Multi-agent Pareto appointment exchanging in hospital patient scheduling

NARCIS (Netherlands)

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:

Optimal load scheduling in commercial and residential microgrids

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Optimizing Unmanned Aircraft System Scheduling

Science.gov (United States)

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

Robust Optimization-Based Generation Self-Scheduling under Uncertain Price

Directory of Open Access Journals (Sweden)

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.

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

International Nuclear Information System (INIS)

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

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

Science.gov (United States)

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.

An introduction to optimal satellite range scheduling

CERN Document Server

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, ...

Global Optimization of Nonlinear Blend-Scheduling Problems

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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.

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

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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.

Optimal Scheduling of Residential Microgrids Considering Virtual Energy Storage System

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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.

Optimal Grid Scheduling Using Improved Artificial Bee Colony Algorithm

OpenAIRE

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...

Robust Optimization for Household Load Scheduling with Uncertain Parameters

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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.

Optimal Scheduling of Domestic Appliances via MILP

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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.

Optimal scheduling using priced timed automata

DEFF Research Database (Denmark)

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....

Optimization of Multiperiod Mixed Train Schedule on High-Speed Railway

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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.

Joint optimization of production scheduling and machine group preventive maintenance

International Nuclear Information System (INIS)

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.

Hybrid glowworm swarm optimization for task scheduling in the cloud environment

Science.gov (United States)

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.

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

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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.

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

Science.gov (United States)

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.

Optimal scheduling for distribution network with redox flow battery storage

International Nuclear Information System (INIS)

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.

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

Science.gov (United States)

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.

A Privacy-Preserving Distributed Optimal Scheduling for Interconnected Microgrids

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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.

customer-teller scheduling system for optimizing banks service

African Journals Online (AJOL)

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 ...

Electric power systems advanced forecasting techniques and optimal generation scheduling

CERN Document Server

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

Discrete Bat Algorithm for Optimal Problem of Permutation Flow Shop Scheduling

Science.gov (United States)

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

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

Science.gov (United States)

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.

Optimal residential smart appliances scheduling considering distribution network constraints

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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

Optimized Treatment Schedules for Chronic Myeloid Leukemia.

Directory of Open Access Journals (Sweden)

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.

Fog computing job scheduling optimization based on bees swarm

Science.gov (United States)

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.

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

International Nuclear Information System (INIS)

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.

Optimal Intermittent Dose Schedules for Chemotherapy Using Genetic Algorithm

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

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

International Nuclear Information System (INIS)

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

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Optimal charging schedule of an electric vehicle fleet

DEFF Research Database (Denmark)

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...

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

Directory of Open Access Journals (Sweden)

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.

An Optimal Scheduling Dispatch of a Microgrid under Risk Assessment

Directory of Open Access Journals (Sweden)

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.

Refrigerator Optimal Scheduling to Minimise the Cost of Operation

Directory of Open Access Journals (Sweden)

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.

Optimal Scheduling of Doctors Outpatient Departments Based on Patients’ Behavior

Directory of Open Access Journals (Sweden)

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.

Computer-aided resource planning and scheduling for radiological services

Science.gov (United States)

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.

Intelligent discrete particle swarm optimization for multiprocessor task scheduling problem

Directory of Open Access Journals (Sweden)

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.

Sensitivity of the optimal parameter settings for a LTE packet scheduler

NARCIS (Netherlands)

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

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

Science.gov (United States)

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.

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

Science.gov (United States)

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

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

DEFF Research Database (Denmark)

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...

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

International Nuclear Information System (INIS)

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

Optimization Model for Capacity Management and Bed Scheduling for Hospital

Science.gov (United States)

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.

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

KAUST Repository

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.

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

International Nuclear Information System (INIS)

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

Verification and Optimization of a PLC Control Schedule

NARCIS (Netherlands)

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

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

Science.gov (United States)

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.

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

Science.gov (United States)

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

A PSO approach for preventive maintenance scheduling optimization

International Nuclear Information System (INIS)

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)

Discrete Optimization Model for Vehicle Routing Problem with Scheduling Side Cosntraints

Science.gov (United States)

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.

Optimal methodology for a machining process scheduling in spot electricity markets

International Nuclear Information System (INIS)

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.

Optimal trajectory planning and train scheduling for urban rail transit systems

CERN Document Server

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...

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

Energy Technology Data Exchange (ETDEWEB)

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

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Resource-Optimal Scheduling Using Priced Timed Automata

DEFF Research Database (Denmark)

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....

Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

Directory of Open Access Journals (Sweden)

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.

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

KAUST Repository

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

Tramp ship routing and scheduling with integrated bunker optimization

DEFF Research Database (Denmark)

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...

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

Directory of Open Access Journals (Sweden)

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.

Joint optimization scheduling for water conservancy projects in complex river networks

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Using Optimization Models for Scheduling in Enterprise Resource Planning Systems

Directory of Open Access Journals (Sweden)

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.

Scheduling with Bus Access Optimization for Distributed Embedded Systems

DEFF Research Database (Denmark)

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....

On using priced timed automata to achieve optimal scheduling

DEFF Research Database (Denmark)

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....

Parametric Optimization of Hospital Design

DEFF Research Database (Denmark)

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....

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

Science.gov (United States)

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

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

International Nuclear Information System (INIS)

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

Optimization and Flight Schedules of Pioneer Routes in Papua Province

Science.gov (United States)

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%.

Resource allocation in IT projects: using schedule optimization

Directory of Open Access Journals (Sweden)

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.

Flexible Job Shop Scheduling Problem Using an Improved Ant Colony Optimization

Directory of Open Access Journals (Sweden)

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.

Schedule optimization study implementation plan

International Nuclear Information System (INIS)

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

Resource Allocation and Outpatient Appointment Scheduling Using Simulation Optimization

Directory of Open Access Journals (Sweden)

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.

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

Directory of Open Access Journals (Sweden)

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.

Routing and Scheduling Optimization Model of Sea Transportation

Science.gov (United States)

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.

Flow shop scheduling algorithm to optimize warehouse activities

Directory of Open Access Journals (Sweden)

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.

Optimal scheduling of micro grids based on single objective programming

Science.gov (United States)

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.

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

OpenAIRE

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...

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

Directory of Open Access Journals (Sweden)

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.

Terminal Control Area Aircraft Scheduling and Trajectory Optimization Approaches

Directory of Open Access Journals (Sweden)

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.

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

NARCIS (Netherlands)

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

Optimizing Intermodal Train Schedules with a Design Balanced Network Design Model

DEFF Research Database (Denmark)

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...

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

Directory of Open Access Journals (Sweden)

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%.

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

DEFF Research Database (Denmark)

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...

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

Directory of Open Access Journals (Sweden)

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.

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

Energy Technology Data Exchange (ETDEWEB)

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.

Nurse Scheduling by Cooperative GA with Effective Mutation Operator

Science.gov (United States)

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.

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

African Journals Online (AJOL)

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 ...

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Energy Technology Data Exchange (ETDEWEB)

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.

BINARY PARTICLE SWARM OPTIMIZATION APPROACH FOR RANDOM GENERATION OUTAGE MAINTENANCE SCHEDULING

Directory of Open Access Journals (Sweden)

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.

Research on optimizing pass schedule of tandem cold mill

International Nuclear Information System (INIS)

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)

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

Directory of Open Access Journals (Sweden)

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

A probabilistic computational framework for bridge network optimal maintenance scheduling

International Nuclear Information System (INIS)

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.

A master surgical scheduling approach for cyclic scheduling in operating room departments

NARCIS (Netherlands)

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

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

Directory of Open Access Journals (Sweden)

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.

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

KAUST Repository

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.

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

Science.gov (United States)

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

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

Directory of Open Access Journals (Sweden)

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.

Optimal scheduling in call centers with a callback option

OpenAIRE

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 ...

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

Science.gov (United States)

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

A short-term operating room surgery scheduling problem integrating multiple nurses roster constraints.

Science.gov (United States)

Xiang, Wei; Yin, Jiao; Lim, Gino

2015-02-01

Operating room (OR) surgery scheduling determines the individual surgery's operation start time and assigns the required resources to each surgery over a schedule period, considering several constraints related to a complete surgery flow and the multiple resources involved. This task plays a decisive role in providing timely treatments for the patients while balancing hospital resource utilization. The originality of the present study is to integrate the surgery scheduling problem with real-life nurse roster constraints such as their role, specialty, qualification and availability. This article proposes a mathematical model and an ant colony optimization (ACO) approach to efficiently solve such surgery scheduling problems. A modified ACO algorithm with a two-level ant graph model is developed to solve such combinatorial optimization problems because of its computational complexity. The outer ant graph represents surgeries, while the inner graph is a dynamic resource graph. Three types of pheromones, i.e. sequence-related, surgery-related, and resource-related pheromone, fitting for a two-level model are defined. The iteration-best and feasible update strategy and local pheromone update rules are adopted to emphasize the information related to the good solution in makespan, and the balanced utilization of resources as well. The performance of the proposed ACO algorithm is then evaluated using the test cases from (1) the published literature data with complete nurse roster constraints, and 2) the real data collected from a hospital in China. The scheduling results using the proposed ACO approach are compared with the test case from both the literature and the real life hospital scheduling. Comparison results with the literature shows that the proposed ACO approach has (1) an 1.5-h reduction in end time; (2) a reduction in variation of resources' working time, i.e. 25% for ORs, 50% for nurses in shift 1 and 86% for nurses in shift 2; (3) an 0.25h reduction in

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

International Nuclear Information System (INIS)

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

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

Science.gov (United States)

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

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

Directory of Open Access Journals (Sweden)

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.

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

Energy Technology Data Exchange (ETDEWEB)

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.

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

Energy Technology Data Exchange (ETDEWEB)

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.

Adaptive track scheduling to optimize concurrency and vectorization in GeantV

International Nuclear Information System (INIS)

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)

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

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

DEFF Research Database (Denmark)

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...

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

International Nuclear Information System (INIS)

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

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

DEFF Research Database (Denmark)

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....

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

International Nuclear Information System (INIS)

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.

A robust optimization approach for energy generation scheduling in microgrids

International Nuclear Information System (INIS)

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.

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

DEFF Research Database (Denmark)

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....

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

Science.gov (United States)

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.

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

Energy Technology Data Exchange (ETDEWEB)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

Directory of Open Access Journals (Sweden)

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.

Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems

CERN Document Server

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...

A model for generating master surgical schedules to allow cyclic scheduling in operating room departments

NARCIS (Netherlands)

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

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

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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.

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

Science.gov (United States)

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.

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

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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.

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Microgrid optimal scheduling considering impact of high penetration wind generation

Science.gov (United States)

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.

Scheduling nursing personnel on a microcomputer.

Science.gov (United States)

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.

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

International Nuclear Information System (INIS)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

International Nuclear Information System (INIS)

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

Optimal Results and Numerical Simulations for Flow Shop Scheduling Problems

Directory of Open Access Journals (Sweden)

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.

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

Energy Technology Data Exchange (ETDEWEB)

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)

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

Energy Technology Data Exchange (ETDEWEB)

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.

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

International Nuclear Information System (INIS)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

OpenAIRE

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...

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

International Nuclear Information System (INIS)

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)

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

International Nuclear Information System (INIS)

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

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

Energy Technology Data Exchange (ETDEWEB)

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)

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

International Nuclear Information System (INIS)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

International Nuclear Information System (INIS)

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.

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

Science.gov (United States)

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

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

DEFF Research Database (Denmark)

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....

Multi-objective group scheduling optimization integrated with preventive maintenance

Science.gov (United States)

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.

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

International Nuclear Information System (INIS)

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.

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

International Nuclear Information System (INIS)

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.

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

NARCIS (Netherlands)

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

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

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

National Research Council Canada - National Science Library

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...

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

OpenAIRE

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...

REPNET: project scheduling and workflow optimization for Construction Projects

Directory of Open Access Journals (Sweden)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

Directory of Open Access Journals (Sweden)

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.

Mechanisms for scheduling games with selfish players

NARCIS (Netherlands)

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

Optimal Temporal Decoupling in Task Scheduling with Preferences

NARCIS (Netherlands)

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

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

KAUST Repository

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

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

KAUST Repository

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

Optimal Rules for Single Machine Scheduling with Stochastic Breakdowns

Directory of Open Access Journals (Sweden)

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.

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

DEFF Research Database (Denmark)

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...

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

International Nuclear Information System (INIS)

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

Schedule-Aware Workflow Management Systems

Science.gov (United States)

Mans, Ronny S.; Russell, Nick C.; van der Aalst, Wil M. P.; Moleman, Arnold J.; Bakker, Piet J. M.

Contemporary workflow management systems offer work-items to users through specific work-lists. Users select the work-items they will perform without having a specific schedule in mind. However, in many environments work needs to be scheduled and performed at particular times. For example, in hospitals many work-items are linked to appointments, e.g., a doctor cannot perform surgery without reserving an operating theater and making sure that the patient is present. One of the problems when applying workflow technology in such domains is the lack of calendar-based scheduling support. In this paper, we present an approach that supports the seamless integration of unscheduled (flow) and scheduled (schedule) tasks. Using CPN Tools we have developed a specification and simulation model for schedule-aware workflow management systems. Based on this a system has been realized that uses YAWL, Microsoft Exchange Server 2007, Outlook, and a dedicated scheduling service. The approach is illustrated using a real-life case study at the AMC hospital in the Netherlands. In addition, we elaborate on the experiences obtained when developing and implementing a system of this scale using formal techniques.

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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

Optimization of Task Scheduling Algorithm through QoS Parameters for Cloud Computing

Directory of Open Access Journals (Sweden)

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.

Seeking optimality in fruit pulping schedules: A case study*

Directory of Open Access Journals (Sweden)

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.

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

Institute of Scientific and Technical Information of China (English)

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.

Development of Watch Schedule Using Rules Approach

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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. 

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

Science.gov (United States)

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.

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

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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.

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

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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.

MEDICAL STAFF SCHEDULING USING SIMULATED ANNEALING

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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.

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

International Nuclear Information System (INIS)

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.

Optimal Algorithms and a PTAS for Cost-Aware Scheduling

NARCIS (Netherlands)

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

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

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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.

Optimizing patient flow in a large hospital surgical centre by means of discrete-event computer simulation models.

Science.gov (United States)

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.

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

International Nuclear Information System (INIS)

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

Car painting process scheduling with harmony search algorithm

Science.gov (United States)

Syahputra, M. F.; Maiyasya, A.; Purnamawati, S.; Abdullah, D.; Albra, W.; Heikal, M.; Abdurrahman, A.; Khaddafi, M.

2018-02-01

Automotive painting program in the process of painting the car body by using robot power, making efficiency in the production system. Production system will be more efficient if pay attention to scheduling of car order which will be done by considering painting body shape of car. Flow shop scheduling is a scheduling model in which the job-job to be processed entirely flows in the same product direction / path. Scheduling problems often arise if there are n jobs to be processed on the machine, which must be specified which must be done first and how to allocate jobs on the machine to obtain a scheduled production process. Harmony Search Algorithm is a metaheuristic optimization algorithm based on music. The algorithm is inspired by observations that lead to music in search of perfect harmony. This musical harmony is in line to find optimal in the optimization process. Based on the tests that have been done, obtained the optimal car sequence with minimum makespan value.

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

DEFF Research Database (Denmark)

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....

Hospital admission planning to optimize major resources utilization under uncertainty

OpenAIRE

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...

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

DEFF Research Database (Denmark)

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...

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

International Nuclear Information System (INIS)

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.

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

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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.

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

DEFF Research Database (Denmark)

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...

Revisiting Symbiotic Job Scheduling

OpenAIRE

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...

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

Directory of Open Access Journals (Sweden)

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.

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

International Nuclear Information System (INIS)

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.

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

Science.gov (United States)

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

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

DEFF Research Database (Denmark)

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...

Construction schedules slack time minimizing

Science.gov (United States)

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.

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

International Nuclear Information System (INIS)

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

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

Directory of Open Access Journals (Sweden)

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.

Multi-objective stochastic scheduling optimization model for connecting a virtual power plant to wind-photovoltaic-electric vehicles considering uncertainties and demand response

International Nuclear Information System (INIS)

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

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

International Nuclear Information System (INIS)

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)

A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

Fractionation schedules for cancers of the head and neck

International Nuclear Information System (INIS)

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

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

Directory of Open Access Journals (Sweden)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

DEFF Research Database (Denmark)

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....

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

International Nuclear Information System (INIS)

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.

Gain-Scheduled Control of a Fossil-Fired Power Plant Boiler

DEFF Research Database (Denmark)

Hangstrup, M.; Stoustrup, Jakob; Andersen, Palle

1999-01-01

-scheduling which interpolates between unstable controllers is not allowed using traditional schemes. The results show that a considerable optimization of the conventional controlled system is obtainable. Also the gain-scheduled optimizing controller is seen to have a superior performance compared to the fixed LTI......In this paper the objective is to optimize the control of a coal fired 250 MW power plant boiler. The conventional control system is supplemented with a multivariable optimizing controller operating in parallel with the conventional control system. Due to the strong dependence of the gains...... and dynamics upon the load, it is beneficial to consider a gain-scheduling control approach. Optimization using complex mu synthesis results in unstable LTI controllers in some operating points of the boiler. A recent gain-scheduling approach allowing for unstable fixed LTI controllers is applied. Gain...

An improved sheep flock heredity algorithm for job shop scheduling and flow shop scheduling problems

Directory of Open Access Journals (Sweden)

Chandramouli Anandaraman

2011-10-01

Full Text Available Job Shop Scheduling Problem (JSSP and Flow Shop Scheduling Problem (FSSP are strong NP-complete combinatorial optimization problems among class of typical production scheduling problems. An improved Sheep Flock Heredity Algorithm (ISFHA is proposed in this paper to find a schedule of operations that can minimize makespan. In ISFHA, the pairwise mutation operation is replaced by a single point mutation process with a probabilistic property which guarantees the feasibility of the solutions in the local search domain. A Robust-Replace (R-R heuristic is introduced in place of chromosomal crossover to enhance the global search and to improve the convergence. The R-R heuristic is found to enhance the exploring potential of the algorithm and enrich the diversity of neighborhoods. Experimental results reveal the effectiveness of the proposed algorithm, whose optimization performance is markedly superior to that of genetic algorithms and is comparable to the best results reported in the literature.

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

Science.gov (United States)

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

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

Science.gov (United States)

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

Endogenous scheduling preferences and congestion

DEFF Research Database (Denmark)

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....

Multi-Satellite Observation Scheduling for Large Area Disaster Emergency Response

Science.gov (United States)

Niu, X. N.; Tang, H.; Wu, L. X.

2018-04-01

an optimal imaging plan, plays a key role in coordinating multiple satellites to monitor the disaster area. In the paper, to generate imaging plan dynamically according to the disaster relief, we propose a dynamic satellite task scheduling method for large area disaster response. First, an initial robust scheduling scheme is generated by a robust satellite scheduling model in which both the profit and the robustness of the schedule are simultaneously maximized. Then, we use a multi-objective optimization model to obtain a series of decomposing schemes. Based on the initial imaging plan, we propose a mixed optimizing algorithm named HA_NSGA-II to allocate the decomposing results thus to obtain an adjusted imaging schedule. A real disaster scenario, i.e., 2008 Wenchuan earthquake, is revisited in terms of rapid response using satellite resources and used to evaluate the performance of the proposed method with state-of-the-art approaches. We conclude that our satellite scheduling model can optimize the usage of satellite resources so as to obtain images in disaster response in a more timely and efficient manner.

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

Directory of Open Access Journals (Sweden)

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.

The robust schedule - A link to improved workflow

DEFF Research Database (Denmark)

Lindhard, Søren; Wandahl, Søren

2012-01-01

-down the contractors, and force them to rigorously adhere to the initial schedule. If delayed the work-pace or manpower has to be increased to observe the schedule. In attempt to improve productivity three independent site-mangers have been interviewed about time-scheduling. Their experiences and opinions have been...... analyzed and weaknesses in existing time scheduling have been found. The findings showed a negative side effect of keeping the schedule to tight. A too tight schedule is inflexible and cannot absorb variability in production. Flexibility is necessary because of the contractors interacting and dependable....... The result is a chaotic, complex and uncontrolled construction site. Furthermore, strict time limits entail the workflow to be optimized under non-optimal conditions. Even though productivity seems to be increasing, productivity per man-hour is decreasing resulting in increased cost. To increase productivity...

Application of coupled symbolic and numeric processing to an advanced scheduling system for plant construction

International Nuclear Information System (INIS)

Kobayashi, Yasuhiro; Takamoto, Masanori; Nonaka, Hisanori; Yamada, Naoyuki

1994-01-01

A scheduling system has been developed by integrating symbolic processing functions for constraint handling and modification guidance, with numeric processing functions for schedule optimization and evaluation. The system is composed of an automatic schedule generation module, interactive schedule revision module and schedule evaluation module. The goal of the problem solving is the flattening of the daily resources requirement throughout the scheduling period. The automatic schedule generation module optimizes the initial schedule according to the formulatable portion of requirement description specified in a predicate-like language. A planning engineer refines the near-goal schedule through a knowledge-based interactive optimization process to obtain the goal schedule which fully covers the requirement description, with the interactive schedule revision module and schedule evaluation module. A scheduling system has been implemented on the basis of the proposed problem solving framework and experimentally applied to real-world sized scheduling problems for plant construction. With a result of the overall plant construction scheduling, a section schedule optimization process is described with the emphasis on the symbolic processing functions. (author)

Schedule Matters: Understanding the Relationship between Schedule Delays and Costs on Overruns

Science.gov (United States)

Majerowicz, Walt; Shinn, Stephen A.

2016-01-01

This paper examines the relationship between schedule delays and cost overruns on complex projects. It is generally accepted by many project practitioners that cost overruns are directly related to schedule delays. But what does "directly related to" actually mean? Some reasons or root causes for schedule delays and associated cost overruns are obvious, if only in hindsight. For example, unrealistic estimates, supply chain difficulties, insufficient schedule margin, technical problems, scope changes, or the occurrence of risk events can negatively impact schedule performance. Other factors driving schedule delays and cost overruns may be less obvious and more difficult to quantify. Examples of these less obvious factors include project complexity, flawed estimating assumptions, over-optimism, political factors, "black swan" events, or even poor leadership and communication. Indeed, is it even possible the schedule itself could be a source of delay and subsequent cost overrun? Through literature review, surveys of project practitioners, and the authors' own experience on NASA programs and projects, the authors will categorize and examine the various factors affecting the relationship between project schedule delays and cost growth. The authors will also propose some ideas for organizations to consider to help create an awareness of the factors which could cause or influence schedule delays and associated cost growth on complex projects.

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

Science.gov (United States)

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

Solving cyclical nurse scheduling problem using preemptive goal programming

Science.gov (United States)

Sundari, V. E.; Mardiyati, S.

2017-07-01

Nurse scheduling system in a hospital is being modeled as a preemptive goal programming problem that is solved by using LINGO software with the objective function to minimize deviation variable at each goal. The scheduling is done cyclically, so every nurse is treated fairly since they have the same work shift portion with the other nurses. By paying attention to the hospital's rules regarding nursing work shift cyclically, it can be obtained that numbers of nurse needed in every ward are 18 nurses and the numbers of scheduling periods are 18 periods where every period consists of 21 days.

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

Energy Technology Data Exchange (ETDEWEB)

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)

Self-scheduling with Microsoft Excel.

Science.gov (United States)

Irvin, S A; Brown, H N

1999-01-01

Excessive time was being spent by the emergency department (ED) staff, head nurse, and unit secretary on a complex 6-week manual self-scheduling system. This issue, plus inevitable errors and staff dissatisfaction, resulted in a manager-lead initiative to automate elements of the scheduling process using Microsoft Excel. The implementation of this initiative included: common coding of all 8-hour and 12-hour shifts, with each 4-hour period represented by a cell; the creation of a 6-week master schedule using the "count-if" function of Excel based on current staffing guidelines; staff time-off requests then entered by the department secretary; the head nurse, with staff input, then fine-tuned the schedule to provide even unit coverage. Outcomes of these changes included an increase in staff satisfaction, time saved by the head nurse, and staff work time saved because there was less arguing about the schedule. Ultimately, the automated self-scheduling method was expanded to the entire 700-bed hospital.

Short-term hydro-thermal-wind complementary scheduling considering uncertainty of wind power using an enhanced multi-objective bee colony optimization algorithm

International Nuclear Information System (INIS)

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.

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

Directory of Open Access Journals (Sweden)

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.

On Multiple Users Scheduling Using Superposition Coding over Rayleigh Fading Channels

KAUST Repository

Zafar, Ammar

2013-02-20

In this letter, numerical results are provided to analyze the gains of multiple users scheduling via superposition coding with successive interference cancellation in comparison with the conventional single user scheduling in Rayleigh blockfading broadcast channels. The information-theoretic optimal power, rate and decoding order allocation for the superposition coding scheme are considered and the corresponding histogram for the optimal number of scheduled users is evaluated. Results show that at optimality there is a high probability that only two or three users are scheduled per channel transmission block. Numerical results for the gains of multiple users scheduling in terms of the long term throughput under hard and proportional fairness as well as for fixed merit weights for the users are also provided. These results show that the performance gain of multiple users scheduling over single user scheduling increases when the total number of users in the network increases, and it can exceed 10% for high number of users

设备维修最优化调度策略方法研究%Equipment Repair Optimization Scheduling Strategy Approach

Institute of Scientific and Technical Information of China (English)

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。

The effects of hospitals' governance on optimal contracts: bargaining vs. contracting.

Science.gov (United States)

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.

Optimal OFDMA Downlink Scheduling Under a Control Signaling Cost Constraint

OpenAIRE

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...

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

Science.gov (United States)

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.

Optimal scheduling for distributed hybrid system with pumped hydro storage

International Nuclear Information System (INIS)

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.

Performance comparison of some evolutionary algorithms on job shop scheduling problems

Science.gov (United States)

Mishra, S. K.; Rao, C. S. P.

2016-09-01

Job Shop Scheduling as a state space search problem belonging to NP-hard category due to its complexity and combinational explosion of states. Several naturally inspire evolutionary methods have been developed to solve Job Shop Scheduling Problems. In this paper the evolutionary methods namely Particles Swarm Optimization, Artificial Intelligence, Invasive Weed Optimization, Bacterial Foraging Optimization, Music Based Harmony Search Algorithms are applied and find tuned to model and solve Job Shop Scheduling Problems. To compare about 250 Bench Mark instances have been used to evaluate the performance of these algorithms. The capabilities of each these algorithms in solving Job Shop Scheduling Problems are outlined.

Distributed Research Project Scheduling Based on Multi-Agent Methods

Directory of Open Access Journals (Sweden)

Constanta Nicoleta Bodea

2011-01-01

Full Text Available Different project planning and scheduling approaches have been developed. The Operational Research (OR provides two major planning techniques: CPM (Critical Path Method and PERT (Program Evaluation and Review Technique. Due to projects complexity and difficulty to use classical methods, new approaches were developed. Artificial Intelligence (AI initially promoted the automatic planner concept, but model-based planning and scheduling methods emerged later on. The paper adresses the project scheduling optimization problem, when projects are seen as Complex Adaptive Systems (CAS. Taken into consideration two different approaches for project scheduling optimization: TCPSP (Time- Constrained Project Scheduling and RCPSP (Resource-Constrained Project Scheduling, the paper focuses on a multiagent implementation in MATLAB for TCSP. Using the research project as a case study, the paper includes a comparison between two multi-agent methods: Genetic Algorithm (GA and Ant Colony Algorithm (ACO.

CQPSO scheduling algorithm for heterogeneous multi-core DAG task model

Science.gov (United States)

Zhai, Wenzheng; Hu, Yue-Li; Ran, Feng

2017-07-01

Efficient task scheduling is critical to achieve high performance in a heterogeneous multi-core computing environment. The paper focuses on the heterogeneous multi-core directed acyclic graph (DAG) task model and proposes a novel task scheduling method based on an improved chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm. A task priority scheduling list was built. A processor with minimum cumulative earliest finish time (EFT) was acted as the object of the first task assignment. The task precedence relationships were satisfied and the total execution time of all tasks was minimized. The experimental results show that the proposed algorithm has the advantage of optimization abilities, simple and feasible, fast convergence, and can be applied to the task scheduling optimization for other heterogeneous and distributed environment.

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

DEFF Research Database (Denmark)

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...

Fractional Programming for Communication Systems—Part II: Uplink Scheduling via Matching

Science.gov (United States)

Shen, Kaiming; Yu, Wei

2018-05-01

This two-part paper develops novel methodologies for using fractional programming (FP) techniques to design and optimize communication systems. Part I of this paper proposes a new quadratic transform for FP and treats its application for continuous optimization problems. In this Part II of the paper, we study discrete problems, such as those involving user scheduling, which are considerably more difficult to solve. Unlike the continuous problems, discrete or mixed discrete-continuous problems normally cannot be recast as convex problems. In contrast to the common heuristic of relaxing the discrete variables, this work reformulates the original problem in an FP form amenable to distributed combinatorial optimization. The paper illustrates this methodology by tackling the important and challenging problem of uplink coordinated multi-cell user scheduling in wireless cellular systems. Uplink scheduling is more challenging than downlink scheduling, because uplink user scheduling decisions significantly affect the interference pattern in nearby cells. Further, the discrete scheduling variable needs to be optimized jointly with continuous variables such as transmit power levels and beamformers. The main idea of the proposed FP approach is to decouple the interaction among the interfering links, thereby permitting a distributed and joint optimization of the discrete and continuous variables with provable convergence. The paper shows that the well-known weighted minimum mean-square-error (WMMSE) algorithm can also be derived from a particular use of FP; but our proposed FP-based method significantly outperforms WMMSE when discrete user scheduling variables are involved, both in term of run-time efficiency and optimizing results.

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

DEFF Research Database (Denmark)

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...

Realistic Scheduling Mechanism for Smart Homes

Directory of Open Access Journals (Sweden)

Danish Mahmood

2016-03-01

Full Text Available In this work, we propose a Realistic Scheduling Mechanism (RSM to reduce user frustration and enhance appliance utility by classifying appliances with respective constraints and their time of use effectively. Algorithms are proposed regarding functioning of home appliances. A 24 hour time slot is divided into four logical sub-time slots, each composed of 360 min or 6 h. In these sub-time slots, only desired appliances (with respect to appliance classification are scheduled to raise appliance utility, restricting power consumption by a dynamically modelled power usage limiter that does not only take the electricity consumer into account but also the electricity supplier. Once appliance, time and power usage limiter modelling is done, we use a nature-inspired heuristic algorithm, Binary Particle Swarm Optimization (BPSO, optimally to form schedules with given constraints representing each sub-time slot. These schedules tend to achieve an equilibrium amongst appliance utility and cost effectiveness. For validation of the proposed RSM, we provide a comparative analysis amongst unscheduled electrical load usage, scheduled directly by BPSO and RSM, reflecting user comfort, which is based upon cost effectiveness and appliance utility.

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

DEFF Research Database (Denmark)

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...

Uplink Packet-Data Scheduling in DS-CDMA Systems

Science.gov (United States)

Choi, Young Woo; Kim, Seong-Lyun

In this letter, we consider the uplink packet scheduling for non-real-time data users in a DS-CDMA system. As an effort to jointly optimize throughput and fairness, we formulate a time-span minimization problem incorporating the time-multiplexing of different simultaneous transmission schemes. Based on simple rules, we propose efficient scheduling algorithms and compare them with the optimal solution obtained by linear programming.

Optimal scheduling for enhanced coal bed methane production through CO2 injection

International Nuclear Information System (INIS)

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

PARTICAL SWARM OPTIMIZATION OF TASK SCHEDULING IN CLOUD COMPUTING

OpenAIRE

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...

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

DEFF Research Database (Denmark)

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...

On program restructuring, scheduling, and communication for parallel processor systems

Energy Technology Data Exchange (ETDEWEB)

Polychronopoulos, Constantine D. [Univ. of Illinois, Urbana, IL (United States)

1986-08-01

This dissertation discusses several software and hardware aspects of program execution on large-scale, high-performance parallel processor systems. The issues covered are program restructuring, partitioning, scheduling and interprocessor communication, synchronization, and hardware design issues of specialized units. All this work was performed focusing on a single goal: to maximize program speedup, or equivalently, to minimize parallel execution time. Parafrase, a Fortran restructuring compiler was used to transform programs in a parallel form and conduct experiments. Two new program restructuring techniques are presented, loop coalescing and subscript blocking. Compile-time and run-time scheduling schemes are covered extensively. Depending on the program construct, these algorithms generate optimal or near-optimal schedules. For the case of arbitrarily nested hybrid loops, two optimal scheduling algorithms for dynamic and static scheduling are presented. Simulation results are given for a new dynamic scheduling algorithm. The performance of this algorithm is compared to that of self-scheduling. Techniques for program partitioning and minimization of interprocessor communication for idealized program models and for real Fortran programs are also discussed. The close relationship between scheduling, interprocessor communication, and synchronization becomes apparent at several points in this work. Finally, the impact of various types of overhead on program speedup and experimental results are presented.

Efficient Scheduling of Scientific Workflows with Energy Reduction Using Novel Discrete Particle Swarm Optimization and Dynamic Voltage Scaling for Computational Grids

Directory of Open Access Journals (Sweden)

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.

Efficient Scheduling of Scientific Workflows with Energy Reduction Using Novel Discrete Particle Swarm Optimization and Dynamic Voltage Scaling for Computational Grids

Science.gov (United States)

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

Maximizing the nurses' preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm

Science.gov (United States)

Jafari, Hamed; Salmasi, Nasser

2015-09-01

The nurse scheduling problem (NSP) has received a great amount of attention in recent years. In the NSP, the goal is to assign shifts to the nurses in order to satisfy the hospital's demand during the planning horizon by considering different objective functions. In this research, we focus on maximizing the nurses' preferences for working shifts and weekends off by considering several important factors such as hospital's policies, labor laws, governmental regulations, and the status of nurses at the end of the previous planning horizon in one of the largest hospitals in Iran i.e., Milad Hospital. Due to the shortage of available nurses, at first, the minimum total number of required nurses is determined. Then, a mathematical programming model is proposed to solve the problem optimally. Since the proposed research problem is NP-hard, a meta-heuristic algorithm based on simulated annealing (SA) is applied to heuristically solve the problem in a reasonable time. An initial feasible solution generator and several novel neighborhood structures are applied to enhance performance of the SA algorithm. Inspired from our observations in Milad hospital, random test problems are generated to evaluate the performance of the SA algorithm. The results of computational experiments indicate that the applied SA algorithm provides solutions with average percentage gap of 5.49 % compared to the upper bounds obtained from the mathematical model. Moreover, the applied SA algorithm provides significantly better solutions in a reasonable time than the schedules provided by the head nurses.

The Synthesis of a Hardware Scheduler for Non-Manifest Loops

NARCIS (Netherlands)

Mansour, O.; Molenkamp, Egbert; Krol, Th.

This paper addresses the hardware implementation of a dynamic scheduler for non-manifest data dependent periodic loops. Static scheduling techniques which are known to give near optimal scheduling-solutions for manifest loops, fail at scheduling non-manifest loops, since they lack the run time

Utilization Bound of Non-preemptive Fixed Priority Schedulers

Science.gov (United States)

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.

TRU Waste Management Program. Cost/schedule optimization analysis

International Nuclear Information System (INIS)

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

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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%.

Cloud computing task scheduling strategy based on improved differential evolution algorithm

Science.gov (United States)

Ge, Junwei; He, Qian; Fang, Yiqiu

2017-04-01

In order to optimize the cloud computing task scheduling scheme, an improved differential evolution algorithm for cloud computing task scheduling is proposed. Firstly, the cloud computing task scheduling model, according to the model of the fitness function, and then used improved optimization calculation of the fitness function of the evolutionary algorithm, according to the evolution of generation of dynamic selection strategy through dynamic mutation strategy to ensure the global and local search ability. The performance test experiment was carried out in the CloudSim simulation platform, the experimental results show that the improved differential evolution algorithm can reduce the cloud computing task execution time and user cost saving, good implementation of the optimal scheduling of cloud computing tasks.

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

International Nuclear Information System (INIS)

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

Nuclear power plant maintenance scheduling dilemma: a genetic algorithm approach

International Nuclear Information System (INIS)

Mahdavi, M.H.; Modarres, M.

2004-01-01

There are huge numbers of components scheduled for maintenance when a nuclear power plant is shut down. Among these components, a number of them are safety related which their operability as well as reliability when plant becomes up is main concerns. Not performing proper maintenance on this class of components/system would impose substantial risk on operating the NPP. In this paper a new approach based on genetic algorithms is presented to optimize the NPP maintenance schedule during shutdown. following this approach the cost incurred by maintenance activities for each schedule is balanced with the risk imposed by the maintenance scheduling plan to the plant operation status when it is up. The risk model implemented in the GA scheduler as its evaluation function is developed on the basis of the probabilistic risk assessment methodology. the Ga optimizers itself is shown to be superior compared to other optimization methods such as the monte carlo technique

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

International Nuclear Information System (INIS)

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

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

Science.gov (United States)

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

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

Planning and Scheduling for Fleets of Earth Observing Satellites

Science.gov (United States)

Frank, Jeremy; Jonsson, Ari; Morris, Robert; Smith, David E.; Norvig, Peter (Technical Monitor)

2001-01-01

We address the problem of scheduling observations for a collection of earth observing satellites. This scheduling task is a difficult optimization problem, potentially involving many satellites, hundreds of requests, constraints on when and how to service each request, and resources such as instruments, recording devices, transmitters, and ground stations. High-fidelity models are required to ensure the validity of schedules; at the same time, the size and complexity of the problem makes it unlikely that systematic optimization search methods will be able to solve them in a reasonable time. This paper presents a constraint-based approach to solving the Earth Observing Satellites (EOS) scheduling problem, and proposes a stochastic heuristic search method for solving it.

Robust and Flexible Scheduling with Evolutionary Computation

DEFF Research Database (Denmark)

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...

Integrated network design and scheduling problems :

Energy Technology Data Exchange (ETDEWEB)

Nurre, Sarah G.; Carlson, Jeffrey J.

2014-01-01

We consider the class of integrated network design and scheduling problems. These problems focus on selecting and scheduling operations that will change the characteristics of a network, while being speci cally concerned with the performance of the network over time. Motivating applications of INDS problems include infrastructure restoration after extreme events and building humanitarian distribution supply chains. While similar models have been proposed, no one has performed an extensive review of INDS problems from their complexity, network and scheduling characteristics, information, and solution methods. We examine INDS problems under a parallel identical machine scheduling environment where the performance of the network is evaluated by solving classic network optimization problems. We classify that all considered INDS problems as NP-Hard and propose a novel heuristic dispatching rule algorithm that selects and schedules sets of arcs based on their interactions in the network. We present computational analysis based on realistic data sets representing the infrastructures of coastal New Hanover County, North Carolina, lower Manhattan, New York, and a realistic arti cial community CLARC County. These tests demonstrate the importance of a dispatching rule to arrive at near-optimal solutions during real-time decision making activities. We extend INDS problems to incorporate release dates which represent the earliest an operation can be performed and exible release dates through the introduction of specialized machine(s) that can perform work to move the release date earlier in time. An online optimization setting is explored where the release date of a component is not known.

A review of metaheuristic scheduling techniques in cloud computing

Directory of Open Access Journals (Sweden)

Mala Kalra

2015-11-01

Full Text Available Cloud computing has become a buzzword in the area of high performance distributed computing as it provides on-demand access to shared pool of resources over Internet in a self-service, dynamically scalable and metered manner. Cloud computing is still in its infancy, so to reap its full benefits, much research is required across a broad array of topics. One of the important research issues which need to be focused for its efficient performance is scheduling. The goal of scheduling is to map tasks to appropriate resources that optimize one or more objectives. Scheduling in cloud computing belongs to a category of problems known as NP-hard problem due to large solution space and thus it takes a long time to find an optimal solution. There are no algorithms which may produce optimal solution within polynomial time to solve these problems. In cloud environment, it is preferable to find suboptimal solution, but in short period of time. Metaheuristic based techniques have been proved to achieve near optimal solutions within reasonable time for such problems. In this paper, we provide an extensive survey and comparative analysis of various scheduling algorithms for cloud and grid environments based on three popular metaheuristic techniques: Ant Colony Optimization (ACO, Genetic Algorithm (GA and Particle Swarm Optimization (PSO, and two novel techniques: League Championship Algorithm (LCA and BAT algorithm.

Surgical scheduling: a lean approach to process improvement.

Science.gov (United States)

Simon, Ross William; Canacari, Elena G

2014-01-01

A large teaching hospital in the northeast United States had an inefficient, paper-based process for scheduling orthopedic surgery that caused delays and contributed to site/side discrepancies. The hospital's leaders formed a team with the goals of developing a safe, effective, patient-centered, timely, efficient, and accurate orthopedic scheduling process; smoothing the schedule so that block time was allocated more evenly; and ensuring correct site/side. Under the resulting process, real-time patient information is entered into a database during the patient's preoperative visit in the surgeon's office. The team found the new process reduced the occurrence of site/side discrepancies to zero, reduced instances of changing the sequence of orthopedic procedures by 70%, and increased patient satisfaction. Copyright © 2014 AORN, Inc. Published by Elsevier Inc. All rights reserved.

Intelligent Scheduling of a Grid-Connected Heat Pump in a Danish Detached House

DEFF Research Database (Denmark)

Gianniou, Panagiota; Foteinaki, Kyriaki; Heller, Alfred

This study proposes a methodology for intelligent scheduling of a heat pump installed in a refurbished grid-connected detached house in Denmark. This scheduling is conducted through the coupling of a dynamic building simulation tool with an optimization tool. The optimization of the operation of ...... thermal comfort conditions. The proposed methodology bridges dynamic building modelling with optimization of real-time operation of HVAC systems offering a detailed model for building physics, especially regarding thermal mass and a stochastic price-based control....... of the system is based on a price-signal considering a three-day period for different weather cases. The results show that the optimal scheduling of the system is successful in terms of reducing the peak load during times when electricity prices are high, thus achieving cost savings as well as maintaining good......This study proposes a methodology for intelligent scheduling of a heat pump installed in a refurbished grid-connected detached house in Denmark. This scheduling is conducted through the coupling of a dynamic building simulation tool with an optimization tool. The optimization of the operation...

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

Directory of Open Access Journals (Sweden)

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.

ENHANCED HYBRID PSO – ACO ALGORITHM FOR GRID SCHEDULING

Directory of Open Access Journals (Sweden)

P. Mathiyalagan

2010-07-01

Full Text Available Grid computing is a high performance computing environment to solve larger scale computational demands. Grid computing contains resource management, task scheduling, security problems, information management and so on. Task scheduling is a fundamental issue in achieving high performance in grid computing systems. A computational GRID is typically heterogeneous in the sense that it combines clusters of varying sizes, and different clusters typically contains processing elements with different level of performance. In this, heuristic approaches based on particle swarm optimization and ant colony optimization algorithms are adopted for solving task scheduling problems in grid environment. Particle Swarm Optimization (PSO is one of the latest evolutionary optimization techniques by nature. It has the better ability of global searching and has been successfully applied to many areas such as, neural network training etc. Due to the linear decreasing of inertia weight in PSO the convergence rate becomes faster, which leads to the minimal makespan time when used for scheduling. To make the convergence rate faster, the PSO algorithm is improved by modifying the inertia parameter, such that it produces better performance and gives an optimized result. The ACO algorithm is improved by modifying the pheromone updating rule. ACO algorithm is hybridized with PSO algorithm for efficient result and better convergence in PSO algorithm.

Simultaneous optimization of planning and scheduling in an oil refinery

NARCIS (Netherlands)

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

Using Integer Programming for Airport Service Planning in Staff Scheduling

Directory of Open Access Journals (Sweden)

W.H. Ip

2010-09-01

Full Text Available Reliability and safety in flight is extremely necessary and that depend on the adoption of proper maintenance system. Therefore, it is essential for aircraft maintenance companies to perform the manpower scheduling efficiently. One of the objectives of this paper is to provide an Integer Programming approach to determine the optimal solutions to aircraft maintenance planning and scheduling and hence the planning and scheduling processes can become more efficient and effective. Another objective is to develop a set of computational schedules for maintenance manpower to cover all scheduled flights. In this paper, a sequential methodology consisting of 3 stages is proposed. They are initial maintenance demand schedule, the maintenance pairing and the maintenance group(s assignment. Since scheduling would split up into different stages, different mathematical techniques have been adopted to cater for their own problem characteristics. Microsoft Excel would be used. Results from the first stage and second stage would be inputted into integer programming model using Microsoft Excel Solver to find the optimal solution. Also, Microsoft Excel VBA is used for devising a scheduling system in order to reduce the manual process and provide a user friendly interface. For the results, all can be obtained optimal solution and the computation time is reasonable and acceptable. Besides, the comparison of the peak time and non-peak time is discussed.

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

Directory of Open Access Journals (Sweden)

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.

A Multiobjective Robust Scheduling Optimization Mode for Multienergy Hybrid System Integrated by Wind Power, Solar Photovoltaic Power, and Pumped Storage Power

Directory of Open Access Journals (Sweden)

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.

Harmonious personnel scheduling

NARCIS (Netherlands)

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,

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

Directory of Open Access Journals (Sweden)

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

SPANR planning and scheduling

Science.gov (United States)

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.

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

DEFF Research Database (Denmark)

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...

Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments

Directory of Open Access Journals (Sweden)

Sonia Yassa

2013-01-01

Full Text Available We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach.

Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments

Science.gov (United States)

Kadima, Hubert; Granado, Bertrand

2013-01-01

We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach. PMID:24319361

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

Directory of Open Access Journals (Sweden)

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.

A comparison of alternative medicare reimbursement policies under optimal hospital pricing.

Science.gov (United States)

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

Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization.

Science.gov (United States)

Huang, Daizheng; Wu, Zhihui

2017-01-01

Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.

Evolutionary Scheduler for the Deep Space Network

Science.gov (United States)

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.

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

OpenAIRE

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...

Application of cultural algorithm to generation scheduling of hydrothermal systems

International Nuclear Information System (INIS)

Yuan Xiaohui; Yuan Yanbin

2006-01-01

The daily generation scheduling of hydrothermal power systems plays an important role in the operation of electric power systems for economics and security, which is a large scale dynamic non-linear constrained optimization problem. It is difficult to solve using traditional optimization methods. This paper proposes a new cultural algorithm to solve the optimal daily generation scheduling of hydrothermal power systems. The approach takes the water transport delay time between connected reservoirs into consideration and can conveniently deal with the complicated hydraulic coupling simultaneously. An example is used to verify the correctness and effectiveness of the proposed cultural algorithm, comparing with both the Lagrange method and the genetic algorithm method. The simulation results demonstrate that the proposed algorithm has rapid convergence speed and higher solution precision. Thus, an effective method is provided to solve the optimal daily generation scheduling of hydrothermal systems

Case mix classification and a benchmark set for surgery scheduling

NARCIS (Netherlands)

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

NUMA-Aware Thread Scheduling for Big Data Transfers over Terabits Network Infrastructure

Directory of Open Access Journals (Sweden)

Taeuk Kim

2018-01-01

Full Text Available The evergrowing trend of big data has led scientists to share and transfer the simulation and analytical data across the geodistributed research and computing facilities. However, the existing data transfer frameworks used for data sharing lack the capability to adopt the attributes of the underlying parallel file systems (PFS. LADS (Layout-Aware Data Scheduling is an end-to-end data transfer tool optimized for terabit network using a layout-aware data scheduling via PFS. However, it does not consider the NUMA (Nonuniform Memory Access architecture. In this paper, we propose a NUMA-aware thread and resource scheduling for optimized data transfer in terabit network. First, we propose distributed RMA buffers to reduce memory controller contention in CPU sockets and then schedule the threads based on CPU socket and NUMA nodes inside CPU socket to reduce memory access latency. We design and implement the proposed resource and thread scheduling in the existing LADS framework. Experimental results showed from 21.7% to 44% improvement with memory-level optimizations in the LADS framework as compared to the baseline without any optimization.

A Novel Assembly Line Scheduling Algorithm Based on CE-PSO

Directory of Open Access Journals (Sweden)

Xiaomei Hu

2015-01-01

Full Text Available With the widespread application of assembly line in enterprises, assembly line scheduling is an important problem in the production since it directly affects the productivity of the whole manufacturing system. The mathematical model of assembly line scheduling problem is put forward and key data are confirmed. A double objective optimization model based on equipment utilization and delivery time loss is built, and optimization solution strategy is described. Based on the idea of solution strategy, assembly line scheduling algorithm based on CE-PSO is proposed to overcome the shortcomings of the standard PSO. Through the simulation experiments of two examples, the validity of the assembly line scheduling algorithm based on CE-PSO is proved.

Optimization and planning of operating theatre activities: an original definition of pathways and process modeling.

Science.gov (United States)

Barbagallo, Simone; Corradi, Luca; de Ville de Goyet, Jean; Iannucci, Marina; Porro, Ivan; Rosso, Nicola; Tanfani, Elena; Testi, Angela

2015-05-17

The Operating Room (OR) is a key resource of all major hospitals, but it also accounts for up 40% of resource costs. Improving cost effectiveness, while maintaining a quality of care, is a universal objective. These goals imply an optimization of planning and a scheduling of the activities involved. This is highly challenging due to the inherent variable and unpredictable nature of surgery. A Business Process Modeling Notation (BPMN 2.0) was used for the representation of the "OR Process" (being defined as the sequence of all of the elementary steps between "patient ready for surgery" to "patient operated upon") as a general pathway ("path"). The path was then both further standardized as much as possible and, at the same time, keeping all of the key-elements that would allow one to address or define the other steps of planning, and the inherent and wide variability in terms of patient specificity. The path was used to schedule OR activity, room-by-room, and day-by-day, feeding the process from a "waiting list database" and using a mathematical optimization model with the objective of ending up in an optimized planning. The OR process was defined with special attention paid to flows, timing and resource involvement. Standardization involved a dynamics operation and defined an expected operating time for each operation. The optimization model has been implemented and tested on real clinical data. The comparison of the results reported with the real data, shows that by using the optimization model, allows for the scheduling of about 30% more patients than in actual practice, as well as to better exploit the OR efficiency, increasing the average operating room utilization rate up to 20%. The optimization of OR activity planning is essential in order to manage the hospital's waiting list. Optimal planning is facilitated by defining the operation as a standard pathway where all variables are taken into account. By allowing a precise scheduling, it feeds the process of

Multiuser switched diversity scheduling schemes

KAUST Repository

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.

Multiuser switched diversity scheduling schemes

KAUST Repository

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.

Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural Network

Directory of Open Access Journals (Sweden)

Kazuhiko Hiramoto

2018-01-01

Full Text Available We propose an adaptive gain scheduled semiactive control method using an artificial neural network for structural systems subject to earthquake disturbance. In order to design a semiactive control system with high control performance against earthquakes with different time and/or frequency properties, multiple semiactive control laws with high performance for each of multiple earthquake disturbances are scheduled with an adaptive manner. Each semiactive control law to be scheduled is designed based on the output emulation approach that has been proposed by the authors. As the adaptive gain scheduling mechanism, we introduce an artificial neural network (ANN. Input signals of the ANN are the measured earthquake disturbance itself, for example, the acceleration, velocity, and displacement. The output of the ANN is the parameter for the scheduling of multiple semiactive control laws each of which has been optimized for a single disturbance. Parameters such as weight and bias in the ANN are optimized by the genetic algorithm (GA. The proposed design method is applied to semiactive control design of a base-isolated building with a semiactive damper. With simulation study, the proposed adaptive gain scheduling method realizes control performance exceeding single semiactive control optimizing the average of the control performance subject to various earthquake disturbances.

Susceptibility of optimal train schedules to stochastic disturbances of process times

DEFF Research Database (Denmark)

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...

Genetic algorithm parameters tuning for resource-constrained project scheduling problem

Science.gov (United States)

Tian, Xingke; Yuan, Shengrui

2018-04-01

Project Scheduling Problem (RCPSP) is a kind of important scheduling problem. To achieve a certain optimal goal such as the shortest duration, the smallest cost, the resource balance and so on, it is required to arrange the start and finish of all tasks under the condition of satisfying project timing constraints and resource constraints. In theory, the problem belongs to the NP-hard problem, and the model is abundant. Many combinatorial optimization problems are special cases of RCPSP, such as job shop scheduling, flow shop scheduling and so on. At present, the genetic algorithm (GA) has been used to deal with the classical RCPSP problem and achieved remarkable results. Vast scholars have also studied the improved genetic algorithm for the RCPSP problem, which makes it to solve the RCPSP problem more efficiently and accurately. However, for the selection of the main parameters of the genetic algorithm, there is no parameter optimization in these studies. Generally, we used the empirical method, but it cannot ensure to meet the optimal parameters. In this paper, the problem was carried out, which is the blind selection of parameters in the process of solving the RCPSP problem. We made sampling analysis, the establishment of proxy model and ultimately solved the optimal parameters.

Real-time disk scheduling in a mixed-media file system

NARCIS (Netherlands)

Bosch, H.G.P.; Mullender, Sape J.

2000-01-01

This paper presents our real-time disk scheduler called the Delta L scheduler, which optimizes unscheduled best-effort disk requests by giving priority to best-effort disk requests while meeting real-time request deadlines. Our scheduler tries to execute real-time disk requests as much as possible

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

Science.gov (United States)

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.

optimal scheduling of petroleum products distribution in nigeria

African Journals Online (AJOL)

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 ...

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

International Nuclear Information System (INIS)

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)

Nontraditional work schedules for pharmacists.

Science.gov (United States)

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.

Real-Time Disk Scheduling in a Mixed-Media File System

NARCIS (Netherlands)

Bosch, H.G.P.; Mullender, Sape J.

2000-01-01

This paper presents our real-time disk scheduler called the L scheduler, which optimizes unscheduled best-effort disk requests by giving priority to best-effort disk requests while meeting real-time request deadlines. Our scheduler tries to execute real-time disk requests as much as possible in the

Artificial intelligence for the CTA Observatory scheduler

Science.gov (United States)

Colomé, Josep; Colomer, Pau; Campreciós, Jordi; Coiffard, Thierry; de Oña, Emma; Pedaletti, Giovanna; Torres, Diego F.; Garcia-Piquer, Alvaro

2014-08-01

The Cherenkov Telescope Array (CTA) project will be the next generation ground-based very high energy gamma-ray instrument. The success of the precursor projects (i.e., HESS, MAGIC, VERITAS) motivated the construction of this large infrastructure that is included in the roadmap of the ESFRI projects since 2008. CTA is planned to start the construction phase in 2015 and will consist of two arrays of Cherenkov telescopes operated as a proposal-driven open observatory. Two sites are foreseen at the southern and northern hemispheres. The CTA observatory will handle several observation modes and will have to operate tens of telescopes with a highly efficient and reliable control. Thus, the CTA planning tool is a key element in the control layer for the optimization of the observatory time. The main purpose of the scheduler for CTA is the allocation of multiple tasks to one single array or to multiple sub-arrays of telescopes, while maximizing the scientific return of the facility and minimizing the operational costs. The scheduler considers long- and short-term varying conditions to optimize the prioritization of tasks. A short-term scheduler provides the system with the capability to adapt, in almost real-time, the selected task to the varying execution constraints (i.e., Targets of Opportunity, health or status of the system components, environment conditions). The scheduling procedure ensures that long-term planning decisions are correctly transferred to the short-term prioritization process for a suitable selection of the next task to execute on the array. In this contribution we present the constraints to CTA task scheduling that helped classifying it as a Flexible Job-Shop Problem case and finding its optimal solution based on Artificial Intelligence techniques. We describe the scheduler prototype that uses a Guarded Discrete Stochastic Neural Network (GDSN), for an easy representation of the possible long- and short-term planning solutions, and Constraint

Flexible job-shop scheduling based on genetic algorithm and simulation validation

Directory of Open Access Journals (Sweden)

Zhou Erming

2017-01-01

Full Text Available This paper selects flexible job-shop scheduling problem as the research object, and Constructs mathematical model aimed at minimizing the maximum makespan. Taking the transmission reverse gear production line of a transmission corporation as an example, genetic algorithm is applied for flexible jobshop scheduling problem to get the specific optimal scheduling results with MATLAB. DELMIA/QUEST based on 3D discrete event simulation is applied to construct the physical model of the production workshop. On the basis of the optimal scheduling results, the logical link of the physical model for the production workshop is established, besides, importing the appropriate process parameters to make virtual simulation on the production workshop. Finally, through analyzing the simulated results, it shows that the scheduling results are effective and reasonable.

Economic and psychological burden of scheduled surgery ...

African Journals Online (AJOL)

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.

Solving a chemical batch scheduling problem by local search

NARCIS (Netherlands)

Brucker, P.; Hurink, Johann L.

1999-01-01

A chemical batch scheduling problem is modelled in two different ways as a discrete optimization problem. Both models are used to solve the batch scheduling problem in a two-phase tabu search procedure. The method is tested on real-world data.

A heuristic model for risk and cost impacts of plant outage maintenance schedule

International Nuclear Information System (INIS)

Mohammad Hadi Hadavi, S.

2009-01-01

Cost and risk are two major competing criteria in maintenance optimization problems. If a plant is forced to shutdown because of accident or fear of accident happening, beside loss of revenue, it causes damage to the credibility and reputation of the business operation. In this paper a heuristic model for incorporating three compelling optimization criteria (i.e., risk, cost, and loss) into a single evaluation function is proposed. Such a model could be used in any evaluation engine of outage maintenance schedule optimizer. It is attempted to make the model realistic and to address the ongoing challenges facing a schedule planner in a simple and commonly understandable fashion. Two simple competing schedules for the NPP feedwater system are examined against the model. The results show that while the model successfully addresses the current challenges for outage maintenance optimization, it properly demonstrates the dynamics of schedule in regards to risk, cost, and losses endured by maintenance schedule, particularly when prolonged outage and lack of maintenance for equipments in need of urgent care are of concern.

Multi-objective demand side scheduling considering the operational safety of appliances

International Nuclear Information System (INIS)

Du, Y.F.; Jiang, L.; Li, Y.Z.; Counsell, J.; Smith, J.S.

2016-01-01

Highlights: • Operational safety of appliances is introduced in multi-objective scheduling. • Relationships between operational safety and other objectives are investigated. • Adopted Pareto approach is compared with Weigh and Constraint approaches. • Decision making of Pareto approach is proposed for final appliances’ scheduling. - Abstract: The safe operation of appliances is of great concern to users. The safety risk increases when the appliances are in operation during periods when users are not at home or when they are asleep. In this paper, multi-objective demand side scheduling is investigated with consideration to the appliances’ operational safety together with the electricity cost and the operational delay. The formulation of appliances’ operational safety is proposed based on users’ at-home status and awake status. Then the relationships between the operational safety and the other two objectives are investigated through the approach of finding the Pareto-optimal front. Moreover, this approach is compared with the Weigh and Constraint approaches. As the Pareto-optimal front consists of a set of optimal solutions, this paper proposes a method to make the final scheduling decision based on the relationships among the multiple objectives. Simulation results demonstrate that the operational safety is improved with the sacrifice of the electricity cost and the operational delay, and that the approach of finding the Pareto-optimal front is effective in presenting comprehensive optimal solutions of the multi-objective demand side scheduling.

The optimality of hospital financing system: the role of physician-manager interactions.

Science.gov (United States)

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.

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

CSIR Research Space (South Africa)

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...

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

Directory of Open Access Journals (Sweden)

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.

A Form 990 Schedule H conundrum: how much of your bad debt might be charity?

Science.gov (United States)

Bailey, Shari; Franklin, David; Hearle, Keith

2010-04-01

IRS Form 990 Schedule H requires hospitals to estimate the amount of bad debt expense attributable to patients eligible for charity under the hospital's charity care policy. Responses to Schedule H, Part III.A.3 open up the entire patient collection process to examination by the IRS, state officials, and the public. Using predictive analytics can help hospitals efficiently identify charity-eligible patients when answering Part III.A.3.

Online Scheduling in Distributed Message Converter Systems

NARCIS (Netherlands)

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

Optimal Power Scheduling for an Islanded Hybrid Microgrid

DEFF Research Database (Denmark)

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...

Scheduling for energy and reliability management on multiprocessor real-time systems

Science.gov (United States)

Qi, Xuan

Scheduling algorithms for multiprocessor real-time systems have been studied for years with many well-recognized algorithms proposed. However, it is still an evolving research area and many problems remain open due to their intrinsic complexities. With the emergence of multicore processors, it is necessary to re-investigate the scheduling problems and design/develop efficient algorithms for better system utilization, low scheduling overhead, high energy efficiency, and better system reliability. Focusing cluster schedulings with optimal global schedulers, we study the utilization bound and scheduling overhead for a class of cluster-optimal schedulers. Then, taking energy/power consumption into consideration, we developed energy-efficient scheduling algorithms for real-time systems, especially for the proliferating embedded systems with limited energy budget. As the commonly deployed energy-saving technique (e.g. dynamic voltage frequency scaling (DVFS)) will significantly affect system reliability, we study schedulers that have intelligent mechanisms to recuperate system reliability to satisfy the quality assurance requirements. Extensive simulation is conducted to evaluate the performance of the proposed algorithms on reduction of scheduling overhead, energy saving, and reliability improvement. The simulation results show that the proposed reliability-aware power management schemes could preserve the system reliability while still achieving substantial energy saving.

Online scheduling of 2-re-entrant flexible manufacturing systems

NARCIS (Netherlands)

Pinxten, J. van; Waqas, U.; Geilen, M.; Basten, T.; Somers, L.

2017-01-01

Online scheduling of operations is essential to optimize productivity of flexible manufacturing systems (FMSs) where manufacturing requests arrive on the fly. An FMS processes products according to a particular flow through processing stations. This work focusses on online scheduling of re-entrant

An Improved Genetic Algorithm for Single-Machine Inverse Scheduling Problem

Directory of Open Access Journals (Sweden)

Jianhui Mou

2014-01-01

Full Text Available The goal of the scheduling is to arrange operations on suitable machines with optimal sequence for corresponding objectives. In order to meet market requirements, scheduling systems must own enough flexibility against uncertain events. These events can change production status or processing parameters, even causing the original schedule to no longer be optimal or even to be infeasible. Traditional scheduling strategies, however, cannot cope with these cases. Therefore, a new idea of scheduling called inverse scheduling has been proposed. In this paper, the inverse scheduling with weighted completion time (SMISP is considered in a single-machine shop environment. In this paper, an improved genetic algorithm (IGA with a local searching strategy is proposed. To improve the performance of IGA, efficient encoding scheme, fitness evaluation mechanism, feasible initialization methods, and a local search procedure have been employed in the paper. Because of the local improving method, the proposed IGA can balance its exploration ability and exploitation ability. We adopt 27 instances to verify the effectiveness of the proposed algorithm. The experimental results illustrated that the proposed algorithm can generate satisfactory solutions. This approach also has been applied to solve the scheduling problem in the real Chinese shipyard and can bring some benefits.

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

Directory of Open Access Journals (Sweden)

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

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

International Nuclear Information System (INIS)

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

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

Science.gov (United States)

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.

Multiple-Machine Scheduling with Learning Effects and Cooperative Games

Directory of Open Access Journals (Sweden)

Yiyuan Zhou

2015-01-01

Full Text Available Multiple-machine scheduling problems with position-based learning effects are studied in this paper. There is an initial schedule in this scheduling problem. The optimal schedule minimizes the sum of the weighted completion times; the difference between the initial total weighted completion time and the minimal total weighted completion time is the cost savings. A multiple-machine sequencing game is introduced to allocate the cost savings. The game is balanced if the normal processing times of jobs that are on the same machine are equal and an equal number of jobs are scheduled on each machine initially.

Joint opportunistic scheduling and network coding for bidirectional relay channel

KAUST Repository

Shaqfeh, Mohammad

2013-07-01

In this paper, we consider a two-way communication system in which two users communicate with each other through an intermediate relay over block-fading channels. We investigate the optimal opportunistic scheduling scheme in order to maximize the long-term average transmission rate in the system assuming symmetric information flow between the two users. Based on the channel state information, the scheduler decides that either one of the users transmits to the relay, or the relay transmits to a single user or broadcasts to both users a combined version of the two users\\' transmitted information by using linear network coding. We obtain the optimal scheduling scheme by using the Lagrangian dual problem. Furthermore, in order to characterize the gains of network coding and opportunistic scheduling, we compare the achievable rate of the system versus suboptimal schemes in which the gains of network coding and opportunistic scheduling are partially exploited. © 2013 IEEE.

Cyclic machine scheduling with tool transportation - additional calculations

NARCIS (Netherlands)

Kuijpers, C.M.H.

2001-01-01

In the PhD Thesis of Kuijpers a cyclic machine scheduling problem with tool transportation is considered. For the problem with two machines, it is shown that there always exists an optimal schedule with a certain structure. This is done by means of an elaborate case study. For a number of cases some

Research and Applications of Shop Scheduling Based on Genetic Algorithms

Directory of Open Access Journals (Sweden)

Hang ZHAO

Full Text Available ABSTRACT Shop Scheduling is an important factor affecting the efficiency of production, efficient scheduling method and a research and application for optimization technology play an important role for manufacturing enterprises to improve production efficiency, reduce production costs and many other aspects. Existing studies have shown that improved genetic algorithm has solved the limitations that existed in the genetic algorithm, the objective function is able to meet customers' needs for shop scheduling, and the future research should focus on the combination of genetic algorithm with other optimized algorithms. In this paper, in order to overcome the shortcomings of early convergence of genetic algorithm and resolve local minimization problem in search process,aiming at mixed flow shop scheduling problem, an improved cyclic search genetic algorithm is put forward, and chromosome coding method and corresponding operation are given.The operation has the nature of inheriting the optimal individual ofthe previous generation and is able to avoid the emergence of local minimum, and cyclic and crossover operation and mutation operation can enhance the diversity of the population and then quickly get the optimal individual, and the effectiveness of the algorithm is validated. Experimental results show that the improved algorithm can well avoid the emergency of local minimum and is rapid in convergence.

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

DEFF Research Database (Denmark)

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....

Axiomatic Design of a Framework for the Comprehensive Optimization of Patient Flows in Hospitals

Directory of Open Access Journals (Sweden)

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.

Interactive Dynamic Mission Scheduling for ASCA

Science.gov (United States)

Antunes, A.; Nagase, F.; Isobe, T.

The Japanese X-ray astronomy satellite ASCA (Advanced Satellite for Cosmology and Astrophysics) mission requires scheduling for each 6-month observation phase, further broken down into weekly schedules at a few minutes resolution. Two tools, SPIKE and NEEDLE, written in Lisp and C, use artificial intelligence (AI) techniques combined with a graphic user interface for fast creation and alteration of mission schedules. These programs consider viewing and satellite attitude constraints as well as observer-requested criteria and present an optimized set of solutions for review by the planner. Six-month schedules at 1 day resolution are created for an oversubscribed set of targets by the SPIKE software, originally written for HST and presently being adapted for EUVE, XTE and AXAF. The NEEDLE code creates weekly schedules at 1 min resolution using in-house orbital routines and creates output for processing by the command generation software. Schedule creation on both the long- and short-term scale is rapid, less than 1 day for long-term, and one hour for short-term.

Responsive versus scheduled feeding in preterm infants (Review)

OpenAIRE

Watson, Julie; McGuire, William

2015-01-01

Scheduled feeding of prescribed enteral volumes remains standard practice for preterm infants. However, feeding preterm infants in response to their feeding and satiation cues (responsive, cue-based, or infant led feeding) rather than at scheduled intervals might enhance parent experience and satisfaction, help in the establishment of independent oral feeding, increase nutrient intake and growth rates, and allow earlier hospital discharge.\\ud \\ud Objectives: To assess the effect of feeding pr...

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

Science.gov (United States)

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

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

Science.gov (United States)

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

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

Directory of Open Access Journals (Sweden)

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

A reduced feedback proportional fair multiuser scheduling scheme

KAUST Repository

Shaqfeh, Mohammad

2011-12-01

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. A 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 propose a novel proportional fair multiuser switched-diversity scheduling scheme and we demonstrate that it can be optimized using a practical and distributed method to obtain the per-user feedback thresholds. We demonstrate by numerical examples that our reduced feedback proportional fair scheduler operates within 0.3 bits/sec/Hz from the achievable rates by the conventional full feedback proportional fair scheduler in Rayleigh fading conditions. © 2011 IEEE.

Technology for planning and scheduling under complex constraints

Science.gov (United States)

Alguire, Karen M.; Pedro Gomes, Carla O.

1997-02-01

Within the context of law enforcement, several problems fall into the category of planning and scheduling under constraints. Examples include resource and personnel scheduling, and court scheduling. In the case of court scheduling, a schedule must be generated considering available resources, e.g., court rooms and personnel. Additionally, there are constraints on individual court cases, e.g., temporal and spatial, and between different cases, e.g., precedence. Finally, there are overall objectives that the schedule should satisfy such as timely processing of cases and optimal use of court facilities. Manually generating a schedule that satisfies all of the constraints is a very time consuming task. As the number of court cases and constraints increases, this becomes increasingly harder to handle without the assistance of automatic scheduling techniques. This paper describes artificial intelligence (AI) technology that has been used to develop several high performance scheduling applications including a military transportation scheduler, a military in-theater airlift scheduler, and a nuclear power plant outage scheduler. We discuss possible law enforcement applications where we feel the same technology could provide long-term benefits to law enforcement agencies and their operations personnel.

Planning and scheduling for petroleum refineries using mathematical programming

Directory of Open Access Journals (Sweden)

Joly M.

2002-01-01

Full Text Available The objective of this paper is the development and solution of nonlinear and mixed-integer (MIP optimization models for real-world planning and scheduling problems in petroleum refineries. Firstly, we present a nonlinear planning model that represents a general refinery topology and allows implementation of nonlinear process models as well as blending relations. The optimization model is able to define new operating points, thus increasing the production of the more valuable products and simultaneously satisfying all specification constraints. The second part addresses scheduling problems in oil refineries, which are formulated as MIP optimization models and rely on both continuous and discrete time representations. Three practical applications closely related to the current refinery scenario are presented. The first one addresses the problem of crude oil inventory management of a refinery that receives several types of crude oil delivered exclusively by a single oil pipeline. Subsequently, two optimization models intended to define the optimal production policy, inventory control and distribution are proposed and solved for the fuel oil and asphalt plant. Finally, the planning model of Moro et al. (1998 is extended in order to sequence decisions at the scheduling level in the liquefied petroleum gas (LPG area for maximization of the production of petrochemical-grade propane and product delivery.

An optimal algorithm for preemptive on-line scheduling

NARCIS (Netherlands)

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

Variable Neighborhood Search for Parallel Machines Scheduling Problem with Step Deteriorating Jobs

Directory of Open Access Journals (Sweden)

Wenming Cheng

2012-01-01

Full Text Available In many real scheduling environments, a job processed later needs longer time than the same job when it starts earlier. This phenomenon is known as scheduling with deteriorating jobs to many industrial applications. In this paper, we study a scheduling problem of minimizing the total completion time on identical parallel machines where the processing time of a job is a step function of its starting time and a deteriorating date that is individual to all jobs. Firstly, a mixed integer programming model is presented for the problem. And then, a modified weight-combination search algorithm and a variable neighborhood search are employed to yield optimal or near-optimal schedule. To evaluate the performance of the proposed algorithms, computational experiments are performed on randomly generated test instances. Finally, computational results show that the proposed approaches obtain near-optimal solutions in a reasonable computational time even for large-sized problems.

Fewer intensive care unit refusals and a higher capacity utilization by using a cyclic surgical case schedule

NARCIS (Netherlands)

van Houdenhoven, Mark; van Oostrum, Jeroen M.; Wullink, Gerhard; Hans, Elias W.; Hurink, Johann L.; Bakker, Jan; Kazemier, Geert

Purpose: Mounting health care costs force hospital managers to maximize utilization of scarce resources and simultaneously improve access to hospital services. This article assesses the benefits of a cyclic case scheduling approach that exploits a master surgical schedule (MSS). An MSS maximizes

Courts, Scheduled Damages, and Medical Malpractice Insurance

DEFF Research Database (Denmark)

Bertoli, Paola; Grembi, Veronica

We assess the impact of the introduction of schedules of non-economic damages (i.e. tiered caps systems) on the behavior of insurers operating in the medical liability market for hospitals while controlling the performance of the judicial system, measured as court backlog. Using a difference......-in-differences strategy on Italian data, we find that the introduction of schedules increases the presence of insurers (i.e. medical liability market attractiveness) only in inefficient judicial districts. In the same way, court inefficiency is attractive to insurers for average values of schedules penetration...... of the market, with an increasing positive impact of inefficiency as the territorial coverage of schedules increases. Finally, no significant impact is registered on paid premiums. Our analysis sheds light on a complex set of elements affecting the decisions of insurers in malpractice markets. The analysis...

Application of Tabu Search Algorithm in Job Shop Scheduling

Directory of Open Access Journals (Sweden)

Betrianis Betrianis

2010-10-01

Full Text Available Tabu Search is one of local search methods which is used to solve the combinatorial optimization problem. This method aimed is to make the searching process of the best solution in a complex combinatorial optimization problem(np hard, ex : job shop scheduling problem, became more effective, in a less computational time but with no guarantee to optimum solution.In this paper, tabu search is used to solve the job shop scheduling problem consists of 3 (three cases, which is ordering package of September, October and November with objective of minimizing makespan (Cmax. For each ordering package, there is a combination for initial solution and tabu list length. These result then  compared with 4 (four other methods using basic dispatching rules such as Shortest Processing Time (SPT, Earliest Due Date (EDD, Most Work Remaining (MWKR dan First Come First Served (FCFS. Scheduling used Tabu Search Algorithm is sensitive for variables changes and gives makespan shorter than scheduling used by other four methods.

Applying dynamic priority scheduling scheme to static systems of pinwheel task model in power-aware scheduling.

Science.gov (United States)

Seol, Ye-In; Kim, Young-Kuk

2014-01-01

Power-aware scheduling reduces CPU energy consumption in hard real-time systems through dynamic voltage scaling (DVS). In this paper, we deal with pinwheel task model which is known as static and predictable task model and could be applied to various embedded or ubiquitous systems. In pinwheel task model, each task's priority is static and its execution sequence could be predetermined. There have been many static approaches to power-aware scheduling in pinwheel task model. But, in this paper, we will show that the dynamic priority scheduling results in power-aware scheduling could be applied to pinwheel task model. This method is more effective than adopting the previous static priority scheduling methods in saving energy consumption and, for the system being still static, it is more tractable and applicable to small sized embedded or ubiquitous computing. Also, we introduce a novel power-aware scheduling algorithm which exploits all slacks under preemptive earliest-deadline first scheduling which is optimal in uniprocessor system. The dynamic priority method presented in this paper could be applied directly to static systems of pinwheel task model. The simulation results show that the proposed algorithm with the algorithmic complexity of O(n) reduces the energy consumption by 10-80% over the existing algorithms.

Joint Network Coding and Opportunistic Scheduling for the Bidirectional Relay Channel

KAUST Repository

Shaqfeh, Mohammad

2013-05-27

In this paper, we consider a two-way communication system in which two users communicate with each other through an intermediate relay over block-fading channels. We investigate the optimal opportunistic scheduling scheme in order to maximize the long-term average transmission rate in the system assuming symmetric information flow between the two users. Based on the channel state information, the scheduler decides that either one of the users transmits to the relay, or the relay transmits to a single user or broadcasts to both users a combined version of the two users’ transmitted information by using linear network coding. We obtain the optimal scheduling scheme by using the Lagrangian dual problem. Furthermore, in order to characterize the gains of network coding and opportunistic scheduling, we compare the achievable rate of the system versus suboptimal schemes in which the gains of network coding and opportunistic scheduling are partially exploited.

Joint Network Coding and Opportunistic Scheduling for the Bidirectional Relay Channel

KAUST Repository

Shaqfeh, Mohammad; Alnuweiri, Hussein; Alouini, Mohamed-Slim; Zafar, Ammar

2013-01-01

In this paper, we consider a two-way communication system in which two users communicate with each other through an intermediate relay over block-fading channels. We investigate the optimal opportunistic scheduling scheme in order to maximize the long-term average transmission rate in the system assuming symmetric information flow between the two users. Based on the channel state information, the scheduler decides that either one of the users transmits to the relay, or the relay transmits to a single user or broadcasts to both users a combined version of the two users’ transmitted information by using linear network coding. We obtain the optimal scheduling scheme by using the Lagrangian dual problem. Furthermore, in order to characterize the gains of network coding and opportunistic scheduling, we compare the achievable rate of the system versus suboptimal schemes in which the gains of network coding and opportunistic scheduling are partially exploited.

Scheduling algorithms for rapid imaging using agile Cubesat constellations

Science.gov (United States)

Nag, Sreeja; Li, Alan S.; Merrick, James H.

2018-02-01

Distributed Space Missions such as formation flight and constellations, are being recognized as important Earth Observation solutions to increase measurement samples over space and time. Cubesats are increasing in size (27U, ∼40 kg in development) with increasing capabilities to host imager payloads. Given the precise attitude control systems emerging in the commercial market, Cubesats now have the ability to slew and capture images within short notice. We propose a modular framework that combines orbital mechanics, attitude control and scheduling optimization to plan the time-varying, full-body orientation of agile Cubesats in a constellation such that they maximize the number of observed images and observation time, within the constraints of Cubesat hardware specifications. The attitude control strategy combines bang-bang and PD control, with constraints such as power consumption, response time, and stability factored into the optimality computations and a possible extension to PID control to account for disturbances. Schedule optimization is performed using dynamic programming with two levels of heuristics, verified and improved upon using mixed integer linear programming. The automated scheduler is expected to run on ground station resources and the resultant schedules uplinked to the satellites for execution, however it can be adapted for onboard scheduling, contingent on Cubesat hardware and software upgrades. The framework is generalizable over small steerable spacecraft, sensor specifications, imaging objectives and regions of interest, and is demonstrated using multiple 20 kg satellites in Low Earth Orbit for two case studies - rapid imaging of Landsat's land and coastal images and extended imaging of global, warm water coral reefs. The proposed algorithm captures up to 161% more Landsat images than nadir-pointing sensors with the same field of view, on a 2-satellite constellation over a 12-h simulation. Integer programming was able to verify that

Preemptive Online Scheduling: Optimal Algorithms for All Speeds

Czech Academy of Sciences Publication Activity Database

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

Multi-objective differential evolution with adaptive Cauchy mutation for short-term multi-objective optimal hydro-thermal scheduling

Energy Technology Data Exchange (ETDEWEB)

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.

Flow-shop scheduling problem under uncertainties: Review and trends

OpenAIRE

Eliana María González-Neira; Jairo R. Montoya-Torres; David Barrera

2017-01-01

Among the different tasks in production logistics, job scheduling is one of the most important at the operational decision-making level to enable organizations to achieve competiveness. Scheduling consists in the allocation of limited resources to activities over time in order to achieve one or more optimization objectives. Flow-shop (FS) scheduling problems encompass the sequencing processes in environments in which the activities or operations are performed in a serial flow. This type of co...

Staff Scheduling for Inbound Call and Customer Contact Centers

OpenAIRE

Fukunaga, Alex; Hamilton, Ed; Fama, Jason; Andre, David; Matan, Ofer; Nourbakhsh, Illah

2002-01-01

The staff scheduling problem is a critical problem in the call center (or, more generally, customer contact center) industry. This article describes DIRECTOR, a staff scheduling system for contact centers. DIRECTOR is a constraint-based system that uses AI search techniques to generate schedules that satisfy and optimize a wide range of constraints and service-quality metrics. DIRECTOR has successfully been deployed at more than 800 contact centers, with significant measurable benefits, some ...

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

CERN Document Server

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.

Runway Scheduling Using Generalized Dynamic Programming

Science.gov (United States)

Montoya, Justin; Wood, Zachary; Rathinam, Sivakumar

2011-01-01

A generalized dynamic programming method for finding a set of pareto optimal solutions for a runway scheduling problem is introduced. The algorithm generates a set of runway fight sequences that are optimal for both runway throughput and delay. Realistic time-based operational constraints are considered, including miles-in-trail separation, runway crossings, and wake vortex separation. The authors also model divergent runway takeoff operations to allow for reduced wake vortex separation. A modeled Dallas/Fort Worth International airport and three baseline heuristics are used to illustrate preliminary benefits of using the generalized dynamic programming method. Simulated traffic levels ranged from 10 aircraft to 30 aircraft with each test case spanning 15 minutes. The optimal solution shows a 40-70 percent decrease in the expected delay per aircraft over the baseline schedulers. Computational results suggest that the algorithm is promising for real-time application with an average computation time of 4.5 seconds. For even faster computation times, two heuristics are developed. As compared to the optimal, the heuristics are within 5% of the expected delay per aircraft and 1% of the expected number of runway operations per hour ad can be 100x faster.

Study on multi-objective flexible job-shop scheduling problem considering energy consumption

Directory of Open Access Journals (Sweden)

Zengqiang Jiang

2014-06-01

Full Text Available Purpose: Build a multi-objective Flexible Job-shop Scheduling Problem(FJSP optimization model, in which the makespan, processing cost, energy consumption and cost-weighted processing quality are considered, then Design a Modified Non-dominated Sorting Genetic Algorithm (NSGA-II based on blood variation for above scheduling model.Design/methodology/approach: A multi-objective optimization theory based on Pareto optimal method is used in carrying out the optimization model. NSGA-II is used to solve the model.Findings: By analyzing the research status and insufficiency of multi-objective FJSP, Find that the difference in scheduling will also have an effect on energy consumption in machining process and environmental emissions. Therefore, job-shop scheduling requires not only guaranteeing the processing quality, time and cost, but also optimizing operation plan of machines and minimizing energy consumption.Originality/value: A multi-objective FJSP optimization model is put forward, in which the makespan, processing cost, energy consumption and cost-weighted processing quality are considered. According to above model, Blood-Variation-based NSGA-II (BVNSGA-II is designed. In which, the chromosome mutation rate is determined after calculating the blood relationship between two cross chromosomes, crossover and mutation strategy of NSGA-II is optimized and the prematurity of population is overcome. Finally, the performance of the proposed model and algorithm is evaluated through a case study, and the results proved the efficiency and feasibility of the proposed model and algorithm.

Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm.

Science.gov (United States)

Abdulhamid, Shafi'i Muhammad; Abd Latiff, Muhammad Shafie; Abdul-Salaam, Gaddafi; Hussain Madni, Syed Hamid

2016-01-01

Cloud computing system is a huge cluster of interconnected servers residing in a datacenter and dynamically provisioned to clients on-demand via a front-end interface. Scientific applications scheduling in the cloud computing environment is identified as NP-hard problem due to the dynamic nature of heterogeneous resources. Recently, a number of metaheuristics optimization schemes have been applied to address the challenges of applications scheduling in the cloud system, without much emphasis on the issue of secure global scheduling. In this paper, scientific applications scheduling techniques using the Global League Championship Algorithm (GBLCA) optimization technique is first presented for global task scheduling in the cloud environment. The experiment is carried out using CloudSim simulator. The experimental results show that, the proposed GBLCA technique produced remarkable performance improvement rate on the makespan that ranges between 14.44% to 46.41%. It also shows significant reduction in the time taken to securely schedule applications as parametrically measured in terms of the response time. In view of the experimental results, the proposed technique provides better-quality scheduling solution that is suitable for scientific applications task execution in the Cloud Computing environment than the MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) scheduling techniques.

Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm

Science.gov (United States)

Abdulhamid, Shafi’i Muhammad; Abd Latiff, Muhammad Shafie; Abdul-Salaam, Gaddafi; Hussain Madni, Syed Hamid

2016-01-01

Cloud computing system is a huge cluster of interconnected servers residing in a datacenter and dynamically provisioned to clients on-demand via a front-end interface. Scientific applications scheduling in the cloud computing environment is identified as NP-hard problem due to the dynamic nature of heterogeneous resources. Recently, a number of metaheuristics optimization schemes have been applied to address the challenges of applications scheduling in the cloud system, without much emphasis on the issue of secure global scheduling. In this paper, scientific applications scheduling techniques using the Global League Championship Algorithm (GBLCA) optimization technique is first presented for global task scheduling in the cloud environment. The experiment is carried out using CloudSim simulator. The experimental results show that, the proposed GBLCA technique produced remarkable performance improvement rate on the makespan that ranges between 14.44% to 46.41%. It also shows significant reduction in the time taken to securely schedule applications as parametrically measured in terms of the response time. In view of the experimental results, the proposed technique provides better-quality scheduling solution that is suitable for scientific applications task execution in the Cloud Computing environment than the MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) scheduling techniques. PMID:27384239

Axiomatic Design of a Framework for the Comprehensive Optimization of Patient Flows in Hospitals

Science.gov (United States)

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.

Optimization-based decision support to assist in logistics planning for hospital evacuations.

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

Genetic algorithm to solve the problems of lectures and practicums scheduling

Science.gov (United States)

Syahputra, M. F.; Apriani, R.; Sawaluddin; Abdullah, D.; Albra, W.; Heikal, M.; Abdurrahman, A.; Khaddafi, M.

2018-02-01

Generally, the scheduling process is done manually. However, this method has a low accuracy level, along with possibilities that a scheduled process collides with another scheduled process. When doing theory class and practicum timetable scheduling process, there are numerous problems, such as lecturer teaching schedule collision, schedule collision with another schedule, practicum lesson schedules that collides with theory class, and the number of classrooms available. In this research, genetic algorithm is implemented to perform theory class and practicum timetable scheduling process. The algorithm will be used to process the data containing lists of lecturers, courses, and class rooms, obtained from information technology department at University of Sumatera Utara. The result of scheduling process using genetic algorithm is the most optimal timetable that conforms to available time slots, class rooms, courses, and lecturer schedules.

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

International Nuclear Information System (INIS)

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

An optimal dynamic interval preventive maintenance scheduling for series systems

International Nuclear Information System (INIS)

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

Energy-efficient approach to minimizing the energy consumption in an extended job-shop scheduling problem

Science.gov (United States)

Tang, Dunbing; Dai, Min

2015-09-01

The traditional production planning and scheduling problems consider performance indicators like time, cost and quality as optimization objectives in manufacturing processes. However, environmentally-friendly factors like energy consumption of production have not been completely taken into consideration. Against this background, this paper addresses an approach to modify a given schedule generated by a production planning and scheduling system in a job shop floor, where machine tools can work at different cutting speeds. It can adjust the cutting speeds of the operations while keeping the original assignment and processing sequence of operations of each job fixed in order to obtain energy savings. First, the proposed approach, based on a mixed integer programming mathematical model, changes the total idle time of the given schedule to minimize energy consumption in the job shop floor while accepting the optimal solution of the scheduling objective, makespan. Then, a genetic-simulated annealing algorithm is used to explore the optimal solution due to the fact that the problem is strongly NP-hard. Finally, the effectiveness of the approach is performed smalland large-size instances, respectively. The experimental results show that the approach can save 5%-10% of the average energy consumption while accepting the optimal solution of the makespan in small-size instances. In addition, the average maximum energy saving ratio can reach to 13%. And it can save approximately 1%-4% of the average energy consumption and approximately 2.4% of the average maximum energy while accepting the near-optimal solution of the makespan in large-size instances. The proposed research provides an interesting point to explore an energy-aware schedule optimization for a traditional production planning and scheduling problem.

Evolutionarily stable learning schedules and cumulative culture in discrete generation models.

Science.gov (United States)

Aoki, Kenichi; Wakano, Joe Yuichiro; Lehmann, Laurent

2012-06-01

Individual learning (e.g., trial-and-error) and social learning (e.g., imitation) are alternative ways of acquiring and expressing the appropriate phenotype in an environment. The optimal choice between using individual learning and/or social learning may be dictated by the life-stage or age of an organism. Of special interest is a learning schedule in which social learning precedes individual learning, because such a schedule is apparently a necessary condition for cumulative culture. Assuming two obligatory learning stages per discrete generation, we obtain the evolutionarily stable learning schedules for the three situations where the environment is constant, fluctuates between generations, or fluctuates within generations. During each learning stage, we assume that an organism may target the optimal phenotype in the current environment by individual learning, and/or the mature phenotype of the previous generation by oblique social learning. In the absence of exogenous costs to learning, the evolutionarily stable learning schedules are predicted to be either pure social learning followed by pure individual learning ("bang-bang" control) or pure individual learning at both stages ("flat" control). Moreover, we find for each situation that the evolutionarily stable learning schedule is also the one that optimizes the learned phenotype at equilibrium. Copyright © 2012 Elsevier Inc. All rights reserved.

Schedulability-Driven Communication Synthesis for Time Triggered Embedded Systems

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2006-01-01

We present an approach to static priority preemptive process scheduling for the synthesis of hard real-time distributed embedded systems where communication plays an important role. The communication model is based on a time-triggered protocol. We have developed an analysis for the communication...... delays proposing four different message scheduling policies over a time-triggered communication channel. Optimization strategies for the synthesis of communication are developed, and the four approaches to message scheduling are compared using extensive experiments...

Schedulability-Driven Communication Synthesis for Time Triggered Embedded Systems

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

1999-01-01

We present an approach to static priority preemptive process scheduling for the synthesis of hard real-time distributed embedded systems where communication plays an important role. The communication model is based on a time-triggered protocol. We have developed an analysis for the communication...... delays proposing four different message scheduling policies over a time-triggered communication channel. Optimization strategies for the synthesis of communication are developed, and the four approaches to message scheduling are compared using extensive experiments....

Schedulability-Driven Communication Synthesis for Time Triggered Embedded Systems

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2004-01-01

We present an approach to static priority preemptive process scheduling for the synthesis of hard real-time distributed embedded systems where communication plays an important role. The communication model is based on a time-triggered protocol. We have developed an analysis for the communication...... delays with four different message scheduling policies over a time-triggered communication channel. Optimization strategies for the synthesis of communication are developed, and the four approaches to message scheduling are compared using extensive experiments....

Using a vision cognitive algorithm to schedule virtual machines

OpenAIRE

Zhao Jiaqi; Mhedheb Yousri; Tao Jie; Jrad Foued; Liu Qinghuai; Streit Achim

2014-01-01

Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NPhard global optimization problem with grand challenges for researchers. This work studies the Virtual Machine (VM) scheduling problem on the...

Scheduling Additional Train Unit Services on Rail Transit Lines

OpenAIRE

Zhibin Jiang; Yuyan Tan; Özgür Yalçınkaya

2014-01-01

This paper deals with the problem of scheduling additional train unit (TU) services in a double parallel rail transit line, and a mixed integer programming (MIP) model is formulated for integration strategies of new trains connected by TUs with the objective of obtaining higher frequencies in some special sections and special time periods due to mass passenger volumes. We took timetable scheduling and TUs scheduling as an integrated optimization model with two objectives: minimizing travel ti...

Designing of vague logic based multilevel feedback queue scheduler

Directory of Open Access Journals (Sweden)

Supriya Raheja

2016-03-01

Full Text Available Multilevel feedback queue scheduler suffers from major issues of scheduling such as starvation for long tasks, fixed number of queues, and static length of time quantum in each queue. These factors directly affect the performance of the scheduler. At many times impreciseness exists in attributes of tasks which make the performance even worse. In this paper, our intent is to improve the performance by providing a solution to these issues. We design a multilevel feedback queue scheduler using a vague set which we call as VMLFQ scheduler. VMLFQ scheduler intelligently handles the impreciseness and defines the optimum number of queues as well as the optimal size of time quantum for each queue. It also resolves the problem of starvation. This paper simulates and analyzes the performance of VMLFQ scheduler with the other multilevel feedback queue techniques using MatLab.

Design issues of an efficient distributed database scheduler for telecom

NARCIS (Netherlands)

Bodlaender, M.P.; Stok, van der P.D.V.

1998-01-01

We optimize the speed of real-time databases by optimizing the scheduler. The performance of a database is directly linked to the environment it operates in, and we use environment characteristics as guidelines for the optimization. A typical telecom environment is investigated, and characteristics

A case study: the initiative to improve RN scheduling at Hamilton Health Sciences.

Science.gov (United States)

Wallace, Laurel-Anne; Pierson, Sharon

2008-01-01

In 2003, Hamilton Health Sciences embarked on an initiative to improve and standardize nursing schedules and scheduling practices. The scheduling project was one of several initiatives undertaken by a corporate-wide Nursing Resource Group established to enhance the work environment and patient care and to ensure appropriate utilization of nursing resources across the organization's five hospitals. This article focuses on major activities undertaken in the scheduling initiative. The step-by-step approach described, plus examples of the scheduling resources developed and samples of extended-tour schedules, will all provide insight, potential strategies and practical help for nursing administrators, human resources (HR) personnel and others interested in improving nurse scheduling.

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

Directory of Open Access Journals (Sweden)

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.

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

DEFF Research Database (Denmark)

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...

Comparison of Different Approaches to the Cutting Plan Scheduling

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Peter Bober

2011-10-01

Full Text Available Allocation of specific cutting plans and their scheduling to individual cutting machines presents a combinatorial optimization problem. In this respect, various approaches and methods are used to arrive to a viable solution. The paper reports three approaches represented by three discreet optimization methods. The first one is back-tracing algorithm and serves as a reference to verify functionality of the other two ones. The second method is optimization using genetic algorithms, and the third one presents heuristic approach to optimization based on anticipated properties of an optimal solution. Research results indicate that genetic algorithms are demanding to calculate though not dependant on the selected objective function. Heuristic algorithm is fast but dependant upon anticipated properties of the optimal solution. Hence, at change of the objective function it has to be changed. When the scheduling by genetic algorithms is solvable in a sufficiently short period of time, it is more appropriate from the practical point than the heuristic algorithm. The back-tracing algorithm usually does not provide a result in a feasible period of time.

Neural Feedback Scheduling of Real-Time Control Tasks

OpenAIRE

Xia, Feng; Tian, Yu-Chu; Sun, Youxian; Dong, Jinxiang

2008-01-01

Many embedded real-time control systems suffer from resource constraints and dynamic workload variations. Although optimal feedback scheduling schemes are in principle capable of maximizing the overall control performance of multitasking control systems, most of them induce excessively large computational overheads associated with the mathematical optimization routines involved and hence are not directly applicable to practical systems. To optimize the overall control performance while minimi...

Reusable rocket engine preventive maintenance scheduling using genetic algorithm

International Nuclear Information System (INIS)

Chen, Tao; Li, Jiawen; Jin, Ping; Cai, Guobiao

2013-01-01

This paper deals with the preventive maintenance (PM) scheduling problem of reusable rocket engine (RRE), which is different from the ordinary repairable systems, by genetic algorithm. Three types of PM activities for RRE are considered and modeled by introducing the concept of effective age. The impacts of PM on all subsystems' aging processes are evaluated based on improvement factor model. Then the reliability of engine is formulated by considering the accumulated time effect. After that, optimization model subjected to reliability constraint is developed for RRE PM scheduling at fixed interval. The optimal PM combination is obtained by minimizing the total cost in the whole life cycle for a supposed engine. Numerical investigations indicate that the subsystem's intrinsic reliability characteristic and the improvement factor of maintain operations are the most important parameters in RRE's PM scheduling management

Stage, tumor growth rate and optimal dose fractionation schedules in the treatment of squamous cell carcinoma of the head and neck

International Nuclear Information System (INIS)

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.)

Using the principles of circadian physiology enhances shift schedule design

International Nuclear Information System (INIS)

Connolly, J.J.; Moore-Ede, M.C.

1987-01-01

Nuclear power plants must operate 24 h, 7 days a week. For the most part, shift schedules currently in use at nuclear power plants have been designed to meet operational needs without considering the biological clocks of the human operators. The development of schedules that also take circadian principles into account is a positive step that can be taken to improve plant safety by optimizing operator alertness. These schedules reduce the probability of human errors especially during backshifts. In addition, training programs that teach round-the-clock workers how to deal with the problems of shiftwork can help to optimize performance and alertness. These programs teach shiftworkers the underlying causes of the sleep problems associated with shiftwork and also provide coping strategies for improving sleep and dealing with the transition between shifts. When these training programs are coupled with an improved schedule, the problems associated with working round-the-clock can be significantly reduced

Scheduling Non-Preemptible Jobs to Minimize Peak Demand

Directory of Open Access Journals (Sweden)

Sean Yaw

2017-10-01

Full Text Available This paper examines an important problem in smart grid energy scheduling; peaks in power demand are proportionally more expensive to generate and provision for. The issue is exacerbated in local microgrids that do not benefit from the aggregate smoothing experienced by large grids. Demand-side scheduling can reduce these peaks by taking advantage of the fact that there is often flexibility in job start times. We focus attention on the case where the jobs are non-preemptible, meaning once started, they run to completion. The associated optimization problem is called the peak demand minimization problem, and has been previously shown to be NP-hard. Our results include an optimal fixed-parameter tractable algorithm, a polynomial-time approximation algorithm, as well as an effective heuristic that can also be used in an online setting of the problem. Simulation results show that these methods can reduce peak demand by up to 50% versus on-demand scheduling for household power jobs.

Review of job shop scheduling research and its new perspectives under Industry 4.0

OpenAIRE

Zhang, Jian; Ding, Guofu; Zou, Yisheng; Qin, Sheng-feng; Fu, Jianlin

2017-01-01

Traditional job shop scheduling is concentrated on centralized scheduling or semi-distributed scheduling. Under the Industry 4.0, the scheduling should deal with a smart and distributed manufacturing system supported by novel and emerging manufacturing technologies such as mass customization, Cyber-Physics Systems, Digital Twin, and SMAC (Social, Mobile, Analytics, Cloud). The scheduling research needs to shift its focus to smart distributed scheduling modeling and optimization. In order to t...

Scheduling Algorithms for Maximizing Throughput with Zero-Forcing Beamforming in a MIMO Wireless System

Science.gov (United States)

Foronda, Augusto; Ohta, Chikara; Tamaki, Hisashi

Dirty paper coding (DPC) is a strategy to achieve the region capacity of multiple input multiple output (MIMO) downlink channels and a DPC scheduler is throughput optimal if users are selected according to their queue states and current rates. However, DPC is difficult to implement in practical systems. One solution, zero-forcing beamforming (ZFBF) strategy has been proposed to achieve the same asymptotic sum rate capacity as that of DPC with an exhaustive search over the entire user set. Some suboptimal user group selection schedulers with reduced complexity based on ZFBF strategy (ZFBF-SUS) and proportional fair (PF) scheduling algorithm (PF-ZFBF) have also been proposed to enhance the throughput and fairness among the users, respectively. However, they are not throughput optimal, fairness and throughput decrease if each user queue length is different due to different users channel quality. Therefore, we propose two different scheduling algorithms: a throughput optimal scheduling algorithm (ZFBF-TO) and a reduced complexity scheduling algorithm (ZFBF-RC). Both are based on ZFBF strategy and, at every time slot, the scheduling algorithms have to select some users based on user channel quality, user queue length and orthogonality among users. Moreover, the proposed algorithms have to produce the rate allocation and power allocation for the selected users based on a modified water filling method. We analyze the schedulers complexity and numerical results show that ZFBF-RC provides throughput and fairness improvements compared to the ZFBF-SUS and PF-ZFBF scheduling algorithms.

Scheduling with Optimized Communication for Time-Triggered Embedded Systems

DEFF Research Database (Denmark)

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...

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

Energy Technology Data Exchange (ETDEWEB)

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)

Scheduling projects with multiskill learning effect.

Science.gov (United States)

Zha, Hong; Zhang, Lianying

2014-01-01

We investigate the project scheduling problem with multiskill learning effect. A new model is proposed to deal with the problem, where both autonomous and induced learning are considered. In order to obtain the optimal solution, a genetic algorithm with specific encoding and decoding schemes is introduced. A numerical example is used to illustrate the proposed model. The computational results show that the learning effect cannot be neglected in project scheduling. By means of determining the level of induced learning, the project manager can balance the project makespan with total cost.

Congestion game scheduling for virtual drug screening optimization

Science.gov (United States)

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.

Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning

Directory of Open Access Journals (Sweden)

Maria Drakaki

2017-02-01

Full Text Available Agent-based intelligent manufacturing control systems are capable to efficiently respond and adapt to environmental changes. Manufacturing system adaptation and evolution can be addressed with learning mechanisms that increase the intelligence of agents. In this paper a manufacturing scheduling method is presented based on Timed Colored Petri Nets (CTPNs and reinforcement learning (RL. CTPNs model the manufacturing system and implement the scheduling. In the search for an optimal solution a scheduling agent uses RL and in particular the Q-learning algorithm. A warehouse order-picking scheduling is presented as a case study to illustrate the method. The proposed scheduling method is compared to existing methods. Simulation and state space results are used to evaluate performance and identify system properties.

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

OpenAIRE

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...

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

Directory of Open Access Journals (Sweden)

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.

Generation-Side Power Scheduling in a Grid-Connected DC Microgrid

DEFF Research Database (Denmark)

Hernández, Adriana Carolina Luna; Aldana, Nelson Leonardo Diaz; Meng, Lexuan

2015-01-01

In this paper, a constrained mixed-integer programming model for scheduling the active power supplied by the generation units in storage-based DC microgrids is presented. The optimization problem minimizes operating costs taking into account a two-stage mode operation of the energy storage system...... so that a more accurate model for optimization of the microgrid operation can be obtained. The model is used in a particular grid-connected DC microgrid that includes two renewable energy sources and an energy storage system which supply a critical load. The results of the scheduling process...

A hybrid approach for minimizing makespan in permutation flowshop scheduling

DEFF Research Database (Denmark)

Govindan, Kannan; Balasundaram, R.; Baskar, N.

2017-01-01

This work proposes a hybrid approach for solving traditional flowshop scheduling problems to reduce the makespan (total completion time). To solve scheduling problems, a combination of Decision Tree (DT) and Scatter Search (SS) algorithms are used. Initially, the DT is used to generate a seed...... solution which is then given input to the SS to obtain optimal / near optimal solutions of makespan. The DT used the entropy function to convert the given problem into a tree structured format / set of rules. The SS provides an extensive investigation of the search space through diversification...

Optimal RTP Based Power Scheduling for Residential Load in Smart Grid

Science.gov (United States)

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.

Scheduling the scheduling task : a time management perspective on scheduling

NARCIS (Netherlands)

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

Military Free Fall Scheduling And Manifest Optimization Model

Science.gov (United States)

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

Job Burnout and its Association With Work Schedules and Job Satisfaction Among Iranian Nurses in a Public Hospital: A Questionnaire Survey

Directory of Open Access Journals (Sweden)

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

HOROPLAN: computer-assisted nurse scheduling using constraint-based programming.

Science.gov (United States)

Darmoni, S J; Fajner, A; Mahé, N; Leforestier, A; Vondracek, M; Stelian, O; Baldenweck, M

1995-01-01

Nurse scheduling is a difficult and time consuming task. The schedule has to determine the day to day shift assignments of each nurse for a specified period of time in a way that satisfies the given requirements as much as possible, taking into account the wishes of nurses as closely as possible. This paper presents a constraint-based, artificial intelligence approach by describing a prototype implementation developed with the Charme language and the first results of its use in the Rouen University Hospital. Horoplan implements a non-cyclical constraint-based scheduling, using some heuristics. Four levels of constraints were defined to give a maximum of flexibility: French level (e.g. number of worked hours in a year), hospital level (e.g. specific day-off), department level (e.g. specific shift) and care unit level (e.g. specific pattern for week-ends). Some constraints must always be verified and can not be overruled and some constraints can be overruled at a certain cost. Rescheduling is possible at any time specially in case of an unscheduled absence.

Prescribed Travel Schedules for Fatigue Management

Science.gov (United States)

Whitmire, Alexandra; Johnston, Smith; Lockley, Steven