resource characteristics by optimizing job execution time. Resource failure in Grid is no longer an exception but a regular occurring event as resources are increasingly being used by the scientific community to solve computationally intensive problems which typically run for days or even months. It is therefore absolutely essential that these long-running applications are able to tolerate failures and avoid re-computations from scratch after resource failure has occurred, to satisfy the user's Quality of Service (QoS requirement. Job Scheduling with Fault Tolerance in Grid Computing using Ant Colony Optimization is proposed to ensure that jobs are executed successfully even when resource failure has occurred. The technique employed in this paper, is the use of resource failure rate, as well as checkpoint-based roll back recovery strategy. Check-pointing aims at reducing the amount of work that is lost upon failure of the system by immediately saving the state of the system. A comparison of the proposed approach with an existing Ant Colony Optimization (ACO algorithm is discussed. The experimental results of the implemented Fault Tolerance scheduling algorithm show that there is an improvement in the user's QoS requirement over the existing ACO algorithm, which has no fault tolerance integrated in it. The performance evaluation of the two algorithms was measured in terms of the three main scheduling performance metrics: makespan, throughput and average turnaround time.

  14. Simulated Stochastic Approximation Annealing for Global Optimization With a Square-Root Cooling Schedule

    Liang, Faming

    2014-04-03

    Simulated annealing has been widely used in the solution of optimization problems. As known by many researchers, the global optima cannot be guaranteed to be located by simulated annealing unless a logarithmic cooling schedule is used. However, the logarithmic cooling schedule is so slow that no one can afford to use this much CPU time. This article proposes a new stochastic optimization algorithm, the so-called simulated stochastic approximation annealing algorithm, which is a combination of simulated annealing and the stochastic approximation Monte Carlo algorithm. Under the framework of stochastic approximation, it is shown that the new algorithm can work with a cooling schedule in which the temperature can decrease much faster than in the logarithmic cooling schedule, for example, a square-root cooling schedule, while guaranteeing the global optima to be reached when the temperature tends to zero. The new algorithm has been tested on a few benchmark optimization problems, including feed-forward neural network training and protein-folding. The numerical results indicate that the new algorithm can significantly outperform simulated annealing and other competitors. Supplementary materials for this article are available online.

  15. Optimization-based sale transactions and hydrothermal scheduling

    Prasannan, B.; Luh, P.B.; Zhang, L.

    1996-01-01

    Selling and purchasing power are important activities for utilities because of potential savings. When a selling utility presents an offer including prices, power levels and durations, a purchasing utility selects power levels and durations within the offered range subject to relevant constraints. The decisionmaking process is complicated because transactions are coupled with system demand and reserve, therefore decisions have to be made in conjunction with the commitment and dispatching of units. Furthermore, transaction decisions have to be made in almost real time in view of the competitiveness of the power market caused by deregulation. In this paper, transactions are analyzed from a selling utility's viewpoint for a system consisting of thermal, hydro and pumped-storage units. To effectively solve the problem, linear sale revenues are approximated by nonlinear functions, and non-profitable options are identified and eliminated from consideration. The multipliers are then updated at the high level by using a modified subgradient method to obtain near optimal solutions quickly. Testing results show that the algorithm produces good sale offers efficiently

  16. A Distributed Particle Swarm Optimization Zlgorithmfor Flexible Job-hop Scheduling Problem

    LIU Sheng--hui

    2017-06-01

    Full Text Available According to the characteristics of the Flexible job shop scheduling problem the minimum makespan as measures we proposed a distributed particle swarm optimization algorithm aiming to solve flexible job shop scheduling problem. The algorithm adopts the method of distributed ideas to solve problems and we are established for two multi agent particle swarm optimization model in this algorithm it can solve the traditional particle swarm optimization algorithm when making decisions in real time according to the emergencies. Finally some benthmark problems were experimented and the results are compared with the traditional algorithm. Experimental results proved that the developed distributed PSO is enough effective and efficient to solve the FJSP and it also verified the reasonableness of the multi}gent particle swarm optimization model.

  17. Discrete Optimization Model for Vehicle Routing Problem with Scheduling Side Cosntraints

    Juliandri, Dedy; Mawengkang, Herman; Bu'ulolo, F.

    2018-01-01

    Vehicle Routing Problem (VRP) is an important element of many logistic systems which involve routing and scheduling of vehicles from a depot to a set of customers node. This is a hard combinatorial optimization problem with the objective to find an optimal set of routes used by a fleet of vehicles to serve the demands a set of customers It is required that these vehicles return to the depot after serving customers’ demand. The problem incorporates time windows, fleet and driver scheduling, pick-up and delivery in the planning horizon. The goal is to determine the scheduling of fleet and driver and routing policies of the vehicles. The objective is to minimize the overall costs of all routes over the planning horizon. We model the problem as a linear mixed integer program. We develop a combination of heuristics and exact method for solving the model.

  18. Operational Excellence through Schedule Optimization and Production Simulation of Application Specific Integrated Circuits.

    Flory, John Andrew [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Padilla, Denise D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Gauthier, John H. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Zwerneman, April Marie [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Miller, Steven P [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2016-05-01

    Upcoming weapon programs require an aggressive increase in Application Specific Integrated Circuit (ASIC) production at Sandia National Laboratories (SNL). SNL has developed unique modeling and optimization tools that have been instrumental in improving ASIC production productivity and efficiency, identifying optimal operational and tactical execution plans under resource constraints, and providing confidence in successful mission execution. With ten products and unprecedented levels of demand, a single set of shared resources, highly variable processes, and the need for external supplier task synchronization, scheduling is an integral part of successful manufacturing. The scheduler uses an iterative multi-objective genetic algorithm and a multi-dimensional performance evaluator. Schedule feasibility is assessed using a discrete event simulation (DES) that incorporates operational uncertainty, variability, and resource availability. The tools provide rapid scenario assessments and responses to variances in the operational environment, and have been used to inform major equipment investments and workforce planning decisions in multiple SNL facilities.

  19. Optimal methodology for a machining process scheduling in spot electricity markets

    Yusta, J.M.; Torres, F.; Khodr, H.M.

    2010-01-01

    Electricity spot markets have introduced hourly variations in the price of electricity. These variations allow the increase of the energy efficiency by the appropriate scheduling and adaptation of the industrial production to the hourly cost of electricity in order to obtain the maximum profit for the industry. In this article a mathematical optimization model simulates costs and the electricity demand of a machining process. The resultant problem is solved using the generalized reduced gradient approach, to find the optimum production schedule that maximizes the industry profit considering the hourly variations of the price of electricity in the spot market. Different price scenarios are studied to analyze the impact of the spot market prices for electricity on the optimal scheduling of the machining process and on the industry profit. The convenience of the application of the proposed model is shown especially in cases of very high electricity prices.

  20. Optimal trajectory planning and train scheduling for urban rail transit systems

    Wang, Yihui; van den Boom, Ton; De Schutter, Bart

    2016-01-01

    This book contributes to making urban rail transport fast, punctual and energy-efficient –significant factors in the importance of public transportation systems to economic, environmental and social requirements at both municipal and national levels. It proposes new methods for shortening passenger travel times and for reducing energy consumption, addressing two major topics: (1) train trajectory planning: the authors derive a nonlinear model for the operation of trains and present several approaches for calculating optimal and energy-efficient trajectories within a given schedule; and (2) train scheduling: the authors develop a train scheduling model for urban rail systems and optimization approaches with which to balance total passenger travel time with energy efficiency and other costs to the operator. Mixed-integer linear programming and pseudospectral methods are among the new methods proposed for single- and multi-train systems for the solution of the nonlinear trajectory planning problem which involv...

  1. Compliance with the vaccination schedule in children hospitalized with pneumonia and associated factors

    da Silva, Amanda Tabosa Pereira; Lima, Eduardo Jorge da Fonseca; Caminha, Maria de Fátima Costa; da Silva, Andresa Tabosa Pereira; Rodrigues, Edil de Albuquerque; dos Santos, Carmina Silva

    2018-01-01

    ABSTRACT OBJECTIVE: To verify the adequacy and factors associated with compliance with the immunization schedule (BCG, DTP-Hib, MMR, PCV-10) in children hospitalized with pneumonia at a pediatric referral hospital in Northeast Brazil. METHODS: This is a cross-sectional, descriptive study with an analytical component, with a sample of 452 children hospitalized with pneumonia at the Instituto de Medicina Integral Prof. Fernando Figueira, between 2010 and 2013. The inclusion criterion was children aged from one month to less than five years of age with proof in the immunization record. The exclusion criterion was the presence of hospital-acquired pneumonia or concomitant disease. We have evaluated the adequacy of the immunization schedule for the BCG, tetravalent, MMR, and 10-valent pneumococcal conjugate (PCV-10) vaccines. We used the chi-square test and Fisher's exact test followed by multivariate Poisson regression, estimating the crude and adjusted prevalence ratios and respective 95% confidence intervals. The variables with p < 0.20 in the univariate analysis were included in the multivariate analysis. RESULTS: There was good adequacy in the immunization schedule, except for PCV-10, which presented a percentage lower than 85%. We have observed an association between adequate compliance with the immunization schedule and education level of the mother (89.9% complete high school), sex of the child (87.2% female), age of the child (94.2% younger than six months), and breastfeeding (84.3% breastfed). CONCLUSIONS: Given the high rate of education level of the mother and the high percentage of breastfeeding, we can understand that there is a better understanding of the health of the child by the mothers studied in this study, showing the effectiveness of public policies for infant feeding. However, children did not have good adequacy of the immunization schedule of PCV-10, one of the main vaccines against pneumonia, which can be one of the main factors in the causes of

  2. Cost Minimization for Joint Energy Management and Production Scheduling Using Particle Swarm Optimization

    Shah, Rahul H.

    Production costs account for the largest share of the overall cost of manufacturing facilities. With the U.S. industrial sector becoming more and more competitive, manufacturers are looking for more cost and resource efficient working practices. Operations management and production planning have shown their capability to dramatically reduce manufacturing costs and increase system robustness. When implementing operations related decision making and planning, two fields that have shown to be most effective are maintenance and energy. Unfortunately, the current research that integrates both is limited. Additionally, these studies fail to consider parameter domains and optimization on joint energy and maintenance driven production planning. Accordingly, production planning methodology that considers maintenance and energy is investigated. Two models are presented to achieve well-rounded operating strategy. The first is a joint energy and maintenance production scheduling model. The second is a cost per part model considering maintenance, energy, and production. The proposed methodology will involve a Time-of-Use electricity demand response program, buffer and holding capacity, station reliability, production rate, station rated power, and more. In practice, the scheduling problem can be used to determine a joint energy, maintenance, and production schedule. Meanwhile, the cost per part model can be used to: (1) test the sensitivity of the obtained optimal production schedule and its corresponding savings by varying key production system parameters; and (2) to determine optimal system parameter combinations when using the joint energy, maintenance, and production planning model. Additionally, a factor analysis on the system parameters is conducted and the corresponding performance of the production schedule under variable parameter conditions, is evaluated. Also, parameter optimization guidelines that incorporate maintenance and energy parameter decision making in the

  3. Optimal joint scheduling of electrical and thermal appliances in a smart home environment

    Shirazi, Elham; Zakariazadeh, Alireza; Jadid, Shahram

    2015-01-01

    Highlights: • Thermal appliances are scheduled based on desired temperature and energy prices. • A discomfort index has been introduced within the home energy scheduling model. • Appliances are scheduled based on activity probability and desired options. • Starting probability depends on the social random factor and consumption behavior. - Abstract: With the development of home area network, residents have the opportunity to schedule their power usage in the home by themselves aiming at reducing electricity expenses. Moreover, as renewable energy sources are deployed in home, a home energy management system needs to consider both energy consumption and generation simultaneously to minimize the energy cost. In this paper, a smart home energy management model has been presented in which electrical and thermal appliances are jointly scheduled. The proposed method aims at minimizing the electricity cost of a residential customer by scheduling various type of appliances considering the residents consumption behavior, seasonal probability, social random factor, discomfort index and appliances starting probability functions. In this model, the home central controller receives the electricity price information, environmental factors data as well as the resident desired options in order to optimally schedule appliances including electrical and thermal. The scheduling approach is tested on a typical home including variety of home appliances, a small wind turbine, photovoltaic panel, combined heat and power unit, boiler and electrical and thermal storages over a 24-h period. The results show that the scheduling of different appliances can be reached simultaneously by using the proposed formulation. Moreover, simulation results evidenced that the proposed home energy management model exhibits a lower cost and, therefore, is more economical.

  4. A derived heuristics based multi-objective optimization procedure for micro-grid scheduling

    Li, Xin; Deb, Kalyanmoy; Fang, Yanjun

    2017-06-01

    With the availability of different types of power generators to be used in an electric micro-grid system, their operation scheduling as the load demand changes with time becomes an important task. Besides satisfying load balance constraints and the generator's rated power, several other practicalities, such as limited availability of grid power and restricted ramping of power output from generators, must all be considered during the operation scheduling process, which makes it difficult to decide whether the optimization results are accurate and satisfactory. In solving such complex practical problems, heuristics-based customized optimization algorithms are suggested. However, due to nonlinear and complex interactions of variables, it is difficult to come up with heuristics in such problems off-hand. In this article, a two-step strategy is proposed in which the first task deciphers important heuristics about the problem and the second task utilizes the derived heuristics to solve the original problem in a computationally fast manner. Specifically, the specific operation scheduling is considered from a two-objective (cost and emission) point of view. The first task develops basic and advanced level knowledge bases offline from a series of prior demand-wise optimization runs and then the second task utilizes them to modify optimized solutions in an application scenario. Results on island and grid connected modes and several pragmatic formulations of the micro-grid operation scheduling problem clearly indicate the merit of the proposed two-step procedure.

  5. Optimizing Intermodal Train Schedules with a Design Balanced Network Design Model

    Pedersen, Michael Berliner; Crainic, Teodor Gabriel

    We present a modeling approach for optimizing intermodal trains schedules based on an infrastructure divided into time-dependent train paths. The formulation can be generalized to a capacitated multi commodity network design model with additional design balance constraints. We present a Tabu Search...

  6. Optimization of Temperature Schedule Parameters on Heat Supply in Power-and-Heat Supply Systems

    V. A. Sednin

    2009-01-01

    Full Text Available The paper considers problems concerning optimization of a temperature schedule in the district heating systems with steam-turbine thermal power stations having average initial steam parameters. It has been shown in the paper that upkeeping of an optimum network water temperature permits to increase an energy efficiency of heat supply due to additional systematic saving of fuel. 

  7. Optimal household appliances scheduling under day-ahead pricing and load-shaping demand response strategies

    Paterakis, N.G.; Erdinç, O.; Bakirtzis, A.G.; Catalao, J.P.S.

    2015-01-01

    In this paper, a detailed home energy management system structure is developed to determine the optimal dayahead appliance scheduling of a smart household under hourly pricing and peak power-limiting (hard and soft power limitation)-based demand response strategies. All types of controllable assets

  8. Robust Optimization-Based Generation Self-Scheduling under Uncertain Price

    Xiao Luo

    2011-01-01

    Full Text Available This paper considers generation self-scheduling in electricity markets under uncertain price. Based on the robust optimization (denoted as RO methodology, a new self-scheduling model, which has a complicated max-min optimization structure, is set up. By using optimal dual theory, the proposed model is reformulated to an ordinary quadratic and quadratic cone programming problems in the cases of box and ellipsoidal uncertainty, respectively. IEEE 30-bus system is used to test the new model. Some comparisons with other methods are done, and the sensitivity with respect to the uncertain set is analyzed. Comparing with the existed uncertain self-scheduling approaches, the new method has twofold characteristics. First, it does not need a prediction of distribution of random variables and just requires an estimated value and the uncertain set of power price. Second, the counterpart of RO corresponding to the self-scheduling is a simple quadratic or quadratic cone programming. This indicates that the reformulated problem can be solved by many ordinary optimization algorithms.

  9. Optimal routes scheduling for municipal waste disposal garbage trucks using evolutionary algorithm and artificial immune system

    Bogna MRÓWCZYŃSKA

    2011-01-01

    Full Text Available This paper describes an application of an evolutionary algorithm and an artificial immune systems to solve a problem of scheduling an optimal route for waste disposal garbage trucks in its daily operation. Problem of an optimisation is formulated and solved using both methods. The results are presented for an area in one of the Polish cities.

  10. Optimization of multi-objective integrated process planning and scheduling problem using a priority based optimization algorithm

    Ausaf, Muhammad Farhan; Gao, Liang; Li, Xinyu

    2015-12-01

    For increasing the overall performance of modern manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the complexity of handling process planning and scheduling simultaneously, most of the research work has been limited to solving the integrated process planning and scheduling (IPPS) problem for a single objective function. As there are many conflicting objectives when dealing with process planning and scheduling, real world problems cannot be fully captured considering only a single objective for optimization. Therefore considering multi-objective IPPS (MOIPPS) problem is inevitable. Unfortunately, only a handful of research papers are available on solving MOIPPS problem. In this paper, an optimization algorithm for solving MOIPPS problem is presented. The proposed algorithm uses a set of dispatching rules coupled with priority assignment to optimize the IPPS problem for various objectives like makespan, total machine load, total tardiness, etc. A fixed sized external archive coupled with a crowding distance mechanism is used to store and maintain the non-dominated solutions. To compare the results with other algorithms, a C-matric based method has been used. Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed method is an efficient approach for solving the MOIPPS problem.

  11. Optimal power system generation scheduling by multi-objective genetic algorithms with preferences

    Zio, E.; Baraldi, P.; Pedroni, N.

    2009-01-01

    Power system generation scheduling is an important issue both from the economical and environmental safety viewpoints. The scheduling involves decisions with regards to the units start-up and shut-down times and to the assignment of the load demands to the committed generating units for minimizing the system operation costs and the emission of atmospheric pollutants. As many other real-world engineering problems, power system generation scheduling involves multiple, conflicting optimization criteria for which there exists no single best solution with respect to all criteria considered. Multi-objective optimization algorithms, based on the principle of Pareto optimality, can then be designed to search for the set of nondominated scheduling solutions from which the decision-maker (DM) must a posteriori choose the preferred alternative. On the other hand, often, information is available a priori regarding the preference values of the DM with respect to the objectives. When possible, it is important to exploit this information during the search so as to focus it on the region of preference of the Pareto-optimal set. In this paper, ways are explored to use this preference information for driving a multi-objective genetic algorithm towards the preferential region of the Pareto-optimal front. Two methods are considered: the first one extends the concept of Pareto dominance by biasing the chromosome replacement step of the algorithm by means of numerical weights that express the DM' s preferences; the second one drives the search algorithm by changing the shape of the dominance region according to linear trade-off functions specified by the DM. The effectiveness of the proposed approaches is first compared on a case study of literature. Then, a nonlinear, constrained, two-objective power generation scheduling problem is effectively tackled

  12. Permutation flow-shop scheduling problem to optimize a quadratic objective function

    Ren, Tao; Zhao, Peng; Zhang, Da; Liu, Bingqian; Yuan, Huawei; Bai, Danyu

    2017-09-01

    A flow-shop scheduling model enables appropriate sequencing for each job and for processing on a set of machines in compliance with identical processing orders. The objective is to achieve a feasible schedule for optimizing a given criterion. Permutation is a special setting of the model in which the processing order of the jobs on the machines is identical for each subsequent step of processing. This article addresses the permutation flow-shop scheduling problem to minimize the criterion of total weighted quadratic completion time. With a probability hypothesis, the asymptotic optimality of the weighted shortest processing time schedule under a consistency condition (WSPT-CC) is proven for sufficiently large-scale problems. However, the worst case performance ratio of the WSPT-CC schedule is the square of the number of machines in certain situations. A discrete differential evolution algorithm, where a new crossover method with multiple-point insertion is used to improve the final outcome, is presented to obtain high-quality solutions for moderate-scale problems. A sequence-independent lower bound is designed for pruning in a branch-and-bound algorithm for small-scale problems. A set of random experiments demonstrates the performance of the lower bound and the effectiveness of the proposed algorithms.

  13. A feasibility study: Selection of a personalized radiotherapy fractionation schedule using spatiotemporal optimization

    Kim, Minsun; Stewart, Robert D.; Phillips, Mark H.

    2015-01-01

    Purpose: To investigate the impact of using spatiotemporal optimization, i.e., intensity-modulated spatial optimization followed by fractionation schedule optimization, to select the patient-specific fractionation schedule that maximizes the tumor biologically equivalent dose (BED) under dose constraints for multiple organs-at-risk (OARs). Methods: Spatiotemporal optimization was applied to a variety of lung tumors in a phantom geometry using a range of tumor sizes and locations. The optimal fractionation schedule for a patient using the linear-quadratic cell survival model depends on the tumor and OAR sensitivity to fraction size (α/β), the effective tumor doubling time (T d ), and the size and location of tumor target relative to one or more OARs (dose distribution). The authors used a spatiotemporal optimization method to identify the optimal number of fractions N that maximizes the 3D tumor BED distribution for 16 lung phantom cases. The selection of the optimal fractionation schedule used equivalent (30-fraction) OAR constraints for the heart (D mean ≤ 45 Gy), lungs (D mean ≤ 20 Gy), cord (D max ≤ 45 Gy), esophagus (D max ≤ 63 Gy), and unspecified tissues (D 05 ≤ 60 Gy). To assess plan quality, the authors compared the minimum, mean, maximum, and D 95 of tumor BED, as well as the equivalent uniform dose (EUD) for optimized plans to conventional intensity-modulated radiation therapy plans prescribing 60 Gy in 30 fractions. A sensitivity analysis was performed to assess the effects of T d (3–100 days), tumor lag-time (T k = 0–10 days), and the size of tumors on optimal fractionation schedule. Results: Using an α/β ratio of 10 Gy, the average values of tumor max, min, mean BED, and D 95 were up to 19%, 21%, 20%, and 19% larger than those from conventional prescription, depending on T d and T k used. Tumor EUD was up to 17% larger than the conventional prescription. For fast proliferating tumors with T d less than 10 days, there was no

  14. A feasibility study: Selection of a personalized radiotherapy fractionation schedule using spatiotemporal optimization

    Kim, Minsun, E-mail: mk688@uw.edu; Stewart, Robert D. [Department of Radiation Oncology, University of Washington, Seattle, Washington 98195-6043 (United States); Phillips, Mark H. [Departments of Radiation Oncology and Neurological Surgery, University of Washington, Seattle, Washington 98195-6043 (United States)

    2015-11-15

    Purpose: To investigate the impact of using spatiotemporal optimization, i.e., intensity-modulated spatial optimization followed by fractionation schedule optimization, to select the patient-specific fractionation schedule that maximizes the tumor biologically equivalent dose (BED) under dose constraints for multiple organs-at-risk (OARs). Methods: Spatiotemporal optimization was applied to a variety of lung tumors in a phantom geometry using a range of tumor sizes and locations. The optimal fractionation schedule for a patient using the linear-quadratic cell survival model depends on the tumor and OAR sensitivity to fraction size (α/β), the effective tumor doubling time (T{sub d}), and the size and location of tumor target relative to one or more OARs (dose distribution). The authors used a spatiotemporal optimization method to identify the optimal number of fractions N that maximizes the 3D tumor BED distribution for 16 lung phantom cases. The selection of the optimal fractionation schedule used equivalent (30-fraction) OAR constraints for the heart (D{sub mean} ≤ 45 Gy), lungs (D{sub mean} ≤ 20 Gy), cord (D{sub max} ≤ 45 Gy), esophagus (D{sub max} ≤ 63 Gy), and unspecified tissues (D{sub 05} ≤ 60 Gy). To assess plan quality, the authors compared the minimum, mean, maximum, and D{sub 95} of tumor BED, as well as the equivalent uniform dose (EUD) for optimized plans to conventional intensity-modulated radiation therapy plans prescribing 60 Gy in 30 fractions. A sensitivity analysis was performed to assess the effects of T{sub d} (3–100 days), tumor lag-time (T{sub k} = 0–10 days), and the size of tumors on optimal fractionation schedule. Results: Using an α/β ratio of 10 Gy, the average values of tumor max, min, mean BED, and D{sub 95} were up to 19%, 21%, 20%, and 19% larger than those from conventional prescription, depending on T{sub d} and T{sub k} used. Tumor EUD was up to 17% larger than the conventional prescription. For fast proliferating

  15. Optimal treatment scheduling of ionizing radiation and sunitinib improves the antitumor activity and allows dose reduction

    Kleibeuker, Esther A; Hooven, Matthijs A ten; Castricum, Kitty C; Honeywell, Richard; Griffioen, Arjan W; Verheul, Henk M; Slotman, Ben J; Thijssen, Victor L

    2015-01-01

    The combination of radiotherapy with sunitinib is clinically hampered by rare but severe side effects and varying results with respect to clinical benefit. We studied different scheduling regimes and dose reduction in sunitinib and radiotherapy in preclinical tumor models to improve potential outcome of this combination treatment strategy. The chicken chorioallantoic membrane (CAM) was used as an angiogenesis in vivo model and as a xenograft model with human tumor cells (HT29 colorectal adenocarcinoma, OE19 esophageal adenocarcinoma). Treatment consisted of ionizing radiation (IR) and sunitinib as single therapy or in combination, using different dose-scheduling regimes. Sunitinib potentiated the inhibitory effect of IR (4 Gy) on angiogenesis. In addition, IR (4 Gy) and sunitinib (4 days of 32.5 mg/kg per day) inhibited tumor growth. Ionizing radiation induced tumor cell apoptosis and reduced proliferation, whereas sunitinib decreased tumor angiogenesis and reduced tumor cell proliferation. When IR was applied before sunitinib, this almost completely inhibited tumor growth, whereas concurrent IR was less effective and IR after sunitinib had no additional effect on tumor growth. Moreover, optimal scheduling allowed a 50% dose reduction in sunitinib while maintaining comparable antitumor effects. This study shows that the therapeutic efficacy of combination therapy improves when proper dose-scheduling is applied. More importantly, optimal treatment regimes permit dose reductions in the angiogenesis inhibitor, which will likely reduce the side effects of combination therapy in the clinical setting. Our study provides important leads to optimize combination treatment in the clinical setting

  16. Resource planning and scheduling of payload for satellite with particle swarm optimization

    Li, Jian; Wang, Cheng

    2007-11-01

    The resource planning and scheduling technology of payload is a key technology to realize an automated control for earth observing satellite with limited resources on satellite, which is implemented to arrange the works states of various payloads to carry out missions by optimizing the scheme of the resources. The scheduling task is a difficult constraint optimization problem with various and mutative requests and constraints. Based on the analysis of the satellite's functions and the payload's resource constraints, a proactive planning and scheduling strategy based on the availability of consumable and replenishable resources in time-order is introduced along with dividing the planning and scheduling period to several pieces. A particle swarm optimization algorithm is proposed to address the problem with an adaptive mutation operator selection, where the swarm is divided into groups with different probabilities to employ various mutation operators viz., differential evolution, Gaussian and random mutation operators. The probabilities are adjusted adaptively by comparing the effectiveness of the groups to select a proper operator. The simulation results have shown the feasibility and effectiveness of the method.

  17. A priority-based heuristic algorithm (PBHA for optimizing integrated process planning and scheduling problem

    Muhammad Farhan Ausaf

    2015-12-01

    Full Text Available Process planning and scheduling are two important components of a manufacturing setup. It is important to integrate them to achieve better global optimality and improved system performance. To find optimal solutions for integrated process planning and scheduling (IPPS problem, numerous algorithm-based approaches exist. Most of these approaches try to use existing meta-heuristic algorithms for solving the IPPS problem. Although these approaches have been shown to be effective in optimizing the IPPS problem, there is still room for improvement in terms of quality of solution and algorithm efficiency, especially for more complicated problems. Dispatching rules have been successfully utilized for solving complicated scheduling problems, but haven’t been considered extensively for the IPPS problem. This approach incorporates dispatching rules with the concept of prioritizing jobs, in an algorithm called priority-based heuristic algorithm (PBHA. PBHA tries to establish job and machine priority for selecting operations. Priority assignment and a set of dispatching rules are simultaneously used to generate both the process plans and schedules for all jobs and machines. The algorithm was tested for a series of benchmark problems. The proposed algorithm was able to achieve superior results for most complex problems presented in recent literature while utilizing lesser computational resources.

  18. Schedulability Analysis and Optimization for the Synthesis of Multi-Cluster Distributed Embedded Systems

    Pop, Paul; Eles, Petru; Peng, Zebo

    2003-01-01

    We present an approach to schedulability analysis for the synthesis of multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. We have also proposed a buffer size and worst case queuing delay analysis for the gateways......, responsible for routing inter-cluster traffic. Optimization heuristics for the priority assignment and synthesis of bus access parameters aimed at producing a schedulable system with minimal buffer needs have been proposed. Extensive experiments and a real-life example show the efficiency of our approaches....

  19. System and method for optimal load and source scheduling in context aware homes

    Shetty, Pradeep; Foslien Graber, Wendy; Mangsuli, Purnaprajna R.; Kolavennu, Soumitri N.; Curtner, Keith L.

    2018-01-23

    A controller for controlling energy consumption in a home includes a constraints engine to define variables for multiple appliances in the home corresponding to various home modes and persona of an occupant of the home. A modeling engine models multiple paths of energy utilization of the multiple appliances to place the home into a desired state from a current context. An optimal scheduler receives the multiple paths of energy utilization and generates a schedule as a function of the multiple paths and a selected persona to place the home in a desired state.

  20. Optimal Scheduling for Retrieval Jobs in Double-Deep AS/RS by Evolutionary Algorithms

    Kuo-Yang Wu

    2013-01-01

    Full Text Available We investigate the optimal scheduling of retrieval jobs for double-deep type Automated Storage and Retrieval Systems (AS/RS in the Flexible Manufacturing System (FMS used in modern industrial production. Three types of evolutionary algorithms, the Genetic Algorithm (GA, the Immune Genetic Algorithm (IGA, and the Particle Swarm Optimization (PSO algorithm, are implemented to obtain the optimal assignments. The objective is to minimize the working distance, that is, the shortest retrieval time travelled by the Storage and Retrieval (S/R machine. Simulation results and comparisons show the advantages and feasibility of the proposed methods.

  1. Optimizing the Steel Plate Storage Yard Crane Scheduling Problem Using a Two Stage Planning/Scheduling Approach

    Hansen, Anders Dohn; Clausen, Jens

    This paper presents the Steel Plate Storage Yard Crane Scheduling Problem. The task is to generate a schedule for two gantry cranes sharing tracks. The schedule must comply with a number of constraints and at the same time be cost efficient. We propose some ideas for a two stage planning...

  2. Joint Scheduling Optimization of Virtual Power Plants and Equitable Profit Distribution Using Shapely Value Theory

    Zhong-fu Tan

    2018-01-01

    Full Text Available The installation capacity of wind and solar photovoltaic power is continually increasing, which makes renewable energy grid connection and power generation an important link of China’s power structure optimization. A virtual power plant (VPP is an important way to help distributed energy resource grid connection and promote renewable energy industry development. To study the economic scheduling problem of various distributed energy resources and the profit distribution problem of VPP alliance, this study builds a separate operation scheduling model for individual VPP and a joint operation scheduling model for VPP alliance, as well as the profit distribution model. The case study verifies the feasibility and effectiveness of the proposed model. The sensitivity analysis provides information about VPP decision-making in accordance with the policy environment development trend.

  3. Joint optimization scheduling for water conservancy projects in complex river networks

    Qin Liu

    2017-01-01

    Full Text Available In this study, we simulated water flow in a water conservancy project consisting of various hydraulic structures, such as sluices, pumping stations, hydropower stations, ship locks, and culverts, and developed a multi-period and multi-variable joint optimization scheduling model for flood control, drainage, and irrigation. In this model, the number of sluice holes, pump units, and hydropower station units to be opened were used as decision variables, and different optimization objectives and constraints were considered. This model was solved with improved genetic algorithms and verified using the Huaian Water Conservancy Project as an example. The results show that the use of the joint optimization scheduling led to a 10% increase in the power generation capacity and a 15% reduction in the total energy consumption. The change in the water level was reduced by 0.25 m upstream of the Yundong Sluice, and by 50% downstream of pumping stations No. 1, No. 2, and No. 4. It is clear that the joint optimization scheduling proposed in this study can effectively improve power generation capacity of the project, minimize operating costs and energy consumption, and enable more stable operation of various hydraulic structures. The results may provide references for the management of water conservancy projects in complex river networks.

  4. Optimization of operating schedule of machines in granite industry using evolutionary algorithms

    Loganthurai, P.; Rajasekaran, V.; Gnanambal, K.

    2014-01-01

    Highlights: • Operating time of machines in granite industries was studied. • Operating time has been optimized using evolutionary algorithms such as PSO, DE. • The maximum demand has been reduced. • Hence the electricity cost of the industry and feeder stress have been reduced. - Abstract: Electrical energy consumption cost plays an important role in the production cost of any industry. The electrical energy consumption cost is calculated as two part tariff, the first part is maximum demand cost and the second part is energy consumption cost or unit cost (kW h). The maximum demand cost can be reduced without affecting the production. This paper focuses on the reduction of maximum demand by proper operating schedule of major equipments. For this analysis, various granite industries are considered. The major equipments in granite industries are cutting machine, polishing machine and compressor. To reduce the maximum demand, the operating time of polishing machine is rescheduled by optimization techniques such as Differential Evolution (DE) and particle swarm optimization (PSO). The maximum demand costs are calculated before and after rescheduling. The results show that if the machines are optimally operated, the cost is reduced. Both DE and PSO algorithms reduce the maximum demand cost at the same rate for all the granite industries. However, the optimum scheduling obtained by DE reduces the feeder power flow than the PSO scheduling

  5. Hospital Readmissions Following Physician Call System Change: A Comparison of Concentrated and Distributed Schedules.

    Yarnell, Christopher J; Shadowitz, Steven; Redelmeier, Donald A

    2016-07-01

    Physician call schedules are a critical element for medical practice and hospital efficiency. We compared readmission rates prior to and after a change in physician call system at Sunnybrook Health Sciences Centre. We studied patients discharged over a decade (2004 through 2013) and identified whether or not each patient was readmitted within the subsequent 28 days. We excluded patients discharged for a surgical, obstetrical, or psychiatric diagnosis. We used time-to-event analysis and time-series analysis to compare rates of readmission prior to and after the physician call system change (January 1, 2009). A total of 89,697 patients were discharged, of whom 10,001 (11%) were subsequently readmitted and 4280 died. The risk of readmission was increased by about 26% following physician call system change (9.7% vs 12.2%, P system change (95% confidence interval, 22%-31%; P system change persisted across patients with diverse ages, estimated readmission risks, and medical diagnoses. The net effect was equal to 7240 additional patient days in the hospital following call system change. A modest increase was observed at a nearby acute care hospital that did not change physician call system, and no increase in risk of death was observed with increased hospital readmissions. We suggest that changes in physician call systems sometimes increase subsequent hospital readmission rates. Further reductions in readmissions may instead require additional resources or ingenuity. Copyright © 2016 Elsevier Inc. All rights reserved.

  6. Preventive maintenance optimization for a multi-component system under changing job shop schedule

    Zhou Xiaojun; Lu Zhiqiang; Xi Lifeng

    2012-01-01

    Variability and small lot size is a common feature for many discrete manufacturing processes designed to meet a wide array of customer needs. Because of this, job shop schedule often has to be continuously updated in reaction to changes in production plan. Generally, the aim of preventive maintenance is to ensure production effectiveness and therefore the preventive maintenance models must have the ability to be adaptive to changes in job shop schedule. In this paper, a dynamic opportunistic preventive maintenance model is developed for a multi-component system with considering changes in job shop schedule. Whenever a job is completed, preventive maintenance opportunities arise for all the components in the system. An optimal maintenance practice is dynamically determined by maximizing the short-term cumulative opportunistic maintenance cost savings for the system. The numerical example shows that the scheme obtained by the proposed model can effectively address the preventive maintenance scheduling problem caused by the changes in job shop schedule and is more efficient than the ones based on two other commonly used preventive maintenance models.

  7. An Improved Harmony Search algorithm for optimal scheduling of the diesel generators in oil rig platforms

    Yadav, Parikshit; Kumar, Rajesh; Panda, S.K.; Chang, C.S.

    2011-01-01

    Harmony Search (HS) algorithm is music based meta-heuristic optimization method which is analogous with the music improvisation process where musician continue to polish the pitches in order to obtain better harmony. The paper focuses on the optimal scheduling of the generators to reduce the fuel consumption in the oil rig platform. The accurate modeling of the specific fuel consumption is significant in this optimization. The specific fuel consumption has been modeled using cubic spline interpolation. The SFC curve is non-linear and discrete in nature, hence conventional methods fail to give optimal solution. HS algorithm has been used for optimal scheduling of the generators of both equal and unequal rating. Furthermore an Improved Harmony Search (IHS) method for generating new solution vectors that enhances accuracy and convergence rate of HS has been employed. The paper also focuses on the impacts of constant parameters on Harmony Search algorithm. Numerical results show that the IHS method has good convergence property. Moreover, the fuel consumption for IHS algorithm is lower when compared to HS and other heuristic or deterministic methods and is a powerful search algorithm for various engineering optimization problems.

  8. An improved Harmony Search algorithm for optimal scheduling of the diesel generators in oil rig platforms

    Yadav, Parikshit; Kumar, Rajesh; Panda, S.K.; Chang, C.S. [Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576 (Singapore)

    2011-02-15

    Harmony Search (HS) algorithm is music based meta-heuristic optimization method which is analogous with the music improvisation process where musician continue to polish the pitches in order to obtain better harmony. The paper focuses on the optimal scheduling of the generators to reduce the fuel consumption in the oil rig platform. The accurate modeling of the specific fuel consumption is significant in this optimization. The specific fuel consumption has been modeled using cubic spline interpolation. The SFC curve is non-linear and discrete in nature, hence conventional methods fail to give optimal solution. HS algorithm has been used for optimal scheduling of the generators of both equal and unequal rating. Furthermore an Improved Harmony Search (IHS) method for generating new solution vectors that enhances accuracy and convergence rate of HS has been employed. The paper also focuses on the impacts of constant parameters on Harmony Search algorithm. Numerical results show that the IHS method has good convergence property. Moreover, the fuel consumption for IHS algorithm is lower when compared to HS and other heuristic or deterministic methods and is a powerful search algorithm for various engineering optimization problems. (author)

  9. Collaborative Optimization of Stop Schedule Plan and Ticket Allotment for the Intercity Train

    Xichun Chen

    2016-01-01

    Full Text Available As regards the ticket allotment issue of the intercity passenger corridor designed for different train grades, the matching relationship between the ticket allotment and the passenger flow demand is studied. The passenger flow conversion equation which is based on the collaborative optimization of the intercity train stop schedule plan and ticket allotment is established. Then the mathematical model aiming at the maximum revenue of intercity train system and the highest satisfaction from the passengers is established. The particle swarm harmony search algorithm is designed to solve the model. The example verifies the effectiveness of the model and algorithm, which indicates that, through the collaborative optimization of the stop schedule plan and ticket allotment for different grades intercity trains, the sectional utilization rate of the train can be improved; meanwhile, the optimum matching between the intercity train revenue and the passenger satisfaction can be realized.

  10. Cloud computing task scheduling strategy based on differential evolution and ant colony optimization

    Ge, Junwei; Cai, Yu; Fang, Yiqiu

    2018-05-01

    This paper proposes a task scheduling strategy DEACO based on the combination of Differential Evolution (DE) and Ant Colony Optimization (ACO), aiming at the single problem of optimization objective in cloud computing task scheduling, this paper combines the shortest task completion time, cost and load balancing. DEACO uses the solution of the DE to initialize the initial pheromone of ACO, reduces the time of collecting the pheromone in ACO in the early, and improves the pheromone updating rule through the load factor. The proposed algorithm is simulated on cloudsim, and compared with the min-min and ACO. The experimental results show that DEACO is more superior in terms of time, cost, and load.

  11. BINARY PARTICLE SWARM OPTIMIZATION APPROACH FOR RANDOM GENERATION OUTAGE MAINTENANCE SCHEDULING

    K. Suresh

    2013-01-01

    Full Text Available This paper presents a methodology for maintenance scheduling (MS of generators using binary particle swarm optimization (BPSO based probabilistic approach. The objective of this paper is to reduce the loss of load probability (LOLP for a power system. The capacity outage probability table (COPT is the initial step in creating maintenance schedule using the probabilistic levelized risk method. This paper proposes BPSO method which is used to construct the COPT. In order to mitigate the effects of probabilistic levelized risk method, BPSO based probabilistic levelized risk method is embarked on a MS problem. In order to validate the effectiveness of the proposed algorithm, case study results for simple five unit system can accomplish a significant levelization in the reliability indices that make possible to evaluate system generation system adequacy in the MS horizon of the power system. The proposed method shows better performance compared with other optimization methods and conventional method with improved search performance.

  12. A New Software for Management, Scheduling, and Optimization for the Light Hydrocarbon Pipeline Network System of Daqing Oilfield

    Yongtu Liang

    2014-01-01

    Full Text Available This paper presents the new software which specifically developed based on Visual Studio 2010 for Daqing Oilfield China includes the most complex light hydrocarbon pipeline network system in Asia, has become a powerful auxiliary tool to manage field data, makes scheduling plans for batching operation, and optimizes pumping plans. Firstly, DMM for recording and managing field data is summarized. Then, the batch scheduling simulation module called SSM for the difficult batch-scheduling issues of the multiple-source pipeline network system is introduced. Finally, SOM, that is Scheduling Optimization Module, is indicated for solving the problem of the pumps being started up/shut-down frequently.

  13. High-Speed Train Stop-Schedule Optimization Based on Passenger Travel Convenience

    Dingjun Chen; Shaoquan Ni; Chang’an Xu; Hongxia Lv; Simin Wang

    2016-01-01

    The stop-schedules for passenger trains are important to the operation planning of high-speed trains, and they decide the quality of passenger service and the transportation efficiency. This paper analyzes the specific manifestation of passenger travel convenience and proposes the concepts of interstation accessibility and degree of accessibility. In consideration of both the economic benefits of railway corporations and the travel convenience of passengers, a multitarget optimization model i...

  14. Optimization of horizontal microcode within and beyond basic blocks: an application of processor scheduling with resources

    Fisher, J.A.

    1979-10-01

    Microprogram optimization is the rearrangement of microcode written vertically, with one operation issued per step, into legal horizontal microinstructions, in which several operations are issued each instruction cycle. The rearrangement is done in a way that approximately minimizes the running time of the code. This problem is identified with the problem of processor scheduling with resource constraints. Thus, the problem of optimizing basic blocks of microcode can be seen to be np-complete; however, approximate methods for basic blocks which have good records in other, similar scheduling environments can be used. In priority list scheduling the tasks are ordered according to some evaluation function, and then schedules are found by repeated scans of the list. Several evaluation functions are shown to perform very well on large samples of various classes of random data-precedence graphs with characteristics similar to those derived from microprograms. A method of spotting resource bottlenecks in the derived data-precedence graph enables one to obtain a resource-considerate evaluation function, in which tasks which contribute directly to or precede bottlenecks have their priorities raised. The complexity of the calculations necessary to compute the lower bound was greatly reduced. A method is suggested for optimizing beyond basic blocks. Groups of basic blocks are treated as if they were one block; the information necessary to control the motion of tasks between blocks is encoded as data-precedence constraints on the conditional tasks. Long paths of code can thus be optimized, with no back branches, by the same methods used for basic blocks. 9 figures, 6 tables.

  15. Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling

    Tan, Kang Miao; Ramachandaramurthy, Vigna K.; Yong, Jia Ying

    2017-01-01

    -to-grid optimization algorithm is implemented and tested in MATLAB software (R2013a, MathWorks, Natick, MA, USA). The performance of the optimization algorithm depends heavily on the setting of the target load, power grid load and capability of the grid-connected electric vehicles. Hence, the performance...... of the proposed algorithm under various target load and electric vehicles’ state of charge selections were analysed. The effectiveness of the vehicle-to-grid scheduling to implement the appropriate peak load shaving and load levelling services for the grid load variance minimization is verified under various...

  16. Reliability-based optimization of maintenance scheduling of mechanical components under fatigue

    Beaurepaire, P.; Valdebenito, M.A.; Schuëller, G.I.; Jensen, H.A.

    2012-01-01

    This study presents the optimization of the maintenance scheduling of mechanical components under fatigue loading. The cracks of damaged structures may be detected during non-destructive inspection and subsequently repaired. Fatigue crack initiation and growth show inherent variability, and as well the outcome of inspection activities. The problem is addressed under the framework of reliability based optimization. The initiation and propagation of fatigue cracks are efficiently modeled using cohesive zone elements. The applicability of the method is demonstrated by a numerical example, which involves a plate with two holes subject to alternating stress. PMID:23564979

  17. Application of genetic algorithms to the maintenance scheduling optimization in a nuclear system basing on reliability

    Lapa, Celso M. Franklin; Pereira, Claudio M.N.A.; Mol, Antonio C. de Abreu

    1999-01-01

    This paper presents a solution based on genetic algorithm and probabilistic safety analysis that can be applied in the optimization of the preventive maintenance politic of nuclear power plant safety systems. The goal of this approach is to improve the average availability of the system through the optimization of the preventive maintenance scheduling politic. The auxiliary feed water system of a two loops pressurized water reactor is used as a sample case, in order to demonstrate the effectiveness of the proposed method. The results, when compared to those obtained by some standard maintenance politics, reveal quantitative gains and operational safety levels. (author)

  18. Simulation optimization based ant colony algorithm for the uncertain quay crane scheduling problem

    Naoufal Rouky

    2019-01-01

    Full Text Available This work is devoted to the study of the Uncertain Quay Crane Scheduling Problem (QCSP, where the loading /unloading times of containers and travel time of quay cranes are considered uncertain. The problem is solved with a Simulation Optimization approach which takes advantage of the great possibilities offered by the simulation to model the real details of the problem and the capacity of the optimization to find solutions with good quality. An Ant Colony Optimization (ACO meta-heuristic hybridized with a Variable Neighborhood Descent (VND local search is proposed to determine the assignments of tasks to quay cranes and the sequences of executions of tasks on each crane. Simulation is used inside the optimization algorithm to generate scenarios in agreement with the probabilities of the distributions of the uncertain parameters, thus, we carry out stochastic evaluations of the solutions found by each ant. The proposed optimization algorithm is tested first for the deterministic case on several well-known benchmark instances. Then, in the stochastic case, since no other work studied exactly the same problem with the same assumptions, the Simulation Optimization approach is compared with the deterministic version. The experimental results show that the optimization algorithm is competitive as compared to the existing methods and that the solutions found by the Simulation Optimization approach are more robust than those found by the optimization algorithm.

  19. TRADING-OFF CONSTRAINTS IN THE PUMP SCHEDULING OPTIMIZATION OF WATER DISTRIBUTION NETWORKS

    Gencer Genço\\u011Flu

    2016-01-01

    Full Text Available Pumps are one of the essential components of water supply systems. Depending of the topography, a water supply system may completely rely on pumping. They may consume non-negligible amount of water authorities' budgets during operation. Besides their energy costs, maintaining the healthiness of pumping systems is another concern for authorities. This study represents a multi-objective optimization method for pump scheduling problem. The optimization objective contains hydraulic and operational constraints. Switching of pumps and usage of electricity tariff are assumed to be key factors for operational reliability and energy consumption and costs of pumping systems. The local optimals for systems operational reliability, energy consumptions and energy costs are investigated resulting from trading-off pump switch and electricity tariff constraints within given set of boundary conditions. In the study, a custom made program is employed that combines genetic algorithm based optimization module with hydraulic network simulation software -EPANET. Developed method is applied on the case study network; N8-3 pressure zone of the Northern Supply of Ankara (Turkey Water Distribution Network. This work offers an efficient method for water authorities aiming to optimize pumping schedules considering expenditures and operational reliability mutually.

  20. Optimal scheduling of biocide dosing for seawater-cooled power and desalination plants

    Mahfouz, Abdullah Bin

    2011-02-13

    Thermal desalination systems are typically integrated with power plants to exploit the excess heat resulting from the power-generation units. Using seawater in cooling the power plant and the desalination system is a common practice in many parts of the world where there is a shortage of freshwater. Biofouling is one of the major problems associated with the usage of seawater in cooling systems. Because of the dynamic variation in the power and water demands as well as the changes in the characteristics of seawater and the process, there is a need to develop an optimal policy for scheduling biocide usage and cleaning maintenance of the heat exchangers. The objective of this article is to introduce a systematic procedure for the optimization of scheduling the dosing of biocide and dechlorination chemicals as well as cleaning maintenance for a power production/thermal desalination plant. A multi-period optimization formulation is developed and solved to determine: the optimal levels of dosing and dechlorination chemicals; the timing of maintenance to clean the heat-exchange surfaces; and the dynamic dependence of the biofilm growth on the applied doses, the seawater-biocide chemistry, the process conditions, and seawater characteristics for each time period. The technical, economic, and environmental considerations of the system are accounted for. A case study is solved to elucidate the applicability of the developed optimization approach. © 2011 Springer-Verlag.

  1. Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment.

    Abdullahi, Mohammed; Ngadi, Md Asri

    2016-01-01

    Cloud computing has attracted significant attention from research community because of rapid migration rate of Information Technology services to its domain. Advances in virtualization technology has made cloud computing very popular as a result of easier deployment of application services. Tasks are submitted to cloud datacenters to be processed on pay as you go fashion. Task scheduling is one the significant research challenges in cloud computing environment. The current formulation of task scheduling problems has been shown to be NP-complete, hence finding the exact solution especially for large problem sizes is intractable. The heterogeneous and dynamic feature of cloud resources makes optimum task scheduling non-trivial. Therefore, efficient task scheduling algorithms are required for optimum resource utilization. Symbiotic Organisms Search (SOS) has been shown to perform competitively with Particle Swarm Optimization (PSO). The aim of this study is to optimize task scheduling in cloud computing environment based on a proposed Simulated Annealing (SA) based SOS (SASOS) in order to improve the convergence rate and quality of solution of SOS. The SOS algorithm has a strong global exploration capability and uses fewer parameters. The systematic reasoning ability of SA is employed to find better solutions on local solution regions, hence, adding exploration ability to SOS. Also, a fitness function is proposed which takes into account the utilization level of virtual machines (VMs) which reduced makespan and degree of imbalance among VMs. CloudSim toolkit was used to evaluate the efficiency of the proposed method using both synthetic and standard workload. Results of simulation showed that hybrid SOS performs better than SOS in terms of convergence speed, response time, degree of imbalance, and makespan.

  2. Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment.

    Mohammed Abdullahi

    Full Text Available Cloud computing has attracted significant attention from research community because of rapid migration rate of Information Technology services to its domain. Advances in virtualization technology has made cloud computing very popular as a result of easier deployment of application services. Tasks are submitted to cloud datacenters to be processed on pay as you go fashion. Task scheduling is one the significant research challenges in cloud computing environment. The current formulation of task scheduling problems has been shown to be NP-complete, hence finding the exact solution especially for large problem sizes is intractable. The heterogeneous and dynamic feature of cloud resources makes optimum task scheduling non-trivial. Therefore, efficient task scheduling algorithms are required for optimum resource utilization. Symbiotic Organisms Search (SOS has been shown to perform competitively with Particle Swarm Optimization (PSO. The aim of this study is to optimize task scheduling in cloud computing environment based on a proposed Simulated Annealing (SA based SOS (SASOS in order to improve the convergence rate and quality of solution of SOS. The SOS algorithm has a strong global exploration capability and uses fewer parameters. The systematic reasoning ability of SA is employed to find better solutions on local solution regions, hence, adding exploration ability to SOS. Also, a fitness function is proposed which takes into account the utilization level of virtual machines (VMs which reduced makespan and degree of imbalance among VMs. CloudSim toolkit was used to evaluate the efficiency of the proposed method using both synthetic and standard workload. Results of simulation showed that hybrid SOS performs better than SOS in terms of convergence speed, response time, degree of imbalance, and makespan.

  3. Proposed algorithm to improve job shop production scheduling using ant colony optimization method

    Pakpahan, Eka KA; Kristina, Sonna; Setiawan, Ari

    2017-12-01

    This paper deals with the determination of job shop production schedule on an automatic environment. On this particular environment, machines and material handling system are integrated and controlled by a computer center where schedule were created and then used to dictate the movement of parts and the operations at each machine. This setting is usually designed to have an unmanned production process for a specified interval time. We consider here parts with various operations requirement. Each operation requires specific cutting tools. These parts are to be scheduled on machines each having identical capability, meaning that each machine is equipped with a similar set of cutting tools therefore is capable of processing any operation. The availability of a particular machine to process a particular operation is determined by the remaining life time of its cutting tools. We proposed an algorithm based on the ant colony optimization method and embedded them on matlab software to generate production schedule which minimize the total processing time of the parts (makespan). We test the algorithm on data provided by real industry and the process shows a very short computation time. This contributes a lot to the flexibility and timelines targeted on an automatic environment.

  4. Hospital admission planning to optimize major resources utilization under uncertainty

    Dellaert, N.P.; Jeunet, J.

    2010-01-01

    Admission policies for elective inpatient services mainly result in the management of a single resource: the operating theatre as it is commonly considered as the most critical and expensive resource in a hospital. However, other bottleneck resources may lead to surgery cancellations, such as bed capacity and nursing staff in Intensive Care (IC) units and bed occupancy in wards or medium care (MC) services. Our incentive is therefore to determine a master schedule of a given number of patient...

  5. Towards Cost and Comfort Based Hybrid Optimization for Residential Load Scheduling in a Smart Grid

    Nadeem Javaid

    2017-10-01

    Full Text Available In a smart grid, several optimization techniques have been developed to schedule load in the residential area. Most of these techniques aim at minimizing the energy consumption cost and the comfort of electricity consumer. Conversely, maintaining a balance between two conflicting objectives: energy consumption cost and user comfort is still a challenging task. Therefore, in this paper, we aim to minimize the electricity cost and user discomfort while taking into account the peak energy consumption. In this regard, we implement and analyse the performance of a traditional dynamic programming (DP technique and two heuristic optimization techniques: genetic algorithm (GA and binary particle swarm optimization (BPSO for residential load management. Based on these techniques, we propose a hybrid scheme named GAPSO for residential load scheduling, so as to optimize the desired objective function. In order to alleviate the complexity of the problem, the multi dimensional knapsack is used to ensure that the load of electricity consumer will not escalate during peak hours. The proposed model is evaluated based on two pricing schemes: day-ahead and critical peak pricing for single and multiple days. Furthermore, feasible regions are calculated and analysed to develop a relationship between power consumption, electricity cost, and user discomfort. The simulation results are compared with GA, BPSO and DP, and validate that the proposed hybrid scheme reflects substantial savings in electricity bills with minimum user discomfort. Moreover, results also show a phenomenal reduction in peak power consumption.

  6. Flexible Job Shop Scheduling Problem Using an Improved Ant Colony Optimization

    Lei Wang

    2017-01-01

    Full Text Available As an extension of the classical job shop scheduling problem, the flexible job shop scheduling problem (FJSP plays an important role in real production systems. In FJSP, an operation is allowed to be processed on more than one alternative machine. It has been proven to be a strongly NP-hard problem. Ant colony optimization (ACO has been proven to be an efficient approach for dealing with FJSP. However, the basic ACO has two main disadvantages including low computational efficiency and local optimum. In order to overcome these two disadvantages, an improved ant colony optimization (IACO is proposed to optimize the makespan for FJSP. The following aspects are done on our improved ant colony optimization algorithm: select machine rule problems, initialize uniform distributed mechanism for ants, change pheromone’s guiding mechanism, select node method, and update pheromone’s mechanism. An actual production instance and two sets of well-known benchmark instances are tested and comparisons with some other approaches verify the effectiveness of the proposed IACO. The results reveal that our proposed IACO can provide better solution in a reasonable computational time.

  7. Optimal distribution of reactivity excess in a system of reactors operating at a variable loading schedule

    Bolsunov, A.A.; Zagrebaev, A.M.; Naumov, V.I.

    1979-01-01

    Considered is the task of reactivity excess distribution optimization in the system of reactors for the purpose of minimazing the summary power production losses at the fixed loading schedule. Mathematical formulation of the task is presented. Given are the curves, characterizing the dependence of possible degree of the reactor power drop on reactivity excees for non-stationary Xe poisoning at different nominal density of neutron flux. Analyzing the results, it is concluded that in case, when the reactors differ only in neutron flux density the reactor with lower neutron flux density should be involved in the variable operation schedule first as the poisoning of this reactor will be less, and therefore, the losses of the system power production will be less. It is advisable to reserve the reactivity excess in the reactor with greater power or in the reactor with higher burnup rate. It is stressed that the obtained results of the optimization task solution point out the possibility of obtaining the certain ecomonic effect and permit to correct the requirements on mobility of separate power units at system approach to NPP operation in a variable loading schedule

  8. Performance deterioration modeling and optimal preventive maintenance strategy under scheduled servicing subject to mission time

    Li Dawei

    2014-08-01

    Full Text Available Servicing is applied periodically in practice with the aim of restoring the system state and prolonging the lifetime. It is generally seen as an imperfect maintenance action which has a chief influence on the maintenance strategy. In order to model the maintenance effect of servicing, this study analyzes the deterioration characteristics of system under scheduled servicing. And then the deterioration model is established from the failure mechanism by compound Poisson process. On the basis of the system damage value and failure mechanism, the failure rate refresh factor is proposed to describe the maintenance effect of servicing. A maintenance strategy is developed which combines the benefits of scheduled servicing and preventive maintenance. Then the optimization model is given to determine the optimal servicing period and preventive maintenance time, with an objective to minimize the system expected life-cycle cost per unit time and a constraint on system survival probability for the duration of mission time. Subject to mission time, it can control the ability of accomplishing the mission at any time so as to ensure the high dependability. An example of water pump rotor relating to scheduled servicing is introduced to illustrate the failure rate refresh factor and the proposed maintenance strategy. Compared with traditional methods, the numerical results show that the failure rate refresh factor can describe the maintenance effect of servicing more intuitively and objectively. It also demonstrates that this maintenance strategy can prolong the lifetime, reduce the total lifetime maintenance cost and guarantee the dependability of system.

  9. Optimal Cluster Mill Pass Scheduling With an Accurate and Rapid New Strip Crown Model

    Malik, Arif S.; Grandhi, Ramana V.; Zipf, Mark E.

    2007-01-01

    Besides the requirement to roll coiled sheet at high levels of productivity, the optimal pass scheduling of cluster-type reversing cold mills presents the added challenge of assigning mill parameters that facilitate the best possible strip flatness. The pressures of intense global competition, and the requirements for increasingly thinner, higher quality specialty sheet products that are more difficult to roll, continue to force metal producers to commission innovative flatness-control technologies. This means that during the on-line computerized set-up of rolling mills, the mathematical model should not only determine the minimum total number of passes and maximum rolling speed, it should simultaneously optimize the pass-schedule so that desired flatness is assured, either by manual or automated means. In many cases today, however, on-line prediction of strip crown and corresponding flatness for the complex cluster-type rolling mills is typically addressed either by trial and error, by approximate deflection models for equivalent vertical roll-stacks, or by non-physical pattern recognition style models. The abundance of the aforementioned methods is largely due to the complexity of cluster-type mill configurations and the lack of deflection models with sufficient accuracy and speed for on-line use. Without adequate assignment of the pass-schedule set-up parameters, it may be difficult or impossible to achieve the required strip flatness. In this paper, we demonstrate optimization of cluster mill pass-schedules using a new accurate and rapid strip crown model. This pass-schedule optimization includes computations of the predicted strip thickness profile to validate mathematical constraints. In contrast to many of the existing methods for on-line prediction of strip crown and flatness on cluster mills, the demonstrated method requires minimal prior tuning and no extensive training with collected mill data. To rapidly and accurately solve the multi-contact problem

  10. Modeling Nurse Scheduling Problem Using 0-1 Goal Programming A Case Study Of Tafo Government Hospital Kumasi-Ghana

    Wallace Agyei

    2015-03-01

    Full Text Available Abstract The problem of scheduling nurses at the Out-Patient Department OPD at Tafo Government Hospital Kumasi Ghana is presented. Currently the schedules are prepared by head nurse who performs this difficult and time consuming task by hand. Due to the existence of many constraints the resulting schedule usually does not guarantee the fairness of distribution of work. The problem was formulated as 0-1goal programming model with the of objective of evenly balancing the workload among nurses and satisfying their preferences as much as possible while complying with the legal and working regulations.. The developed model was then solved using LINGO14.0 software. The resulting schedules based on 0-1goal programming model balanced the workload in terms of the distribution of shift duties fairness in terms of the number of consecutive night duties and satisfied the preferences of the nurses. This is an improvement over the schedules done manually.

  11. Optimization of Task Scheduling Algorithm through QoS Parameters for Cloud Computing

    Monika

    2016-01-01

    Full Text Available Cloud computing is an incipient innovation which broadly spreads among analysts. It furnishes clients with foundation, stage and programming as enhancement which is easily available by means of web. A cloud is a sort of parallel and conveyed framework comprising of a gathering of virtualized PCs that are utilized to execute various tasks to accomplish good execution time, accomplish due date and usage of its assets. The scheduling issue can be seen as the finding an ideal task of assignments over the accessible arrangement of assets with the goal that we can accomplish the wanted objectives for tasks. This paper presents an optimal algorithm for scheduling tasks to get their waiting time as a QoS parameter. The algorithm is simulated using Cloudsim simulator and experiments are carried out to help clients to make sense of the bottleneck of utilizing no. of virtual machine parallely.

  12. MDTM: Optimizing Data Transfer using Multicore-Aware I/O Scheduling

    Zhang, Liang [Fermilab; Demar, Phil [Fermilab; Wu, Wenji [Fermilab; Kim, Bockjoo [Florida U.

    2017-05-09

    Bulk data transfer is facing significant challenges in the coming era of big data. There are multiple performance bottlenecks along the end-to-end path from the source to destination storage system. The limitations of current generation data transfer tools themselves can have a significant impact on end-to-end data transfer rates. In this paper, we identify the issues that lead to underperformance of these tools, and present a new data transfer tool with an innovative I/O scheduler called MDTM. The MDTM scheduler exploits underlying multicore layouts to optimize throughput by reducing delay and contention for I/O reading and writing operations. With our evaluations, we show how MDTM successfully avoids NUMA-based congestion and significantly improves end-to-end data transfer rates across high-speed wide area networks.

  13. MDTM: Optimizing Data Transfer using Multicore-Aware I/O Scheduling

    Zhang, Liang [Fermilab; Demar, Phil [Fermilab; Wu, Wenji [Fermilab; Kim, Bockjoo [Florida U.

    2017-01-01

    Bulk data transfer is facing significant challenges in the coming era of big data. There are multiple performance bottlenecks along the end-to-end path from the source to destination storage system. The limitations of current generation data transfer tools themselves can have a significant impact on end-to-end data transfer rates. In this paper, we identify the issues that lead to underperformance of these tools, and present a new data transfer tool with an innovative I/O scheduler called MDTM. The MDTM scheduler exploits underlying multicore layouts to optimize throughput by reducing delay and contention for I/O reading and writing operations. With our evaluations, we show how MDTM successfully avoids NUMA-based congestion and significantly improves end-to-end data transfer rates across high-speed wide area networks.

  14. Solving Multi-Resource Constrained Project Scheduling Problem using Ant Colony Optimization

    Hsiang-Hsi Huang

    2015-01-01

    Full Text Available This paper applied Ant Colony Optimization (ACO to develop a resource constraints scheduling model to achieve the resource allocation optimization and the shortest completion time of a project under resource constraints and the activities precedence requirement for projects. Resource leveling is also discussed and has to be achieved under the resource allocation optimization in this research. Testing cases and examples adopted from the international test bank were studied for verifying the effectiveness of the proposed model. The results showed that the solutions of different cases all have a better performance within a reasonable time. These can be obtained through ACO algorithm under the same constrained conditions. A program was written for the proposed model that is able to automatically produce the project resource requirement figure after the project duration is solved.

  15. Metroplex Optimization Model Expansion and Analysis: The Airline Fleet, Route, and Schedule Optimization Model (AFRS-OM)

    Sherry, Lance; Ferguson, John; Hoffman, Karla; Donohue, George; Beradino, Frank

    2012-01-01

    This report describes the Airline Fleet, Route, and Schedule Optimization Model (AFRS-OM) that is designed to provide insights into airline decision-making with regards to markets served, schedule of flights on these markets, the type of aircraft assigned to each scheduled flight, load factors, airfares, and airline profits. The main inputs to the model are hedged fuel prices, airport capacity limits, and candidate markets. Embedded in the model are aircraft performance and associated cost factors, and willingness-to-pay (i.e. demand vs. airfare curves). Case studies demonstrate the application of the model for analysis of the effects of increased capacity and changes in operating costs (e.g. fuel prices). Although there are differences between airports (due to differences in the magnitude of travel demand and sensitivity to airfare), the system is more sensitive to changes in fuel prices than capacity. Further, the benefits of modernization in the form of increased capacity could be undermined by increases in hedged fuel prices

  16. Improved teaching-learning-based and JAYA optimization algorithms for solving flexible flow shop scheduling problems

    Buddala, Raviteja; Mahapatra, Siba Sankar

    2017-11-01

    Flexible flow shop (or a hybrid flow shop) scheduling problem is an extension of classical flow shop scheduling problem. In a simple flow shop configuration, a job having `g' operations is performed on `g' operation centres (stages) with each stage having only one machine. If any stage contains more than one machine for providing alternate processing facility, then the problem becomes a flexible flow shop problem (FFSP). FFSP which contains all the complexities involved in a simple flow shop and parallel machine scheduling problems is a well-known NP-hard (Non-deterministic polynomial time) problem. Owing to high computational complexity involved in solving these problems, it is not always possible to obtain an optimal solution in a reasonable computation time. To obtain near-optimal solutions in a reasonable computation time, a large variety of meta-heuristics have been proposed in the past. However, tuning algorithm-specific parameters for solving FFSP is rather tricky and time consuming. To address this limitation, teaching-learning-based optimization (TLBO) and JAYA algorithm are chosen for the study because these are not only recent meta-heuristics but they do not require tuning of algorithm-specific parameters. Although these algorithms seem to be elegant, they lose solution diversity after few iterations and get trapped at the local optima. To alleviate such drawback, a new local search procedure is proposed in this paper to improve the solution quality. Further, mutation strategy (inspired from genetic algorithm) is incorporated in the basic algorithm to maintain solution diversity in the population. Computational experiments have been conducted on standard benchmark problems to calculate makespan and computational time. It is found that the rate of convergence of TLBO is superior to JAYA. From the results, it is found that TLBO and JAYA outperform many algorithms reported in the literature and can be treated as efficient methods for solving the FFSP.

  17. A Review On Job Shop Scheduling Using Non-Conventional Optimization Algorithm

    K.Mallikarjuna; Venkatesh.G

    2014-01-01

    A great deal of research has been focused on solving job shop scheduling problem (∫J), over the last four decades, resulting in a wide variety of approaches. Recently much effort has been concentrated on hybrid methods to solve ∫J, as a single technique cannot solve this stubborn problem. As a result much effort has recently been concentrated on techniques that lead to combinatorial optimization methods and a meta-strategy which guides the search out of local optima. In this p...

  18. Optimal scheduling for electric heat booster under day-ahead electricity and heat pricing

    Cai, Hanmin; You, Shi; Bindner, Henrik W.

    2017-01-01

    Multi-energy system (MES) operation calls for active management of flexible resources across energy sectors to improve efficiency and meet challenging environmental targets. Electric heat booster, a solution for Domestic Hot Water (DHW) preparation under Low-Temperature-District-Heating (LTDH......) context, is identified as one of aforementioned flexible resources for electricity and heat sectors. This paper extends the concept of optimal load scheduling under day-ahead pricing from electricity sector only to both electricity and heat sectors. A case study constructing day-ahead energy prices...

  19. Optimal Scheduling of Railway Track Possessions in Large-Scale Projects with Multiple Construction Works

    Li, Rui; Roberti, Roberto

    2017-01-01

    satisfying different operational constraints and minimizing the total construction cost. To find an optimal solution of the RTPSP, this paper proposes an approach that, first, transfers the nominal market prices into track-possession-based real prices, and then generates a schedule of the construction works...... by solving a mixed-integer linear-programming model for the given track blocking proposal. The proposed approach is tested on a real-life case study from the Danish railway infrastructure manager. The results show that, in 2 h of computing time, the approach is able to provide solutions that are within 0...

  20. A Bee Colony Optimization Approach for Mixed Blocking Constraints Flow Shop Scheduling Problems

    Mostafa Khorramizadeh

    2015-01-01

    Full Text Available The flow shop scheduling problems with mixed blocking constraints with minimization of makespan are investigated. The Taguchi orthogonal arrays and path relinking along with some efficient local search methods are used to develop a metaheuristic algorithm based on bee colony optimization. In order to compare the performance of the proposed algorithm, two well-known test problems are considered. Computational results show that the presented algorithm has comparative performance with well-known algorithms of the literature, especially for the large sized problems.

  1. Optimal Research and Numerical Simulation for Scheduling No-Wait Flow Shop in Steel Production

    Huawei Yuan

    2013-01-01

    Full Text Available This paper considers the m-machine flow shop scheduling problem with the no-wait constraint to minimize total completion time which is the typical model in steel production. First, the asymptotic optimality of the Shortest Processing Time (SPT first rule is proven for this problem. To further evaluate the performance of the algorithm, a new lower bound with performance guarantee is designed. At the end of the paper, numerical simulations show the effectiveness of the proposed algorithm and lower bound.

  2. Optimized Scheduling of Smart Meter Data Access for Real-time Voltage Quality Monitoring

    Kemal, Mohammed Seifu; Olsen, Rasmus Løvenstein; Schwefel, Hans-Peter

    2018-01-01

    Abstract—Active low-voltage distribution grids that support high integration of distributed generation such as photovoltaics and wind turbines require real-time voltage monitoring. At the same time, countries in Europe such as Denmark have close to 100% rollout of smart metering infrastructure....... The metering infrastructure has limitations to provide real-time measurements with small-time granularity. This paper presents an algorithm for optimized scheduling of smart meter data access to provide real-time voltage quality monitoring. The algorithm is analyzed using a real distribution grid in Denmark...

  3. Work schedule flexibility is associated with emotional exhaustion among registered nurses in Swiss hospitals: A cross-sectional study.

    Dhaini, Suzanne R; Denhaerynck, Kris; Bachnick, Stefanie; Schwendimann, René; Schubert, Maria; De Geest, Sabina; Simon, Michael

    2018-06-01

    Emotional exhaustion among healthcare workers is a widely investigated, well-recognized problem, the incidence of which has recently been linked to work environment factors, particularly work/family conflict. However, another environmental feature that may be equally influential, but that is more amenable to nurse manager action, remains less recognized: shift schedule flexibility. This study's main purposes were to assess variations in work schedule flexibility between Swiss acute care hospital units, and to investigate associations between psychosocial work environment (e.g. work schedule flexibility) and self-reported emotional exhaustion among registered nurses. This is a secondary analysis of data collected for the multi-center observational cross-sectional Match RN study, which included a national sample of 23 hospitals and 1833 registered nurses across Switzerland. Overall, self-reported work schedule flexibility among registered nurses was limited: 32% of participants reported little or no influence in planning their own shifts. Work schedule flexibility (β -0.11; CI -0.16; -0.06) and perceived nurse manager ability (β -0.30; CI -0.49; -0.10) were negatively related to self-reported emotional exhaustion. Work-family conflict (β 0.39; CI 0.33; 0.45) was positively correlated to emotional exhaustion. The study results indicate that managerial efforts to improve working environments, including special efforts to improve work schedule flexibility, might play an important role in promoting nurses' emotional health. Copyright © 2018 Elsevier Ltd. All rights reserved.

  4. Optimized Charging Scheduling with Single Mobile Charger for Wireless Rechargeable Sensor Networks

    Qihua Wang

    2017-11-01

    Full Text Available Due to the rapid development of wireless charging technology, the recharging issue in wireless rechargeable sensor network (WRSN has been a popular research problem in the past few years. The weakness of previous work is that charging route planning is not reasonable. In this work, a dynamic optimal scheduling scheme aiming to maximize the vacation time ratio of a single mobile changer for WRSN is proposed. In the proposed scheme, the wireless sensor network is divided into several sub-networks according to the initial topology of deployed sensor networks. After comprehensive analysis of energy states, working state and constraints for different sensor nodes in WRSN, we transform the optimized charging path problem of the whole network into the local optimization problem of the sub networks. The optimized charging path with respect to dynamic network topology in each sub-network is obtained by solving an optimization problem, and the lifetime of the deployed wireless sensor network can be prolonged. Simulation results show that the proposed scheme has good and reliable performance for a small wireless rechargeable sensor network.

  5. Adaptive track scheduling to optimize concurrency and vectorization in GeantV

    Apostolakis, J; Brun, R; Carminati, F; Gheata, A; Novak, M; Wenzel, S; Bandieramonte, M; Bitzes, G; Canal, P; Elvira, V D; Jun, S Y; Lima, G; Licht, J C De Fine; Duhem, L; Sehgal, R; Shadura, O

    2015-01-01

    The GeantV project is focused on the R and D of new particle transport techniques to maximize parallelism on multiple levels, profiting from the use of both SIMD instructions and co-processors for the CPU-intensive calculations specific to this type of applications. In our approach, vectors of tracks belonging to multiple events and matching different locality criteria must be gathered and dispatched to algorithms having vector signatures. While the transport propagates tracks and changes their individual states, data locality becomes harder to maintain. The scheduling policy has to be changed to maintain efficient vectors while keeping an optimal level of concurrency. The model has complex dynamics requiring tuning the thresholds to switch between the normal regime and special modes, i.e. prioritizing events to allow flushing memory, adding new events in the transport pipeline to boost locality, dynamically adjusting the particle vector size or switching between vector to single track mode when vectorization causes only overhead. This work requires a comprehensive study for optimizing these parameters to make the behaviour of the scheduler self-adapting, presenting here its initial results. (paper)

  6. Fuzzy linear programming based optimal fuel scheduling incorporating blending/transloading facilities

    Djukanovic, M.; Babic, B.; Milosevic, B. [Electrical Engineering Inst. Nikola Tesla, Belgrade (Yugoslavia); Sobajic, D.J. [EPRI, Palo Alto, CA (United States). Power System Control; Pao, Y.H. [Case Western Reserve Univ., Cleveland, OH (United States)]|[AI WARE, Inc., Cleveland, OH (United States)

    1996-05-01

    In this paper the blending/transloading facilities are modeled using an interactive fuzzy linear programming (FLP), in order to allow the decision-maker to solve the problem of uncertainty of input information within the fuel scheduling optimization. An interactive decision-making process is formulated in which decision-maker can learn to recognize good solutions by considering all possibilities of fuzziness. The application of the fuzzy formulation is accompanied by a careful examination of the definition of fuzziness, appropriateness of the membership function and interpretation of results. The proposed concept provides a decision support system with integration-oriented features, whereby the decision-maker can learn to recognize the relative importance of factors in the specific domain of optimal fuel scheduling (OFS) problem. The formulation of a fuzzy linear programming problem to obtain a reasonable nonfuzzy solution under consideration of the ambiguity of parameters, represented by fuzzy numbers, is introduced. An additional advantage of the FLP formulation is its ability to deal with multi-objective problems.

  7. Robust Optimization of the Self- scheduling and Market Involvement for an Electricity Producer

    Lima, Ricardo

    2015-01-01

    This work address the optimization under uncertainty of the self-scheduling, forward contracting, and pool involvement of an electricity producer operating a mixed power generation station, which combines thermal, hydro and wind sources, and uses a two-stage adaptive robust optimization approach. In this problem the wind power production and the electricity pool price are considered to be uncertain, and are described by uncertainty convex sets. Two variants of a constraint generation algorithm are proposed, namely a primal and dual version, and they are used to solve two case studies based on two different producers. Their market strategies are investigated for three different scenarios, corresponding to as many instances of electricity price forecasts. The effect of the producers’ approach, whether conservative or more risk prone, is also investigated by solving each instance for multiple values of the so-called budget parameter. It was possible to conclude that this parameter influences markedly the producers’ strategy, in terms of scheduling, profit, forward contracting, and pool involvement. Regarding the computational results, these show that for some instances, the two variants of the algorithms have a similar performance, while for a particular subset of them one variant has a clear superiority

  8. Robust Optimization of the Self- scheduling and Market Involvement for an Electricity Producer

    Lima, Ricardo

    2015-01-07

    This work address the optimization under uncertainty of the self-scheduling, forward contracting, and pool involvement of an electricity producer operating a mixed power generation station, which combines thermal, hydro and wind sources, and uses a two-stage adaptive robust optimization approach. In this problem the wind power production and the electricity pool price are considered to be uncertain, and are described by uncertainty convex sets. Two variants of a constraint generation algorithm are proposed, namely a primal and dual version, and they are used to solve two case studies based on two different producers. Their market strategies are investigated for three different scenarios, corresponding to as many instances of electricity price forecasts. The effect of the producers’ approach, whether conservative or more risk prone, is also investigated by solving each instance for multiple values of the so-called budget parameter. It was possible to conclude that this parameter influences markedly the producers’ strategy, in terms of scheduling, profit, forward contracting, and pool involvement. Regarding the computational results, these show that for some instances, the two variants of the algorithms have a similar performance, while for a particular subset of them one variant has a clear superiority

  9. A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems.

    Li, Xuejun; Xu, Jia; Yang, Yun

    2015-01-01

    Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.

  10. A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems

    Xuejun Li

    2015-01-01

    Full Text Available Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO and Particle Swarm Optimization (PSO have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.

  11. An Optimizing Space Data-Communications Scheduling Method and Algorithm with Interference Mitigation, Generalized for a Broad Class of Optimization Problems

    Rash, James

    2014-01-01

    NASA's space data-communications infrastructure-the Space Network and the Ground Network-provide scheduled (as well as some limited types of unscheduled) data-communications services to user spacecraft. The Space Network operates several orbiting geostationary platforms (the Tracking and Data Relay Satellite System (TDRSS)), each with its own servicedelivery antennas onboard. The Ground Network operates service-delivery antennas at ground stations located around the world. Together, these networks enable data transfer between user spacecraft and their mission control centers on Earth. Scheduling data-communications events for spacecraft that use the NASA communications infrastructure-the relay satellites and the ground stations-can be accomplished today with software having an operational heritage dating from the 1980s or earlier. An implementation of the scheduling methods and algorithms disclosed and formally specified herein will produce globally optimized schedules with not only optimized service delivery by the space data-communications infrastructure but also optimized satisfaction of all user requirements and prescribed constraints, including radio frequency interference (RFI) constraints. Evolutionary algorithms, a class of probabilistic strategies for searching large solution spaces, is the essential technology invoked and exploited in this disclosure. Also disclosed are secondary methods and algorithms for optimizing the execution efficiency of the schedule-generation algorithms themselves. The scheduling methods and algorithms as presented are adaptable to accommodate the complexity of scheduling the civilian and/or military data-communications infrastructure within the expected range of future users and space- or ground-based service-delivery assets. Finally, the problem itself, and the methods and algorithms, are generalized and specified formally. The generalized methods and algorithms are applicable to a very broad class of combinatorial-optimization

  12. Optimizing Air Transportation Service to Metroplex Airports. Par 2; Analysis Using the Airline Schedule Optimization Model (ASOM)

    Donoue, George; Hoffman, Karla; Sherry, Lance; Ferguson, John; Kara, Abdul Qadar

    2010-01-01

    The air transportation system is a significant driver of the U.S. economy, providing safe, affordable, and rapid transportation. During the past three decades airspace and airport capacity has not grown in step with demand for air transportation; the failure to increase capacity at the same rate as the growth in demand results in unreliable service and systemic delay. This report describes the results of an analysis of airline strategic decision-making that affects geographic access, economic access, and airline finances, extending the analysis of these factors using historic data (from Part 1 of the report). The Airline Schedule Optimization Model (ASOM) was used to evaluate how exogenous factors (passenger demand, airline operating costs, and airport capacity limits) affect geographic access (markets-served, scheduled flights, aircraft size), economic access (airfares), airline finances (profit), and air transportation efficiency (aircraft size). This analysis captures the impact of the implementation of airport capacity limits, as well as the effect of increased hedged fuel prices, which serve as a proxy for increased costs per flight that might occur if auctions or congestion pricing are imposed; also incorporated are demand elasticity curves based on historical data that provide information about how passenger demand is affected by airfare changes.

  13. Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling

    Kang Miao Tan

    2017-11-01

    Full Text Available The introduction of electric vehicles into the transportation sector helps reduce global warming and carbon emissions. The interaction between electric vehicles and the power grid has spurred the emergence of a smart grid technology, denoted as vehicle-to grid-technology. Vehicle-to-grid technology manages the energy exchange between a large fleet of electric vehicles and the power grid to accomplish shared advantages for the vehicle owners and the power utility. This paper presents an optimal scheduling of vehicle-to-grid using the genetic algorithm to minimize the power grid load variance. This is achieved by allowing electric vehicles charging (grid-to-vehicle whenever the actual power grid loading is lower than the target loading, while conducting electric vehicle discharging (vehicle-to-grid whenever the actual power grid loading is higher than the target loading. The vehicle-to-grid optimization algorithm is implemented and tested in MATLAB software (R2013a, MathWorks, Natick, MA, USA. The performance of the optimization algorithm depends heavily on the setting of the target load, power grid load and capability of the grid-connected electric vehicles. Hence, the performance of the proposed algorithm under various target load and electric vehicles’ state of charge selections were analysed. The effectiveness of the vehicle-to-grid scheduling to implement the appropriate peak load shaving and load levelling services for the grid load variance minimization is verified under various simulation investigations. This research proposal also recommends an appropriate setting for the power utility in terms of the selection of the target load based on the electric vehicle historical data.

  14. Optimizing antibiotic usage in hospitals: a qualitative study of the perspectives of hospital managers.

    Broom, A; Gibson, A F; Broom, J; Kirby, E; Yarwood, T; Post, J J

    2016-11-01

    Antibiotic optimization in hospitals is an increasingly critical priority in the context of proliferating resistance. Despite the emphasis on doctors, optimizing antibiotic use within hospitals requires an understanding of how different stakeholders, including non-prescribers, influence practice and practice change. This study was designed to understand Australian hospital managers' perspectives on antimicrobial resistance, managing antibiotic governance, and negotiating clinical vis-à-vis managerial priorities. Twenty-three managers in three hospitals participated in qualitative semi-structured interviews in Australia in 2014 and 2015. Data were systematically coded and thematically analysed. The findings demonstrate, from a managerial perspective: (1) competing demands that can hinder the prioritization of antibiotic governance; (2) ineffectiveness of audit and monitoring methods that limit rationalization for change; (3) limited clinical education and feedback to doctors; and (4) management-directed change processes are constrained by the perceived absence of a 'culture of accountability' for antimicrobial use amongst doctors. Hospital managers report considerable structural and interprofessional challenges to actualizing antibiotic optimization and governance. These challenges place optimization as a lower priority vis-à-vis other issues that management are confronted with in hospital settings, and emphasize the importance of antimicrobial stewardship (AMS) programmes that engage management in understanding and addressing the barriers to change. Copyright © 2016 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

  15. Modeling and optimization of tissue 10B concentration and dosimetry for arbitrary BPA-F infusion schedules in humans

    Kiger, W.S. III; Newton, T.H.; Palmer, M.R.

    2000-01-01

    Separate compartmental models have been derived for the concentration of 10 B resulting from BPA-F infusion in the central vascular space (i.e., blood or, more appropriately, plasma) and in glioblastoma multiforme and normal brain. By coupling the model for the temporal variation of 10 B concentration in the central vascular space with that for tissue, the dynamic behavior of the 10 B concentration and the resulting dosimetry in the relevant tissues and blood may be predicted for arbitrary infusion schedules. This coupled model may be used as a tool for identifying the optimal time for BNCT irradiation and optimal BPA-F infusion schedule (i.e., temporal targeting) in humans without the need for expensive and time-consuming pharmacokinetic studies for every infusion schedule considered. This model was used to analyze the concentration profiles resulting from a wide range of infusion schedules and their implications for dosimetry. (author)

  16. Optimal scheduling for enhanced coal bed methane production through CO2 injection

    Huang, Yuping; Zheng, Qipeng P.; Fan, Neng; Aminian, Kashy

    2014-01-01

    Highlights: • A novel deterministic optimization model for CO 2 -ECBM production scheduling. • Maximize the total profit from both sales of natural gas and CO 2 credits trading in the carbon market. • A stochastic model incorporating uncertainties and dynamics of NG price and CO 2 credit. - Abstract: Enhanced coal bed methane production with CO 2 injection (CO 2 -ECBM) is an effective technology for accessing the natural gas embedded in the traditionally unmineable coal seams. The revenue via this production process is generated not only by the sales of coal bed methane, but also by trading CO 2 credits in the carbon market. As the technology of CO 2 -ECBM becomes mature, its commercialization opportunities are also springing up. This paper proposes applicable mathematical models for CO 2 -ECBM production and compares the impacts of their production schedules on the total profit. A novel basic deterministic model for CO 2 -ECBM production including the technical and chemical details is proposed and then a multistage stochastic programming model is formulated in order to address uncertainties of natural gas price and CO 2 credit. Both models are nonlinear programming problems, which are solved by commercial nonlinear programming software BARON via GAMS. Numerical experiments show the benefits (e.g., expected profit gain) of using stochastic models versus deterministic models

  17. Optimal short-term operation schedule of a hydropower plant in a competitive electricity market

    Perez-Diaz, Juan I.; Wilhelmi, Jose R.; Arevalo, Luis A.

    2010-01-01

    This paper presents a dynamic programming model to solve the short-term scheduling problem of a hydropower plant that sells energy in a pool-based electricity market with the objective of maximizing the revenue. This is a nonlinear and non-concave problem subject to strong technical and strategic constraints, and in which discrete and continuous variables take part. The model described in this paper determines, in each hour of the planning horizon (typically from one day to one week), both the optimal number of units in operation (unit commitment) and the power to be generated by the committed units (generation dispatch). The power generated by each unit is considered as a nonlinear function of the actual water discharge and volume of the associated reservoir. The dependence of the units' efficiency and operating limits with the available gross head is also accounted for in this model. The application of this model to a real hydropower plant demonstrates its capabilities in providing the operation schedule that maximizes the revenue of the hydro plant while satisfying several constraints of different classes. In addition, the use of this model as a supporting tool to estimate the economic feasibility of a hydropower plant development project is also analyzed in the paper. (author)

  18. Joint optimization of green vehicle scheduling and routing problem with time-varying speeds

    Zhang, Dezhi; Wang, Xin; Ni, Nan; Zhang, Zhuo

    2018-01-01

    Based on an analysis of the congestion effect and changes in the speed of vehicle flow during morning and evening peaks in a large- or medium-sized city, the piecewise function is used to capture the rules of the time-varying speed of vehicles, which are very important in modelling their fuel consumption and CO2 emission. A joint optimization model of the green vehicle scheduling and routing problem with time-varying speeds is presented in this study. Extra wages during nonworking periods and soft time-window constraints are considered. A heuristic algorithm based on the adaptive large neighborhood search algorithm is also presented. Finally, a numerical simulation example is provided to illustrate the optimization model and its algorithm. Results show that, (1) the shortest route is not necessarily the route that consumes the least energy, (2) the departure time influences the vehicle fuel consumption and CO2 emissions and the optimal departure time saves on fuel consumption and reduces CO2 emissions by up to 5.4%, and (3) extra driver wages have significant effects on routing and departure time slot decisions. PMID:29466370

  19. Schedule Optimization of Imaging Missions for Multiple Satellites and Ground Stations Using Genetic Algorithm

    Lee, Junghyun; Kim, Heewon; Chung, Hyun; Kim, Haedong; Choi, Sujin; Jung, Okchul; Chung, Daewon; Ko, Kwanghee

    2018-04-01

    In this paper, we propose a method that uses a genetic algorithm for the dynamic schedule optimization of imaging missions for multiple satellites and ground systems. In particular, the visibility conflicts of communication and mission operation using satellite resources (electric power and onboard memory) are integrated in sequence. Resource consumption and restoration are considered in the optimization process. Image acquisition is an essential part of satellite missions and is performed via a series of subtasks such as command uplink, image capturing, image storing, and image downlink. An objective function for optimization is designed to maximize the usability by considering the following components: user-assigned priority, resource consumption, and image-acquisition time. For the simulation, a series of hypothetical imaging missions are allocated to a multi-satellite control system comprising five satellites and three ground stations having S- and X-band antennas. To demonstrate the performance of the proposed method, simulations are performed via three operation modes: general, commercial, and tactical.

  20. Multiobjective Joint Optimization of Production Scheduling and Maintenance Planning in the Flexible Job-Shop Problem

    Jianfei Ye

    2015-01-01

    Full Text Available In order to solve the joint optimization of production scheduling and maintenance planning problem in the flexible job-shop, a multiobjective joint optimization model considering the maximum completion time and maintenance costs per unit time is established based on the concept of flexible job-shop and preventive maintenance. A weighted sum method is adopted to eliminate the index dimension. In addition, a double-coded genetic algorithm is designed according to the problem characteristics. The best result under the circumstances of joint decision-making is obtained through multiple simulation experiments, which proves the validity of the algorithm. We can prove the superiority of joint optimization model by comparing the result of joint decision-making project with the result of independent decision-making project under fixed preventive maintenance period. This study will enrich and expand the theoretical framework and analytical methods of this problem; it provides a scientific decision analysis method for enterprise to make production plan and maintenance plan.

  1. Optimal Scheduling of Biogas-Solar-Wind Renewable Portfolio for Multi-Carrier Energy Supplies

    Zhou, Bin; Xu, Da; Li, Canbing

    2018-01-01

    the mitigation of renewable intermittency and the efficient utilization of batteries, and a multi-carrier generation scheduling scheme is further presented to dynamically optimize dispatch factors in the coupling matrix for energy-efficient con-version and storage, while different energy demands of end......This paper proposes a multi-source multi-product framework for coupled multi-carrier energy supplies with a biogas-solar-wind hybrid renewable system. In this framework, the biogas-solar-wind complementarities are fully exploited based on digesting thermodynamic effects for the synergetic...... interactions of electricity, gas and heating energy flows, and a coupling matrix is formulated for the modeling of production, conversion, storage, and consumption of different energy carriers. The multi-energy complementarity of biogas-solar-wind renewable portfolio can be utilized to facilitate...

  2. Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

    Li, Jun-qing; Pan, Quan-ke; Mao, Kun

    2014-01-01

    A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm. PMID:24883414

  3. Short-term hydro-thermal scheduling using particle swarm optimization method

    Yu, Binghui; Yuan, Xiaohui; Wang, Jinwen

    2007-01-01

    The approaches based on different particle swarm optimization (PSO) techniques are applied to solve the short-term hydro-thermal scheduling problem. In the proposed methods, many constraints of the hydro-thermal system, such as power balance, water balance, reservoir volume limits and the operation limits of hydro and thermal plants, are considered. The feasibility of the proposed algorithm is demonstrated through an example system, and the results are compared with the results of a genetic algorithm and evolutionary programming approaches. The experimental results show that all the PSO algorithms have the ability to achieve nearly global solutions, but a local version of PSO with inertia weight appears to be the best amongst all the PSOs in terms of high quality solution

  4. Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

    Jun-qing Li

    2014-01-01

    Full Text Available A hybrid algorithm which combines particle swarm optimization (PSO and iterated local search (ILS is proposed for solving the hybrid flowshop scheduling (HFS problem with preventive maintenance (PM activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron’s benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm.

  5. Hybrid particle swarm optimization for hybrid flowshop scheduling problem with maintenance activities.

    Li, Jun-qing; Pan, Quan-ke; Mao, Kun

    2014-01-01

    A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm.

  6. Energy-Aware Real-Time Task Scheduling for Heterogeneous Multiprocessors with Particle Swarm Optimization Algorithm

    Weizhe Zhang

    2014-01-01

    Full Text Available Energy consumption in computer systems has become a more and more important issue. High energy consumption has already damaged the environment to some extent, especially in heterogeneous multiprocessors. In this paper, we first formulate and describe the energy-aware real-time task scheduling problem in heterogeneous multiprocessors. Then we propose a particle swarm optimization (PSO based algorithm, which can successfully reduce the energy cost and the time for searching feasible solutions. Experimental results show that the PSO-based energy-aware metaheuristic uses 40%–50% less energy than the GA-based and SFLA-based algorithms and spends 10% less time than the SFLA-based algorithm in finding the solutions. Besides, it can also find 19% more feasible solutions than the SFLA-based algorithm.

  7. On Several Fundamental Problems of Optimization, Estimation, and Scheduling in Wireless Communications

    Gao, Qian

    For both the conventional radio frequency and the comparably recent optical wireless communication systems, extensive effort from the academia had been made in improving the network spectrum efficiency and/or reducing the error rate. To achieve these goals, many fundamental challenges such as power efficient constellation design, nonlinear distortion mitigation, channel training design, network scheduling and etc. need to be properly addressed. In this dissertation, novel schemes are proposed accordingly to deal with specific problems falling in category of these challenges. Rigorous proofs and analyses are provided for each of our work to make a fair comparison with the corresponding peer works to clearly demonstrate the advantages. The first part of this dissertation considers a multi-carrier optical wireless system employing intensity modulation (IM) and direct detection (DD). A block-wise constellation design is presented, which treats the DC-bias that conventionally used solely for biasing purpose as an information basis. Our scheme, we term it MSM-JDCM, takes advantage of the compactness of sphere packing in a higher dimensional space, and in turn power efficient constellations are obtained by solving an advanced convex optimization problem. Besides the significant power gains, the MSM-JDCM has many other merits such as being capable of mitigating nonlinear distortion by including a peak-to-power ratio (PAPR) constraint, minimizing inter-symbol-interference (ISI) caused by frequency-selective fading with a novel precoder designed and embedded, and further reducing the bit-error-rate (BER) by combining with an optimized labeling scheme. The second part addresses several optimization problems in a multi-color visible light communication system, including power efficient constellation design, joint pre-equalizer and constellation design, and modeling of different structured channels with cross-talks. Our novel constellation design scheme, termed CSK-Advanced, is

  8. Hybrid Particle Swarm Optimization based Day-Ahead Self-Scheduling for Thermal Generator in Competitive Electricity Market

    Pindoriya, Naran M.; Singh, S.N.; Østergaard, Jacob

    2009-01-01

    in day-ahead energy market subject to operational constraints and 2) at the same time, to minimize the risk due to uncertainty in price forecast. Therefore, it is a conflicting bi-objective optimization problem which has both binary and continuous optimization variables considered as constrained mixed......This paper presents a hybrid particle swarm optimization algorithm (HPSO) to solve the day-ahead self-scheduling for thermal power producer in competitive electricity market. The objective functions considered to model the self-scheduling problem are 1) to maximize the profit from selling energy...... integer nonlinear programming. To demonstrate the effectiveness of the proposed method for self-scheduling in a day-ahead energy market, the locational margin price (LMP) forecast uncertainty in PJM electricity market is considered. An adaptive wavelet neural network (AWNN) is used to forecast the day...

  9. Short term hydroelectric power system scheduling with wind turbine generators using the multi-pass iteration particle swarm optimization approach

    Lee, T.-Y.

    2008-01-01

    This paper uses multi-pass iteration particle swarm optimization (MIPSO) to solve short term hydroelectric generation scheduling of a power system with wind turbine generators. MIPSO is a new algorithm for solving nonlinear optimal scheduling problems. A new index called iteration best (IB) is incorporated into particle swarm optimization (PSO) to improve solution quality. The concept of multi-pass dynamic programming is applied to modify PSO further and improve computation efficiency. The feasible operational regions of the hydro units and pumped storage plants over the whole scheduling time range must be determined before applying MIPSO to the problem. Wind turbine power generation then shaves the power system load curves. Next, MIPSO calculates hydroelectric generation scheduling. It begins with a coarse time stage and searching space and refines the time interval between two time stages and the search spacing pass by pass (iteration). With the cooperation of agents called particles, the near optimal solution of the scheduling problem can be effectively reached. The effects of wind speed uncertainty were also considered in this paper. The feasibility of the new algorithm is demonstrated by a numerical example, and MIPSO solution quality and computation efficiency are compared to those of other algorithms

  10. Optimizing Water Use and Hydropower Production in Operational Reservoir System Scheduling with RiverWare

    Magee, T. M.; Zagona, E. A.

    2017-12-01

    Practical operational optimization of multipurpose reservoir systems is challenging for several reasons. Each purpose has its own constraints which may conflict with those of other purposes. While hydropower generation typically provides the bulk of the revenue, it is also among the lowest priority purposes. Each river system has important details that are specific to the location such as hydrology, reservoir storage capacity, physical limitations, bottlenecks, and the continuing evolution of operational policy. In addition, reservoir operations models include discrete, nonlinear, and nonconvex physical processes and if-then operating policies. Typically, the forecast horizon for scheduling needs to be extended far into the future to avoid near term (e.g., a few hours or a day) scheduling decisions that result in undesirable future states; this makes the computational effort much larger than may be expected. Put together, these challenges lead to large and customized mathematical optimization problems which must be solved efficiently to be of practical use. In addition, the solution process must be robust in an operational setting. We discuss a unique modeling approach in RiverWare that meets these challenges in an operational setting. The approach combines a Preemptive Linear Goal Programming optimization model to handle prioritized policies complimented by preprocessing and postprocessing with Rulebased Simulation to improve the solution with regard to nonlinearities, discrete issues, and if-then logic. An interactive policy language with a graphical user interface allows modelers to customize both the optimization and simulation based on the unique aspects of the policy for their system while the routine physical aspect of operations are modeled automatically. The modeler is aided by a set of compiled predefined functions and functions shared by other modelers. We illustrate the success of the approach with examples from daily use at the Tennessee Valley

  11. Multiple time-scale optimization scheduling for islanded microgrids including PV, wind turbine, diesel generator and batteries

    Xiao, Zhao xia; Nan, Jiakai; Guerrero, Josep M.

    2017-01-01

    A multiple time-scale optimization scheduling including day ahead and short time for an islanded microgrid is presented. In this paper, the microgrid under study includes photovoltaics (PV), wind turbine (WT), diesel generator (DG), batteries, and shiftable loads. The study considers the maximum...... efficiency operation area for the diesel engine and the cost of the battery charge/discharge cycle losses. The day-ahead generation scheduling takes into account the minimum operational cost and the maximum load satisfaction as the objective function. Short-term optimal dispatch is based on minimizing...

  12. Optimal Charging Schedule Planning and Economic Analysis for Electric Bus Charging Stations

    Rong-Ceng Leou

    2017-04-01

    Full Text Available The battery capacity of electric buses (EB used for public transportation is greater than that of electric cars, and the charging power is also several times greater than that used in electric cars; this can result in high energy consumption and negatively impact power distribution networks. This paper proposes a framework to determine the optimal contracted power capacity and charging schedule of an EB charging station in such a way that energy costs can be reduced. A mathematical model of controlled charging, which includes the capacity and energy charges of the station, was developed to minimize costs. The constraints of the model include the charging characteristics of an EB and the operational guidelines of the bus company. A practical EB charging station was used to verify the proposed model. The financial viability of this EB charging station is also studied in this paper. The economic analysis model for this charging station considers investment and operational costs, and the operational revenue. Sensitivity analyses with respect to some key parameters are also performed in this paper. Based on actual operational routes and EB charging schemes, test results indicate that the EB charging station investment is feasible, and the planning model proposed can be used to determine optimal station power capacity and minimize energy costs.

  13. On the optimal scheduling of periodic tests and maintenance for reliable redundant components

    Courtois, Pierre-Jacques; Delsarte, Philippe

    2006-01-01

    Periodically, some m of the n redundant components of a dependable system may have to be taken out of service for inspection, testing or preventive maintenance. The system is then constrained to operate with lower (n-m) redundancy and thus with less reliability during these periods. However, more frequent periodic inspections decrease the probability that a component fail undetected in the time interval between successive inspections. An optimal time schedule of periodic preventive operations arises from these two conflicting factors, balancing the loss of redundancy during inspections against the reliability benefits of more frequent inspections. Considering no other factor than this decreased redundancy at inspection time, this paper demonstrates the existence of an optimal interval between inspections, which maximizes the mean time between system failures. By suitable transformations and variable identifications, an analytic closed form expression of the optimum is obtained for the general (m, n) case. The optimum is shown to be unique within the ranges of parameter values valid in practice; its expression is easy to evaluate and shown to be useful to analyze and understand the influence of these parameters. Inspections are assumed to be perfect, i.e. they cause no component failure by themselves and leave no failure undetected. In this sense, the optimum determines a lowest bound for the system failure rate that can be achieved by a system of n-redundant components, m of which require for inspection or maintenance recurrent periods of unavailability of length t. The model and its general closed form solution are believed to be new . Previous work had computed optimal values for an estimation of a time average of system unavailability, but by numerical procedures only and with different numerical approximations, other objectives and model assumptions (one component only inspected at a time), and taking into account failures caused by testing itself, repair and

  14. A Study of an Appointment Scheduling System for Outpatients at the United States Air Force Academy Hospital.

    1988-07-30

    8a. NAME OF FUNDING/SPONSORING 8b. OFFICE SYMBOL 9. PROCUREMENT INSTRUMENT IDENTIFICATION NUMBER ORGANIZATION (If applicable) 8c. ADDRESS (City, State...Services are provided in General Dentistry, Oral Surgery, Periodontics, Prosthodontics, Endodontics , and Orthodontics (MHR 1987, 4-5). The hospital also...appointment D. Shields 4 clerks using a rotary wheel file. Schedules were forwarded to outpatien records to pull the patient record prior to the clinic

  15. Discrete particle swarm optimization to solve multi-objective limited-wait hybrid flow shop scheduling problem

    Santosa, B.; Siswanto, N.; Fiqihesa

    2018-04-01

    This paper proposes a discrete Particle Swam Optimization (PSO) to solve limited-wait hybrid flowshop scheduing problem with multi objectives. Flow shop schedulimg represents the condition when several machines are arranged in series and each job must be processed at each machine with same sequence. The objective functions are minimizing completion time (makespan), total tardiness time, and total machine idle time. Flow shop scheduling model always grows to cope with the real production system accurately. Since flow shop scheduling is a NP-Hard problem then the most suitable method to solve is metaheuristics. One of metaheuristics algorithm is Particle Swarm Optimization (PSO), an algorithm which is based on the behavior of a swarm. Originally, PSO was intended to solve continuous optimization problems. Since flow shop scheduling is a discrete optimization problem, then, we need to modify PSO to fit the problem. The modification is done by using probability transition matrix mechanism. While to handle multi objectives problem, we use Pareto Optimal (MPSO). The results of MPSO is better than the PSO because the MPSO solution set produced higher probability to find the optimal solution. Besides the MPSO solution set is closer to the optimal solution

  16. Optimal deployment schedule of an active twist rotor for performance enhancement and vibration reduction in high-speed flights

    Young H. YOU

    2017-08-01

    Full Text Available The best active twist schedules exploiting various waveform types are sought taking advantage of the global search algorithm for the reduction of hub vibration and/or power required of a rotor in high-speed conditions. The active twist schedules include two non-harmonic inputs formed based on segmented step functions as well as the simple harmonic waveform input. An advanced Particle Swarm assisted Genetic Algorithm (PSGA is employed for the optimizer. A rotorcraft Computational Structural Dynamics (CSD code CAMRAD II is used to perform the rotor aeromechanics analysis. A Computation Fluid Dynamics (CFD code is coupled with CSD for verification and some physical insights. The PSGA optimization results are verified against the parameter sweep study performed using the harmonic actuation. The optimum twist schedules according to the performance and/or vibration reduction strategy are obtained and their optimization gains are compared between the actuation cases. A two-phase non-harmonic actuation schedule demonstrates the best outcome in decreasing the power required while a four-phase non-harmonic schedule results in the best vibration reduction as well as the simultaneous reductions in the power required and vibration. The mechanism of reduction to the performance gains is identified illustrating the section airloads, angle-of-attack distribution, and elastic twist deformation predicted by the present approaches.

  17. Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

    Maryam Mousavi

    Full Text Available Flexible manufacturing system (FMS enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs. An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA, particle swarm optimization (PSO, and hybrid GA-PSO to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

  18. Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

    Mousavi, Maryam; Yap, Hwa Jen; Musa, Siti Nurmaya; Tahriri, Farzad; Md Dawal, Siti Zawiah

    2017-01-01

    Flexible manufacturing system (FMS) enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

  19. Optimal coordinated scheduling of combined heat and power fuel cell, wind, and photovoltaic units in micro grids considering uncertainties

    Bornapour, Mosayeb; Hooshmand, Rahmat-Allah; Khodabakhshian, Amin; Parastegari, Moein

    2016-01-01

    In this paper, a stochastic model is proposed for coordinated scheduling of combined heat and power units in micro grid considering wind turbine and photovoltaic units. Uncertainties of electrical market price; the speed of wind and solar radiation are considered using a scenario-based method. In the method, scenarios are generated using roulette wheel mechanism based on probability distribution functions of input random variables. Using this method, the probabilistic specifics of the problem are distributed and the problem is converted to a deterministic one. The type of the objective function, coordinated scheduling of combined heat and power, wind turbine, and photovoltaic units change this problem to a mixed integer nonlinear one. Therefore to solve this problem modified particle swarm optimization algorithm is employed. The mentioned uncertainties lead to an increase in profit. Moreover, the optimal coordinated scheduling of renewable energy resources and thermal units in micro grids increase the total profit. In order to evaluate the performance of the proposed method, its performance is executed on modified 33 bus distributed system as a micro grid. - Highlights: • Stochastic model is proposed for coordinated scheduling of renewable energy sources. • The effect of combined heat and power is considered. • Maximizing profits of micro grid is considered as objective function. • Considering the uncertainties of problem lead to profit increasing. • Optimal scheduling of renewable energy sources and thermal units increases profit.

  20. Residual Stress Developed During the Cure of Thermosetting Polymers: Optimizing Cure Schedule to Minimize Stress.

    Kropka, Jamie Michael; Stavig, Mark E.; Jaramillo, Rex

    2016-06-01

    When thermosetting polymers are used to bond or encapsulate electrical, mechanical or optical assemblies, residual stress, which often affects the performance and/or reliability of these devices, develops within the structure. The Thin-Disk-on-Cylinder structural response test is demonstrated as a powerful tool to design epoxy encapsulant cure schedules to reduce residual stress, even when all the details of the material evolution during cure are not explicitly known. The test's ability to (1) distinguish between cohesive and adhesive failure modes and (2) demonstrate methodologies to eliminate failure and reduce residual stress, make choices of cure schedules that optimize stress in the encapsulant unambiguous. For the 828/DEA/GMB material in the Thin-Disk-on-Cylinder geometry, the stress associated with cure is significant and outweighs that associated with cool down from the final cure temperature to room temperature (for measured lid strain, Scure I > I I e+h erma * II) * The difference between the final cure temperature and 1 1 -- the temperature at which the material gels, Tf-T ge i, was demonstrated to be a primary factor in determining the residual stress associated with cure. Increasing T f -T ge i leads to a reduction in cure stress that is described as being associated with balancing some of the 828/DEA/GMB cure shrinkage with thermal expansion. The ability to tune residual stress associated with cure by controlling T f -T ge i would be anticipated to translate to other thermosetting encapsulation materials, but the times and temperatures appropriate for a given material may vary widely.

  1. Integrated Optimization of Service-Oriented Train Plan and Schedule on Intercity Rail Network with Varying Demand

    Wenliang Zhou

    2015-01-01

    Full Text Available For a better service level of a train operating plan, we propose an integrated optimization method of train planning and train scheduling, which generally are optimized, respectively. Based on the cost analysis of both passengers travelling and enterprises operation, and the constraint analysis of trains operation, we construct a multiobjective function and build an integrated optimization model with the aim of reducing both passenger travel costs and enterprise operating costs. Then, a solving algorithm is established based on the simulated annealing algorithm. Finally, using as an example the Changzhutan intercity rail network, as an example we analyze the optimized results and the influence of the model parameters on the results.

  2. An Improved Particle Swarm Optimization for Selective Single Machine Scheduling with Sequence Dependent Setup Costs and Downstream Demands

    Kun Li

    2015-01-01

    Full Text Available This paper investigates a special single machine scheduling problem derived from practical industries, namely, the selective single machine scheduling with sequence dependent setup costs and downstream demands. Different from traditional single machine scheduling, this problem further takes into account the selection of jobs and the demands of downstream lines. This problem is formulated as a mixed integer linear programming model and an improved particle swarm optimization (PSO is proposed to solve it. To enhance the exploitation ability of the PSO, an adaptive neighborhood search with different search depth is developed based on the decision characteristics of the problem. To improve the search diversity and make the proposed PSO algorithm capable of getting out of local optimum, an elite solution pool is introduced into the PSO. Computational results based on extensive test instances show that the proposed PSO can obtain optimal solutions for small size problems and outperform the CPLEX and some other powerful algorithms for large size problems.

  3. Optimal stochastic reactive power scheduling in a microgrid considering voltage droop scheme of DGs and uncertainty of wind farms

    Khorramdel, Benyamin; Raoofat, Mahdi

    2012-01-01

    Distributed Generators (DGs) in a microgrid may operate in three different reactive power control strategies, including PV, PQ and voltage droop schemes. This paper proposes a new stochastic programming approach for reactive power scheduling of a microgrid, considering the uncertainty of wind farms. The proposed algorithm firstly finds the expected optimal operating point of each DG in V-Q plane while the wind speed is a probabilistic variable. A multi-objective function with goals of loss minimization, reactive power reserve maximization and voltage security margin maximization is optimized using a four-stage multi-objective nonlinear programming. Then, using Monte Carlo simulation enhanced by scenario reduction technique, the proposed algorithm simulates actual condition and finds optimal operating strategy of DGs. Also, if any DGs are scheduled to operate in voltage droop scheme, the optimum droop is determined. Also, in the second part of the research, to enhance the optimality of the results, PSO algorithm is used for the multi-objective optimization problem. Numerical examples on IEEE 34-bus test system including two wind turbines are studied. The results show the benefits of voltage droop scheme for mitigating the impacts of the uncertainty of wind. Also, the results show preference of PSO method in the proposed approach. -- Highlights: ► Reactive power scheduling in a microgrid considering loss and voltage security. ► Stochastic nature of wind farms affects reactive power scheduling and is considered. ► Advantages of using the voltage droop characteristics of DGs in voltage security are shown. ► Power loss, voltage security and VAR reserve are three goals of a multi-objective optimization. ► Monte Carlo method with scenario reduction is used to determine optimal control strategy of DGs.

  4. Performance evaluation of different types of particle representation procedures of Particle Swarm Optimization in Job-shop Scheduling Problems

    Izah Anuar, Nurul; Saptari, Adi

    2016-02-01

    This paper addresses the types of particle representation (encoding) procedures in a population-based stochastic optimization technique in solving scheduling problems known in the job-shop manufacturing environment. It intends to evaluate and compare the performance of different particle representation procedures in Particle Swarm Optimization (PSO) in the case of solving Job-shop Scheduling Problems (JSP). Particle representation procedures refer to the mapping between the particle position in PSO and the scheduling solution in JSP. It is an important step to be carried out so that each particle in PSO can represent a schedule in JSP. Three procedures such as Operation and Particle Position Sequence (OPPS), random keys representation and random-key encoding scheme are used in this study. These procedures have been tested on FT06 and FT10 benchmark problems available in the OR-Library, where the objective function is to minimize the makespan by the use of MATLAB software. Based on the experimental results, it is discovered that OPPS gives the best performance in solving both benchmark problems. The contribution of this paper is the fact that it demonstrates to the practitioners involved in complex scheduling problems that different particle representation procedures can have significant effects on the performance of PSO in solving JSP.

  5. Scheduling Multilevel Deadline-Constrained Scientific Workflows on Clouds Based on Cost Optimization

    Maciej Malawski

    2015-01-01

    Full Text Available This paper presents a cost optimization model for scheduling scientific workflows on IaaS clouds such as Amazon EC2 or RackSpace. We assume multiple IaaS clouds with heterogeneous virtual machine instances, with limited number of instances per cloud and hourly billing. Input and output data are stored on a cloud object store such as Amazon S3. Applications are scientific workflows modeled as DAGs as in the Pegasus Workflow Management System. We assume that tasks in the workflows are grouped into levels of identical tasks. Our model is specified using mathematical programming languages (AMPL and CMPL and allows us to minimize the cost of workflow execution under deadline constraints. We present results obtained using our model and the benchmark workflows representing real scientific applications in a variety of domains. The data used for evaluation come from the synthetic workflows and from general purpose cloud benchmarks, as well as from the data measured in our own experiments with Montage, an astronomical application, executed on Amazon EC2 cloud. We indicate how this model can be used for scenarios that require resource planning for scientific workflows and their ensembles.

  6. Optimal RTP Based Power Scheduling for Residential Load in Smart Grid

    Joshi, Hemant I.; Pandya, Vivek J.

    2015-12-01

    To match supply and demand, shifting of load from peak period to off-peak period is one of the effective solutions. Presently flat rate tariff is used in major part of the world. This type of tariff doesn't give incentives to the customers if they use electrical energy during off-peak period. If real time pricing (RTP) tariff is used, consumers can be encouraged to use energy during off-peak period. Due to advancement in information and communication technology, two-way communications is possible between consumers and utility. To implement this technique in smart grid, home energy controller (HEC), smart meters, home area network (HAN) and communication link between consumers and utility are required. HEC interacts automatically by running an algorithm to find optimal energy consumption schedule for each consumer. However, all the consumers are not allowed to shift their load simultaneously during off-peak period to avoid rebound peak condition. Peak to average ratio (PAR) is considered while carrying out minimization problem. Linear programming problem (LPP) method is used for minimization. The simulation results of this work show the effectiveness of the minimization method adopted. The hardware work is in progress and the program based on the method described here will be made to solve real problem.

  7. Multi-objective flexible job-shop scheduling problem using modified discrete particle swarm optimization.

    Huang, Song; Tian, Na; Wang, Yan; Ji, Zhicheng

    2016-01-01

    Taking resource allocation into account, flexible job shop problem (FJSP) is a class of complex scheduling problem in manufacturing system. In order to utilize the machine resources rationally, multi-objective particle swarm optimization (MOPSO) integrating with variable neighborhood search is introduced to address FJSP efficiently. Firstly, the assignment rules (AL) and dispatching rules (DR) are provided to initialize the population. And then special discrete operators are designed to produce new individuals and earliest completion machine (ECM) is adopted in the disturbance operator to escape the optima. Secondly, personal-best archives (cognitive memories) and global-best archive (social memory), which are updated by the predefined non-dominated archive update strategy, are simultaneously designed to preserve non-dominated individuals and select personal-best positions and the global-best position. Finally, three neighborhoods are provided to search the neighborhoods of global-best archive for enhancing local search ability. The proposed algorithm is evaluated by using Kacem instances and Brdata instances, and a comparison with other approaches shows the effectiveness of the proposed algorithm for FJSP.

  8. Near-Optimal Heuristics for Just-In-Time Jobs Maximization in Flow Shop Scheduling

    Helio Yochihiro Fuchigami

    2018-04-01

    Full Text Available The number of just-in-time jobs maximization in a permutation flow shop scheduling problem is considered. A mixed integer linear programming model to represent the problem as well as solution approaches based on enumeration and constructive heuristics were proposed and computationally implemented. Instances with up to 10 jobs and five machines are solved by the mathematical model in an acceptable running time (3.3 min on average while the enumeration method consumes, on average, 1.5 s. The 10 constructive heuristics proposed show they are practical especially for large-scale instances (up to 100 jobs and 20 machines, with very good-quality results and efficient running times. The best two heuristics obtain near-optimal solutions, with only 0.6% and 0.8% average relative deviations. They prove to be better than adaptations of the NEH heuristic (well-known for providing very good solutions for makespan minimization in flow shop for the considered problem.

  9. A Local and Global Search Combine Particle Swarm Optimization Algorithm for Job-Shop Scheduling to Minimize Makespan

    Zhigang Lian

    2010-01-01

    Full Text Available The Job-shop scheduling problem (JSSP is a branch of production scheduling, which is among the hardest combinatorial optimization problems. Many different approaches have been applied to optimize JSSP, but for some JSSP even with moderate size cannot be solved to guarantee optimality. The original particle swarm optimization algorithm (OPSOA, generally, is used to solve continuous problems, and rarely to optimize discrete problems such as JSSP. In OPSOA, through research I find that it has a tendency to get stuck in a near optimal solution especially for middle and large size problems. The local and global search combine particle swarm optimization algorithm (LGSCPSOA is used to solve JSSP, where particle-updating mechanism benefits from the searching experience of one particle itself, the best of all particles in the swarm, and the best of particles in neighborhood population. The new coding method is used in LGSCPSOA to optimize JSSP, and it gets all sequences are feasible solutions. Three representative instances are made computational experiment, and simulation shows that the LGSCPSOA is efficacious for JSSP to minimize makespan.

  10. A Three-Stage Optimization Algorithm for the Stochastic Parallel Machine Scheduling Problem with Adjustable Production Rates

    Rui Zhang

    2013-01-01

    Full Text Available We consider a parallel machine scheduling problem with random processing/setup times and adjustable production rates. The objective functions to be minimized consist of two parts; the first part is related with the due date performance (i.e., the tardiness of the jobs, while the second part is related with the setting of machine speeds. Therefore, the decision variables include both the production schedule (sequences of jobs and the production rate of each machine. The optimization process, however, is significantly complicated by the stochastic factors in the manufacturing system. To address the difficulty, a simulation-based three-stage optimization framework is presented in this paper for high-quality robust solutions to the integrated scheduling problem. The first stage (crude optimization is featured by the ordinal optimization theory, the second stage (finer optimization is implemented with a metaheuristic called differential evolution, and the third stage (fine-tuning is characterized by a perturbation-based local search. Finally, computational experiments are conducted to verify the effectiveness of the proposed approach. Sensitivity analysis and practical implications are also discussed.

  11. Optimal Scheduling of Distributed Energy Resources and Responsive Loads in Islanded Microgrids Considering Voltage and Frequency Security Constraints

    Vahedipour-Dahraie, Mostafa; Najafi, Hamid Reza; Anvari-Moghaddam, Amjad

    2018-01-01

    in islanded MGs with regard to voltage and frequency security constraints. Based on the proposed model, scheduling of the controllable units in both supply and demand sides is done in a way not only to maximize the expected profit of MG operator (MGO), but also to minimize the energy payments of customers...... on the system’s performance in terms of voltage and frequency stability. Moreover, optimal coordination of DERs and responsive loads can increase the expected profit of MGO significantly. The effectiveness of the proposed scheduling approach is verified on an islanded MG test system over a 24-h period....

  12. Evolutionary Hybrid Particle Swarm Optimization Algorithm for Solving NP-Hard No-Wait Flow Shop Scheduling Problems

    Laxmi A. Bewoor

    2017-10-01

    Full Text Available The no-wait flow shop is a flowshop in which the scheduling of jobs is continuous and simultaneous through all machines without waiting for any consecutive machines. The scheduling of a no-wait flow shop requires finding an appropriate sequence of jobs for scheduling, which in turn reduces total processing time. The classical brute force method for finding the probabilities of scheduling for improving the utilization of resources may become trapped in local optima, and this problem can hence be observed as a typical NP-hard combinatorial optimization problem that requires finding a near optimal solution with heuristic and metaheuristic techniques. This paper proposes an effective hybrid Particle Swarm Optimization (PSO metaheuristic algorithm for solving no-wait flow shop scheduling problems with the objective of minimizing the total flow time of jobs. This Proposed Hybrid Particle Swarm Optimization (PHPSO algorithm presents a solution by the random key representation rule for converting the continuous position information values of particles to a discrete job permutation. The proposed algorithm initializes population efficiently with the Nawaz-Enscore-Ham (NEH heuristic technique and uses an evolutionary search guided by the mechanism of PSO, as well as simulated annealing based on a local neighborhood search to avoid getting stuck in local optima and to provide the appropriate balance of global exploration and local exploitation. Extensive computational experiments are carried out based on Taillard’s benchmark suite. Computational results and comparisons with existing metaheuristics show that the PHPSO algorithm outperforms the existing methods in terms of quality search and robustness for the problem considered. The improvement in solution quality is confirmed by statistical tests of significance.

  13. Economies of scale and optimal size of hospitals: Empirical results for Danish public hospitals

    Kristensen, Troels

    number of beds per hospital is estimated to be 275 beds per site. Sensitivity analysis to partial changes in model parameters yields a joint 95% confidence interval in the range 130 - 585 beds per site. Conclusions: The results indicate that it may be appropriate to consolidate the production of small...... the current configuration of Danish hospitals is subject to scale economies that may justify such plans and to estimate an optimal hospital size. Methods: We estimate cost functions using panel data on total costs, DRG-weighted casemix, and number : We estimate cost functions using panel data on total costs......, DRG-weighted casemix, and number of beds for three years from 2004-2006. A short-run cost function is used to derive estimates of long-run scale economies by applying the envelope condition. Results: We identify moderate to significant long-run economies of scale when applying two alternative We...

  14. Capacitated vehicle-routing problem model for scheduled solid waste collection and route optimization using PSO algorithm.

    Hannan, M A; Akhtar, Mahmuda; Begum, R A; Basri, H; Hussain, A; Scavino, Edgar

    2018-01-01

    Waste collection widely depends on the route optimization problem that involves a large amount of expenditure in terms of capital, labor, and variable operational costs. Thus, the more waste collection route is optimized, the more reduction in different costs and environmental effect will be. This study proposes a modified particle swarm optimization (PSO) algorithm in a capacitated vehicle-routing problem (CVRP) model to determine the best waste collection and route optimization solutions. In this study, threshold waste level (TWL) and scheduling concepts are applied in the PSO-based CVRP model under different datasets. The obtained results from different datasets show that the proposed algorithmic CVRP model provides the best waste collection and route optimization in terms of travel distance, total waste, waste collection efficiency, and tightness at 70-75% of TWL. The obtained results for 1 week scheduling show that 70% of TWL performs better than all node consideration in terms of collected waste, distance, tightness, efficiency, fuel consumption, and cost. The proposed optimized model can serve as a valuable tool for waste collection and route optimization toward reducing socioeconomic and environmental impacts. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Optimization of design and erection methods to minimize the construction time-schedule of EPR plants

    Pierrat, Michel; L'Huby, Yvan; Decelle, Alain

    1999-01-01

    This paper presents the results of the investigations made during the Basic Design of the EPR project (European Pressurized water Reactor) to shorten the construction schedule. A 57 months construction schedule can be reached for the first unit. The investigations concern both design and construction methods. (author)

  16. Scheduling and Optimization of Fault-Tolerant Embedded Systems with Transparency/Performance Trade-Offs

    Izosimov, Viacheslav; Pop, Paul; Eles, Petru

    2012-01-01

    In this article, we propose a strategy for the synthesis of fault-tolerant schedules and for the mapping of fault-tolerant applications. Our techniques handle transparency/performance trade-offs and use the faultoccurrence information to reduce the overhead due to fault tolerance. Processes...... and messages are statically scheduled, and we use process reexecution for recovering from multiple transient faults. We propose a finegrained transparent recovery, where the property of transparency can be selectively applied to processes and messages. Transparency hides the recovery actions in a selected part...... of the application so that they do not affect the schedule of other processes and messages. While leading to longer schedules, transparent recovery has the advantage of both improved debuggability and less memory needed to store the faulttolerant schedules....

  17. MODELLING TEMPORAL SCHEDULE OF URBAN TRAINS USING AGENT-BASED SIMULATION AND NSGA2-BASED MULTIOBJECTIVE OPTIMIZATION APPROACHES

    M. Sahelgozin

    2015-12-01

    Full Text Available Increasing distances between locations of residence and services leads to a large number of daily commutes in urban areas. Developing subway systems has been taken into consideration of transportation managers as a response to this huge amount of travel demands. In developments of subway infrastructures, representing a temporal schedule for trains is an important task; because an appropriately designed timetable decreases Total passenger travel times, Total Operation Costs and Energy Consumption of trains. Since these variables are not positively correlated, subway scheduling is considered as a multi-criteria optimization problem. Therefore, proposing a proper solution for subway scheduling has been always a controversial issue. On the other hand, research on a phenomenon requires a summarized representation of the real world that is known as Model. In this study, it is attempted to model temporal schedule of urban trains that can be applied in Multi-Criteria Subway Schedule Optimization (MCSSO problems. At first, a conceptual framework is represented for MCSSO. Then, an agent-based simulation environment is implemented to perform Sensitivity Analysis (SA that is used to extract the interrelations between the framework components. These interrelations is then taken into account in order to construct the proposed model. In order to evaluate performance of the model in MCSSO problems, Tehran subway line no. 1 is considered as the case study. Results of the study show that the model was able to generate an acceptable distribution of Pareto-optimal solutions which are applicable in the real situations while solving a MCSSO is the goal. Also, the accuracy of the model in representing the operation of subway systems was significant.

  18. Application-Oriented Optimal Shift Schedule Extraction for a Dual-Motor Electric Bus with Automated Manual Transmission

    Mingjie Zhao

    2018-02-01

    Full Text Available The conventional battery electric buses (BEBs have limited potential to optimize the energy consumption and reach a better dynamic performance. A practical dual-motor equipped with 4-speed Automated Manual Transmission (AMT propulsion system is proposed, which can eliminate the traction interruption in conventional AMT. A discrete model of the dual-motor-AMT electric bus (DMAEB is built and used to optimize the gear shift schedule. Dynamic programming (DP algorithm is applied to find the optimal results where the efficiency and shift time of each gear are considered to handle the application problem of global optimization. A rational penalty factor and a proper shift time delay based on bench test results are set to reduce the shift frequency by 82.5% in Chinese-World Transient Vehicle Cycle (C-WTVC. Two perspectives of applicable shift rule extraction methods, i.e., the classification method based on optimal operating points and clustering method based on optimal shifting points, are explored and compared. Eventually, the hardware-in-the-loop (HIL simulation results demonstrate that the proposed structure and extracted shift schedule can realize a significant improvement in reducing energy loss by 20.13% compared to traditional empirical strategies.

  19. A rectangle bin packing optimization approach to the signal scheduling problem in the FlexRay static segment

    Rui ZHAO; Gui-he QIN; Jia-qiao LIU

    2016-01-01

    As FlexRay communication protocol is extensively used in distributed real-time applications on vehicles, signal scheduling in FlexRay network becomes a critical issue to ensure the safe and efficient operation of time-critical applications. In this study, we propose a rectangle bin packing optimization approach to schedule communication signals with timing constraints into the FlexRay static segment at minimum bandwidth cost. The proposed approach, which is based on integer linear program-ming (ILP), supports both the slot assignment mechanisms provided by the latest version of the FlexRay specification, namely, the single sender slot multiplexing, and multiple sender slot multiplexing mechanisms. Extensive experiments on a synthetic and an automotive X-by-wire system case study demonstrate that the proposed approach has a well optimized performance.

  20. Susceptibility of optimal train schedules to stochastic disturbances of process times

    Larsen, Rune; Pranzo, Marco; D’Ariano, Andrea

    2013-01-01

    study, an advanced branch and bound algorithm, on average, outperforms a First In First Out scheduling rule both in deterministic and stochastic traffic scenarios. However, the characteristic of the stochastic processes and the way a stochastic instance is handled turn out to have a serious impact...... and dwell times). In fact, the objective of railway traffic management is to reduce delay propagation and to increase disturbance robustness of train schedules at a network scale. We present a quantitative study of traffic disturbances and their effects on the schedules computed by simple and advanced...

  1. Optimal stochastic scheduling of CHP-PEMFC, WT, PV units and hydrogen storage in reconfigurable micro grids considering reliability enhancement

    Bornapour, Mosayeb; Hooshmand, Rahmat-Allah; Khodabakhshian, Amin; Parastegari, Moein

    2017-01-01

    Highlights: • Stochastic model is proposed for coordinated scheduling of renewable energy sources. • The effect of combined heat and power is considered. • Uncertainties of wind speed, solar radiation and electricity market price are considered. • Profit maximization, emission and AENS minimization are considered as objective functions. • Modified firefly algorithm is employed to solve the problem. - Abstract: Nowadays the operation of renewable energy sources and combined heat and power (CHP) units is increased in micro grids; therefore, to reach optimal performance, optimal scheduling of these units is required. In this regard, in this paper a micro grid consisting of proton exchange membrane fuel cell-combined heat and power (PEMFC-CHP), wind turbines (WT) and photovoltaic (PV) units, is modeled to determine the optimal scheduling state of these units by considering uncertain behavior of renewable energy resources. For this purpose, a scenario-based method is used for modeling the uncertainties of electrical market price, the wind speed, and solar irradiance. It should be noted that the hydrogen storage strategy is also applied in this study for PEMFC-CHP units. Market profit, total emission production, and average energy not supplied (AENS) are the objective functions considered in this paper simultaneously. Consideration of the above-mentioned objective functions converts the proposed problem to a mixed integer nonlinear programming. To solve this problem, a multi-objective firefly algorithm is used. The uncertainties of parameters convert the mixed integer nonlinear programming problem to a stochastic mixed integer nonlinear programming problem. Moreover, optimal coordinated scheduling of renewable energy resources and thermal units in micro-grids improve the value of the objective functions. Simulation results obtained from a modified 33-bus distributed network as a micro grid illustrates the effectiveness of the proposed method.

  2. Efficient Hybrid Genetic Based Multi Dimensional Host Load Aware Algorithm for Scheduling and Optimization of Virtual Machines

    Thiruvenkadam, T; Karthikeyani, V

    2014-01-01

    Mapping the virtual machines to the physical machines cluster is called the VM placement. Placing the VM in the appropriate host is necessary for ensuring the effective resource utilization and minimizing the datacenter cost as well as power. Here we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We developed the algorithm based on two different methods, first initial VM packing is done by...

  3. Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes

    Zunaira Nadeem

    2018-04-01

    Full Text Available In this paper, we design a controller for home energy management based on following meta-heuristic algorithms: teaching learning-based optimization (TLBO, genetic algorithm (GA, firefly algorithm (FA and optimal stopping rule (OSR theory. The principal goal of designing this controller is to reduce the energy consumption of residential sectors while reducing consumer’s electricity bill and maximizing user comfort. Additionally, we propose three hybrid schemes OSR-GA, OSR-TLBO and OSR-FA, by combining the best features of existing algorithms. We have also optimized the desired parameters: peak to average ratio, energy consumption, cost, and user comfort (appliance waiting time for 20, 50, 100 and 200 heterogeneous homes in two steps. In the first step, we obtain the optimal scheduling of home appliances implementing our aforementioned hybrid schemes for single and multiple homes while considering user preferences and threshold base policy. In the second step, we formulate our problem through chance constrained optimization. Simulation results show that proposed hybrid scheduling schemes outperformed for single and multiple homes and they shift the consumer load demand exceeding a predefined threshold to the hours where the electricity price is low thus following the threshold base policy. This helps to reduce electricity cost while considering the comfort of a user by minimizing delay and peak to average ratio. In addition, chance-constrained optimization is used to ensure the scheduling of appliances while considering the uncertainties of a load hence smoothing the load curtailment. The major focus is to keep the appliances power consumption within the power constraint, while keeping power consumption below a pre-defined acceptable level. Moreover, the feasible regions of appliances electricity consumption are calculated which show the relationship between cost and energy consumption and cost and waiting time.

  4. Optimal Day-ahead Charging Scheduling of Electric Vehicles through an Aggregative Game Model

    Liu, Zhaoxi; Wu, Qiuwei; Huang, Shaojun

    2017-01-01

    The electric vehicle (EV) market has been growing rapidly around the world. With large scale deployment of EVs in power systems, both the grid and EV owners will benefit if the flexible demand of EV charging is properly managed through the electricity market. When EV charging demand is considerable...... in a grid, it will impact spot prices in the electricity market and consequently influence the charging scheduling itself. The interaction between the spot prices and the EV demand needs to be considered in the EV charging scheduling, otherwise it will lead to a higher charging cost. A day-ahead EV charging...... scheduling based on an aggregative game model is proposed in this paper. The impacts of the EV demand on the electricity prices are formulated with the game model in the scheduling considering possible actions of other EVs. The existence and uniqueness of the pure strategy Nash equilibrium are proved...

  5. Optimal Scheduling and Operating Target (OPTAR) Cost Model for Aircraft Carriers in the Fleet Response Plan

    York, Michael A

    2008-01-01

    .... This capability is developed through the Fleet Readiness Training Plan (FRTP) where the Navy's carriers are scheduled in staggered 32-month cycles consisting of four phases of progressive readiness levels...

  6. Robust optimization for load scheduling of a smart home with photovoltaic system

    Wang, Chengshan; Zhou, Yue; Jiao, Bingqi; Wang, Yamin; Liu, Wenjian; Wang, Dan

    2015-01-01

    Highlights: • Robust household load scheduling is presented for smart homes with PV system. • A robust counterpart is formulated to deal with PV output uncertainty. • The robust counterpart is finally transformed to a quadratic programming problem. • Load schedules with different robustness can be made by the proposed method. • Feed-in tariff and PV output would affect the significance of the proposed method. - Abstract: In this paper, a robust approach is developed to tackle the uncertainty of PV power output for load scheduling of smart homes integrated with household PV system. Specifically, a robust formulation is proposed and further transformed to an equivalent quadratic programming problem. Day-ahead load schedules with different robustness can be generated by solving the proposed robust formulation with different predefined parameters. The validity and advantage of the proposed approach has been verified by simulation results. Also, the effects of feed-in tariff and PV output have been evaluated

  7. Optimal Scheduling of a Battery Energy Storage System with Electric Vehicles’ Auxiliary for a Distribution Network with Renewable Energy Integration

    Yuqing Yang

    2015-09-01

    Full Text Available With global conventional energy depletion, as well as environmental pollution, utilizing renewable energy for power supply is the only way for human beings to survive. Currently, distributed generation incorporated into a distribution network has become the new trend, with the advantages of controllability, flexibility and tremendous potential. However, the fluctuation of distributed energy resources (DERs is still the main concern for accurate deployment. Thus, a battery energy storage system (BESS has to be involved to mitigate the bad effects of DERs’ integration. In this paper, optimal scheduling strategies for BESS operation have been proposed, to assist with consuming the renewable energy, reduce the active power loss, alleviate the voltage fluctuation and minimize the electricity cost. Besides, the electric vehicles (EVs considered as the auxiliary technique are also introduced to attenuate the DERs’ influence. Moreover, both day-ahead and real-time operation scheduling strategies were presented under the consideration with the constraints of BESS and the EVs’ operation, and the optimization was tackled by a fuzzy mathematical method and an improved particle swarm optimization (IPSO algorithm. Furthermore, the test system for the proposed strategies is a real distribution network with renewable energy integration. After simulation, the proposed scheduling strategies have been verified to be extremely effective for the enhancement of the distribution network characteristics.

  8. An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling

    Lu, Songfeng; Sun, Chengfu; Lu, Zhengding [School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China)

    2010-03-15

    This paper presents a modified quantum-behaved particle swarm optimization (QPSO) for short-term combined economic emission scheduling (CEES) of hydrothermal power systems with several equality and inequality constraints. The hydrothermal scheduling is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. The bi-objective problem is converted into a single objective one by price penalty factor. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability. In this study, heuristic strategies are proposed to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also employed to meet the reservoir storage volumes constraints. To show the efficiency of the proposed method, different case studies are carried out and QPSO-DM is compared with the differential evolution (DE), the particle swarm optimization (PSO) with same heuristic strategies in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed method is capable of yielding higher-quality solutions stably and efficiently in the short-term hydrothermal scheduling than any other tested optimization algorithms. (author)

  9. An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling

    Lu Songfeng [School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China); Sun Chengfu, E-mail: ajason_369@sina.co [School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China); Lu Zhengding [School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China)

    2010-03-15

    This paper presents a modified quantum-behaved particle swarm optimization (QPSO) for short-term combined economic emission scheduling (CEES) of hydrothermal power systems with several equality and inequality constraints. The hydrothermal scheduling is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. The bi-objective problem is converted into a single objective one by price penalty factor. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability. In this study, heuristic strategies are proposed to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also employed to meet the reservoir storage volumes constraints. To show the efficiency of the proposed method, different case studies are carried out and QPSO-DM is compared with the differential evolution (DE), the particle swarm optimization (PSO) with same heuristic strategies in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed method is capable of yielding higher-quality solutions stably and efficiently in the short-term hydrothermal scheduling than any other tested optimization algorithms.

  10. An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling

    Lu Songfeng; Sun Chengfu; Lu Zhengding

    2010-01-01

    This paper presents a modified quantum-behaved particle swarm optimization (QPSO) for short-term combined economic emission scheduling (CEES) of hydrothermal power systems with several equality and inequality constraints. The hydrothermal scheduling is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. The bi-objective problem is converted into a single objective one by price penalty factor. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability. In this study, heuristic strategies are proposed to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also employed to meet the reservoir storage volumes constraints. To show the efficiency of the proposed method, different case studies are carried out and QPSO-DM is compared with the differential evolution (DE), the particle swarm optimization (PSO) with same heuristic strategies in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed method is capable of yielding higher-quality solutions stably and efficiently in the short-term hydrothermal scheduling than any other tested optimization algorithms.

  11. Optimizing Music Learning: Exploring How Blocked and Interleaved Practice Schedules Affect Advanced Performance.

    Carter, Christine E; Grahn, Jessica A

    2016-01-01

    Repetition is the most commonly used practice strategy by musicians. Although blocks of repetition continue to be suggested in the pedagogical literature, work in the field of cognitive psychology suggests that repeated events receive less processing, thereby reducing the potential for long-term learning. Motor skill learning and sport psychology research offer an alternative. Instead of using a blocked practice schedule, with practice completed on one task before moving on to the next task, an interleaved schedule can be used, in which practice is frequently alternated between tasks. This frequent alternation involves more effortful processing, resulting in increased long-term learning. The finding that practicing in an interleaved schedule leads to better retention than practicing in a blocked schedule has been labeled the "contextual interference effect." While the effect has been observed across a wide variety of fields, few studies have researched this phenomenon in a music-learning context, despite the broad potential for application to music practice. This study compared the effects of blocked and interleaved practice schedules on advanced clarinet performance in an ecologically valid context. Ten clarinetists were given one concerto exposition and one technical excerpt to practice in a blocked schedule (12 min per piece) and a second concerto exposition and technical excerpt to practice in an interleaved schedule (3 min per piece, alternating until a total of 12 min of practice were completed on each piece). Participants sight-read the four pieces prior to practice and performed them at the end of practice and again one day later. The sight-reading and two performance run-throughs of each piece were recorded and given to three professional clarinetists to rate using a percentage scale. Overall, whenever there was a ratings difference between the conditions, pieces practiced in the interleaved schedule were rated better than those in the blocked schedule

  12. Optimizing Music Learning: Exploring How Blocked and Interleaved Practice Schedules Affect Advanced Performance

    Carter, Christine E.; Grahn, Jessica A.

    2016-01-01

    Repetition is the most commonly used practice strategy by musicians. Although blocks of repetition continue to be suggested in the pedagogical literature, work in the field of cognitive psychology suggests that repeated events receive less processing, thereby reducing the potential for long-term learning. Motor skill learning and sport psychology research offer an alternative. Instead of using a blocked practice schedule, with practice completed on one task before moving on to the next task, an interleaved schedule can be used, in which practice is frequently alternated between tasks. This frequent alternation involves more effortful processing, resulting in increased long-term learning. The finding that practicing in an interleaved schedule leads to better retention than practicing in a blocked schedule has been labeled the “contextual interference effect.” While the effect has been observed across a wide variety of fields, few studies have researched this phenomenon in a music-learning context, despite the broad potential for application to music practice. This study compared the effects of blocked and interleaved practice schedules on advanced clarinet performance in an ecologically valid context. Ten clarinetists were given one concerto exposition and one technical excerpt to practice in a blocked schedule (12 min per piece) and a second concerto exposition and technical excerpt to practice in an interleaved schedule (3 min per piece, alternating until a total of 12 min of practice were completed on each piece). Participants sight-read the four pieces prior to practice and performed them at the end of practice and again one day later. The sight-reading and two performance run-throughs of each piece were recorded and given to three professional clarinetists to rate using a percentage scale. Overall, whenever there was a ratings difference between the conditions, pieces practiced in the interleaved schedule were rated better than those in the blocked schedule

  13. Optimizing music learning: Exploring how blocked and interleaved practice schedules affect advanced performance

    Christine E Carter

    2016-08-01

    Full Text Available Repetition is the most commonly used practice strategy by musicians. Although blocks of repetition continue to be suggested in the pedagogical literature, work in the field of cognitive psychology suggests that repeated events receive less processing, thereby reducing the potential for long-term learning. Motor skill learning and sport psychology research offer an alternative. Instead of using a blocked practice schedule, with practice completed on one task before moving on to the next task, an interleaved schedule can be used, in which practice is frequently alternated between tasks. This frequent alternation involves more effortful processing, resulting in increased long-term learning. The finding that practicing in an interleaved schedule leads to better retention than practicing in a blocked schedule has been labeled the contextual interference effect. While the effect has been observed across a wide variety of fields, few studies have researched this phenomenon in a music-learning context, despite the broad potential for application to music practice. This study compared the effects of blocked and interleaved practice schedules on advanced clarinet performance in an ecologically valid context. Ten clarinetists were given one concerto exposition and one technical excerpt to practice in a blocked schedule (twelve minutes per piece and a second concerto exposition and technical excerpt to practice in an interleaved schedule (three minutes per piece, alternating until a total of twelve minutes of practice were completed on each piece. Participants sight-read the four pieces prior to practice and performed them at the end of practice and again one day later. The sight-reading and two performance run-throughs of each piece were recorded and given to three professional clarinetists to rate using a percentage scale. Overall, whenever there was a ratings difference between the conditions, pieces practiced in the interleaved schedule were rated

  14. Optimizing Fire Department Operations Through Work Schedule Analysis, Alternative Staffing, and Nonproductive Time Reduction

    2014-09-01

    and disadvantages ; for example, although the majority of fire departments in the United States abide by the 24/48- schedule, this schedule also leads...costs. The local bargaining group also falls into these criteria. The local bargaining unit must be agreeable to the policy changes. They cannot... agreeable to the local bargaining group particularly when it is addressed in an article of the current bargaining agreement. Additionally, the local

  15. Study on Cloud Computing Resource Scheduling Strategy Based on the Ant Colony Optimization Algorithm

    Lingna He; Qingshui Li; Linan Zhu

    2012-01-01

    In order to replace the traditional Internet software usage patterns and enterprise management mode, this paper proposes a new business calculation mode- cloud computing, resources scheduling strategy is the key technology in cloud computing, Based on the study of cloud computing system structure and the mode of operation, The key research for cloud computing the process of the work scheduling and resource allocation problems based on ant colony algorithm , Detailed analysis and design of the...

  16. Comparison of different chaotic maps in particle swarm optimization algorithm for long-term cascaded hydroelectric system scheduling

    He Yaoyao; Zhou Jianzhong; Xiang Xiuqiao; Chen Heng; Qin Hui

    2009-01-01

    The goal of this paper is to present a novel chaotic particle swarm optimization (CPSO) algorithm and compares the efficiency of three one-dimensional chaotic maps within symmetrical region for long-term cascaded hydroelectric system scheduling. The introduced chaotic maps improve the global optimal capability of CPSO algorithm. Moreover, a piecewise linear interpolation function is employed to transform all constraints into restrict upriver water level for implementing the maximum of objective function. Numerical results and comparisons demonstrate the effect and speed of different algorithms on a practical hydro-system.

  17. Optimal model-based deficit irrigation scheduling using AquaCrop: a simulation study with cotton, potato and tomato

    Linker, Raphael; Ioslovich, Ilya; Sylaios, Georgios

    2016-01-01

    -smooth behavior of the objective function and the fact that it involves multiple integer variables. We developed an optimization scheme for generating sub-optimal irrigation schedules that take implicitly into account the response of the crop to water stress, and used these as initial guesses for a full......Water shortage is the main limiting factor for agricultural productivity in many countries and improving water use efficiency in agriculture has been the focus of numerous studies. The usual approach to limit water consumption in agriculture is to apply water quotas and in such a situation farmers...... variables are the irrigation amounts for each day of the season. The objective function is the expected yield calculated with the use of a model. In the present work we solved this optimization problem for three crops modeled by the model AquaCrop. This optimization problem is non-trivial due to the non...

  18. SU-F-19A-08: Optimal Time Release Schedule of In-Situ Drug Release During Permanent Prostate Brachytherapy

    Cormack, R; Ngwa, W; Makrigiorgos, G; Tangutoori, S; Rajiv, K; Sridhar, S

    2014-01-01

    Purpose: Permanent prostate brachytherapy spacers can be used to deliver sustained doses of radiosentitizing drug directly to the target, in order to enhance the radiation effect. Implantable nanoplatforms for chemo-radiation therapy (INCeRTs) have a maximum drug capacity and can be engineered to control the drug release schedule. The optimal schedule for sensitization during continuous low dose rate irradiation is unknown. This work studies the optimal release schedule of drug for both traditional sensitizers, and those that work by suppressing DNA repair processes. Methods: Six brachytherapy treatment plans were used to model the anatomy, implant geometry and calculate the spatial distribution of radiation dose and drug concentrations for a range of drug diffusion parameters. Three state partial differential equations (cells healthy, damaged or dead) modeled the effect of continuous radiation (radiosensitivities α,β) and cellular repair (time tr) on a cell population. Radiosensitization was modeled as concentration dependent change in α,β or tr which with variable duration under the constraint of fixed total drug release. Average cell kill was used to measure effectiveness. Sensitization by means of both enhanced damage and reduced repair were studied. Results: Optimal release duration is dependent on the concentration of radiosensitizer compared to the saturation concentration (csat) above which additional sensitization does not occur. Long duration drug release when enhancing α or β maximizes cell death when drug concentrations are generally over csat. Short term release is optimal for concentrations below saturation. Sensitization by suppressing repair has a similar though less distinct trend that is more affected by the radiation dose distribution. Conclusion: Models of sustained local radiosensitization show potential to increase the effectiveness of radiation in permanent prostate brachytherapy. INCeRTs with high drug capacity produce the greatest

  19. Optimizing Travel Time to Outpatient Interventional Radiology Procedures in a Multi-Site Hospital System Using a Google Maps Application.

    Mandel, Jacob E; Morel-Ovalle, Louis; Boas, Franz E; Ziv, Etay; Yarmohammadi, Hooman; Deipolyi, Amy; Mohabir, Heeralall R; Erinjeri, Joseph P

    2018-02-20

    The purpose of this study is to determine whether a custom Google Maps application can optimize site selection when scheduling outpatient interventional radiology (IR) procedures within a multi-site hospital system. The Google Maps for Business Application Programming Interface (API) was used to develop an internal web application that uses real-time traffic data to determine estimated travel time (ETT; minutes) and estimated travel distance (ETD; miles) from a patient's home to each a nearby IR facility in our hospital system. Hypothetical patient home addresses based on the 33 cities comprising our institution's catchment area were used to determine the optimal IR site for hypothetical patients traveling from each city based on real-time traffic conditions. For 10/33 (30%) cities, there was discordance between the optimal IR site based on ETT and the optimal IR site based on ETD at non-rush hour time or rush hour time. By choosing to travel to an IR site based on ETT rather than ETD, patients from discordant cities were predicted to save an average of 7.29 min during non-rush hour (p = 0.03), and 28.80 min during rush hour (p travel time when more than one location providing IR procedures is available within the same hospital system.

  20. Optimal Scheduling of Integrated Energy Systems with Combined Heat and Power Generation, Photovoltaic and Energy Storage Considering Battery Lifetime Loss

    Yongli Wang

    2018-06-01

    Full Text Available Integrated energy systems (IESs are considered a trending solution for the energy crisis and environmental problems. However, the diversity of energy sources and the complexity of the IES have brought challenges to the economic operation of IESs. Aiming at achieving optimal scheduling of components, an IES operation optimization model including photovoltaic, combined heat and power generation system (CHP and battery energy storage is developed in this paper. The goal of the optimization model is to minimize the operation cost under the system constraints. For the optimization process, an optimization principle is conducted, which achieves maximized utilization of photovoltaic by adjusting the controllable units such as energy storage and gas turbine, as well as taking into account the battery lifetime loss. In addition, an integrated energy system project is taken as a research case to validate the effectiveness of the model via the improved differential evolution algorithm (IDEA. The comparison between IDEA and a traditional differential evolution algorithm shows that IDEA could find the optimal solution faster, owing to the double variation differential strategy. The simulation results in three different battery states which show that the battery lifetime loss is an inevitable factor in the optimization model, and the optimized operation cost in 2016 drastically decreased compared with actual operation data.

  1. Mix-oriented manufacturing control (MOMC): a quasi-optimal procedure for dynamic scheduling control

    Cristofari, Marco; Caron, Franco; McDuffie, Ernest L.

    1997-12-01

    The total system throughput (ST) is one of the most important decision variables at the planning/scheduling phase of a manufacturing system. Material requirement planning (MRP) and master production schedule (MPS) are based on the assumption that ST is known. All the subsequent developments (e.g. jobs- release, system work-load, input-product mix, etc.) depends on such an assumption. If this assumption is incorrect, the production activity control (PAC) will not be able to satisfy the planned targets during the scheduling phase. Delays and bottlenecks will be unavoidable in the system. In case of random flexible manufacturing system (FMS) (or, in general, job-shop production), the measure of ST can not be evaluated a priori without running simulations or observing the actual flow of the operations in the system. The way entities enter the system (sequencing and percentage of the input products) effects the value of ST in such a way that estimation based on historical data are highly risky. The methodology proposed in this paper allows the scheduler to assess the analytical functions which link ST, and other output performance variables, to the input product mix (IPM). This way the robustness of the scheduling plan can be verified before the actual release of the jobs into the system.

  2. A PMBGA to Optimize the Selection of Rules for Job Shop Scheduling Based on the Giffler-Thompson Algorithm

    Rui Zhang

    2012-01-01

    Full Text Available Most existing research on the job shop scheduling problem has been focused on the minimization of makespan (i.e., the completion time of the last job. However, in the fiercely competitive market nowadays, delivery punctuality is more important for maintaining a high service reputation. So in this paper, we aim at solving job shop scheduling problems with the total weighted tardiness objective. Several dispatching rules are adopted in the Giffler-Thompson algorithm for constructing active schedules. It is noticeable that the rule selections for scheduling consecutive operations are not mutually independent but actually interrelated. Under such circumstances, a probabilistic model-building genetic algorithm (PMBGA is proposed to optimize the sequence of selected rules. First, we use Bayesian networks to model the distribution characteristics of high-quality solutions in the population. Then, the new generation of individuals is produced by sampling the established Bayesian network. Finally, some elitist individuals are further improved by a special local search module based on parameter perturbation. The superiority of the proposed approach is verified by extensive computational experiments and comparisons.

  3. A modified genetic algorithm for time and cost optimization of an additive manufacturing single-machine scheduling

    M. Fera

    2018-09-01

    Full Text Available Additive Manufacturing (AM is a process of joining materials to make objects from 3D model data, usually layer by layer, as opposed to subtractive manufacturing methodologies. Selective Laser Melting, commercially known as Direct Metal Laser Sintering (DMLS®, is the most diffused additive process in today’s manufacturing industry. Introduction of a DMLS® machine in a production department has remarkable effects not only on industrial design but also on production planning, for example, on machine scheduling. Scheduling for a traditional single machine can employ consolidated models. Scheduling of an AM machine presents new issues because it must consider the capability of producing different geometries, simultaneously. The aim of this paper is to provide a mathematical model for an AM/SLM machine scheduling. The complexity of the model is NP-HARD, so possible solutions must be found by metaheuristic algorithms, e.g., Genetic Algorithms. Genetic Algorithms solve sequential optimization problems by handling vectors; in the present paper, we must modify them to handle a matrix. The effectiveness of the proposed algorithms will be tested on a test case formed by a 30 Part Number production plan with a high variability in complexity, distinct due dates and low production volumes.

  4. A Reconfigured Whale Optimization Technique (RWOT for Renewable Electrical Energy Optimal Scheduling Impact on Sustainable Development Applied to Damietta Seaport, Egypt

    Noha H. El-Amary

    2018-03-01

    Full Text Available This paper studies the effect on the rate of growth of carbon dioxide emission in seaports’ atmosphere of replacing a part of the fossil fuel electrical power generation by clean renewable electrical energies, through two different scheduling strategies. The increased rate of harmful greenhouse gas emissions due to conventional electrical power generation severely affects the whole global atmosphere. Carbon dioxide and other greenhouse gases emissions are responsible for a significant share of global warming. Developing countries participate in this environmental distortion to a great percentage. Two different suggested strategies for renewable electrical energy scheduling are discussed in this paper, to attain a sustainable green port by the utilization of two mutual sequential clean renewable energies, which are biomass and photovoltaic (PV energy. The first strategy, which is called the eco-availability mode, is a simple method. It is based on operating the renewable electrical energy sources during the available time of operation, taking into consideration the simple and basic technical issues only, without considering the sophisticated technical and economical models. The available operation time is determined by the environmental condition. This strategy is addressed to result on the maximum available Biomass and PV energy generation based on the least environmental and technical conditions (panel efficiency, minimum average daily sunshine hours per month, minimum average solar insolation per month. The second strategy, which is called the Intelligent Scheduling (IS mode, relies on an intelligent Reconfigured Whale Optimization Technique (RWOT based-model. In this strategy, some additional technical and economical issues are considered. The studied renewable electrical energy generation system is considered in two scenarios, which are with and without storage units. The objective (cost function of the scheduling optimization problem, for

  5. Joint optimization of CQI calculation and interference mitigation for user scheduling in MIMO-OFDM systems

    Sadek, Mirette; Aï ssa, Sonia

    2011-01-01

    In MIMO-OFDM multiuser systems, user scheduling is employed as a means of multiple access. In a downlink scenario, users that share the same subcarriers of an OFDM symbol are separated through precoding in order to achieve space division multiple access (SDMA). User scheduling techniques rely on channel knowledge at the transmitter, namely, the so-called channel quality indicator (CQI). In this paper, we implement a leakage-based precoding algorithm whose purpose is twofold. First, it is used to compute a reliable CQI based on a group of precoding vectors that are adapted to the channel. Then, it implements user scheduling through using the optimum vectors for precoding, thus minimizing interference among users. We also introduce the concept of resource block size adaptivity. The resource block (RB) is defined as the least unit in an OFDM symbol that a user can be assigned to. We propose a variable RB size that adapts to the channel conditions. © 2011 IEEE.

  6. Optimization of workflow scheduling in Utility Management System with hierarchical neural network

    Srdjan Vukmirovic

    2011-08-01

    Full Text Available Grid computing could be the future computing paradigm for enterprise applications, one of its benefits being that it can be used for executing large scale applications. Utility Management Systems execute very large numbers of workflows with very high resource requirements. This paper proposes architecture for a new scheduling mechanism that dynamically executes a scheduling algorithm using feedback about the current status Grid nodes. Two Artificial Neural Networks were created in order to solve the scheduling problem. A case study is created for the Meter Data Management system with measurements from the Smart Metering system for the city of Novi Sad, Serbia. Performance tests show that significant improvement of overall execution time can be achieved by Hierarchical Artificial Neural Networks.

  7. Joint optimization of CQI calculation and interference mitigation for user scheduling in MIMO-OFDM systems

    Sadek, Mirette

    2011-05-01

    In MIMO-OFDM multiuser systems, user scheduling is employed as a means of multiple access. In a downlink scenario, users that share the same subcarriers of an OFDM symbol are separated through precoding in order to achieve space division multiple access (SDMA). User scheduling techniques rely on channel knowledge at the transmitter, namely, the so-called channel quality indicator (CQI). In this paper, we implement a leakage-based precoding algorithm whose purpose is twofold. First, it is used to compute a reliable CQI based on a group of precoding vectors that are adapted to the channel. Then, it implements user scheduling through using the optimum vectors for precoding, thus minimizing interference among users. We also introduce the concept of resource block size adaptivity. The resource block (RB) is defined as the least unit in an OFDM symbol that a user can be assigned to. We propose a variable RB size that adapts to the channel conditions. © 2011 IEEE.

  8. Optimal production scheduling for energy efficiency improvement in biofuel feedstock preprocessing considering work-in-process particle separation

    Li, Lin; Sun, Zeyi; Yao, Xufeng; Wang, Donghai

    2016-01-01

    Biofuel is considered a promising alternative to traditional liquid transportation fuels. The large-scale substitution of biofuel can greatly enhance global energy security and mitigate greenhouse gas emissions. One major concern of the broad adoption of biofuel is the intensive energy consumption in biofuel manufacturing. This paper focuses on the energy efficiency improvement of biofuel feedstock preprocessing, a major process of cellulosic biofuel manufacturing. An improved scheme of the feedstock preprocessing considering work-in-process particle separation is introduced to reduce energy waste and improve energy efficiency. A scheduling model based on the improved scheme is also developed to identify an optimal production schedule that can minimize the energy consumption of the feedstock preprocessing under production target constraint. A numerical case study is used to illustrate the effectiveness of the proposed method. The research outcome is expected to improve the energy efficiency and enhance the environmental sustainability of biomass feedstock preprocessing. - Highlights: • A novel method to schedule production in biofuel feedstock preprocessing process. • Systems modeling approach is used. • Capable of optimize preprocessing to reduce energy waste and improve energy efficiency. • A numerical case is used to illustrate the effectiveness of the method. • Energy consumption per unit production can be significantly reduced.

  9. Multi-objective parallel particle swarm optimization for day-ahead Vehicle-to-Grid scheduling

    Soares, Joao; Vale, Zita; Canizes, Bruno

    2013-01-01

    This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle-To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming...... to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow...

  10. Schedulability Analysis and Optimization for the Synthesis of Multi-Cluster Distributed Embedded Systems

    Pop, Paul; Eles, Petru; Peng, Zebo

    2003-01-01

    An approach to schedulability analysis for the synthesis of multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways, is presented. A buffer size and worst case queuing delay analysis for the gateways, responsible for routing...... inter-cluster traffic, is also proposed. Optimisation heuristics for the priority assignment and synthesis of bus access parameters aimed at producing a schedulable system with minimal buffer needs have been proposed. Extensive experiments and a real-life example show the efficiency of the approaches....

  11. Effects of activity-rest schedules on physiological strain and spinal load in hospital-based porters.

    Beynon, C; Burke, J; Doran, D; Nevill, A

    2000-10-01

    Workers in physically demanding occupations require rest breaks to recover from physiological stress and biomechanical loading. Physiological stress can increase the risk of developing musculoskeletal disorders and repeated loading of the spine may increase the potential for incurring back pain. The aim of the study was to assess the impact of an altered activity-rest schedule on physiological and spinal loading in hospital-based porters. An existing 4-h activity-rest schedule was obtained from observations on eight male porters. This schedule formed the normal trial, which included two 5- and one 15-min breaks. An alternative 4-h schedule was proposed (experimental condition) that had two breaks each of 12.5 min. It was hypothesized that the experimental trial is more effective in promoting recovery from physiological strain and spinal shrinkage than the normal trial, due to the 5-min breaks being insufficient to allow physiological variables to return to resting levels or the intervertebral discs to reabsorb fluid. Ten males performed both test conditions and oxygen uptake VO2, heart rate, minute ventilation VE, perceived exertion and spinal shrinkage were recorded. There were no significant differences in any of the measured variables between the two trials (p > 0.05). Median heart rates were 78 (range 71-93) and 82 (71-90) beats.min(-1) for the normal trial and the experimental trial respectively, indicating that the activity was of low intensity. The light intensity was corroborated by the oxygen uptakes (0.75, range 0.65-0.94 1.min(-1)). Spinal shrinkage occurred to the same extent in the two trials (2.12 +/- 3.16 mm and 2.88 +/- 2.92 mm in the normal trial and the experimental trial respectively). Varying the length and positioning of the rest breaks did not significantly affect the physiological responses or magnitude of spinal shrinkage between the two trials. More physically demanding work than the porters' schedule should induce greater physiological

  12. Inter-terminal transfer between port terminals. A continuous mathematical programming model to optimize scheduling and deployment of transport units

    Morales Fusco, P.; Pedrielli, G.; Zhou, C.; Hay Lee, L.; Peng Chew, E.

    2016-07-01

    In most large port cities, the challenge of inter-terminal transfers (ITT) prevails due to the long distance between multiple terminals. The quantity of containers requiring movement between terminals as they connect from pre-carrier to on-carrier is increasing with the formation of the mega-alliances. The paper proposes a continuous time mathematical programming model to optimize the deployment and schedule of trucks and barges to minimize the number of operating transporters, their makespan, costs and the distance travelled by the containers by choosing the right combination of transporters and container movements while fulfilling time window restrictions imposed on reception of the containers. A multi-step routing problem is developed where transporters can travel from one terminal to another and/or load or unload containers from a specific batch at each step. The model proves successful in identifying the costless schedule and means of transportation. And a sensibility analysis over the parameters used is provided. (Author)

  13. Monitoring Strategies in Permeable Pavement Systems to Optimize Maintenance Scheduling - abstract

    As the surface in a permeable pavement system clogs and performance decreases, maintenance is required to preserve the design function. Currently, guidance is limited for scheduling maintenance on an as needed basis. Previous research has shown that surface clogging in a permea...

  14. Resource-Constrained Optimal Scheduling of Synchronous Dataflow Graphs via Timed Automata

    Ahmad, W.; de Groote, Robert; Holzenspies, P.K.F.; Stoelinga, Mariëlle Ida Antoinette; van de Pol, Jan Cornelis

    Synchronous dataflow (SDF) graphs are a widely used formalism for modelling, analysing and realising streaming applications, both on a single processor and in a multiprocessing context. Efficient schedules are essential to obtain maximal throughput under the constraint of available resources. This

  15. Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control

    Meng Xiong

    2015-08-01

    Full Text Available Energy storage devices are expected to be more frequently implemented in wind farms in near future. In this paper, both pumped hydro and fly wheel storage systems are used to assist a wind farm to smooth the power fluctuations. Due to the significant difference in the response speeds of the two storages types, the wind farm coordination with two types of energy storage is a problem. This paper presents two methods for the coordination problem: a two-level hierarchical model predictive control (MPC method and a single-level MPC method. In the single-level MPC method, only one MPC controller coordinates the wind farm and the two storage systems to follow the grid scheduling. Alternatively, in the two-level MPC method, two MPC controllers are used to coordinate the wind farm and the two storage systems. The structure of two level MPC consists of outer level and inner level MPC. They run alternatively to perform real-time scheduling and then stop, thus obtaining long-term scheduling results and sending some results to the inner level as input. The single-level MPC method performs both long- and short-term scheduling tasks in each interval. The simulation results show that the methods proposed can improve the utilization of wind power and reduce wind power spillage. In addition, the single-level MPC and the two-level MPC are not interchangeable. The single-level MPC has the advantage of following the grid schedule while the two-level MPC can reduce the optimization time by 60%.

  16. Two-stage stochastic day-ahead optimal resource scheduling in a distribution network with intensive use of distributed energy resources

    Sousa, Tiago; Ghazvini, Mohammad Ali Fotouhi; Morais, Hugo

    2015-01-01

    The integration of renewable sources and electric vehicles will introduce new uncertainties to the optimal resource scheduling, namely at the distribution level. These uncertainties are mainly originated by the power generated by renewables sources and by the electric vehicles charge requirements....... This paper proposes a two-state stochastic programming approach to solve the day-ahead optimal resource scheduling problem. The case study considers a 33-bus distribution network with 66 distributed generation units and 1000 electric vehicles....

  17. A Multi-Verse Optimizer with Levy Flights for Numerical Optimization and Its Application in Test Scheduling for Network-on-Chip.

    Cong Hu

    Full Text Available We propose a new meta-heuristic algorithm named Levy flights multi-verse optimizer (LFMVO, which incorporates Levy flights into multi-verse optimizer (MVO algorithm to solve numerical and engineering optimization problems. The Original MVO easily falls into stagnation when wormholes stochastically re-span a number of universes (solutions around the best universe achieved over the course of iterations. Since Levy flights are superior in exploring unknown, large-scale search space, they are integrated into the previous best universe to force MVO out of stagnation. We test this method on three sets of 23 well-known benchmark test functions and an NP complete problem of test scheduling for Network-on-Chip (NoC. Experimental results prove that the proposed LFMVO is more competitive than its peers in both the quality of the resulting solutions and convergence speed.

  18. Multiple R&D projects scheduling optimization with improved particle swarm algorithm.

    Liu, Mengqi; Shan, Miyuan; Wu, Juan

    2014-01-01

    For most enterprises, in order to win the initiative in the fierce competition of market, a key step is to improve their R&D ability to meet the various demands of customers more timely and less costly. This paper discusses the features of multiple R&D environments in large make-to-order enterprises under constrained human resource and budget, and puts forward a multi-project scheduling model during a certain period. Furthermore, we make some improvements to existed particle swarm algorithm and apply the one developed here to the resource-constrained multi-project scheduling model for a simulation experiment. Simultaneously, the feasibility of model and the validity of algorithm are proved in the experiment.

  19. A Mutualism Quantum Genetic Algorithm to Optimize the Flow Shop Scheduling with Pickup and Delivery Considerations

    Jinwei Gu

    2015-01-01

    Full Text Available A mutualism quantum genetic algorithm (MQGA is proposed for an integrated supply chain scheduling with the materials pickup, flow shop scheduling, and the finished products delivery. The objective is to minimize the makespan, that is, the arrival time of the last finished product to the customer. In MQGA, a new symbiosis strategy named mutualism is proposed to adjust the size of each population dynamically by regarding the mutual influence relation of the two subpopulations. A hybrid Q-bit coding method and a local speeding-up method are designed to increase the diversity of genes, and a checking routine is carried out to ensure the feasibility of each solution; that is, the total physical space of each delivery batch could not exceed the capacity of the vehicle. Compared with the modified genetic algorithm (MGA and the quantum-inspired genetic algorithm (QGA, the effectiveness and efficiency of the MQGA are validated by numerical experiments.

  20. Unified approach for the optimization of energy and water in multipurpose batch plants using a flexible scheduling framework

    Adekola, O

    2013-05-01

    Full Text Available & Engineering Chemistry Research Vol. 52(25)/ pp 8488-8506 Unified Approach for the Optimization of Energy and Water in Multipurpose Batch Plants Using a Flexible Scheduling Framework Omobolanle Adekola,† Jane D. Stamp,† Thokozani Majozi,*,†,‡ Anurag... Garg,§ and Santanu Bandyopadhyay⊥ †Department of Chemical Engineering, University of Pretoria, Lynnwood Road, Pretoria, 0002, South Africa ‡Modelling and Digital S ien e, S , Meiring aud oad, retoria, 02, South Africa §Centre...

  1. Optimization of nas lemoore scheduling to support a growing aircraft population

    2017-03-01

    service it. Both heuristics model the amount of fuel available in the trucks as in MSISCHE. For the current policy, each hot skid has its own queue...reflect the official policy or position of the Department of Defense or the U.S. Government. IRB number ____N/A____. 12a. DISTRIBUTION / AVAILABILITY ...well as constraints imposed by limited military operating area (MOA) availability . Given the complexity of this scheduling problem, attempting to

  2. Optimal Routing and Scheduling of Charge for Electric Vehicles: Case Study

    Barco, John; Guerra, Andres; Muñoz, Luis; Quijano, Nicanor

    2013-01-01

    In Colombia, there is an increasing interest about improving public transportation. One of the proposed strategies in that way is the use battery electric vehicles (BEVs). One of the new challenges is the BEVs routing problem, which is subjected to the traditional issues of the routing problems, and must also consider the particularities of autonomy, charge and battery degradation of the BEVs. In this work, a scheme that coordinates the routing, scheduling of charge and operating costs of BEV...

  3. Optimized Waterspace Management and Scheduling Using Mixed-Integer Linear Programming

    2016-01-01

    PANAMA CITY DIVISION PANAMA CITY, FLORIDA 32407-7001 5(3257...task. This paper is outlined as follows: in Section 2, we discuss the general setup of the the MCM scheduling problem, including the definition of the...Suite 1425 Arlington, VA 22203-1995 Naval Surface Warfare Center, Panama City Division 1 ATTN: Technical Library 110 Vernon Avenue Panama City, FL 32407 27

  4. Decoupling algorithms from schedules for easy optimization of image processing pipelines

    Adams, Andrew; Paris, Sylvain; Levoy, Marc; Ragan-Kelley, Jonathan Millar; Amarasinghe, Saman P.; Durand, Fredo

    2012-01-01

    Using existing programming tools, writing high-performance image processing code requires sacrificing readability, portability, and modularity. We argue that this is a consequence of conflating what computations define the algorithm, with decisions about storage and the order of computation. We refer to these latter two concerns as the schedule, including choices of tiling, fusion, recomputation vs. storage, vectorization, and parallelism. We propose a representation for feed-forward imagi...

  5. Reducing Cancelations on the Day of Scheduled Surgery at a Children's Hospital.

    Pratap, Jayant Nick; Varughese, Anna M; Mercurio, Patti; Lynch, Terri; Lonnemann, Teresa; Ellis, Andrea; Rugg, John; Stone, W Ray; Bedinghaus, Cindi

    2015-05-01

    Cancelation on the day of surgery (DoSC) represents a costly wastage of operating room (OR) time and causes inconvenience, emotional distress, and financial cost to families. A quality improvement project sought to reduce lost OR time due to cancelation. Key drivers of the process included effective 2-way communication with families, compliance with fasting rules, and decision-making on patient illness before the day of surgery. A multidisciplinary team conducted serial tests of change addressing the various key drivers. Interventions were simplified, colorful, personalized preoperative instruction sheets and text-message reminders to caregivers' cellphones, as well as a defined institutional decision-making pathway to permit rescheduling before the day of surgery in case of patient illness concerns. After initial smaller-scale testing, the interventions were implemented across all patients and sites. Data were collected from the hospital information technology system and analyzed by using control charts and statistical process control methods. Mean OR time lost due to DoSC was decreased from a baseline of 5.7 to 3.6 hours/day in testing with a subset of surgical services at the hospital's base campus, and then from 6.6 hours to 5.5 hours/day when implemented across all services at both surgical sites. By applying quality improvement methods, significant reductions were made in time lost due to DoSC. The impact can be significant by improving institutional resource utilization. Copyright © 2015 by the American Academy of Pediatrics.

  6. Optimization of the scheduled maintenance on the power units of the nuclear power plants with WWER

    Skalozubov, V.I.; Kovrizhkin, Yu.L.; Kolykhanov, V.N.; Kochneva, V.Yu.; Urbanskij, V.V.

    2008-01-01

    The advanced international and domestic experience in the field of the maintenance optimization of the power units of NPPs, as well, as on the base of the planning optimization, the maintenance organization and carrying out, the technical maintenance and repair control system automatization, the testing and monitoring optimization during the service process, the modernization of the technology and technical tools of the maintenance service and control is represented

  7. A Biobjective Optimization Model for Deadline Satisfaction in Line-of-Balance Scheduling with Work Interruptions Consideration

    Xin Zou

    2018-01-01

    Full Text Available The line-of-balance (LOB technique has demonstrated many advantages in scheduling repetitive projects, one of which is that it allows more than one crew to be hired by an activity concurrently. The deadline satisfaction problem in LOB scheduling (DSPLOB aims to find an LOB schedule such that the project is completed within a given deadline and the total number of crews is minimized. Previous studies required a strict application of crew work continuity, which may lead to a decline in the competitiveness of solutions. This paper introduces work interruptions into the DSPLOB and presents a biobjective optimization model that can balance the two conflicting objectives of minimizing the total number of crews and maximizing work continuity. An efficient version of the ϵ-constraint method is customized to find all feasible tradeoff solutions. Then, these solutions are further improved by an automated procedure to reduce the number of interruptions for each activity without deteriorating the performance in both the objectives. The effectiveness and practicability of the proposed model are verified using a considerable number of instances. The results show that introducing work interruptions provides more flexibility in reducing the total number of crews under the LOB framework, especially for serial projects with a tight deadline constraint.

  8. Optimization of the heat treatment schedule for next european dipole (NED) powder in tube $Nb_{3}Sn$ strand

    Boutboul, T; den Ouden, A; Pedrini, D; Volpini, G

    2009-01-01

    A Nb3Sn strand was successfully developed by the company SMI for Next European Dipole (NED) activity and on the basis of Powder-In-Tube (PIT) method. This strand, after the standard reaction recommended by the firm (84 h @ 675 oC), presents attractive performances as a critical current density in the non-copper part of ~ 2500 A/mm2 for 4.2 K and 12 T applied field, an effective filament diameter of ~ 50 μm and limited flux jumps at low magnetic fields. Heat treatment optimization studies are currently performed at CERN to try to optimize the strand electric abilities. For this purpose, various heat treatment schedules were already investigated with a plateau temperature as low as 625 oC. The preliminary results of these studies are summarized here.

  9. Optimal Scheduling of a Battery-Based Energy Storage System for a Microgrid with High Penetration of Renewable Sources

    Dulout, Jeremy; Hernández, Adriana Carolina Luna; Anvari-Moghaddam, Amjad

    2017-01-01

    A new scheduling method is proposed to manage efficiently the integration of renewable sources in microgrids (MGs) with energy storage systems (ESSs). The purpose of this work is to take into account the main stress factors influencing the ageing mechanisms of a battery energy storage system (BESS......) in order to make an optimal dispatch of resources in the microgrid and enhance the storage system lifetime while minimizing the cost of electric consumption. The load demand and generation profiles are derived from the analysis of consumption and renewable production (solar photovoltaic sources and wind...... turbines) of the Western Denmark electric grid. Thus, the proposed microgrid is mainly fed by renewable sources and few electricity is coming from the main grid (which helps operating costs minimization). In this respect, a cost analysis is performed to find the optimal hourly power output of the BESS...

  10. Short-term cascaded hydroelectric system scheduling based on chaotic particle swarm optimization using improved logistic map

    He, Yaoyao; Yang, Shanlin; Xu, Qifa

    2013-07-01

    In order to solve the model of short-term cascaded hydroelectric system scheduling, a novel chaotic particle swarm optimization (CPSO) algorithm using improved logistic map is introduced, which uses the water discharge as the decision variables combined with the death penalty function. According to the principle of maximum power generation, the proposed approach makes use of the ergodicity, symmetry and stochastic property of improved logistic chaotic map for enhancing the performance of particle swarm optimization (PSO) algorithm. The new hybrid method has been examined and tested on two test functions and a practical cascaded hydroelectric system. The experimental results show that the effectiveness and robustness of the proposed CPSO algorithm in comparison with other traditional algorithms.

  11. The optimal scheduling of decentralised co-generation plants in microgrids; Optimale Einsatzplanung von Kraft-Waerme-Kopplungsanlagen in Microgrids

    Gunkel, David [TU Dresden (Germany). Lehrstuhl fuer Energiewirtschaft; Hess, Tobias; Schegner, Peter [TU Dresden (Germany). Inst. fuer Elektrische Energieversorgung und Hochspannungstechnik

    2011-07-01

    The daily operational scheduling of decentralised unit is an important optimization task of power systems. This proceeding deals with planning of small scaled co-generation power units for district heating in a microgrid. This power system can be mathematically formulated and solved by an optimization algorithm. The solution process consists of a unit commitment and dispatch. The starting unit commitment is characterised by a mixed integer nonlinear problem defining the on-off-state of all units. Subsequently, the dispatch distributes the generation requirements to every committed unit considering thermal demand. The dispatching is based on a mixed integer linear problem. Additionally, it presents a way for flexible reducing the outage reserve related to the operational condition. The given microgrid operates in an islanding mode. The method can also be applied in a grid connected model considering the possible requirements of a grid operator. (orig.)

  12. An Optimization Scheduling Model for Wind Power and Thermal Power with Energy Storage System considering Carbon Emission Trading

    Huan-huan Li

    2015-01-01

    Full Text Available Wind power has the characteristics of randomness and intermittence, which influences power system safety and stable operation. To alleviate the effect of wind power grid connection and improve power system’s wind power consumptive capability, this paper took emission trading and energy storage system into consideration and built an optimization model for thermal-wind power system and energy storage systems collaborative scheduling. A simulation based on 10 thermal units and wind farms with 2800 MW installed capacity verified the correctness of the models put forward by this paper. According to the simulation results, the introduction of carbon emission trading can improve wind power consumptive capability and cut down the average coal consumption per unit of power. The introduction of energy storage system can smooth wind power output curve and suppress power fluctuations. The optimization effects achieve the best when both of carbon emission trading and energy storage system work at the same time.

  13. Radiation therapy for painful bone metastases. Aiming at optimal treatment schedules

    Murakami, Ryuji; Saito, Ryuichi; Miyazaki, Toshiyuki; Takahashi, Mutsumasa

    2001-01-01

    The purpose of this study is to evaluate the pain relief obtained by radiation therapy for painful bone metastases, with a special regard to general condition. Between June 1998 and May 2000, 54 patients with 86 painful bone metastases were treated with radiation therapy whose effects could be evaluated for a minimum period of 6 months or until death. Treatment schedules were 3 Gy/fraction/day (30-36 Gy/10-12 fractions) in usual cases (61 lesions), 4-8 Gy/fraction/day (8-20 Gy/1-5 fractions) in patients with a poor general condition (9 lesions), and 2 Gy/fraction/day (40-50 Gy/20-25 fractions) in lesions with a large radiation field (16 lesions). Complete pain relief without medication (CR) was achieved in 40 lesions (47%). Significant predictors for CR were primary site (p=0.0003), performance status (p=0.0060), pain score (p=00190), narcotic score (p<0.0001), and prognosis (p<0.0001), but no difference was found in CR among treatment schedules. No evidence of severe radiation-induced complication was seen. General condition (performance status and prognosis) has an influence on pain relief. Compared with the daily 2 Gy protocol, the daily 3 Gy protocol has the advantage of shorter treatment time. The treatment schedule should be assessed in patients with a large radiation field and/or poor general condition. Especially for the patients with poor general condition, combined pain medication should be considered. (author)

  14. Optimizing the dosing schedule of l-asparaginase improves its anti-tumor activity in breast tumor-bearing mice

    Shoya Shiromizu

    2018-04-01

    Full Text Available Proliferation of acute lymphoblastic leukemic cells is nutritionally dependent on the external supply of asparagine. l-asparaginase, an enzyme hydrolyzing l-asparagine in blood, is used for treatment of acute lymphoblastic leukemic and other related blood cancers. Although previous studies demonstrated that l-asparaginase suppresses the proliferation of cultured solid tumor cells, it remains unclear whether this enzyme prevents the growth of solid tumors in vivo. In this study, we demonstrated the importance of optimizing dosing schedules for the anti-tumor activity of l-asparaginase in 4T1 breast tumor-bearing mice. Cultures of several types of murine solid tumor cells were dependent on the external supply of asparagine. Among them, we selected murine 4T1 breast cancer cells and implanted them into BALB/c female mice kept under standardized light/dark cycle conditions. The growth of 4T1 tumor cells implanted in mice was significantly suppressed by intravenous administration of l-asparaginase during the light phase, whereas its administration during the dark phase failed to show significant anti-tumor activity. Decreases in plasma asparagine levels due to the administration of l-asparaginase were closely related to the dosing time-dependency of its anti-tumor effects. These results suggest that the anti-tumor efficacy of l-asparaginase in breast tumor-bearing mice is improved by optimizing the dosing schedule. Keywords: l-asparaginase, Asparagine, Solid tumor, Chrono-pharmacotherapy

  15. Depositional features of the Middle Jurassic formation of Field N and their influence on optimal drilling schedule

    Mishina, D; Rukavishnikov, V; Belozerov, B; Bochkov, A

    2015-01-01

    The Middle Jurassic formation of Field N represented by 4 hydrodynamically connected layers (J5-6, J4, J3 and J2) contains 42% of the field STOIIP. The J2-6 formation is characterized as a gas-oil-condensate massive lithologically and tectonically screened accumulation with a gas cap (J2, J3 layers) and bottom water (J5-6 layer). Oil is predominantly in the J3 and J4 layers. There is a high risk of early gas coning from gas-bearing layers to oil producing wells determined on the basis of production test results, which can significantly decrease the life of the well. To select a more optimal drilling schedule, it is necessary to take the risk of early gas coning into account and determine distinctive features within the gas- saturated zone that can reduce it. The presence of a thick shale barrier between the J2 and J3 layers with thicknesses varying from 0 to 30 m is recognized as the beginning of a transgression cycle, and if the gas cap is only in the J2 layer, this barrier with the thickness of more than 5 m can extensively prevent early gas coning into oil producing wells. The integration of geological information represented by the probability map constructed and petrophysical information represented by the kh map provide the more precise determination of an optimal drilling schedule

  16. Optimizing the dosing schedule of l-asparaginase improves its anti-tumor activity in breast tumor-bearing mice.

    Shiromizu, Shoya; Kusunose, Naoki; Matsunaga, Naoya; Koyanagi, Satoru; Ohdo, Shigehiro

    2018-04-01

    Proliferation of acute lymphoblastic leukemic cells is nutritionally dependent on the external supply of asparagine. l-asparaginase, an enzyme hydrolyzing l-asparagine in blood, is used for treatment of acute lymphoblastic leukemic and other related blood cancers. Although previous studies demonstrated that l-asparaginase suppresses the proliferation of cultured solid tumor cells, it remains unclear whether this enzyme prevents the growth of solid tumors in vivo. In this study, we demonstrated the importance of optimizing dosing schedules for the anti-tumor activity of l-asparaginase in 4T1 breast tumor-bearing mice. Cultures of several types of murine solid tumor cells were dependent on the external supply of asparagine. Among them, we selected murine 4T1 breast cancer cells and implanted them into BALB/c female mice kept under standardized light/dark cycle conditions. The growth of 4T1 tumor cells implanted in mice was significantly suppressed by intravenous administration of l-asparaginase during the light phase, whereas its administration during the dark phase failed to show significant anti-tumor activity. Decreases in plasma asparagine levels due to the administration of l-asparaginase were closely related to the dosing time-dependency of its anti-tumor effects. These results suggest that the anti-tumor efficacy of l-asparaginase in breast tumor-bearing mice is improved by optimizing the dosing schedule. Copyright © 2018 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

  17. An Optimization of Manufacturing Systems using a Feedback Control Scheduling Model

    Ikome, John M.; Kanakana, Grace M.

    2018-03-01

    In complex production system that involves multiple process, unplanned disruption often turn to make the entire production system vulnerable to a number of problems which leads to customer’s dissatisfaction. However, this problem has been an ongoing problem that requires a research and methods to streamline the entire process or develop a model that will address it, in contrast to this, we have developed a feedback scheduling model that can minimize some of this problem and after a number of experiment, it shows that some of this problems can be eliminated if the correct remedial actions are implemented on time.

  18. An Optimal Scheduling Algorithm with a Competitive Factor for Real-Time Systems

    1991-07-29

    real - time systems in which the value of a task is proportional to its computation time. The system obtains the value of a given task if the task completes by its deadline. Otherwise, the system obtains no value for the task. When such a system is underloaded (i.e. there exists a schedule for which all tasks meet their deadlines), Dertouzos [6] showed that the earliest deadline first algorithm will achieve 100% of the possible value. We consider the case of a possibly overloaded system and present an algorithm which: 1. behaves like the earliest deadline first

  19. Ant colony optimization and event-based dynamic task scheduling and staffing for software projects

    Ellappan, Vijayan; Ashwini, J.

    2017-11-01

    In programming change organizations from medium to inconceivable scale broadens, the issue of wander orchestrating is amazingly unusual and testing undertaking despite considering it a manual system. Programming wander-organizing requirements to deal with the issue of undertaking arranging and in addition the issue of human resource portion (also called staffing) in light of the way that most of the advantages in programming ventures are individuals. We propose a machine learning approach with finds respond in due order regarding booking by taking in the present arranging courses of action and an event based scheduler revives the endeavour arranging system moulded by the learning computation in perspective of the conformity in event like the begin with the Ander, the instant at what time possessions be free starting to ended errands, and the time when delegates stick together otherwise depart the wander inside the item change plan. The route toward invigorating the timetable structure by the even based scheduler makes the arranging method dynamic. It uses structure components to exhibit the interrelated surges of endeavours, slip-ups and singular all through different progression organizes and is adjusted to mechanical data. It increases past programming wander movement ask about by taking a gander at a survey based process with a one of a kind model, organizing it with the data based system for peril assessment and cost estimation, and using a choice showing stage.

  20. Simulated Annealing-Based Ant Colony Algorithm for Tugboat Scheduling Optimization

    Qi Xu

    2012-01-01

    Full Text Available As the “first service station” for ships in the whole port logistics system, the tugboat operation system is one of the most important systems in port logistics. This paper formulated the tugboat scheduling problem as a multiprocessor task scheduling problem (MTSP after analyzing the characteristics of tugboat operation. The model considers factors of multianchorage bases, different operation modes, and three stages of operations (berthing/shifting-berth/unberthing. The objective is to minimize the total operation times for all tugboats in a port. A hybrid simulated annealing-based ant colony algorithm is proposed to solve the addressed problem. By the numerical experiments without the shifting-berth operation, the effectiveness was verified, and the fact that more effective sailing may be possible if tugboats return to the anchorage base timely was pointed out; by the experiments with the shifting-berth operation, one can see that the objective is most sensitive to the proportion of the shifting-berth operation, influenced slightly by the tugboat deployment scheme, and not sensitive to the handling operation times.

  1. Optimally Scheduling Distribution of the MH-60S Helicopter and Pilots to Combat Support (HC) Squadrons

    Culver, Cory

    2002-01-01

    ... (Optimal Transition HC Allocation Model) that minimizes lost flying days. OTHCAM takes into account variable training durations, travel times and tour lengths as well as manpower and aircraft constraints...

  2. Capacity optimization and scheduling of a multiproduct manufacturing facility for biotech products.

    Shaik, Munawar A; Dhakre, Ankita; Rathore, Anurag S; Patil, Nitin

    2014-01-01

    A general mathematical framework has been proposed in this work for scheduling of a multiproduct and multipurpose facility involving manufacturing of biotech products. The specific problem involves several batch operations occurring in multiple units involving fixed processing time, unlimited storage policy, transition times, shared units, and deterministic and fixed data in the given time horizon. The different batch operations are modeled using state-task network representation. Two different mathematical formulations are proposed based on discrete- and continuous-time representations leading to a mixed-integer linear programming model which is solved using General Algebraic Modeling System software. A case study based on a real facility is presented to illustrate the potential and applicability of the proposed models. The continuous-time model required less number of events and has a smaller problem size compared to the discrete-time model. © 2014 American Institute of Chemical Engineers.

  3. Mixed-integer nonlinear approach for the optimal scheduling of a head-dependent hydro chain

    Catalao, J.P.S.; Pousinho, H.M.I. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Mendes, V.M.F. [Department of Electrical Engineering and Automation, Instituto Superior de Engenharia de Lisboa, R. Conselheiro Emidio Navarro, 1950-062 Lisbon (Portugal)

    2010-08-15

    This paper is on the problem of short-term hydro scheduling (STHS), particularly concerning a head-dependent hydro chain. We propose a novel mixed-integer nonlinear programming (MINLP) approach, considering hydroelectric power generation as a nonlinear function of water discharge and of the head. As a new contribution to earlier studies, we model the on-off behavior of the hydro plants using integer variables, in order to avoid water discharges at forbidden areas. Thus, an enhanced STHS is provided due to the more realistic modeling presented in this paper. Our approach has been applied successfully to solve a test case based on one of the Portuguese cascaded hydro systems with a negligible computational time requirement. (author)

  4. Portfolio optimization using Mixture Design of Experiments. Scheduling trades within electricity markets

    Oliveira, Francisco Alexandre de; Paiva, Anderson Paulo de; Lima, Jose Wanderley Marangon; Balestrassi, Pedro Paulo; Mendes, Rona Rinston Amaury [Federal Univ. of Itajuba, Minas Gerais (Brazil)

    2011-01-15

    Deregulation of the electricity sector has given rise to several approaches to defining optimal portfolios of energy contracts. Financial tools - requiring substantial adjustments - are usually used to determine risk and return. This article presents a novel approach to adjusting the conditional value at risk (CVaR) metric to the mix of contracts on the energy markets; the approach uses Mixture Design of Experiments (MDE). In this kind of experimental strategy, the design factors are treated as proportions in a mixture system considered quite adequate for treating portfolios in general. Instead of using traditional linear programming, the concept of desirability function is here used to combine the multi-response, nonlinear objective functions for mean with the variance of a specific portfolio obtained through MDE. The maximization of the desirability function is implied in the portfolio optimization, generating an efficient recruitment frontier. This approach offers three main contributions: it includes risk aversion in the optimization routine, it assesses interaction between contracts, and it lessens the computational effort required to solve the constrained nonlinear optimization problem. A case study based on the Brazilian energy market is used to illustrate the proposal. The numerical results verify the proposal's adequacy. (author)

  5. Optimal resource allocation and load scheduling for a multi-commodity smart energy system

    Blaauwbroek, N.; Nguyen, H.P.; Shi, H.; Kamphuis, I.G.; Kling, W.L.; Konsman, M.J.

    2015-01-01

    The increasing introduction of district heating systems together with hybrid energy appliances as heat pumps and micro-combined heat and power installations, results in new opportunities for optimizing the available resources in multi-commodity smart energy systems, including electricity, heat and

  6. Portfolio optimization using Mixture Design of Experiments. Scheduling trades within electricity markets

    Oliveira, Francisco Alexandre de; Paiva, Anderson Paulo de; Lima, Jose Wanderley Marangon; Balestrassi, Pedro Paulo; Mendes, Rona Rinston Amaury

    2011-01-01

    Deregulation of the electricity sector has given rise to several approaches to defining optimal portfolios of energy contracts. Financial tools - requiring substantial adjustments - are usually used to determine risk and return. This article presents a novel approach to adjusting the conditional value at risk (CVaR) metric to the mix of contracts on the energy markets; the approach uses Mixture Design of Experiments (MDE). In this kind of experimental strategy, the design factors are treated as proportions in a mixture system considered quite adequate for treating portfolios in general. Instead of using traditional linear programming, the concept of desirability function is here used to combine the multi-response, nonlinear objective functions for mean with the variance of a specific portfolio obtained through MDE. The maximization of the desirability function is implied in the portfolio optimization, generating an efficient recruitment frontier. This approach offers three main contributions: it includes risk aversion in the optimization routine, it assesses interaction between contracts, and it lessens the computational effort required to solve the constrained nonlinear optimization problem. A case study based on the Brazilian energy market is used to illustrate the proposal. The numerical results verify the proposal's adequacy. (author)

  7. Optimal Power Scheduling for a Grid-Connected Hybrid PV-Wind-Battery Microgrid System

    Hernández, Adriana Carolina Luna; Aldana, Nelson Leonardo Diaz; Savaghebi, Mehdi

    2016-01-01

    mathematical model, wherein the cost of energy purchased from the main grid is minimized and profits for selling energy generated by photovoltaic arrays are maximized by considering both physical constraints and requirements for a feasible deployment in the real system. The optimization model is tested...

  8. Optimal estimation and scheduling in aquifer management using the rapid feedback control method

    Ghorbanidehno, Hojat; Kokkinaki, Amalia; Kitanidis, Peter K.; Darve, Eric

    2017-12-01

    Management of water resources systems often involves a large number of parameters, as in the case of large, spatially heterogeneous aquifers, and a large number of "noisy" observations, as in the case of pressure observation in wells. Optimizing the operation of such systems requires both searching among many possible solutions and utilizing new information as it becomes available. However, the computational cost of this task increases rapidly with the size of the problem to the extent that textbook optimization methods are practically impossible to apply. In this paper, we present a new computationally efficient technique as a practical alternative for optimally operating large-scale dynamical systems. The proposed method, which we term Rapid Feedback Controller (RFC), provides a practical approach for combined monitoring, parameter estimation, uncertainty quantification, and optimal control for linear and nonlinear systems with a quadratic cost function. For illustration, we consider the case of a weakly nonlinear uncertain dynamical system with a quadratic objective function, specifically a two-dimensional heterogeneous aquifer management problem. To validate our method, we compare our results with the linear quadratic Gaussian (LQG) method, which is the basic approach for feedback control. We show that the computational cost of the RFC scales only linearly with the number of unknowns, a great improvement compared to the basic LQG control with a computational cost that scales quadratically. We demonstrate that the RFC method can obtain the optimal control values at a greatly reduced computational cost compared to the conventional LQG algorithm with small and controllable losses in the accuracy of the state and parameter estimation.

  9. An Integrated and Optimal Joint Scheduling of Energy Resources to Feed Electrical, Thermal and Potable Water Demands in Remote Area

    R. Ghaffarpour

    2016-12-01

    Full Text Available The continuous spread of distributed energy resources (DERs such as combined heating and power (CHP, diesel generators, boilers and renewable energy sources provide an effective solution to energy related problems to serve the power and heat demands with minimum cost. Moreover, the DERs may play a significant role for supplying power and heat in rural areas, where grid electricity is not available. Also, some dry areas may face water scarcity and salinity problems. So, one important solution is the use of DERs to drive desalination units in order to solve water scarcity and salinity problems. In this study, the optimal scheduling of DERs and reverse osmosis (RO desalination unit that feed the required electric, thermal and potable water demands are determined. The present paper describes the operation constraints and cost function of components of the system in detail. Operation constraints of generation units as well as feasible region of operation CHP in dual dependency characteristic are taken into account. To confirm the performance of the proposed model the approach is tested on a realistic remote area over a 24-h period. The results show that the economical scheduling of DERs and desalination units can be obtained using proposed methodology by implementing the proposed formulation.

  10. A Scheduling Model for the Re-entrant Manufacturing System and Its Optimization by NSGA-II

    Masoud Rabbani

    2016-11-01

    Full Text Available In this study, a two-objective mixed-integer linear programming model (MILP for multi-product re-entrant flow shop scheduling problem has been designed. As a result, two objectives are considered. One of them is maximization of the production rate and the other is the minimization of processing time. The system has m stations and can process several products in a moment. The re-entrant flow shop scheduling problem is well known as NP-hard problem and its complexity has been discussed by several researchers. Given that NSGA-II algorithm is one of the strongest and most applicable algorithm in solving multi-objective optimization problems, it is used to solve this problem. To increase algorithm performance, Taguchi technique is used to design experiments for algorithm’s parameters. Numerical experiments are proposed to show the efficiency and effectiveness of the model. Finally, the results of NSGA-II are compared with SPEA2 algorithm (Strength Pareto Evolutionary Algorithm 2. The experimental results show that the proposed algorithm performs significantly better than the SPEA2.

  11. A Flexible Job Shop Scheduling Problem with Controllable Processing Times to Optimize Total Cost of Delay and Processing

    Hadi Mokhtari

    2015-11-01

    Full Text Available In this paper, the flexible job shop scheduling problem with machine flexibility and controllable process times is studied. The main idea is that the processing times of operations may be controlled by consumptions of additional resources. The purpose of this paper to find the best trade-off between processing cost and delay cost in order to minimize the total costs. The proposed model, flexible job shop scheduling with controllable processing times (FJCPT, is formulated as an integer non-linear programming (INLP model and then it is converted into an integer linear programming (ILP model. Due to NP-hardness of FJCPT, conventional analytic optimization methods are not efficient. Hence, in order to solve the problem, a Scatter Search (SS, as an efficient metaheuristic method, is developed. To show the effectiveness of the proposed method, numerical experiments are conducted. The efficiency of the proposed algorithm is compared with that of a genetic algorithm (GA available in the literature for solving FJSP problem. The results showed that the proposed SS provide better solutions than the existing GA.

  12. Trade-off between learning and exploitation: the Pareto-optimal versus evolutionarily stable learning schedule in cumulative cultural evolution.

    Wakano, Joe Yuichiro; Miura, Chiaki

    2014-02-01

    Inheritance of culture is achieved by social learning and improvement is achieved by individual learning. To realize cumulative cultural evolution, social and individual learning should be performed in this order in one's life. However, it is not clear whether such a learning schedule can evolve by the maximization of individual fitness. Here we study optimal allocation of lifetime to learning and exploitation in a two-stage life history model under a constant environment. We show that the learning schedule by which high cultural level is achieved through cumulative cultural evolution is unlikely to evolve as a result of the maximization of individual fitness, if there exists a trade-off between the time spent in learning and the time spent in exploiting the knowledge that has been learned in earlier stages of one's life. Collapse of a fully developed culture is predicted by a game-theoretical analysis where individuals behave selfishly, e.g., less learning and more exploiting. The present study suggests that such factors as group selection, the ability of learning-while-working ("on the job training"), or environmental fluctuation might be important in the realization of rapid and cumulative cultural evolution that is observed in humans. Copyright © 2013 Elsevier Inc. All rights reserved.

  13. An Improved Version of Discrete Particle Swarm Optimization for Flexible Job Shop Scheduling Problem with Fuzzy Processing Time

    Song Huang

    2016-01-01

    Full Text Available The fuzzy processing time occasionally exists in job shop scheduling problem of flexible manufacturing system. To deal with fuzzy processing time, fuzzy flexible job shop model was established in several papers and has attracted numerous researchers’ attention recently. In our research, an improved version of discrete particle swarm optimization (IDPSO is designed to solve flexible job shop scheduling problem with fuzzy processing time (FJSPF. In IDPSO, heuristic initial methods based on triangular fuzzy number are developed, and a combination of six initial methods is applied to initialize machine assignment and random method is used to initialize operation sequence. Then, some simple and effective discrete operators are employed to update particle’s position and generate new particles. In order to guide the particles effectively, we extend global best position to a set with several global best positions. Finally, experiments are designed to investigate the impact of four parameters in IDPSO by Taguchi method, and IDPSO is tested on five instances and compared with some state-of-the-art algorithms. The experimental results show that the proposed algorithm can obtain better solutions for FJSPF and is more competitive than the compared algorithms.

  14. A new multi-objective optimization model for preventive maintenance and replacement scheduling of multi-component systems

    Moghaddam, Kamran S.; Usher, John S.

    2011-07-01

    In this article, a new multi-objective optimization model is developed to determine the optimal preventive maintenance and replacement schedules in a repairable and maintainable multi-component system. In this model, the planning horizon is divided into discrete and equally-sized periods in which three possible actions must be planned for each component, namely maintenance, replacement, or do nothing. The objective is to determine a plan of actions for each component in the system while minimizing the total cost and maximizing overall system reliability simultaneously over the planning horizon. Because of the complexity, combinatorial and highly nonlinear structure of the mathematical model, two metaheuristic solution methods, generational genetic algorithm, and a simulated annealing are applied to tackle the problem. The Pareto optimal solutions that provide good tradeoffs between the total cost and the overall reliability of the system can be obtained by the solution approach. Such a modeling approach should be useful for maintenance planners and engineers tasked with the problem of developing recommended maintenance plans for complex systems of components.

  15. An Adaptive Procedure for Task Scheduling Optimization in Mobile Cloud Computing

    Pham Phuoc Hung

    2015-01-01

    Full Text Available Nowadays, mobile cloud computing (MCC has emerged as a new paradigm which enables offloading computation-intensive, resource-consuming tasks up to a powerful computing platform in cloud, leaving only simple jobs to the capacity-limited thin client devices such as smartphones, tablets, Apple’s iWatch, and Google Glass. However, it still faces many challenges due to inherent problems of thin clients, especially the slow processing and low network connectivity. So far, a number of research studies have been carried out, trying to eliminate these problems, yet few have been found efficient. In this paper, we present an enhanced architecture, taking advantage of collaboration of thin clients and conventional desktop or laptop computers, known as thick clients, particularly aiming at improving cloud access. Additionally, we introduce an innovative genetic approach for task scheduling such that the processing time is minimized, while considering network contention and cloud cost. Our simulation shows that the proposed approach is more cost-effective and achieves better performance compared with others.

  16. Cascading Delay Risk of Airline Workforce Deployments with Crew Pairing and Schedule Optimization.

    Chung, Sai Ho; Ma, Hoi Lam; Chan, Hing Kai

    2017-08-01

    This article concerns the assignment of buffer time between two connected flights and the number of reserve crews in crew pairing to mitigate flight disruption due to flight arrival delay. Insufficient crew members for a flight will lead to flight disruptions such as delays or cancellations. In reality, most of these disruption cases are due to arrival delays of the previous flights. To tackle this problem, many research studies have examined the assignment method based on the historical flight arrival delay data of the concerned flights. However, flight arrival delays can be triggered by numerous factors. Accordingly, this article proposes a new forecasting approach using a cascade neural network, which considers a massive amount of historical flight arrival and departure data. The approach also incorporates learning ability so that unknown relationships behind the data can be revealed. Based on the expected flight arrival delay, the buffer time can be determined and a new dynamic reserve crew strategy can then be used to determine the required number of reserve crews. Numerical experiments are carried out based on one year of flight data obtained from 112 airports around the world. The results demonstrate that by predicting the flight departure delay as the input for the prediction of the flight arrival delay, the prediction accuracy can be increased. Moreover, by using the new dynamic reserve crew strategy, the total crew cost can be reduced. This significantly benefits airlines in flight schedule stability and cost saving in the current big data era. © 2016 Society for Risk Analysis.

  17. Modeling Optimal Scheduling for Pumping System to Minimize Operation Cost and Enhance Operation Reliability

    Yin Luo

    2012-01-01

    Full Text Available Traditional pump scheduling models neglect the operation reliability which directly relates with the unscheduled maintenance cost and the wear cost during the operation. Just for this, based on the assumption that the vibration directly relates with the operation reliability and the degree of wear, it could express the operation reliability as the normalization of the vibration level. The characteristic of the vibration with the operation point was studied, it could be concluded that idealized flow versus vibration plot should be a distinct bathtub shape. There is a narrow sweet spot (80 to 100 percent BEP to obtain low vibration levels in this shape, and the vibration also follows similar law with the square of the rotation speed without resonance phenomena. Then, the operation reliability could be modeled as the function of the capacity and rotation speed of the pump and add this function to the traditional model to form the new. And contrast with the tradition method, the result shown that the new model could fix the result produced by the traditional, make the pump operate in low vibration, then the operation reliability could increase and the maintenance cost could decrease.

  18. A short-term scheduling for the optimal operation of biorefineries

    Grisi, E.F.; Yusta, J.M.; Khodr, H.M.

    2011-01-01

    This work presents an analysis of the inherent potentialities and characteristics of the sugarcane industries that produce sugar, bioethanol, biogas and bioelectricity and that are being described as ''Biorefineries''. These Biorefineries are capable of producing bio-energy under diverse forms, intended for their own internal consumption and for external sales and marketing. A complex model and simulation are carried out of the processes of a sugarcane industry, with the data characteristic as well as the production costs, prices of products and considerations on the energy demand by basic processes. A Mixed-Integer Linear Programming (MILP) optimization problem formulation and an analysis of optimal solutions in short-term operation are described, taking into account the production cost functions of each commodity and the incomes obtained from selling electricity and other products. The objective is to maximize the hourly plant economic profit in the different scenarios considered in a real case study. (author)

  19. Optimal Day-Ahead Scheduling of a Hybrid Electric Grid Using Weather Forecasts

    2013-12-01

    with 214 turbines [22]. In July 2011, the DoD declared that a complete study of 217 wind farm projects proposed in 35 states and Puerto Rico found...14. SUBJECT TERMS Hybrid electric grid , Microgrid , Hybrid renewable energy system , energy management center, optimization, Day...electric grid. In the case of a hybrid electric grid (HEG), or hybrid renewable energy system (HRES) where the microgrid can be connected to the commercial

  20. Productivity growth, case mix and optimal size of hospitals. A 16-year study of the Norwegian hospital sector.

    Anthun, Kjartan Sarheim; Kittelsen, Sverre Andreas Campbell; Magnussen, Jon

    2017-04-01

    This paper analyses productivity growth in the Norwegian hospital sector over a period of 16 years, 1999-2014. This period was characterized by a large ownership reform with subsequent hospital reorganizations and mergers. We describe how technological change, technical productivity, scale efficiency and the estimated optimal size of hospitals have evolved during this period. Hospital admissions were grouped into diagnosis-related groups using a fixed-grouper logic. Four composite outputs were defined and inputs were measured as operating costs. Productivity and efficiency were estimated with bootstrapped data envelopment analyses. Mean productivity increased by 24.6% points from 1999 to 2014, an average annual change of 1.5%. There was a substantial growth in productivity and hospital size following the ownership reform. After the reform (2003-2014), average annual growth was case mix between hospitals, and thus provides a framework for future studies. The study adds to the discussion on optimal hospital size. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. Optimal LNG (liquefied natural gas) regasification scheduling for import terminals with storage

    Trotter, Ian M.; Gomes, Marília Fernandes Maciel; Braga, Marcelo José; Brochmann, Bjørn; Lie, Ole Nikolai

    2016-01-01

    We describe a stochastic dynamic programming model for maximising the revenue generated by regasification of LNG (liquefied natural gas) from storage tanks at importation terminals in relation to a natural gas spot market. We present three numerical resolution strategies: a posterior optimal strategy, a rolling intrinsic strategy and a full option strategy based on a least-squares Monte Carlo algorithm. We then compare model simulation results to the observed behaviour of three LNG importation terminals in the UK for the period April 2011 to April 2012, and find that there was low correlation between the observed regasification decisions of the operators and those suggested by the three simulated strategies. However, the actions suggested by the model simulations would have generated significantly higher revenues, suggesting that the facilities might have been operated sub-optimally. A further numerical experiment shows that increasing the storage and regasification capacities of a facility can significantly increase the achievable revenue, even without altering the amount of LNG received, by allowing operators more flexibility to defer regasification. - Highlights: • We present a revenue maximisation model for LNG (liquefied natural gas) storage tanks at import terminals. • Three resolution strategies: posterior optimal, rolling intrinsic and full option. • The full option strategy is based on a least-squares Monte Carlo algorithm. • Model simulations show potential for higher revenue in three UK LNG terminals. • Numerical experiments show how storage and regasification capacities affect revenue.

  2. Optimizing the Internal Medicine Clinic at Evans Army Community Hospital

    Bonilla, Jose

    2003-01-01

    ...) 2002, the Internal Medicine (IM) clinic at Evans Army Community Hospital, Fort Carson, Colorado, failed to meet access to care standards for routine appointments, and was only marginally successful in meeting standards for urgent appointments...

  3. Does Operation Scheduling Make a Difference: Tapping the Potential of Optimized Design for Skipping-Stop Strategy in Reducing Bus Emissions

    Xumei Chen

    2017-09-01

    Full Text Available The idea of corporate social responsibility has promoted bus operation agencies to rethink how to provide not only efficient but also environmentally friendly services for residents. A study on the potential of using an optimized design of skip-stop services, one of the essential operational strategies in practice, to reduce emissions is conducted in this paper. The underlying scheduling problem is formulated as a nonlinear programming problem with the primary objective of optimizing the total costs for both passengers and operating agencies, as well as with the secondary objective of minimizing bus emissions. A solution method is developed to solve the problem. A real-world case of Route 16 in Beijing is studied, in which the optimal scheduling strategy that maximizes the cost savings and environmental benefits is determined. The costs and emissions of the proposed scheduling strategy are compared with the optimal scheduling with skip-stop services without considering bus emissions. The results show that the proposed scheduling strategy outperforms the other operating strategy with respect to operational costs and bus emissions. A sensitivity study is then conducted to investigate the impact of the fleet size in operations and passenger demand on the effectiveness of the proposed stop-skipping strategy considering bus emissions.

  4. Energy Optimization for Distributed Energy Resources Scheduling with Enhancements in Voltage Stability Margin

    Morais, Hugo; Sousa, Tiago; Perez, Angel

    2016-01-01

    to evaluate the resulting multiobjective optimization problem: the sum-weighted Pareto front and an adapted goal programming methodology. With this new methodology, the system operators can consider both the costs and voltage stability. Priority can be assigned to one objective function according...... to the operating scenario. Additionally, it is possible to evaluate the impact of the distributed generation and the electric vehicles in the management of voltage stability in the future electric networks. One detailed case study considering a distribution network with high penetration of distributed energy...

  5. Optimized scheduling technique of null subcarriers for peak power control in 3GPP LTE downlink.

    Cho, Soobum; Park, Sang Kyu

    2014-01-01

    Orthogonal frequency division multiple access (OFDMA) is a key multiple access technique for the long term evolution (LTE) downlink. However, high peak-to-average power ratio (PAPR) can cause the degradation of power efficiency. The well-known PAPR reduction technique, dummy sequence insertion (DSI), can be a realistic solution because of its structural simplicity. However, the large usage of subcarriers for the dummy sequences may decrease the transmitted data rate in the DSI scheme. In this paper, a novel DSI scheme is applied to the LTE system. Firstly, we obtain the null subcarriers in single-input single-output (SISO) and multiple-input multiple-output (MIMO) systems, respectively; then, optimized dummy sequences are inserted into the obtained null subcarrier. Simulation results show that Walsh-Hadamard transform (WHT) sequence is the best for the dummy sequence and the ratio of 16 to 20 for the WHT and randomly generated sequences has the maximum PAPR reduction performance. The number of near optimal iteration is derived to prevent exhausted iterations. It is also shown that there is no bit error rate (BER) degradation with the proposed technique in LTE downlink system.

  6. Optimal Scheduling of an Regional Integrated Energy System with Energy Storage Systems for Service Regulation

    Hengrui Ma

    2018-01-01

    Full Text Available Ancillary services are critical to maintaining the safe and stable operation of power systems that contain a high penetration level of renewable energy resources. As a high-quality regulation resource, the regional integrated energy system (RIES with energy storage system (ESS can effectively adjust the non-negligible frequency offset caused by the renewable energy integration into the power system, and help solve the problem of power system frequency stability. In this paper, the optimization model aiming at regional integrated energy system as a participant in the regulation market based on pay-for-performance is established. Meanwhile YALMIP + CPLEX is used to simulate and analyze the total operating cost under different dispatch modes. This paper uses the actual operation model of the PJM regulation market to guide the optimal allocation of regulation resource in the regional integrated energy system, and provides a balance between the power trading revenue and regulation market revenue in order to achieve the maximum profit.

  7. Optimizing Instruction Scheduling and Register Allocation for Register-File-Connected Clustered VLIW Architectures

    Tang, Haijing; Wang, Siye; Zhang, Yanjun

    2013-01-01

    Clustering has become a common trend in very long instruction words (VLIW) architecture to solve the problem of area, energy consumption, and design complexity. Register-file-connected clustered (RFCC) VLIW architecture uses the mechanism of global register file to accomplish the inter-cluster data communications, thus eliminating the performance and energy consumption penalty caused by explicit inter-cluster data move operations in traditional bus-connected clustered (BCC) VLIW architecture. However, the limit number of access ports to the global register file has become an issue which must be well addressed; otherwise the performance and energy consumption would be harmed. In this paper, we presented compiler optimization techniques for an RFCC VLIW architecture called Lily, which is designed for encryption systems. These techniques aim at optimizing performance and energy consumption for Lily architecture, through appropriate manipulation of the code generation process to maintain a better management of the accesses to the global register file. All the techniques have been implemented and evaluated. The result shows that our techniques can significantly reduce the penalty of performance and energy consumption due to access port limitation of global register file. PMID:23970841

  8. Optimizing Instruction Scheduling and Register Allocation for Register-File-Connected Clustered VLIW Architectures

    Haijing Tang

    2013-01-01

    Full Text Available Clustering has become a common trend in very long instruction words (VLIW architecture to solve the problem of area, energy consumption, and design complexity. Register-file-connected clustered (RFCC VLIW architecture uses the mechanism of global register file to accomplish the inter-cluster data communications, thus eliminating the performance and energy consumption penalty caused by explicit inter-cluster data move operations in traditional bus-connected clustered (BCC VLIW architecture. However, the limit number of access ports to the global register file has become an issue which must be well addressed; otherwise the performance and energy consumption would be harmed. In this paper, we presented compiler optimization techniques for an RFCC VLIW architecture called Lily, which is designed for encryption systems. These techniques aim at optimizing performance and energy consumption for Lily architecture, through appropriate manipulation of the code generation process to maintain a better management of the accesses to the global register file. All the techniques have been implemented and evaluated. The result shows that our techniques can significantly reduce the penalty of performance and energy consumption due to access port limitation of global register file.

  9. Two-Stage Optimal Scheduling of Electric Vehicle Charging based on Transactive Control

    Liu, Zhaoxi; Wu, Qiuwei; Ma, Kang

    2018-01-01

    In this paper, a two-stage optimal charging scheme based on transactive control is proposed for the aggregator to manage day-ahead electricity procurement and real-time EV charging management in order to minimize its total operating cost. The day-ahead electricity procurement considers both the day......-ahead energy cost and expected real-time operation cost. In the real-time charging management, the cost of employing the charging flexibility from the EV owners is explicitly modelled. The aggregator uses a transactive market to manage the real-time charging demand to provide the regulating power. A model...... predictive control (MPC) based method is proposed for the aggregator to clear the transactive market. The realtime charging decisions of the EVs are determined by the clearing of the proposed transactive market according to the realtime requests and preferences of the EV owners. As such, the aggregators...

  10. Optimization Scheduling Model for Wind-thermal Power System Considering the Dynamic penalty factor

    PENG, Siyu; LUO, Jianchun; WANG, Yunyu; YANG, Jun; RAN, Hong; PENG, Xiaodong; HUANG, Ming; LIU, Wanyu

    2018-03-01

    In this paper, a new dynamic economic dispatch model for power system is presented.Objective function of the proposed model presents a major novelty in the dynamic economic dispatch including wind farm: introduced the “Dynamic penalty factor”, This factor could be computed by using fuzzy logic considering both the variable nature of active wind power and power demand, and it could change the wind curtailment cost according to the different state of the power system. Case studies were carried out on the IEEE30 system. Results show that the proposed optimization model could mitigate the wind curtailment and the total cost effectively, demonstrate the validity and effectiveness of the proposed model.

  11. Hospital admission planning to optimize major resources utilization under uncertainty

    Dellaert, N.P.; Jeunet, J.

    2010-01-01

    Admission policies for elective inpatient services mainly result in the management of a single resource: the operating theatre as it is commonly considered as the most critical and expensive resource in a hospital. However, other bottleneck resources may lead to surgery cancellations, such as bed

  12. Optimization of hospital ward resources with patient relocation using Markov chain modeling

    Andersen, Anders Reenberg; Nielsen, Bo Friis; Reinhardt, Line Blander

    2017-01-01

    available to the hospital. Patient flow is modeled using a homogeneous continuous-time Markov chain and optimization is conducted using a local search heuristic. Our model accounts for patient relocation, which has not been done analytically in literature with similar scope. The study objective is to ensure...... are distributed. Furthermore, our heuristic is found to efficiently derive the optimal solution. Applying our model to the hospital case, we found that relocation of daily arrivals can be reduced by 11.7% by re-distributing beds that are already available to the hospital....

  13. Scheduling optimization of a real-world multi product pipeline network; Otimizacao das operacoes de transporte de derivados de petroleo em redes de dutos

    Boschetto, Suelen N.; Felizari, Luiz C.; Magatao, Leandro; Stebel, Sergio L.; Neves Junior, Flavio; Lueders, Ricardo; Arruda, Lucia V.R. de [Universidade Tecnologica Federal do Parana (UTFPR), Curitiba, PR (Brazil); Ribas, Paulo Cesar; Bernardo, Luiz F.J. [PETROBRAS S.A., Rio de Janeiro, RJ (Brazil). Centro de Pesquisas (CENPES)

    2008-07-01

    This work develops an optimization structure to aid the operational decision-making of scheduling activities in a real world pipeline network. The proposed approach is based on a decomposition method to address complex problems with high computational burden. The Pre-analysis makes a previous evaluation of a batch sequencing, getting information to be entered into optimization block. The continuous time Mixed Integer Linear Program (MILP) model gets such information and calculates the scheduling. The models are applied to a pipeline network that connects different areas including refineries, terminals, and final clients. Many oil derivatives (e.g. gasoline, liquefied petroleum gas, naphtha) can be sent or received in this network. The computational burden to determine a short-term scheduling within the considered scenario is a relevant issue. Many insights have been derived from the obtained solutions, which are given in a reduced computational time for oil industrial-size scenarios. (author)

  14. Erratum to ''Johnson's algorithm : A key to solve optimally or approximately flowshop scheduling problems with unavailability periods'' [International Journal of Production Economics 121 (2009) 81-87

    Rapine , Christophe

    2013-01-01

    International audience; In Allaoui H., Artiba A, ''Johnson's algorithm : A key to solve optimally or approximately flowshop scheduling problems with unavailability periods'' [International Journal of Production Economics 121 (2009)] the authors propose optimality conditions for the Johnson sequence in presence of one unavailability period on the first machine and pretend for a performance guarantee of 2 when several unavailability periods may occur. We establish in this note that these condit...

  15. The impact of chief executive officer optimism on hospital strategic decision making.

    Langabeer, James R; Yao, Emery

    2012-01-01

    Previous strategic decision making research has focused mostly on the analytical positioning approach, which broadly emphasizes an alignment between rationality and the external environment. In this study, we propose that hospital chief executive optimism (or the general tendency to expect positive future outcomes) will moderate the relationship between comprehensively rational decision-making process and organizational performance. The purpose of this study was to explore the impact that dispositional optimism has on the well-established relationship between rational decision-making processes and organizational performance. Specifically, we hypothesized that optimism will moderate the relationship between the level of rationality and the organization's performance. We further suggest that this relationship will be more negative for those with high, as opposed to low, optimism. We surveyed 168 hospital CEOs and used moderated hierarchical regression methods to statically test our hypothesis. On the basis of a survey study of 168 hospital CEOs, we found evidence of a complex interplay of optimism in the rationality-organizational performance relationship. More specifically, we found that the two-way interactions between optimism and rational decision making were negatively associated with performance and that where optimism was the highest, the rationality-performance relationship was the most negative. Executive optimism was positively associated with organizational performance. We also found that greater perceived environmental turbulence, when interacting with optimism, did not have a significant interaction effect on the rationality-performance relationship. These findings suggest potential for broader participation in strategic processes and the use of organizational development techniques that assess executive disposition and traits for recruitment processes, because CEO optimism influences hospital-level processes. Research implications include incorporating

  16. Optimal charging scheduling for large-scale EV (electric vehicle) deployment based on the interaction of the smart-grid and intelligent-transport systems

    Luo, Yugong; Zhu, Tao; Wan, Shuang; Zhang, Shuwei; Li, Keqiang

    2016-01-01

    The widespread use of electric vehicles (EVs) is becoming an imminent trend. Research has been done on the scheduling of EVs from the perspective of the charging characteristic, improvement in the safety and economy of the power grid, or the traffic jams in the transport system caused by a large number of EVs driven to charging stations. There is a lack of systematic studies considering EVs, the power grid, and the transport system all together. In this paper, a novel optimal charging scheduling strategy for different types of EVs is proposed based on not only transport system information, such as road length, vehicle velocity and waiting time, but also grid system information, such as load deviation and node voltage. In addition, a charging scheduling simulation platform suitable for large-scale EV deployment is developed based on actual charging scenarios. The simulation results show that the improvements in both the transport system efficiency and the grid system operation can be obtained by using the optimal strategy, such as the node voltage drop is decreased, the power loss is reduced, and the load curve is optimized. - Highlights: • A novel optimal charging scheduling strategy is proposed for different electric vehicles (EVs). • A simulation platform suitable for large-scale EV deployment is established. • The traffic congestion near the charging and battery-switch stations is relieved. • The safety and economy problems of the distribution network are solved. • The peak-to-valley load of the distribution system is reduced.

  17. Optimizing Lighting Design for Hospital Wards by Defining User Zones

    Thuesen, Niels; Stidsen, Lone; Kirkegaard, Poul Henning

    2011-01-01

    of lighting design, so it has the ability to support the different users activity and behavior on the ward. By using RFID tracking and manual observations we have analyzed and evaluated the ward functionality as working environment for the staff. The method creates a higher understanding of the ward...... of lighting design in private and public settings are often not similar. The purpose of this article is therefore present a approach dividing the hospital ward in 3 user zones for patients, staff and visitors. The main user of the zone should be in control of the light scenario and thereby a refining...

  18. Real-time integration of optimal generation scheduling with MPC for the energy management of a renewable hydrogen-based microgrid

    Petrollese, Mario; Valverde, Luis; Cocco, Daniele; Cau, Giorgio; Guerra, José

    2016-01-01

    Highlights: • Energy management strategy for a renewable hydrogen-based microgrid. • Integration of optimal generation scheduling with a model predictive control. • Experimental tests are carried out simulating typical summer and winter days. • Effective improvement in performance and reduction in microgrid operating cost are achieved. - Abstract: This paper presents a novel control strategy for the optimal management of microgrids with high penetration of renewable energy sources and different energy storage systems. The control strategy is based on the integration of optimal generation scheduling with a model predictive control in order to achieve both long and short-term optimal planning. In particular, long-term optimization of the various microgrid components is obtained by the adoption of an optimal generation scheduling, in which a statistical approach is used to take into account weather and load forecasting uncertainties. The real-time management of the microgrid is instead entrusted to a model predictive controller, which has the important feature of using the results obtained by the optimal generation scheduling. The proposed control strategy was tested in a laboratory-scale microgrid present at the University of Seville, which is composed of an electronic power source that emulates a photovoltaic system, a battery bank and a hydrogen production and storage system. Two different experimental tests that simulate a summer and a winter day were carried out over a 24-h period to verify the reliability and performance enhancement of the control system. Results show an effective improvement in performance in terms of reduction of the microgrid operating cost and greater involvement of the hydrogen storage system for the maintenance of a spinning reserve in batteries.

  19. Short-term bulk energy storage system scheduling for load leveling in unit commitment: modeling, optimization, and sensitivity analysis.

    Hemmati, Reza; Saboori, Hedayat

    2016-05-01

    Energy storage systems (ESSs) have experienced a very rapid growth in recent years and are expected to be a promising tool in order to improving power system reliability and being economically efficient. The ESSs possess many potential benefits in various areas in the electric power systems. One of the main benefits of an ESS, especially a bulk unit, relies on smoothing the load pattern by decreasing on-peak and increasing off-peak loads, known as load leveling. These devices require new methods and tools in order to model and optimize their effects in the power system studies. In this respect, this paper will model bulk ESSs based on the several technical characteristics, introduce the proposed model in the thermal unit commitment (UC) problem, and analyze it with respect to the various sensitive parameters. The technical limitations of the thermal units and transmission network constraints are also considered in the model. The proposed model is a Mixed Integer Linear Programming (MILP) which can be easily solved by strong commercial solvers (for instance CPLEX) and it is appropriate to be used in the practical large scale networks. The results of implementing the proposed model on a test system reveal that proper load leveling through optimum storage scheduling leads to considerable operation cost reduction with respect to the storage system characteristics.

  20. Short-term bulk energy storage system scheduling for load leveling in unit commitment: modeling, optimization, and sensitivity analysis

    Hemmati, Reza; Saboori, Hedayat

    2016-01-01

    Energy storage systems (ESSs) have experienced a very rapid growth in recent years and are expected to be a promising tool in order to improving power system reliability and being economically efficient. The ESSs possess many potential benefits in various areas in the electric power systems. One of the main benefits of an ESS, especially a bulk unit, relies on smoothing the load pattern by decreasing on-peak and increasing off-peak loads, known as load leveling. These devices require new methods and tools in order to model and optimize their effects in the power system studies. In this respect, this paper will model bulk ESSs based on the several technical characteristics, introduce the proposed model in the thermal unit commitment (UC) problem, and analyze it with respect to the various sensitive parameters. The technical limitations of the thermal units and transmission network constraints are also considered in the model. The proposed model is a Mixed Integer Linear Programming (MILP) which can be easily solved by strong commercial solvers (for instance CPLEX) and it is appropriate to be used in the practical large scale networks. The results of implementing the proposed model on a test system reveal that proper load leveling through optimum storage scheduling leads to considerable operation cost reduction with respect to the storage system characteristics. PMID:27222741

  1. Optimizing Transmission and Shutdown for Energy-Efficient Real-time Packet Scheduling in Clustered Ad Hoc Networks

    Rajkumar Ragunathan

    2005-01-01

    Full Text Available Energy efficiency is imperative to enable the deployment of ad hoc networks. Conventional power management focuses independently on the physical or MAC layer and approaches differ depending on the abstraction level. At the physical layer, the fundamental tradeoff between transmission rate and energy is exploited, which leads to transmit as slow as possible. At MAC level, power reduction techniques aim to transmit as fast as possible to maximize the radios power-off interval. The two approaches seem conflicting and it is not obvious which one is the most appropriate. We propose a transmission strategy that optimally mixes both techniques in a multiuser context. We present a cross-layer solution considering the transceiver power characteristics, the varying system load, and the dynamic channel constraints. Based on this, we derive a low-complexity online scheduling algorithm. Results considering an -ary quadrature amplitude modulation radio show that for a range of scenarios a large power reduction is achieved, compared to the case where only scaling or shutdown is considered.

  2. A multi-objective optimization of the active and reactive resource scheduling at a distribution level in a smart grid context

    Sousa, Tiago; Morais, Hugo; Vale, Zita

    2015-01-01

    In the traditional paradigm, the large power plants supply the reactive power required at a transmission level and the capacitors and transformer tap changer were also used at a distribution level. However, in a near future will be necessary to schedule both active and reactive power...... at a distribution level, due to the high number of resources connected in distribution levels. This paper proposes a new multi-objective methodology to deal with the optimal resource scheduling considering the distributed generation, electric vehicles and capacitor banks for the joint active and reactive power...... scheduling. The proposed methodology considers the minimization of the cost (economic perspective) of all distributed resources, and the minimization of the voltage magnitude difference (technical perspective) in all buses. The Pareto front is determined and a fuzzy-based mechanism is applied to present...

  3. A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response

    Ju, Liwei; Tan, Zhongfu; Yuan, Jinyun; Tan, Qingkun; Li, Huanhuan; Dong, Fugui

    2016-01-01

    Highlights: • Our research focuses on Virtual Power Plant (VPP). • Virtual Power Plant consists of WPP, PV, CGT, ESSs and DRPs. • Robust optimization theory is introduced to analyze uncertainties. • A bi-level stochastic scheduling optimization model is proposed for VPP. • Models are built to measure the impacts of ESSs and DERPs on VPP operation. - Abstract: To reduce the uncertain influence of wind power and solar photovoltaic power on virtual power plant (VPP) operation, robust optimization theory (ROT) is introduced to build a stochastic scheduling model for VPP considering the uncertainty, price-based demand response (PBDR) and incentive-based demand response (IBDR). First, the VPP components are described including the wind power plant (WPP), photovoltaic generators (PV), convention gas turbine (CGT), energy storage systems (ESSs) and demand resource providers (DRPs). Then, a scenario generation and reduction frame is proposed for analyzing and simulating output stochastics based on the interval method and the Kantorovich distance. Second, a bi-level robust scheduling model is proposed with a double robust coefficient for WPP and PV. In the upper layer model, the maximum VPP operation income is taken as the optimization objective for building the scheduling model with the day-ahead prediction output of WPP and PV. In the lower layer model, the day-ahead scheduling scheme is revised with the actual output of the WPP and PV under the objectives of the minimum system net load and the minimum system operation cost. Finally, the independent micro-grid in a coastal island in eastern China is used for the simulation analysis. The results illustrate that the model can overcome the influence of uncertainty on VPP operations and reduce the system power shortage cost by connecting the day-ahead scheduling with the real-time scheduling. ROT could provide a flexible decision tool for decision makers, effectively addressing system uncertainties. ESSs could

  4. A comparison of alternative medicare reimbursement policies under optimal hospital pricing.

    Dittman, D A; Morey, R C

    1983-01-01

    This paper applies and extends the use of a nonlinear hospital pricing model, recently posited in the literature by Dittman and Morey [1]. That model applied a hospital profit-maximizing behavior and studied the effects of optimal pricing of hospital ancillary services on the incidence of payment by private insurance companies and the Medicare trust fund. Here, we examine variations of the above model where both hospital profit-maximizing and profit-satisficing postures are of interest. We apply the model to three types of Medicare reimbursement policies currently in use or under legislative mandate to implement. The policies differ according to hospital size and whether cross-subsidies are allowed. We are interested in determining the effects of profit-maximizing and -satisficing behaviors of these three reimbursement policies on the levels of profits received, and on the respective implications for private payors and the Medicare trust fund. PMID:6347973

  5. A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm

    Fanrong Kong

    2017-09-01

    Full Text Available To alleviate the emission of greenhouse gas and the dependence on fossil fuel, Plug-in Hybrid Electrical Vehicles (PHEVs have gained an increasing popularity in current decades. Due to the fluctuating electricity prices in the power market, a charging schedule is very influential to driving cost. Although the next-day electricity prices can be obtained in a day-ahead power market, a driving plan is not easily made in advance. Although PHEV owners can input a next-day plan into a charging system, e.g., aggregators, day-ahead, it is a very trivial task to do everyday. Moreover, the driving plan may not be very accurate. To address this problem, in this paper, we analyze energy demands according to a PHEV owner’s historical driving records and build a personalized statistic driving model. Based on the model and the electricity spot prices, a rolling optimization strategy is proposed to help make a charging decision in the current time slot. On one hand, by employing a heuristic algorithm, the schedule is made according to the situations in the following time slots. On the other hand, however, after the current time slot, the schedule will be remade according to the next tens of time slots. Hence, the schedule is made by a dynamic rolling optimization, but it only decides the charging decision in the current time slot. In this way, the fluctuation of electricity prices and driving routine are both involved in the scheduling. Moreover, it is not necessary for PHEV owners to input a day-ahead driving plan. By the optimization simulation, the results demonstrate that the proposed method is feasible to help owners save charging costs and also meet requirements for driving.

  6. Deterministic and stochastic approach for safety and reliability optimization of captive power plant maintenance scheduling using GA/SA-based hybrid techniques: A comparison of results

    Mohanta, Dusmanta Kumar; Sadhu, Pradip Kumar; Chakrabarti, R.

    2007-01-01

    This paper presents a comparison of results for optimization of captive power plant maintenance scheduling using genetic algorithm (GA) as well as hybrid GA/simulated annealing (SA) techniques. As utilities catered by captive power plants are very sensitive to power failure, therefore both deterministic and stochastic reliability objective functions have been considered to incorporate statutory safety regulations for maintenance of boilers, turbines and generators. The significant contribution of this paper is to incorporate stochastic feature of generating units and that of load using levelized risk method. Another significant contribution of this paper is to evaluate confidence interval for loss of load probability (LOLP) because some variations from optimum schedule are anticipated while executing maintenance schedules due to different real-life unforeseen exigencies. Such exigencies are incorporated in terms of near-optimum schedules obtained from hybrid GA/SA technique during the final stages of convergence. Case studies corroborate that same optimum schedules are obtained using GA and hybrid GA/SA for respective deterministic and stochastic formulations. The comparison of results in terms of interval of confidence for LOLP indicates that levelized risk method adequately incorporates the stochastic nature of power system as compared with levelized reserve method. Also the interval of confidence for LOLP denotes the possible risk in a quantified manner and it is of immense use from perspective of captive power plants intended for quality power

  7. 设备维修最优化调度策略方法研究%Equipment Repair Optimization Scheduling Strategy Approach

    张宏铭

    2014-01-01

    Under the condition of information technology, wartime equipment repair optimization scheduling problem is a key problem in the process of repair security. In this paper, according to the PSO algorithm based model is proposed for the wartime equipment repair security scheduling strategy, maximize the wartime repair support system effectiveness. Im-proved the PSO algorithm, solved the local optimization algorithm, compared with the FCFS repair security scheduling strat-egy, The simulation experiments show that the POS algorithm to scheduling performance is improved obviously.%信息化条件下,战时装备维修优化调度问题是装备维修保障过程中的关键问题。本文根据PSO算法建立模型提出了战时装备维修保障调度策略,最大限度的提高战时维修保障系统的效能,同时对PSO算法进行改进,解决算法中的局部最优化问题,最后与基于FCFS算法的维修保障调度策略进行对比,通过仿真实验证明PSO算法对调度性能有明显改善。

  8. Technical Report: Optimizing the Slab Yard Planning and Crane Scheduling Problem using a Two-Stage Approach

    Hansen, Anders Dohn; Clausen, Jens

    2008-01-01

    In this paper, we present The Slab Yard Planning and Crane Scheduling Problem. The problem has its origin in steel production facilities with a large throughput. A slab yard is used as a buffer for slabs that are needed in the upcoming production. Slabs are transported by cranes and the problem...... considered here, is concerned with the generation of schedules for these. The problem is decomposed and modeled in two parts, namely a planning problem and a scheduling problem. In the planning problem a set of crane operations is created to take the yard from its current state to a desired goal state...... schedule for the cranes is generated, where each operation is assigned to a crane and is given a specific time of initiation. For both models, a thorough description of the modeling details is given along with a specification of objective criteria. Variants of the models are presented as well. Preliminary...

  9. Short-term hydro-thermal-wind complementary scheduling considering uncertainty of wind power using an enhanced multi-objective bee colony optimization algorithm

    Zhou, Jianzhong; Lu, Peng; Li, Yuanzheng; Wang, Chao; Yuan, Liu; Mo, Li

    2016-01-01

    Highlights: • HTWCS system is established while considering uncertainty of wind power. • An enhanced multi-objective bee colony optimization algorithm is proposed. • Some heuristic repairing strategies are designed to handle various constraints. • HTWCS problem with economic/environment objectives is solved by EMOBCO. - Abstract: This paper presents a short-term economic/environmental hydro-thermal-wind complementary scheduling (HTWCS) system considering uncertainty of wind power, as well as various complicated non-linear constraints. HTWCS system is formulated as a multi-objective optimization problem to optimize conflictive objectives, i.e., economic and environmental criteria. Then an enhanced multi-objective bee colony optimization algorithm (EMOBCO) is proposed to solve this problem, which adopts Elite archive set, adaptive mutation/selection mechanism and local searching strategy to improve global searching ability of standard bee colony optimization (BCO). Especially, a novel constraints-repairing strategy with compressing decision space and a violation-adjustment method are used to handle various hydraulic and electric constraints. Finally, a daily scheduling simulation case of hydro-thermal-wind system is conducted to verify feasibility and effectiveness of the proposed EMOBCO in solving HTWCS problem. The simulation results indicate that the proposed EMOBCO can provide lower economic cost and smaller pollutant emission than other method established recently while considering various complex constraints in HTWCS problem.

  10. Computer-aided resource planning and scheduling for radiological services

    Garcia, Hong-Mei C.; Yun, David Y.; Ge, Yiqun; Khan, Javed I.

    1996-05-01

    There exists tremendous opportunity in hospital-wide resource optimization based on system integration. This paper defines the resource planning and scheduling requirements integral to PACS, RIS and HIS integration. An multi-site case study is conducted to define the requirements. A well-tested planning and scheduling methodology, called Constrained Resource Planning model, has been applied to the chosen problem of radiological service optimization. This investigation focuses on resource optimization issues for minimizing the turnaround time to increase clinical efficiency and customer satisfaction, particularly in cases where the scheduling of multiple exams are required for a patient. How best to combine the information system efficiency and human intelligence in improving radiological services is described. Finally, an architecture for interfacing a computer-aided resource planning and scheduling tool with the existing PACS, HIS and RIS implementation is presented.

  11. Revisiting Symbiotic Job Scheduling

    Eyerman , Stijn; Michaud , Pierre; Rogiest , Wouter

    2015-01-01

    International audience; —Symbiotic job scheduling exploits the fact that in a system with shared resources, the performance of jobs is impacted by the behavior of other co-running jobs. By coscheduling combinations of jobs that have low interference, the performance of a system can be increased. In this paper, we investigate the impact of using symbiotic job scheduling for increasing throughput. We find that even for a theoretically optimal scheduler, this impact is very low, despite the subs...

  12. The optimality of hospital financing system: the role of physician-manager interactions.

    Crainich, David; Leleu, Hervé; Mauleon, Ana

    2008-12-01

    The ability of a prospective payment system to ensure an optimal level of both quality and cost reducing activities in the hospital industry has been stressed by Ma (Ma, J Econ Manage Strategy 8(2):93-112, 1994) whose analysis assumes that decisions about quality and costs are made by a single agent. This paper examines whether this result holds when the main decisions made within the hospital are shared between physicians (quality of treatment) and hospital managers (cost reduction). Ma's conclusions appear to be relevant in the US context (where the hospital managers pay the whole cost of treatment). Nonetheless, when physicians partly reimburse hospitals for the treatment cost as it is the case in many European countries, we show that the ability of a prospective payment system to achieve both objectives is sensitive to the type of interaction (simultaneous, sequential or joint decision-making) between the agents. Our analysis suggests that regulation policies in the hospital sector should not be exclusively focused on the financing system but should also take the interaction between physicians and hospital managers into account.

  13. Optimizing patient flow in a large hospital surgical centre by means of discrete-event computer simulation models.

    Ferreira, Rodrigo B; Coelli, Fernando C; Pereira, Wagner C A; Almeida, Renan M V R

    2008-12-01

    This study used the discrete-events computer simulation methodology to model a large hospital surgical centre (SC), in order to analyse the impact of increases in the number of post-anaesthetic beds (PABs), of changes in surgical room scheduling strategies and of increases in surgery numbers. The used inputs were: number of surgeries per day, type of surgical room scheduling, anaesthesia and surgery duration, surgical teams' specialty and number of PABs, and the main outputs were: number of surgeries per day, surgical rooms' use rate and blocking rate, surgical teams' use rate, patients' blocking rate, surgery delays (minutes) and the occurrence of postponed surgeries. Two basic strategies were implemented: in the first strategy, the number of PABs was increased under two assumptions: (a) following the scheduling plan actually used by the hospital (the 'rigid' scheduling - surgical rooms were previously assigned and assignments could not be changed) and (b) following a 'flexible' scheduling (surgical rooms, when available, could be freely used by any surgical team). In the second, the same analysis was performed, increasing the number of patients (up to the system 'feasible maximum') but fixing the number of PABs, in order to evaluate the impact of the number of patients over surgery delays. It was observed that the introduction of a flexible scheduling/increase in PABs would lead to a significant improvement in the SC productivity.

  14. Multi-objective stochastic scheduling optimization model for connecting a virtual power plant to wind-photovoltaic-electric vehicles considering uncertainties and demand response

    Ju, Liwei; Li, Huanhuan; Zhao, Junwei; Chen, Kangting; Tan, Qingkun; Tan, Zhongfu

    2016-01-01

    Highlights: • Our research focuses on virtual power plant. • Electric vehicle group and demand response are integrated into virtual power plant. • Stochastic chance constraint planning is applied to overcome uncertainties. • A multi-objective stochastic scheduling model is proposed for virtual power plant. • A three-stage hybrid intelligent solution algorithm is proposed for solving the model. - Abstract: A stochastic chance-constrained planning method is applied to build a multi-objective optimization model for virtual power plant scheduling. Firstly, the implementation cost of demand response is calculated using the system income difference. Secondly, a wind power plant, photovoltaic power, an electric vehicle group and a conventional power plant are aggregated into a virtual power plant. A stochastic scheduling model is proposed for the virtual power plant, considering uncertainties under three objective functions. Thirdly, a three-stage hybrid intelligent solution algorithm is proposed, featuring the particle swarm optimization algorithm, the entropy weight method and the fuzzy satisfaction theory. Finally, the Yunnan distributed power demonstration project in China is utilized for example analysis. Simulation results demonstrate that when considering uncertainties, the system will reduce the grid connection of the wind power plant and photovoltaic power to decrease the power shortage punishment cost. The average reduction of the system power shortage punishment cost and the operation revenue of virtual power plant are 61.5% and 1.76%, respectively, while the average increase of the system abandoned energy cost is 40.4%. The output of the virtual power plant exhibits a reverse distribution with the confidence degree of the uncertainty variable. The proposed algorithm rapidly calculates a global optimal set. The electric vehicle group could provide spinning reserve to ensure stability of the output of the virtual power plant. Demand response could

  15. Efficient Scheduling of Scientific Workflows with Energy Reduction Using Novel Discrete Particle Swarm Optimization and Dynamic Voltage Scaling for Computational Grids

    M. Christobel

    2015-01-01

    Full Text Available One of the most significant and the topmost parameters in the real world computing environment is energy. Minimizing energy imposes benefits like reduction in power consumption, decrease in cooling rates of the computing processors, provision of a green environment, and so forth. In fact, computation time and energy are directly proportional to each other and the minimization of computation time may yield a cost effective energy consumption. Proficient scheduling of Bag-of-Tasks in the grid environment ravages in minimum computation time. In this paper, a novel discrete particle swarm optimization (DPSO algorithm based on the particle’s best position (pbDPSO and global best position (gbDPSO is adopted to find the global optimal solution for higher dimensions. This novel DPSO yields better schedule with minimum computation time compared to Earliest Deadline First (EDF and First Come First Serve (FCFS algorithms which comparably reduces energy. Other scheduling parameters, such as job completion ratio and lateness, are also calculated and compared with EDF and FCFS. An energy improvement of up to 28% was obtained when Makespan Conservative Energy Reduction (MCER and Dynamic Voltage Scaling (DVS were used in the proposed DPSO algorithm.

  16. Efficient Scheduling of Scientific Workflows with Energy Reduction Using Novel Discrete Particle Swarm Optimization and Dynamic Voltage Scaling for Computational Grids

    Christobel, M.; Tamil Selvi, S.; Benedict, Shajulin

    2015-01-01

    One of the most significant and the topmost parameters in the real world computing environment is energy. Minimizing energy imposes benefits like reduction in power consumption, decrease in cooling rates of the computing processors, provision of a green environment, and so forth. In fact, computation time and energy are directly proportional to each other and the minimization of computation time may yield a cost effective energy consumption. Proficient scheduling of Bag-of-Tasks in the grid environment ravages in minimum computation time. In this paper, a novel discrete particle swarm optimization (DPSO) algorithm based on the particle's best position (pbDPSO) and global best position (gbDPSO) is adopted to find the global optimal solution for higher dimensions. This novel DPSO yields better schedule with minimum computation time compared to Earliest Deadline First (EDF) and First Come First Serve (FCFS) algorithms which comparably reduces energy. Other scheduling parameters, such as job completion ratio and lateness, are also calculated and compared with EDF and FCFS. An energy improvement of up to 28% was obtained when Makespan Conservative Energy Reduction (MCER) and Dynamic Voltage Scaling (DVS) were used in the proposed DPSO algorithm. PMID:26075296

  17. A Multiobjective Robust Scheduling Optimization Mode for Multienergy Hybrid System Integrated by Wind Power, Solar Photovoltaic Power, and Pumped Storage Power

    Lihui Zhang

    2017-01-01

    Full Text Available Wind power plant (WPP, photovoltaic generators (PV, cell-gas turbine (CGT, and pumped storage power station (PHSP are integrated into multienergy hybrid system (MEHS. Firstly, this paper presents MEHS structure and constructs a scheduling model with the objective functions of maximum economic benefit and minimum power output fluctuation. Secondly, in order to relieve the uncertainty influence of WPP and PV on system, robust stochastic theory is introduced to describe uncertainty and propose a multiobjective stochastic scheduling optimization mode by transforming constraint conditions with uncertain variables. Finally, a 9.6 MW WPP, a 6.5 MW PV, three CGT units, and an upper reservoir with 10 MW·h equivalent capacity are chosen as simulation system. The results show MEHS system can achieve the best operation result by using the multienergy hybrid generation characteristic. PHSP could shave peak and fill valley of load curve by optimizing pumping storage and inflowing generating behaviors based on the load supply and demand status and the available power of WPP and PV. Robust coefficients can relieve the uncertainty of WPP and PV and provide flexible scheduling decision tools for decision-makers with different risk attitudes by setting different robust coefficients, which could maximize economic benefits and minimize operation risks at the same time.

  18. Axiomatic Design of a Framework for the Comprehensive Optimization of Patient Flows in Hospitals

    Gabriele Arcidiacono

    2017-01-01

    Full Text Available Lean Management and Six Sigma are nowadays applied not only to the manufacturing industry but also to service industry and public administration. The manifold variables affecting the Health Care system minimize the effect of a narrow Lean intervention. Therefore, this paper aims to discuss a comprehensive, system-based approach to achieve a factual holistic optimization of patient flows. This paper debates the efficacy of Lean principles applied to the optimization of patient flows and related activities, structures, and resources, developing a theoretical framework based on the principles of the Axiomatic Design. The demand for patient-oriented and efficient health services leads to use these methodologies to improve hospital processes. In the framework, patients with similar characteristics are clustered in families to achieve homogeneous flows through the value stream. An optimization checklist is outlined as the result of the mapping between Functional Requirements and Design Parameters, with the right sequence of the steps to optimize the patient flow according to the principles of Axiomatic Design. The Axiomatic Design-based top-down implementation of Health Care evidence, according to Lean principles, results in a holistic optimization of hospital patient flows, by reducing the complexity of the system.

  19. Axiomatic Design of a Framework for the Comprehensive Optimization of Patient Flows in Hospitals

    Arcidiacono, Gabriele; Matt, Dominik T.; Rauch, Erwin

    2017-01-01

    Lean Management and Six Sigma are nowadays applied not only to the manufacturing industry but also to service industry and public administration. The manifold variables affecting the Health Care system minimize the effect of a narrow Lean intervention. Therefore, this paper aims to discuss a comprehensive, system-based approach to achieve a factual holistic optimization of patient flows. This paper debates the efficacy of Lean principles applied to the optimization of patient flows and related activities, structures, and resources, developing a theoretical framework based on the principles of the Axiomatic Design. The demand for patient-oriented and efficient health services leads to use these methodologies to improve hospital processes. In the framework, patients with similar characteristics are clustered in families to achieve homogeneous flows through the value stream. An optimization checklist is outlined as the result of the mapping between Functional Requirements and Design Parameters, with the right sequence of the steps to optimize the patient flow according to the principles of Axiomatic Design. The Axiomatic Design-based top-down implementation of Health Care evidence, according to Lean principles, results in a holistic optimization of hospital patient flows, by reducing the complexity of the system. © 2017 Gabriele Arcidiacono et al.

  20. Pruning-Based, Energy-Optimal, Deterministic I/O Device Scheduling for Hard Real-Time Systems

    2005-02-01

    However, DPM via I/O device scheduling for hard real - time systems has received relatively little attention. In this paper,we present an offline I/O...polynomial time. We present experimental results to show that EDS and MDO reduce the energy consumption of I/O devices significantly for hard real - time systems .

  1. Optimization-based decision support to assist in logistics planning for hospital evacuations.

    Glick, Roger; Bish, Douglas R; Agca, Esra

    2013-01-01

    The evacuation of the hospital is a very complex process and evacuation planning is an important part of a hospital's emergency management plan. There are numerous factors that affect the evacuation plan including the nature of threat, availability of resources and staff the characteristics of the evacuee population, and risk to patients and staff. The safety and health of patients is of fundamental importance, but safely moving patients to alternative care facilities while under threat is a very challenging task. This article describes the logistical issues and complexities involved in planning and execution of hospital evacuations. Furthermore, this article provides examples of how optimization-based decision support tools can help evacuation planners to better plan for complex evacuations by providing real-world solutions to various evacuation scenarios.

  2. Let’s have a meeting: How hospitals use scheduled meetings to support cross-boundary collaboration

    Prætorius, Thim; Hasle, Peter; Nielsen, Anders Paarup

    2018-01-01

    of meetings, we add to previous research studying when meetings are used (e.g., task and input uncertainty). In particular, we focus on meetings’ role for achieving collaboration across occupational and departmental boundaries and for developing the collaboration skills component of organizational social...... and help those involved to solve concrete care tasks. Meetings are often inter-disciplinary, thereby having the potential to develop relations, common goals and trust (organizational social capital components) across occupational and departmental boundaries. For hospital managers, our findings...... are important because they can use meetings to respond to the pressing need for more and better intra-organizational health care collaboration. Using meetings sensibly also allows hospitals to benefit from the positive outcomes of collaboration and social capital (e.g., knowledge sharing, performance...

  3. Optimal Scheduling of Material Handling Devices in a PCB Production Line: Problem Formulation and a Polynomial Algorithm

    Ada Che

    2008-01-01

    Full Text Available Modern automated production lines usually use one or multiple computer-controlled robots or hoists for material handling between workstations. A typical application of such lines is an automated electroplating line for processing printed circuit boards (PCBs. In these systems, cyclic production policy is widely used due to large lot size and simplicity of implementation. This paper addresses cyclic scheduling of a multihoist electroplating line with constant processing times. The objective is to minimize the cycle time, or equivalently to maximize the production throughput, for a given number of hoists. We propose a mathematical model and a polynomial algorithm for this scheduling problem. Computational results on randomly generated instances are reported.

  4. A proposed simulation optimization model framework for emergency department problems in public hospital

    Ibrahim, Ireen Munira; Liong, Choong-Yeun; Bakar, Sakhinah Abu; Ahmad, Norazura; Najmuddin, Ahmad Farid

    2015-12-01

    The Emergency Department (ED) is a very complex system with limited resources to support increase in demand. ED services are considered as good quality if they can meet the patient's expectation. Long waiting times and length of stay is always the main problem faced by the management. The management of ED should give greater emphasis on their capacity of resources in order to increase the quality of services, which conforms to patient satisfaction. This paper is a review of work in progress of a study being conducted in a government hospital in Selangor, Malaysia. This paper proposed a simulation optimization model framework which is used to study ED operations and problems as well as to find an optimal solution to the problems. The integration of simulation and optimization is hoped can assist management in decision making process regarding their resource capacity planning in order to improve current and future ED operations.

  5. Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid

    Jian, Linni; Zheng, Yanchong; Xiao, Xinping; Chan, C.C.

    2015-01-01

    Highlights: • A novel event-triggered scheduling scheme for vehicle-to-grid (V2G) operation is proposed. • New scheme can handle the uncertainty arising from stochastic connection of electric vehicles. • New scheme aims at minimizing the overall load variance of power grid by V2G operation. • Method to evaluate the performance of proposed scheme is elaborated and demonstrated. - Abstract: Vehicle-to-grid (V2G) operation of plug-in electric vehicles (PEVs) is attracting increasing attention since it can assist to improve the efficiency and reliability of power grid, as well as reduce the operating cost and greenhouse gas emission of electric vehicles. Within the scheme of V2G operation, PEVs are expected to serve as a novel distributed energy storage system (ESS) to help achieve the balance between supply and demand of power grid. One of the key difficulties concerning its practical implementation lies in that the availability of PEVs as ESS for grid remains highly uncertain due to their mobility as transportation tools. To address this issue, a novel event-triggered scheduling scheme for V2G operation based on the scenario of stochastic PEV connection to smart grid is proposed in this paper. Firstly, the mathematical model is formulated. Secondly, the preparation of input data for systematic evaluation is introduced and the case study is conducted. Finally, statistic analysis results demonstrate that our proposed V2G scheduling scheme can dramatically smooth out the fluctuation in power load profiles

  6. Optimization of municipal waste collection scheduling and routing using vehicle assignment problem (case study of Surabaya city waste collection)

    Ramdhani, M. N.; Baihaqi, I.; Siswanto, N.

    2018-04-01

    Waste collection and disposal become a major problem for many metropolitan cities. Growing population, limited vehicles, and increased road traffic make the waste transportation become more complex. Waste collection involves some key considerations, such as vehicle assignment, vehicle routes, and vehicle scheduling. In the scheduling process, each vehicle has a scheduled departure that serve each route. Therefore, vehicle’s assignments should consider the time required to finish one assigment on that route. The objective of this study is to minimize the number of vehicles needed to serve all routes by developing a mathematical model which uses assignment problem approach. The first step is to generated possible routes from the existing routes, followed by vehicle assignments for those certain routes. The result of the model shows fewer vehicles required to perform waste collection asa well as the the number of journeys that the vehicle to collect the waste to the landfill. The comparison of existing conditions with the model result indicates that the latter’s has better condition than the existing condition because each vehicle with certain route has an equal workload, all the result’s model has the maximum of two journeys for each route.

  7. Minimizing patient waiting time in emergency department of public hospital using simulation optimization approach

    Ibrahim, Ireen Munira; Liong, Choong-Yeun; Bakar, Sakhinah Abu; Ahmad, Norazura; Najmuddin, Ahmad Farid

    2017-04-01

    Emergency department (ED) is the main unit of a hospital that provides emergency treatment. Operating 24 hours a day with limited number of resources invites more problems to the current chaotic situation in some hospitals in Malaysia. Delays in getting treatments that caused patients to wait for a long period of time are among the frequent complaints against government hospitals. Therefore, the ED management needs a model that can be used to examine and understand resource capacity which can assist the hospital managers to reduce patients waiting time. Simulation model was developed based on 24 hours data collection. The model developed using Arena simulation replicates the actual ED's operations of a public hospital in Selangor, Malaysia. The OptQuest optimization in Arena is used to find the possible combinations of a number of resources that can minimize patients waiting time while increasing the number of patients served. The simulation model was modified for improvement based on results from OptQuest. The improvement model significantly improves ED's efficiency with an average of 32% reduction in average patients waiting times and 25% increase in the total number of patients served.

  8. Job Burnout and its Association With Work Schedules and Job Satisfaction Among Iranian Nurses in a Public Hospital: A Questionnaire Survey

    Asghari

    2016-08-01

    Full Text Available Background Job burnout, defined as a syndrome derived from prolonged exposure to stressors at work, is often observed in health care workers. Shift work and job satisfaction are considered two of the occupational risks for burnout in nurses. Nurses have stress and health complaints. In addition, nurses are likely to job burnout. Objectives The current study aimed to determine the prevalence of job burnout and its association with work schedules and job satisfaction among Iranian nurses in a public hospital. Methods This cross-sectional study was conducted in one of the largest Iranian public hospitals among 362 nurses (response rate: 80.44% in Tehran, Iran. The Maslach burnout inventory (MBI-22 and demographic factors questionnaire were used in the present study. The relationship between job burnout with work schedules and job satisfaction was investigated with multiple logistic regression analysis. Results The mean age and work experience of the participants were 36.14 ± 8.59 and 15.23 ±9.30 years, respectively. The result indicated a relatively high prevalence of burnout (particularly, personal accomplishment among the study population. In general, 64.4% of participants reported low personal accomplishment level. The nurses engaged in shift work reported higher levels of emotional exhaustion (odds ratio (OR = 1.02, 95% confidence interval (CI = 1.006 - 1.041, P-value = 0.008; there was no relationship between work schedules with depersonalization and personal accomplishment. The result showed significant relationship between job satisfaction and emotional exhaustion (OR = 0.945, 95% CI = 0.928 - 0.963, P-value < 0.001 and personal accomplishment (OR = 1.003, 95% CI = 1.014 - 1.058, P-value = 0.001. Conclusions The current study revealed that the Iranian nurses are exposed to a considerable risk of personal accomplishment. Also, job burnout is in association with shift working and low job satisfaction level. In this regard, working pressure

  9. Multi-objective differential evolution with adaptive Cauchy mutation for short-term multi-objective optimal hydro-thermal scheduling

    Qin Hui [College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China); Zhou Jianzhong, E-mail: jz.zhou@hust.edu.c [College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China); Lu Youlin; Wang Ying; Zhang Yongchuan [College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China)

    2010-04-15

    A new multi-objective optimization method based on differential evolution with adaptive Cauchy mutation (MODE-ACM) is presented to solve short-term multi-objective optimal hydro-thermal scheduling (MOOHS) problem. Besides fuel cost, the pollutant gas emission is also optimized as an objective. The water transport delay between connected reservoirs and the effect of valve-point loading of thermal units are also taken into account in the presented problem formulation. The proposed algorithm adopts an elitist archive to retain non-dominated solutions obtained during the evolutionary process. It modifies the DE's operators to make it suit for multi-objective optimization (MOO) problems and improve its performance. Furthermore, to avoid premature convergence, an adaptive Cauchy mutation is proposed to preserve the diversity of population. An effective constraints handling method is utilized to handle the complex equality and inequality constraints. The effectiveness of the proposed algorithm is tested on a hydro-thermal system consisting of four cascaded hydro plants and three thermal units. The results obtained by MODE-ACM are compared with several previous studies. It is found that the results obtained by MODE-ACM are superior in terms of fuel cost as well as emission output, consuming a shorter time. Thus it can be a viable alternative to generate optimal trade-offs for short-term MOOHS problem.

  10. The effects of hospitals' governance on optimal contracts: bargaining vs. contracting.

    Galizzi, Matteo M; Miraldo, Marisa

    2011-03-01

    We propose a two-stage model to study the impact of different hospitals' governance frameworks on the optimal contracts designed by third-party payers when patients' disease severity is the private information of the hospital. In the second stage, doctors and managers interact within either a bargaining or a contracting scenario. In the contracting scenario, managers offer a contract that determines the payment to doctors, and doctors decide how many patients to treat. In the bargaining scenario, doctors and managers strategically negotiate on both the payment to doctors and the number of patients to treat. We derive the equilibrium doctors' payments and number of treated patients under both scenarios. We then derive the optimal contract offered by the government to the hospital in the first stage. Results show that when the cost of capital is sufficiently low, the informational rent is lower, and the social welfare is higher, in the contracting scenario. Copyright © 2010 Elsevier B.V. All rights reserved.

  11. Optimizing antibiotic usage in adults admitted with fever by a multifaceted intervention in an Indonesian governmental hospital.

    Hadi, U.; Keuter, M.; Asten, H.A.G.H. van; Broek, P. van den

    2008-01-01

    OBJECTIVE: To optimize antimicrobial treatment of patients with fever upon admission to the department of internal medicine of Dr Soetomo Hospital in Surabaya, Indonesia. METHOD: Prospective intervention study. The intervention comprised development of a consensus guideline, an official declaration

  12. A New Method Based on Simulation-Optimization Approach to Find Optimal Solution in Dynamic Job-shop Scheduling Problem with Breakdown and Rework

    Farzad Amirkhani

    2017-03-01

    The proposed method is implemented on classical job-shop problems with objective of makespan and results are compared with mixed integer programming model. Moreover, the appropriate dispatching priorities are achieved for dynamic job-shop problem minimizing a multi-objective criteria. The results show that simulation-based optimization are highly capable to capture the main characteristics of the shop and produce optimal/near-optimal solutions with highly credibility degree.

  13. Optimal design of modular cogeneration plants for hospital facilities and robustness evaluation of the results

    Gimelli, A.; Muccillo, M.; Sannino, R.

    2017-01-01

    Highlights: • A specific methodology has been set up based on genetic optimization algorithm. • Results highlight a tradeoff between primary energy savings (TPES) and simple payback (SPB). • Optimized plant configurations show TPES exceeding 18% and SPB of approximately three years. • The study aims to identify the most stable plant solutions through the robust design optimization. • The research shows how a deterministic definition of the decision variables could lead to an overestimation of the results. - Abstract: The widespread adoption of combined heat and power generation is widely recognized as a strategic goal to achieve significant primary energy savings and lower carbon dioxide emissions. In this context, the purpose of this research is to evaluate the potential of cogeneration based on reciprocating gas engines for some Italian hospital buildings. Comparative analyses have been conducted based on the load profiles of two specific hospital facilities and through the study of the cogeneration system-user interaction. To this end, a specific methodology has been set up by coupling a specifically developed calculation algorithm to a genetic optimization algorithm, and a multi-objective approach has been adopted. The results from the optimization problem highlight a clear trade-off between total primary energy savings (TPES) and simple payback period (SPB). Optimized plant configurations and management strategies show TPES exceeding 18% for the reference hospital facilities and multi–gas engine solutions along with a minimum SPB of approximately three years, thereby justifying the European regulation promoting cogeneration. However, designing a CHP plant for a specific energetic, legislative or market scenario does not guarantee good performance when these scenarios change. For this reason, the proposed methodology has been enhanced in order to focus on some innovative aspects. In particular, this study proposes an uncommon and effective approach

  14. [A program for optimizing the use of antimicrobials (PROA): experience in a regional hospital].

    Ugalde-Espiñeira, J; Bilbao-Aguirregomezcorta, J; Sanjuan-López, A Z; Floristán-Imízcoz, C; Elorduy-Otazua, L; Viciola-García, M

    2016-08-01

    Programs for optimizing the use of antibiotics (PROA) or antimicrobial stewardship programs are multidisciplinary programs developed in response to the increase of antibiotic resistant bacteria, the objective of which are to improve clinical results, to minimize adverse events and to reduce costs associated with the use of antimicrobials. The implementation of a PROA program in a 128-bed general hospital and the results obtained at 6 months are here reported. An intervention quasi-experimental study with historical control group was designed with the objective of assessing the impact of a PROA program with a non-restrictive intervention model to help prescription, with a direct and bidirectional intervention. The basis of the program is an optimization audit of the use of antimicrobials with not imposed personalized recommendations and the use of information technologies applied to this setting. The impact on the pharmaceutical consumption and costs, cost per process, mean hospital stay, percentage of readmissions to the hospital are described. A total of 307 audits were performed. In 65.8% of cases, treatment was discontinued between the 7th and the 10th day. The main reasons of treatment discontinuation were completeness of treatment (43.6%) and lack of indication (14.7%). The reduction of pharmaceutical expenditure was 8.59% (P = 0.049) and 5.61% of the consumption in DDD/100 stays (P=0.180). The costs by processes in general surgery showed a 3.14% decrease (p=0.000). The results obtained support the efficiency of these programs in small size hospitals with limited resources.

  15. Optimization Technique With Sensitivity Analysis On Menu Scheduling For Boarding School Student Aged 13-18 Using “Sufahani-Ismail Algorithm”

    Sudin, Azila M.; Sufahani, Suliadi

    2018-04-01

    Boarding school student aged 13-18 need to eat nutritious meals which contains proper calories, vitality and nutrients for appropriate development with a specific end goal to repair and upkeep the body tissues. Furthermore, it averts undesired diseases and contamination. Serving healthier food is a noteworthy stride towards accomplishing that goal. However, arranging a nutritious and balance menu manually is convoluted, wasteful and tedious. Therefore, the aim of this study is to develop a mathematical model with an optimization technique for menu scheduling that fulfill the whole supplement prerequisite for boarding school student, reduce processing time, minimize the budget and furthermore serve assortment type of food each day. It additionally gives the flexibility for the cook to choose any food to be considered in the beginning of the process and change any favored menu even after the ideal arrangement and optimal solution has been obtained. This is called sensitivity analysis. A recalculation procedure will be performed in light of the ideal arrangement and seven days menu was produced. The data was gathered from the Malaysian Ministry of Education and schools authorities. Menu arranging is a known optimization problem. Therefore Binary Programming alongside optimization technique and “Sufahani-Ismail Algorithm” were utilized to take care of this issue. In future, this model can be implemented to other menu problem, for example, for sports, endless disease patients, militaries, colleges, healing facilities and nursing homes.

  16. Stage, tumor growth rate and optimal dose fractionation schedules in the treatment of squamous cell carcinoma of the head and neck

    Sugawara, Tadashi; Morita, Mamoru; Aihara, Toshinori; Tanaka, Osamu

    1983-01-01

    In 77 patients with cancer of the head and neck, 45 patients received radiotherapy alone, while 32 patients with T 1 or T 2 glottic cancer received combined therapy with laryngomicrosurgery performed prior to or during the course of irradiation. These T 1 and T 2 groups were separately analyzed from other T 1 and T 2 groups as T sub(LMS). Local recurrence rates were compared concerning overall time and fraction size in following three subgroups, i.e., T sub(LMS), Tsub(1+2), and Tsub(3+4). No significant correlation was detected between total dose converted to partial tolerance (PT) and local control in all subgroups except for Tsub(3+4), in which local recurrence rate was rather higher in high PT range. Local control was significantly dependent on overall treatment time and fraction size, differently by tumor stage. Favorable fractionation schedules considered as optimal for T sub(LMS) were those in which treatment was given 5 times weekly with a daily dose rate more than 196 rad in a shorter overall time of less than 42 days. In contrast to T sub(LMS), effective schedules for Tsub(3+4) consisted of longer overall time of more than 60 days and a lower daily dose rate of less than 153 rad. In Tsub(1+2), the optimal schedule was needed to be altered according to rapidness of progression of disease characterised by duration of the complaints, which was statistically proved to be significantly shorter in Tsub(1+2) than Tsub(3+4). Rapidity-adjusted schedule consisted of a shorter over-all time of less than 49 days with a daily dose rate of more than 175 rad 5 fractions a week for a case having a duration of complaints of less than 2.9 months, and a longer overall time of more than 50 days with a daily dose rate of less than 174 rad for a case having a duration of more than 3 months. (J.P.N.)

  17. Operation costs and pollutant emissions reduction by definition of new collection scheduling and optimization of MSW collection routes using GIS. The case study of Barreiro, Portugal.

    Zsigraiova, Zdena; Semiao, Viriato; Beijoco, Filipa

    2013-04-01

    This work proposes an innovative methodology for the reduction of the operation costs and pollutant emissions involved in the waste collection and transportation. Its innovative feature lies in combining vehicle route optimization with that of waste collection scheduling. The latter uses historical data of the filling rate of each container individually to establish the daily circuits of collection points to be visited, which is more realistic than the usual assumption of a single average fill-up rate common to all the system containers. Moreover, this allows for the ahead planning of the collection scheduling, which permits a better system management. The optimization process of the routes to be travelled makes recourse to Geographical Information Systems (GISs) and uses interchangeably two optimization criteria: total spent time and travelled distance. Furthermore, rather than using average values, the relevant parameters influencing fuel consumption and pollutant emissions, such as vehicle speed in different roads and loading weight, are taken into consideration. The established methodology is applied to the glass-waste collection and transportation system of Amarsul S.A., in Barreiro. Moreover, to isolate the influence of the dynamic load on fuel consumption and pollutant emissions a sensitivity analysis of the vehicle loading process is performed. For that, two hypothetical scenarios are tested: one with the collected volume increasing exponentially along the collection path; the other assuming that the collected volume decreases exponentially along the same path. The results evidence unquestionable beneficial impacts of the optimization on both the operation costs (labor and vehicles maintenance and fuel consumption) and pollutant emissions, regardless the optimization criterion used. Nonetheless, such impact is particularly relevant when optimizing for time yielding substantial improvements to the existing system: potential reductions of 62% for the total

  18. Revisiting the NEH algorithm- the power of job insertion technique for optimizing the makespan in permutation flow shop scheduling

    A. Baskar

    2016-04-01

    Full Text Available Permutation flow shop scheduling problems have been an interesting area of research for over six decades. Out of the several parameters, minimization of makespan has been studied much over the years. The problems are widely regarded as NP-Complete if the number of machines is more than three. As the computation time grows exponentially with respect to the problem size, heuristics and meta-heuristics have been proposed by many authors that give reasonably accurate and acceptable results. The NEH algorithm proposed in 1983 is still considered as one of the best simple, constructive heuristics for the minimization of makespan. This paper analyses the powerful job insertion technique used by NEH algorithm and proposes seven new variants, the complexity level remains same. 120 numbers of problem instances proposed by Taillard have been used for the purpose of validating the algorithms. Out of the seven, three produce better results than the original NEH algorithm.

  19. Clinical trials in hospitalized heart failure patients: targeting interventions to optimal phenotypic subpopulations.

    Vaduganathan, Muthiah; Butler, Javed; Roessig, Lothar; Fonarow, Gregg C; Greene, Stephen J; Metra, Marco; Cotter, Gadi; Kupfer, Stuart; Zalewski, Andrew; Sato, Naoki; Filippatos, Gerasimos; Gheorghiade, Mihai

    2015-07-01

    With one possible exception, the last decade of clinical trials in hospitalized heart failure (HHF) patients has failed to demonstrate improvement in long-term clinical outcomes. This trend necessitates a need to evaluate optimal drug development strategies and standards of trial conduct. It has become increasingly important to recognize the heterogeneity among HHF patients and the differential characterization of novel drug candidates. Targeting these agents to specific subpopulations may afford optimal net response related to the particular mode of action of the drug. Analyses of previous trials demonstrate profound differences in the baseline characteristics of patients enrolled across global regions and participating sites. Such differences may influence risks for events and interpretation of results. Therefore, the actual execution of trials and the epidemiology of HHF populations at the investigative sites must be taken into consideration. Collaboration among participating sites including the provision of registry data tailored to the planned development program will optimize trial conduct. Observational data prior to study initiation may enable sites to feedback and engage in protocol development to allow for feasible and valid clinical trial conduct. This site-centered, epidemiology-based network environment may facilitate studies in specific patient populations and promote optimal data collection and clear interpretation of drug safety and efficacy. This review summarizes the roundtable discussion held by a multidisciplinary team of representatives from academia, National Institutes of Health, industry, regulatory agencies, payers, and contract and academic research organizations to answer the question: Who should be targeted for novel therapies in HHF?

  20. Refinery scheduling

    Magalhaes, Marcus V.; Fraga, Eder T. [PETROBRAS, Rio de Janeiro, RJ (Brazil); Shah, Nilay [Imperial College, London (United Kingdom)

    2004-07-01

    This work addresses the refinery scheduling problem using mathematical programming techniques. The solution adopted was to decompose the entire refinery model into a crude oil scheduling and a product scheduling problem. The envelope for the crude oil scheduling problem is composed of a terminal, a pipeline and the crude area of a refinery, including the crude distillation units. The solution method adopted includes a decomposition technique based on the topology of the system. The envelope for the product scheduling comprises all tanks, process units and products found in a refinery. Once crude scheduling decisions are Also available the product scheduling is solved using a rolling horizon algorithm. All models were tested with real data from PETROBRAS' REFAP refinery, located in Canoas, Southern Brazil. (author)

  1. Test scheduling optimization for 3D network-on-chip based on cloud evolutionary algorithm of Pareto multi-objective

    Xu, Chuanpei; Niu, Junhao; Ling, Jing; Wang, Suyan

    2018-03-01

    In this paper, we present a parallel test strategy for bandwidth division multiplexing under the test access mechanism bandwidth constraint. The Pareto solution set is combined with a cloud evolutionary algorithm to optimize the test time and power consumption of a three-dimensional network-on-chip (3D NoC). In the proposed method, all individuals in the population are sorted in non-dominated order and allocated to the corresponding level. Individuals with extreme and similar characteristics are then removed. To increase the diversity of the population and prevent the algorithm from becoming stuck around local optima, a competition strategy is designed for the individuals. Finally, we adopt an elite reservation strategy and update the individuals according to the cloud model. Experimental results show that the proposed algorithm converges to the optimal Pareto solution set rapidly and accurately. This not only obtains the shortest test time, but also optimizes the power consumption of the 3D NoC.

  2. An optimization design proposal of automated guided vehicles for mixed type transportation in hospital environments.

    González, Domingo; Romero, Luis; Espinosa, María Del Mar; Domínguez, Manuel

    2017-01-01

    The aim of this paper is to present an optimization proposal in the automated guided vehicles design used in hospital logistics, as well as to analyze the impact of its implementation in a real environment. This proposal is based on the design of those elements that would allow the vehicles to deliver an extra cart by the towing method. So, the proposal intention is to improve the productivity and the performance of the current vehicles by using a transportation method of combined carts. The study has been developed following concurrent engineering premises from three different viewpoints. First, the sequence of operations has been described, and second, a proposal of design of the equipment has been undertaken. Finally, the impact of the proposal has been analyzed according to real data from the Hospital Universitario Rio Hortega in Valladolid (Spain). In this particular case, by the implementation of the analyzed proposal in the hospital a reduction of over 35% of the current time of use can be achieved. This result may allow adding new tasks to the vehicles, and according to this, both a new kind of vehicle and a specific module can be developed in order to get a better performance.

  3. An optimization design proposal of automated guided vehicles for mixed type transportation in hospital environments.

    Domingo González

    Full Text Available The aim of this paper is to present an optimization proposal in the automated guided vehicles design used in hospital logistics, as well as to analyze the impact of its implementation in a real environment.This proposal is based on the design of those elements that would allow the vehicles to deliver an extra cart by the towing method. So, the proposal intention is to improve the productivity and the performance of the current vehicles by using a transportation method of combined carts.The study has been developed following concurrent engineering premises from three different viewpoints. First, the sequence of operations has been described, and second, a proposal of design of the equipment has been undertaken. Finally, the impact of the proposal has been analyzed according to real data from the Hospital Universitario Rio Hortega in Valladolid (Spain. In this particular case, by the implementation of the analyzed proposal in the hospital a reduction of over 35% of the current time of use can be achieved. This result may allow adding new tasks to the vehicles, and according to this, both a new kind of vehicle and a specific module can be developed in order to get a better performance.

  4. Sleep duration as a mediator between an alternating day and night shift work schedule and metabolic syndrome among female hospital employees.

    Korsiak, Jill; Tranmer, Joan; Day, Andrew; Aronson, Kristan J

    2018-02-01

    The main objective was to determine whether sleep duration on work shifts mediates the relationship between a current alternating day and night shift work schedule and metabolic syndrome among female hospital employees. The secondary objective was to assess whether cumulative lifetime shift work exposure was associated with metabolic syndrome. In this cross-sectional study of 294 female hospital employees, sleep duration was measured with the ActiGraph GT3X+. Shift work status was determined through self-report. Investigation of the total, direct and indirect effects between shift work, sleep duration on work shifts and metabolic syndrome was conducted using regression path analysis. Logistic regression was used to determine the association between cumulative shift work exposure and metabolic syndrome. Shift work is strongly associated with metabolic syndrome (OR Total =2.72, 95% CI 1.38 to 5.36), and the relationship is attenuated when work shift sleep duration is added to the model (OR Direct =1.18, 95% CI 0.49 to 2.89). Sleep duration is an important intermediate between shift work and metabolic syndrome (OR Indirect =2.25, 95% CI 1.27 to 4.26). Cumulative shift work exposure is not associated with metabolic syndrome in this population. Sleep duration mediates the association between a current alternating day-night shift work pattern and metabolic syndrome. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  5. Gradient decent based multi-objective cultural differential evolution for short-term hydrothermal optimal scheduling of economic emission with integrating wind power and photovoltaic power

    Zhang, Huifeng; Yue, Dong; Xie, Xiangpeng; Dou, Chunxia; Sun, Feng

    2017-01-01

    With the integration of wind power and photovoltaic power, optimal operation of hydrothermal power system becomes great challenge due to its non-convex, stochastic and complex-coupled constrained characteristics. This paper extends short-term hydrothermal system optimal model into short-term hydrothermal optimal scheduling of economic emission while considering integrated intermittent energy resources (SHOSEE-IIER). For properly solving SHOSEE-IIER problem, a gradient decent based multi-objective cultural differential evolution (GD-MOCDE) is proposed to improve the optimal efficiency of SHOSEE-IIER combined with three designed knowledge structures, which mainly enhances search ability of differential evolution in the shortest way. With considering those complex-coupled and stochastic constraints, a heuristic constraint-handling measurement is utilized to tackle with them both in coarse and fine tuning way, and probability constraint-handling procedures are taken to properly handle those stochastic constraints combined with their probability density functions. Ultimately, those approaches are implemented on five test systems, which testify the optimization efficiency of proposed GD-MOCDE and constraint-handling efficiency for system load balance, water balance and stochastic constraint-handling measurements, those obtained results reveal that the proposed GD-MOCDE can properly solve the SHOSEE-IIER problem combined with those constraint-handling approaches. - Highlights: • Gradient decent method is proposed to improve mutation operator. • Hydrothermal system is extended to hybrid energy system. • The uncertainty constraint is converted into deterministic constraint. • The results show the viability and efficiency of proposed algorithm.

  6. Statistical modelling for precision agriculture: A case study in optimal environmental schedules for Agaricus Bisporus production via variable domain functional regression

    Panayi, Efstathios; Kyriakides, George

    2017-01-01

    Quantifying the effects of environmental factors over the duration of the growing process on Agaricus Bisporus (button mushroom) yields has been difficult, as common functional data analysis approaches require fixed length functional data. The data available from commercial growers, however, is of variable duration, due to commercial considerations. We employ a recently proposed regression technique termed Variable-Domain Functional Regression in order to be able to accommodate these irregular-length datasets. In this way, we are able to quantify the contribution of covariates such as temperature, humidity and water spraying volumes across the growing process, and for different lengths of growing processes. Our results indicate that optimal oxygen and temperature levels vary across the growing cycle and we propose environmental schedules for these covariates to optimise overall yields. PMID:28961254

  7. Short-term hydro generation scheduling of Xiluodu and Xiangjiaba cascade hydropower stations using improved binary-real coded bee colony optimization algorithm

    Lu, Peng; Zhou, Jianzhong; Wang, Chao; Qiao, Qi; Mo, Li

    2015-01-01

    Highlights: • STHGS problem is decomposed into two parallel sub-problems of UC and ELD. • Binary coded BCO is used to solve UC sub-problem with 0–1 discrete variables. • Real coded BCO is used to solve ELD sub-problem with continuous variables. • Some heuristic repairing strategies are designed to handle various constraints. • The STHGS of Xiluodu and Xiangjiaba cascade stations is solved by IB-RBCO. - Abstract: Short-term hydro generation scheduling (STHGS) of cascade hydropower stations is a typical nonlinear mixed integer optimization problem to minimize the total water consumption while simultaneously meeting the grid requirements and other hydraulic and electrical constraints. In this paper, STHGS problem is decomposed into two parallel sub-problems of unit commitment (UC) and economic load dispatch (ELD), and the methodology of improved binary-real coded bee colony optimization (IB-RBCO) algorithm is proposed to solve them. Firstly, the improved binary coded BCO is used to solve the UC sub-problem with 0–1 discrete variables, and the heuristic repairing strategy for unit state constrains is applied to generate the feasible unit commitment schedule. Then, the improved real coded BCO is used to solve the ELD sub-problem with continuous variables, and an effective method is introduced to handle various unit operation constraints. Especially, the new updating strategy of DE/best/2/bin method with dynamic parameter control mechanism is applied to real coded BCO to improve the search ability of IB-RBCO. Finally, to verify the feasibility and effectiveness of the proposed IB-RBCO method, it is applied to solve the STHGS problem of Xiluodu and Xiangjiaba cascaded hydropower stations, and the simulating results are compared with other intelligence algorithms. The simulation results demonstrate that the proposed IB-RBCO method can get higher-quality solutions with less water consumption and shorter calculating time when facing the complex STHGS problem

  8. TH-302, a hypoxia-activated prodrug with broad in vivo preclinical combination therapy efficacy: optimization of dosing regimens and schedules.

    Liu, Qian; Sun, Jessica D; Wang, Jingli; Ahluwalia, Dharmendra; Baker, Amanda F; Cranmer, Lee D; Ferraro, Damien; Wang, Yan; Duan, Jian-Xin; Ammons, W Steve; Curd, John G; Matteucci, Mark D; Hart, Charles P

    2012-06-01

    Subregional hypoxia is a common feature of tumors and is recognized as a limiting factor for the success of radiotherapy and chemotherapy. TH-302, a hypoxia-activated prodrug selectively targeting hypoxic regions of solid tumors, delivers a cytotoxic warhead to the tumor, while maintaining relatively low systemic toxicity. The antitumor activity, different dosing sequences, and dosing regimens of TH-302 in combination with commonly used conventional chemotherapeutics were investigated in human tumor xenograft models. Seven chemotherapeutic drugs (docetaxel, cisplatin, pemetrexed, irinotecan, doxorubicin, gemcitabine, and temozolomide) were tested in combination with TH-302 in eleven human xenograft models, including non-small cell lung cancer (NSCLC), colon cancer, prostate cancer, fibrosarcoma, melanoma, and pancreatic cancer. The antitumor activity of docetaxel, cisplatin, pemetrexed, irinotecan, doxorubicin, gemcitabine, and temozolomide was increased when combined with TH-302 in nine out of eleven models tested. Administration of TH-302 2-8 h prior to the other chemotherapeutics yielded superior efficacy versus other sequences tested. Simultaneous administration of TH-302 and chemotherapeutics increased toxicity versus schedules with dosing separations. In a dosing optimization study, TH-302 administered daily at 50 mg/kg intraperitoneally for 5 days per week in the H460 NSCLC model showed the optimal response with minimal toxicity. TH-302 enhances the activity of a wide range of conventional anti-neoplastic agents in a broad panel of in vivo xenograft models. These data highlight in vivo effects of schedule and order of drug administration in regimen efficacy and toxicity and have relevance to the design of human regimens incorporating TH-302.

  9. Una formulación matemática y de solución para programar cirugías con restricciones de recursos humanos en el hospital público A mathematical formulation and solution to schedule surgeries with human resource constraints in a public hospital

    Lorena Pradenas Rojas

    2012-08-01

    Full Text Available Actualmente, los hospitales públicos nacionales e internacionales presentan demandas que sobrepasan la capacidad de atención, lo que ha provocado un creciente interés por usar herramientas de gestión en los centros clínicos que les permita realizar de forma eficiente y eficaz la entrega de servicios a los distintos pacientes. El presente estudio aporta una nueva forma de abordar el problema de programación de cirugías, desde la programación matemática, presentando un modelo de optimización multiobjetivo y un algoritmo metaheurístico implementado computacionalmente, que permite la programación semanal de intervenciones quirúrgicas, cumpliendo con los requerimientos de pabellones y personal especializado necesario para su realización. Se utiliza una instancia de prueba, donde el tiempo de ejecución del algoritmo, implementado en C++, fue de siete minutos para 191 cirugías en lista de espera. El tiempo alcanzado es considerablemente menor a la programación realizada con un sistema manual, como los actualmente usados en hospitales públicos.Currently, national and international public hospitals have demands that exceed their service capacity, which has caused a growing interest in management sciences to deliver these medical centers the tools that will enable them to perform efficiently and effectively, delivering services to different patients. This study provides a new way of approaching the problem of surgical scheduling using mathematical programming, presenting a multi-objective optimization model and a metaheuristic algorithm implemented computationall. The solution allows weekly schedule of surgical procedure and complying with the requirements of the flag and expertise necessary for realization. We ordered test instances where the execution time of the algorithm, coded in C++, was 7 minutes for a 191 surgeries waiting list, which is a considerable less amount of time to this schedule than using a manual approach. The latest

  10. Mid and long-term optimize scheduling of cascade hydro-power stations based on modified GA-POA method

    J. Li

    2018-06-01

    Full Text Available In this paper, to explore the efficiency and rationality of the cascade combined generation, a cascade combined optimal model with the maximum generating capacity is established, and solving the model by the modified GA-POA method. It provides a useful reference for the joint development of cascade hydro-power stations in large river basins. The typical annual runoff data are selected to calculate the difference between the calculated results under different representative years. The results show that the cascade operation of cascaded hydro-power stations can significantly increase the overall power generation of cascade and ease the flood risk caused by concentration of flood season.

  11. Hospitals

    Department of Homeland Security — This database contains locations of Hospitals for 50 states and Washington D.C. , Puerto Rico and US territories. The dataset only includes hospital facilities and...

  12. Schedule Analytics

    2016-04-30

    Warfare, Naval Sea Systems Command Acquisition Cycle Time : Defining the Problem David Tate, Institute for Defense Analyses Schedule Analytics Jennifer...research was comprised of the following high- level steps :  Identify and review primary data sources 1...research. However, detailed reviews of the OMB IT Dashboard data revealed that schedule data is highly aggregated. Program start date and program end date

  13. Mechanisms for scheduling games with selfish players

    Hoeksma, R.P.

    2015-01-01

    Many challenges in operations research involve optimization. In particular, scheduling treats the optimal planning of tasks. This thesis focuses on machine scheduling models, where a number of tasks, called jobs, need to be scheduled on one or more machines. The outcome is determined by which job is

  14. Optimization Schedule of Solar Energy House%太阳能小屋的最优设计

    赵国喜; 代林帅

    2013-01-01

    研究了太阳能发电装置的电池组件选配问题,建立了双目标规划模型,并进行了求解。有效地设计出太阳能光伏电池板最优铺设方案,并给出了光伏电池逆转换器配置办法,提出了一种比较经济的太阳能小屋设计办法。%Double objective programming mode is established and solved based on battery component matching problem with solar energy generation device. The optimal laying scenario for solar photovoltaic panels is designed effectively, and configuration method of photovoltaic battery inverse converter is given. An economical solar house design is proposed.

  15. A novel approach using flexible scheduling and aggregation to optimize demand response in the developing interactive grid market architecture

    Reihani, Ehsan; Motalleb, Mahdi; Thornton, Matsu; Ghorbani, Reza

    2016-01-01

    Highlights: • Designing a DR market to increase renewable resources and decrease air pollution. • Explaining two economic models for DR market for selling available DR quantities. • Optimal allocating DR quantity to houses under each DR aggregator control. • Proposing a discomfort cost function for residential DR resources. • Performing a sensitivity analysis on discomfort cost function coefficients. - Abstract: With the increasing presence of intermittent renewable energy generation sources, variable control over loads and energy storage devices on the grid become even more important to maintain this balance. Increasing renewable energy penetration depends on both technical and economic factors. Distribution system consumers can contribute to grid stability by controlling residential electrical device power consumed by water heaters and battery storage systems. Coupled with dynamic supply pricing strategies, a comprehensive system for demand response (DR) exist. Proper DR management will allow greater integration of renewable energy sources partially replacing energy demand currently met by the combustion of fossil-fuels. An enticing economic framework providing increased value to consumers compensates them for reduced control of devices placed under a DR aggregator. Much work has already been done to develop more effective ways to implement DR control systems. Utilizing an integrated approach that combines consumer requirements into aggregate pools, and provides a dynamic response to market and grid conditions, we have developed a mathematical model that can quantify control parameters for optimum demand response and decide which resources to switch and when. In this model, optimization is achieved as a function of cost savings vs. customer comfort using mathematical market analysis. Two market modeling approaches—the Cournot and SFE—are presented and compared. A quadratic function is used for presenting the cost function of each DRA (Demand

  16. A Monte-Carlo-Based Method for the Optimal Placement and Operation Scheduling of Sewer Mining Units in Urban Wastewater Networks

    Eleftheria Psarrou

    2018-02-01

    Full Text Available Pressures on water resources, which have increased significantly nowadays mainly due to rapid urbanization, population growth and climate change impacts, necessitate the development of innovative wastewater treatment and reuse technologies. In this context, a mid-scale decentralized technology concerning wastewater reuse is that of sewer mining. It is based on extracting wastewater from a wastewater system, treating it on-site and producing recycled water applicable for non-potable uses. Despite the technology’s considerable benefits, several challenges hinder its implementation. Sewer mining disturbs biochemical processes inside sewers and affects hydrogen sulfide build-up, resulting in odor, corrosion and health-related problems. In this study, a tool for optimal sewer mining unit placement aiming to minimize hydrogen sulfide production is presented. The Monte-Carlo method coupled with the Environmental Protection Agency’s Storm Water Management Model (SWMM is used to conduct multiple simulations of the network. The network’s response when sewage is extracted from it is also examined. Additionally, the study deals with optimal pumping scheduling. The overall methodology is applied in a sewer network in Greece providing useful results. It can therefore assist in selecting appropriate locations for sewer mining implementation, with the focus on eliminating hydrogen sulfide-associated problems while simultaneously ensuring that higher water needs are satisfied.

  17. Multi-objective optimization of short-term hydrothermal scheduling using non-dominated sorting gravitational search algorithm with chaotic mutation

    Tian, Hao; Yuan, Xiaohui; Ji, Bin; Chen, Zhihuan

    2014-01-01

    Highlights: • An improved non-dominated sorting gravitational search algorithm (NSGSA-CM) is proposed. • NSGSA-CM is used to solve the problem of short-term multi-objective hydrothermal scheduling. • We enhance the search capability of NSGSA-CM by chaotic mutation. • New strategies are devised to handle various constraints in NSGSA-CM. • We obtain better compromise solutions with less fuel cost and emissions. - Abstract: This paper proposes a non-dominated sorting gravitational search algorithm with chaotic mutation (NSGSA-CM) to solve short-term economic/environmental hydrothermal scheduling (SEEHTS) problem. The SEEHTS problem is formulated as a multi-objective optimization problem with many equality and inequality constraints. By introducing the concept of non-dominated sorting and crowding distance, NSGSA-CM can optimize two objectives of fuel cost and pollutant emission simultaneously and obtain a set of Pareto optimal solutions in one trial. In order to improve the performance of NSGSA-CM, the paper introduces particle memory character and population social information in velocity update process. And a chaotic mutation is adopted to prevent the premature convergence. Furthermore, NSGSA-CM utilizes an elitism strategy which selects better solutions in parent and offspring populations based on their non-domination rank and crowding distance to update new generations. When dealing with the constraints of the SEEHTS, new strategies without penalty factors are proposed. In order to handle the water dynamic balance and system load balance constraints, this paper uses a combined strategy which adjusts the violation averagely to each decision variable at first and adjusts the rest violation randomly later. Meanwhile, a new symmetrical adjustment strategy by modifying the discharges at current and later interval without breaking water dynamic balance is adopted to handle reservoir storage constraints. To test the performance of the proposed NSGSA

  18. Evaluation of the Optimal Connection of Power Transformers in the Substations of a Hospital

    Carlos Javier Renedo

    2018-02-01

    Full Text Available Transformers are installed in power distribution systems to perform changes in supply voltage. Large consumers often have several transformers installed in parallel to ensure continuity of supply in the event of failure. These machines can achieve very high efficiency, but their efficiency is not constant since it depends on the power demanded at each time. Therefore, the level of efficiency that correspond to the operation of a specific transformer depends on two factors: machine technical characteristics and electrical load. In this work, the authors have proposed a methodology which shows the optimal number of transformers to be connected at each period in the substations of a large Spanish hospital, in order to achieve the maximum seasonal efficiency of these machines. The results of the energy saving are determined with respect to the current situation, in which all the transformers are permanently connected. On the other hand, the European Union has established a new regulation that sets the minimum energy efficiency requirements for new power transformers. This efficiency improvement is proposed to be applied gradually in two stages, a first limit came into force in 2015, while a more restrictive approach will appear in the year 2021. This work has also studied the potential energy savings that would occur when the substations of the hospital have more efficient transformers complying with the new European Regulation 548/2014.

  19. Optimization is required when using linked hospital and laboratory data to investigate respiratory infections.

    Lim, Faye J; Blyth, Christopher C; de Klerk, Nicholas; Valenti, Beverly; Rouhiainen, Oliver J; Wu, Dominic Yu-An; Jansz, Christopher S; Moore, Hannah C

    2016-01-01

    Despite a recommendation for microbiological testing, only 45% of children hospitalized for respiratory infections in our previous data linkage study linked to a microbiological record. We conducted a chart review to validate linked microbiological data. The chart review consisted of children aged data linkage study. Poisson regression was used to identify factors predicting the likelihood of microbiological tests in the chart review cohort. From the chart review, 77% of 746 records had a microbiological test performed compared with 46% of 18,687 records from our previous data linkage study. Of those undergoing testing, 66% of the chart review and 64% of data linkage records had ≥1 respiratory pathogen(s) detected. In the chart review cohort, frequency of testing was highest in children admitted to metropolitan hospitals. Validation studies are essential to ensure the quality of linked data. Our previous data linkage study failed to capture all relevant microbiological records. Findings will be used to optimize extraction protocols for future linkage studies. Copyright © 2016 Elsevier Inc. All rights reserved.

  20. Optimization of sodium bicarbonate injection for acid scrubbing in hospital waste incineration plant

    Rozainee, M.; Salleh, M.; Mutahharah, M.M.; Anwar Johari

    2010-01-01

    Optimization of sodium bicarbonate (NaHCO 3 ) injection for acid hydrochloric (HCl) scrubbing was conducted on a hospital waste incineration plant. The plant employs a rotary kiln system having burning capacity of 350 kg/h hospital waste (average calorific value of 17.4 MJ/kg) and is operated on a 24 hr/ day basis. Currently, NaHCO 3 injection rate is 25 kg/h as recommended by manufacturer to meet the Department of Environment (DOE) standard emission limit of 200 mg/Nm 3 HCl. Testing of HCl emission at various injection rates of 25, 20, 15 and 10 kg/ h results in HCl final concentration in the range of 0.58-7.13, 5.63-7.74, 0.07-2.99 and 3-28 mg/Nm 3 respectively. The results showed that NaHCO 3 injection rate as low as 10 kg/ h could still meet the HCl stipulated emission limit. Economic comparison between 25 and 10 kg/ h injection rates showed that total saving on NaHCO 3 and disposal of fly ash was RM 22,000 per month (equivalent to saving RM 260,000 per year) when using 10 kg/ h injection rate. It was concluded from the study that optimum injection rate would not only save cost and reduce wastage but also reduce bag house loading rate and prolong the life span of filter bags. (author)

  1. Applied Mathematical Optimization Technique on Menu Scheduling for Boarding School Student Using Delete-Reshuffle-Reoptimize Algorithm

    Sufahani, Suliadi; Mohamad, Mahathir; Roslan, Rozaini; Ghazali Kamardan, M.; Che-Him, Norziha; Ali, Maselan; Khalid, Kamal; Nazri, E. M.; Ahmad, Asmala

    2018-04-01

    Boarding school student needs to eat well balanced nutritious food which includes proper calories, vitality and supplements for legitimate development, keeping in mind the end goal is to repair and support the body tissues and averting undesired ailments and disease. Serving healthier menu is a noteworthy stride towards accomplishing that goal. Be that as it may, arranging a nutritious and adjusted menu physically is confounded, wasteful and tedious. This study intends to build up a scientific mathematical model for eating routine arranging that improves and meets the vital supplement consumption for boarding school student aged 13-18 and in addition saving the financial plan. It likewise gives the adaptability for the cook to change any favoured menu even after the ideal arrangement has been produced. A recalculation procedure will be performed in view of the ideal arrangement. The information was gathered from the the Ministry of Education and boarding schools’ authorities. Menu arranging is a notable enhancement issue and part of well-established optimization problem. The model was fathomed by utilizing Binary Programming and “Delete-Reshuffle-Reoptimize Algortihm (DDRA)”.

  2. Using queuing theory and simulation model to optimize hospital pharmacy performance.

    Bahadori, Mohammadkarim; Mohammadnejhad, Seyed Mohsen; Ravangard, Ramin; Teymourzadeh, Ehsan

    2014-03-01

    Hospital pharmacy is responsible for controlling and monitoring the medication use process and ensures the timely access to safe, effective and economical use of drugs and medicines for patients and hospital staff. This study aimed to optimize the management of studied outpatient pharmacy by developing suitable queuing theory and simulation technique. A descriptive-analytical study conducted in a military hospital in Iran, Tehran in 2013. A sample of 220 patients referred to the outpatient pharmacy of the hospital in two shifts, morning and evening, was selected to collect the necessary data to determine the arrival rate, service rate, and other data needed to calculate the patients flow and queuing network performance variables. After the initial analysis of collected data using the software SPSS 18, the pharmacy queuing network performance indicators were calculated for both shifts. Then, based on collected data and to provide appropriate solutions, the queuing system of current situation for both shifts was modeled and simulated using the software ARENA 12 and 4 scenarios were explored. Results showed that the queue characteristics of the studied pharmacy during the situation analysis were very undesirable in both morning and evening shifts. The average numbers of patients in the pharmacy were 19.21 and 14.66 in the morning and evening, respectively. The average times spent in the system by clients were 39 minutes in the morning and 35 minutes in the evening. The system utilization in the morning and evening were, respectively, 25% and 21%. The simulation results showed that reducing the staff in the morning from 2 to 1 in the receiving prescriptions stage didn't change the queue performance indicators. Increasing one staff in filling prescription drugs could cause a decrease of 10 persons in the average queue length and 18 minutes and 14 seconds in the average waiting time. On the other hand, simulation results showed that in the evening, decreasing the staff

  3. Using Queuing Theory and Simulation Model to Optimize Hospital Pharmacy Performance

    Bahadori, Mohammadkarim; Mohammadnejhad, Seyed Mohsen; Ravangard, Ramin; Teymourzadeh, Ehsan

    2014-01-01

    Background: Hospital pharmacy is responsible for controlling and monitoring the medication use process and ensures the timely access to safe, effective and economical use of drugs and medicines for patients and hospital staff. Objectives: This study aimed to optimize the management of studied outpatient pharmacy by developing suitable queuing theory and simulation technique. Patients and Methods: A descriptive-analytical study conducted in a military hospital in Iran, Tehran in 2013. A sample of 220 patients referred to the outpatient pharmacy of the hospital in two shifts, morning and evening, was selected to collect the necessary data to determine the arrival rate, service rate, and other data needed to calculate the patients flow and queuing network performance variables. After the initial analysis of collected data using the software SPSS 18, the pharmacy queuing network performance indicators were calculated for both shifts. Then, based on collected data and to provide appropriate solutions, the queuing system of current situation for both shifts was modeled and simulated using the software ARENA 12 and 4 scenarios were explored. Results: Results showed that the queue characteristics of the studied pharmacy during the situation analysis were very undesirable in both morning and evening shifts. The average numbers of patients in the pharmacy were 19.21 and 14.66 in the morning and evening, respectively. The average times spent in the system by clients were 39 minutes in the morning and 35 minutes in the evening. The system utilization in the morning and evening were, respectively, 25% and 21%. The simulation results showed that reducing the staff in the morning from 2 to 1 in the receiving prescriptions stage didn't change the queue performance indicators. Increasing one staff in filling prescription drugs could cause a decrease of 10 persons in the average queue length and 18 minutes and 14 seconds in the average waiting time. On the other hand, simulation

  4. Harmonious personnel scheduling

    Fijn van Draat, Laurens; Post, Gerhard F.; Veltman, Bart; Winkelhuijzen, Wessel

    2006-01-01

    The area of personnel scheduling is very broad. Here we focus on the ‘shift assignment problem’. Our aim is to discuss how ORTEC HARMONY handles this planning problem. In particular we go into the structure of the optimization engine in ORTEC HARMONY, which uses techniques from genetic algorithms,

  5. An corrigendum on the paper : Solving the job-shop scheduling problem optimally by dynamic programming (Computers and Operations Research 39(12) (2968–2977) (S0305054812000500) (10.1016/j.cor.2012.02.024))

    van Hoorn, Jelke J.; Nogueira, Agustín; Ojea, Ignacio; Gromicho Dos Santos, Joaquim Antonio

    2017-01-01

    In [1] an algorithm is proposed for solving the job-shop scheduling problem optimally using a dynamic programming strategy. This is, according to our knowledge, the first exact algorithm for the Job Shop problem which is not based on integer linear programming and branch and bound. Despite the

  6. Optimal Decision Model for Sustainable Hospital Building Renovation—A Case Study of a Vacant School Building Converting into a Community Public Hospital

    Juan, Yi-Kai; Cheng, Yu-Ching; Perng, Yeng-Horng; Castro-Lacouture, Daniel

    2016-01-01

    Much attention has been paid to hospitals environments since modern pandemics have emerged. The building sector is considered to be the largest world energy consumer, so many global organizations are attempting to create a sustainable environment in building construction by reducing energy consumption. Therefore, maintaining high standards of hygiene while reducing energy consumption has become a major task for hospitals. This study develops a decision model based on genetic algorithms and A* graph search algorithms to evaluate existing hospital environmental conditions and to recommend an optimal scheme of sustainable renovation strategies, considering trade-offs among minimal renovation cost, maximum quality improvement, and low environmental impact. Reusing vacant buildings is a global and sustainable trend. In Taiwan, for example, more and more school space will be unoccupied due to a rapidly declining birth rate. Integrating medical care with local community elder-care efforts becomes important because of the aging population. This research introduces a model that converts a simulated vacant school building into a community public hospital renovation project in order to validate the solutions made by hospital managers and suggested by the system. The result reveals that the system performs well and its solutions are more successful than the actions undertaken by decision-makers. This system can improve traditional hospital building condition assessment while making it more effective and efficient. PMID:27347986

  7. Optimal Decision Model for Sustainable Hospital Building Renovation-A Case Study of a Vacant School Building Converting into a Community Public Hospital.

    Juan, Yi-Kai; Cheng, Yu-Ching; Perng, Yeng-Horng; Castro-Lacouture, Daniel

    2016-06-24

    Much attention has been paid to hospitals environments since modern pandemics have emerged. The building sector is considered to be the largest world energy consumer, so many global organizations are attempting to create a sustainable environment in building construction by reducing energy consumption. Therefore, maintaining high standards of hygiene while reducing energy consumption has become a major task for hospitals. This study develops a decision model based on genetic algorithms and A* graph search algorithms to evaluate existing hospital environmental conditions and to recommend an optimal scheme of sustainable renovation strategies, considering trade-offs among minimal renovation cost, maximum quality improvement, and low environmental impact. Reusing vacant buildings is a global and sustainable trend. In Taiwan, for example, more and more school space will be unoccupied due to a rapidly declining birth rate. Integrating medical care with local community elder-care efforts becomes important because of the aging population. This research introduces a model that converts a simulated vacant school building into a community public hospital renovation project in order to validate the solutions made by hospital managers and suggested by the system. The result reveals that the system performs well and its solutions are more successful than the actions undertaken by decision-makers. This system can improve traditional hospital building condition assessment while making it more effective and efficient.

  8. Development of Watch Schedule Using Rules Approach

    Jurkevicius, Darius; Vasilecas, Olegas

    The software for schedule creation and optimization solves a difficult, important and practical problem. The proposed solution is an online employee portal where administrator users can create and manage watch schedules and employee requests. Each employee can login with his/her own account and see his/her assignments, manage requests, etc. Employees set as administrators can perform the employee scheduling online, manage requests, etc. This scheduling software allows users not only to see the initial and optimized watch schedule in a simple and understandable form, but also to create special rules and criteria and input their business. The system using rules automatically will generate watch schedule.

  9. Construction schedules slack time minimizing

    Krzemiński, Michał

    2017-07-01

    The article presents two copyright models for minimizing downtime working brigades. Models have been developed for construction schedules performed using the method of work uniform. Application of flow shop models is possible and useful for the implementation of large objects, which can be divided into plots. The article also presents a condition describing gives which model should be used, as well as a brief example of optimization schedule. The optimization results confirm the legitimacy of the work on the newly-developed models.

  10. Hospitals

    Mullins, Michael

    2013-01-01

    The challenge could be briefly seen in these terms: hospitals as places for treatment where there’s a technology focus and hospitals for healing where there’s a human focus. In the 60s - 70s wave of new hospital building, an emphasis on technology can be seen. It’s time to move from the technology...... focus. It is not enough to consider only the factors of function within architecture, hygiene, economy and logistics. We also need to look at aspects of aesthetics, bringing nature into the building, art, color, acoustics, volume and space as we perceive them. Contemporary methods and advances...... placed, accessible, provided with plenty of greenery, and maximize sensory impressions, providing sounds, smells, sight and the possibility to be touched. This is a very well documented area I can say. Hygiene, in terms of architecture can give attention to hand wash facilities and their positioning...

  11. SPANR planning and scheduling

    Freund, Richard F.; Braun, Tracy D.; Kussow, Matthew; Godfrey, Michael; Koyama, Terry

    2001-07-01

    SPANR (Schedule, Plan, Assess Networked Resources) is (i) a pre-run, off-line planning and (ii) a runtime, just-in-time scheduling mechanism. It is designed to support primarily commercial applications in that it optimizes throughput rather than individual jobs (unless they have highest priority). Thus it is a tool for a commercial production manager to maximize total work. First the SPANR Planner is presented showing the ability to do predictive 'what-if' planning. It can answer such questions as, (i) what is the overall effect of acquiring new hardware or (ii) what would be the effect of a different scheduler. The ability of the SPANR Planner to formulate in advance tree-trimming strategies is useful in several commercial applications, such as electronic design or pharmaceutical simulations. The SPANR Planner is demonstrated using a variety of benchmarks. The SPANR Runtime Scheduler (RS) is briefly presented. The SPANR RS can provide benefit for several commercial applications, such as airframe design and financial applications. Finally a design is shown whereby SPANR can provide scheduling advice to most resource management systems.

  12. Glycaemic control in diabetic patients during hospital admission is not optimal

    Hellquist, Fanny; Budde, Line; Feldt-Rasmussen, Bo Friis

    2011-01-01

    of admission was collected, including: bedside p-glucose readings, scheduled and supplemental insulin treatment. RESULTS: In total, 111 observation days were included from 37 diabetic patients (27 medical and ten surgical). P-glucose was measured on average four and 2.5 times daily at the medical...... was not given despite being indicated in 37% of the elevated glucose episodes. Increments in scheduled insulin dose were rarely observed despite being indicated. CONCLUSION: Despite acceptable median p-glucose levels, hyperglycaemia was frequent. The number of glucose readings was low and clinical inertia...

  13. The anti-hepatitis drug use effect and inventory management optimization from the perspective of hospital drug supply chain.

    Liu, Zhanyu

    2017-09-01

    By analyzing the current hospital anti hepatitis drug use, dosage, indications and drug resistance, this article studied the drug inventory management and cost optimization. The author used drug utilization evaluation method, analyzed the amount and kind distribution of anti hepatitis drugs and made dynamic monitoring of inventory. At the same time, the author puts forward an effective scheme of drug classification management, uses the ABC classification method to classify the drugs according to the average daily dose of drugs, and implements the automatic replenishment plan. The design of pharmaceutical services supply chain includes drug procurement platform, warehouse management system and connect to the hospital system through data exchange. Through the statistical analysis of drug inventory, we put forward the countermeasures of drug logistics optimization. The results showed that drug replenishment plan can effectively improve drugs inventory efficiency.

  14. A pre-analysis for the optimal operational scheduling of a pipeline network; Uma pre-analise do problema de otimizacao da programacao das operacoes de uma malha dutoviaria

    Czaikowski, Daniel I.; Brondani, William M.; Arantes, Lucas G.; Boschetto, Suelen N.; Lueders, Ricardo; Magatao, Leandro; Stebel, Sergio L. [Universidade Tecnologica Federal do Parana (UTFPR), Curitiba, PR (Brazil); Ribas, Paulo C. [PETROBRAS S.A., Rio de Janeiro, RJ (Brazil). Centro de Pesquisas (CENPES)

    2008-07-01

    This work suggests a Pre-analysis in the input parameters of an optimization system (Bonacin et al., 2007; Boschetto et al., 2008). The proposed method is based on programming techniques that use lists of objects threaded, where objects are elements belonging to the same class, according to the concept of the object-oriented programming. The Preanalysis makes a previous evaluation of a batch sequencing, getting information to be entered into an optimization block. The continuous time Mixed Integer Linear Programming (MILP) model gets such information and determines the scheduling. The models are applied on a pipeline network that connects different areas including refineries, terminals, and final clients. Many oil derivatives (e.g. gasoline, liquefied petroleum gas, naphtha) can be sent or received in this network. The Pre-analysis objective is to reduce the computational time of an MILP model, and the proposed approach can aid the decision-making process to obtain a more detailed scheduling. (author)

  15. Nurse Scheduling by Cooperative GA with Effective Mutation Operator

    Ohki, Makoto

    In this paper, we propose an effective mutation operators for Cooperative Genetic Algorithm (CGA) to be applied to a practical Nurse Scheduling Problem (NSP). The nurse scheduling is a very difficult task, because NSP is a complex combinatorial optimizing problem for which many requirements must be considered. In real hospitals, the schedule changes frequently. The changes of the shift schedule yields various problems, for example, a fall in the nursing level. We describe a technique of the reoptimization of the nurse schedule in response to a change. The conventional CGA is superior in ability for local search by means of its crossover operator, but often stagnates at the unfavorable situation because it is inferior to ability for global search. When the optimization stagnates for long generation cycle, a searching point, population in this case, would be caught in a wide local minimum area. To escape such local minimum area, small change in a population should be required. Based on such consideration, we propose a mutation operator activated depending on the optimization speed. When the optimization stagnates, in other words, when the optimization speed decreases, the mutation yields small changes in the population. Then the population is able to escape from a local minimum area by means of the mutation. However, this mutation operator requires two well-defined parameters. This means that user have to consider the value of these parameters carefully. To solve this problem, we propose a periodic mutation operator which has only one parameter to define itself. This simplified mutation operator is effective over a wide range of the parameter value.

  16. Application of genetic algorithms to the maintenance scheduling optimization in a nuclear system basing on reliability; Aplicacao de algoritmos geneticos na otimizacao da politica de manutencoes preventivas de um sistema nuclear centrada em confiabilidade

    Lapa, Celso M. Franklin; Pereira, Claudio M.N.A.; Mol, Antonio C. de Abreu [Instituto de Engenharia Nuclear (IEN), Rio de Janeiro, RJ (Brazil)

    1999-07-01

    This paper presents a solution based on genetic algorithm and probabilistic safety analysis that can be applied in the optimization of the preventive maintenance politic of nuclear power plant safety systems. The goal of this approach is to improve the average availability of the system through the optimization of the preventive maintenance scheduling politic. The auxiliary feed water system of a two loops pressurized water reactor is used as a sample case, in order to demonstrate the effectiveness of the proposed method. The results, when compared to those obtained by some standard maintenance politics, reveal quantitative gains and operational safety levels. (author)

  17. Optimized biofilm-based systems for removal of pharmaceuticals from hospital waste water

    Andersen, Henrik R; Chhetri, Ravi; Hansen, Kamilla

    Discharge of hospital wastewater is of increasing concern, as hospitals are identified as chemical pollution source due to pharmaceutical content. This project seeks to develop the most efficient and economically feasible technology to remove pharmaceuticals from wastewater, regardless of the poi...

  18. Optimism During Hospitalization for First Acute Myocardial Infarction and Long-Term Mortality Risk: A Prospective Cohort Study.

    Weiss-Faratci, Netanela; Lurie, Ido; Benyamini, Yael; Cohen, Gali; Goldbourt, Uri; Gerber, Yariv

    2017-01-01

    To assess the association between dispositional optimism, defined as generalized positive expectations about the future, and long-term mortality in young survivors of myocardial infarction (MI). A subcohort of 664 patients 65 years and younger, drawn from the longitudinal Israel Study of First Acute Myocardial Infarction, completed an adapted Life Orientation Test (LOT) questionnaire during their index hospitalization between February 15, 1992, and February 15, 1993. Additional sociodemographic, clinical, and psychosocial variables were assessed at baseline; mortality follow-up lasted through December 31, 2015. Cox proportional hazards regression models were fit to assess the hazard ratios for mortality associated with LOT-derived optimism. The mean age of the participants was 52.4±8.6 years; 98 (15%) were women. The median follow-up period was 22.4 years (25th-75th percentiles, 16.1-22.8 years), during which 284 patients (43%) had died. The mean LOT score was 16.5±4.1. Incidence density rates for mortality in increasing optimism tertiles were 25.4, 25.8, and 16.0 per 1000 person-years, respectively (Poptimism during hospitalization for MI were associated with reduced mortality over a 2-decade follow-up period. Optimism training and positive psychology should be examined as part of psychosocial interventions and rehabilitation after MI. Copyright © 2016 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

  19. Forecasting COPD hospitalization in the clinic: optimizing the chronic respiratory questionnaire.

    Abascal-Bolado, Beatriz; Novotny, Paul J; Sloan, Jeff A; Karpman, Craig; Dulohery, Megan M; Benzo, Roberto P

    2015-01-01

    Forecasting hospitalization in patients with COPD has gained significant interest in the field of COPD care. There is a need to find simple tools that can help clinicians to stratify the risk of hospitalization in these patients at the time of care. The perception of quality of life has been reported to be independently associated with hospitalizations, but questionnaires are impractical for daily clinical use. Individual questions from valid questionnaires can have robust predictive abilities, as has been suggested in previous reports, as a way to use patient-reported outcomes to forecast important events like hospitalizations in COPD. Our primary aim was to assess the predictive value of individual questions from the Chronic Respiratory Questionnaire Self-Assessment Survey (CRQ-SAS) on the risk of hospitalization and to develop a clinically relevant and simple algorithm that clinicians can use in routine practice to identify patients with an increased risk of hospitalization. A total of 493 patients with COPD prospectively recruited from an outpatient pulmonary clinic completed the CRQ-SAS, demographic information, pulmonary function testing, and clinical outcomes. The cohort had a mean age of 70 years, was 54% male, with forced expiratory volume in 1 second percentage predicted 42.8±16.7, and modified Medical Research Council dyspnea scale score of 2±1.13. Our analysis validated the original CRQ-SAS domains. Importantly, recursive partitioning analysis identified three CRQ-SAS items regarding fear or panic of breathlessness, dyspnea with basic activities of daily living, and depressive symptoms that were highly predictive of hospitalization. We propose a robust (area under the curve =0.70) but short and easy algorithm for daily clinical care to forecast hospitalizations in patients with COPD. We identified three themes - fear of breathlessness, dyspnea with basic activities of daily living, and depressive symptoms - as important patient-reported outcomes to

  20. Scheduling the scheduling task : a time management perspective on scheduling

    Larco Martinelli, J.A.; Wiers, V.C.S.; Fransoo, J.C.

    2013-01-01

    Time is the most critical resource at the disposal of schedulers. Hence, an adequate management of time from the schedulers may impact positively on the scheduler’s productivity and responsiveness to uncertain scheduling environments. This paper presents a field study of how schedulers make use of

  1. REDUCING AND OPTIMIZING THE CYCLE TIME OF PATIENTS DISCHARGE PROCESS IN A HOSPITAL USING SIX SIGMA DMAIC APPROACH

    S. Arun Vijay

    2014-06-01

    Full Text Available A lengthy and in-efficient process of discharging in-patients from the Hospital is an essential component that needs to be addressed in order to improve the quality of Health care facility. Even though, several quality methodologies are adopted to improve such services in Hospitals, the implementation of Six Sigma DMAIC methodology to improve the Hospital discharge process is much limited in the Literature. Thus, the objective of this research is to reduce the cycle time of the Patients discharge process using Six Sigma DMAIC Model in a multidisciplinary hospital setting in India. This study had been conducted through the five phases of the Six Sigma DMAIC Model using different Quality tools and techniques. This study suggested various improvement strategies to reduce the cycle time of Patients discharge process and after its implementation; there is a 61% reduction in the cycle time of the Patients discharge process. Also, a control pl an check sheet has been developed to sustain the Improvements obtained. This Study would be an eye opener for the Health Care Managers to reduce and optimize the cycle time of Patients discharge process in Hospitals using Six Sigma DMAIC Model.

  2. Scheduling nursing personnel on a microcomputer.

    Liao, C J; Kao, C Y

    1997-01-01

    Suggests that with the shortage of nursing personnel, hospital administrators have to pay more attention to the needs of nurses to retain and recruit them. Also asserts that improving nurses' schedules is one of the most economic ways for the hospital administration to create a better working environment for nurses. Develops an algorithm for scheduling nursing personnel. Contrary to the current hospital approach, which schedules nurses on a person-by-person basis, the proposed algorithm constructs schedules on a day-by-day basis. The algorithm has inherent flexibility in handling a variety of possible constraints and goals, similar to other non-cyclical approaches. But, unlike most other non-cyclical approaches, it can also generate a quality schedule in a short time on a microcomputer. The algorithm was coded in C language and run on a microcomputer. The developed software is currently implemented at a leading hospital in Taiwan. The response to the initial implementation is quite promising.

  3. Modeling an integrated hospital management planning problem using integer optimization approach

    Sitepu, Suryati; Mawengkang, Herman; Irvan

    2017-09-01

    Hospital is a very important institution to provide health care for people. It is not surprising that nowadays the people’s demands for hospital is increasing. However, due to the rising cost of healthcare services, hospitals need to consider efficiencies in order to overcome these two problems. This paper deals with an integrated strategy of staff capacity management and bed allocation planning to tackle these problems. Mathematically, the strategy can be modeled as an integer linear programming problem. We solve the model using a direct neighborhood search approach, based on the notion of superbasic variables.

  4. Nontraditional work schedules for pharmacists.

    Mahaney, Lynnae; Sanborn, Michael; Alexander, Emily

    2008-11-15

    Nontraditional work schedules for pharmacists at three institutions are described. The demand for pharmacists and health care in general continues to increase, yet significant material changes are occurring in the pharmacy work force. These changing demographics, coupled with historical vacancy rates and turnover trends for pharmacy staff, require an increased emphasis on workplace changes that can improve staff recruitment and retention. At William S. Middleton Memorial Veterans Affairs Hospital in Madison, Wisconsin, creative pharmacist work schedules and roles are now mainstays to the recruitment and retention of staff. The major challenge that such scheduling presents is the 8 hours needed to prepare a six-week schedule. Baylor Medical Center at Grapevine in Dallas, Texas, has a total of 45 pharmacy employees, and slightly less than half of the 24.5 full-time-equivalent staff work full-time, with most preferring to work one, two, or three days per week. As long as the coverage needs of the facility are met, Envision Telepharmacy in Alpine, Texas, allows almost any scheduling arrangement preferred by individual pharmacists or the pharmacist group covering the facility. Staffing involves a great variety of shift lengths and intervals, with shifts ranging from 2 to 10 hours. Pharmacy leaders must be increasingly aware of opportunities to provide staff with unique scheduling and operational enhancements that can provide for a better work-life balance. Compressed workweeks, job-sharing, and team scheduling were the most common types of alternative work schedules implemented at three different institutions.

  5. A randomized phase 3 study on the optimization of the combination of bevacizumab with FOLFOX/OXXEL in the treatment of patients with metastatic colorectal cancer-OBELICS (Optimization of BEvacizumab scheduLIng within Chemotherapy Scheme).

    Avallone, Antonio; Piccirillo, Maria Carmela; Aloj, Luigi; Nasti, Guglielmo; Delrio, Paolo; Izzo, Francesco; Di Gennaro, Elena; Tatangelo, Fabiana; Granata, Vincenza; Cavalcanti, Ernesta; Maiolino, Piera; Bianco, Francesco; Aprea, Pasquale; De Bellis, Mario; Pecori, Biagio; Rosati, Gerardo; Carlomagno, Chiara; Bertolini, Alessandro; Gallo, Ciro; Romano, Carmela; Leone, Alessandra; Caracò, Corradina; de Lutio di Castelguidone, Elisabetta; Daniele, Gennaro; Catalano, Orlando; Botti, Gerardo; Petrillo, Antonella; Romano, Giovanni M; Iaffaioli, Vincenzo R; Lastoria, Secondo; Perrone, Francesco; Budillon, Alfredo

    2016-02-08

    study could optimize bevacizumab scheduling in combination with chemotherapy in mCRC patients. Moreover, correlative studies could improve the knowledge of the mechanisms by which bevacizumab enhance chemotherapy effect and could identify early predictors of response. EudraCT Number: 2011-004997-27 TRIAL REGISTRATION: ClinicalTrials.gove number, NCT01718873.

  6. Optimal stochastic coordinated scheduling of proton exchange membrane fuel cell-combined heat and power, wind and photovoltaic units in micro grids considering hydrogen storage

    Bornapour, Mosayeb; Hooshmand, Rahmat-Allah; Khodabakhshian, Amin; Parastegari, Moein

    2017-01-01

    Highlights: •Stochastic model is proposed for coordinated scheduling of renewable energy sources. •The effect of combined heat and power is considered. •Hydrogen storage is considered for fuel cells. •Maximizing profits of micro grid is considered as objective function. •Considering the uncertainties of problem lead to profit increasing. -- Abstract: Nowadays, renewable energy sources and combined heat and power units are extremely used in micro grids, so it is necessary to schedule these units to improve the performance of the system. In this regard, a stochastic model is proposed in this paper to schedule proton exchange membrane fuel cell-combined heat and power, wind turbines, and photovoltaic units coordinately in a micro grid while considering hydrogen storage. Hydrogen storage strategy is considered for the operation of proton exchange membrane fuel cell-combined heat and power units. To consider stochastic generation of renewable energy source units in this paper, a scenario-based method is used. In this method, the uncertainties of electrical market price, the wind speed, and solar irradiance are considered. This stochastic scheduling problem is a mixed integer- nonlinear programming which considers the proposed objective function and variables of coordinated scheduling of PEMFC-CHP, wind turbines and photovoltaic units. It also considers hydrogen storage strategy and converts it to a mixed integer nonlinear problem. In this study a modified firefly algorithm is used to solve the problem. This method is examined on modified 33-bus distributed network as a MG for its performance.

  7. A master surgical scheduling approach for cyclic scheduling in operating room departments

    van Oostrum, Jeroen M.; van Houdenhoven, M.; Hurink, Johann L.; Hans, Elias W.; Wullink, Gerhard; Kazemier, G.

    This paper addresses the problem of operating room (OR) scheduling at the tactical level of hospital planning and control. Hospitals repetitively construct operating room schedules, which is a time-consuming, tedious, and complex task. The stochasticity of the durations of surgical procedures

  8. A model for generating master surgical schedules to allow cyclic scheduling in operating room departments

    van Oostrum, J.M.; van Houdenhoven, M.; Hurink, Johann L.; Hans, Elias W.; Wullink, Gerhard; Kazemier, G.

    2005-01-01

    This paper addresses the problem of operating room scheduling at the tactical level of hospital planning and control. Hospitals repetitively construct operating room schedules, which is a time consuming tedious and complex task. The stochasticity of the durations of surgical procedures complicates

  9. Forecasting COPD hospitalization in the clinic: optimizing the chronic respiratory questionnaire

    Abascal-Bolado B

    2015-10-01

    Full Text Available Beatriz Abascal-Bolado,1 Paul J Novotny,2 Jeff A Sloan,2 Craig Karpman,3 Megan M Dulohery,3 Roberto P Benzo31Pulmonary Division, Instituto de Investigación Sanitaria Valdecilla (IDIVAL, Santander, Spain; 2Department of Cancer Center Statistics, Health Science Research, 3Mindful Breathing Laboratory, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USAPurpose: Forecasting hospitalization in patients with COPD has gained significant interest in the field of COPD care. There is a need to find simple tools that can help clinicians to stratify the risk of hospitalization in these patients at the time of care. The perception of quality of life has been reported to be independently associated with hospitalizations, but questionnaires are impractical for daily clinical use. Individual questions from valid questionnaires can have robust predictive abilities, as has been suggested in previous reports, as a way to use patient-reported outcomes to forecast important events like hospitalizations in COPD. Our primary aim was to assess the predictive value of individual questions from the Chronic Respiratory Questionnaire Self-Assessment Survey (CRQ-SAS on the risk of hospitalization and to develop a clinically relevant and simple algorithm that clinicians can use in routine practice to identify patients with an increased risk of hospitalization.Patients and methods: A total of 493 patients with COPD prospectively recruited from an outpatient pulmonary clinic completed the CRQ-SAS, demographic information, pulmonary function testing, and clinical outcomes. The cohort had a mean age of 70 years, was 54% male, with forced expiratory volume in 1 second percentage predicted 42.8±16.7, and modified Medical Research Council dyspnea scale score of 2±1.13.Results: Our analysis validated the original CRQ-SAS domains. Importantly, recursive partitioning analysis identified three CRQ-SAS items regarding fear or panic of breathlessness

  10. Optimization of maintenance scheduling with genetic algorithms regarding the storage behavior during the availability prognosis of power plants; Optimierung der Instandhaltungsplanung mit genetischen Algorithmen unter Beruecksichtigung des Speicherverhaltens bei der Verfuegbarkeitsprognose von Kraftwerksanlagen

    Warnecke, Martin

    2008-12-19

    In the age of the liberalized energy market the power plant raisers and operators have to consider several factors when choosing the fuel type and dimensioning their power plants, e.g. emissions, erection cost and last but not least operating cost. Simulations assuming different scenarios are required. The rivaling aspects of erection cost, partially dependant availability, maintenance philosophy and operating cost are motivating the optimization of maintenance scheduling and the availability prognosis which are the topic of this thesis. The focus of this thesis is on the scheduling of the time based maintenance strategy. This strategy defines the time spans between the repeating inspections of each component. This is based on the experience of operators and manufacturers. The mathematic problem itself is especially challenging because of strong interdependencies between the single components due to synergy effects. Each component has its own theoretically optimal lifetime and maintenance period. Yet as part of a compound it might be more cost efficient in the long run to maintain some components together shifting some of them forward or backward. The thereby caused interdependencies constitute a non-linear, mixed-whole-numbered calculation of the cost approximation. For the optimization of this maintenance scheduling a new approach was developed. It was realized that the problem couldn't be solved satisfyingly with classic optimization algorithms. Afterwards a solution based on ''genetic algorithms'' was developed. In the meantime the methods for the availability prognosis of complex power plant facilities were enhanced. Especially a new component with storage behavior (with optional losses) was added to the prognosis tool. This storage model integrates the behavior of a storage into the computing time reduced Monte-Carlo-Method. (orig.)

  11. [Role of clinical pharmacist in the therapeutical optimization in geriatric outpatient hospital].

    Jean-Bart, E; Faure, R; Omrani, S; Guilli, T; Roubaud, C; Krolak-Salmon, P; Mouchoux, C

    2014-05-01

    Cares in outpatient hospital for elderly patients is a period of interest for multidisciplinary reassessment and pharmaceutical care of the prescription. The objective is to present the implementation of the pharmaceutical care activity at the outpatient hospital. Between August and October 2011, elderly patients hospitalized in the outpatient hospital for a brief appraisal had a pharmaceutical care. The clinician introduced pharmaceutical reviews in the synthesis letter for general practitioner. An analysis of the activity was carried out over 3 months. A pharmaceutical care had been realized for 67 patients, mean age of 81.7 years. Among medical related problems identified, 39.6% were for potentially unnecessary medication. A stop was proposed for 44% of pharmaceutical interventions. A total of 91 pharmaceutical interventions and 13 recommendations were made and 34% of patients had potentially inappropriate medication. According to the objective to reduce the therapeutics contributing to the iatrogenesis, this approach allowed us to undertake a multidisciplinary collaboration oriented toward the relay between hospital and city cares. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

  12. A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm

    Kong, Fanrong; Jiang, Jianhui; Ding, Zhigang

    2017-01-01

    To alleviate the emission of greenhouse gas and the dependence on fossil fuel, Plug-in Hybrid Electrical Vehicles (PHEVs) have gained an increasing popularity in current decades. Due to the fluctuating electricity prices in the power market, a charging schedule is very influential to driving cost...

  13. Robust and Flexible Scheduling with Evolutionary Computation

    Jensen, Mikkel T.

    Over the last ten years, there have been numerous applications of evolutionary algorithms to a variety of scheduling problems. Like most other research on heuristic scheduling, the primary aim of the research has been on deterministic formulations of the problems. This is in contrast to real world...... scheduling problems which are usually not deterministic. Usually at the time the schedule is made some information about the problem and processing environment is available, but this information is uncertain and likely to change during schedule execution. Changes frequently encountered in scheduling...... environments include machine breakdowns, uncertain processing times, workers getting sick, materials being delayed and the appearance of new jobs. These possible environmental changes mean that a schedule which was optimal for the information available at the time of scheduling can end up being highly...

  14. Endogenous scheduling preferences and congestion

    Fosgerau, Mogens; Small, Kenneth

    2010-01-01

    and leisure, but agglomeration economies at home and at work lead to scheduling preferences forming endogenously. Using bottleneck congestion technology, we obtain an equilibrium queuing pattern consistent with a general version of the Vickrey bottleneck model. However, the policy implications are different....... Compared to the predictions of an analyst observing untolled equilibrium and taking scheduling preferences as exogenous, we find that both the optimal capacity and the marginal external cost of congestion have changed. The benefits of tolling are greater, and the optimal time varying toll is different....

  15. Outcomes In Two Massachusetts Hospital Systems Give Reason For Optimism About Communication-And-Resolution Programs.

    Mello, Michelle M; Kachalia, Allen; Roche, Stephanie; Niel, Melinda Van; Buchsbaum, Lisa; Dodson, Suzanne; Folcarelli, Patricia; Benjamin, Evan M; Sands, Kenneth E

    2017-10-01

    Through communication-and-resolution programs, hospitals and liability insurers communicate with patients when adverse events occur; investigate and explain what happened; and, where appropriate, apologize and proactively offer compensation. Using data recorded by program staff members and from surveys of involved clinicians, we examined case outcomes of a program used by two academic medical centers and two of their community hospitals in Massachusetts in the period 2013-15. The hospitals demonstrated good adherence to the program protocol. Ninety-one percent of the program events did not meet compensation eligibility criteria, and those events that did were not costly to resolve (the median payment was $75,000). Only 5 percent of events led to malpractice claims or lawsuits. Clinicians were supportive of the program but desired better communication about it from staff members. Our findings suggest that communication-and-resolution programs will not lead to higher liability costs when hospitals adhere to their commitment to offer compensation proactively. Project HOPE—The People-to-People Health Foundation, Inc.

  16. The management of diabetic foot ulcers in Danish hospitals is not optimal

    Kirketerp-Møller, Klaus; Svendsen, Ole Lander; Jansen, Rasmus Bo

    2015-01-01

    in the treatment. The objective of this study was to describe the treatment practices at the time the guidelines were launched. METHODS: A questionnaire-based survey was conducted among Danish hospital departments working with diabetic feet. All public departments were invited by e-mail to participate...

  17. Practical quantum appointment scheduling

    Touchette, Dave; Lovitz, Benjamin; Lütkenhaus, Norbert

    2018-04-01

    We propose a protocol based on coherent states and linear optics operations for solving the appointment-scheduling problem. Our main protocol leaks strictly less information about each party's input than the optimal classical protocol, even when considering experimental errors. Along with the ability to generate constant-amplitude coherent states over two modes, this protocol requires the ability to transfer these modes back-and-forth between the two parties multiple times with very low losses. The implementation requirements are thus still challenging. Along the way, we develop tools to study quantum information cost of interactive protocols in the finite regime.

  18. Net returns, fiscal risks, and the optimal patient mix for a profit-maximizing hospital.

    Ozatalay, S; Broyles, R

    1987-10-01

    As is well recognized, the provisions of PL98-21 not only transfer financial risks from the Medicare program to the hospital but also induce institutions to adjust the diagnostic mix of Medicare beneficiaries so as to maximize net income or minimize the net loss. This paper employs variation in the set of net returns as the sole measure of financial risk and develops a model that identifies the mix of beneficiaries that maximizes net income, subject to a given level of risk. The results indicate that the provisions of PL98-21 induce the institution to deny admission to elderly patients presenting conditions for which the net return is relatively low and the variance in the cost per case is large. Further, the paper suggests that the treatment of beneficiaries at a level commensurate with previous periods or the preferences of physicians may jeopardize the viability and solvency of Medicare-dependent hospitals.

  19. Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

    Soares, João; Valle, Zita; Morais, Hugo

    2013-01-01

    This paper presents a decision support Tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy ressource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application...... of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network...... constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance...

  20. Economic and psychological burden of scheduled surgery ...

    Background: Cancellation of scheduled surgery creates a financial burden for hospitals, caregivers and ..... costs and disregard some of the aspects mentioned in the ..... cancellation of elective surgical procedures in a Spanish general.

  1. Multidrug resistance circumvention by a new triazinoaminopiperidine derivative S9788 in vitro: definition of the optimal schedule and comparison with verapamil.

    Julia, A. M.; Roché, H.; Berlion, M.; Lucas, C.; Milano, G.; Robert, J.; Bizzari, J. P.; Canal, P.

    1994-01-01

    The current work was undertaken to investigate the importance of exposure sequence and duration in achieving the maximum reversal action of S9788 on doxorubicin (DOX) cytotoxicity against cells that exhibit the (MDR) multidrug resistance phenotype: the MCF7/DOX cell line. Accumulation and release of DOX were examined in this cell line. The reversal effect was compared with that obtained with verapamil. S9788 activity was schedule dependent: when comparing incubation with S9788 before or after treatment with DOX, the best reversal factor was obtained in the case of a post-treatment incubation (65.6 +/- 7.7 vs 20.8 +/- 7.0). S9788 was a more potent modulating agent than verapamil, whatever the schedule of exposure of the cells to the reversal agent. The reversal of resistance after short-term DOX exposures was caused not only by prolonged cellular accumulation of DOX, but also by its prolonged retention after transfer of cells to DOX-free medium. A relationship was noted between cellular exposure to DOX and the cytotoxic effect, and so the reversal of resistance induced by S9788 appears to be directly linked to the level of cell exposure to DOX. This work provided a rationale for improving the schedule of administration of S9788 in clinical trials. PMID:8180016

  2. Solving no-wait two-stage flexible flow shop scheduling problem with unrelated parallel machines and rework time by the adjusted discrete Multi Objective Invasive Weed Optimization and fuzzy dominance approach

    Jafarzadeh, Hassan; Moradinasab, Nazanin; Gerami, Ali

    2017-07-01

    Adjusted discrete Multi-Objective Invasive Weed Optimization (DMOIWO) algorithm, which uses fuzzy dominant approach for ordering, has been proposed to solve No-wait two-stage flexible flow shop scheduling problem. Design/methodology/approach: No-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times and probable rework in both stations, different ready times for all jobs and rework times for both stations as well as unrelated parallel machines with regards to the simultaneous minimization of maximum job completion time and average latency functions have been investigated in a multi-objective manner. In this study, the parameter setting has been carried out using Taguchi Method based on the quality indicator for beater performance of the algorithm. Findings: The results of this algorithm have been compared with those of conventional, multi-objective algorithms to show the better performance of the proposed algorithm. The results clearly indicated the greater performance of the proposed algorithm. Originality/value: This study provides an efficient method for solving multi objective no-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times, probable rework in both stations, different ready times for all jobs, rework times for both stations and unrelated parallel machines which are the real constraints.

  3. Solving no-wait two-stage flexible flow shop scheduling problem with unrelated parallel machines and rework time by the adjusted discrete Multi Objective Invasive Weed Optimization and fuzzy dominance approach

    Jafarzadeh, Hassan; Moradinasab, Nazanin; Gerami, Ali

    2017-01-01

    Adjusted discrete Multi-Objective Invasive Weed Optimization (DMOIWO) algorithm, which uses fuzzy dominant approach for ordering, has been proposed to solve No-wait two-stage flexible flow shop scheduling problem. Design/methodology/approach: No-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times and probable rework in both stations, different ready times for all jobs and rework times for both stations as well as unrelated parallel machines with regards to the simultaneous minimization of maximum job completion time and average latency functions have been investigated in a multi-objective manner. In this study, the parameter setting has been carried out using Taguchi Method based on the quality indicator for beater performance of the algorithm. Findings: The results of this algorithm have been compared with those of conventional, multi-objective algorithms to show the better performance of the proposed algorithm. The results clearly indicated the greater performance of the proposed algorithm. Originality/value: This study provides an efficient method for solving multi objective no-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times, probable rework in both stations, different ready times for all jobs, rework times for both stations and unrelated parallel machines which are the real constraints.

  4. Evaluation of different initial solution algorithms to be used in the heuristics optimization to solve the energy resource scheduling in smart grids

    Sousa, Tiago; Morais, Hugo; Castro, Rui

    2016-01-01

    vehicles. The proposed algorithms proved to present results very close to the optimal with a small difference between 0.1%. A deterministic technique is used as comparison and it took around 26 h to obtain the optimal one. On the other hand, the simulated annealing was able of obtaining results around 1...

  5. HEMŞİRE ÇİZELGELEMESİNDE ESNEK VARDİYA PLANLAMASI VE HASTANE UYGULAMASI (FLEXIBLE SHIFT PLANNING IN NURSE SCHEDULING AND AN APPLICATION OF THE HOSPITAL

    Yücel ÖZTÜRKOĞLU

    2014-01-01

    Full Text Available ÖZ: Müşteri tatmininin her geçen gün daha da zorlaştığı hizmet sektörlerinde, işverenler sunulan hizmetin kalitesini arttırmak ve hizmetin devamlılığını sağlamak için yeni arayışlar içine girmekteler. İşverenler, müşteriden önce hizmeti sunan kişilerin memnuniyetini sağlayarak rekabet gücünü arttırma yollarına başvurmaktadırlar. Bu yollardan biri de çalışma sürelerinin esnekleştirilmesidir. Bu çalışmada, hizmet sektörleri arasında önemli bir yere sahip olan hastanelerde ki hemşire çizelgeleme problemi için tam sayılı matematiksel bir model oluşturulmuştur. Oluşturulan modelde, klasik çizelgeleme modellerinin aksine hemşirelerin işe başlama saatlerine esneklik getirilmiştir. Modelin başlıca amacı, hemşirelerin kendi tercihlerine göre haftalık çizelgelerinin oluşturulmasıdır. Oluşturulan model, gerçek veriler kullanılarak bir üniversite hastanesinin genel cerrahi bölümünde denenmiştir. Modelin, %99,6 oranında hemşire tercihlerini yerine getirdiği görülmektedir. Anahtar Kelimeler: Esnek Çalışma Saatleri, Hemşire Çizelgelemesi, Matematiksel Model, Tam Sayılı Programlama. ABSTRACT: In the service industry, customer satisfaction becomes more difficult with each passing day, employers have looked for new paradigms and ways to make service quality better and to keep service facilities reliable. Therefore, with the purpose of increasing their competitive power, employers give more importance to their employees who have direct relation with customers than they do to their customer. One of the new paradigms is make working hours flexible. In this study, an integer programming model is proposed for the nurse scheduling problem in the hospitals which are one of the most important service industries. On the contrary of classical nurse scheduling model, developed model has made flexible to starting time. The main aim of the model is make a schedule for nurses according to

  6. Multi-Objective Scheduling Optimization Based on a Modified Non-Dominated Sorting Genetic Algorithm-II in Voltage Source Converter−Multi-Terminal High Voltage DC Grid-Connected Offshore Wind Farms with Battery Energy Storage Systems

    Ho-Young Kim

    2017-07-01

    Full Text Available Improving the performance of power systems has become a challenging task for system operators in an open access environment. This paper presents an optimization approach for solving the multi-objective scheduling problem using a modified non-dominated sorting genetic algorithm in a hybrid network of meshed alternating current (AC/wind farm grids. This approach considers voltage and power control modes based on multi-terminal voltage source converter high-voltage direct current (MTDC and battery energy storage systems (BESS. To enhance the hybrid network station performance, we implement an optimal process based on the battery energy storage system operational strategy for multi-objective scheduling over a 24 h demand profile. Furthermore, the proposed approach is formulated as a master problem and a set of sub-problems associated with the hybrid network station to improve the overall computational efficiency using Benders’ decomposition. Based on the results of the simulations conducted on modified institute of electrical and electronics engineers (IEEE-14 bus and IEEE-118 bus test systems, we demonstrate and confirm the applicability, effectiveness and validity of the proposed approach.

  7. The relationship between traits optimism and anxiety and health-related quality of life in patients hospitalized for chronic diseases: data from the SATISQOL study

    2013-01-01

    Background The impact of psychological factors is often taken into account in the evaluation of quality of life. However, the effect of optimism and trait anxiety remains controversial and they are rarely studied simultaneously. We aimed to study the effect of this factor on health-related quality of life (HRQOL) of patients after a hospitalization in relation with their chronic disease. Methods Using cross-sectional data from the SATISQOL cohort, we conducted a multicentric study, including patients hospitalized for an intervention in connection with their chronic disease. Six months after hospitalization, patients completed a generic HRQOL questionnaire (SF-36), and the STAI and LOT-R questionnaires to evaluate optimism and trait anxiety. We studied the effect of each trait on HRQOL separately, and simultaneously, taking account of their interaction in 3 models, using an ANOVA. Results In this study, 1529 patients were included in three participating hospitals and there existed wide diversity in the chronic diseases in our population. The HRQOL score increased for all dimensions of SF36 between 15,8 and 44,5 when the level of anxiety decreased (p optimism (optimism on HRQOL. In the model 3, assessing the effect of both anxiety and optimism on HRQOL, and their interaction, the HRQOL score for all dimensions of the SF36 increased when the level of anxiety decreased (p optimism (p optimism was significant for the Social Functioning dimension (p = 0.0021). Conclusions Optimism and trait anxiety appeared to be significantly correlated with HRQOL. Furthermore, an interaction existed between the trait anxiety and optimism for some dimensions of SF36. Contrary to optimism, it seems essential to evaluate trait anxiety in future studies about HRQOL, since it could represent a confounding factor. PMID:23914779

  8. The management of diabetic foot ulcers in Danish hospitals is not optimal

    Kirketerp-Møller, Klaus; Svendsen, Ole Lander; Jansen, Rasmus Bo

    2015-01-01

    INTRODUCTION: The diabetic foot is a complicated health issue which ideally involves several different specialists to ensure the most effective treatment. The Danish Health and Medicines Authority recently published a national guideline to address the implementation of multidisciplinary teams...... in the treatment. The objective of this study was to describe the treatment practices at the time the guidelines were launched. METHODS: A questionnaire-based survey was conducted among Danish hospital departments working with diabetic feet. All public departments were invited by e-mail to participate......) were mostly orthopaedic surgeons. A classification system of the diabetic foot was rarely or never used, and eight respondents (42%) reported having a multidisciplinary team in accordance with the national guidelines. 73% of the respondents performed some form of surgical intervention on diabetic feet...

  9. Evolutionary Scheduler for the Deep Space Network

    Guillaume, Alexandre; Lee, Seungwon; Wang, Yeou-Fang; Zheng, Hua; Chau, Savio; Tung, Yu-Wen; Terrile, Richard J.; Hovden, Robert

    2010-01-01

    A computer program assists human schedulers in satisfying, to the maximum extent possible, competing demands from multiple spacecraft missions for utilization of the transmitting/receiving Earth stations of NASA s Deep Space Network. The program embodies a concept of optimal scheduling to attain multiple objectives in the presence of multiple constraints.

  10. Activity Based Costing (ABC as an Approach to Optimize Purchasing Performance in Hospitality Industry

    Mohamed S. El-Deeb

    2011-07-01

    Full Text Available ABC (Activity Based Costing system has proved success in both products and services. The researchers propose using a new model through the application of ABC approach that can be implemented in purchasing department as one of the most dynamic departments in service sector to optimize purchasing activities performance. The researchers propose purchasing measures, targeting customers’ loyalty ensuring the continuous flow of supplies. The researchers used the questionnaire as a tool of data collection method for verifying the hypothesis of the research. Data obtained was analyzed by using Statistical Package for Social Sciences (SPSS. The results of the research based on limited survey that have been distributed to number of hotels in Great Cairo region. Our research was targeting three hundred purchasing manager and staff through five star hotels. It is recognized that further research is necessary to establish the exact nature of the causal linkages between proposed performance measures and strategic intent in order to gain insights into practice elsewhere.

  11. An optimal painless treatment for early hemorrhoids; our experience in Government Medical College and Hospital

    Singal, R; Gupta, S; Dalal, AK; Dalal, U; Attri, AK

    2013-01-01

    Objective - To evaluate the efficacy of Infrared Coagulation Therapy (IRC) for hemorrhoids. IRC is a painless, safe and successful procedure. Place and duration of study - Department of Surgery, Government Medical College and Hospital, Sector-32, Chandigarh, India, from August 2006 to October 2008. The choice of procedure depends on the patient's symptoms, the extent of the hemorrhoidal disease, and the experience of the surgeon along with the availability of the techniques/instruments. Materials and methods - This is a prospective study done from August 2006 to October 2008. Total number of 155 patients was included in the study. Infrared Coagulation Therapy (IRC) was performed through a special designed proctoscope. Patients excluded were with coagulopathy disorders, fissure in ano, and anal ulcers. Results - It is an outpatient Department (OPD), non-surgical, ambulatory, painless and bloodless procedure, without any hospital stay. Early recovery and minimal recurrence of hemorrhoids were noted without any morbidity or mortality. We have studied 155 patients, treated with IRC on OPD basis. Surgery was required in few patients in whom IRC failed or was contraindicated. Out of the total 155 patients, 127 came for follow up. After the 1st sitting of IRC therapy: out of 127; 43 patients got a total relief, mass shrinkage was of > 75% in 57 cases and 75% relief in 15 cases and >50 % relief in 11 patients. In the 3rd sitting out of 26/84 cases: 13 cases got a total relief and 13 cases refused to take the third sitting; however, in 7 cases the hemorrhoidal mass shrank up to 50% after the two sittings. These 14 were operated as there was no relief from bleeding after giving two sittings of IRC. Our opinion is that, in the above 14 cases, the patient might have not followed the instructions properly for dietary habits. Conclusion - IRC is a safe, simple and effective procedure for early hemorrhoids without any complications. IRC is nowadays the world’s leading office

  12. The preanalytical optimization of blood cultures: a review and the clinical importance of benchmarking in 5 Belgian hospitals.

    Willems, Elise; Smismans, Annick; Cartuyvels, Reinoud; Coppens, Guy; Van Vaerenbergh, Kristien; Van den Abeele, Anne-Marie; Frans, Johan

    2012-05-01

    Bloodstream infections remain a major challenge in medicine. Optimal detection of pathogens is only possible if the quality of preanalytical factors is thoroughly controlled. Since the laboratory is responsible for this preanalytical phase, the quality control of critical factors should be integrated in its quality control program. The numerous recommendations regarding blood culture collection contain controversies. Only an unambiguous guideline permits standardization and interlaboratory quality control. We present an evidence-based concise guideline of critical preanalytical determinants for blood culture collection and summarize key performance indicators with their concomitant target values. In an attempt to benchmark, we compared the true-positive rate, contamination rate, and collected blood volume of blood culture bottles in 5 Belgian hospital laboratories. The true-positive blood culture rate fell within previously defined acceptation criteria by Baron et al. (2005) in all 5 hospitals, whereas the contamination rate exceeded the target value in 4 locations. Most unexpected, in each of the 5 laboratories, more than one third of the blood culture bottles were incorrectly filled, irrespective of the manufacturer of the blood culture vials. As a consequence of this shortcoming, one manufacturer recently developed an automatic blood volume monitoring system. In conclusion, clear recommendations for standardized blood culture collection combined with quality control of critical factors of the preanalytical phase are essential for diagnostic blood culture improvement. Copyright © 2012 Elsevier Inc. All rights reserved.

  13. [Programs for optimizing the use of antibiotics (PROA) in Spanish hospitals: GEIH-SEIMC, SEFH and SEMPSPH consensus document].

    Rodríguez-Baño, Jesús; Paño-Pardo, José Ramón; Alvarez-Rocha, Luis; Asensio, Angel; Calbo, Esther; Cercenado, Emilia; Cisneros, José Miguel; Cobo, Javier; Delgado, Olga; Garnacho-Montero, José; Grau, Santiago; Horcajada, Juan Pablo; Hornero, Ana; Murillas-Angoiti, Javier; Oliver, Antonio; Padilla, Belén; Pasquau, Juan; Pujol, Miquel; Ruiz-Garbajosa, Patricia; San Juan, Rafael; Sierra, Rafael

    2012-01-01

    The antimicrobial agents are unique drugs for several reasons. First, their efficacy is higher than other drugs in terms of reduction of morbidity and mortality. Also, antibiotics are the only group of drugs associated with ecological effects, because their administration may contribute to the emergence and spread of microbial resistance. Finally, they are used by almost all medical specialties. Appropriate use of antimicrobials is very complex because of the important advances in the management of infectious diseases and the spread of antibiotic resistance. Thus, the implementation of programs for optimizing the use of antibiotics in hospitals (called PROA in this document) is necessary. This consensus document defines the objectives of the PROA (namely, to improve the clinical results of patients with infections, to minimise the adverse events associated to the use of antimicrobials including the emergence and spread of antibiotic resistance, and to ensure the use of the most cost-efficacious treatments), and provides recommendations for the implementation of these programs in Spanish hospitals. The key aspects of the recommendations are as follows. Multidisciplinary antibiotic teams should be formed, under the auspices of the Infection Committees. The PROA need to be considered as part of institutional programs and the strategic objectives of the hospital. The PROA should include specific objectives based on measurable indicators, and activities aimed at improving the use of antimicrobials, mainly through educational activities and interventions based more on training activities directed to prescribers than just on restrictive measures. Copyright © 2011 Elsevier España, S.L. All rights reserved.

  14. Constructing optimal experience for the hospitalized newborn through neuro-based music therapy.

    Shoemark, Helen; Hanson-Abromeit, Deanna; Stewart, Lauren

    2015-01-01

    Music-based intervention for hospitalized newborn infants has traditionally been based in a biomedical model, with physiological stability as the prime objective. More recent applications are grounded in other theories, including attachment, trauma and neurological models in which infant, parent and the dyadic interaction may be viewed as a dynamic system bound by the common context of the neonatal intensive care unit (NICU). The immature state of the preterm infant's auditory processing system requires a careful and individualized approach for the introduction of purposeful auditory experience intended to support development. The infant's experience of an unpredictable auditory environment is further compromised by a potential lack of meaningful auditory stimulation. Parents often feel disconnected from their own capacities to nurture their infant with potentially life-long implications for the infant's neurobehavioral and psychological well-being. This perspectives paper will outline some neurological considerations for auditory processing in the premature infant to frame a premise for music-based interventions. A hypothetical clinical case will illustrate the application of music by a music therapist with an infant and family in NICU.

  15. Constructing optimal experience for the hospitalized newborn through neuro-based music therapy

    Helen eShoemark

    2015-09-01

    Full Text Available Music-based intervention for hospitalized newborn infants has traditionally been based in a biomedical model, with physiological stability as the prime objective. More recent applications are grounded in other theories, including attachment, trauma and neurological models in which infant, parent and the dyadic interaction may be viewed as a dynamic system bound by the common context of the NICU. The immature state of the preterm infant’s neurological system and particularly auditory system means that no assumptions can be made about auditory processing and stimulation should proceed with caution. The infant’s experience of an unpredictable auditory environment is further compromised by a potential lack of meaningful auditory stimulation. Parents often feel disconnected from their own capacities to nurture their infant. The implications for the infant’s neurobehavioral and psychological well-being are life-long. This perspectives paper will outline the likely neurological considerations for auditory processing in the premature infant as well as establishing a premise for music-based interventions. A hypothetical clinical case will illustrate the application of music by a music therapist with an infant and family in NICU.

  16. WE-H-BRA-03: Development of a Model to Include the Evolution of Resistant Tumor Subpopulations Into the Treatment Optimization Process for Schedules Involving Targeted Agents in Chemoradiation Therapy

    Grassberger, C; Paganetti, H

    2016-01-01

    Purpose: To develop a model that includes the process of resistance development into the treatment optimization process for schedules that include targeted therapies. Further, to validate the approach using clinical data and to apply the model to assess the optimal induction period with targeted agents before curative treatment with chemo-radiation in stage III lung cancer. Methods: Growth of the tumor and its subpopulations is modeled by Gompertzian growth dynamics, resistance induction as a stochastic process. Chemotherapy induced cell kill is modeled by log-cell kill dynamics, targeted agents similarly but restricted to the sensitive population. Radiation induced cell kill is assumed to follow the linear-quadratic model. The validation patient data consist of a cohort of lung cancer patients treated with tyrosine kinase inhibitors that had longitudinal imaging data available. Results: The resistance induction model was successfully validated using clinical trial data from 49 patients treated with targeted agents. The observed recurrence kinetics, with tumors progressing from 1.4–63 months, result in tumor growth equaling a median volume doubling time of 92 days [34–248] and a median fraction of pre-existing resistance of 0.035 [0–0.22], in agreement with previous clinical studies. The model revealed widely varying optimal time points for the use of curative therapy, reaching from ∼1m to >6m depending on the patient’s growth rate and amount of pre-existing resistance. This demonstrates the importance of patient-specific treatment schedules when targeted agents are incorporated into the treatment. Conclusion: We developed a model including evolutionary dynamics of resistant sub-populations with traditional chemotherapy and radiation cell kill models. Fitting to clinical data yielded patient specific growth rates and resistant fraction in agreement with previous studies. Further application of the model demonstrated how proper timing of chemo

  17. WE-H-BRA-03: Development of a Model to Include the Evolution of Resistant Tumor Subpopulations Into the Treatment Optimization Process for Schedules Involving Targeted Agents in Chemoradiation Therapy

    Grassberger, C; Paganetti, H [Massachusetts General Hospital, Boston, MA (United States)

    2016-06-15

    Purpose: To develop a model that includes the process of resistance development into the treatment optimization process for schedules that include targeted therapies. Further, to validate the approach using clinical data and to apply the model to assess the optimal induction period with targeted agents before curative treatment with chemo-radiation in stage III lung cancer. Methods: Growth of the tumor and its subpopulations is modeled by Gompertzian growth dynamics, resistance induction as a stochastic process. Chemotherapy induced cell kill is modeled by log-cell kill dynamics, targeted agents similarly but restricted to the sensitive population. Radiation induced cell kill is assumed to follow the linear-quadratic model. The validation patient data consist of a cohort of lung cancer patients treated with tyrosine kinase inhibitors that had longitudinal imaging data available. Results: The resistance induction model was successfully validated using clinical trial data from 49 patients treated with targeted agents. The observed recurrence kinetics, with tumors progressing from 1.4–63 months, result in tumor growth equaling a median volume doubling time of 92 days [34–248] and a median fraction of pre-existing resistance of 0.035 [0–0.22], in agreement with previous clinical studies. The model revealed widely varying optimal time points for the use of curative therapy, reaching from ∼1m to >6m depending on the patient’s growth rate and amount of pre-existing resistance. This demonstrates the importance of patient-specific treatment schedules when targeted agents are incorporated into the treatment. Conclusion: We developed a model including evolutionary dynamics of resistant sub-populations with traditional chemotherapy and radiation cell kill models. Fitting to clinical data yielded patient specific growth rates and resistant fraction in agreement with previous studies. Further application of the model demonstrated how proper timing of chemo

  18. Optimal Load-Tracking Operation of Grid-Connected Solid Oxide Fuel Cells through Set Point Scheduling and Combined L1-MPC Control

    Siwei Han

    2018-03-01

    Full Text Available An optimal load-tracking operation strategy for a grid-connected tubular solid oxide fuel cell (SOFC is studied based on the steady-state analysis of the system thermodynamics and electrochemistry. Control of the SOFC is achieved by a two-level hierarchical control system. In the upper level, optimal setpoints of output voltage and the current corresponding to unit load demand is obtained through a nonlinear optimization by minimizing the SOFC’s internal power waste. In the lower level, a combined L1-MPC control strategy is designed to achieve fast set point tracking under system nonlinearities, while maintaining a constant fuel utilization factor. To prevent fuel starvation during the transient state resulting from the output power surging, a fuel flow constraint is imposed on the MPC with direct electron balance calculation. The proposed control schemes are testified on the grid-connected SOFC model.

  19. Integrating Multi-Domain Distributed Energy Systems with Electric Vehicle PQ Flexibility: Optimal Design and Operation Scheduling for Sustainable Low-Voltage Distribution Grids

    Morvaj, Boran; Knezovic, Katarina; Evins, Ralph

    2016-01-01

    on the grid operation, in addition to coordinated charging, is analysed. Results showed that when the system can be optimally designed, emissions decrease by 64% and additionally 32% with proactive EV integration, whereas EV reactive power control enables integration of larger EV amounts and provides...... in the stable operation. The model was applied to a real low-voltage Danish distribution grid where measurement data is available on hourly basis in order to determine EV flexibility impacts on carbon emissions, as well as the benefits of optimal DES design. The influence of EV reactive power control...

  20. Using integrated control methodology to optimize energy performance for the guest rooms in UAE hospitality sector

    AlFaris, Fadi; Abu-Hijleh, Bassam; Abdul-Ameer, Alaa

    2016-01-01

    Highlights: • Energy efficiency in 4 and 5 star luxury hotels in the United Arab Emirates. • The normalized energy use index (EUI) ranges between 241.5 and 348.4 kWh/m"2/year for post 2003 hotels. • The normalized energy use index (EUI) ranges between 348.4 and 511.1 kWh/m"2/year for pre 2003 hotels. • Integrated HVAC and lighting control strategies can reduce total energy consumption by up to 31.5%. - Abstract: The hospitality sector is growing rapidly in the UAE and especially in Dubai. As a result, it contributes substantially in the UAE's carbon footprint. This research was conducted to measure, evaluate and increase the energy efficiency in 4 and 5 star luxury hotels in UAE. Energy benchmarking analysis was used to analyze the energy data of 19 hotel buildings to differentiate between usual and best practice of energy performance. Moreover, the normalized energy use index (EUI) kWh/m"2/year has been identified for the best, usual and poor practice hotels. It was found that the normalized EUI ranges between 241.5 kWh/m"2/year or less as a best practice to more than 361.3 kWh/m"2/year of the poor energy practice for the hotels constructed after the year of 2003. Whereas the hotels' energy data showed higher values for those constructed before 2003, as the normalized EUI varies between 348.4 kWh/m"2/year as best practice to more than 511.1 kWh/m"2/year. An integrated control strategy has been employed to improve the energy performance and assess the energy saving for the guestroom. This technique showed that the overall energy performance improvement reached to 31.5% out of entire energy consumption of the hotel including electricity and gas. This reduction resulted in 43.2% savings from the cooling system and 13.2% from the lighting system due to the installing of the integrated control system in the guestrooms.

  1. Multiobjective Synergistic Scheduling Optimization Model for Wind Power and Plug-In Hybrid Electric Vehicles under Different Grid-Connected Modes

    Liwei Ju

    2014-01-01

    Full Text Available In order to promote grid’s wind power absorptive capacity and to overcome the adverse impacts of wind power on the stable operation of power system, this paper establishes benefit contrastive analysis models of wind power and plug-in hybrid electric vehicles (PHEVs under the optimization goal of minimum coal consumption and pollutant emission considering multigrid connected modes. Then, a two-step adaptive solving algorithm is put forward to get the optimal system operation scheme with the highest membership degree based on the improved ε constraints method and fuzzy decision theory. Thirdly, the IEEE36 nodes 10-unit system is used as the simulation system. Finally, the sensitive analysis for PHEV’s grid connected number is made. The result shows the proposed algorithm is feasible and effective to solve the model. PHEV’s grid connection could achieve load shifting effect and promote wind power grid connection. Especially, the optimization goals reach the optimum in fully optimal charging mode. As PHEV’s number increases, both abandoned wind and thermal power generation cost would decrease and the peak and valley difference of load curve would gradually be reduced.

  2. Calendario de vacunación en los nuevos médicos residentes procedentes de Perú: Hospital Universitario Ramón y Cajal Vaccine schedule in new resident physicians from Peru: Ramon y Cajal University Hospital

    Gian C. Navarro Chumbes

    2011-03-01

    Full Text Available Introducción: En los últimos años España se ha convertido en un país receptor de inmigrantes y el personal sanitario no es una excepción. Al momento de valorar el estado vacunal de los médicos residentes de inicio es importante tener en cuenta su procedencia. Objetivo: Realizar una revisión sobre la vacunación existente en el Perú, antecedentes y seroprevalencia de las patologías inmunoprevenibles en el personal sanitario peruano; y en base a lo encontrado determinar las pautas de vacunación a seguir en caso no se aporte documentación de vacunación previa para aquellos médicos residentes de inicio que provengan del Perú. Material y Métodos: Revisión bibliográfica. Resultados: Los datos encontrados sugieren que no existe evidencia que nos indique una correcta pauta de vacunación en los médicos residentes que proceden de Perú. Conclusiones: A todo médico residente de inicio proveniente de Perú que no aporte cartilla de vacunación se le procederá a vacunar como si fuese un adulto no vacunado, siempre teniendo en cuenta que si existe documentación de dosis previas se completarán las pautas sin reiniciar o repetir las dosis.Introduction: In recent years, Spain has been receiving immigrants of all working sectors and health staff is not an exception. When evaluating a vaccine schedule of first year resident physicians, it is important to know where they are arriving from. Objective: Make a review about vaccination in Peru, background and seroprevalence of immunopreventable pathology in Peruvian health staff; and bearing in mind the information, determine a vaccine schedule for first year resident physicians from Peru whose information is not available because they do not have previous vaccination documents. Method and materials: We made a bibliographic review. Results: The information obtained suggests that there is no evidence of correct vaccination in first year resident physicians from Peru. Conclusions: All first year

  3. MEDICAL STAFF SCHEDULING USING SIMULATED ANNEALING

    Ladislav Rosocha

    2015-07-01

    Full Text Available Purpose: The efficiency of medical staff is a fundamental feature of healthcare facilities quality. Therefore the better implementation of their preferences into the scheduling problem might not only rise the work-life balance of doctors and nurses, but also may result into better patient care. This paper focuses on optimization of medical staff preferences considering the scheduling problem.Methodology/Approach: We propose a medical staff scheduling algorithm based on simulated annealing, a well-known method from statistical thermodynamics. We define hard constraints, which are linked to legal and working regulations, and minimize the violations of soft constraints, which are related to the quality of work, psychic, and work-life balance of staff.Findings: On a sample of 60 physicians and nurses from gynecology department we generated monthly schedules and optimized their preferences in terms of soft constraints. Our results indicate that the final value of objective function optimized by proposed algorithm is more than 18-times better in violations of soft constraints than initially generated random schedule that satisfied hard constraints.Research Limitation/implication: Even though the global optimality of final outcome is not guaranteed, desirable solutionwas obtained in reasonable time. Originality/Value of paper: We show that designed algorithm is able to successfully generate schedules regarding hard and soft constraints. Moreover, presented method is significantly faster than standard schedule generation and is able to effectively reschedule due to the local neighborhood search characteristics of simulated annealing.

  4. Using the reference curve of a water supply system for determining the optimal operation schedule; Utilizacion de la curva de consgina de un abastecimiento de agua para determinar el regimen de explotacion mas eficiente

    Iglesias Rey, P. L.; Martinez Solano, F. J.; Fuertes Miquel, V. S.; Lopez Patino, G.

    2007-07-01

    A new water inlet point in the network can modify the water supply schedule so that the distribution of flow to supply from the different points would be a problem to determine in each case. To present work uses the reference curve concept of a water supply system to propose a method that determines the appropriate distribution of water supplied using simulation models. The methodology is based on looking for the grater power efficiency in the system, assuming equal production costs in the different sources. The obtained conclusions allows to know some parameters that influence in the location of the optimal production system. At the same time, the analysis of two examples shows the reach of the propose methodology. (Author) 6 refs.

  5. Multiuser switched diversity scheduling schemes

    Shaqfeh, Mohammad; Alnuweiri, Hussein M.; Alouini, Mohamed-Slim

    2012-01-01

    Multiuser switched-diversity scheduling schemes were recently proposed in order to overcome the heavy feedback requirements of conventional opportunistic scheduling schemes by applying a threshold-based, distributed, and ordered scheduling mechanism. The main idea behind these schemes is that slight reduction in the prospected multiuser diversity gains is an acceptable trade-off for great savings in terms of required channel-state-information feedback messages. In this work, we characterize the achievable rate region of multiuser switched diversity systems and compare it with the rate region of full feedback multiuser diversity systems. We propose also a novel proportional fair multiuser switched-based scheduling scheme and we demonstrate that it can be optimized using a practical and distributed method to obtain the feedback thresholds. We finally demonstrate by numerical examples that switched-diversity scheduling schemes operate within 0.3 bits/sec/Hz from the ultimate network capacity of full feedback systems in Rayleigh fading conditions. © 2012 IEEE.

  6. Multiuser switched diversity scheduling schemes

    Shaqfeh, Mohammad

    2012-09-01

    Multiuser switched-diversity scheduling schemes were recently proposed in order to overcome the heavy feedback requirements of conventional opportunistic scheduling schemes by applying a threshold-based, distributed, and ordered scheduling mechanism. The main idea behind these schemes is that slight reduction in the prospected multiuser diversity gains is an acceptable trade-off for great savings in terms of required channel-state-information feedback messages. In this work, we characterize the achievable rate region of multiuser switched diversity systems and compare it with the rate region of full feedback multiuser diversity systems. We propose also a novel proportional fair multiuser switched-based scheduling scheme and we demonstrate that it can be optimized using a practical and distributed method to obtain the feedback thresholds. We finally demonstrate by numerical examples that switched-diversity scheduling schemes operate within 0.3 bits/sec/Hz from the ultimate network capacity of full feedback systems in Rayleigh fading conditions. © 2012 IEEE.

  7. Cancelamento de cirurgias programadas em um hospital-escola: um estudo exploratório La cancelación de cirugías programadas en un hospital-escuela: un estudio exploratorio Cancellation of scheduled surgeries in a university hospital: an exploratory study

    Jacqueline Borges Cavalcante

    2000-08-01

    seleccionados y sometidos a la cirugía en el periodo de septiembre a diciembre de 1996. Se analizaron los datos cuantitativamente y se presentaron en tablas. Los resultados demuestran que de las 1.145 cirugías programadas en el periodo seleccionado, 379 (33% fueron suspendidas. Los servicios afectados fueron: Cirugía General, Oftalmología, Cirugía de Cabeza y Cuello, Traumatología y Ortopedia, Otorrinolaringología, Nefrología y Trasplante Renal, y Proctología. Se hacen necesarias investigaciones en esta área para saber las razones que determinaron la cancelación de esas cirugías, así como la participación de enfermería en el estudio de esta problemática.In spite of the extensive available literature on surgery patients' preparation and on the performance of surgeries, the focus given to the cancellation of the surgical act has been quite restricted. This study aims at identifying the number of scheduled and cancelled surgeries as well as the services that are mostly affected by such cancellations and was carried out in the surgery service of a big public university hospital located in the metropolitan area of Fortaleza, Ceará. The data were collected through surgery registration books, daily maps of surgery schedules and from the files of patients scheduled for surgery from September to December, 1996. The gathered data were analyzed quantitatively and introduced in charts. The results demonstrate that from the 1,145 surgeries programmed in the selected period, 379 (33% had been cancelled. The mostly prejudiced services were General Surgery, Ophthalmology, Head and Neck Surgery, Trauma and Orthopedics, Otorhinolaryngology, Nephrology and Renal Transplant, and Proctology. Further investigation in this area in order to know the determinant causes of surgery cancellation as well as the participation of nursing in the study of this problem are necessary.

  8. The impact of service-specific staffing, case scheduling, turnovers, and first-case starts on anesthesia group and operating room productivity: a tutorial using data from an Australian hospital.

    McIntosh, Catherine; Dexter, Franklin; Epstein, Richard H

    2006-12-01

    In this tutorial, we consider the impact of operating room (OR) management on anesthesia group and OR labor productivity and costs. Most of the tutorial focuses on the steps required for each facility to refine its OR allocations using its own data collected during patient care. Data from a hospital in Australia are used throughout to illustrate the methods. OR allocation is a two-stage process. During the initial tactical stage of allocating OR time, OR capacity ("block time") is adjusted. For operational decision-making on a shorter-term basis, the existing workload can be considered fixed. Staffing is matched to that workload based on maximizing the efficiency of use of OR time. Scheduling cases and making decisions on the day of surgery to increase OR efficiency are worthwhile interventions to increase anesthesia group productivity. However, by far, the most important step is the appropriate refinement of OR allocations (i.e., planning service-specific staffing) 2-3 mo before the day of surgery. Reducing surgical and/or turnover times and delays in first-case-of-the-day starts generally provides small reductions in OR labor costs. Results vary widely because they are highly sensitive both to the OR allocations (i.e., staffing) and to the appropriateness of those OR allocations.

  9. Case mix classification and a benchmark set for surgery scheduling

    Leeftink, Gréanne; Hans, Erwin W.

    Numerous benchmark sets exist for combinatorial optimization problems. However, in healthcare scheduling, only a few benchmark sets are known, mainly focused on nurse rostering. One of the most studied topics in the healthcare scheduling literature is surgery scheduling, for which there is no widely

  10. Nitrogen Fertilization for Optimizing the Quality and Yield of Shade Grown Cuban Cigar Tobacco: Required Nitrogen Amounts, Application Schedules, Adequate Leaf Nitrogen Levels, and Early Season Diagnostic Tests

    Borges A

    2014-12-01

    Full Text Available Nitrogen (N fertilizers have a decisive influence on the yield and quality of tobacco. Yield, percentage of plant N, wrapper leaf quality, and nicotine content are all important quality characteristics in tobacco growing. This work is an attempt to provide a tool for optimizing mineral N nutrition for Cuban cigar tobacco, using a strategy that links N supply with leaf N concentration and wrapper yield. Similar approaches developed worldwide have mainly involved Virginia and Burley tobacco types but not Cuban cigar tobacco. The objective of the current work is to identify the effects of fertilizer N levels and timing of application on each of the mentioned quality factors for shade grown Cuban cigar tobacco. Another purpose is to explore the usefulness of a quick method of assessing the N status of plants based on measuring leaf transmission at two different wavelengths (650 and 940 nm. The experiments were done in the main tobacco growing area of Cuba (Vueltabajo. In each experiment, nine separate treatments were used covering different levels and times of fertilizer N application. The same experiment was carried out in three different years (2005-2006, 2006-2007, 2007-2008 but as the results were similar only one set of data is described (2006-2007. The patterns of response to N fertilizer of all four quality measurements, including yield and wrapper leaf quality, were similar in the different replications of the experiments. The optimal fertilizer level was 140-190 kg N/ha (40% applied on days 8-10 after transplanting and 60% on days 18-20 after transplanting. The optimal N concentration of leaves taken at the central foliar level of the middle stalk position was 4.3-4.7% at harvest time. Leaf transmission measurements by means of the SPAD-502 Chlorophyll Meter in the early stages of growth were correlated with leaf chlorophyll and N concentration and provide an excellent guide for predicting Cuban cigar tobacco wrapper leaf yield.

  11. Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes

    Muqaddas Naz

    2018-02-01

    Full Text Available With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce electricity cost and peak load demand, demand side management (DSM is modeled as an optimization problem, and the solution is obtained by applying meta-heuristic techniques with different pricing schemes. In this paper, an optimization technique, the hybrid gray wolf differential evolution (HGWDE, is proposed by merging enhanced differential evolution (EDE and gray wolf optimization (GWO scheme using real-time pricing (RTP and critical peak pricing (CPP. Load shifting is performed from on-peak hours to off-peak hours depending on the electricity cost defined by the utility. However, there is a trade-off between user comfort and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the peak to average ratio (PAR is reduced to 53.02%, 29.02% and 26.55%, while the electricity bill is reduced to 12.81%, 12.012% and 12.95%, respectively, for the 15-, 30- and 60-min operational time interval (OTI. On the other hand, the PAR and electricity bill are reduced to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff.

  12. Type A behavior pattern, accident optimism and fatalism: an investigation into non-compliance with safety work behaviors among hospital nurses.

    Ugwu, Fabian O; Onyishi, Ike E; Ugwu, Chidi; Onyishi, Charity N

    2015-01-01

    Safety work behavior has continued to attract the interest of organizational researchers and practitioners especially in the health sector. The goal of the study was to investigate whether personality type A, accident optimism and fatalism could predict non-compliance with safety work behaviors among hospital nurses. One hundred and fifty-nine nursing staff sampled from three government-owned hospitals in a state in southeast Nigeria, participated in the study. Data were collected through Type A Behavior Scale (TABS), Accident Optimism, Fatalism and Compliance with Safety Behavior (CSB) Scales. Our results showed that personality type A, accident optimism and fatalism were all related to non-compliance with safety work behaviors. Personality type A individuals tend to comply less with safety work behaviors than personality type B individuals. In addition, optimistic and fatalistic views about accidents and existing safety rules also have implications for compliance with safety work behaviors.

  13. A Two-Level Optimal Scheduling Strategy for Central Air-Conditioners Based on Metal Model with Comprehensive State-Queueing Control Models

    Yebai Qi

    2017-12-01

    Full Text Available Unlike some thermostatically controlled appliances (TCAs with small capacities, Central Air-conditioner (CAC has huge potential for demand response because of its large capacity. This paper presents a new CAC control strategy under multiple constraints. The CAC is modeled by three main modules: CAC central unit, water pumps, and temperature simulation of terminal users. The CAC’s power consumption is mainly determined by users’ load ratio. As the information and communication system have become the central nervous system of the smart grid, big data analysis is of great significance. Assuming that reliable two-way communication systems are preset, an integrated parameter priority list (IPPL control strategy is used to control and monitor CAC. A new intelligent algorithm, Space Exploration and Unimodal Region Elimination (SEUMRE algorithm, is introduced for solving the optimization problem of demand response targets generation under multiple constraints with the help of big data analysis. In this paper, influences and constrain factors, such as price and users’ comfortable levels are taken into account to satisfy the need of actual situation. Simulation results show that the proposed approach, when comparing with other typical optimization algorithms, yields better performances and efficiency.

  14. AP1000 construction schedule

    Winters, J.W.

    2001-01-01

    Westinghouse performed this study as part of EPRI interest in advancing the use of computer aided processes to reduce the cost of nuclear power plants. EPRI believed that if one could relate appropriate portions of an advanced light water reactor plant model to activities in its construction sequence, and this relationship could be portrayed visually, then optimization of the construction sequence could be developed as never before. By seeing a 3-D representation of the plant at any point in its construction sequence, more informed decisions can be made on the feasibility or attractiveness of follow on or parallel steps in the sequence. The 3-D representation of construction as a function of time (4-D) could also increase the confidence of potential investors concerning the viability of the schedule and the plant ultimate cost. This study performed by Westinghouse confirmed that it is useful to be able to visualize a plant construction in 3-D as a function of time in order to optimize the sequence of construction activities. (author)

  15. Utilization Bound of Non-preemptive Fixed Priority Schedulers

    Park, Moonju; Chae, Jinseok

    It is known that the schedulability of a non-preemptive task set with fixed priority can be determined in pseudo-polynomial time. However, since Rate Monotonic scheduling is not optimal for non-preemptive scheduling, the applicability of existing polynomial time tests that provide sufficient schedulability conditions, such as Liu and Layland's bound, is limited. This letter proposes a new sufficient condition for non-preemptive fixed priority scheduling that can be used for any fixed priority assignment scheme. It is also shown that the proposed schedulability test has a tighter utilization bound than existing test methods.

  16. Immunization Schedules for Adults

    ... ACIP Vaccination Recommendations Why Immunize? Vaccines: The Basics Immunization Schedule for Adults (19 Years of Age and ... diseases that can be prevented by vaccines . 2018 Immunization Schedule Recommended Vaccinations for Adults by Age and ...

  17. Instant Childhood Immunization Schedule

    ... Recommendations Why Immunize? Vaccines: The Basics Instant Childhood Immunization Schedule Recommend on Facebook Tweet Share Compartir Get ... date. See Disclaimer for additional details. Based on Immunization Schedule for Children 0 through 6 Years of ...

  18. Online Scheduling in Distributed Message Converter Systems

    Risse, Thomas; Wombacher, Andreas; Surridge, Mike; Taylor, Steve; Aberer, Karl

    The optimal distribution of jobs among hosts in distributed environments is an important factor to achieve high performance. The optimal strategy depends on the application. In this paper we present a new online scheduling strategy for distributed EDI converter system. The strategy is based on the

  19. Fractionation schedules for cancers of the head and neck

    Harari, Paul M.

    1995-01-01

    Purpose/Objective: This refresher course reviews current research activity and treatment results in the field of radiation therapy fractionation. The presentation emphasizes worldwide studies of altered fractionation, highlighting head and neck cancer as the primary teaching model. Basic radiobiological principles guiding the development of altered fractionation regimens, and advancing the understanding of fractionation effects on normal and tumor tissue are reviewed. A 'standard' prescription of 2 Gy x 35 fractions = 70 Gy may not provide the optimal balance between primary tumor control and late normal tissue effects for all patients with squamous cell carcinoma of the head and neck. The last decade has witnessed the treatment of thousands of head and neck cancer patients with curative radiotherapy using altered fractination schedules designed to improve overall treatment results. Although the number of different fractionation regimens currently being investigated continues to increase, the common guiding principles behind their design are relatively simple. Common fractionation terminology (i.e., accelerated hyperfractionation) will be reviewed, as well as a brief summary of radiobiological concepts pertaining to tumor potential doubling time, tumor proliferation kinetics, overall treatment time and fraction size-dependence of acute and late tissue effects. Several well known head and neck fractionation schedules from around the world (Manchester Christie Hospital-United Kingdom, Princess Margaret Hospital-Canada, Massachusetts General Hospital-USA, MD Anderson Hospital-USA, University of Florida-USA, Mount Vernon Hospital CHART-United Kingdom, RTOG and EORTC trials-USA and Europe) will be summarized with regard to design-rationale, treatment technique and results. The design of several current cooperative group trials investigating altered head and neck fractionation will be presented, as well as concepts prompting the pilot evaluation of several brand new

  20. Optimization and Management of Naval Hospital Bremerton's Military-Medicare Population by Market Analysis of the Naval Hospital Bremerton Empanelled Population

    Coefield, Ocie

    2001-01-01

    The purpose of this research project was to determine whether Naval Hospital Bremerton could meet the service demands for the care of the over 65 military-Medicare eligible population within the catchment area...