Exact methods for time constrained routing and related scheduling problems
Kohl, Niklas
1995-01-01
real difference is how the coordinating master problem - a concave non-differentiable maximization problem - is solved. We show how the constrained shortest path problem can be solved efficiently, and present a number of different strategies for solving the master problem. The lower bound obtainable...
Exact methods for time constrained routing and related scheduling problems
Kohl, Niklas
1995-01-01
This dissertation presents a number of optimization methods for the Vehicle Routing Problem with Time Windows (VRPTW). The VRPTW is a generalization of the well known capacity constrained Vehicle Routing Problem (VRP), where a fleet of vehicles based at a central depot must service a set of custo......This dissertation presents a number of optimization methods for the Vehicle Routing Problem with Time Windows (VRPTW). The VRPTW is a generalization of the well known capacity constrained Vehicle Routing Problem (VRP), where a fleet of vehicles based at a central depot must service a set...
Routing and scheduling problems
Reinhardt, Line Blander
be that the objects routed have an availability time window and a delivery time window or that locations on the path have a service time window. When routing moving transportation objects such as vehicles and vessels schedules are made in connection with the routing. Such schedules represent the time for the presence...... to a destination on a predefined network, the routing and scheduling of vessels in a liner shipping network given a demand forecast to be covered, the routing of manpower and vehicles transporting disabled passengers in an airport and the vehicle routing with time windows where one version studied includes edge...... of a connection between two locations. This could be an urban bus schedule where busses are routed and this routing creates a bus schedule which the passengers between locations use. In this thesis various routing and scheduling problems will be presented. The topics covered will be routing from an origin...
SCHEDULING PROBLEMS-AN OVERVIEW
Asmuliardi MULUK; Hasan AKPOLAT; Jichao XU
2003-01-01
There seems to be a significant gap between the theoretical and the practical aspects of scheduling problems in the job shop environment. Theoretically, scheduling systems are designed on the basis of an optimum approach to the scheduling model. However in the practice, the optimum that is built into the scheduling applications seems to face some challenges when dealing with the dynamic character of a scheduling system, for instance machine breakdown or change of orders. Scheduling systems have become quite complex in the past few years. Competitive business environments and shorter product life cycles are the imminent challenges being faced by many companies these days.These challenges push companies to anticipate a demand driven supply chain in their business environment. A demand-driven supply chain incorporates the customer view into the supply chain processes. As a consequence of this, scheduling as a core process of the demand-driven supply chain must also reflect the customer view. In addition, other approaches to solving scheduling problems, for instance approaches based on human factors, prefer the scheduling system to be more flexible in both design and implementation. After discussion of these factors, the authors propose the integration of a different set of criteria for the development of scheduling systems which not only appears to have a better flexibility but also increased customer-focus.
Routing and scheduling problems
Reinhardt, Line Blander
In today’s globalized society, transport contributes to our daily life in many different ways. The production of the parts for a shelf ready product may take place on several continents and our travel between home and work, vacation travel and business trips has increased in distance the last......, the effectiveness of the network is of importance aiming at satisfying as many costumer demands as possible at a low cost. Routing represent a path between locations such as an origin and destination for the object routed. Sometimes routing has a time dimension as well as the physical paths. This may...... to a destination on a predefined network, the routing and scheduling of vessels in a liner shipping network given a demand forecast to be covered, the routing of manpower and vehicles transporting disabled passengers in an airport and the vehicle routing with time windows where one version studied includes edge...
A Scheduling Problem for Hospital Operating Theatre
Sufahani, Suliadi F; Ismail, Zuhaimy
2012-01-01
This paper provides a classification of real scheduling problems. Various ways have been examined and described on the problem. Scheduling problem faces a tremendous challenges and difficulties in order to meet the preferences of the consumer. Dealing with scheduling problem is complicated, inefficient and time-consuming. This study aims to develop a mathematical model for scheduling the operating theatre during peak and off peak time. Scheduling problem is a well known optimization problem and the goal is to find the best possible optimal solution. In this paper, we used integer linear programming technique for scheduling problem in a high level of synthesis. In addition, time and resource constrained scheduling was used. An optimal result was fully obtained by using the software GLPK/AMPL. This model can be adopted to solve other scheduling problems, such as the Lecture Theatre, Cinemas and Work Shift.
Li, Shanlin; Li, Maoqin
2015-01-01
We consider an integrated production and distribution scheduling problem faced by a typical make-to-order manufacturer which relies on a third-party logistics (3PL) provider for finished product delivery to customers. In the beginning of a planning horizon, the manufacturer has received a set of orders to be processed on a single production line. Completed orders are delivered to customers by a finite number of vehicles provided by the 3PL company which follows a fixed daily or weekly shipping schedule such that the vehicles have fixed departure dates which are not part of the decisions. The problem is to find a feasible schedule that minimizes one of the following objective functions when processing times and weights are oppositely ordered: (1) the total weight of late orders and (2) the number of vehicles used subject to the condition that the total weight of late orders is minimum. We show that both problems are solvable in polynomial time.
The Vessel Schedule Recovery Problem
Brouer, Berit Dangaard; Plum, Christian Edinger Munk; Vaaben, Bo;
Maritime transportation is the backbone of world trade and is accountable for around 3% of the worlds CO2 emissions. We present the Vessel Schedule Recovery Problem (VSRP) to evaluate a given disruption scenario and to select a recovery action balancing the trade off between increased bunker cons...... consumption and the impact on the remaining network and the customer service level. The model is applied to 4 real cases from Maersk Line. Solutions are comparable or superior to those chosen by operations managers. Cost savings of up to 58% may be achieved....
Constraint-based scheduling applying constraint programming to scheduling problems
Baptiste, Philippe; Nuijten, Wim
2001-01-01
Constraint Programming is a problem-solving paradigm that establishes a clear distinction between two pivotal aspects of a problem: (1) a precise definition of the constraints that define the problem to be solved and (2) the algorithms and heuristics enabling the selection of decisions to solve the problem. It is because of these capabilities that Constraint Programming is increasingly being employed as a problem-solving tool to solve scheduling problems. Hence the development of Constraint-Based Scheduling as a field of study. The aim of this book is to provide an overview of the most widely used Constraint-Based Scheduling techniques. Following the principles of Constraint Programming, the book consists of three distinct parts: The first chapter introduces the basic principles of Constraint Programming and provides a model of the constraints that are the most often encountered in scheduling problems. Chapters 2, 3, 4, and 5 are focused on the propagation of resource constraints, which usually are responsibl...
Shanlin Li
2015-01-01
Full Text Available We consider an integrated production and distribution scheduling problem faced by a typical make-to-order manufacturer which relies on a third-party logistics (3PL provider for finished product delivery to customers. In the beginning of a planning horizon, the manufacturer has received a set of orders to be processed on a single production line. Completed orders are delivered to customers by a finite number of vehicles provided by the 3PL company which follows a fixed daily or weekly shipping schedule such that the vehicles have fixed departure dates which are not part of the decisions. The problem is to find a feasible schedule that minimizes one of the following objective functions when processing times and weights are oppositely ordered: (1 the total weight of late orders and (2 the number of vehicles used subject to the condition that the total weight of late orders is minimum. We show that both problems are solvable in polynomial time.
On green routing and scheduling problem
Touati, Nora
2012-01-01
The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools.
On green routing and scheduling problem
Touati, Nora; Jost, Vincent
2011-01-01
The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools.
An Augmented Lagrangian Approach for Scheduling Problems
Nishi, Tatsushi; Konishi, Masami
The paper describes an augmented Lagrangian decomposition and coordination approach for solving single machine scheduling problems to minimize the total weighted tardiness. The problem belongs to the class of NP-hard combinatorial optimization problem. We propose an augmented Lagrangian decomposition and coordination approach, which is commonly used for continuous optimization problems, for solving scheduling problems despite the fact that the problem is nonconvex and non-differentiable. The proposed method shows a good convergence to a feasible solution without heuristically constructing a feasible solution. The performance of the proposed method is compared with that of an ordinary Lagrangian relaxation.
Integrated network design and scheduling problems :
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.
The Home Care Crew Scheduling Problem
Rasmussen, Matias Sevel; Justesen, Tor
In the Home Care Crew Scheduling Problem (HCCSP) a staff of caretakers has to be assigned a number of visits, such that the total number of assigned visits is maximised. The visits have different locations and positions in time, and travelling time and time windows must be respected. The challenge...... clustering of the visits based on the problem structure. The algorithm is tested on real-life problem instances and we obtain solutions that are better than current practice in all cases....
Effects on sleep-related problems and self-reported health after a change of shift schedule.
Karlson, Björn; Eek, Frida; Orbaek, Palle; Osterberg, Kai
2009-04-01
This study prospectively examined the effects of a change of shift schedule from a fast forward-rotating schedule to a slowly backward-rotating one. The initial schedule had a forward rotation from mornings to afternoons to nights over 6 consecutive days, with 2 days on each shift followed by 4 days off before the next iteration of the cycle, whereas the new schedule had a slower backward rotation from mornings to nights to afternoons, with 3 days on a given shift followed by 3 days off before the next shift. Shift workers (n = 118) were compared with a reference group of daytime workers (n = 67) from the same manufacturing plant by means of questionnaires covering subjective health, sleep and fatigue, recovery ability, satisfaction with work hours, work-family interface, and job demands, control, and support. Data were collected 6 months before implementing the new schedule and at a follow-up 15 months later. As predicted, on most dimensions measured the shift workers displayed clear improvements from initially poorer scores than daytime workers, and the daytime workers displayed no improvements.
ALGORITHM FOR SOLVING EXTREME SCHEDULING PROBLEMS
Gennady A. Berketov
2015-01-01
The article considers the original algorithmfor solving the generalized problem ofscheduling theory, based on the branch and bound method. Task schedulingperform works (operations) and restrictions on resources used often occur with scheduling discrete manufacturing operations, optimizing network implementationschedules of scientific, economic or technical projects. Tools to solve suchproblems are included in the decisionsupport system ACS in many businesses.The effectiveness of the proposed ...
Abstraction and control techniques for non-stationary scheduling problems
Innocenti, Giacomo
2009-01-01
The paper faces the problem of scheduling from a new perspective, trying to bridge the gap between classical heuristic approaches and system identification and control strategies. To this aim, a complete mathematical formulation of a general scheduling process is derived, beginning from very broad assumptions. This allows a greater freedom of manipulation and guarantee the resolution of the identification (and control) techniques. Both an adaptive and a switching strategies are presented in relation to the performances of a simple Round Robin algorithm.
Genetic Algorithms for Satellite Scheduling Problems
Fatos Xhafa
2012-01-01
Full Text Available Recently there has been a growing interest in mission operations scheduling problem. The problem, in a variety of formulations, arises in management of satellite/space missions requiring efficient allocation of user requests to make possible the communication between operations teams and spacecraft systems. Not only large space agencies, such as ESA (European Space Agency and NASA, but also smaller research institutions and universities can establish nowadays their satellite mission, and thus need intelligent systems to automate the allocation of ground station services to space missions. In this paper, we present some relevant formulations of the satellite scheduling viewed as a family of problems and identify various forms of optimization objectives. The main complexities, due highly constrained nature, windows accessibility and visibility, multi-objectives and conflicting objectives are examined. Then, we discuss the resolution of the problem through different heuristic methods. In particular, we focus on the version of ground station scheduling, for which we present computational results obtained with Genetic Algorithms using the STK simulation toolkit.
The Home Care Crew Scheduling Problem:
Rasmussen, Matias Sevel; Justesen, Tor; Dohn, Anders
In the Home Care Crew Scheduling Problem a staff of caretakers has to be assigned a number of visits to patients' homes, such that the overall service level is maximised. The problem is a generalisation of the vehicle routing problem with time windows. Required travel time between visits and time...... windows of the visits must be respected. The challenge when assigning visits to caretakers lies in the existence of soft preference constraints and in temporal dependencies between the start times of visits. We model the problem as a set partitioning problem with side constraints and develop an exact...... branch-and-price solution algorithm, as this method has previously given solid results for classical vehicle routing problems. Temporal dependencies are modelled as generalised precedence constraints and enforced through the branching. We introduce a novel visit clustering approach based on the soft...
Algorithms for Assembly-Type Flowshop Scheduling Problem
无
2000-01-01
An assembly-type flowshop scheduling problem with minimizing makespan is considered in this paper. The problem of scheduling for minimizing makespan is first addressed, and then a new heuristic algorithm is proposed for it.
A Hybrid Genetic Algorithm for the Job Shop Scheduling Problem
Gonçalves, José Fernando; Mendes, J. J. M.; Resende, Maurício G. C.
2005-01-01
This paper presents a hybrid genetic algorithm for the Job Shop Scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set o...
Cooperated Bayesian algorithm for distributed scheduling problem
QIANG Lei; XIAO Tian-yuan
2006-01-01
This paper presents a new distributed Bayesian optimization algorithm (BOA) to overcome the efficiency problem when solving NP scheduling problems.The proposed approach integrates BOA into the co-evolutionary schema,which builds up a concurrent computing environment.A new search strategy is also introduced for local optimization process.It integrates the reinforcement learning(RL) mechanism into the BOA search processes,and then uses the mixed probability information from BOA (post-probability) and RL (pre-probability) to enhance the cooperation between different local controllers,which improves the optimization ability of the algorithm.The experiment shows that the new algorithm does better in both optimization (2.2%) and convergence (11.7%),compared with classic BOA.
Distributing Flexibility to Enhance Robustness in Task Scheduling Problems
Wilmer, D.; Klos, T.B.; Wilson, M.
2013-01-01
Temporal scheduling problems occur naturally in many diverse application domains such as manufacturing, transportation, health and education. A scheduling problem arises if we have a set of temporal events (or variables) and some constraints on those events, and we have to find a schedule, which is
Minimizing the Makespan for Scheduling Problems with General Deterioration Effects
Xianyu Yu
2013-01-01
Full Text Available This paper investigates the scheduling problems with general deterioration models. By the deterioration models, the actual processing time functions of jobs depend not only on the scheduled position in the job sequence but also on the total weighted normal processing times of the jobs already processed. In this paper, the objective is to minimize the makespan. For the single-machine scheduling problems with general deterioration effects, we show that the considered problems are polynomially solvable. For the flow shop scheduling problems with general deterioration effects, we also show that the problems can be optimally solved in polynomial time under the proposed conditions.
Solving project scheduling problems by minimum cut computations
Möhring, R.H.; Schulz, A.S.; Stork, F.; Uetz, M.J.
2003-01-01
In project scheduling, a set of precedence-constrained jobs has to be scheduled so as to minimize a given objective. In resource-constrained project scheduling, the jobs additionally compete for scarce resources. Due to its universality, the latter problem has a variety of applications in manufactur
Job shop scheduling problem based on DNA computing
Yin Zhixiang; Cui Jianzhong; Yang Yan; Ma Ying
2006-01-01
To solve job shop scheduling problem, a new approach-DNA computing is used in solving job shop scheduling problem. The approach using DNA computing to solve job shop scheduling is divided into three stands. Finally, optimum solutions are obtained by sequencing. A small job shop scheduling problem is solved in DNA computing, and the "operations" of the computation were performed with standard protocols, as ligation, synthesis, electrophoresis etc. This work represents further evidence for the ability of DNA computing to solve NP-complete search problems.
Paprocka, I.; Kempa, W. M.; Grabowik, C.; Kalinowski, K.; Krenczyk, D.
2016-08-01
In the paper a survey of predictive and reactive scheduling methods is done in order to evaluate how the ability of prediction of reliability characteristics influences over robustness criteria. The most important reliability characteristics are: Mean Time to Failure, Mean Time of Repair. Survey analysis is done for a job shop scheduling problem. The paper answers the question: what method generates robust schedules in the case of a bottleneck failure occurrence before, at the beginning of planned maintenance actions or after planned maintenance actions? Efficiency of predictive schedules is evaluated using criteria: makespan, total tardiness, flow time, idle time. Efficiency of reactive schedules is evaluated using: solution robustness criterion and quality robustness criterion. This paper is the continuation of the research conducted in the paper [1], where the survey of predictive and reactive scheduling methods is done only for small size scheduling problems.
Automated problem scheduling and reduction of synchronization delay effects
Saltz, Joel H.
1987-01-01
It is anticipated that in order to make effective use of many future high performance architectures, programs will have to exhibit at least a medium grained parallelism. A framework is presented for partitioning very sparse triangular systems of linear equations that is designed to produce favorable preformance results in a wide variety of parallel architectures. Efficient methods for solving these systems are of interest because: (1) they provide a useful model problem for use in exploring heuristics for the aggregation, mapping and scheduling of relatively fine grained computations whose data dependencies are specified by directed acrylic graphs, and (2) because such efficient methods can find direct application in the development of parallel algorithms for scientific computation. Simple expressions are derived that describe how to schedule computational work with varying degrees of granularity. The Encore Multimax was used as a hardware simulator to investigate the performance effects of using the partitioning techniques presented in shared memory architectures with varying relative synchronization costs.
An Iterated Local Search Algorithm for a Place Scheduling Problem
Shicheng Hu
2013-01-01
Full Text Available We study the place scheduling problem which has many application backgrounds in realities. For the block manufacturing project with special manufacturing platform requirements, we propose a place resource schedule problem. First, the mathematical model for the place resource schedule problem is given. On the basis of resource-constrained project scheduling problem and packing problem, we develop a hybrid heuristic method which combines priority rules and three-dimensional best fit algorithm, in which the priority rules determine the scheduling order and the three-dimensional best fit algorithm solves the placement. After this method is used to get an initial solution, the iterated local search is employed to get an improvement. Finally, we use a set of simulation data to demonstrate the steps of the proposed method and verify its feasibility.
Job shop scheduling problem with late work criterion
Piroozfard, Hamed; Wong, Kuan Yew
2015-05-01
Scheduling is considered as a key task in many industries, such as project based scheduling, crew scheduling, flight scheduling, machine scheduling, etc. In the machine scheduling area, the job shop scheduling problems are considered to be important and highly complex, in which they are characterized as NP-hard. The job shop scheduling problems with late work criterion and non-preemptive jobs are addressed in this paper. Late work criterion is a fairly new objective function. It is a qualitative measure and concerns with late parts of the jobs, unlike classical objective functions that are quantitative measures. In this work, simulated annealing was presented to solve the scheduling problem. In addition, operation based representation was used to encode the solution, and a neighbourhood search structure was employed to search for the new solutions. The case studies are Lawrence instances that were taken from the Operations Research Library. Computational results of this probabilistic meta-heuristic algorithm were compared with a conventional genetic algorithm, and a conclusion was made based on the algorithm and problem.
The Novel Heuristic for Data Transmission Dynamic Scheduling Problems
Hao Xu
2013-01-01
Full Text Available The data transmission dynamic scheduling is a process that allocates the ground stations and available time windows to the data transmission tasks dynamically for improving the resource utilization. A novel heuristic is proposed to solve the data transmission dynamic scheduling problem. The characteristic of this heuristic is the dynamic hybridization of simple rules. Experimental results suggest that the proposed algorithm is correct, feasible, and available. The dynamic hybridization of simple rules can largely improve the efficiency of scheduling.
METHODS FOR THE SHIFT DESIGN AND PERSONNEL TASK SCHEDULING PROBLEM
Lapègue, Tanguy; Bellenguez-Morineau, Odile; Prot, Damien
2014-01-01
Colloque avec actes et comité de lecture. internationale.; International audience; This paper introduces an overview of the methods that have been used in the literature to solve the Shift Design and Personnel Task Scheduling Problem with Equity. Basically, this problem aims at designing a schedule while assigning fixed tasks, that cannot be preempted, to an heterogeneous workforce. Such problem may occur in several contexts, where industrial activity requires a sharp and efficient management...
A DIFFERENTIAL EVOLUTION ALGORITHM DEVELOPED FOR A NURSE SCHEDULING PROBLEM
Shahnazari-Shahrezaei, P.
2012-11-01
Full Text Available Nurse scheduling is a type of manpower allocation problem that tries to satisfy hospital managers objectives and nurses preferences as much as possible by generating fair shift schedules. This paper presents a nurse scheduling problem based on a real case study, and proposes two meta-heuristics a differential evolution algorithm (DE and a greedy randomised adaptive search procedure (GRASP to solve it. To investigate the efficiency of the proposed algorithms, two problems are solved. Furthermore, some comparison metrics are applied to examine the reliability of the proposed algorithms. The computational results in this paper show that the proposed DE outperforms the GRASP.
Handling Deafness Problem of Scheduled Multi-Channel Polling MACs
Jiang, Fulong; Liu, Hao; Shi, Longxing
Combining scheduled channel polling with channel diversity is a promising way for a MAC protocol to achieve high energy efficiency and performance under both light and heavy traffic conditions. However, the deafness problem may cancel out the benefit of channel diversity. In this paper, we first investigate the deafness problem of scheduled multi-channel polling MACs with experiments. Then we propose and evaluate two schemes to handle the deafness problem. Our experiment shows that deafness is a significant reason for performance degradation in scheduled multi-channel polling MACs. A proper scheme should be chosen depending on the traffic pattern and the design objective.
Hybrid IP/CP Methods for Solving Sports Scheduling Problems
Rasmussen, Rasmus Vinther
2006-01-01
The field of sports scheduling comprises a challenging research areawith a great variety of hard combinatorial optimization problems andchallenging practical applications. This dissertation gives acomprehensive survey of the area and a number of new contributionsare presented. First a general sol...
Classification of routing and scheduling problems in liner shipping
Hjortshøj Kjeldsen, Karina
A classification scheme for routing and scheduling problems in liner shipping is developed and subsequently used to classify existing literature on the subject. Based on the classification the articles are grouped, and the main characteristics of each group and article are described. The grouping...... may serve as a catalyst towards developing a model or a group of models that covers the main problems within routing and scheduling in liner shipping....
Solving University Scheduling Problem Using Hybrid Approach
Aftab Ahmed Shaikh
2011-10-01
Full Text Available In universities scheduling curriculum activity is an essential job. Primarily, scheduling is a distribution of limited resources under interrelated constraints. The set of hard constraints demand the highest priority and should not to be violated at any cost, while the maximum soft constraints satisfaction mounts the quality scale of solution. In this research paper, a novel bisected approach is introduced that is comprisesd of GA (Genetic Algorithm as well as Backtracking Recursive Search. The employed technique deals with both hard and soft constraints successively. The first phase decisively is focused over elimination of all the hard constraints bounded violations and eventually produces partial solution for subsequent step. The second phase is supposed to draw the best possible solution on the search space. Promising results are obtained by implementation on the real dataset. The key points of the research approach are to get assurance of hard constraints removal from the dataset and minimizing computational time for GA by initializing pre-processed set of chromosomes.
Aheuristic Algorithm for Berth Scheduling Problem in Container Ports
张海滨
2011-01-01
In this paper,the berth scheduling problem is transformed into a special two-dimensional packing problem with some constraints.A nonlinear programming model for the problem is established,and a heuristic algorithm is proposed to solve the model.Simulation
Re-scheduling in railways: the rolling stock balancing problem
G. Budai-Balke (Gabriella); G. Maróti (Gábor); R. Dekker (Rommert); D. Huisman (Dennis); L.G. Kroon (Leo)
2007-01-01
textabstractThis paper addresses the Rolling Stock Balancing Problem (RSBP). This problem arises at a passenger railway operator when the rolling stock has to be re-scheduled due to changing circumstances. These problems arise both in the planning process and during operations. The RSBP has as inpu
Solving very large vehicle scheduling problems in public mass transit
Loebel, A.; Groetschel, M.
1994-12-31
In public mass transit the problem of scheduling vehicles belonging to different depots such that a set of given timetabled trips is operated by exactly one vehicle arises. This Multiple-Depot-Vehicle-Scheduling-Problem (MDVSP) is a special case of a multi-commodity-flow-problem and is NP-hard. We discuss the polyhedron associated with the MDVSP, determine its dimension and give various classes of facet defining inequalities. These polyhedral results can be used in a branch and cut algorithm and have proven to be successful on very large scale real-world problems from Hamburger Hochbahn AG with up to 27 million variables.
Flow-shop scheduling problem under uncertainties: Review and trends
Eliana María González-Neira
2017-03-01
Full Text Available 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 configuration includes assembly lines and the chemical, electronic, food, and metallurgical industries, among others. Scheduling has been mostly investigated for the deterministic cases, in which all parameters are known in advance and do not vary over time. Nevertheless, in real-world situations, events are frequently subject to uncertainties that can affect the decision-making process. Thus, it is important to study scheduling and sequencing activities under uncertainties since they can cause infeasibilities and disturbances. The purpose of this paper is to provide a general overview of the FS scheduling problem under uncertainties and its role in production logistics and to draw up opportunities for further research. To this end, 100 papers about FS and flexible flow-shop scheduling problems published from 2001 to October 2016 were analyzed and classified. Trends in the reviewed literature are presented and finally some research opportunities in the field are proposed.
Single machine stochastic JIT scheduling problem subject to machine breakdowns
2008-01-01
In this paper we research the single machine stochastic JIT scheduling problem subject to the machine breakdowns for preemptive-resume and preemptive-repeat.The objective function of the problem is the sum of squared deviations of the job-expected completion times from the due date.For preemptive-resume,we show that the optimal sequence of the SSDE problem is V-shaped with respect to expected processing times.And a dynamic programming algorithm with the pseudopolynomial time complexity is given.We discuss the difference between the SSDE problem and the ESSD problem and show that the optimal solution of the SSDE problem is a good approximate optimal solution of the ESSD problem,and the optimal solution of the SSDE problem is an optimal solution of the ESSD problem under some conditions.For preemptive-repeat,the stochastic JIT scheduling problem has not been solved since the variances of the completion times cannot be computed.We replace the ESSD problem by the SSDE problem.We show that the optimal sequence of the SSDE problem is V-shaped with respect to the expected occupying times.And a dynamic programming algorithm with the pseudopolynomial time complexity is given.A new thought is advanced for the research of the preemptive-repeat stochastic JIT scheduling problem.
Single machine stochastic JIT scheduling problem subject to machine breakdowns
TANG HengYong; ZHAO ChuanLi; CHENG CongDian
2008-01-01
In this paper we research the single machine stochastic JIT scheduling problem subject to the machine breakdowns for preemptive-resume and preemptive-repeat. The objective function of the problem is the sum of squared deviations of the job-expected completion times from the due date. For preemptive-resume, we show that the optimal sequence of the SSDE problem is V-shaped with respect to expected processing times. And a dynamic programming algorithm with the pseudopolynomial time complexity is, given. We discuss the difference between the SSDE problem and the ESSD problem and show that the optimal solution of the SSDE problem is a good approximate optimal solution of the ESSD problem, and the optimal solution of the SSDE problem is an optimal solution of the ESSD problem under some conditions. For preemptive-repeat, the stochastic JIT scheduling problem has not been solved since the variances of the completion times cannot be computed. We replace the ESSD problem by the SSDE problem. We show that the optimal sequence of the SSDE problem is V-shaped with respect to the expected occupying times. And a dynamic programming algorithm with the pseudopolynomial time complexity is given. A new thought is advanced for the research of the preemptive-repeat stochastic JIT scheduling problem.
An evolutionary approach for solving the job shop scheduling problem in a service industry
Milad Yousefi
2015-03-01
Full Text Available In this paper, an evolutionary-based approach based on the discrete particle swarm optimization (DPSO algorithm is developed for finding the optimum schedule of a registration problem in a university. Minimizing the makespan, which is the total length of the schedule, in a real-world case study is considered as the target function. Since the selected case study has the characteristics of job shop scheduling problem (JSSP, it is categorized as a NP-hard problem which makes it difficult to be solved by conventional mathematical approaches in relatively short computation time.
Dynamic Scheduling for Cloud Reliability using Transportation Problem
P. Balasubramanie
2012-01-01
Full Text Available Problem statement: Cloud is purely a dynamic environment and the existing task scheduling algorithms are mostly static and considered various parameters like time, cost, make span, speed, scalability, throughput, resource utilization, scheduling success rate and so on. Available scheduling algorithms are mostly heuristic in nature and more complex, time consuming and does not consider reliability and availability of the cloud computing environment. Therefore there is a need to implement a scheduling algorithm that can improve the availability and reliability in cloud environment. Approach: We propose a new algorithm using modified linear programming problem transportation based task scheduling and resource allocation for decentralized dynamic cloud computing. The Main objective is to improve the reliability of cloud computing environment by considering the resources available and itâs working status of each Cluster periodically and maximizes the profit for the cloud providers by minimizing the total cost for scheduling, allocation and execution cost and minimizing total turn-around, total waiting time and total execution time. Our proposed algorithm also utilizes task historical values such as past success rate, failure rate of task in each Cluster and previous execution time and total cost for various Clusters for each task from Task Info Container (TFC for tasks scheduling resource allocation for near future. Results: Our approach TP Scheduling (Transpotation Problem based responded for various tasks assigned by clients in poisson arrival pattern and achieved the improved reliability in dynamic decentralized cloud environment. Conclusion: With our proposed TP Scheduling algorithn we improve the Reliability of the decentralized dynamic cloud computing.
SOLVABLE CASES OF THE NO-WAIT FLOWSHOP SCHEDULING PROBLEM
VANDERVEEN, JAA; VANDAL, R
1991-01-01
The no-wait flow-shop scheduling problem (NWFSSP) with a makespan objective function is considered. As is well known, this problem is NP-hard for three or more machines. Therefore, it is interesting to consider special cases, i.e. special structured processing time matrices, that allow polynomial
The Simultaneous Vehicle Scheduling and Passenger Service Problem
Petersen, Hanne Løhmann; Larsen, Allan; Madsen, Oli B.G.
2013-01-01
, by modifying the timetable. The planning approach is referred to as the simultaneous vehicle scheduling and passenger service problem (SVSPSP). The SVSPSP is modelled as an integer programming problem and solved using a large neighborhood search metaheuristic. The proposed framework is tested on data inspired...
Algorithms for semi on-line multiprocessor scheduling problems
无
2002-01-01
In the classical multiprocessor scheduling problems, it is assumed that the problems are considered in off-line or on-line environment. But in practice, problems are often not really off-line or on-line but somehow in between. This means that, with respect to the on-line problem, some further information about the tasks is available, which allows the improvement of the performance of the best possible algorithms. Problems of this class are called semi on-line ones. The authors studied two semi on-line multiprocessor scheduling problems, in which, the total processing time of all tasks is known in advance, or all processing times lie in a given interval. They proposed approximation algorithms for minimizing the makespan and analyzed their performance guarantee. The algorithms improve the known results for 3 or more processor cases in the literature.
A Solution Methodology for the Variable-Level Scheduling Problem
1991-03-01
distribution unlimited 13. ABSTRACT (Maxtmum 200 words) This study looked at a specific scheduling problem for a Department of Defense agency. A heuristic algorithm was...the DoD problem in this research. Bala Shetty’s article, " A Heuristic Algorithm for a Network Problem with Variable Upper Bounds", also uses a...could be used, but this would undoubtably have long comrt ational times. The research, then, involving a heuristic algorithm to generate an effective
The Simultaneous Vehicle Scheduling and Passenger Service Problem
Petersen, Hanne Løhmann; Larsen, Allan; Madsen, Oli B.G.;
2013-01-01
, by modifying the timetable. The planning approach is referred to as the simultaneous vehicle scheduling and passenger service problem (SVSPSP). The SVSPSP is modelled as an integer programming problem and solved using a large neighborhood search metaheuristic. The proposed framework is tested on data inspired......Passengers using public transport systems often experience waiting times when transferring between two scheduled services. In this paper we propose a planning approach that seeks to obtain a favourable trade-off between the two contrasting objectives, passenger service and operating cost...
Scheduling and order acceptance for the customised stochastic lot scheduling problem
van Foreest, Nicky D.; Wijngaard, Jacob; van der Vaart, Taco
2010-01-01
This paper develops and analyses several customer order acceptance policies to achieve high bottleneck utilisation for the customised stochastic lot scheduling problem (CSLSP) with large setups and strict order due dates. To compare the policies, simulation is used as the main tool, due to the compl
Grain Emergency Vehicle Scheduling Problem with Time and Demand Uncertainty
Jiang DongQing
2014-01-01
Full Text Available Grain transportation plays an important role in many relief and emergency supply chains. In this paper, we take the grain emergency vehicle scheduling model between multiwarehouses as the research object. Under the emergency environment, the aim of the problem is to maximize the utilization of vehicles and minimize the delay time. The randomness of the key parameters in grain emergency vehicle scheduling, such as time and demand, is determined through statistical analysis and the model is solved through robust optimization method. Besides, we apply the numerical examples for experimental analysis. We compare the robust optimization model with classic model to illustrate the differences and similarities between them. The results show that the uncertainty of both time and demand has great influence on the efficiency of grain emergency vehicle scheduling problem.
Waters, Melissa B; Lerman, Dorothea C; Hovanetz, Alyson N
2009-01-01
The separate and combined effects of visual schedules and extinction plus differential reinforcement of other behavior (DRO) were evaluated to decrease transition-related problem behavior of 2 children diagnosed with autism. Visual schedules alone were ineffective in reducing problem behavior when transitioning from preferred to nonpreferred activities. Problem behavior decreased for both participants when extinction and DRO were introduced, regardless of whether visual schedules were also used.
APPLYING PARTICLE SWARM OPTIMIZATION TO JOB-SHOP SCHEDULING PROBLEM
Xia Weijun; Wu Zhiming; Zhang Wei; Yang Genke
2004-01-01
A new heuristic algorithm is proposed for the problem of finding the minimum makespan in the job-shop scheduling problem. The new algorithm is based on the principles of particle swarm optimization (PSO). PSO employs a collaborative population-based search, which is inspired by the social behavior of bird flocking. It combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. Simulated annealing (SA) employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule. By reasonably combining these two different search algorithms, a general, fast and easily implemented hybrid optimization algorithm, named HPSO, is developed. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated by applying it to some benchmark job-shop scheduling problems and comparing results with other algorithms in literature. Comparing results indicate that PSO-based algorithm is a viable and effective approach for the job-shop scheduling problem.
A new polynomial algorithm for a parallel identical scheduling problem
无
2007-01-01
A precedence order is defined based on the release dates of jobs'direct successors.Using the defined precedence order and Heap Sort,a new polynomial algorithm is provided which aims tO solve the parallel scheduling problem P|pj=1,Tj,outtree|∑ Cj.The new algorithm is shown to be more compact and easier to implement.
Classification of Ship Routing and Scheduling Problems in Liner Shipping
Kjeldsen, Karina Hjortshøj
2011-01-01
This article provides a classification scheme for ship routing and scheduling problems in liner shipping in line with the current and future operational conditions of the liner shipping industry. Based on the classification, the literature is divided into groups whose main characteristics...
Optimal Results and Numerical Simulations for Flow Shop Scheduling Problems
Tao Ren
2012-01-01
Full Text Available This paper considers the m-machine flow shop problem with two objectives: makespan with release dates and total quadratic completion time, respectively. For Fm|rj|Cmax, we prove the asymptotic optimality for any dense scheduling when the problem scale is large enough. For Fm‖ΣCj2, improvement strategy with local search is presented to promote the performance of the classical SPT heuristic. At the end of the paper, simulations show the effectiveness of the improvement strategy.
Application of genetic algorithm to the technological operations scheduling problem
Lujić, R.
2008-04-01
Full Text Available The basic enterprise task is to satisfy customer requirements: due date, price and quality. Based on experiences from engineers practice of work in Croatian enterprises it could be concluded that enterprises are not able to fulfil obligations to the customer in a way of due dates. One of the basic reasons lies in inappropriate scheduling model that has not had possibility to make plan variants. The paper shows how genetic algorithm could be successfully applied in scheduling model to solve the problem of plan variant. As a basic selection in the paper 3-tournament steady-state selection has been applied.
Extended precedence preservative crossover for job shop scheduling problems
Ong, Chung Sin; Moin, Noor Hasnah; Omar, Mohd
2013-04-01
Job shop scheduling problems (JSSP) is one of difficult combinatorial scheduling problems. A wide range of genetic algorithms based on the two parents crossover have been applied to solve the problem but multi parents (more than two parents) crossover in solving the JSSP is still lacking. This paper proposes the extended precedence preservative crossover (EPPX) which uses multi parents for recombination in the genetic algorithms. EPPX is a variation of the precedence preservative crossover (PPX) which is one of the crossovers that perform well to find the solutions for the JSSP. EPPX is based on a vector to determine the gene selected in recombination for the next generation. Legalization of children (offspring) can be eliminated due to the JSSP representation encoded by using permutation with repetition that guarantees the feasibility of chromosomes. The simulations are performed on a set of benchmarks from the literatures and the results are compared to ensure the sustainability of multi parents recombination in solving the JSSP.
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...
A Heuristic Approach for International Crude Oil Transportation Scheduling Problems
Yin, Sisi; Nishi, Tatsushi; Izuno, Tsukasa
In this paper, we propose a heuristic algorithm to solve a practical ship scheduling problem for international crude oil transportation. The problem is considered as a vehicle routing problem with split deliveries. The objective of this paper is to find an optimal assignment of tankers, a sequence of visiting and loading volume simultaneously in order to minimize the total distance satisfying the capacity of tankers. A savings-based meta-heuristic algorithm with lot sizing parameters and volume assignment heuristic is developed. The proposed method is applied to solve a case study with real data. Computational results demonstrate the effectiveness of the heuristic algorithm compared with that of human operators.
Global Optimization of Nonlinear Blend-Scheduling Problems
Pedro A. Castillo Castillo
2017-04-01
Full Text Available The scheduling of gasoline-blending operations is an important problem in the oil refining industry. This problem not only exhibits the combinatorial nature that is intrinsic to scheduling problems, but also non-convex nonlinear behavior, due to the blending of various materials with different quality properties. In this work, a global optimization algorithm is proposed to solve a previously published continuous-time mixed-integer nonlinear scheduling model for gasoline blending. The model includes blend recipe optimization, the distribution problem, and several important operational features and constraints. The algorithm employs piecewise McCormick relaxation (PMCR and normalized multiparametric disaggregation technique (NMDT to compute estimates of the global optimum. These techniques partition the domain of one of the variables in a bilinear term and generate convex relaxations for each partition. By increasing the number of partitions and reducing the domain of the variables, the algorithm is able to refine the estimates of the global solution. The algorithm is compared to two commercial global solvers and two heuristic methods by solving four examples from the literature. Results show that the proposed global optimization algorithm performs on par with commercial solvers but is not as fast as heuristic approaches.
A canned food scheduling problem with batch due date
Chung, Tsui-Ping; Liao, Ching-Jong; Smith, Milton
2014-09-01
This article considers a canned food scheduling problem where jobs are grouped into several batches. Jobs can be sent to the next operation only when all the jobs in the same batch have finished their processing, i.e. jobs in a batch, have a common due date. This batch due date problem is quite common in canned food factories, but there is no efficient heuristic to solve the problem. The problem can be formulated as an identical parallel machine problem with batch due date to minimize the total tardiness. Since the problem is NP hard, two heuristics are proposed to find the near-optimal solution. Computational results comparing the effectiveness and efficiency of the two proposed heuristics with an existing heuristic are reported and discussed.
Waters, Melissa B.; Lerman, Dorothea C.; Hovanetz, Alyson N.
2009-01-01
The separate and combined effects of visual schedules and extinction plus differential reinforcement of other behavior (DRO) were evaluated to decrease transition-related problem behavior of 2 children diagnosed with autism. Visual schedules alone were ineffective in reducing problem behavior when transitioning from preferred to nonpreferred…
An Indirect Genetic Algorithm for a Nurse Scheduling Problem
Aickelin, Uwe
2008-01-01
This paper describes a Genetic Algorithms approach to a manpower-scheduling problem arising at a major UK hospital. Although Genetic Algorithms have been successfully used for similar problems in the past, they always had to overcome the limitations of the classical Genetic Algorithms paradigm in handling the conflict between objectives and constraints. The approach taken here is to use an indirect coding based on permutations of the nurses, and a heuristic decoder that builds schedules from these permutations. Computational experiments based on 52 weeks of live data are used to evaluate three different decoders with varying levels of intelligence, and four well-known crossover operators. Results are further enhanced by introducing a hybrid crossover operator and by making use of simple bounds to reduce the size of the solution space. The results reveal that the proposed algorithm is able to find high quality solutions and is both faster and more flexible than a recently published Tabu Search approach.
The Nonpermutation Flowshop Scheduling Problem: Adjustment and Bounding Procedures
Anis Gharbi
2014-01-01
Full Text Available We consider the makespan minimization in a flowshop environment where the job sequence does not have to be the same for all the machines. Contrarily to the classical permutation flowshop scheduling problem, this strongly NP-hard problem received very scant attention in the literature. In this paper, some improved single-machine-based adjustment procedures are proposed, and a new two-machine-based one is introduced. Based on these adjustments, new lower and upper bounding schemes are derived. Our experimental analysis shows that the proposed procedures provide promising results.
Clonal Selection Based Memetic Algorithm for Job Shop Scheduling Problems
Jin-hui Yang; Liang Sun; Heow Pueh Lee; Yun Qian; Yan-chun Liang
2008-01-01
A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exploration and exploitation. In the clonal selection mechanism, clonal selection, hypermutation and receptor edit theories are presented to construct an evolutionary searching mechanism which is used for exploration. In the local search mechanism, a simulated annealing local search algorithm based on Nowicki and Smutnicki's neighborhood is presented to exploit local optima. The proposed algorithm is examined using some well-known benchmark problems. Numerical results validate the effectiveness of the proposed algorithm.
Artificial immune algorithm for multi-depot vehicle scheduling problems
Wu, Zhongyi; Wang, Donggen; Xia, Linyuan; Chen, Xiaoling
2008-10-01
In the fast-developing logistics and supply chain management fields, one of the key problems in the decision support system is that how to arrange, for a lot of customers and suppliers, the supplier-to-customer assignment and produce a detailed supply schedule under a set of constraints. Solutions to the multi-depot vehicle scheduling problems (MDVRP) help in solving this problem in case of transportation applications. The objective of the MDVSP is to minimize the total distance covered by all vehicles, which can be considered as delivery costs or time consumption. The MDVSP is one of nondeterministic polynomial-time hard (NP-hard) problem which cannot be solved to optimality within polynomial bounded computational time. Many different approaches have been developed to tackle MDVSP, such as exact algorithm (EA), one-stage approach (OSA), two-phase heuristic method (TPHM), tabu search algorithm (TSA), genetic algorithm (GA) and hierarchical multiplex structure (HIMS). Most of the methods mentioned above are time consuming and have high risk to result in local optimum. In this paper, a new search algorithm is proposed to solve MDVSP based on Artificial Immune Systems (AIS), which are inspirited by vertebrate immune systems. The proposed AIS algorithm is tested with 30 customers and 6 vehicles located in 3 depots. Experimental results show that the artificial immune system algorithm is an effective and efficient method for solving MDVSP problems.
LOGISTICS SCHEDULING: ANALYSIS OF TWO-STAGE PROBLEMS
Yung-Chia CHANG; Chung-Yee LEE
2003-01-01
This paper studies the coordination effects between stages for scheduling problems where decision-making is a two-stage process. Two stages are considered as one system. The system can be a supply chain that links two stages, one stage representing a manufacturer; and the other, a distributor.It also can represent a single manufacturer, while each stage represents a different department responsible for a part of operations. A problem that jointly considers both stages in order to achieve ideal overall system performance is defined as a system problem. In practice, at times, it might not be feasible for the two stages to make coordinated decisions due to (i) the lack of channels that allow decision makers at the two stages to cooperate, and/or (ii) the optimal solution to the system problem is too difficult (or costly) to achieve.Two practical approaches are applied to solve a variant of two-stage logistic scheduling problems. The Forward Approach is defined as a solution procedure by which the first stage of the system problem is solved first, followed by the second stage. Similarly, the Backward Approach is defined as a solution procedure by which the second stage of the system problem is solved prior to solving the first stage. In each approach, two stages are solved sequentially and the solution generated is treated as a heuristic solution with respect to the corresponding system problem. When decision makers at two stages make decisions locally without considering consequences to the entire system,ineffectiveness may result - even when each stage optimally solves its own problem. The trade-off between the time complexity and the solution quality is the main concern. This paper provides the worst-case performance analysis for each approach.
Integrated Production-Distribution Scheduling Problem with Multiple Independent Manufacturers
Jianhong Hao
2015-01-01
Full Text Available We consider the nonstandard parts supply chain with a public service platform for machinery integration in China. The platform assigns orders placed by a machinery enterprise to multiple independent manufacturers who produce nonstandard parts and makes production schedule and batch delivery schedule for each manufacturer in a coordinate manner. Each manufacturer has only one plant with parallel machines and is located at a location far away from other manufacturers. Orders are first processed at the plants and then directly shipped from the plants to the enterprise in order to be finished before a given deadline. We study the above integrated production-distribution scheduling problem with multiple manufacturers to maximize a weight sum of the profit of each manufacturer under the constraints that all orders are finished before the deadline and the profit of each manufacturer is not negative. According to the optimal condition analysis, we formulate the problem as a mixed integer programming model and use CPLEX to solve it.
Solving a manpower scheduling problem for airline catering using metaheuristics
Ho, Sin C.; Leung, Janny M. Y.
2010-01-01
We study a manpower scheduling problem with job time-windows and job-skills compatibility constraints. This problem is motivated by airline catering operations, whereby airline meals and other supplies are delivered to aircrafts on the tarmac just before the flights take-off. Jobs (flights) must...... be serviced within a given time-window by a team consisting of a driver and loader. Each driver/loader has the skills to service some, but not all, of the airline/aircraft/ configuration of the jobs. Given the jobs to be serviced and the roster of workers for each shift, the problem is to form teams...... annealing heuristic approach to solve the problem. Computational experiments show that the tabu search approach outperforms the simulated annealing approach, and is capable of finding good solutions....
Diversity Controlling Genetic Algorithm for Order Acceptance and Scheduling Problem
Cheng Chen
2014-01-01
Full Text Available Selection and scheduling are an important topic in production systems. To tackle the order acceptance and scheduling problem on a single machine with release dates, tardiness penalty, and sequence-dependent setup times, in this paper a diversity controlling genetic algorithm (DCGA is proposed, in which a diversified population is maintained during the whole search process through survival selection considering both the fitness and the diversity of individuals. To measure the similarity between individuals, a modified Hamming distance without considering the unaccepted orders in the chromosome is adopted. The proposed DCGA was validated on 1500 benchmark instances with up to 100 orders. Compared with the state-of-the-art algorithms, the experimental results show that DCGA improves the solution quality obtained significantly, in terms of the deviation from upper bound.
A Bayesian Optimisation Algorithm for the Nurse Scheduling Problem
Jingpeng, Li
2008-01-01
A Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurses assignment. Unlike our previous work that used Gas to implement implicit learning, the learning in the proposed algorithm is explicit, ie. Eventually, we will be able to identify and mix building blocks directly. The Bayesian optimization algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated, ie in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again usin...
组合优化调度问题求解方法%The Approach to Solving Combinatorial Optimization Schedule Problems
张居阳; 孙吉贵
2003-01-01
Optimization schedule problem is this kind of problem that people often meet in the field of industrial manufacture,transportation and traffic. A good schedule scheme can improve the efficiency of production and reduce the cost of production. So scholars in all of the related fields have high regard for schedule problem at all times. This paper describes the method and technology about combinatorial optimization schedule problems. The research state and advances in this field are reviewed and surveyed. At the end of the paper an approach to solving Job Shop problem,a representative paradigm in schedule problem ,is introduced and discussed concretely.
A MULTICRITERIA PERMUTATION FLOWSHOP SCHEDULING PROBLEM WITH SETUP TIMES
M.Saravanan
2014-07-01
Full Text Available The permutation flow shop scheduling problem has been completely concentrated on in late decades, both from single objective and additionally from multi-objective points of view. To the best of our information, little has been carried out with respect to the multi-objective flow shop with sequence dependent setup times are acknowledged. As setup times and multi-criteria problems are significant in industry, we must concentrate on this area. We propose a simple and powerful meta-heuristic algorithm as artificial immune system for the sequence dependent setup time’s flow shop problem with several criteria. The objective functions are framed to simultaneously minimize the makespan time, tardiness time, earliness time and total completion time. The proposed approach is in conjunction with the constructive heuristic of Nawaz et al. evaluated using benchmark problems taken from Taillard and compared with the prevailing Simulated annealing approach and B-Grasp approach. Computational experiments indicate that the proposed algorithm is better than the SA approach and B-Grasp approach in all cases and can be very well applied to find better schedule.
On the Integrated Job Scheduling and Constrained Network Routing Problem
Gamst, Mette
This paper examines the NP-hard problem of scheduling a number of jobs on a finite set of machines such that the overall profit of executed jobs is maximized. Each job demands a number of resources, which must be sent to the executing machine via constrained paths. Furthermore, two resource demand...... transmissions cannot use the same edge in the same time period. An exact solution approach based on Dantzig-Wolfe decomposition is proposed along with several heuristics. The methods are computationally evaluated on test instances arising from telecommunications with up to 500 jobs and 500 machines. Results...
Combinatorial particle swarm optimization for solving blocking flowshop scheduling problem
Mansour Eddaly
2016-10-01
Full Text Available This paper addresses to the flowshop scheduling problem with blocking constraints. The objective is to minimize the makespan criterion. We propose a hybrid combinatorial particle swarm optimization algorithm (HCPSO as a resolution technique for solving this problem. At the initialization, different priority rules are exploited. Experimental study and statistical analysis were performed to select the most adapted one for this problem. Then, the swarm behavior is tested for solving a combinatorial optimization problem such as a sequencing problem under constraints. Finally, an iterated local search algorithm based on probabilistic perturbation is sequentially introduced to the particle swarm optimization algorithm for improving the quality of solution. The computational results show that our approach is able to improve several best known solutions of the literature. In fact, 76 solutions among 120 were improved. Moreover, HCPSO outperforms the compared methods in terms of quality of solutions in short time requirements. Also, the performance of the proposed approach is evaluated according to a real-world industrial problem.
The Distributed Assembly Parallel Machine Scheduling Problem with eligibility constraints.
Sara Hatami
2015-01-01
Full Text Available In this paper we jointly consider realistic scheduling extensions: First we study the distributed unrelated parallel machines problems by which there is a set of identical factories with parallel machines in a production stage. Jobs have to be assigned to factories and to machines. Additionally, there is an assembly stage with a single assembly machine. Finished jobs at the manufacturing stage are assembled into final products in this second assembly stage. These two joint features are referred to as the distributed assembly parallel machine scheduling problem or DAPMSP. The objective is to minimize the makespan in the assembly stage. Due to technological constraints, machines cannot be left empty and some jobs might be processed on certain factories only. We propose a mathematical model and two high performing heuristics. The model is tested with two state-of-the-art solvers and, together with the heuristics, 2220 instances are solved in a comprehensive computational experiments. Results show that the proposed model is able to solve moderately-sized instances and one of the heuristics is fast, giving close to optimal solutions in less than half a second in the worst case.
JIT single machine scheduling problem with periodic preventive maintenance
Shahriari, Mohammadreza; Shoja, Naghi; Zade, Amir Ebrahimi; Barak, Sasan; Sharifi, Mani
2016-03-01
This article investigates a JIT single machine scheduling problem with a periodic preventive maintenance. Also to maintain the quality of the products, there is a limitation on the maximum number of allowable jobs in each period. The proposed bi-objective mixed integer model minimizes total earliness-tardiness and makespan simultaneously. Due to the computational complexity of the problem, multi-objective particle swarm optimization (MOPSO) algorithm is implemented. Also, as well as MOPSO, two other optimization algorithms are used for comparing the results. Eventually, Taguchi method with metrics analysis is presented to tune the algorithms' parameters and a multiple criterion decision making technique based on the technique for order of preference by similarity to ideal solution is applied to choose the best algorithm. Comparison results confirmed the supremacy of MOPSO to the other algorithms.
A NEW APPROACH TO JOB SHOP-SCHEDULING PROBLEM
Ramezanali Mahdavinejad
2007-03-01
Full Text Available In this paper, single-processors job shop scheduling problems are solved by a heuristic algorithm based on the hybrid of priority dispatching rules according to an ant colony optimization algorithm. The objective function is to minimize the makespan i.e. total completion time , in which a simultanous presence of various kinds of ferons is allowed. The process of finding the best solution will be improved by using the suitable hybrid of priority dispatching rules. Ant colony optimization algorithm, not only promote the ability of this proposed algorithm, but also decreases the total working time because of decreasing in setup times and modifying the working production line. By solving some problems as samples (i.e. Fisher's & Thomson's problems, this algorithm is compared with the others. The results show that when the size of the problem becomes larger the deviation from lower limit increases, but its rate decreases with the size of the problems, so that it reaches to its limit.
Cyclic delivery-scheduling problem with synchronization of vehicles\\' arrivals at logistic centers
Katarzyna Zofia Gdowska
2015-12-01
Full Text Available Background: In this paper a cyclic delivery-scheduling problem with vehicles serving fixed routes is presented. Each vehicle is assigned to one route to which some manufacturers' warehouses and logistics centers belong. A vehicle is to be loaded at a manufacturer's warehouse, then to deliver goods to a logistics center and may be also loaded there with other goods and to transport them to the next node along the route. One logistic center belongs to several routes, so the goods delivered by one vehicle may continue their journey by another truck. For every route the frequency of the vehicle is fixed and known. The objective here is to obtain such synchronization of vehicles arrivals in logistics centers, so that it is possible to organize their arrivals in repeatable blocks. Methods: In the paper the cyclic delivery-scheduling problem with vehicles serving fixed routes is formulated as a MIP model. Due to the fixed routes and desirable synchronization of vehicles arrivals in shared points this problem seems to be similar to the public transit network timetabling problem. Because of that the model presented here was based on a model dedicated to the public transit network timetabling problem, where optimization criterion was to maximize synchronization of vehicles' arrivals at the shared nodes. Results: Mixed integer programming model was employed for solving several cases of cyclic delivery-scheduling problem with vehicles serving fixed routes. Computational experiments are reported and obtained results are presented. Conclusions: The mixed integer programming model for the cyclic delivery-scheduling problem with synchronization of vehicles arrivals at logistic centers presented in this paper can be utilized for generating schedules for a group of vehicles serving fixed long routes. It may result in reducing total operational cost related to this group of vehicles as well as in reducing the goods travel time from the place of origin to their
Analysis of dispatching rules application on scheduling problem in flexible-flow shop production
Rakićević Zoran M.
2014-01-01
Full Text Available In this paper we analyzed a group of simple heuristic methods, which are used for solving the scheduling problem in manufacturing and services. The analysis was performed on the scheduling problem in a flexible-flow shop production, which is known by the English term - Flexible-Flow Shop (FFS. The task is to determine the schedule of processing multiple products on multiple machines, where all the products have the same sequence of processing and for each process there are multiple machines available. For this FFS problem we present the corresponding mathematical model of mixed integer programming. Among potential methods for solving the set task, we consider simple heuristics because the original scheduling problem is NP-hard and finding the exact optimal solution would require unacceptably long computing time. Heuristic methods are based on priority rules that are performed based on the relations of importance between products and their processing time on individual machines. Heuristic methods are widely used for solving practical problems, which was the motivation for the analysis performed in this paper. The aim of the analysis is to identify those priority rules, from a set of considered, which provide a good solution to a hypothetical scheduling problem example, where the evaluation of solution is performed using different criteria functions. The analysis that is presented in the paper was obtained by using the computer program LEKIN. The main results of the analysis indicated that priority rules give different solutions to the problem of FFS and that each of these solutions is a significantly good result in terms of some of the considered criteria functions.
Approximation for a scheduling problem with application in wireless networks
无
2010-01-01
A network of many sensors and a base station that are deployed over a region is considered.Each sensor has a transmission range,an interference range and a carrier sensing range,which are r,αr and βr,respectively.In this paper,we study the minimum latency conflict-aware many-to-one data aggregation scheduling problem:Given locations of sensors along with a base station,a subset of all sensors,and parameters r,α and β,to find a schedule in which the data of each sensor in the subset can be transmitted to the base station with no conflicts,such that the latency is minimized.We designe an algorithm based on maximal independent sets,which has a latency bound of(a+19b)R + Δb-a + 5 time slots,where a and b are two constant integers relying on α and β,Δ is the maximum degree of network topology,and R is the trivial lower bound of latency.Here Δ contributes to an additive factor instead of a multiplicative one,thus our algorithm is nearly a constant(a+19b)-ratio.
Nurse Scheduling System based on Dynamic Weighted Maximal Constraint Satisfaction Problem
Hattori, Hiromitsu; Isomura, Atsushi; Ito, Takayuki; Ozono, Tadachika; Shintani, Toramatsu
Scheduling has been an important research field in Artificial Intelligence. Because typical scheduling problems could be modeled as a Constraint Satisfaction Problem(CSP), several constraint satisfaction techniques have been proposed. In order to handle the different levels of importance of the constraints, solving a problem as a Weighted Maximal Constraint Satisfaction Problem(W-MaxCSP) is an promising approach. However, there exists the case where unexpected events are added and some sudden changes are required, i.e., the case with dynamic changes in scheduling problems. In this paper, we describe such dynamic scheduling problem as a Dynamic Weighted Maximal Constraint Satisfaction Problem(DW-MaxCSP) in which constraints would changes dynamically. Generally, it is undesirable to determine vastly modified schedule even if re-scheduling is needed. A new schedule should be close to the current one as much as possible. In order to obtain stable solutions, we propose the methodology to maintain portions of the current schedule using the provisional soft constraints, which explicitly penalize the changes from the current schedule. We have experimentally confirmed the efficacy of re-scheduling based on our method with provisional constraints. In this paper, we construct the nurse scheduling system for applying the proposed scheduling method.
Exact and Metaheuristic Approaches for a Bi-Objective School Bus Scheduling Problem
2015-01-01
As a class of hard combinatorial optimization problems, the school bus routing problem has received considerable attention in the last decades. For a multi-school system, given the bus trips for each school, the school bus scheduling problem aims at optimizing bus schedules to serve all the trips within the school time windows. In this paper, we propose two approaches for solving the bi-objective school bus scheduling problem: an exact method of mixed integer programming (MIP) and a metaheuri...
Models and Strategies for Variants of the Job Shop Scheduling Problem
Grimes, Diarmuid
2011-01-01
Recently, a variety of constraint programming and Boolean satisfiability approaches to scheduling problems have been introduced. They have in common the use of relatively simple propagation mechanisms and an adaptive way to focus on the most constrained part of the problem. In some cases, these methods compare favorably to more classical constraint programming methods relying on propagation algorithms for global unary or cumulative resource constraints and dedicated search heuristics. In particular, we described an approach that combines restarting, with a generic adaptive heuristic and solution guided branching on a simple model based on a decomposition of disjunctive constraints. In this paper, we introduce an adaptation of this technique for an important subclass of job shop scheduling problems (JSPs), where the objective function involves minimization of earliness/tardiness costs. We further show that our technique can be improved by adding domain specific information for one variant of the JSP (involving...
MODIFIED BOTTLENECK-BASED PROCEDURE FOR LARGE-SCALE FLOW-SHOP SCHEDULING PROBLEMS WITH BOTTLENECK
ZUO Yan; GU Hanyu; XI Yugeng
2006-01-01
A new bottleneck-based heuristic for large-scale flow-shop scheduling problems with a bottleneck is proposed, which is simpler but more tailored than the shifting bottleneck (SB)procedure. In this algorithm, a schedule for the bottleneck machine is first constructed optimally and then the non-bottleneck machines are scheduled around the bottleneck schedule by some effective dispatching rules. Computational results show that the modified bottleneck-based procedure can achieve a tradeoff between solution quality and computational time comparing with SB procedure for medium-size problems. Furthermore it can obtain a good solution in quite short time for large-scale scheduling problems.
MULTICRITERIA HYBRID FLOW SHOP SCHEDULING PROBLEM: LITERATURE REVIEW, ANALYSIS, AND FUTURE RESEARCH
Marcia de Fatima Morais
2014-12-01
Full Text Available This research focuses on the Hybrid Flow Shop production scheduling problem, which is one of the most difficult problems to solve. The literature points to several studies that focus the Hybrid Flow Shop scheduling problem with monocriteria functions. Despite of the fact that, many real world problems involve several objective functions, they can often compete and conflict, leading researchers to concentrate direct their efforts on the development of methods that take consider this variant into consideration. The goal of the study is to review and analyze the methods in order to solve the Hybrid Flow Shop production scheduling problem with multicriteria functions in the literature. The analyses were performed using several papers that have been published over the years, also the parallel machines types, the approach used to develop solution methods, the type of method develop, the objective function, the performance criterion adopted, and the additional constraints considered. The results of the reviewing and analysis of 46 papers showed opportunities for future research on this topic, including the following: (i use uniform and dedicated parallel machines, (ii use exact and metaheuristics approaches, (iv develop lower and uppers bounds, relations of dominance and different search strategies to improve the computational time of the exact methods, (v develop other types of metaheuristic, (vi work with anticipatory setups, and (vii add constraints faced by the production systems itself.
A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problems
Narendhar, S.; T Amudha
2012-01-01
Bio-Inspired computing is the subset of Nature-Inspired computing. Job Shop Scheduling Problem is categorized under popular scheduling problems. In this research work, Bacterial Foraging Optimization was hybridized with Ant Colony Optimization and a new technique Hybrid Bacterial Foraging Optimization for solving Job Shop Scheduling Problem was proposed. The optimal solutions obtained by proposed Hybrid Bacterial Foraging Optimization algorithms are much better when compared with the solution...
An Analysis of Robust Workforce Scheduling Models for a Nurse Rostering Problem
2007-03-01
12 Moz and Pato ...problem and the nurse rerostering problem. The nurse rostering problem has received much attention in the staff scheduling literature (Moz and Pato , 2007...Most recently, Moz and Pato (2007) developed constructive heuristics and genetic algorithms to re-roster a schedule following a disruption. Their use
A Class of Single Machine Scheduling Problems with Variable Processing Time
无
2001-01-01
In this paper, single machine scheduling problems with variableprocessing time are raised. The criterions of the problem considered are minimizing scheduling length of all jobs, flow time and number of tardy jobs and so on. The complexity of the problem is determined.
Performance comparison of some evolutionary algorithms on job shop scheduling problems
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.
Rolling optimization algorithm based on collision window for single machine scheduling problem
Wang Changjun; Xi Yugeng
2005-01-01
Focusing on the single machine scheduling problem which minimizes the total completion time in the presence of dynamic job arrivals, a rolling optimization scheduling algorithm is proposed based on the analysis of the character and structure of scheduling. An optimal scheduling strategy in collision window is presented. Performance evaluation of this algorithm is given. Simulation indicates that the proposed algorithm is better than other common heuristic algorithms on both the total performance and stability.
Jianjun Qi; Bo Guo; Hongtao Lei; Tao Zhang
2014-01-01
This paper considers a project scheduling problem with the objective of minimizing resource availability costs appealed to finish al activities before the deadline. There are finish-start type precedence relations among the activities which require some kinds of renewable resources. We predigest the process of sol-ving the resource availability cost problem (RACP) by using start time of each activity to code the schedule. Then, a novel heuris-tic algorithm is proposed to make the process of looking for the best solution efficiently. And then pseudo particle swarm optimiza-tion (PPSO) combined with PSO and path relinking procedure is presented to solve the RACP. Final y, comparative computational experiments are designed and the computational results show that the proposed method is very effective to solve RACP.
Research on remanufacturing scheduling problem based on critical chain management
Cui, Y.; Guan, Z.; He, C.; Yue, L.
2017-06-01
Remanufacturing is the recycling process of waste products as “as good as new products”, compared with materials recycling, remanufacturing represents a higher form of recycling. The typical structure of remanufacturing system consists of three parts: disassembly workshop, remanufacturing workshop and assembly workshop. However, the management of production planning and control activities can differ greatly from management activities in traditional manufacturing. Scheduling in a remanufacturing environment is more complex and the scheduler must deal with more uncertainty than in a traditional manufacturing environment. In order to properly schedule in a remanufacturing environment the schedule must be able to cope with several complicating factors which increase variability. This paper introduced and discussed seven complicating characteristics that require significant changes in production planning and control activities, in order to provide a new method for remanufacturing production scheduling system.
Stanimirović Ivan
2009-01-01
Full Text Available We introduce a heuristic method for the single resource constrained project scheduling problem, based on the dynamic programming solution of the knapsack problem. This method schedules projects with one type of resources, in the non-preemptive case: once started an activity is not interrupted and runs to completion. We compare the implementation of this method with well-known heuristic scheduling method, called Minimum Slack First (known also as Gray-Kidd algorithm, as well as with Microsoft Project.
The Simultaneous Vehicle Scheduling and Passenger Service Problem
Petersen, Hanne Løhmann; Larsen, Allan; Madsen, Oli B.G.
Passengers using public transport systems often experience waiting times when transferring between two scheduled services. We propose a planning approach which seeks to obtain a favorable trade-off between the conflicting objectives passenger service and operating cost, by allowing some moderate...... by the express-bus network in the Greater Copenhagen Area. The results are encouraging and indicate a potential decrease of passenger waiting times in the network of 10-20%, with the vehicle scheduling costs remaining largely unaffected....
Comparison of heuristic approaches for the multiple depot vehicle scheduling problem
A.S. Pepin; G. Desaulniers (Guy); A. Hertz (Alain); D. Huisman (Dennis)
2006-01-01
textabstractGiven a set of timetabled tasks, the multi-depot vehicle scheduling problem is a well-known problem that consists of determining least-cost schedules for vehicles assigned to several depots such that each task is accomplished exactly once by a vehicle. In this paper, we propose to compar
Polynomial-time approximation schemes for scheduling problems with time lags
X. Zhang (Xiandong); S.L. van de Velde (Steef)
2010-01-01
textabstractWe identify two classes of machine scheduling problems with time lags that possess Polynomial-Time Approximation Schemes (PTASs). These classes together, one for minimizing makespan and one for minimizing total completion time, include many well-studied time lag scheduling problems. The
A tabu-search heuristic for solving the multi-depot vehicle scheduling problem
Gilmar D'Agostini Oliveira Casalinho
2014-08-01
Full Text Available Currently the logistical problems are relying quite significantly on Operational Research in order to achieve greater efficiency in their operations. Among the problems related to the vehicles scheduling in a logistics system, the Multiple Depot Vehicle Scheduling Problem (MDVSP has been addressed in several studies. The MDVSP presupposes the existence of depots that affect the planning of sequences to which travel must be performed. Often, exact methods cannot solve large instances encountered in practice and in order to take them into account, several heuristic approaches are being developed. The aim of this study was thus to solve the MDVSP using a meta-heuristic based on tabu-search method. The main motivation for this work came from the indication that only recently the use of meta-heuristics is being applied to MDVSP context (Pepin et al. 2008 and, also, the limitations listed by Rohde (2008 in his study, which used the branch-and-bound in one of the steps of the heuristic presented to solve the problem, which has increased the time resolution. The research method for solving this problem was based on adaptations of traditional techniques of Operational Research, and provided resolutions presenting very competitive results for the MDVSP such as the cost of the objective function, number of vehicles used and computational time.
An Improved Shuffled Frog-Leaping Algorithm for Flexible Job Shop Scheduling Problem
Kong Lu
2015-02-01
Full Text Available The flexible job shop scheduling problem is a well-known combinatorial optimization problem. This paper proposes an improved shuffled frog-leaping algorithm to solve the flexible job shop scheduling problem. The algorithm possesses an adjustment sequence to design the strategy of local searching and an extremal optimization in information exchange. The computational result shows that the proposed algorithm has a powerful search capability in solving the flexible job shop scheduling problem compared with other heuristic algorithms, such as the genetic algorithm, tabu search and ant colony optimization. Moreover, the results also show that the improved strategies could improve the performance of the algorithm effectively.
An Optimization Model for Scheduling Problems with Two-Dimensional Spatial Resource Constraint
Garcia, Christopher; Rabadi, Ghaith
2010-01-01
Traditional scheduling problems involve determining temporal assignments for a set of jobs in order to optimize some objective. Some scheduling problems also require the use of limited resources, which adds another dimension of complexity. In this paper we introduce a spatial resource-constrained scheduling problem that can arise in assembly, warehousing, cross-docking, inventory management, and other areas of logistics and supply chain management. This scheduling problem involves a twodimensional rectangular area as a limited resource. Each job, in addition to having temporal requirements, has a width and a height and utilizes a certain amount of space inside the area. We propose an optimization model for scheduling the jobs while respecting all temporal and spatial constraints.
Single-Machine Group Scheduling Problems with Deterioration to Minimize the Sum of Completion Times
Yong He
2012-01-01
Full Text Available We consider two single-machine group scheduling problems with deteriorating group setup and job processing times. That is, the job processing times and group setup times are linearly increasing (or decreasing functions of their starting times. Jobs in each group have the same deteriorating rate. The objective of scheduling problems is to minimize the sum of completion times. We show that the sum of completion times minimization problems remains polynomially solvable under the agreeable conditions.
An integrated approach for modeling and solving the scheduling problem of container handling systems
CHEN Lu; XI Li-feng; CAI Jian-guo; BOSTEL Nathalie; DEJAX Pierre
2006-01-01
An integrated model is presented to schedule the container handling system. The objective is to improve the cooperation between different types of equipments, and to increase the productivity of the terminal. The problem is formulated as a Hybrid Flow Shop Scheduling problem with precedence constraint, setup times and blocking (HFSS-B). A tabu search algorithm is proposed to solve this problem. The quality and efficiency of the proposed algorithm is analyzed from the computational point of view.
Ordinal scheduling problem and its asymptotically optimal algorithms on parallel machine system
TAN Zhiyi; HE Yong
2004-01-01
Focusing on the ordinal scheduling problem on a parallel machine system, we discuss the background of ordinal scheduling and the motivation of ordinal algorithms. In addition, for the ordinal scheduling problem on identical parallel machines with the objective to maximize the minimum machine load, we then give two asymptotically optimal algorithm classes which have worst-case ratios very close to the upper bound of the problem for any given m. These results greatly improve the results proposed by He Yong and Tan Zhiyi in 2002.
The problem of scheduling for the linear section of a single-track railway
Akimova, Elena N.; Gainanov, Damir N.; Golubev, Oleg A.; Kolmogortsev, Ilya D.; Konygin, Anton V.
2016-06-01
The paper is devoted to the problem of scheduling for the linear section of a single-track railway: how to organize the flow in both directions in the most efficient way. In this paper, the authors propose an algorithm for scheduling, examine the properties of this algorithm and perform the computational experiments.
A New Algorithm for Resource Constraint Project Scheduling Problem Based on Multi-Agent Systems
何曙光; 齐二石; 李钢
2003-01-01
The resource constrained project scheduling problem (RCPSP) and a decision-making model based on multi-agent systems (MAS) and general equilibrium marketing are proposed. An algorithm leading to the resource allocation decision involved in RCPSP has also been developed. And this algorithm can be used in the multi-project scheduling field as well.Finally, an illustration is given.
Multi-objective Mobile Robot Scheduling Problem with Dynamic Time Windows
Dang, Vinh Quang; Nielsen, Izabela Ewa; Steger-Jensen, Kenn
2012-01-01
This paper deals with the problem of scheduling feeding tasks of a single mobile robot which has capability of supplying parts to feeders on pro-duction lines. The performance criterion is to minimize the total traveling time of the robot and the total tardiness of the feeding tasks being schedul...
Benbouzid-Sitayeb, Fatima; Ammi, Ismaïl; Varnier, Christophe; Zerhouni, Noureddine
2008-01-01
International audience; In this paper, an integrated ACO approach to solve joint production and preventive maintenance scheduling problem in permutation flowshops is considered. A newly developed antcolony algorithm is proposed and analyzed for solving this problem, based on a common representation of production and maintenance data, to obtain a joint schedule that is, subsequently, improved by a new local search procedure. The goal is to optimize a common objective function which takes into ...
An estimation of distribution algorithm (EDA) variant with QGA for Flowshop scheduling problem
Latif, Muhammad Shahid; Hong, Zhou; Ali, Amir
2014-04-01
In this research article, a hybrid approach is presented which based on well-known meta-heuristics algorithms. This study based on integration of Quantum Genetic Algorithm (QGA) and Estimation of Distribution Algorithm, EDA, (for simplicity we use Q-EDA) for flowshop scheduling, a well-known NP hard Problem, while focusing on the total flow time minimization criterion. A relatively new method has been adopted for the encoding of jobs sequence in flowshop known as angel rotations instead of random keys, so QGA become more efficient. Further, EDA has been integrated to update the population of QGA by making a probability model. This probabilistic model is built and used to generate new candidate solutions which comprised on best individuals, obtained after several repetitions of proposed (Q-EDA) approach. As both heuristics based on probabilistic characteristics, so exhibits excellent learning capability and have minimum chances of being trapped in local optima. The results obtained during this study are presented and compared with contemporary approaches in literature. The current hybrid Q-EDA has implemented on different benchmark problems. The experiments has showed better convergence and results. It is concluded that hybrid Q-EDA algorithm can generally produce better results while implemented for Flowshop Scheduling Problem (FSSP).
Optimizing a multi-objectives flow shop scheduling problem by a novel genetic algorithm
R. Tavakkoli-Moghaddam
2013-06-01
Full Text Available Flow-shop problems, as a typical manufacturing challenge, have become an interesting area of research. The primary concern is that the solution space is huge and, therefore, the set of feasible solutions cannot be enumerated one by one. In this paper, we present an efficient solution strategy based on a genetic algorithm (GA to minimize the makespan, total waiting time and total tardiness in a flow shop consisting of n jobs and m machines. The primary objective is to minimize the job waiting time before performing the related operations. This is a major concern for some industries such as food and chemical for planning and production scheduling. In these industries, there is a probability of the decay and deterioration of the products prior to accomplishment of operations in workstation, due to the increase in the waiting time. We develop a model for a flowshop scheduling problem, which uses the planner-specified weights for handling a multi-objective optimization problem. These weights represent the priority of planning objectives given by managers. The results of the proposed GA and classic GA are analyzed by the analysis of variance (ANOVA method and the results are discussed.
Multi-agent Optimization Design for Multi-resource Job Shop Scheduling Problems
Xue, Fan; Fan, Wei
As a practical generalization of the job shop scheduling problem, multi-resource job shop scheduling problem (MRJSSP) is discussed in this paper. In this problem, operations may be processed by a type of resources and jobs have individual deadlines. How to design and optimize this problem with DSAFO, a novel multi-agent algorithm, is introduced in detail by a case study, including problem analysis, agent role specification, and parameter selection. Experimental results show the effectiveness and efficiency of designing and optimizing MRJSSPs with multi-agent.
Hybrid Genetic Algorithm with Multiparents Crossover for Job Shop Scheduling Problems
Noor Hasnah Moin
2015-01-01
Full Text Available The job shop scheduling problem (JSSP is one of the well-known hard combinatorial scheduling problems. This paper proposes a hybrid genetic algorithm with multiparents crossover for JSSP. The multiparents crossover operator known as extended precedence preservative crossover (EPPX is able to recombine more than two parents to generate a single new offspring distinguished from common crossover operators that recombine only two parents. This algorithm also embeds a schedule generation procedure to generate full-active schedule that satisfies precedence constraints in order to reduce the search space. Once a schedule is obtained, a neighborhood search is applied to exploit the search space for better solutions and to enhance the GA. This hybrid genetic algorithm is simulated on a set of benchmarks from the literatures and the results are compared with other approaches to ensure the sustainability of this algorithm in solving JSSP. The results suggest that the implementation of multiparents crossover produces competitive results.
Polynomial-time approximation schemes for scheduling problems with time lags
Zhang, Xiandong; Velde, Steef
2010-01-01
textabstractWe identify two classes of machine scheduling problems with time lags that possess Polynomial-Time Approximation Schemes (PTASs). These classes together, one for minimizing makespan and one for minimizing total completion time, include many well-studied time lag scheduling problems. The running times of these approximation schemes are polynomial in the number of jobs, but exponential in the number of machines and the ratio between the largest time lag and the smallest positive ope...
A Multiobjective Optimization Approach to Solve a Parallel Machines Scheduling Problem
2010-01-01
A multiobjective optimization problem which focuses on parallel machines scheduling is considered. This problem consists of scheduling independent jobs on identical parallel machines with release dates, due dates, and sequence-dependent setup times. The preemption of jobs is forbidden. The aim is to minimize two different objectives: makespan and total tardiness. The contribution of this paper is to propose first a new mathematical model for this specific p...
Solving scheduling tournament problems using a new version of CLONALG
Pérez-Cáceres, Leslie; Riff, María Cristina
2015-01-01
The travelling tournament problem (TTP) is an important and well-known problem within the collective sports research community. The problem is NP-hard which makes difficult finding quality solution in short amount of time. Recently a new kind of TTP has been proposed 'The Relaxed Travelling Tournament Problem'. This version of the problem allows teams to have some days off during the tournament. In this paper, we propose an immune algorithm that is able to solve both problem versions. The algorithm uses moves which are based on the team home/away patterns. One of these moves has been specially designed for the relaxed travel tournament instances. We have tested the algorithm using well-known problem benchmarks and the results obtained are very encouraging.
Behnam Barzegar
2012-01-01
Full Text Available Scheduled production system leads to avoiding stock accumulations, losses reduction, decreasing or even eliminating idol machines, and effort to better benefitting from machines for on time responding customer orders and supplying requested materials in suitable time. In flexible job-shop scheduling production systems, we could reduce time and costs by transferring and delivering operations on existing machines, that is, among NP-hard problems. The scheduling objective minimizes the maximal completion time of all the operations, which is denoted by Makespan. Different methods and algorithms have been presented for solving this problem. Having a reasonable scheduled production system has significant influence on improving effectiveness and attaining to organization goals. In this paper, new algorithm were proposed for flexible job-shop scheduling problem systems (FJSSP-GSPN that is based on gravitational search algorithm (GSA. In the proposed method, the flexible job-shop scheduling problem systems was modeled by color Petri net and CPN tool and then a scheduled job was programmed by GSA algorithm. The experimental results showed that the proposed method has reasonable performance in comparison with other algorithms.
The Home Care Crew Scheduling Problem: Preference-based visit clustering and temporal dependencies
Rasmussen, Matias Sevel; Justesen, Tor Fog; Dohn, Anders Høeg
2012-01-01
In the Home Care Crew Scheduling Problem a staff of home carers has to be assigned a number of visits to patients’ homes, such that the overall service level is maximised. The problem is a generalisation of the vehicle routing problem with time windows. Required travel time between visits and tim...
Multi-trip vehicle routing and scheduling problem with time window in real life
Sze, San-Nah; Chiew, Kang-Leng; Sze, Jeeu-Fong
2012-09-01
This paper studies a manpower scheduling problem with multiple maintenance operations and vehicle routing considerations. Service teams located at a common service centre are required to travel to different customer sites. All customers must be served within given time window, which are known in advance. The scheduling process must take into consideration complex constraints such as a meal break during the team's shift, multiple travelling trips, synchronisation of service teams and working shifts. The main objective of this study is to develop a heuristic that can generate high quality solution in short time for large problem instances. A Two-stage Scheduling Heuristic is developed for different variants of the problem. Empirical results show that the proposed solution performs effectively and efficiently. In addition, our proposed approximation algorithm is very flexible and can be easily adapted to different scheduling environments and operational requirements.
Variable Neighborhood Search for Parallel Machines Scheduling Problem with Step Deteriorating Jobs
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.
Yongyi Shou
2014-01-01
Full Text Available A multiagent evolutionary algorithm is proposed to solve the resource-constrained project portfolio selection and scheduling problem. The proposed algorithm has a dual level structure. In the upper level a set of agents make decisions to select appropriate project portfolios. Each agent selects its project portfolio independently. The neighborhood competition operator and self-learning operator are designed to improve the agent’s energy, that is, the portfolio profit. In the lower level the selected projects are scheduled simultaneously and completion times are computed to estimate the expected portfolio profit. A priority rule-based heuristic is used by each agent to solve the multiproject scheduling problem. A set of instances were generated systematically from the widely used Patterson set. Computational experiments confirmed that the proposed evolutionary algorithm is effective for the resource-constrained project portfolio selection and scheduling problem.
An Iterative Layered Tabu Search Algorithm for Complex Job Shop Scheduling Problem
LIUMin; DONGMingyu; WUCheng
2005-01-01
In this paper, aiming at the complex characteristics that there exist two interrelated decision processes: job-assignment decision and job-sequencing decision in the complex job shop scheduling problem with parallel machines and technical constraints, we propose an Iterative layered tabu search algorithm (ILTSA), which combines the iterative and layered mechanism with tabu search algorithm. In ILTSA, we define the notation of the optimization layer including the job-assignment optimization layer and the job-sequencing optimization layer which correspond to the above two interrelated decision processes respectively. On the basis, we use the corresponding tabu search algorithms in different optimization layers and switch iteratively the above two tabu search algorithms between the two optimization layers to improve the performance of the scheduling algorithm effectively. In the above two TS algorithms, the measuring functions are the objective of the whole scheduling problem. At last, we make numerical computations for different scale scheduling problems of minimizing the makespan and minimizing the total number of tardy jobs respectively, and numerical computational results show that ILTSA is very efficient and suitable for solving larger scale job shop scheduling problem with parallel machines and technical constraints. Also, we apply successfully ILTSA to a practical complex job shop scheduling problem with parallel machines and technical constraints in one of the largest cotton colored weaving enterprises in China.
A branch-and-price algorithm for the long-term home care scheduling problem
Gamst, Mette; Jensen, Thomas Sejr
2012-01-01
propose a branchand-price algorithm for the long-term home care scheduling problem. The pricing problem generates a one-day plan for an employee, and the master problem merges the plans with respect to regularity constraints. The method is capable of generating plans with up to 44 visits during one week.......In several countries, home care is provided for certain citizens living at home. The long-term home care scheduling problem is to generate work plans such that a high quality of service is maintained, the work hours of the employees are respected, and the overall cost is kept as low as possible. We...
Ant Colony Optimization for Solving Solid Waste Collection Scheduling Problems
Z. Ismail
2009-01-01
Full Text Available Problem statement: Southern Waste Management environment (SWM environment is a company responsible for the collection and disposal of solid waste for the city of Johor Bahru, a city with over one million populations. The company is implementing an integrated solid waste management system where it involved in the optimization of resources to ensure the effectiveness of its services. Formulating this real life problem into vehicle routing problem with stochastic demand model and using some designed algorithms to minimize operation cost of solid waste management. Approach: The implementation of Ant Colony Optimization (ACO for solving solid waste collection problem as a VRPSD model was described. A set of data modified from the well known 50 customers problems were used to find the route such that the expected traveling cost was minimized. The total cost was minimized by adopting a preventive restocking policy which was trading off the extra cost of returning to depot after a stock-out with the cost of returning depot for restocking before a stock-out actually occurs. For comparison purposes, Simulated Annealing (SA was used to generate the solution under the same condition. Results: For the problem size with 12 customers with vehicle capacity 10 units, both algorithms obtained the same best cost which is 69.4358 units. But the percentage deviations of averages from the associated best cost are 0.1322 and 0.7064 for ACS and SA. The results indicated that for all demand ranges, proposed ACO algorithm showed better performance than SA algorithm. Conclusion: SA was able to obtain good solutions for small ranges especially small size of problem. For ACS, it is always provide good results for all tested ranges and problems sizes of the tested problem.
Mathematical Model and Hybrid Scatter Search for Cost Driven Job-shop Scheduling Problem
Bai Jie
2011-07-01
Full Text Available Job-shop scheduling problem (JSP is one of the most well-known machine scheduling problems and one of the strongly NP-hard combinatorial optimization problems. Cost optimization is an attractive and critical research and development area for both academic and industrial societies. This paper presents a cost driven model of the job-shop scheduling problem in which the solutions are driven by business inputs, such as the cost of the product transitions, revenue loss due to the machine idle time and earliness/tardiness penalty. And then, a new hybrid scatter search algorithm is proposed to solve the cost driven job-shop scheduling problem by introducing the simulated annealing (SA into the improvement method of scatter search (SS. In order to illustrate the effectiveness of the hybrid method, some test problems are generated, and the performance of the proposed method is compared with other evolutionary algorithms such as genetic algorithm and simulated annealing. The experimental simulation tests show that the hybrid method is quite effective at solving the cost driven job-shop scheduling problem.
On scheduling models for the frequency interval assignment problem with cumulative interferences
Kiatmanaroj, Kata; Artigues, Christian; Houssin, Laurent
2016-05-01
In this article, models and methods for solving a real-life frequency assignment problem based on scheduling theory are investigated. A realistic frequency assignment problem involving cumulative interference constraints in which the aim is to maximize the number of assigned users is considered. If interferences are assumed to be binary, a multiple carrier frequency assignment problem can be treated as a disjunctive scheduling problem since a user requesting a number of contiguous frequencies can be considered as a non-preemptive task with a processing time, and two interfering users can be modelled through a disjunctive constraint on the corresponding tasks. A binary interference version of the problem is constructed and a disjunctive scheduling model is derived. Based on the binary representation, two models are proposed. The first one relies on an interference matrix and the second one considers maximal cliques. A third, cumulative, model that yields a new class of scheduling problems is also proposed. Computational experiments show that the case-study frequency assignment problem can be solved efficiently with disjunctive scheduling techniques.
Ant Colony Optimization for Solving Solid Waste Collection Scheduling Problems
Z. Ismail; S. L. Loh
2009-01-01
Problem statement: Southern Waste Management environment (SWM environment) is a company responsible for the collection and disposal of solid waste for the city of Johor Bahru, a city with over one million populations...
Ship Block Transportation Scheduling Problem Based on Greedy Algorithm
Chong Wang
2016-05-01
Full Text Available Ship block transportation problems are crucial issues to address in reducing the construction cost and improving the productivity of shipyards. Shipyards aim to maximize the workload balance of transporters with time constraint such that all blocks should be transported during the planning horizon. This process leads to three types of penalty time: empty transporter travel time, delay time, and tardy time. This study aims to minimize the sum of the penalty time. First, this study presents the problem of ship block transportation with the generalization of the block transportation restriction on the multi-type transporter. Second, the problem is transformed into the classical traveling salesman problem and assignment problem through a reasonable model simplification and by adding a virtual node to the proposed directed graph. Then, a heuristic algorithm based on greedy algorithm is proposed to assign blocks to available transporters and sequencing blocks for each transporter simultaneously. Finally, the numerical experiment method is used to validate the model, and its result shows that the proposed algorithm is effective in realizing the efficient use of the transporters in shipyards. Numerical simulation results demonstrate the promising application of the proposed method to efficiently improve the utilization of transporters and to reduce the cost of ship block logistics for shipyards.
A Study on the Enhanced Best Performance Algorithm for the Just-in-Time Scheduling Problem
Sivashan Chetty
2015-01-01
Full Text Available The Just-In-Time (JIT scheduling problem is an important subject of study. It essentially constitutes the problem of scheduling critical business resources in an attempt to optimize given business objectives. This problem is NP-Hard in nature, hence requiring efficient solution techniques. To solve the JIT scheduling problem presented in this study, a new local search metaheuristic algorithm, namely, the enhanced Best Performance Algorithm (eBPA, is introduced. This is part of the initial study of the algorithm for scheduling problems. The current problem setting is the allocation of a large number of jobs required to be scheduled on multiple and identical machines which run in parallel. The due date of a job is characterized by a window frame of time, rather than a specific point in time. The performance of the eBPA is compared against Tabu Search (TS and Simulated Annealing (SA. SA and TS are well-known local search metaheuristic algorithms. The results show the potential of the eBPA as a metaheuristic algorithm.
Packing, Scheduling and Covering Problems in a Game-Theoretic Perspective
Kleiman, Elena
2011-01-01
Many packing, scheduling and covering problems that were previously considered by computer science literature in the context of various transportation and production problems, appear also suitable for describing and modeling various fundamental aspects in networks optimization such as routing, resource allocation, congestion control, etc. Various combinatorial problems were already studied from the game theoretic standpoint, and we attempt to complement to this body of research. Specifically, we consider the bin packing problem both in the classic and parametric versions, the job scheduling problem and the machine covering problem in various machine models. We suggest new interpretations of such problems in the context of modern networks and study these problems from a game theoretic perspective by modeling them as games, and then concerning various game theoretic concepts in these games by combining tools from game theory and the traditional combinatorial optimization. In the framework of this research we in...
An Improved Genetic Algorithm for Single-Machine Inverse Scheduling Problem
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.
A branch and cut approach to the multiproduct pipeline scheduling problem
Souza Filho, Erito Marques de; Bahiense, Laura; Ferreira Filho, Virgilio Jose Martins [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil)
2009-07-01
Pipelines are known as the most reliable and economical mode of transportation for petroleum and its derivatives, especially when large amounts of products have to be pumped for large distances. We address the short-term schedule of a pipeline system comprising the distribution of several petroleum derivatives from a single oil refinery to several depots, connected to local consumer markets, through a single multi-product pipeline. The major difficulties faced in these operations are related to the satisfaction of product demands by the various consumer markets, and operational constraints such as the maximum sizes of contiguous pumping packs, and the immiscible products. Several researchers have developed models and techniques for this short-term pipeline scheduling problem. Two different methodologies have been proposed in the literature: heuristic search techniques and exact methods. In this paper, we use a branch-and cut algorithm, performed in Xpress-MP{sup T}M, and compare the solutions obtained with that ones obtained before using the Variable Neighborhood Search metaheuristic. The computational results showed a significant improvement of performance in relation to previous algorithm. (author)
Number of Tardy Jobs of Single Machine Scheduling Problem with Variable Processing Time
无
1999-01-01
The number of tardy jobs of the single machine scheduling problem with a variable processing time is studied in accordance with the published instances of traffic transportation management engineering. It is proved by 3-partition problem that if the problem is of ready time and common deadline-constrained, its complexity is NP-hard in the strong sense. Finally, a polynomial algorithm for solving unit processing time and common deadline problems is proposed.
Application of a hybrid generation/utility assessment heuristic to a class of scheduling problems
Heyward, Ann O.
1989-01-01
A two-stage heuristic solution approach for a class of multiobjective, n-job, 1-machine scheduling problems is described. Minimization of job-to-job interference for n jobs is sought. The first stage generates alternative schedule sequences by interchanging pairs of schedule elements. The set of alternative sequences can represent nodes of a decision tree; each node is reached via decision to interchange job elements. The second stage selects the parent node for the next generation of alternative sequences through automated paired comparison of objective performance for all current nodes. An application of the heuristic approach to communications satellite systems planning is presented.
Chih-Chiang Lin
2010-01-01
Full Text Available The broadcast scheduling problem (BSP in packet radio networks is a well-known NP-complete combinatorial optimization problem. The broadcast scheduling avoids packet collisions by allowing only one node transmission in each collision domain of a time division multiple access (TDMA network. It also improves the transmission utilization by assigning one transmission time slot to one or more nodes; thus, each node transmits at least once in each time frame. An optimum transmission schedule could minimize the length of a time frame while minimizing the number of idle nodes. In this paper, we propose a new iterated local search (ILS algorithm that consists of two special perturbation and local search operators to solve the BSPs. Computational experiments are applied to benchmark data sets and randomly generated problem instances. The experimental results show that our ILS approach is effective in solving the problems with only a few runtimes, even for very large networks with 2,500 nodes.
Constraint optimization model of a scheduling problem for a robotic arm in automatic systems
Kristiansen, Ewa; Smith, Stephen F.; Kristiansen, Morten
2014-01-01
In this paper, we investigate the problem of scheduling a 6 DOF robotic arm to carry out a sequence of spray painting tasks. The duration of any given painting task is process dependent and fixed, but the duration of an “intertask”, corresponding to the process of relocating and reorienting...... the robot arm from one painting task to the next one, is influenced by the order of tasks and must be minimized by the scheduler. There are multiple solutions for reaching any given painting task and tasks can be performed in either of two different directions. Further complicating the problem...... 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...
A fuzzy modeling for single machine scheduling problem with deteriorating jobs
Mohammad Mahavi Mazdeh
2010-06-01
Full Text Available This paper addresses a bi-criteria scheduling problem with deteriorating jobs on a single machine. We develop a model for a single machine bi-criteria scheduling problem (SMBSP with the aim of minimizing total tardiness and work in process (WIP costs. WIP cost increases as a job passes through a series of stages in the production process. Due to the uncertainty involved in real-world scheduling problems, it is sometimes unrealistic or even impossible to acquire exact input data. Hence, we consider the SMBSP under the hypothesis of fuzzy L-R processing time's knowledge and fuzzy L-R due date. The effectiveness of the proposed model and the denoted methodology is demonstrated through a test problem.
Scheduling Internal Audit Activities: A Stochastic Combinatorial Optimization Problem
Rossi, R.; Tarim, S.A.; Hnich, B.; Prestwich, S.; Karacaer, S.
2010-01-01
The problem of finding the optimal timing of audit activities within an organisation has been addressed by many researchers. We propose a stochastic programming formulation with Mixed Integer Linear Programming (MILP) and Constraint Programming (CP) certainty-equivalent models. In experiments neithe
Cyclic flow shop scheduling problem with two-machine cells
Bożejko Wojciech
2017-06-01
Full Text Available In the paper a variant of cyclic production with setups and two-machine cell is considered. One of the stages of the problem solving consists of assigning each operation to the machine on which it will be carried out. The total number of such assignments is exponential. We propose a polynomial time algorithm finding the optimal operations to machines assignment.
Scheduling Internal Audit Activities: A Stochastic Combinatorial Optimization Problem
Rossi, R.; Tarim, S.A.; Hnich, B.; Prestwich, S.; Karacaer, S.
2010-01-01
The problem of finding the optimal timing of audit activities within an organisation has been addressed by many researchers. We propose a stochastic programming formulation with Mixed Integer Linear Programming (MILP) and Constraint Programming (CP) certainty-equivalent models. In experiments
Solving Job-Shop Scheduling Problems by Genetic Algorithms Based on Building Block Hypothesis
CHENG Rong; CHEN You-ping; LI Zhi-gang
2006-01-01
In this paper, we propose a new genetic algorithm for job-shop scheduling problems(JSP). The proposed method uses the operation-based representation, based on schema theorem and building block hypothesis, a new crossover is proposed: By selecting short, low order highly fit schemas to genetic operator, the crossover can exchange meaningful ordering information of parents effectively and can search the global optimization. Simulation results on MT benchmark problem coded by C + + show that our genetic operators are very powerful and suitable to job-shop scheduling problems and our method outperforms the previous GA-based approaches.
The Simultaneous Vehicle Scheduling and Passenger Service Problem with Flexible Dwell Times
Fonseca, Joao Filipe Paiva; Larsen, Allan; van der Hurk, Evelien;
times at important transfer points based on expected passenger ows. We introduce a compact mixed integer linear formulation of the SVSPSP-FDT able to address small instances. We also present a meta-heuristic approach to solve medium/large instances of the problem. The e ectiveness of the proposed......In this talk, we deal with a generalization of the well-known Vehicle Scheduling Problem(VSP) that we call Simultaneous Vehicle Scheduling and Passenger Service Problem with Flexible Dwell Times (SVSPSP-FDT). The SVSPSP-FDT generalizes the VSP because the original timetables of the trips can...
Shang-Kuan Chen
2016-01-01
Full Text Available In nuclear power plant construction scheduling, a project is generally defined by its dependent preparation time, the time required for construction, and its reactor installation time. The issues of multiple construction teams and multiple reactor installation teams are considered. In this paper, a hierarchical particle swarm optimization algorithm is proposed to solve the nuclear power plant construction scheduling problem and minimize the occurrence of projects failing to achieve deliverables within applicable due times and deadlines.
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.
Wang, Lui; Valenzuela-Rendon, Manuel
1993-01-01
The Space Station Freedom will require the supply of items in a regular fashion. A schedule for the delivery of these items is not easy to design due to the large span of time involved and the possibility of cancellations and changes in shuttle flights. This paper presents the basic concepts of a genetic algorithm model, and also presents the results of an effort to apply genetic algorithms to the design of propellant resupply schedules. As part of this effort, a simple simulator and an encoding by which a genetic algorithm can find near optimal schedules have been developed. Additionally, this paper proposes ways in which robust schedules, i.e., schedules that can tolerate small changes, can be found using genetic algorithms.
Solving the Scheduling Problem in Computational Grid using Artificial Bee Colony Algorithm
Seyyed Mohsen Hashemi
2013-07-01
Full Text Available Scheduling tasks on computational grids is known as NP-complete problem. Scheduling tasks in Grid computing, means assigning tasks to resources such that the time termination and average waiting time criteria and the number of required machines are optimized. Based on heuristic or meta-heuristic search have been proposed to obtain optimal solutions. The presented method tries to optimize all of the mentioned criteria with artificial bee colony system with consideration to precedence of tasks. Bee colony optimization is one of algorithms which categorized in swarm intelligence that can be used in optimization problems. This algorithm is based on the intelligent behavior of honey bees in foraging process. The result shows using bees for solving scheduling problem in computational grid makes better finish time and average waiting time.
Deterministic and randomized scheduling problems under the lp norm on two identical machines
LIN Ling; TAN Zhi-yi; HE Yong
2005-01-01
Parallel machine scheduling problems, which are important discrete optimization problems, may occur in many applications. For example, load balancing in network communication channel assignment, parallel processing in large-size computing, task arrangement in flexible manufacturing systems, etc., are multiprocessor scheduling problem. In the traditional parallel machine scheduling problems, it is assumed that the problems are considered in offline or online environment. But in practice, problems are often not really offline or online but somehow in-between. This means that, with respect to the online problem, some further information about the tasks is available, which allows the improvement of the performance of the best possible algorithms. Problems of this class are called semi-online ones. In this paper, the semi-online problem P2|decr|lp (p＞1) is considered where jobs come in non-increasing order of their processing times and the objective is to minimize the sum of the lp norm of every machine's load. It is shown that LS algorithm is optimal for any lp norm, which extends the results known in the literature. Furthermore, randomized lower bounds for the problems P2|online|lp and P2|decr|lp are presented.
Yahong Zheng
2014-05-01
Full Text Available Purpose: This paper focuses on a classic optimization problem in operations research, the flexible job shop scheduling problem (FJSP, to discuss the method to deal with uncertainty in a manufacturing system.Design/methodology/approach: In this paper, condition based maintenance (CBM, a kind of preventive maintenance, is suggested to reduce unavailability of machines. Different to the simultaneous scheduling algorithm (SSA used in the previous article (Neale & Cameron,1979, an inserting algorithm (IA is applied, in which firstly a pre-schedule is obtained through heuristic algorithm and then maintenance tasks are inserted into the pre-schedule scheme.Findings: It is encouraging that a new better solution for an instance in benchmark of FJSP is obtained in this research. Moreover, factually SSA used in literature for solving normal FJSPPM (FJSP with PM is not suitable for the dynamic FJSPPM. Through application in the benchmark of normal FJSPPM, it is found that although IA obtains inferior results compared to SSA used in literature, it performs much better in executing speed.Originality/value: Different to traditional scheduling of FJSP, uncertainty of machines is taken into account, which increases the complexity of the problem. An inserting algorithm (IA is proposed to solve the dynamic scheduling problem. It is stated that the quality of the final result depends much on the quality of the pre-schedule obtained during the procedure of solving a normal FJSP. In order to find the best solution of FJSP, a comparative study of three heuristics is carried out, the integrated GA, ACO and ABC. In the comparative study, we find that GA performs best in the three heuristic algorithms. Meanwhile, a new better solution for an instance in benchmark of FJSP is obtained in this research.
Heuristics methods for the flow shop scheduling problem with separated setup times
Marcelo Seido Nagano
2012-06-01
Full Text Available This paper deals with the permutation flow shop scheduling problem with separated machine setup times. As a result of an investigation on the problem characteristics, four heuristics methods are proposed with procedures of the construction sequencing solution by an analogy with the asymmetric traveling salesman problem with the objective of minimizing makespan. Experimental results show that one of the new heuristics methods proposed provide high quality solutions in comparisons with the evaluated methods considered in the literature.
Optimization and Robustness in Planning and Scheduling Problems. Application to Container Terminals
Rodríguez Molins, Mario
2015-01-01
Despite the continuous evolution in computers and information technology, real-world combinatorial optimization problems are NP-problems, in particular in the domain of planning and scheduling. Thus, although exact techniques from the Operations Research (OR) field, such as Linear Programming, could be applied to solve optimization problems, they are difficult to apply in real-world scenarios since they usually require too much computational time, i.e: an optimized solution is ...
Multi-product valid inequalities for the discrete lot-sizing and scheduling problem
Gicquel, Céline; Minoux, Michel
2015-01-01
International audience; We consider a problem arising in the context of industrial production planning, namely the multi-product discrete lot-sizing and scheduling problem with sequence-dependent changeover costs. We aim at developing an exact solution approach based on a Cut & Branch procedure for this combinatorial optimization problem. To achieve this, we propose a new family of multi-product valid inequalities which corresponds to taking into account the conflicts between different produc...
Solving a manpower scheduling problem for airline catering using tabu search
Ho, Sin C.; Leung, Janny M. Y.
We study a manpower scheduling problem with job time-windows and job-skills compatibility constraints. This problem is motivated by airline catering operations, whereby airline meals and other supplies are delivered to aircrafts on the tarmac just before the flights take off. Jobs (flights) must ...... the problem. The tabu search approach employs strategic oscillation and diversification to try to explore a broad region of the solution space. Computational examples suggest that the tabu search approach can find good solutions....
Flowshop Scheduling Problems with a Position-Dependent Exponential Learning Effect
Mingbao Cheng
2013-01-01
Full Text Available We consider a permutation flowshop scheduling problem with a position-dependent exponential learning effect. The objective is to minimize the performance criteria of makespan and the total flow time. For the two-machine flow shop scheduling case, we show that Johnson’s rule is not an optimal algorithm for minimizing the makespan given the exponential learning effect. Furthermore, by using the shortest total processing times first (STPT rule, we construct the worst-case performance ratios for both criteria. Finally, a polynomial-time algorithm is proposed for special cases of the studied problem.
无
2005-01-01
The optimality of a fuzzy logic alternative to the usual treatment of uncertainties in a scheduling system using fuzzy numbers is examined formally. Processing times and due dates are fuzzified and presented by fuzzy numbers. With introducing the necessity measure, we compare fuzzy completion times of jobs with fuzzy due dates to decide whether jobs are tardy. The object is to minimize the numbers of tardy jobs.The efficient solution method for this problem is proposed. And deterministic counterpart of this single machine scheduling problem is a special case of fuzzy version.
Greedy and metaheuristics for the offline scheduling problem in grid computing
Gamst, Mette
In grid computing a number of geographically distributed resources connected through a wide area network, are utilized as one computations unit. The NP-hard offline scheduling problem in grid computing consists of assigning jobs to resources in advance. In this paper, five greedy heuristics and two...... metaheuristics for solving the offline scheduling problem are introduced. Computationally evaluating the heuristics shows that all heuristics find useful solutions with a gap of 20\\% between upper and lower bounds. The metaheuristics give better results than the greedy heuristics, but also have larger time usage...
A new genetic algorithm for flexible job-shop scheduling problems
Driss, Imen; Mouss, Kinza Nadia; Laggoun, Assia [University of Batna, Batna (Algeria)
2015-03-15
Flexible job-shop scheduling problem (FJSP), which is proved to be NP-hard, is an extension of the classical job-shop scheduling problem. In this paper, we propose a new genetic algorithm (NGA) to solve FJSP to minimize makespan. This new algorithm uses a new chromosome representation and adopts different strategies for crossover and mutation. The proposed algorithm is validated on a series of benchmark data sets and tested on data from a drug manufacturing company. Experimental results prove that the NGA is more efficient and competitive than some other existing algorithms.
Solution of the NP-hard total tardiness minimization problem in scheduling theory
Lazarev, A. A.
2007-06-01
The classical NP-hard (in the ordinary sense) problem of scheduling jobs in order to minimize the total tardiness for a single machine 1‖Σ T j is considered. An NP-hard instance of the problem is completely analyzed. A procedure for partitioning the initial set of jobs into subsets is proposed. Algorithms are constructed for finding an optimal schedule depending on the number of subsets. The complexity of the algorithms is O( n 2Σ p j ), where n is the number of jobs and p j is the processing time of the jth job ( j = 1, 2, …, n).
Reactive Scheduling Presentation for an Open Shop problem focused on job\\\\\\'s due dates
hadi naseri
2016-02-01
Full Text Available scheduling consists of assignment resources in order to perform a set of tasks in a given time horizon, that optimizes the usage of available resources. Most of researches in open shop district (area have stationary and certain status, means the situation that all data are certain and won't change in time horizon. Whereas real world scheduling problems are rarely stationary and certain. Reactive programming is a researching district that considers and peruses each changes and uncertain assumptions in real world scheduling problems, so in this paper, first we express a mixed integer programming model to produce initial scheduling for open shop problem, in continue we will generalize the presented model to it's equivalent reactive programming with due date's changing. Finally we will express an algorithm due to necessity for revise the primal scheduling. This algorithm and whole models have been implemented and ran in AIMMS software environment and the computational results have been reported.
Two parameter-tuned metaheuristic algorithms for the multi-level lot sizing and scheduling problem
S.M.T. Fatemi Ghomi
2012-10-01
Full Text Available This paper addresses the problem of lot sizing and scheduling problem for n-products and m-machines in flow shop environment where setups among machines are sequence-dependent and can be carried over. Many products must be produced under capacity constraints and allowing backorders. Since lot sizing and scheduling problems are well-known strongly NP-hard, much attention has been given to heuristics and metaheuristics methods. This paper presents two metaheuristics algorithms namely, Genetic Algorithm (GA and Imperialist Competitive Algorithm (ICA. Moreover, Taguchi robust design methodology is employed to calibrate the parameters of the algorithms for different size problems. In addition, the parameter-tuned algorithms are compared against a presented lower bound on randomly generated problems. At the end, comprehensive numerical examples are presented to demonstrate the effectiveness of the proposed algorithms. The results showed that the performance of both GA and ICA are very promising and ICA outperforms GA statistically.
Solving multi-objective job shop scheduling problems using a non-dominated sorting genetic algorithm
Piroozfard, Hamed; Wong, Kuan Yew
2015-05-01
The efforts of finding optimal schedules for the job shop scheduling problems are highly important for many real-world industrial applications. In this paper, a multi-objective based job shop scheduling problem by simultaneously minimizing makespan and tardiness is taken into account. The problem is considered to be more complex due to the multiple business criteria that must be satisfied. To solve the problem more efficiently and to obtain a set of non-dominated solutions, a meta-heuristic based non-dominated sorting genetic algorithm is presented. In addition, task based representation is used for solution encoding, and tournament selection that is based on rank and crowding distance is applied for offspring selection. Swapping and insertion mutations are employed to increase diversity of population and to perform intensive search. To evaluate the modified non-dominated sorting genetic algorithm, a set of modified benchmarking job shop problems obtained from the OR-Library is used, and the results are considered based on the number of non-dominated solutions and quality of schedules obtained by the algorithm.
Sekhri Larbi
2014-12-01
Full Text Available The optimal resources allocation to tasks was the primary objective of the research dealing with scheduling problems. These problems are characterized by their complexity, known as NP-hard in most cases. Currently with the evolution of technology, classical methods are inadequate because they degrade system performance (inflexibility, inefficient resources using policy, etc.. In the context of parallel and distributed systems, several computing units process multitasking applications in concurrent way. Main goal of such process is to schedule tasks and map them on the appropriate machines to achieve the optimal overall system performance (Minimize the Make-span and balance the load among the machines. In this paper we present a Time Petri Net (TPN based approach to solve the scheduling problem by mapping each entity (tasks, resources and constraints to correspondent one in the TPN. In this case, the scheduling problem can be reduced to finding an optimal sequence of transitions leading from an initial marking to a final one. Our approach improves the classical mapping algorithms by introducing a control over resources allocation and by taking into consideration the resource balancing aspect leading to an acceptable state of the system. The approach is applied to a specific class of problems where the machines are parallel and identical. This class is analyzed by using the TiNA (Time Net Analyzer tool software developed in the LAAS laboratory (Toulouse, France.
An Integer Linear Programming Solution to the Telescope Network Scheduling Problem
Lampoudi, Sotiria; Eastman, Jason
2015-01-01
Telescope networks are gaining traction due to their promise of higher resource utilization than single telescopes and as enablers of novel astronomical observation modes. However, as telescope network sizes increase, the possibility of scheduling them completely or even semi-manually disappears. In an earlier paper, a step towards software telescope scheduling was made with the specification of the Reservation formalism, through the use of which astronomers can express their complex observation needs and preferences. In this paper we build on that work. We present a solution to the discretized version of the problem of scheduling a telescope network. We derive a solvable integer linear programming (ILP) model based on the Reservation formalism. We show computational results verifying its correctness, and confirm that our Gurobi-based implementation can address problems of realistic size. Finally, we extend the ILP model to also handle the novel observation requests that can be specified using the more advanc...
Marwati, Rini; Yulianti, Kartika; Pangestu, Herny Wulandari
2016-02-01
A fuzzy evolutionary algorithm is an integration of an evolutionary algorithm and a fuzzy system. In this paper, we present an application of a genetic algorithm to a fuzzy evolutionary algorithm to detect and to solve chromosomes conflict. A chromosome conflict is identified by existence of any two genes in a chromosome that has the same values as two genes in another chromosome. Based on this approach, we construct an algorithm to solve a lecture scheduling problem. Time codes, lecture codes, lecturer codes, and room codes are defined as genes. They are collected to become chromosomes. As a result, the conflicted schedule turns into chromosomes conflict. Built in the Delphi program, results show that the conflicted lecture schedule problem is solvable by this algorithm.
Comparison of heuristics for an economic lot scheduling problem with deliberated coproduction
Pilar I. Vidal-Carreras
2009-12-01
Full Text Available We built on the Economic Lot Scheduling Problem Scheduling (ELSP literature by making some modifications in order to introduce new constraints which had not been thoroughly studied with a view to simulating specific real situations. Specifically, our aim is to propose and simulate different scheduling policies for a new ELSP variant: Deliberated Coproduction. This problem comprises a product system in an ELSP environment in which we may choose if more than one product can be produced on the machine at a given time. We expressly consider the option of coproducing two products whose demand is not substitutable. In order to draw conclusions, a simulation model and its results were developed in the article by employing modified Bomberger data which include two items that could be produced simultaneously.
Mostafa Kazemi
2012-10-01
Full Text Available In this paper, we consider job shop scheduling and machine location problem, simultaneously. Processing, transportation, and setup times are defined as deterministic parameters. The purpose of this paper is to determine machine location and job scheduling such that the make span and transportation cost is minimized. Therefore, the proposed model is a multi-objective problem one, where the first objective function minimizes make span and another minimizes the transportation cost. To solve the multi-objective problem, two methods are evaluated. Considering combination of job shop scheduling problem and machine location problem makes the proposed model more complex than job shop scheduling problem, which is an NP-hard problem. Therefore, to solve the proposed model, genetic algorithm as a meta-heuristic algorithm is implemented. To show the efficiency of the proposed genetic algorithm, 6×6 job shop scheduling problems are considered.
Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm
Lvjiang Yin
2016-12-01
Full Text Available Nowadays, manufacturing enterprises face the challenge of just-in-time (JIT production and energy saving. Therefore, study of JIT production and energy consumption is necessary and important in manufacturing sectors. Moreover, energy saving can be attained by the operational method and turn off/on idle machine method, which also increases the complexity of problem solving. Thus, most researchers still focus on small scale problems with one objective: a single machine environment. However, the scheduling problem is a multi-objective optimization problem in real applications. In this paper, a single machine scheduling model with controllable processing and sequence dependence setup times is developed for minimizing the total earliness/tardiness (E/T, cost, and energy consumption simultaneously. An effective multi-objective evolutionary algorithm called local multi-objective evolutionary algorithm (LMOEA is presented to tackle this multi-objective scheduling problem. To accommodate the characteristic of the problem, a new solution representation is proposed, which can convert discrete combinational problems into continuous problems. Additionally, a multiple local search strategy with self-adaptive mechanism is introduced into the proposed algorithm to enhance the exploitation ability. The performance of the proposed algorithm is evaluated by instances with comparison to other multi-objective meta-heuristics such as Nondominated Sorting Genetic Algorithm II (NSGA-II, Strength Pareto Evolutionary Algorithm 2 (SPEA2, Multiobjective Particle Swarm Optimization (OMOPSO, and Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D. Experimental results demonstrate that the proposed LMOEA algorithm outperforms its counterparts for this kind of scheduling problems.
Muller, Laurent Flindt
2009-01-01
We present an application of an Adaptive Large Neighborhood Search (ALNS) algorithm to the Resource-constrained Project Scheduling Problem (RCPSP). The ALNS framework was first proposed by Pisinger and Røpke [19] and can be described as a large neighborhood search algorithm with an adaptive layer...
A Heuristic Algorithm for the Two-Machine Flowshop Group Scheduling Problem
王秀利; 吴惕华
2002-01-01
This paper presents the two-machine flowshop group scheduling problem with the optimal objective ofmaximum lateness. A dominance rule within group and a dominance rule between groups are established. Thesedominance rules along with a previously established dominance rule are used to develop a heuristic algorithm.Experimental results are given and analyzed.
Tabu search for the job-shop scheduling problem with multi-purpose machines
Hurink, Johann; Jurisch, Bernd; Thole, Monika
1994-01-01
In this paper we study the following generalization of the job-shop scheduling problem. Each operation can be performed by one machine out of a set of machines given for this operation. The processing time does not depend on the machine which has been chosen for processing the operation. This proble
An Improved Multiobjective PSO for the Scheduling Problem of Panel Block Construction
Zhi Yang
2016-01-01
Full Text Available Uncertainty is common in ship construction. However, few studies have focused on scheduling problems under uncertainty in shipbuilding. This paper formulates the scheduling problem of panel block construction as a multiobjective fuzzy flow shop scheduling problem (FSSP with a fuzzy processing time, a fuzzy due date, and the just-in-time (JIT concept. An improved multiobjective particle swarm optimization called MOPSO-M is developed to solve the scheduling problem. MOPSO-M utilizes a ranked-order-value rule to convert the continuous position of particles into the discrete permutations of jobs, and an available mapping is employed to obtain the precedence-based permutation of the jobs. In addition, to improve the performance of MOPSO-M, archive maintenance is combined with global best position selection, and mutation and a velocity constriction mechanism are introduced into the algorithm. The feasibility and effectiveness of MOPSO-M are assessed in comparison with general MOPSO and nondominated sorting genetic algorithm-II (NSGA-II.
Wei-min Ma
2015-01-01
Full Text Available We consider parallel-machine scheduling problems with past-sequence-dependent (psd delivery times and aging maintenance. The delivery time is proportional to the waiting time in the system. Each machine has an aging maintenance activity. We develop polynomial algorithms to three versions of the problem to minimize the total absolute deviation of job completion times, the total load, and the total completion time.
Hoogeveen, J.A.; Van de Velde, S.L.
1994-12-31
We address the problem of scheduling n jobs in a flow shop environment that consists of a batching machine with capacity c and a standard machine with capacity 1 such that total completion time is minimized. We formulate this problem as a pidgin integer programming problem by using the concept of position dependent completion times; to this formulation we apply Lagrangian relaxation to obtain a strong lower bound. We show that this lower bound dominates the bound that was obtained by Ahmadi et al. by applying Lagrangian relaxation to an ordinary formulation of this problem.
Exact and Metaheuristic Approaches for a Bi-Objective School Bus Scheduling Problem.
Chen, Xiaopan; Kong, Yunfeng; Dang, Lanxue; Hou, Yane; Ye, Xinyue
2015-01-01
As a class of hard combinatorial optimization problems, the school bus routing problem has received considerable attention in the last decades. For a multi-school system, given the bus trips for each school, the school bus scheduling problem aims at optimizing bus schedules to serve all the trips within the school time windows. In this paper, we propose two approaches for solving the bi-objective school bus scheduling problem: an exact method of mixed integer programming (MIP) and a metaheuristic method which combines simulated annealing with local search. We develop MIP formulations for homogenous and heterogeneous fleet problems respectively and solve the models by MIP solver CPLEX. The bus type-based formulation for heterogeneous fleet problem reduces the model complexity in terms of the number of decision variables and constraints. The metaheuristic method is a two-stage framework for minimizing the number of buses to be used as well as the total travel distance of buses. We evaluate the proposed MIP and the metaheuristic method on two benchmark datasets, showing that on both instances, our metaheuristic method significantly outperforms the respective state-of-the-art methods.
Exact and Metaheuristic Approaches for a Bi-Objective School Bus Scheduling Problem.
Xiaopan Chen
Full Text Available As a class of hard combinatorial optimization problems, the school bus routing problem has received considerable attention in the last decades. For a multi-school system, given the bus trips for each school, the school bus scheduling problem aims at optimizing bus schedules to serve all the trips within the school time windows. In this paper, we propose two approaches for solving the bi-objective school bus scheduling problem: an exact method of mixed integer programming (MIP and a metaheuristic method which combines simulated annealing with local search. We develop MIP formulations for homogenous and heterogeneous fleet problems respectively and solve the models by MIP solver CPLEX. The bus type-based formulation for heterogeneous fleet problem reduces the model complexity in terms of the number of decision variables and constraints. The metaheuristic method is a two-stage framework for minimizing the number of buses to be used as well as the total travel distance of buses. We evaluate the proposed MIP and the metaheuristic method on two benchmark datasets, showing that on both instances, our metaheuristic method significantly outperforms the respective state-of-the-art methods.
Mohamed A.A.-F. Mansour
2011-01-01
Full Text Available Problem statement: In this article we address the multi-objective Periodic Maintenance Scheduling Problem (PMSP of scheduling a set of cyclic maintenance operations for a given set of machines through a specified planning period to minimize the total variance of workforce levels measured in man-hours and maintenance costs with equal weights. Approach: The article proposed a mixed integer non-linear math programming model and a linearised model for the PMSP. Also, we proposed a Genetic Algorithm (GA for solving the problem using a new genome representation considered as a new addition to the maintenance scheduling literature. The algorithms were compared on a set of representative test problems. Results: The developed GA proves its capability and superiority to find good solutions for the PMSP and outperforms solutions found by the commercial optimization package CPLEX. The results indicated that the developed algorithms were able to identify optimal solutions for small size problems up to 5 machines and 6 planning periods.The GAs defined solutions in 22 seconds consuming less than two kilobytes with a reliability of 0.84 while the nonlinear and linear models consumes on average 705 and 37 kilobytes respectively. Conclusion: The developed GA could define solutions of average performance of 0.34 and 0.8 for the linearized algorithm compared with lower bound defined by the nonlinear math programming model. We hope to expand the developed algorithms for integrating maintenance planning and aggregate production planning problems.
Solving Resource-constrained Multiple Project Scheduling Problem Using Timed Colored Petri Nets
WU Yu; ZHUANG Xin-cun; SONG Guo-hui; XU Xiao-dong; LI Cong-xin
2009-01-01
To solve the resource-constrained multiple project scheduling problem (RCMPSP) more effectively, a method based on timed colored Petri net (TCPN) was proposed. In this methodology, firstly a novel mapping mechanism between traditional network diagram such as CPM (critical path method)/PERT (program evaluation and review technique) and TCPN was presented. Then a primary TCPN (PTCPN) for solving RCMPSP was modeled based on the proposed mapping mechanism. Meanwhile, the object PTCPN was used to simulate the multiple projects scheduling and to find the approximately optimal value of RCMPSP. Finally, the performance of the proposed approach for solving RCMPSP was validated by executing a mould manufacturing example.
An Improved Ant Colony Algorithm for a Single-machine Scheduling Problem with Setup Times
YE Qiang; LIU Xinbao; LIU Lin; YANG Shanglin
2006-01-01
Motivated by industrial applications we study a single-machine scheduling problem in which all the jobs are mutually independent and available at time zero. The machine processes the jobs sequentially and it is not idle if there is any job to be processed. The operation of each job cannot be interrupted. The machine cannot process more than one job at a time. A setup time is needed if the machine switches from one type of job to another. The objective is to find an optimal schedule with the minimal total jobs' completion time. While the sum of jobs' processing time is always a constant, the objective is to minimize the sum of setup times. Ant colony optimization (ACO) is a meta-heuristic that has recently been applied to scheduling problem. In this paper we propose an improved ACO-Branching Ant Colony with Dynamic Perturbation (DPBAC) algorithm for the single-machine scheduling problem. DPBAC improves traditional ACO in following aspects: introducing Branching Method to choose starting points; improving state transition rules; introducing Mutation Method to shorten tours; improving pheromone updating rules and introducing Conditional Dynamic Perturbation Strategy. Computational results show that DPBAC algorithm is superior to the traditional ACO algorithm.
Minimizing the total tardiness for the tool change scheduling problem on parallel machines
Antonio Costa
2016-04-01
Full Text Available This paper deals with the total tardiness minimization problem in a parallel machines manufacturing environment where tool change operations have to be scheduled along with jobs. The mentioned issue belongs to the family of scheduling problems under deterministic machine availability restrictions. A new model that considers the effects of the tool wear on the quality characteristics of the worked product is proposed. Since no mathematical programming-based approach has been developed by literature so far, two distinct mixed integer linear programming models, able to schedule jobs as well as tool change activities along the provided production horizon, have been devised. The former is an adaptation of a well-known model presented by the relevant literature for the single machine scheduling problem with tool changes. The latter has been specifically developed for the issue at hand. After a theoretical analysis aimed at revealing the differences between the proposed mathematical models in terms of computational complexity, an extensive experimental campaign has been fulfilled to assess performances of the proposed methods under the CPU time viewpoint. Obtained results have been statistically analyzed through a properly arranged ANOVA analysis.
Learning to repair plans and schedules using a relational (deictic representation
J. Palombarini
2010-09-01
Full Text Available Unplanned and abnormal events may have a significant impact on the feasibility of plans and schedules which requires to repair them 'on-the-fly' to guarantee due date compliance of orders-in-progress and negotiating delivery conditions for new orders. In this work, a repair-based rescheduling approach based on the integration of intensive simulations with logical and relational reinforcement learning is proposed. Based on a relational (deictic representation of schedule states, a number of repair operators have been designed to guide the search towards a goal state. The knowledge generated via simulation is encoded in a relational regression tree for the Q-value function defining the utility of applying a given repair operator at a given schedule state. A prototype implementation in Prolog language is discussed using a representative example of three batch extruders processing orders for four different products. The learning curve for the problem of inserting a new order vividly illustrates the advantages of logical and relational learning in rescheduling.
CAPACITATED LOT SIZING AND SCHEDULING PROBLEMS USING HYBRID GA/TS APPROACHES
无
2003-01-01
The capacitated lot sizing and scheduling problem that involves in determining the production amounts and release dates for several items over a given planning horizon are given to meet dynamic order demand without incurring backloggings. The problem considering overtime capacity is studied. The mathematical model is presented, and a genetic algorithm (GA) approach is developed to solve the problem. The initial solutions are generated after using heuristic method. Capacity balancing procedure is employed to stipulate the feasibility of the solutions. In addition, a technique based on Tabu search (TS) is inserted into the genetic algorithm to deal with the scheduled overtime and help the convergence of algorithm. Computational simulation is conducted to test the efficiency of the proposed hybrid approach, which turns out to improve both the solution quality and execution speed.
Chang, Yung-Chia; Li, Vincent C.; Chiang, Chia-Ju
2014-04-01
Make-to-order or direct-order business models that require close interaction between production and distribution activities have been adopted by many enterprises in order to be competitive in demanding markets. This article considers an integrated production and distribution scheduling problem in which jobs are first processed by one of the unrelated parallel machines and then distributed to corresponding customers by capacitated vehicles without intermediate inventory. The objective is to find a joint production and distribution schedule so that the weighted sum of total weighted job delivery time and the total distribution cost is minimized. This article presents a mathematical model for describing the problem and designs an algorithm using ant colony optimization. Computational experiments illustrate that the algorithm developed is capable of generating near-optimal solutions. The computational results also demonstrate the value of integrating production and distribution in the model for the studied problem.
Single-machine group scheduling problems with deteriorating and learning effect
Xingong, Zhang; Yong, Wang; Shikun, Bai
2016-07-01
The concepts of deteriorating jobs and learning effects have been individually studied in many scheduling problems. However, most studies considering the deteriorating and learning effects ignore the fact that production efficiency can be increased by grouping various parts and products with similar designs and/or production processes. This phenomenon is known as 'group technology' in the literature. In this paper, a new group scheduling model with deteriorating and learning effects is proposed, where learning effect depends not only on job position, but also on the position of the corresponding job group; deteriorating effect depends on its starting time of the job. This paper shows that the makespan and the total completion time problems remain polynomial optimal solvable under the proposed model. In addition, a polynomial optimal solution is also presented to minimise the maximum lateness problem under certain agreeable restriction.
Accounting for Cache Related Pre-emption Delays in Hierarchical Scheduling with Local EDF Scheduler
Lunniss, W.; Altmeyer, S.; Davis, R.I.
2014-01-01
Hierarchical scheduling provides a means of composing multiple real-time applications onto a single processor such that the temporal requirements of each application are met. This has become a popular technique in industry as it allows applications from multiple vendors as well as legacy application
A hybrid algorithm for flexible job-shop scheduling problem with setup times
Ameni Azzouz
2017-01-01
Full Text Available Job-shop scheduling problem is one of the most important fields in manufacturing optimization where a set of n jobs must be processed on a set of m specified machines. Each job consists of a specific set of operations, which have to be processed according to a given order. The Flexible Job Shop problem (FJSP is a generalization of the above-mentioned problem, where each operation can be processed by a set of resources and has a processing time depending on the resource used. The FJSP problems cover two difficulties, namely, machine assignment problem and operation sequencing problem. This paper addresses the flexible job-shop scheduling problem with sequence-dependent setup times to minimize two kinds of objectives function: makespan and bi-criteria objective function. For that, we propose a hybrid algorithm based on genetic algorithm (GA and variable neighbourhood search (VNS to solve this problem. To evaluate the performance of our algorithm, we compare our results with other methods existing in literature. All the results show the superiority of our algorithm against the available ones in terms of solution quality.
Solving a large-scale precedence constrained scheduling problem with elastic jobs using tabu search
Pedersen, C.R.; Rasmussen, R.V.; Andersen, Kim Allan
2007-01-01
This paper presents a solution method for minimizing makespan of a practical large-scale scheduling problem with elastic jobs. The jobs are processed on three servers and restricted by precedence constraints, time windows and capacity limitations. We derive a new method for approximating the server...... exploitation of the elastic jobs and solve the problem using a tabu search procedure. Finding an initial feasible solution is in general -complete, but the tabu search procedure includes a specialized heuristic for solving this problem. The solution method has proven to be very efficient and leads...
Recent trends in solving the deterministic resource constrained Project Scheduling Problem
Karam, Ahmed; Lazarova-Molnar, Sanja
2013-01-01
, a number of new and promising meta-heuristic approaches for solving the RCPSP problem have emerged. In this paper, we provide a detailed review of the most recent approaches for solving the RCPSP that have been proposed in literature. In particular, we present a comparison, classification and analysis......, based on a number of relevant metrics. Extensive numerical results based on well-known benchmark problem instance sets of size J30, J60 and J120 from Project Scheduling Problem Library (PSPLIB), as well as comparisons among state-of-the-art hybrid meta-heuristic algorithms demonstrate the effectiveness...
An Optimal Algorithm for a Class of Parallel Machines Scheduling Problem
常俊林; 邵惠鹤
2004-01-01
This paper considers the parallel machines scheduling problem where jobs are subject to different release times. A constructive heuristic is first proposed to solve the problem in a modest amount of computer time. In general, the quality of the solutions provided by heuristics degrades with the increase of the probiem's scale. Combined the global search ability of genetic algorithm, this paper proposed a hybrid heuristic to improve the quality of solutions further. The computational results show that the hybrid heuristic combines the advantages of heuristic and genetic algorithm effectively and can provide very good solutions to some large problems in a reasonable amount of computer time.
Optimising the Slab Yard Planning and Crane Scheduling Problem using a two-stage heuristic
Hansen, Anders Dohn; Clausen, Jens
2010-01-01
In this paper, we present the Slab Yard Planning and Crane Scheduling Problem. The problem has its origin in steel production facilities with a large throughput. A slab yard is used as a buffer for slabs that are needed in the upcoming production. Slabs are transported by cranes and the problem...... tests are run on a generic setup with simulated data. The test results are very promising. The production delays are reduced significantly in the new solutions compared with the corresponding delays observed in a simulation of manual planning....
Meta-RaPS Algorithm for the Aerial Refueling Scheduling Problem
Kaplan, Sezgin; Arin, Arif; Rabadi, Ghaith
2011-01-01
The Aerial Refueling Scheduling Problem (ARSP) can be defined as determining the refueling completion times for each fighter aircraft (job) on multiple tankers (machines). ARSP assumes that jobs have different release times and due dates, The total weighted tardiness is used to evaluate schedule's quality. Therefore, ARSP can be modeled as a parallel machine scheduling with release limes and due dates to minimize the total weighted tardiness. Since ARSP is NP-hard, it will be more appropriate to develop a pproimate or heuristic algorithm to obtain solutions in reasonable computation limes. In this paper, Meta-Raps-ATC algorithm is implemented to create high quality solutions. Meta-RaPS (Meta-heuristic for Randomized Priority Search) is a recent and promising meta heuristic that is applied by introducing randomness to a construction heuristic. The Apparent Tardiness Rule (ATC), which is a good rule for scheduling problems with tardiness objective, is used to construct initial solutions which are improved by an exchanging operation. Results are presented for generated instances.
Ramli, Razamin; Tein, Lim Huai
2016-08-01
A good work schedule can improve hospital operations by providing better coverage with appropriate staffing levels in managing nurse personnel. Hence, constructing the best nurse work schedule is the appropriate effort. In doing so, an improved selection operator in the Evolutionary Algorithm (EA) strategy for a nurse scheduling problem (NSP) is proposed. The smart and efficient scheduling procedures were considered. Computation of the performance of each potential solution or schedule was done through fitness evaluation. The best so far solution was obtained via special Maximax&Maximin (MM) parent selection operator embedded in the EA, which fulfilled all constraints considered in the NSP.
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.
A Genetic Algorithm Approach for Solving a Flexible Job ShopScheduling Problem
Sayedmohammadreza Vaghefinezhad
2012-05-01
Full Text Available Flexible job shop scheduling has been noticed as an effective manufacturing system to cope with rapid development in todays competitive environment. Flexible job shop scheduling problem (FJSSP is known as a NP-hard in the field of the optimization problem. Assuming the dynamic state of the real world, make these problems more and more complicated. Most studies in the field of FJSSP have only focused on minimizing the total makespan. In this paper, a mathematical model for FJSSP has been developed. The objective function is maximizing the total profit while meeting some constraints. Considering time-varying raw material and selling price and dissimilar demand for each period, are attempts that have been done to decrease gaps between reality and the model. A manufacturer that produces various parts of gas valves has been used as a case study. The scheduling problem for multi part, multi period, and multi operation with parallel machines has been solved by genetic algorithm (GA. The best obtained answer determines the economic amount of production by different machines that belong to predefined operations for each part to satisfy customer demand in each period.
Duan, Qian-Qian; Yang, Gen-Ke; Pan, Chang-Chun
2014-01-01
A hybrid optimization algorithm combining finite state method (FSM) and genetic algorithm (GA) is proposed to solve the crude oil scheduling problem. The FSM and GA are combined to take the advantage of each method and compensate deficiencies of individual methods. In the proposed algorithm, the finite state method makes up for the weakness of GA which is poor at local searching ability. The heuristic returned by the FSM can guide the GA algorithm towards good solutions. The idea behind this is that we can generate promising substructure or partial solution by using FSM. Furthermore, the FSM can guarantee that the entire solution space is uniformly covered. Therefore, the combination of the two algorithms has better global performance than the existing GA or FSM which is operated individually. Finally, a real-life crude oil scheduling problem from the literature is used for conducting simulation. The experimental results validate that the proposed method outperforms the state-of-art GA method.
A Model for Bus Crew Scheduling Problem with Multiple Duty Types
Mingming Chen
2012-01-01
Full Text Available This paper presents an approach for solving the bus crew scheduling problem which considers early, day, and late duty modes with time shift and work intensity constraints. Furthermore, the constraint with the least crew number of a certain duty (e.g., day duty has also been considered. An optimization model is formulated as a 0-1 integer programming problem to improve the efficiency of crew scheduling at the minimum expense of total idle time of crew for a circle bus line. Correspondingly, a heuristic algorithm utilizing the tabu search algorithm has been proposed to solve the model. Finally, the proposed model and algorithm are successfully tested by a case study.
An Artificial Bee Colony Algorithm for the Job Shop Scheduling Problem with Random Processing Times
Rui Zhang
2011-09-01
Full Text Available Due to the influence of unpredictable random events, the processing time of each operation should be treated as random variables if we aim at a robust production schedule. However, compared with the extensive research on the deterministic model, the stochastic job shop scheduling problem (SJSSP has not received sufficient attention. In this paper, we propose an artificial bee colony (ABC algorithm for SJSSP with the objective of minimizing the maximum lateness (which is an index of service quality. First, we propose a performance estimate for preliminary screening of the candidate solutions. Then, the K-armed bandit model is utilized for reducing the computational burden in the exact evaluation (through Monte Carlo simulation process. Finally, the computational results on different-scale test problems validate the effectiveness and efficiency of the proposed approach.
Tabrizi, Babak H.; Farid Ghaderi, Seyed
2016-09-01
Simultaneous planning of project scheduling and material procurement can improve the project execution costs. Hence, the issue has been addressed here by a mixed-integer programming model. The proposed model facilitates the procurement decisions by accounting for a number of suppliers offering a distinctive discount formula from which to purchase the required materials. It is aimed at developing schedules with the best net present value regarding the obtained benefit and costs of the project execution. A genetic algorithm is applied to deal with the problem, in addition to a modified version equipped with a variable neighbourhood search. The underlying factors of the solution methods are calibrated by the Taguchi method to obtain robust solutions. The performance of the aforementioned methods is compared for different problem sizes, in which the utilized local search proved efficient. Finally, a sensitivity analysis is carried out to check the effect of inflation on the objective function value.
A Modified Biogeography-Based Optimization for the Flexible Job Shop Scheduling Problem
Yuzhen Yang
2015-01-01
Full Text Available The flexible job shop scheduling problem (FJSSP is a practical extension of classical job shop scheduling problem that is known to be NP-hard. In this paper, an effective modified biogeography-based optimization (MBBO algorithm with machine-based shifting is proposed to solve FJSSP with makespan minimization. The MBBO attaches great importance to the balance between exploration and exploitation. At the initialization stage, different strategies which correspond to two-vector representation are proposed to generate the initial habitats. At global phase, different migration and mutation operators are properly designed. At local phase, a machine-based shifting decoding strategy and a local search based on insertion to the habitat with best makespan are introduced to enhance the exploitation ability. A series of experiments on two well-known benchmark instances are performed. The comparisons between MBBO and other famous algorithms as well as BBO variants prove the effectiveness and efficiency of MBBO in solving FJSSP.
Parallel genetic algorithms with migration for the hybrid flow shop scheduling problem
K. Belkadi
2006-01-01
Full Text Available This paper addresses scheduling problems in hybrid flow shop-like systems with a migration parallel genetic algorithm (PGA_MIG. This parallel genetic algorithm model allows genetic diversity by the application of selection and reproduction mechanisms nearer to nature. The space structure of the population is modified by dividing it into disjoined subpopulations. From time to time, individuals are exchanged between the different subpopulations (migration. Influence of parameters and dedicated strategies are studied. These parameters are the number of independent subpopulations, the interconnection topology between subpopulations, the choice/replacement strategy of the migrant individuals, and the migration frequency. A comparison between the sequential and parallel version of genetic algorithm (GA is provided. This comparison relates to the quality of the solution and the execution time of the two versions. The efficiency of the parallel model highly depends on the parameters and especially on the migration frequency. In the same way this parallel model gives a significant improvement of computational time if it is implemented on a parallel architecture which offers an acceptable number of processors (as many processors as subpopulations.
Mazur Michał
2015-09-01
Full Text Available A new approach to solving realistic car assembly scheduling problems for mixed model assembly line is presented. It is proposed to decompose the problem into two subproblems: 1 a sequencing problem that generates admissible car sequences fulfilling capacity constraints for all car models in the production plan, 2 a scheduling problem that determines an admissible car sequence with shortest makespan. The details of this approach are illustrated by a simple numerical example.
无
2001-01-01
In this paper, we give a mathematical model for earliness-tardiness job scheduling problem with a common due window on parallel and non-identical machines. Because the job scheduling problem discussed in the paper contains a problem of minimizing make-span, which is NP-complete on parallel and uniform machines, a heuristic algorithm is presented to find an approximate solution for the scheduling problem after proving an important theorem. Two numerical examples illustrate that the heuristic algorithm is very useful and effective in obtaining the near-optimal solution.
M Kamrul AHSAN; De-bi TSAO
2003-01-01
In this paper we formulate a bi-criteria search strategy of a heuristic learning algorithm for solving multiple resource-constrained project scheduling problems. The heuristic solves problems in two phases. In the pre-processing phase, the algorithm estimates distance between a state and the goal state and measures complexity of problem instances. In the search phase, the algorithm uses estimates of the pre-processing phase to further estimate distances to the goal state. The search continues in a stepwise generation of a series of intermediate states through search path evaluation process with backtracking. Developments of intermediate states are exclusively based on a bi-criteria new state selection technique where we consider resource utilization and duration estimate to the goal state. We also propose a variable weighting technique based on initial problem complexity measures. Introducing this technique allows the algorithm to efficiently solve complex project scheduling problems. A numerical example illustrates the algorithm and performance is evaluated by extensive experimentation with various problem parameters. Computational results indicate significance of the algorithm in terms of solution quality and computational performance.
A Multiobjective Optimization Approach to Solve a Parallel Machines Scheduling Problem
Xiaohui Li
2010-01-01
Full Text Available A multiobjective optimization problem which focuses on parallel machines scheduling is considered. This problem consists of scheduling independent jobs on identical parallel machines with release dates, due dates, and sequence-dependent setup times. The preemption of jobs is forbidden. The aim is to minimize two different objectives: makespan and total tardiness. The contribution of this paper is to propose first a new mathematical model for this specific problem. Then, since this problem is NP hard in the strong sense, two well-known approximated methods, NSGA-II and SPEA-II, are adopted to solve it. Experimental results show the advantages of NSGA-II for the studied problem. An exact method is then applied to be compared with NSGA-II algorithm in order to prove the efficiency of the former. Experimental results show the advantages of NSGA-II for the studied problem. Computational experiments show that on all the tested instances, our NSGA-II algorithm was able to get the optimal solutions.
Heuristic and Exact Algorithms for the Two-Machine Just in Time Job Shop Scheduling Problem
Mohammed Al-Salem
2016-01-01
Full Text Available The problem addressed in this paper is the two-machine job shop scheduling problem when the objective is to minimize the total earliness and tardiness from a common due date (CDD for a set of jobs when their weights equal 1 (unweighted problem. This objective became very significant after the introduction of the Just in Time manufacturing approach. A procedure to determine whether the CDD is restricted or unrestricted is developed and a semirestricted CDD is defined. Algorithms are introduced to find the optimal solution when the CDD is unrestricted and semirestricted. When the CDD is restricted, which is a much harder problem, a heuristic algorithm is proposed to find approximate solutions. Through computational experiments, the heuristic algorithms’ performance is evaluated with problems up to 500 jobs.
Hybrid Metaheuristics for Solving a Fuzzy Single Batch-Processing Machine Scheduling Problem
S. Molla-Alizadeh-Zavardehi
2014-01-01
Full Text Available This paper deals with a problem of minimizing total weighted tardiness of jobs in a real-world single batch-processing machine (SBPM scheduling in the presence of fuzzy due date. In this paper, first a fuzzy mixed integer linear programming model is developed. Then, due to the complexity of the problem, which is NP-hard, we design two hybrid metaheuristics called GA-VNS and VNS-SA applying the advantages of genetic algorithm (GA, variable neighborhood search (VNS, and simulated annealing (SA frameworks. Besides, we propose three fuzzy earliest due date heuristics to solve the given problem. Through computational experiments with several random test problems, a robust calibration is applied on the parameters. Finally, computational results on different-scale test problems are presented to compare the proposed algorithms.
Literature Review on the Hybrid Flow Shop Scheduling Problem with Unrelated Parallel Machines
Eliana Marcela Peña Tibaduiza
2017-01-01
Full Text Available Context: The flow shop hybrid problem with unrelated parallel machines has been less studied in the academia compared to the flow shop hybrid with identical processors. For this reason, there are few reports about the kind of application of this problem in industries. Method: A literature review of the state of the art on flow-shop scheduling problem was conducted by collecting and analyzing academic papers on several scientific databases. For this aim, a search query was constructed using keywords defining the problem and checking the inclusion of unrelated parallel machines in such definition; as a result, 50 papers were finally selected for this study. Results: A classification of the problem according to the characteristics of the production system was performed, also solution methods, constraints and objective functions commonly used are presented. Conclusions: An increasing trend is observed in studies of flow shop with multiple stages, but few are based on industry case-studies.
A Novel Joint Problem of Routing, Scheduling, and Variable-Width Channel Allocation in WMNs
Chun-Cheng Lin
2014-01-01
Full Text Available This paper investigates a novel joint problem of routing, scheduling, and channel allocation for single-radio multichannel wireless mesh networks in which multiple channel widths can be adjusted dynamically through a new software technology so that more concurrent transmissions and suppressed overlapping channel interference can be achieved. Although the previous works have studied this joint problem, their linear programming models for the problem were not incorporated with some delicate constraints. As a result, this paper first constructs a linear programming model with more practical concerns and then proposes a simulated annealing approach with a novel encoding mechanism, in which the configurations of multiple time slots are devised to characterize the dynamic transmission process. Experimental results show that our approach can find the same or similar solutions as the optimal solutions for smaller-scale problems and can efficiently find good-quality solutions for a variety of larger-scale problems.
Hybrid metaheuristics for solving a fuzzy single batch-processing machine scheduling problem.
Molla-Alizadeh-Zavardehi, S; Tavakkoli-Moghaddam, R; Lotfi, F Hosseinzadeh
2014-01-01
This paper deals with a problem of minimizing total weighted tardiness of jobs in a real-world single batch-processing machine (SBPM) scheduling in the presence of fuzzy due date. In this paper, first a fuzzy mixed integer linear programming model is developed. Then, due to the complexity of the problem, which is NP-hard, we design two hybrid metaheuristics called GA-VNS and VNS-SA applying the advantages of genetic algorithm (GA), variable neighborhood search (VNS), and simulated annealing (SA) frameworks. Besides, we propose three fuzzy earliest due date heuristics to solve the given problem. Through computational experiments with several random test problems, a robust calibration is applied on the parameters. Finally, computational results on different-scale test problems are presented to compare the proposed algorithms.
Study on multi-objective flexible job-shop scheduling problem considering energy consumption
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.
Robust Proactive Project Scheduling Model for the Stochastic Discrete Time/Cost Trade-Off Problem
Hongbo Li
2015-01-01
Full Text Available We study the project budget version of the stochastic discrete time/cost trade-off problem (SDTCTP-B from the viewpoint of the robustness in the scheduling. Given the project budget and a set of activity execution modes, each with uncertain activity time and cost, the objective of the SDTCTP-B is to minimize the expected project makespan by determining each activity’s mode and starting time. By modeling the activity time and cost using interval numbers, we propose a proactive project scheduling model for the SDTCTP-B based on robust optimization theory. Our model can generate robust baseline schedules that enable a freely adjustable level of robustness. We convert our model into its robust counterpart using a form of the mixed-integer programming model. Extensive experiments are performed on a large number of randomly generated networks to validate our model. Moreover, simulation is used to investigate the trade-off between the advantages and the disadvantages of our robust proactive project scheduling model.
A Genetic Algorithm-based Heuristic for Part-Feeding Mobile Robot Scheduling Problem
Dang, Vinh Quang; Nielsen, Izabela Ewa; Bocewicz, Grzegorz
2012-01-01
This present study deals with the problem of sequencing feeding tasks of a single mobile robot with manipulation arm which is able to provide parts or components for feeders of machines in a manufacturing cell. The mobile robot has to be scheduled in order to keep machines within the cell produci....... A genetic algorithm-based heuristic is developed to find the near optimal solution for the problem. A case study is implemented at an impeller production line in a factory to demonstrate the result of the proposed approach....
A Genetic Algorithm-based Heuristic for Part-Feeding Mobile Robot Scheduling Problem
Dang, Vinh Quang; Nielsen, Izabela Ewa; Bocewicz, Grzegorz
2012-01-01
This present study deals with the problem of sequencing feeding tasks of a single mobile robot with manipulation arm which is able to provide parts or components for feeders of machines in a manufacturing cell. The mobile robot has to be scheduled in order to keep machines within the cell produci....... A genetic algorithm-based heuristic is developed to find the near optimal solution for the problem. A case study is implemented at an impeller production line in a factory to demonstrate the result of the proposed approach....
SCHEDULING PROBLEMS OF STATIONARY OBJECTS WITH THE PROCESSOR IN ONE-DIMENSIONAL ZONE
N. A. Dunichkina
2015-01-01
Full Text Available We consider the mathematical model in which an operating processor serves the set of the stationary objects positioned in a one-dimensional working zone. The processor performs two voyages between the uttermost points of the zone: the forward or direct one, where certain objects are served, and the return one, where remaining objects are served. Servicing of the object cannot start earlier than its ready date. The individual penalty function is assigned to every object, the function depending on the servicing completion time. Minimized criteria of schedule quality are assumed to be total service duration and total penalty. We formulate and study optimization problems with one and two criteria. Proposed algorithms are based on dynamic programming and Pareto principle, the implementations of these algorithms are demonstrated on numerical examples. We show that the algorithm for the problem of processing time minimization is polynomial, and that the problem of total penalty minimization is NP-hard. Correspondingly, the bicriteria problem with the mentioned evaluation criteria is fundamentally intractable, computational complexity of the schedule structure algorithm is exponential. The model describes the fuel supply processes to the diesel-electrical dredgers which extract non-metallic building materials (sand, gravel in large-scale areas of inland waterways. Similar models and optimization problems are important, for example, in applications like the control of satellite group refueling and regular civil aircraft refueling.The article is published in the author’s wording.
Renata Melo e Silva de Oliveira
2015-03-01
Full Text Available Scheduling is a key factor for operations management as well as for business success. From industrial Job-shop Scheduling problems (JSSP, many optimization challenges have emerged since de 1960s when improvements have been continuously required such as bottlenecks allocation, lead-time reductions and reducing response time to requests. With this in perspective, this work aims to discuss 3 different optimization models for minimizing Makespan. Those 3 models were applied on 17 classical problems of examples JSSP and produced different outputs. The first model resorts on Mixed and Integer Programming (MIP and it resulted on optimizing 60% of the studied problems. The other models were based on Constraint Programming (CP and approached the problem in two different ways: a model CP1 is a standard IBM algorithm whereof restrictions have an interval structure that fail to solve 53% of the proposed instances, b Model CP-2 approaches the problem with disjunctive constraints and optimized 88% of the instances. In this work, each model is individually analyzed and then compared considering: i Optimization success performance, ii Computational processing time, iii Greatest Resource Utilization and, iv Minimum Work-in-process Inventory. Results demonstrated that CP-2 presented best results on criteria i and ii, but MIP was superior on criteria iii and iv and those findings are discussed at the final section of this work.
New scheduling rules for a dynamic flexible flow line problem with sequence-dependent setup times
Kia, Hamidreza; Ghodsypour, Seyed Hassan; Davoudpour, Hamid
2017-01-01
In the literature, the application of multi-objective dynamic scheduling problem and simple priority rules are widely studied. Although these rules are not efficient enough due to simplicity and lack of general insight, composite dispatching rules have a very suitable performance because they result from experiments. In this paper, a dynamic flexible flow line problem with sequence-dependent setup times is studied. The objective of the problem is minimization of mean flow time and mean tardiness. A 0-1 mixed integer model of the problem is formulated. Since the problem is NP-hard, four new composite dispatching rules are proposed to solve it by applying genetic programming framework and choosing proper operators. Furthermore, a discrete-event simulation model is made to examine the performances of scheduling rules considering four new heuristic rules and the six adapted heuristic rules from the literature. It is clear from the experimental results that composite dispatching rules that are formed from genetic programming have a better performance in minimization of mean flow time and mean tardiness than others.
New scheduling rules for a dynamic flexible flow line problem with sequence-dependent setup times
Kia, Hamidreza; Ghodsypour, Seyed Hassan; Davoudpour, Hamid
2017-01-01
In the literature, the application of multi-objective dynamic scheduling problem and simple priority rules are widely studied. Although these rules are not efficient enough due to simplicity and lack of general insight, composite dispatching rules have a very suitable performance because they result from experiments. In this paper, a dynamic flexible flow line problem with sequence-dependent setup times is studied. The objective of the problem is minimization of mean flow time and mean tardiness. A 0-1 mixed integer model of the problem is formulated. Since the problem is NP-hard, four new composite dispatching rules are proposed to solve it by applying genetic programming framework and choosing proper operators. Furthermore, a discrete-event simulation model is made to examine the performances of scheduling rules considering four new heuristic rules and the six adapted heuristic rules from the literature. It is clear from the experimental results that composite dispatching rules that are formed from genetic programming have a better performance in minimization of mean flow time and mean tardiness than others.
Extension of the Dynasearch to the Two-Machine Permutation Flowshop Scheduling Problem
Tanaka, Shunji
The purpose of this study is to construct a solution algorithm for the two-machine permutation flowshop problem based on the dynasearch. The dynasearch is an efficient local search algorithm that employs a special neighborhood structure called dynasearch swap neighborhood. Its primary advantage is that the neighborhood of a solution can be explored in polynomial time although it is composed of an exponential number of solutions. The dynasearch for machine scheduling was originally developed for the single-machine total weighted tardiness problem. Then, it was extended to the problem with idle time and setup times. This study further extends the dynasearch to the two-machine permutation flowshop problem and its effectiveness is examined by numerical experiments for both total weighted tardiness and total weighted earliness-tardiness objectives.
A Decision Support Method for Truck Scheduling and Storage Allocation Problem at Container
CAO Jinxin; SHI Oixin; Der-Horng Lee
2008-01-01
Truck scheduling and storage allocation, as two separate subproblems in port operations, have been deeply studied in past decades. However, from the operational point of view, they are highly interde-pendent. Storage allocation for import containers has to balance the travel time and queuing time of each container in yard. This paper proposed an integer programming model handling these two problems as a whole. The objective of this model is to reduce congestion and waiting time of container trucks in the termi-nal so as to decrease the makespan of discharging containers. Due to the inherent complexity of the prob-lem, a genetic algorithm and a greedy heuristic algorithm are designed to attain near optimal solutions. It shows that the heuristic algorithm can achieve the optimal solution for small-scale problems. The solutions of small- and large-scale problems obtained from the heuristic algorithm are better than those from the ge-netic algorithm.
Cache related pre-emption delays in hierarchical scheduling
Lunniss, W.; Altmeyer, S.; Lipari, G.; Davis, R.I.
2016-01-01
Hierarchical scheduling provides a means of composing multiple real-time applications onto a single processor such that the temporal requirements of each application are met. This has become a popular technique in industry as it allows applications from multiple vendors as well as legacy application
The Combinatorial Multi-Mode Resource Constrained Multi-Project Scheduling Problem
Denis Pinha
2016-11-01
Full Text Available This paper presents the formulation and solution of the Combinatorial Multi-Mode Resource Constrained Multi-Project Scheduling Problem. The focus of the proposed method is not on finding a single optimal solution, instead on presenting multiple feasible solutions, with cost and duration information to the project manager. The motivation for developing such an approach is due in part to practical situations where the definition of optimal changes on a regular basis. The proposed approach empowers the project manager to determine what is optimal, on a given day, under the current constraints, such as, change of priorities, lack of skilled worker. The proposed method utilizes a simulation approach to determine feasible solutions, under the current constraints. Resources can be non-consumable, consumable, or doubly constrained. The paper also presents a real-life case study dealing with scheduling of ship repair activities.
Sunxin Wang
2014-01-01
Full Text Available This paper presents a combination of variable neighbourhood search and mathematical programming to minimize the sum of earliness and tardiness penalty costs of all operations for just-in-time job-shop scheduling problem (JITJSSP. Unlike classical E/T scheduling problem with each job having its earliness or tardiness penalty cost, each operation in this paper has its earliness and tardiness penalties, which are paid if the operation is completed before or after its due date. Our hybrid algorithm combines (i a variable neighbourhood search procedure to explore the huge feasible solution spaces efficiently by alternating the swap and insertion neighbourhood structures and (ii a mathematical programming model to optimize the completion times of the operations for a given solution in each iteration procedure. Additionally, a threshold accepting mechanism is proposed to diversify the local search of variable neighbourhood search. Computational results on the 72 benchmark instances show that our algorithm can obtain the best known solution for 40 problems, and the best known solutions for 33 problems are updated.
Robust Parallel Machine Scheduling Problem with Uncertainties and Sequence-Dependent Setup Time
Hongtao Hu
2016-01-01
Full Text Available A parallel machine scheduling problem in plastic production is studied in this paper. In this problem, the processing time and arrival time are uncertain but lie in their respective intervals. In addition, each job must be processed together with a mold while jobs which belong to one family can share the same mold. Therefore, time changing mold is required for two consecutive jobs that belong to different families, which is known as sequence-dependent setup time. This paper aims to identify a robust schedule by min–max regret criterion. It is proved that the scenario incurring maximal regret for each feasible solution lies in finite extreme scenarios. A mixed integer linear programming formulation and an exact algorithm are proposed to solve the problem. Moreover, a modified artificial bee colony algorithm is developed to solve large-scale problems. The performance of the presented algorithm is evaluated through extensive computational experiments and the results show that the proposed algorithm surpasses the exact method in terms of objective value and computational time.
J. S. Sadaghiani
2014-04-01
Full Text Available Flexible job shop scheduling problem is a key factor of using efficiently in production systems. This paper attempts to simultaneously optimize three objectives including minimization of the make span, total workload and maximum workload of jobs. Since the multi objective flexible job shop scheduling problem is strongly NP-Hard, an integrated heuristic approach has been used to solve it. The proposed approach was based on a floating search procedure that has used some heuristic algorithms. Within floating search procedure utilize local heuristic algorithms; it makes the considered problem into two sections including assigning and sequencing sub problem. First of all search is done upon assignment space achieving an acceptable solution and then search would continue on sequencing space based on a heuristic algorithm. This paper has used a multi-objective approach for producing Pareto solution. Thus proposed approach was adapted on NSGA II algorithm and evaluated Pareto-archives. The elements and parameters of the proposed algorithms were adjusted upon preliminary experiments. Finally, computational results were used to analyze efficiency of the proposed algorithm and this results showed that the proposed algorithm capable to produce efficient solutions.
Solving a Production Scheduling Problem by Means of Two Biobjective Metaheuristic Procedures
Toncovich, Adrián; Oliveros Colay, María José; Moreno, José María; Corral, Jiménez; Corral, Rafael
2009-11-01
Production planning and scheduling problems emphasize the need for the availability of management tools that can help to assure proper service levels to customers, maintaining, at the same time, the production costs at acceptable levels and maximizing the utilization of the production facilities. In this case, a production scheduling problem that arises in the context of the activities of a company dedicated to the manufacturing of furniture for children and teenagers is addressed. Two bicriteria metaheuristic procedures are proposed to solve the sequencing problem in a production equipment that constitutes the bottleneck of the production process of the company. The production scheduling problem can be characterized as a general flow shop with sequence dependant setup times and additional inventory constraints. Two objectives are simultaneously taken into account when the quality of the candidate solutions is evaluated: the minimization of completion time of all jobs, or makespan, and the minimization of the total flow time of all jobs. Both procedures are based on a local search strategy that responds to the structure of the simulated annealing metaheuristic. In this case, both metaheuristic approaches generate a set of solutions that provides an approximation to the optimal Pareto front. In order to evaluate the performance of the proposed techniques a series of experiments was conducted. After analyzing the results, it can be said that the solutions provided by both approaches are adequate from the viewpoint of the quality as well as the computational effort involved in their generation. Nevertheless, a further refinement of the proposed procedures should be implemented with the aim of facilitating a quasi-automatic definition of the solution parameters.
Solving a manpower scheduling problem for airline catering using tabu search
Ho, Sin C.; Leung, Janny M. Y.
and assign teams and start-times for the jobs, so as to service as many flights as possible. Only teams with the appropriate skills can be assigned to a flight. Workload balance among the teams is also a consideration. We present a model formulation and investigate a tabu-search heuristic approach to solve......We study a manpower scheduling problem with job time-windows and job-skills compatibility constraints. This problem is motivated by airline catering operations, whereby airline meals and other supplies are delivered to aircrafts on the tarmac just before the flights take off. Jobs (flights) must...... be serviced within a given time-window by a team consisting of a driver and a loader. Each driver/loader has the skills to service some, but not all, of the airline/aircraft/configuration of the jobs. Given the jobs to be serviced and the roster of workers for each shift, the problem is to form teams...
A New Local Search Algorithm for the Job Shop Scheduling Problem
HuangWen-qi; YinAi-hua
2003-01-01
In this paper, the job shop scheduling problem concerned with minimizing make-span is discussed, and a new local search algorithm is proposed for it. This local search method is based on an improved shifting bottleneck procedure and Tabu Search technique. This new local search is different from the previous Tabu Search (TS) proposed by other authors, which is because the improved shifting bottleneck procedure is a new technology that is provided by us for the problem, and two remarkable strategies--intensification and diversification of TS are modified. To demonstrate the performance, our algorithm has been tested on many common problem instances (benchmarks) with various sizes and levels of hardness and compared with other algorithms, especially the latest TS in the literatures.Computational experiments show that this algorithm is effective and efficient.
Local Search Algorithm with Hybrid Neighborhood and Its Application to Job Shop Scheduling Problem
黄文奇; 曾立平
2004-01-01
A new local search method with hybrid neighborhood for Job shop scheduling problem is developed. The proposed hybrid neighborhood is not only efficient in local search, but also can help overcome entrapments while search procedure get trapped at local optima and carry the search to areas of the feasible set with better prospect. New strategies used for breaking out of entrapments are presented and they are helpful for the procedure to improve local optima. A performance comparison of the proposed method with some best-performing algorithms on all 10-job, 10-machine benchmark problems and the other two problems generated by Fisher and Thompson ( ie. , FT6 and FT20) is made. The experiment results show the better optimal performance of the proposed algorithm.
A New Local Search Algorithm for the Job Shop Scheduling Problem
Huang Wen-qi; Yin Ai-hua
2003-01-01
In this paper, the job shop scheduling problem concerned with minimizing make-span is discussed, and a new local search algorithm is proposed for it. This local search method is based on an improved shifting bottleneck procedure and Tabu Search technique. This new local search is different from the previous Tabu Search (TS) proposed by other authors, which is because the improved shifting bottleneck procedure is a new technology that is provided by us for the problem, and two remarkable strategies--intensification and diversification of TS are modified. To demonstrate the performance, our algorithm has been tested on many common problem instances (benchmarks)with various sizes and levels of hardness and compared with other algorithms, especially the latest TS in the literatures.Computational experiments show that this algorithm is effective and efficient.
Benders-based approach for an integrated Lot-Sizing and Scheduling problem
ouerfelli hala
2012-07-01
Full Text Available The main concern of the current paper is to present mathematical model and a decision method for production planning issues of a manufacturing organization. We aim at integrating the medium term and the short term as two levels of decision. These consist in periodical planning with determining the intended produced quantity and scheduling the functioning of machines. It is worth noting that in the literature there exist only few works on the issue of integration because of the shortage of numerical results. Thus, the integrated model presented here allows us to take into consideration the scheduling constraints in the Lot-sizing model. A recent algorithm, based on a heuristic approach to find a production planning with a feasible schedule for each period, has recently been published in which the two levels of decision were applied. In this paper, some of these ideas are developed in order to get an optimal solution. For this, an exact algorithm of Benders’ decomposition method is adopted to the integration problem. This has been proved efficient with reliance primarily on modeling view and the link between the two levels of decision and secondly on the numerical view.
Hong-an Yang
2014-01-01
Full Text Available We focus on solving Stochastic Job Shop Scheduling Problem (SJSSP with random processing time to minimize the expected sum of earliness and tardiness costs of all jobs. To further enhance the efficiency of the simulation optimization technique of embedding Evolutionary Strategy in Ordinal Optimization (ESOO which is based on Monte Carlo simulation, we embed Optimal Computing Budget Allocation (OCBA technique into the exploration stage of ESOO to optimize the performance evaluation process by controlling the allocation of simulation times. However, while pursuing a good set of schedules, “super individuals,” which can absorb most of the given computation while others hardly get any simulation budget, may emerge according to the allocating equation of OCBA. Consequently, the schedules cannot be evaluated exactly, and thus the probability of correct selection (PCS tends to be low. Therefore, we modify OCBA to balance the computation allocation: (1 set a threshold of simulation times to detect “super individuals” and (2 follow an exclusion mechanism to marginalize them. Finally, the proposed approach is applied to an SJSSP comprising 8 jobs on 8 machines with random processing time in truncated normal, uniform, and exponential distributions, respectively. The results demonstrate that our method outperforms the ESOO method by achieving better solutions.
Lei Wang
2017-01-01
Full Text Available In real-world manufacturing systems, production scheduling systems are often implemented under random or dynamic events like machine failure, unexpected processing times, stochastic arrival of the urgent orders, cancellation of the orders, and so on. These dynamic events will lead the initial scheduling scheme to be nonoptimal and/or infeasible. Hence, appropriate dynamic rescheduling approaches are needed to overcome the dynamic events. In this paper, we propose a dynamic rescheduling method based on variable interval rescheduling strategy (VIRS to deal with the dynamic flexible job shop scheduling problem considering machine failure, urgent job arrival, and job damage as disruptions. On the other hand, an improved genetic algorithm (GA is proposed for minimizing makespan. In our improved GA, a mix of random initialization population by combining initialization machine and initialization operation with random initialization is designed for generating high-quality initial population. In addition, the elitist strategy (ES and improved population diversity strategy (IPDS are used to avoid falling into the local optimal solution. Experimental results for static and several dynamic events in the FJSP show that our method is feasible and effective.
Hossein Erfani
2012-09-01
Full Text Available With regard to the fact of the rapid growth of distributed systems and their large spectrum of usage of proposing and representing controlling solutions and optimization of task execution procedures is one of the most important issues. Task scheduling in distributed systems has determining role in improving efficiency in applications such as communication, routing, production plans and project management. The most important issues of good schedule are minimizing makespan and average of waiting time. However, the recent and previous effort usually focused on minimizing makespan. This article presents and analyze a new method based on Ant Colony Optimization (ACO algorithm with considerations to precedence and communication cost for task scheduling problem. In the mentioned method in addition to optimization of finish time, average of waiting time and number of needed processors are also optimized. In this method, by using of a new heuristic list, an algorithm based on ant colony is proposed. The results obtained in comparison with the latest similar models of random search algorithms, proves the higher efficiency of algorithm.
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.
An adaptive large neighborhood search heuristic for the Electric Vehicle Scheduling Problem
Wen, M.; Linde, Esben; Røpke, Stefan;
2016-01-01
This paper addresses the Electric Vehicle Scheduling Problem (E-VSP), in which a set of timetabled bus trips, each starting from and ending at specific locations and at specific times, should be carried out by a set of electric buses or vehicles based at a number of depots with limited driving...... ranges. The electric vehicles are allowed to be recharged fully or partially at any of the given recharging stations. The objective is to firstly minimize the number of vehicles needed to cover all the timetabled trips, and secondly to minimize the total traveling distance, which is equivalent...
An Efficient Estimation of Distribution Algorithm for Job Shop Scheduling Problem
He, Xiao-Juan; Zeng, Jian-Chao; Xue, Song-Dong; Wang, Li-Fang
An estimation of distribution algorithm with probability model based on permutation information of neighboring operations for job shop scheduling problem was proposed. The probability model was given using frequency information of pair-wise operations neighboring. Then the structure of optimal individual was marked and the operations of optimal individual were partitioned to some independent sub-blocks. To avoid repeating search in same area and improve search speed, each sub-block was taken as a whole to be adjusted. Also, stochastic adjustment to the operations within each sub-block was introduced to enhance the local search ability. The experimental results show that the proposed algorithm is more robust and efficient.
A Grafted Genetic Algorithm for the Job-Shop Scheduling Problem
LI Xiang-jun; WANG Shu-zhen; XU Guo-hua
2004-01-01
The standard genetic algorithm has limitations of a low convergence rate and premature convergence in solving the job-shop scheduling problem.To overcome these limitations,this paper presents a new improved hybrid genetic algorithm on the basis of the idea of graft in botany.Through the introduction of a grafted population and crossover probability matrix,this algorithm accelerates the convergence rate greatly and also increases the ability to fight premature convergence.Finally,the approach is tested on a set of standard instances taken from the literature and compared with other approaches.The computation results validate the effectiveness of the proposed algorithm.
A Two-Stage Assembly-Type Flowshop Scheduling Problem for Minimizing Total Tardiness
Ju-Yong Lee
2016-01-01
Full Text Available This research considers a two-stage assembly-type flowshop scheduling problem with the objective of minimizing the total tardiness. The first stage consists of two independent machines, and the second stage consists of a single machine. Two types of components are fabricated in the first stage, and then they are assembled in the second stage. Dominance properties and lower bounds are developed, and a branch and bound algorithm is presented that uses these properties and lower bounds as well as an upper bound obtained from a heuristic algorithm. The algorithm performance is evaluated using a series of computational experiments on randomly generated instances and the results are reported.
An improved Genetic Algorithm of Bi-level Coding for Flexible Job Shop Scheduling Problems
Ye Li
2014-07-01
Full Text Available The current study presents an improved genetic algorithm(GA for the flexible job shop scheduling problem (FJSP. The coding is divided into working sequence level and machine level and two effective crossover operators and mutation operators are designed for the generation and reduce the disruptive effects of genetic operators. The algorithm is tested on instances of 10 working sequences and 10 machines. Computational results show that the proposed GA was successfully and efficiently applied to the FJSP. The results were compared with other approaches, such as traditional GA and GA with neural network. Compared to traditional genetic algorithm, the proposed approach yields significant improvement in solution quality.
Hansen, Anders Dohn; Clausen, Jens
2008-01-01
In this paper, we present The Slab Yard Planning and Crane Scheduling Problem. The problem has its origin in steel production facilities with a large throughput. A slab yard is used as a buffer for slabs that are needed in the upcoming production. Slabs are transported by cranes and the problem c...
限位排序的若干结果%Some Results on Position Restriction Scheduling Problems
陈友军; 林诒勋
2008-01-01
In this paper,we first consider the position restriction scheduling problems on a single machine.The problems have been solved in certain special cases,especially for those obtained by restricting the processing time pj=1.We introduce the bipartite matching algorithm to provide some polynomial-time algorithms to solve them.Then we further consider a problem on unrelated processors.
Kaplan, Sezgin; Rabadi, Ghaith
2013-01-01
This article addresses the aerial refuelling scheduling problem (ARSP), where a set of fighter jets (jobs) with certain ready times must be refuelled from tankers (machines) by their due dates; otherwise, they reach a low fuel level (deadline) incurring a high cost. ARSP is an identical parallel machine scheduling problem with release times and due date-to-deadline windows to minimize the total weighted tardiness. A simulated annealing (SA) and metaheuristic for randomized priority search (Meta-RaPS) with the newly introduced composite dispatching rule, apparent piecewise tardiness cost with ready times (APTCR), are applied to the problem. Computational experiments compared the algorithms' solutions to optimal solutions for small problems and to each other for larger problems. To obtain optimal solutions, a mixed integer program with a piecewise weighted tardiness objective function was solved for up to 12 jobs. The results show that Meta-RaPS performs better in terms of average relative error but SA is more efficient.
Processing time tolerance-based ACO algorithm for solving job-shop scheduling problem
Luo, Yabo; Waden, Yongo P.
2017-06-01
Ordinarily, Job Shop Scheduling Problem (JSSP) is known as NP-hard problem which has uncertainty and complexity that cannot be handled by a linear method. Thus, currently studies on JSSP are concentrated mainly on applying different methods of improving the heuristics for optimizing the JSSP. However, there still exist many problems for efficient optimization in the JSSP, namely, low efficiency and poor reliability, which can easily trap the optimization process of JSSP into local optima. Therefore, to solve this problem, a study on Ant Colony Optimization (ACO) algorithm combined with constraint handling tactics is carried out in this paper. Further, the problem is subdivided into three parts: (1) Analysis of processing time tolerance-based constraint features in the JSSP which is performed by the constraint satisfying model; (2) Satisfying the constraints by considering the consistency technology and the constraint spreading algorithm in order to improve the performance of ACO algorithm. Hence, the JSSP model based on the improved ACO algorithm is constructed; (3) The effectiveness of the proposed method based on reliability and efficiency is shown through comparative experiments which are performed on benchmark problems. Consequently, the results obtained by the proposed method are better, and the applied technique can be used in optimizing JSSP.
无
2007-01-01
A modified bottleneck-based (MB) heuristic for large-scale job-shop scheduling problems with a welldefined bottleneck is suggested,which is simpler but more tailored than the shifting bottleneck (SB) procedure.In this algorithm,the bottleneck is first scheduled optimally while the non-bottleneck machines are subordinated around the solutions of the bottleneck schedule by some effective dispatching rules.Computational results indicate that the MB heuristic can achieve a better tradeoff between solution quality and computational time compared to SB procedure for medium-size problems.Furthermore,it can obtain a good solution in a short time for large-scale job-shop scheduling problems.
Xu, Ye; Wang, Ling; Wang, Shengyao; Liu, Min
2014-09-01
In this article, an effective hybrid immune algorithm (HIA) is presented to solve the distributed permutation flow-shop scheduling problem (DPFSP). First, a decoding method is proposed to transfer a job permutation sequence to a feasible schedule considering both factory dispatching and job sequencing. Secondly, a local search with four search operators is presented based on the characteristics of the problem. Thirdly, a special crossover operator is designed for the DPFSP, and mutation and vaccination operators are also applied within the framework of the HIA to perform an immune search. The influence of parameter setting on the HIA is investigated based on the Taguchi method of design of experiment. Extensive numerical testing results based on 420 small-sized instances and 720 large-sized instances are provided. The effectiveness of the HIA is demonstrated by comparison with some existing heuristic algorithms and the variable neighbourhood descent methods. New best known solutions are obtained by the HIA for 17 out of 420 small-sized instances and 585 out of 720 large-sized instances.
张素君; 顾幸生
2015-01-01
An effective discrete artificial bee colony(DABC) algorithm is proposed for the flow shop scheduling problem with intermediate buffers (IBFSP) in order to minimize the maximum completion time (i.e makespan). The effective combination of the insertion and swap operator is applied to producing neighborhood individual at the employed bee phase. The tournament selection is adopted to avoid falling into local optima, while, the optimized insert operator embeds in onlooker bee phase for further searching the neighborhood solution to enhance the local search ability of algorithm. The tournament selection with size 2 is again applied and a better selected solution will be performed destruction and construction of iterated greedy (IG) algorithm, and then the result replaces the worse one. Simulation results show that our algorithm has a better performance compared with the HDDE and CHS which were proposed recently. It provides the better known solutions for the makespan criterion to flow shop scheduling problem with limited buffers for the Car benchmark by Carlier and Rec benchmark by Reeves. The convergence curves show that the algorithm not only has faster convergence speed but also has better convergence value.
Solving the Resource Constrained Project Scheduling Problem to Minimize the Financial Failure Risk
Zhi Jie Chen
2012-04-01
Full Text Available In practice, a project usually involves cash in- and out-flows associated with each activity. This paper aims to minimize the payment failure risk during the project execution for the resource-constrained project scheduling problem (RCPSP. In such models, the money-time value, which is the product of the net cash in-flow and the time length from the completion time of each activity to the project deadline, provides a financial evaluation of project cash availability. The cash availability of a project schedule is defined as the sum of these money-time values associated with all activities, which is mathematically equivalent to the minimization objective of total weighted completion time. This paper presents four memetic algorithms (MAs which differ in the construction of initial population and restart strategy, and a double variable neighborhood search algorithm for solving the RCPSP problem. An experiment is conducted to evaluate the performance of these algorithms based on the same number of solutions calculated using ProGen generated benchmark instances. The results indicate that the MAs with regret biased sampling rule to generate initial and restart populations outperforms the other algorithms in terms of solution quality.
A hybrid flow shop model for an ice cream production scheduling problem
Imma Ribas Vila
2009-07-01
Full Text Available Normal 0 21 false false false ES X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Taula normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} In this paper we address the scheduling problem that comes from an ice cream manufacturing company. This production system can be modelled as a three stage nowait hybrid flow shop with batch dependent setup costs. To contribute reducing the gap between theory and practice we have considered the real constraints and the criteria used by planners. The problem considered has been formulated as a mixed integer programming. Further, two competitive heuristic procedures have been developed and one of them will be proposed to schedule in the ice cream factory.
A Hybrid Multiobjective Evolutionary Approach for Flexible Job-Shop Scheduling Problems
Jian Xiong
2012-01-01
Full Text Available This paper addresses multiobjective flexible job-shop scheduling problem (FJSP with three simultaneously considered objectives: minimizing makespan, minimizing total workload, and minimizing maximal workload. A hybrid multiobjective evolutionary approach (H-MOEA is developed to solve the problem. According to the characteristic of FJSP, a modified crowding distance measure is introduced to maintain the diversity of individuals. In the proposed H-MOEA, well-designed chromosome representation and genetic operators are developed for FJSP. Moreover, a local search procedure based on critical path theory is incorporated in H-MOEA to improve the convergence ability of the algorithm. Experiment results on several well-known benchmark instances demonstrate the efficiency and stability of the proposed algorithm. The comparison with other recently published approaches validates that H-MOEA can obtain Pareto-optimal solutions with better quality and/or diversity.
A Multiobjective Iterated Greedy Algorithm for Truck Scheduling in Cross-Dock Problems
B. Naderi
2014-01-01
Full Text Available The cross-docking system is a new distribution strategy which can reduce inventories, lead times, and improve responding time to customers. This paper considers biobjective problem of truck scheduling in cross-docking systems with temporary storage. The objectives are minimizing both makespan and total tardiness. For this problem, it proposes a multiobjective iterated greedy algorithm employing advance features such as modified crowding selection, restart phase, and local search. To evaluate the proposed algorithm for performance, it is compared with two available algorithms, subpopulation particle swarm optimization-II and strength Pareto evolutionary algorithm-II. The comparison shows that the proposed multiobjective iterated greedy algorithm shows high performance and outperforms the other two algorithms.
Mathematical Model and Algorithm for the Reefer Mechanic Scheduling Problem at Seaports
Jiantong Zhang
2017-01-01
Full Text Available With the development of seaborne logistics, the international trade of goods transported in refrigerated containers is growing fast. Refrigerated containers, also known as reefers, are used in transportation of temperature sensitive cargo, such as perishable fruits. This trend brings new challenges to terminal managers, that is, how to efficiently arrange mechanics to plug and unplug power for the reefers (i.e., tasks at yards. This work investigates the reefer mechanics scheduling problem at container ports. To minimize the sum of the total tardiness of all tasks and the total working distance of all mechanics, we formulate a mathematical model. For the resolution of this problem, we propose a DE algorithm which is combined with efficient heuristics, local search strategies, and parameter adaption scheme. The proposed algorithm is tested and validated through numerical experiments. Computational results demonstrate the effectiveness and efficiency of the proposed algorithm.
Souad Mekni
2014-11-01
Full Text Available In this paper, a modified invasive weed optimization (IWO algorithm is presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs with the criteria to minimize the maximum completion time (makespan, the total workload of machines and the workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve continuous optimization problems that’s why the heuristic rule the Smallest Position Value (SPV is used to convert the continuous position values to the discrete job sequences. The computational experiments show that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it is able to find the optimal and best-known solutions on the instances studied.
Wang, Chun; Ji, Zhicheng; Wang, Yan
2017-07-01
In this paper, multi-objective flexible job shop scheduling problem (MOFJSP) was studied with the objects to minimize makespan, total workload and critical workload. A variable neighborhood evolutionary algorithm (VNEA) was proposed to obtain a set of Pareto optimal solutions. First, two novel crowded operators in terms of the decision space and object space were proposed, and they were respectively used in mating selection and environmental selection. Then, two well-designed neighborhood structures were used in local search, which consider the problem characteristics and can hold fast convergence. Finally, extensive comparison was carried out with the state-of-the-art methods specially presented for solving MOFJSP on well-known benchmark instances. The results show that the proposed VNEA is more effective than other algorithms in solving MOFJSP.
Amir Abbas Najafi
2009-01-01
Full Text Available Resource investment problem with discounted cash flows (RIPDCFs is a class of project scheduling problem. In RIPDCF, the availability levels of the resources are considered decision variables, and the goal is to find a schedule such that the net present value of the project cash flows optimizes. In this paper, we consider a new RIPDCF in which tardiness of project is permitted with defined penalty. We mathematically formulated the problem and developed a heuristic method to solve it. The results of the performance analysis of the proposed method show an effective solution approach to the problem.
V. Oladokun
2011-12-01
Full Text Available Resource-Constrained Project Scheduling Problem (RCPSP is a Non Polynomial (NP - Hard optimization problem that considers how to assign activities to available resources in order to meet predefined objectives. The problem is usually characterized by precedence relationship between activities with limited capacity of renewable resources. In an environment where resources are limited, projects still have to be finished on time, within the approved budget and in accordance with the preset specifications. Inherently, these tend to make RCPSP, a multi-objective problem. However, it has been treated as a single objective problem with project makespan often recognized as the most relevant objective. As a result of not understanding the multi-objective dimension of some projects, where these objectives need to be simultaneously considered, distraction and conflict of interest have ultimately lead to abandoned or totally failed projects. The aim of this article is to holistically review the relevance and applicability of multi-objective performance dimension of RCPSP in an environment where optimal use of limited resources is important.
Yan Sun
2015-01-01
Full Text Available We explore a freight routing problem wherein the aim is to assign optimal routes to move commodities through a multimodal transportation network. This problem belongs to the operational level of service network planning. The following formulation characteristics will be comprehensively considered: (1 multicommodity flow routing; (2 a capacitated multimodal transportation network with schedule-based rail services and time-flexible road services; (3 carbon dioxide emissions consideration; and (4 a generalized costs optimum oriented to customer demands. The specific planning of freight routing is thus defined as a capacitated time-sensitive multicommodity multimodal generalized shortest path problem. To solve this problem systematically, we first establish a node-arc-based mixed integer nonlinear programming model that combines the above formulation characteristics in a comprehensive manner. Then, we develop a linearization method to transform the proposed model into a linear one. Finally, a computational experiment from the Chinese inland container export business is presented to demonstrate the feasibility of the model and linearization method. The computational results indicate that implementing the proposed model and linearization method in the mathematical programming software Lingo can effectively solve the large-scale practical multicommodity multimodal transportation routing problem.
A Heuristic Genetic Algorithm for No-Wait Flowshop Scheduling Problem
无
2007-01-01
No-wait flowshop scheduling problems with the objective to minimize the total flow time is an important sequencing problem in the field of developing production plans and has a wide engineering background.Genetic algorithm (GA) has the capability of global convergence and has been proven effective to solve NP-hard combinatorial optimization problems, while simple heuristics have the advantage of fast local convergence and can be easily implemented.In order to avoid the defect of slow convergence or premature, a heuristic genetic algorithm is proposed by incorporating the simple heuristics and local search into the traditional genetic algorithm.In this hybridized algorithm, the structural information of no-wait flowshops and high-effective heuristics are incorporated to design a new method for generating initial generation and a new crossover operator.The computational results show the developed heuristic genetic algorithm is efficient and the quality of its solution has advantage over the best known algorithm.It is suitable for solving the large scale practical problems and lays a foundation for the application of meta-heuristic algorithms in industrial production.
Maximizing Total Profit in Two-agent Problem of Order Acceptance and Scheduling
Mohammad Reisi-Nafchi
2017-03-01
Full Text Available In competitive markets, attracting potential customers and keeping current customers is a survival condition for each company. So, paying attention to the requests of customers is important and vital. In this paper, the problem of order acceptance and scheduling has been studied, in which two types of customers or agents compete in a single machine environment. The objective is maximizing sum of the total profit of first agent's accepted orders and the total revenue of second agent. Therefore, only the first agent has penalty and its penalty function is lateness and the second agent's orders have a common due date and this agent does not accept any tardy order. To solve the problem, a mathematical programming, a heuristic algorithm and a pseudo-polynomial dynamic programming algorithm are proposed. Computational results confirm the ability of solving all problem instances up to 70 orders size optimally and also 93.12% of problem instances up to 150 orders size by dynamic programming.
P Chitra; P Venkatesh; R Rajaram
2011-04-01
The task scheduling problem in heterogeneous distributed computing systems is a multiobjective optimization problem (MOP). In heterogeneous distributed computing systems (HDCS), there is a possibility of processor and network failures and this affects the applications running on the HDCS. To reduce the impact of failures on an application running on HDCS, scheduling algorithms must be devised which minimize not only the schedule length (makespan) but also the failure probability of the application (reliability). These objectives are conﬂicting and it is not possible to minimize both objectives at the same time. Thus, it is needed to develop scheduling algorithms which account both for schedule length and the failure probability. Multiobjective Evolutionary Computation algorithms (MOEAs) are well-suited for Multiobjective task scheduling on heterogeneous environment. The two Multi-Objective Evolutionary Algorithms such as Multiobjective Genetic Algorithm (MOGA) and Multiobjective Evolutionary Programming (MOEP) with non-dominated sorting are developed and compared for the various random task graphs and also for a real-time numerical application graph. The metrics for evaluating the convergence and diversity of the obtained non-dominated solutions by the two algorithms are reported. The simulation results conﬁrm that the proposed algorithms can be used for solving the task scheduling at reduced computational times compared to the weighted-sum based biobjective algorithm in the literature.
A Mixed Integer Linear Program for Solving a Multiple Route Taxi Scheduling Problem
Montoya, Justin Vincent; Wood, Zachary Paul; Rathinam, Sivakumar; Malik, Waqar Ahmad
2010-01-01
Aircraft movements on taxiways at busy airports often create bottlenecks. This paper introduces a mixed integer linear program to solve a Multiple Route Aircraft Taxi Scheduling Problem. The outputs of the model are in the form of optimal taxi schedules, which include routing decisions for taxiing aircraft. The model extends an existing single route formulation to include routing decisions. An efficient comparison framework compares the multi-route formulation and the single route formulation. The multi-route model is exercised for east side airport surface traffic at Dallas/Fort Worth International Airport to determine if any arrival taxi time savings can be achieved by allowing arrivals to have two taxi routes: a route that crosses an active departure runway and a perimeter route that avoids the crossing. Results indicate that the multi-route formulation yields reduced arrival taxi times over the single route formulation only when a perimeter taxiway is used. In conditions where the departure aircraft are given an optimal and fixed takeoff sequence, accumulative arrival taxi time savings in the multi-route formulation can be as high as 3.6 hours more than the single route formulation. If the departure sequence is not optimal, the multi-route formulation results in less taxi time savings made over the single route formulation, but the average arrival taxi time is significantly decreased.
Using Improved Ant Colony Algorithm to Investigate EMU Circulation Scheduling Problem
Yu Zhou
2014-01-01
Full Text Available High-speed railway is one of the most important ways to solve the long-standing travel difficulty problem in China. However, due to the high acquisition and maintenance cost, it is impossible for decision-making departments to purchase enough EMUs to satisfy the explosive travel demand. Therefore, there is an urgent need to study how to utilize EMU more efficiently and reduce costs in the case of completing a given task in train diagram. In this paper, an EMU circulation scheduling model is built based on train diagram constraints, maintenance constraints, and so forth; in the model solving process, an improved ACA algorithm has been designed. A case study is conducted to verify the feasibility of the model. Moreover, contrast tests have been carried out to compare the efficiency between the improved ACA and the traditional approaches. The results reveal that improved ACA method can solve the model with less time and the quality of each representative index is much better, which means that efficiency of the improved ACA method is higher and better scheduling scheme can be obtained.
A Branch and Bound Algorithm for Project Scheduling Problem with Spatial Resource Constraints
Shicheng Hu
2015-01-01
Full Text Available With respect to the block assembly schedule in a shipbuilding enterprise, a spatial resource constrained project scheduling problem (SRCPSP is proposed, which aims to minimize the makespan of a project under the constraints of the availability of a two-dimensional spatial resource and the precedence relationship between tasks. In order to solve SRCPSP to the optimum, a branch and bound algorithm (BB is developed. For the BB-SRCPSP, first, an implicitly enumerative branch scheme is presented. Secondly, a precedence based lower bound, as well as an effective dominance rule, is employed for pruning. Next, a heuristic based algorithm is used to decide the order of a node to be selected for expansion such that the efficiency of the algorithm is further improved. In addition, a maximal space based arrangement is applied to the configuration of the areas required each day in an available area. Finally, the simulation experiment is conducted to illustrate the effectiveness of the BB-SRCPSP.
A Literature Survey for Earliness/Tardiness Scheduling Problems with Learning Effect
Mesut Cemil İŞLER
2009-02-01
Full Text Available When a task or work is done continuously, there will be an experience so following times needs of required resources (manpower, materials, etc. will be reduced. This learning curve described first by Wright. Wright determined how workmanship costs decreased while proceed plain increasing. This investigations correctness found consistent by plain producers. Learning effect is an effect that, works can be done in shorter time in the rate of repeat of work with repeating same or similar works in production process. Nowadays classical production systems adapted more acceptable systems with new approaches. Just in time production system (JIT philosophy is one of the most important production system philosophies. JIT which is known production without stock stands on using all product resources optimum. Minimization problem of Earliness/Tardiness finishing penalty, which we can describe Just in time scheduling, appeared by inspired from JIT philosophy. In this study, there is literature survey which directed to earliness/tardiness performance criteria and learning effect processing in scheduling and as a result of this it is obtained some establishing for literature.
Three hybridization models based on local search scheme for job shop scheduling problem
Balbi Fraga, Tatiana
2015-05-01
This work presents three different hybridization models based on the general schema of Local Search Heuristics, named Hybrid Successive Application, Hybrid Neighborhood, and Hybrid Improved Neighborhood. Despite similar approaches might have already been presented in the literature in other contexts, in this work these models are applied to analyzes the solution of the job shop scheduling problem, with the heuristics Taboo Search and Particle Swarm Optimization. Besides, we investigate some aspects that must be considered in order to achieve better solutions than those obtained by the original heuristics. The results demonstrate that the algorithms derived from these three hybrid models are more robust than the original algorithms and able to get better results than those found by the single Taboo Search.
An imperialist competitive algorithm for solving the production scheduling problem in open pit mine
Mojtaba Mokhtarian Asl
2016-06-01
Full Text Available Production scheduling (planning of an open-pit mine is the procedure during which the rock blocks are assigned to different production periods in a way that the highest net present value of the project achieved subject to operational constraints. The paper introduces a new and computationally less expensive meta-heuristic technique known as imperialist competitive algorithm (ICA for long-term production planning of open pit mines. The proposed algorithm modifies the original rules of the assimilation process. The ICA performance for different levels of the control factors has been studied and the results are presented. The result showed that ICA could be efficiently applied on mine production planning problem.
Souza Filho, Erito M.; Bahiense, Laura; Ferreira Filho, Virgilio J.M. [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE); Lima, Leonardo [Centro Federal de Educacao Tecnologica Celso Sukow da Fonseca (CEFET-RJ), Rio de Janeiro, RJ (Brazil)
2008-07-01
Pipeline are known as the most reliable and economical mode of transportation for petroleum and its derivatives, especially when large amounts of products have to be pumped for large distances. In this work we address the short-term schedule of a pipeline system comprising the distribution of several petroleum derivatives from a single oil refinery to several depots, connected to local consumer markets, through a single multi-product pipeline. We propose an integer linear programming formulation and a variable neighborhood search meta-heuristic in order to compare the performances of the exact and heuristic approaches to the problem. Computational tests in C language and MOSEL/XPRESS-MP language are performed over a real Brazilian pipeline system. (author)
Scheduling of flow shop problems on 3 machines in fuzzy environment with double transport facility
Sathish, Shakeela; Ganesan, K.
2016-06-01
Flow shop scheduling is a decision making problem in production and manufacturing field which has a significant impact on the performance of an organization. When the machines on which jobs are to be processed are placed at different places, the transportation time plays a significant role in production. Further two different transport agents where 1st takes the job from 1st machine to 2nd machine and then returns back to the first machine and the 2nd takes the job from 2nd machine to 3rd machine and then returns back to the 2nd machine are also considered. We propose a method to minimize the total make span; without converting the fuzzy processing time to classical numbers by using a new type of fuzzy arithmetic and a fuzzy ranking method. A numerical example is provided to explain the proposed method.
Hui Lu
2014-01-01
Full Text Available Test task scheduling problem (TTSP is a complex optimization problem and has many local optima. In this paper, a hybrid chaotic multiobjective evolutionary algorithm based on decomposition (CMOEA/D is presented to avoid becoming trapped in local optima and to obtain high quality solutions. First, we propose an improving integrated encoding scheme (IES to increase the efficiency. Then ten chaotic maps are applied into the multiobjective evolutionary algorithm based on decomposition (MOEA/D in three phases, that is, initial population and crossover and mutation operators. To identify a good approach for hybrid MOEA/D and chaos and indicate the effectiveness of the improving IES several experiments are performed. The Pareto front and the statistical results demonstrate that different chaotic maps in different phases have different effects for solving the TTSP especially the circle map and ICMIC map. The similarity degree of distribution between chaotic maps and the problem is a very essential factor for the application of chaotic maps. In addition, the experiments of comparisons of CMOEA/D and variable neighborhood MOEA/D (VNM indicate that our algorithm has the best performance in solving the TTSP.
Decision theory for computing variable and value ordering decisions for scheduling problems
Linden, Theodore A.
1993-01-01
Heuristics that guide search are critical when solving large planning and scheduling problems, but most variable and value ordering heuristics are sensitive to only one feature of the search state. One wants to combine evidence from all features of the search state into a subjective probability that a value choice is best, but there has been no solid semantics for merging evidence when it is conceived in these terms. Instead, variable and value ordering decisions should be viewed as problems in decision theory. This led to two key insights: (1) The fundamental concept that allows heuristic evidence to be merged is the net incremental utility that will be achieved by assigning a value to a variable. Probability distributions about net incremental utility can merge evidence from the utility function, binary constraints, resource constraints, and other problem features. The subjective probability that a value is the best choice is then derived from probability distributions about net incremental utility. (2) The methods used for rumor control in Bayesian Networks are the primary way to prevent cycling in the computation of probable net incremental utility. These insights lead to semantically justifiable ways to compute heuristic variable and value ordering decisions that merge evidence from all available features of the search state.
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.
Magalhaes, Marcus V.; Fraga, Eder T. [PETROBRAS, Rio de Janeiro, RJ (Brazil); Shah, Nilay [Imperial College, London (United Kingdom)
2004-07-01
This work addresses the refinery scheduling problem using mathematical programming techniques. The solution adopted was to decompose the entire refinery model into a crude oil scheduling and a product scheduling problem. The envelope for the crude oil scheduling problem is composed of a terminal, a pipeline and the crude area of a refinery, including the crude distillation units. The solution method adopted includes a decomposition technique based on the topology of the system. The envelope for the product scheduling comprises all tanks, process units and products found in a refinery. Once crude scheduling decisions are Also available the product scheduling is solved using a rolling horizon algorithm. All models were tested with real data from PETROBRAS' REFAP refinery, located in Canoas, Southern Brazil. (author)
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.
H Kazemipoor
2012-04-01
Full Text Available A multi-skilled project scheduling problem (MSPSP has been generally presented to schedule a project with staff members as resources. Each activity in project network requires different skills and also staff members have different skills, too. This causes the MSPSP becomes a special type of a multi-mode resource-constrained project scheduling problem (MM-RCPSP with a huge number of modes. Given the importance of this issue, in this paper, a mixed integer linear programming for the MSPSP is presented. Due to the complexity of the problem, a meta-heuristic algorithm is proposed in order to find near optimal solutions. To validate performance of the algorithm, results are compared against exact solutions solved by the LINGO solver. The results are promising and show that optimal or near-optimal solutions are derived for small instances and good solutions for larger instances in reasonable time.
M. Saravanan
2014-03-01
Full Text Available A Hybrid flow shop scheduling is characterized ‘n’ jobs ‘m’ machines with ‘M’ stages by unidirectional flow of work with a variety of jobs being processed sequentially in a single-pass manner. The paper addresses the multi-stage hybrid flow shop scheduling problems with missing operations. It occurs in many practical situations such as stainless steel manufacturing company. The essential complexity of the problem necessitates the application of meta-heuristics to solve hybrid flow shop scheduling. The proposed Simulated Annealing algorithm (SA compared with Particle Swarm Optimization (PSO with the objective of minimization of makespan. It is show that the SA algorithm is efficient in finding out good quality solutions for the hybrid flow shop problems with missing operations.
Chen, Miawjane; Yan, Shangyao; Wang, Sin-Siang; Liu, Chiu-Lan
2015-02-01
An effective project schedule is essential for enterprises to increase their efficiency of project execution, to maximize profit, and to minimize wastage of resources. Heuristic algorithms have been developed to efficiently solve the complicated multi-mode resource-constrained project scheduling problem with discounted cash flows (MRCPSPDCF) that characterize real problems. However, the solutions obtained in past studies have been approximate and are difficult to evaluate in terms of optimality. In this study, a generalized network flow model, embedded in a time-precedence network, is proposed to formulate the MRCPSPDCF with the payment at activity completion times. Mathematically, the model is formulated as an integer network flow problem with side constraints, which can be efficiently solved for optimality, using existing mathematical programming software. To evaluate the model performance, numerical tests are performed. The test results indicate that the model could be a useful planning tool for project scheduling in the real world.
Scheduling theory, algorithms, and systems
Pinedo, Michael L
2016-01-01
This new edition of the well-established text Scheduling: Theory, Algorithms, and Systems provides an up-to-date coverage of important theoretical models in the scheduling literature as well as important scheduling problems that appear in the real world. The accompanying website includes supplementary material in the form of slide-shows from industry as well as movies that show actual implementations of scheduling systems. The main structure of the book, as per previous editions, consists of three parts. The first part focuses on deterministic scheduling and the related combinatorial problems. The second part covers probabilistic scheduling models; in this part it is assumed that processing times and other problem data are random and not known in advance. The third part deals with scheduling in practice; it covers heuristics that are popular with practitioners and discusses system design and implementation issues. All three parts of this new edition have been revamped, streamlined, and extended. The reference...
HE Yan; LIU Fei; CAO Hua-jun; LI Cong-bo
2005-01-01
The issue of reducing energy consumption for the job-shop scheduling problem in machining systems is addressed, whose dual objectives are to minimize both the energy consumption and the makespan. First, the biobjective model for the job-shop scheduling problem is proposed. The objective function value of the model represents synthesized optimization of energy consumption and makespan. Then, a heuristic algorithm is developed to locate the optimal or near optimal solutions of the model based on the Tabu search mechanism. Finally, the experimental case is presented to demonstrate the effectiveness of the proposed model and the algorithm.
Huang, Song; Tian, Na; Wang, Yan; Ji, Zhicheng
2016-01-01
Taking resource allocation into account, flexible job shop problem (FJSP) is a class of complex scheduling problem in manufacturing system. In order to utilize the machine resources rationally, multi-objective particle swarm optimization (MOPSO) integrating with variable neighborhood search is introduced to address FJSP efficiently. Firstly, the assignment rules (AL) and dispatching rules (DR) are provided to initialize the population. And then special discrete operators are designed to produce new individuals and earliest completion machine (ECM) is adopted in the disturbance operator to escape the optima. Secondly, personal-best archives (cognitive memories) and global-best archive (social memory), which are updated by the predefined non-dominated archive update strategy, are simultaneously designed to preserve non-dominated individuals and select personal-best positions and the global-best position. Finally, three neighborhoods are provided to search the neighborhoods of global-best archive for enhancing local search ability. The proposed algorithm is evaluated by using Kacem instances and Brdata instances, and a comparison with other approaches shows the effectiveness of the proposed algorithm for FJSP.
Memetic algorithm for multi-mode resource-constrained project scheduling problems
Shixin Liu; Di Chen; Yifan Wang
2014-01-01
A memetic algorithm (MA) for a multi-mode resource-constrained project scheduling problem (MRCPSP) is pro-posed. We use a new fitness function and two very effective lo-cal search procedures in the proposed MA. The fitness function makes use of a mechanism cal ed “strategic oscil ation” to make the search process have a higher probability to visit solutions around a“feasible boundary”. One of the local search procedures aims at improving the lower bound of project makespan to be less than a known upper bound, and another aims at improving a solution of an MRCPSP instance accepting infeasible solutions based on the new fitness function in the search process. A de-tailed computational experiment is set up using instances from the problem instance library PSPLIB. Computational results show that the proposed MA is very competitive with the state-of-the-art algorithms. The MA obtains improved solutions for one instance of set J30.
Mohammad Hossein Sadeghi
2013-08-01
Full Text Available In this paper, two different sub-problems are considered to solve a resource constrained project scheduling problem (RCPSP, namely i assignment of modes to tasks and ii scheduling of these tasks in order to minimize the makespan of the project. The modified electromagnetism-like algorithm deals with the first problem to create an assignment of modes to activities. This list is used to generate a project schedule. When a new assignment is made, it is necessary to fix all mode dependent requirements of the project activities and to generate a random schedule with the serial SGS method. A local search will optimize the sequence of the activities. Also in this paper, a new penalty function has been proposed for solutions which are infeasible with respect to non-renewable resources. Performance of the proposed algorithm has been compared with the best algorithms published so far on the basis of CPU time and number of generated schedules stopping criteria. Reported results indicate excellent performance of the algorithm.
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.
Yingni Zhai
2014-10-01
Full Text Available Purpose: A decomposition heuristics based on multi-bottleneck machines for large-scale job shop scheduling problems (JSP is proposed.Design/methodology/approach: In the algorithm, a number of sub-problems are constructed by iteratively decomposing the large-scale JSP according to the process route of each job. And then the solution of the large-scale JSP can be obtained by iteratively solving the sub-problems. In order to improve the sub-problems' solving efficiency and the solution quality, a detection method for multi-bottleneck machines based on critical path is proposed. Therewith the unscheduled operations can be decomposed into bottleneck operations and non-bottleneck operations. According to the principle of “Bottleneck leads the performance of the whole manufacturing system” in TOC (Theory Of Constraints, the bottleneck operations are scheduled by genetic algorithm for high solution quality, and the non-bottleneck operations are scheduled by dispatching rules for the improvement of the solving efficiency.Findings: In the process of the sub-problems' construction, partial operations in the previous scheduled sub-problem are divided into the successive sub-problem for re-optimization. This strategy can improve the solution quality of the algorithm. In the process of solving the sub-problems, the strategy that evaluating the chromosome's fitness by predicting the global scheduling objective value can improve the solution quality.Research limitations/implications: In this research, there are some assumptions which reduce the complexity of the large-scale scheduling problem. They are as follows: The processing route of each job is predetermined, and the processing time of each operation is fixed. There is no machine breakdown, and no preemption of the operations is allowed. The assumptions should be considered if the algorithm is used in the actual job shop.Originality/value: The research provides an efficient scheduling method for the
Ethics problems and theories in public relations
Grunig, James E.
2015-01-01
Public relations professionals encounter ethical problems as individuals who make decisions about their professional lives. They also serve as ethical counselors to organizations, a role in which they help organizations behave in ethical, responsible, and sustainable ways. This introduction defines ethics and social responsibility and discusses the possibilities and obstacles that public relations professionals face in the role of ethical counselor. Seven research problems in public relations...
Hamed Piroozfard
2016-01-01
Full Text Available Scheduling is considered as an important topic in production management and combinatorial optimization in which it ubiquitously exists in most of the real-world applications. The attempts of finding optimal or near optimal solutions for the job shop scheduling problems are deemed important, because they are characterized as highly complex and NP-hard problems. This paper describes the development of a hybrid genetic algorithm for solving the nonpreemptive job shop scheduling problems with the objective of minimizing makespan. In order to solve the presented problem more effectively, an operation-based representation was used to enable the construction of feasible schedules. In addition, a new knowledge-based operator was designed based on the problem’s characteristics in order to use machines’ idle times to improve the solution quality, and it was developed in the context of function evaluation. A machine based precedence preserving order-based crossover was proposed to generate the offspring. Furthermore, a simulated annealing based neighborhood search technique was used to improve the local exploitation ability of the algorithm and to increase its population diversity. In order to prove the efficiency and effectiveness of the proposed algorithm, numerous benchmarked instances were collected from the Operations Research Library. Computational results of the proposed hybrid genetic algorithm demonstrate its effectiveness.
Value Analysis of Engine Maintenance Scheduling relative to Fuel Burn and Minimal Operating Costs
Curran, R.; Van der Zwan, F.M.; Ouwehand, A.; Ghijs, S.S.A.
2010-01-01
The paper presents the results from a study in collaboration with an airline that looked at modeling the relationship of maintenance and fuel burn costs relative to minimizing the life cycle cost relative to schedule. The work has verified that the bucket theory presented in the paper is a correct a
M.G. Baldoquin de la Peña
2014-06-01
Full Text Available Some of the complex logistical problems faced by companies combine the needs for strategic and tactical decisions concerning the interrelated issues of clustering, scheduling, and routing. Various strategies can be used to solve these problems. We present a problem of this type, involving a company whose fundamental objective is the commercialization of its product in the domestic market. The paper focuses on a model of and method for a solution to the problem of scheduling visits to customers, taking into account the relationship with other phases of product marketing. The model is nonlinear, involves binary and continuous variables, and solved heuristically. Computational experiments show that the proposed solution performed very well for both real-life and theoretical instances.
EA/G-GA for Single Machine Scheduling Problems with Earliness/Tardiness Costs
Yuh-Min Chen
2011-06-01
Full Text Available An Estimation of Distribution Algorithm (EDA, which depends on explicitly sampling mechanisms based on probabilistic models with information extracted from the parental solutions to generate new solutions, has constituted one of the major research areas in the field of evolutionary computation. The fact that no genetic operators are used in EDAs is a major characteristic differentiating EDAs from other genetic algorithms (GAs. This advantage, however, could lead to premature convergence of EDAs as the probabilistic models are no longer generating diversified solutions. In our previous research [1], we have presented the evidences that EDAs suffer from the drawback of premature convergency, thus several important guidelines are provided for the design of effective EDAs. In this paper, we validated one guideline for incorporating other meta-heuristics into the EDAs. An algorithm named “EA/G-GA” is proposed by selecting a well-known EDA, EA/G, to work with GAs. The proposed algorithm was tested on the NP-Hard single machine scheduling problems with the total weighted earliness/tardiness cost in a just-in-time environment. The experimental results indicated that the EA/G-GA outperforms the compared algorithms statistically significantly across different stopping criteria and demonstrated the robustness of the proposed algorithm. Consequently, this paper is of interest and importance in the field of EDAs.
Improved Hysteretic Noisy Chaotic Neural Network for Broadcast Scheduling Problem in WMNs
Ming Sun
2013-01-01
Full Text Available It has been proven that the noise-tuning-based hysteretic noisy chaotic neural network (NHNCNN can use the noise tuning factor to improve the optimization performance obviously at lower initial noise levels while can not at initial higher noise levels. In order to improve the optimization performance of the NHNCNN at initial higher noise levels, we introduce a new noise tuning factor into the activation function and propose an improved hysteretic noisy chaotic neural network (IHNCNN model. By regulating the value of the newly introduced noise tuning factor, both noise levels of the activation function and hysteretic dynamics in the IHNCNN can be adjusted to help to improve the global optimization ability as the initial noise amplitude is higher. As a result, the IHNCNN can exhibit better optimization performance at initial higher noise levels. In order to demonstrate the advantage of the IHNCNN over the NHNCNN, the IHNCNN combined with gradual expansion scheme (GES is applied to solve broadcast scheduling problem (BSP in wireless multihop networks (WMNs. The aim of BSP is to design an optimal time-division multiple-access (TDMA frame structure with minimal frame length and maximal channel utilization. Simulation results in BSP show the superiority of the IHNCNN.
A hierarchical scheduling problem with a well-solvable second stage
J.B.G. Frenk (Hans); A.H.G. Rinnooy Kan (Alexander); L. Stougie (Leen)
1984-01-01
textabstractIn the hierarchical scheduling model to be considered, the decision at the aggregate level to acquire a number of identical machines has to be based on probabilistic information about the jobs that have to be scheduled on these machines at the detailed level. The objective is to minimize
Will Lunniss
2014-04-01
Full Text Available In multitasking real-time systems, the choice of scheduling algorithm is an important factor to ensure that response time requirements are met while maximising limited system resources. Two popular scheduling algorithms include fixed priority (FP and earliest deadline first (EDF. While they have been studied in great detail before, they have not been compared when taking into account cache related pre-emption delays (CRPD. Memory and cache are split into a number of blocks containing instructions and data. During a pre-emption, cache blocks from the pre-empting task can evict those of the pre-empted task. When the pre-empted task is resumed, if it then has to re-load the evicted blocks, CRPD are introduced which then affect the schedulability of the task. In this paper we compare FP and EDF scheduling algorithms in the presence of CRPD using the state-of-the-art CRPD analysis. We find that when CRPD is accounted for, the performance gains offered by EDF over FP, while still notable, are diminished. Furthermore, we find that under scenarios that cause relatively high CRPD, task layout optimisation techniques can be applied to allow FP to schedule tasksets at a similar processor utilisation to EDF. Thus making the choice of the task layout in memory as important as the choice of scheduling algorithm. This is very relevant for industry, as it is much cheaper and simpler to adjust the task layout through the linker than it is to switch the scheduling algorithm.
Julien Maheut
2013-07-01
Full Text Available Purpose: The purpose of this paper is to present an algorithm that solves the supply network configuration and operations scheduling problem in a mass customization company that faces alternative operations for one specific tool machine order in a multiplant context. Design/methodology/approach: To achieve this objective, the supply chain network configuration and operations scheduling problem is presented. A model based on stroke graphs allows the design of an algorithm that enumerates all the feasible solutions. The algorithm considers the arrival of a new customized order proposal which has to be inserted into a scheduled program. A selection function is then used to choose the solutions to be simulated in a specific simulation tool implemented in a Decision Support System. Findings and Originality/value: The algorithm itself proves efficient to find all feasible solutions when alternative operations must be considered. The stroke structure is successfully used to schedule operations when considering more than one manufacturing and supply option in each step. Research limitations/implications: This paper includes only the algorithm structure for a one-by-one, sequenced introduction of new products into the list of units to be manufactured. Therefore, the lotsizing process is done on a lot-per-lot basis. Moreover, the validation analysis is done through a case study and no generalization can be done without risk. Practical implications: The result of this research would help stakeholders to determine all the feasible and practical solutions for their problem. It would also allow to assessing the total costs and delivery times of each solution. Moreover, the Decision Support System proves useful to assess alternative solutions. Originality/value: This research offers a simple algorithm that helps solve the supply network configuration problem and, simultaneously, the scheduling problem by considering alternative operations. The proposed system
Depression and Related Problems in University Students
Field, Tiffany; Diego, Miguel; Pelaez, Martha; Deeds, Osvelia; Delgado, Jeannette
2012-01-01
Method: Depression and related problems were studied in a sample of 283 university students. Results: The students with high depression scores also had high scores on anxiety, intrusive thoughts, controlling intrusive thoughts and sleep disturbances scales. A stepwise regression suggested that those problems contributed to a significant proportion…
Depression and Related Problems in University Students
Field, Tiffany; Diego, Miguel; Pelaez, Martha; Deeds, Osvelia; Delgado, Jeannette
2012-01-01
Method: Depression and related problems were studied in a sample of 283 university students. Results: The students with high depression scores also had high scores on anxiety, intrusive thoughts, controlling intrusive thoughts and sleep disturbances scales. A stepwise regression suggested that those problems contributed to a significant proportion…
Minimizing makespan in a two-stage hybrid flow shop scheduling problem with open shop in one stage
DONG Jian-ming; HU Jue-liang; CHEN Yong
2013-01-01
This paper considers a scheduling problem in two-stage hybrid flow shop, where the first stage consists of two machines formed an open shop and the other stage has only one machine. The objective is to minimize the makespan, i.e., the maximum completion time of all jobs. We first show the problem is NP-hard in the strong sense, then we present two heuristics to solve the problem. Computational experiments show that the combined algorithm of the two heuristics performs well on randomly generated problem instances.
Lahimer, Asma; Haouari, Mohamed
2011-01-01
This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The problem even in its simplest form is NP-hard in the strong sense. The great deal of interest for this problem, besides its theoretical complexity, is animated by needs of various manufacturing and computing systems. We propose a new approach based on limited discrepancy search to solve the problem. Our method is tested with reference to a proposed lower bound as well as the best-known solutions in literature. Computational results show that the developed approach is efficient in particular for large-size problems.
Birgin, Ernesto G.; Ronconi, Débora P.
2012-10-01
The single machine scheduling problem with a common due date and non-identical ready times for the jobs is examined in this work. Performance is measured by the minimization of the weighted sum of earliness and tardiness penalties of the jobs. Since this problem is NP-hard, the application of constructive heuristics that exploit specific characteristics of the problem to improve their performance is investigated. The proposed approaches are examined through a computational comparative study on a set of 280 benchmark test problems with up to 1000 jobs.
An Integer Linear Programming Model for the Radiotherapy Treatment Scheduling Problem
Burke, Edmund K; Petrovic, Sanja
2011-01-01
Radiotherapy represents an important phase of treatment for a large number of cancer patients. It is essential that resources used to deliver this treatment are employed effectively. This paper presents a new integer linear programming model for real-world radiotherapy treatment scheduling and analyses the effectiveness of using this model on a daily basis in a hospital. Experiments are conducted varying the days on which schedules can be created. Results obtained using real-world data from the Nottingham University Hospitals NHS Trust, UK, are presented and show how the proposed model can be used with different policies in order to achieve good quality schedules.
Task-Scheduling in Cloud Computing using Credit Based Assignment Problem
Mousumi Paul
2011-10-01
Full Text Available This Cloud computing is a latest new computing paradigm where applications, data and IT services are provided across dynamic and geographically dispersed organization. Job scheduling systemproblem is a nucleus and demanding issue in Cloud Computing. How to utilize Cloud computing resources proficiently and gain the maximum profits with job scheduling system is one of the Cloud computing service providers’ ultimate objectives. In this paper we have used credit based scheduling decision to evaluate the entire group of task in the task queue and find the minimal completion time of alltask. Here cost matrix has been generated as the fair tendency of a task to be assigned in a resource.
Trunfio, Roberto
2015-06-01
In a recent article, Guo, Cheng and Wang proposed a randomized search algorithm, called modified generalized extremal optimization (MGEO), to solve the quay crane scheduling problem for container groups under the assumption that schedules are unidirectional. The authors claim that the proposed algorithm is capable of finding new best solutions with respect to a well-known set of benchmark instances taken from the literature. However, as shown in this note, there are some errors in their work that can be detected by analysing the Gantt charts of two solutions provided by MGEO. In addition, some comments on the method used to evaluate the schedule corresponding to a task-to-quay crane assignment and on the search scheme of the proposed algorithm are provided. Finally, to assess the effectiveness of the proposed algorithm, the computational experiments are repeated and additional computational experiments are provided.
A. Shirzadeh Chaleshtarti
2014-01-01
Full Text Available A lot of projects in real life are subject to some kinds of nonrenewable resources that are not exactly similar to the type defined in the project scheduling literature. The difference stems from the fact that, in those projects, contrary to the common assumption in the project scheduling literature, nonrenewable resources are not available in full amount at the beginning of the project, but they are procured along the project horizon. In this paper, we study this different type of nonrenewable resources. To that end, we extend the resource constrained project scheduling problem (RCPSP by this resource type (RCPSP-NR and customize four basic branch and bound algorithms of RCPSP for it, including precedence tree, extension alternatives, minimal delaying alternatives, and minimal forbidden sets. Several bounding and fathoming rules are introduced to the algorithms to shorten the enumeration process. We perform comprehensive experimental analysis using the four customized algorithms and also CPLEX solver.
DTS: Building custom, intelligent schedulers
Hansson, Othar; Mayer, Andrew
1994-01-01
DTS is a decision-theoretic scheduler, built on top of a flexible toolkit -- this paper focuses on how the toolkit might be reused in future NASA mission schedulers. The toolkit includes a user-customizable scheduling interface, and a 'Just-For-You' optimization engine. The customizable interface is built on two metaphors: objects and dynamic graphs. Objects help to structure problem specifications and related data, while dynamic graphs simplify the specification of graphical schedule editors (such as Gantt charts). The interface can be used with any 'back-end' scheduler, through dynamically-loaded code, interprocess communication, or a shared database. The 'Just-For-You' optimization engine includes user-specific utility functions, automatically compiled heuristic evaluations, and a postprocessing facility for enforcing scheduling policies. The optimization engine is based on BPS, the Bayesian Problem-Solver (1,2), which introduced a similar approach to solving single-agent and adversarial graph search problems.
Vanden Berghe, Greet
2012-01-01
Personnel scheduling can become a particularly difficult optimisation problem due to human factors. And yet: people working in healthcare, transportation and other round the clock service regimes perform their duties based on a schedule that was often manually constructed. The unrewarding manual scheduling task deserves more attention from the timetabling community so as to support computation of fair and good quality results. The present abstract touches upon a set of particular characterist...
The single-machine scheduling problems with deteriorating jobs and learning effect
无
2006-01-01
In this paper we consider a single-machine scheduling model with deteriorating jobs and simultaneous learning, and we introduce polynomial solutions for single machine makespan minimization, total flow times minimization and maximum lateness minimization corresponding to the first and second special cases of our model under some agreeable conditions. However,corresponding to the third special case of our model, we show that the optimal schedules may be different from those of the classical version for the above objective functions.
The nurse scheduling problem: a goal programming and nonlinear optimization approaches
Hakim, L.; Bakhtiar, T.; Jaharuddin
2017-01-01
Nurses scheduling is an activity of allocating nurses to conduct a set of tasks at certain room at a hospital or health centre within a certain period. One of obstacles in the nurse scheduling is the lack of resources in order to fulfil the needs of the hospital. Nurse scheduling which is undertaken manually will be at risk of not fulfilling some nursing rules set by the hospital. Therefore, this study aimed to perform scheduling models that satisfy all the specific rules set by the management of Bogor State Hospital. We have developed three models to overcome the scheduling needs. Model 1 is designed to schedule nurses who are solely assigned to a certain inpatient unit and Model 2 is constructed to manage nurses who are assigned to an inpatient room as well as at Polyclinic room as conjunct nurses. As the assignment of nurses on each shift is uneven, then we propose Model 3 to minimize the variance of the workload in order to achieve equitable assignment on every shift. The first two models are formulated in goal programming framework, while the last model is in nonlinear optimization form.
Tao Ren
2014-01-01
Full Text Available We address the scheduling problem for a no-wait flow shop to optimize total completion time with release dates. With the tool of asymptotic analysis, we prove that the objective values of two SPTA-based algorithms converge to the optimal value for sufficiently large-sized problems. To further enhance the performance of the SPTA-based algorithms, an improvement scheme based on local search is provided for moderate scale problems. New lower bound is presented for evaluating the asymptotic optimality of the algorithms. Numerical simulations demonstrate the effectiveness of the proposed algorithms.
Hamidreza Haddad
2012-04-01
Full Text Available This paper tackles the single machine scheduling problem with dependent setup time and precedence constraints. The primary objective of this paper is minimization of total weighted tardiness. Since the complexity of the resulted problem is NP-hard we use metaheuristics method to solve the resulted model. The proposed model of this paper uses genetic algorithm to solve the problem in reasonable amount of time. Because of high sensitivity of GA to its initial values of parameters, a Taguchi approach is presented to calibrate its parameters. Computational experiments validate the effectiveness and capability of proposed method.
Seyyed Mohammad Hassan Hosseini
2016-05-01
Full Text Available Scheduling problem for the hybrid flow shop scheduling problem (HFSP followed by an assembly stage considering aging effects additional preventive and maintenance activities is studied in this paper. In this production system, a number of products of different kinds are produced. Each product is assembled with a set of several parts. The first stage is a hybrid flow shop to produce parts. All machines can process all kinds of parts in this stage but each machine can process only one part at the same time. The second stage is a single assembly machine or a single assembly team of workers. The aim is to schedule the parts on the machines and assembly sequence and also determine when the preventive maintenance activities get done in order to minimize the completion time of all products (makespan. A mathematical modeling is presented and its validation is shown by solving an example in small scale. Since this problem has been proved strongly NP-hard, in order to solve the problem in medium and large scale, four heuristic algorithms is proposed based on the Johnson’s algorithm. The numerical experiments are used to run the mathematical model and evaluate the performance of the proposed algorithms.
Johan Soewanda; Tanti Octavia; Iwan Halim Sahputra
2007-01-01
This paper discusses the application of Robust Hybrid Genetic Algorithm to solve a flow-shop scheduling problem. The proposed algorithm attempted to reach minimum makespan. PT. FSCM Manufacturing Indonesia Plant 4's case was used as a test case to evaluate the performance of the proposed algorithm. The proposed algorithm was compared to Ant Colony, Genetic-Tabu, Hybrid Genetic Algorithm, and the company's algorithm. We found that Robust Hybrid Genetic produces statistically better result than...
武善玉; 张平; 李方; 古锋; 潘毅
2016-01-01
To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems (SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm (HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.
Mohammad Hassan Sebt
2015-11-01
Full Text Available In this paper, a new genetic algorithm (GA is presented for solving the multi-mode resource-constrained project scheduling problem (MRCPSP with minimization of project makespan as the objective subject to resource and precedence constraints. A random key and the related mode list (ML representation scheme are used as encoding schemes and the multi-mode serial schedule generation scheme (MSSGS is considered as the decoding procedure. In this paper, a simple, efficient fitness function is proposed which has better performance compared to the other fitness functions in the literature. Defining a new mutation operator for ML is the other contribution of the current study. Comparing the results of the proposed GA with other approaches using the well-known benchmark sets in PSPLIB validates the effectiveness of the proposed algorithm to solve the MRCPSP.
Drug-related problems in hospitalised patients
van den Bemt, PMLA; Egberts, TCG; de Jong-van den Berg, LTW; Brouwers, JRBJ
2000-01-01
Drug-related problems include medication errors (involving an error in the process of prescribing, dispensing, or administering a drug, whether there are adverse consequences or not) and adverse drug reactions (any response to a drug which is noxious and unintended, and which occurs at doses normall
Drug-related problems in hospitalised patients
van den Bemt, PMLA; Egberts, TCG; de Jong-van den Berg, LTW; Brouwers, JRBJ
Drug-related problems include medication errors (involving an error in the process of prescribing, dispensing, or administering a drug, whether there are adverse consequences or not) and adverse drug reactions (any response to a drug which is noxious and unintended, and which occurs at doses
The rocket problem in general relativity
Henriques, Pedro G
2011-01-01
We derive the covariant optimality conditions for rocket trajectories in general relativity, with and without a bound on the magnitude of the proper acceleration. The resulting theory is then applied to solve two specific problems: the minimum fuel consumption transfer between two galaxies in a FLRW model, and between two stable circular orbits in the Schwarzschild spacetime.
Alcohol Related Problems and the Hispanic
Garcia, Louis S.
1977-01-01
Although Hispanic women report high rates of abstinence, more Hispanic men report alcohol related problems than Anglos, Blacks, or Asians and report more heavy drinking. Yet little has been done to develop or fund culturally specific alcoholism prevention, treatment and rehabilitation programs for the Hispanics. (NQ)
Pathological Gambling and Related Problems among Adolescents.
Ladouceur, Robert; Boudreault, Normand; Jacques, Christian; Vitaro, Frank
1999-01-01
Evaluates the prevalence of pathological gambling and related problems among 3,426 students in junior and senior high schools in Quebec City. Results indicate that 77% have gambled in the last twelve months and 13% gamble at least once a week. Results also reveal that pathological gambling is associated with drug and alcohol use, poor grades, and…
The Swedish Experiment with Localised Control of Time Schedules: Policy Problem Representations
Ronnberg, Linda
2007-01-01
Swedish compulsory schools are the most autonomous in Europe regarding time allocation and time management. Still, the Swedish state decided to take this even further, when introducing an experiment that permits some compulsory schools to abandon the regulations of the national time schedule. The aim of this study is to explore and analyse the…
The Green Ship Routing and Scheduling Problem (GSRSP): A conceptual approach
Kontovas, Christos A.
2014-01-01
Recent reviews of the literature on ship routing and scheduling note the increased attention to environmental issues. This is an area of paramount importance for international shipping and will be even more so in the future. This short communication is motivated by the increasing attention...
Analysis of a stochastic lot scheduling problem with strict due-dates
van Foreest, Nicolaas; Wijngaard, Jacob; Boucherie, Richard; van Dijk, Nico M.
2017-01-01
This chapter considers admission control and scheduling rules for a single machine production environment. Orders arrive at a single machine and can be grouped into serveral product families. Each order has a family dependent due-date, production duration, and reward. When an order cannot be served
Space languages: Solving the classic scheduling problem in Ada and Lisp
Davis, Stephen; Hays, Dan; Wolfsberger, John W.
1988-01-01
The comparison of programming languages is best seen while evaluating similar systems. The strengths and weaknesses of both languages were investigated as the scheduler was being implemented. Some features used in both languages shall be object-oriented paradigms, parallel programming, search and production heuristics, and other classical artificial intelligence implementations.
Liou, Cheng-Dar; Hsieh, Yi-Chih; Chen, Yin-Yann
2013-01-01
This article investigates the two-machine flow-shop group scheduling problem (GSP) with sequence-dependent setup and removal times, and job transportation times between machines. The objective is to minimise the total completion time. As known, this problem is an NP-hard problem and generalises the typical two-machine GSPs. In this article, a new encoding scheme based on permutation representation is proposed to transform a random job permutation to a feasible permutation for GSPs. The proposed encoding scheme simultaneously determines both the sequence of jobs in each group and the sequence of groups. By reasonably combining particle swarm optimisation (PSO) and genetic algorithm (GA), we develop a fast and easily implemented hybrid algorithm (HA) for solving the considered problems. The effectiveness and efficiency of the proposed HA are demonstrated and compared with those of standard PSO and GA by numerical results of various tested instances with group numbers up to 20. In addition, three different lower bounds are developed to evaluate the solution quality of the HA. Limited numerical results indicate that the proposed HA is a viable and effective approach for the studied two-machine flow-shop group scheduling problem.
Schutten, Johannes M.J.
1996-01-01
We consider the problem of scheduling n jobs on m identical parallel machines to minimize a regular cost function. The standard list scheduling algorithm converts a list into a feasible schedule by focusing on the job start times. We prove that list schedules are dominant for this type of problem.
Hui Du
2016-01-01
Full Text Available To produce the final product, parts need to be fabricated in the process stages and thereafter several parts are joined under the assembly operations based on the predefined bill of materials. But assembly relationship between the assembly parts and components has not been considered in general job shop scheduling problem model. The aim of this research is to find the schedule which minimizes completion time of Assembly Job Shop Scheduling Problem (AJSSP. Since the complexity of AJSSP is NP-hard, a hybrid particle swarm optimization (HPSO algorithm integrated PSO with Artificial Immune is proposed and developed to solve AJSSP. The selection strategy based on antibody density makes the particles of HPSO maintain the diversity during the iterative process, thus overcoming the defect of premature convergence. Then HPSO algorithm is applied into a case study development from classical FT06. Finally, the effect of key parameters on the proposed algorithm is analyzed and discussed regarding how to select the parameters. The experiment result confirmed its practice and effectiveness.
Hadi Mokhtari
2013-01-01
Full Text Available In this paper, the problem of received order scheduling by a manufacturer, with the measure of maximum completion times of orders, has been formulated and then an analytical approach has been devised for its solution. At the beginning of a planning period, the manufacturer receives a number of orders from customers, each of which requires two different stages for processing. In order to minimize the work in process inventories, the no-wait condition between two operations of each order is regarded. Then, the equality of obtained schedules is proved by machine idle time minimization, as objective, with the schedules obtained by maximum completion time minimization. A concept entitled “Order pairing” has been defined and an algorithm for achieving optimal order pairs which is based on symmetric assignment problem has been presented. Using the established order pairs, an upper bound has been developed based on contribution of every order pair out of total machines idle time. Out of different states of improving upper bound, 12 potential situations of order pairs sequencing have been also evaluated and then the upper bound improvement has been proved in each situation, separately. Finally, a heuristic algorithm has been developed based on attained results of pair improvement and a case study in printing industry has been investigated and analyzed to approve its applicability.
Chunhua Ju
2012-01-01
Full Text Available Managing multiple project is a complex task involving the unrelenting pressures of time and cost. Many studies have proposed various tools and techniques for single-project scheduling; however, the literature further considering multimode or multiproject issues occurring in the real world is rather scarce. In this paper, design structure matrix (DSM and an improved artificial immune network algorithm (aiNet are developed to solve a multi-mode resource-constrained scheduling problem. Firstly, the DSM is used to simplify the mathematic model of multi-project scheduling problem. Subsequently, aiNet algorithm comprised of clonal selection, negative selection, and network suppression is adopted to realize the local searching and global searching, which will assure that it has a powerful searching ability and also avoids the possible combinatorial explosion. Finally, the approach is tested on a set of randomly cases generated from ProGen. The computational results validate the effectiveness of the proposed algorithm comparing with other famous metaheuristic algorithms such as genetic algorithm (GA, simulated annealing algorithm (SA, and ant colony optimization (ACO.
Bai, Danyu
2015-08-01
This paper discusses the flow shop scheduling problem to minimise the total quadratic completion time (TQCT) with release dates in offline and online environments. For this NP-hard problem, the investigation is focused on the performance of two online algorithms based on the Shortest Processing Time among Available jobs rule. Theoretical results indicate the asymptotic optimality of the algorithms as the problem scale is sufficiently large. To further enhance the quality of the original solutions, the improvement scheme is provided for these algorithms. A new lower bound with performance guarantee is provided, and computational experiments show the effectiveness of these heuristics. Moreover, several results of the single-machine TQCT problem with release dates are also obtained for the deduction of the main theorem.
Ada Che
2008-01-01
Full Text Available Modern automated production lines usually use one or multiple computer-controlled robots or hoists for material handling between workstations. A typical application of such lines is an automated electroplating line for processing printed circuit boards (PCBs. In these systems, cyclic production policy is widely used due to large lot size and simplicity of implementation. This paper addresses cyclic scheduling of a multihoist electroplating line with constant processing times. The objective is to minimize the cycle time, or equivalently to maximize the production throughput, for a given number of hoists. We propose a mathematical model and a polynomial algorithm for this scheduling problem. Computational results on randomly generated instances are reported.
A. Khodadadi
2014-04-01
Full Text Available In most manufacturing and distribution systems, semi-finished jobs are transferred from one processing facility to another by transporters such as automated guided vehicles and conveyors and finished jobs are delivered to customers or warehouses by vehicles such as trucks. Most machine scheduling models assume either that there are a finite number of transporters for delivering jobs or that jobs are delivered instantaneously from one location to another without transportation time involved. In this study we study a new simple heuristic algorithm for a ‘3-machine, n-job’ flow shop scheduling problem in which transportation time and break down times of machines are considered. A heuristic approach method to find optimal and near optimal sequence minimizing the total elapsed time.
Study on disruption management scheduling problem of flow shop under supply chain environment
Bo Hong Guang
2016-01-01
Full Text Available This paper presents a disruption scheduling model for an environment of proportional two-machine no-wait flow shop. To achieve the objects of minimization of weighted sum of makespan and minimization of weighted sum of tardiness, we introduce a revised PSO algorithm which is designed with a neighborhood search structure. According to the experiment, the effectivity of the method proposed is proven.
Wang, Hongfeng; Fu, Yaping; Huang, Min; Wang, Junwei
2016-03-01
The operation process design is one of the key issues in the manufacturing and service sectors. As a typical operation process, the scheduling with consideration of the deteriorating effect has been widely studied; however, the current literature only studied single function requirement and rarely considered the multiple function requirements which are critical for a real-world scheduling process. In this article, two function requirements are involved in the design of a scheduling process with consideration of the deteriorating effect and then formulated into two objectives of a mathematical programming model. A novel multiobjective evolutionary algorithm is proposed to solve this model with combination of three strategies, i.e. a multiple population scheme, a rule-based local search method and an elitist preserve strategy. To validate the proposed model and algorithm, a series of randomly-generated instances are tested and the experimental results indicate that the model is effective and the proposed algorithm can achieve the satisfactory performance which outperforms the other state-of-the-art multiobjective evolutionary algorithms, such as nondominated sorting genetic algorithm II and multiobjective evolutionary algorithm based on decomposition, on all the test instances.
Acute marijuana effects on response-reinforcer relations under multiple variable-interval schedules.
Lane, S D; Cherek, D R; Pietras, C J; Tcheremissine, O V
2004-07-01
Acute marijuana administration may alter response-reinforcer relationships via a change in reinforcer efficacy, but may also impair coordination and motor function. One approach to evaluating drug effects on both motor function and reinforcer efficacy involves fitting the matching law equation to data obtained under multiple variable interval (VI) schedules. The present report describes an experiment that examined the effects of acute marijuana on response properties using this approach. Six human subjects responded under a multiple VI schedule for monetary reinforcers after smoking placebo and two active doses of marijuana. The low marijuana dose produced unsystematic changes in responding. As measured by the matching law equation parameters (k and rB), at the high dose five subjects showed a decrease-motor-related properties of response rate and four subjects' responding indicated a decrease in reinforcer efficacy. These data raise the possibility that, at high doses, marijuana administration alters both motor function and reinforcer efficacy.
Shang-Chia Liu
2015-01-01
Full Text Available In the recent 20 years, scheduling with learning effect has received considerable attention. However, considering the learning effect along with release time is limited. In light of these observations, in this paper, we investigate a single-machine problem with sum of processing times based learning and ready times where the objective is to minimize the makespan. For solving this problem, we build a branch-and-bound algorithm and a heuristic algorithm for the optimal solution and near-optimal solution, respectively. The computational experiments indicate that the branch-and-bound algorithm can perform well the problem instances up to 24 jobs in terms of CPU time and node numbers, and the average error percentage of the proposed heuristic algorithm is less than 0.5%.
Maziar Yazdani
2017-01-01
Full Text Available This research focuses on a scheduling problem with multiple unavailability periods and distinct due dates. The objective is to minimize the sum of maximum earliness and tardiness of jobs. In order to optimize the problem exactly a mathematical model is proposed. However due to computational difficulties for large instances of the considered problem a modified variable neighborhood search (VNS is developed. In basic VNS, the searching process to achieve to global optimum or near global optimum solution is totally random, and it is known as one of the weaknesses of this algorithm. To tackle this weakness, a VNS algorithm is combined with a knowledge module. In the proposed VNS, knowledge module extracts the knowledge of good solution and save them in memory and feed it back to the algorithm during the search process. Computational results show that the proposed algorithm is efficient and effective.
Travel-related hepatitis B: risk factors and prevention using an accelerated vaccination schedule.
Keystone, Jay S
2005-10-01
Rates of global travel and tourism are increasing dramatically, especially to regions with medium or high endemicity for hepatitis A and B, such as Asia, Africa, Latin America, and the Middle East. International travelers to these areas should be protected against both hepatitis A and B, regardless of their anticipated length of stay. However, many travelers depart within weeks of planning their trip (too late to complete the accelerated 0-, 1-, 2-month regimen for hepatitis B), and a majority of those traveling depart without being vaccinated. Although extended-stay travelers are at high risk for hepatitis B, short-stay travelers also are at risk. The most commonly encountered risk factors for travel-related hepatitis B are casual sexual activity with a new partner, medical and dental care abroad, and in the expatriate community, adoption of children who are hepatitis B carriers. Although efficacy studies of accelerated schedules for hepatitis B immunization have not been conducted, the results of immunogenicity studies in healthy volunteers who received an accelerated, 3-dose regimen on a 0-,7-, and 21-day schedule suggest that excellent, rapid, and long-term protection will be conferred. More data are needed to assess the efficacy of accelerated schedules in persons aged >40 years and to determine whether a fourth dose of hepatitis B vaccine is needed in all age groups.
Igaki, T; Sakagami, T
2001-06-01
Two experiments were conducted to investigate the relation between resistance to change and preference. Four pigeons responded in concurrent chained schedules in which variable-interval (VI) 60-s schedules were arranged in the initial link. In Experiment 1, VI and fixed-interval (FI) schedules of equal mean reinforcement rates were arranged in the terminal link. Response rates were higher in the initial link leading to VI terminal link. Under the prefeeding test, the initial-link response rates leading to VI terminal link were more resistant to change than were those leading to FI terminal link, but under the extinction test there were no consistent differences between the two initial-link response rates. In Experiment 2, FI value of the terminal link was manipulated so that pigeons maintained approximately equal responding in the initial link. The two initial-link response rates showed equal resistance to change under the prefeeding and extinction tests. Thus, the data suggest that although the use of extinction as a manipulation to study resistance to change is questioned, resistance to change and preference are different measures of a single object.
Lu Lin
2009-10-01
Full Text Available Estimation of Distribution Algorithm (EDA is a new kinds of colony evolution algorithm, through counting excellent information of individuals of the present colony EDA construct probability distribution model, then sample the model produces newt generation. To solve the NP-Hard question as EDA searching optimum network structure a new Maximum Entropy Distribution Algorithm (MEEDA is provided. The algorithm takes Jaynes principle as the basis, makes use of the maximum entropy of random variables to estimate the minimum bias probability distribution of random variables, and then regard it as the evolution model of the algorithm, which produces the optimal/near optimal solution. Then this paper presents a rough programming model for job shop scheduling under uncertain information problem. The method overcomes the defects of traditional methods which need pre-set authorized characteristics or amount described attributes, designs multi-objective optimization mechanism and expands the application space of a rough set in the issue of job shop scheduling under uncertain information environment. Due to the complexity of the proposed model, traditional algorithms have low capability in producing a feasible solution. We use MEEDA in order to enable a definition of a solution within a reasonable amount of time. We assume that machine flexibility in processing operations to decrease the complexity of the proposed model. Muth and Thompson’s benchmark problems tests are used to verify and validate the proposed rough programming model and its algorithm. The computational results obtained by MEEDA are compared with GA. The compared results prove the effectiveness of MEEDA in the job shop scheduling problem under uncertain information environment.
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.
Hybrid search and the dial-a-ride problem with transfer scheduling constraints
2015-01-01
In a conventional dial-a-ride-system passengers are moved with the same vehicle between their pickup and their drop-off location. In a dial-a-ride-system with transfer, it is possible (or even standard) that passengers change the vehicle once or several times. Transfer Scheduling Constraints (TSC) are imposed in order to ensure that the comfort of the transfer remains on an acceptable level by avoiding too short or too long transfer times but also for limiting the total riding time between th...
C. Christober Asir Rajan
2008-06-01
Full Text Available The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. This also means that it is desirable to find the optimal unit commitment in the power system for the next H hours. A 66-bus utility power system in India demonstrates the effectiveness of the proposed approach; extensive studies have also been performed for different IEEE test systems consist of 24, 57 and 175 buses. Numerical results are shown comparing the cost solutions and computation time obtained by different intelligence and conventional methods.
Johan Soewanda
2007-01-01
Full Text Available This paper discusses the application of Robust Hybrid Genetic Algorithm to solve a flow-shop scheduling problem. The proposed algorithm attempted to reach minimum makespan. PT. FSCM Manufacturing Indonesia Plant 4's case was used as a test case to evaluate the performance of the proposed algorithm. The proposed algorithm was compared to Ant Colony, Genetic-Tabu, Hybrid Genetic Algorithm, and the company's algorithm. We found that Robust Hybrid Genetic produces statistically better result than the company's, but the same as Ant Colony, Genetic-Tabu, and Hybrid Genetic. In addition, Robust Hybrid Genetic Algorithm required less computational time than Hybrid Genetic Algorithm
Yueyue Liu
2015-01-01
Full Text Available This paper studies a production scheduling problem with deteriorating jobs, which frequently arises in contemporary manufacturing environments. The objective is to find an optimal sequence of the set of jobs to minimize the total weighted tardiness, which is an indicator of service quality. The problem belongs to the class of NP-hard. When the number of jobs increases, the computational time required by an optimization algorithm to solve the problem will increase exponentially. To tackle large-scale problems efficiently, a two-stage method is presented in this paper. We partition the set of jobs into a few subsets by applying a neural network approach and thereby transform the large-scale problem into a series of small-scale problems. Then, we employ an improved metaheuristic algorithm (called GTS which combines genetic algorithm with tabu search to find the solution for each subproblem. Finally, we integrate the obtained sequences for each subset of jobs and produce the final complete solution by enumeration. A fair comparison has been made between the two-stage method and the GTS without decomposition, and the experimental results show that the solution quality of the two-stage method is much better than that of GTS for large-scale problems.
Wen, Min; Krapper, Emil; Larsen, Jesper;
2011-01-01
. The objective is to minimize the total delivery cost that is a weighted sum of two kinds of delivery costs. A multilevel variable neighborhood search heuristic is proposed for the problem. In a preprocessing step, the problem size is reduced through an aggregation procedure. Thereafter, the aggregated weekly...... planning problem is decomposed into daily planning problems, each of which is solved by a variable neighborhood search. Finally, the solution of the aggregated problem is expanded to that of the original problem. The method is implemented and tested on real‐life data consisting of up to 2,000 orders per...... week. Computational results show that the aggregation procedure and the decomposition strategy are very effective in solving this large scale problem, and our solutions are superior to the industrial solutions given the constraints considered in this work....
Considering Competition to Solve a Flight Schedule and Aircraft Routing Problem for Small Airlines
J. Díaz-Ramírez
2012-08-01
Full Text Available For the case of low-cost airlines, which are characterized by having a single fleet with a small number of airplanes, ina previous work, a heuristic algorithm (AFS-MRA was developed to simultaneously find the flight schedule and theaircraft routes subject to maintenance constraints. This work advances this algorithm by incorporating competition inthe planning process (MAFS-MRA.Within a time frame with a given demand data, competition is seen as a game with two players (one airline and all itscompetitors, where the strategies are all the potential origin-destinations that could be included in the flight schedule,and the payment matrix contains the objective function coefficients that depend on the market share and the routespreviously selected.Numerical experimentation was undertaken using real data for the case of two airlines that operate at TolucaInternational Airport in Mexico. It was found that, by considering competition, the occupation improves to 3% and thatthe number of flights required to satisfy the demand was reduced to 21%. Besides, the updating process reduces theprofit computation error in almost 80%, as compared to the real market behavior for the period under study.
Shujin Qin
2016-01-01
Full Text Available Workforce scheduling is an important and common task for projects with high labour intensities. It becomes particularly complex when employees have multiple skills and the employees’ productivity changes along with their learning of knowledge according to the tasks they are assigned to. Till now, in this context, only little work has considered the minimum quality limit of tasks and the quality learning effect. In this research, the workforce scheduling model is developed for assigning tasks to multiskilled workforce by considering learning of knowledge and requirements of project quality. By using piecewise linearization to learning curve, the mixed 0-1 nonlinear programming model (MNLP is transformed into a mixed 0-1 linear programming model (MLP. After that, the MLP model is further improved by taking account of the upper bound of employees’ experiences accumulation, and the stable performance of mature employees. Computational experiments are provided using randomly generated instances based on the investigation of a software company. The results demonstrate that the proposed MLPs can precisely approach the original MNLP model but can be calculated in much less time.
Hybrid Multi-Objective Particle Swarm Optimization for Flexible Job Shop Scheduling Problem
S. V. Kamble
2015-03-01
Full Text Available Hybrid algorithm based on Particle Swarm Optimization (PSO and Simulated annealing (SA is proposed, to solve Flexible Job Shop Scheduling with five objectives to be minimized simultaneously: makespan, maximal machine workload, total workload, machine idle time & total tardiness. Rescheduling strategy used to shuffle workload once the machine breakdown takes place in proposed algorithm. The hybrid algorithm combines the high global search efficiency of PSO with the powerful ability to avoid being trapped in local minimum of SA. A hybrid multi-objective PSO (MPSO and SA algorithm is proposed to identify an approximation of the pareto front for Flexible job shop scheduling (FJSSP. Pareto front and crowding distance is used for identify the fitness of particle. MPSO is significant to global search and SA used to local search. The proposed MPSO algorithm is experimentally applied on two benchmark data set. The result shows that the proposed algorithm is better in term quality of non-dominated solution compared to the other algorithms in the literature.
Yue, Lei; Guan, Zailin; Saif, Ullah; Zhang, Fei; Wang, Hao
2016-01-01
Group scheduling is significant for efficient and cost effective production system. However, there exist setup times between the groups, which require to decrease it by sequencing groups in an efficient way. Current research is focused on a sequence dependent group scheduling problem with an aim to minimize the makespan in addition to minimize the total weighted tardiness simultaneously. In most of the production scheduling problems, the processing time of jobs is assumed as fixed. However, the actual processing time of jobs may be reduced due to "learning effect". The integration of sequence dependent group scheduling problem with learning effects has been rarely considered in literature. Therefore, current research considers a single machine group scheduling problem with sequence dependent setup times and learning effects simultaneously. A novel hybrid Pareto artificial bee colony algorithm (HPABC) with some steps of genetic algorithm is proposed for current problem to get Pareto solutions. Furthermore, five different sizes of test problems (small, small medium, medium, large medium, large) are tested using proposed HPABC. Taguchi method is used to tune the effective parameters of the proposed HPABC for each problem category. The performance of HPABC is compared with three famous multi objective optimization algorithms, improved strength Pareto evolutionary algorithm (SPEA2), non-dominated sorting genetic algorithm II (NSGAII) and particle swarm optimization algorithm (PSO). Results indicate that HPABC outperforms SPEA2, NSGAII and PSO and gives better Pareto optimal solutions in terms of diversity and quality for almost all the instances of the different sizes of problems.
刘民; 李法朝; 吴澄
2003-01-01
Measuring the difference between fuzzy numbers is often needed in many fuzzy optimizationproblems such as manufacturing system production line scheduling with uncertainty environments. In thispaper, based on the distance function of plane R2 and the level importance function, we establish theUID-metric and LPID-metric of measuring the difference between fuzzy numbers, and discuss the basicproperties of UID-metric and LPID-metric, and prove that fuzzy number spaces are metric spaces aboutUID-metric and LPID-metric if and only if the level importance function /(λ) ≠ 0 almost everywhere on [0,1]. Further, we discuss the convergence, separability and completeness of UID-metric and LPID-metricbased on the norms of plane R2. Finally, we analyze the characteristics of UID-metric and LPID-metric bysome application examples.
Granja, C; Almada-Lobo, B; Janela, F; Seabra, J; Mendes, A
2014-12-01
As patient's length of stay in waiting lists increases, governments are looking for strategies to control the problem. Agreements were created with private providers to diminish the workload in the public sector. However, the growth of the private sector is not following the demand for care. Given this context, new management strategies have to be considered in order to minimize patient length of stay in waiting lists while reducing the costs and increasing (or at least maintaining) the quality of care. Appointment scheduling systems are today known to be proficient in the optimization of health care services. Their utilization is focused on increasing the usage of human resources, medical equipment and reducing the patient waiting times. In this paper, a simulation-based optimization approach to the Patient Admission Scheduling Problem is presented. Modeling tools and simulation techniques are used in the optimization of a diagnostic imaging department. The proposed techniques have demonstrated to be effective in the evaluation of diagnostic imaging workflows. A simulated annealing algorithm was used to optimize the patient admission sequence towards minimizing the total completion and total waiting of patients. The obtained results showed average reductions of 5% on the total completion and 38% on the patients' total waiting time. Copyright © 2014 Elsevier Inc. All rights reserved.
Song Huang
2016-01-01
Full Text Available The fuzzy processing time occasionally exists in job shop scheduling problem of flexible manufacturing system. To deal with fuzzy processing time, fuzzy flexible job shop model was established in several papers and has attracted numerous researchers’ attention recently. In our research, an improved version of discrete particle swarm optimization (IDPSO is designed to solve flexible job shop scheduling problem with fuzzy processing time (FJSPF. In IDPSO, heuristic initial methods based on triangular fuzzy number are developed, and a combination of six initial methods is applied to initialize machine assignment and random method is used to initialize operation sequence. Then, some simple and effective discrete operators are employed to update particle’s position and generate new particles. In order to guide the particles effectively, we extend global best position to a set with several global best positions. Finally, experiments are designed to investigate the impact of four parameters in IDPSO by Taguchi method, and IDPSO is tested on five instances and compared with some state-of-the-art algorithms. The experimental results show that the proposed algorithm can obtain better solutions for FJSPF and is more competitive than the compared algorithms.
Gamst, M.
2014-01-01
arrived at the machine. Furthermore, two resource demand transmissions cannot use the same edge in the same time period. The problem has application in grid computing, where a number of geographically distributed machines work together for solving large problems. The machines are connected through...
Hany Seidgar
2016-01-01
Full Text Available This paper investigates a scheduling combined manpower-vehicle routing problem with a central depot in and a set of multi-skilled manpower for serving to customers. Teams are in different range of competencies that it will affect the service time duration. Vehicles are in different moving speeds and costs and not all the vehicles are capable to move toward all the customers’ sites. The objective is to minimize the total cost of servicing, routing, and lateness penalties. This paper presents a mixed integer programming model and two meta-heuristic approaches of genetic algorithm (GA and artificial bee colony algorithm (ABC are developed to solve the generated problems. Furthermore, Taguchi experimental design method is applied to set the proper values of parameters. The available results show the higher performance of proposed GA compared with ABC, in quality of solutions.
An effective co-evolutionary quantum genetic algorithm for the no-wait flow shop scheduling problem
Guanlong Deng
2015-12-01
Full Text Available This article proposes a competitive co-evolutionary quantum genetic algorithm for the no-wait flow shop scheduling problem with the criterion to minimize makespan, which is a renowned NP-hard combinatorial optimization problem. An innovative coding and decoding mechanism is proposed. The mechanism uses square matrix to represent the quantum individual and adapts the quantum rotation gate to update the quantum individual. In the algorithm framework, the store-with-diversity is proposed to maintain the diversity of the population. Moreover, a competitive co-evolution strategy is introduced to enhance the evolutionary pressure and accelerate the convergence. The store-with-diversity and competitive co-evolution are designed to keep a balance between exploration and exploitation. Simulations based on a benchmark set and comparisons with several existing algorithms demonstrate the effectiveness and robustness of the proposed algorithm.
Wang, Deyun; Grunder, Olivier; EL Moudni, Abdellah
2014-08-01
This paper considers an integrated lot sizing and scheduling problem for a production-distribution environment with arbitrary job volumes and distinct due dates considerations. In the problem, jobs are firstly batch processed on a batching machine at production stage and then delivered to a pre-specified customer at the subsequent delivery stage by a capacitated vehicle. Each job is associated with a distinct due date and a distinct volume, and has to be delivered to the customer before its due date, i.e. delay is not allowed. The processing time of a batch is a constant independent of the jobs it contains. In production, a constant set-up time as well as a constant set-up cost is required before the first job of this batch is processed. In delivery, a constant delivery time as well as a constant delivery cost is needed for each round-trip delivery between the factory and the customer. Moreover, it is supposed that a job that arrives at the customer before its due date will incur a customer inventory cost. The objective is to find a coordinated lot sizing and scheduling scheme such that the total cost is minimised while guaranteeing a certain customer service level. A mixed integer formulation is proposed for this problem, and then a genetic algorithm is developed to solve it. To evaluate the performance of the proposed genetic algorithm, a lower bound on the objective value is established. Computational experiments show that the proposed genetic algorithm performs well on randomly generated problem instances.
Ahmad Zeraatkar Moghaddam
2012-01-01
Full Text Available This paper presents a mathematical model for the problem of minimizing the maximum lateness on a single machine when the deteriorated jobs are delivered to each customer in various size batches. In reality, this issue may happen within a supply chain in which delivering goods to customers entails cost. Under such situation, keeping completed jobs to deliver in batches may result in reducing delivery costs. In literature review of batch scheduling, minimizing the maximum lateness is known as NP-Hard problem; therefore the present issue aiming at minimizing the costs of delivering, in addition to the aforementioned objective function, remains an NP-Hard problem. In order to solve the proposed model, a Simulation annealing meta-heuristic is used, where the parameters are calibrated by Taguchi approach and the results are compared to the global optimal values generated by Lingo 10 software. Furthermore, in order to check the efficiency of proposed method to solve larger scales of problem, a lower bound is generated. The results are also analyzed based on the effective factors of the problem. Computational study validates the efficiency and the accuracy of the presented model.
Relative locality and the soccer ball problem
Amelino-Camelia, Giovanni; Kowalski-Glikman, Jerzy; Smolin, Lee
2011-01-01
We consider the behavior of macroscopic bodies within the framework of relative locality, which is a recent proposal for Planck scale modifications of the relativistic dynamics of particles which are described as arising from deformations in the geometry of momentum space. These lead to the addition of non-linear terms to the energy-momentum relations and conservation laws, which are suppressed by powers of ratio between the energy E of the particles involved and the Planck mass M_P. We consider and resolve a common objection against such proposals, which is that, even if the corrections are small for elementary particles in current experiments, they are huge when applied to composite systems such as soccer balls, planets and stars, with energies E_{macro} much larger than M_P. We show that this "soccer-ball problem" does not arise within the framework of relative locality, because the non-linear effects for the dynamics of a composite system with N elementary particles appear at most of order E_{macro}/ N M_...
Baniamerian, Ali; Bashiri, Mahdi; Zabihi, Fahime
2017-04-01
Cross-docking is a new warehousing policy in logistics which is widely used all over the world and attracts many researchers attention to study about in last decade. In the literature, economic aspects has been often studied, while one of the most significant factors for being successful in the competitive global market is improving quality of customer servicing and focusing on customer satisfaction. In this paper, we introduce a vehicle routing and scheduling problem with cross-docking and time windows in a three-echelon supply chain that considers customer satisfaction. A set of homogeneous vehicles collect products from suppliers and after consolidation process in the cross-dock, immediately deliver them to customers. A mixed integer linear programming model is presented for this problem to minimize transportation cost and early/tardy deliveries with scheduling of inbound and outbound vehicles to increase customer satisfaction. A two phase genetic algorithm (GA) is developed for the problem. For investigating the performance of the algorithm, it was compared with exact and lower bound solutions in small and large-size instances, respectively. Results show that there are at least 86.6% customer satisfaction by the proposed method, whereas customer satisfaction in the classical model is at most 33.3%. Numerical examples results show that the proposed two phase algorithm could achieve optimal solutions in small-size instances. Also in large-size instances, the proposed two phase algorithm could achieve better solutions with less gap from the lower bound in less computational time in comparison with the classic GA.
Relating Actor Analysis Methods to Policy Problems
Van der Lei, T.E.
2009-01-01
For a policy analyst the policy problem is the starting point for the policy analysis process. During this process the policy analyst structures the policy problem and makes a choice for an appropriate set of methods or techniques to analyze the problem (Goeller 1984). The methods of the policy anal
Vinícius Vilar Jacob
2016-01-01
Full Text Available This paper addresses a single-machine scheduling problem with sequence-dependent family setup times. In this problem the jobs are classified into families according to their similarity characteristics. Setup times are required on each occasion when the machine switches from processing jobs in one family to jobs in another family. The performance measure to be minimized is the total tardiness with respect to the given due dates of the jobs. The problem is classified as NP-hard in the ordinary sense. Since the computational complexity associated with the mathematical formulation of the problem makes it difficult for optimization solvers to deal with large-sized instances in reasonable solution time, efficient heuristic algorithms are needed to obtain near-optimal solutions. In this work we propose three heuristics based on the Iterated Local Search (ILS metaheuristic. The first heuristic is a basic ILS, the second uses a dynamic perturbation size, and the third uses a Path Relinking (PR technique as an intensification strategy. We carry out comprehensive computational and statistical experiments in order to analyze the performance of the proposed heuristics. The computational experiments show that the ILS heuristics outperform a genetic algorithm proposed in the literature. The ILS heuristic with dynamic perturbation size and PR intensification has a superior performance compared to other heuristics.
Claudio F. M. Toledo
2015-01-01
Full Text Available This paper presents the synchronized and integrated two-level lot sizing and scheduling problem (SITLSP. This problem is found in beverage production, foundry, glass industry, and electrofused grains, where the production processes have usually two interdependent levels with sequence-dependent setups in each level. For instance, in the first level of soft drink production, raw materials are stored in tanks flowing to production lines in the second level. The amount and the time the raw materials and products have to be stored and produced should be determined. A synchronization problem occurs because the production in lines and the storage in tanks have to be compatible with each other throughout the time horizon. The SITLSP and its mathematical model are described in detail by this paper. The lack of similar models in the literature has led us to also propose a set of instances for the SITLSP, based on data provided by a soft drink company. Thus, a set of benchmark results for these problem instances are established using an exact method available in an optimization package. Moreover, results for two relaxations proved that the modeling methodology could be useful in real-world applications.
Tahir Nadeem MALIK; Salman ZAFAR; Saaqib HAROON
2015-01-01
Short-term hydrothermal scheduling (STHTS) is a non-linear and complex optimization problem with a set of oper-ational hydraulic and thermal constraints. Earlier, this problem has been addressed by several classical techniques;however, due to limitations such as non-linearity and non-convexity in cost curves, artificial intelligence tools based techniques are being used to solve the STHTS problem. In this paper an improved chaotic hybrid differential evolution (ICHDE) algorithm is proposed to find an optimal solution to this problem taking into account practical constraints. A self-adjusted parameter setting is obtained in differential evolution (DE) with the application of chaos theory, and a chaotic hybridized local search mechanism is embedded in DE to effectively prevent it from premature convergence. Furthermore, heuristic constraint handling techniques without any penalty factor setting are adopted to handle the complex hydraulic and thermal constraints. The superiority and effectiveness of the developed methodology are evaluated by its application in two illustrated hydrothermal test systems taken from the literature. The transmission line losses, prohibited discharge zones of hydel plants, and ramp rate limits of thermal plants are also taken into account. The simulation results reveal that the proposed technique is competent to produce an encouraging solution as com-pared with other recently established evolutionary approaches.
Rasmussen, Karina; O'Neill, Robert E
2006-01-01
The current study assessed the effects of fixed-time reinforcement schedules on problem behavior of students with emotional-behavioral disorders in a clinical day-treatment classroom setting. Three elementary-aged students with a variety of emotional and behavioral problems participated in the study. Initial functional assessments indicated that social attention was the maintaining reinforcer for their verbally disruptive behavior. Baseline phases were alternated with phases in which attention was provided on fixed-time schedules in the context of an ABAB design. The results indicated that the provision of attention on fixed-time schedules substantially reduced the participants' rate of verbal disruptions. These decreases were maintained during initial thinning of the schedules. The results provide one of the first examples that such an intervention can be successfully implemented in a classroom setting.
Meysam Mousavi, S.; Tavakkoli-Moghaddam, Reza; Jolai, Fariborz
2013-10-01
This article considers the design of cross-docking systems under uncertainty in a model that consists of two phases: (1) a strategic-based decision-making process for selecting the location of cross-docks to operate, and (2) an operational-based decision-making process for vehicle routing scheduling with multiple cross-docks. This logistic system contains three echelons, namely suppliers, cross-docks and retailers, in an uncertain environment. In the first phase, a new multi-period cross-dock location model is introduced to determine the minimum number of cross-docks among a set of location sites so that each retailer demand should be met. Then, in the second phase, a new vehicle routing scheduling model with multiple cross-docks is formulated in which each vehicle is able to pickup from or deliver to more than one supplier or retailer, and the pickup and delivery routes start and end at the corresponding cross-dock. This article is the first attempt to introduce an integrated model for cross-docking systems design under a fuzzy environment. To solve the presented two-phase mixed-integer programming (MIP) model, a new fuzzy mathematical programming-based possibilistic approach is used. Furthermore, experimental tests are carried out to demonstrate the effectiveness of the presented model. The computational results reveal the applicability and suitability of the developed fuzzy possibilistic two-phase model in a variety of problems in the domain of cross-docking systems.
X. Zhang (Xiandong)
2010-01-01
textabstractScheduling is essential when activities need to be allocated to scarce resources over time. Motivated by the problem of scheduling barges along container terminals in the Port of Rotterdam, this thesis designs and analyzes algorithms for various on-line and off-line scheduling problems
A column generation approach for solving the patient admission scheduling problem
Range, Troels Martin; Lusby, Richard Martin; Larsen, Jesper
2014-01-01
, different variants of this problem. In this paper we consider one such variant and propose an optimization-based heuristic building on branch-and-bound, column generation, and dynamic constraint aggregation to solve it. We achieve tighter lower bounds than previously reported in the literature and...
The initial value problem as it relates to numerical relativity
Tichy, Wolfgang
2016-01-01
Spacetime is foliated by spatial hypersurfaces in the 3+1 split of General Relativity. The initial value problem then consists of specifying initial data for all relevant fields on one such a spatial hypersurface. These fields are the 3-metric and extrinsic curvature together with matter fields such as fluid velocity, energy density and rest mass density. There is a lot of freedom in choosing such initial data. This freedom corresponds to the physical state of the system at the initial time. At the same time the initial data have to satisfy the Hamiltonian and momentum constraint equations of General Relativity and can thus not be chosen completely freely. We discuss the conformal transverse traceless and conformal thin sandwich decompositions that are commonly used in the construction of constraint satisfying initial data. These decompositions allow us to specify certain free data that describe the physical nature of the system. The remaining metric fields are then determined by solving elliptic equations de...
Mazur Michał
2015-09-01
Full Text Available A Constraint Logic Programming (CLP tool for solving the problem discussed in Part 1 of the paper has been designed. It is outlined and discussed in the paper. The program has been used for solving a real-world car assembly scheduling problem.
H. Gong; L. Tang; C.W. Duin
2010-01-01
Motivated by applications in iron and steel industry, we consider a two-stage flow shop scheduling problem where the first machine is a batching machine subject to the blocking constraint and the second machine is a discrete machine with shared setup times. We show that the problem is strongly NP-ha
Gong, H.; Tang, L.; Duin, C.W.
2010-01-01
Motivated by applications in iron and steel industry, we consider a two-stage flow shop scheduling problem where the first machine is a batching machine subject to the blocking constraint and the second machine is a discrete machine with shared setup times. We show that the problem is strongly
加工时间恶化的两个成组加工排序问题%TWO SCHEDULING PROBLEMS IN GROUP TECHNOLOGY WITH DETERIORATING JOBS
程明宝; 孙世杰
2005-01-01
This paper considers single-machine scheduling problems in group technology with the jobs processing times being simple linear functions of their start times.The objective functions are the minimizing of makespan and total weighted completion time.Some optimal conditions and algorithms are given and the fact that the problem of total weighted completion times is NP-hard is proved.
Shangchia Liu
2015-01-01
Full Text Available In the field of distributed decision making, different agents share a common processing resource, and each agent wants to minimize a cost function depending on its jobs only. These issues arise in different application contexts, including real-time systems, integrated service networks, industrial districts, and telecommunication systems. Motivated by its importance on practical applications, we consider two-agent scheduling on a single machine where the objective is to minimize the total completion time of the jobs of the first agent with the restriction that an upper bound is allowed the total completion time of the jobs for the second agent. For solving the proposed problem, a branch-and-bound and three simulated annealing algorithms are developed for the optimal solution, respectively. In addition, the extensive computational experiments are also conducted to test the performance of the algorithms.
Farahmand-Mehr Mohammad
2014-01-01
Full Text Available In this paper, a hybrid flow shop scheduling problem with a new approach considering time lags and sequence-dependent setup time in realistic situations is presented. Since few works have been implemented in this field, the necessity of finding better solutions is a motivation to extend heuristic or meta-heuristic algorithms. This type of production system is found in industries such as food processing, chemical, textile, metallurgical, printed circuit board, and automobile manufacturing. A mixed integer linear programming (MILP model is proposed to minimize the makespan. Since this problem is known as NP-Hard class, a meta-heuristic algorithm, named Genetic Algorithm (GA, and three heuristic algorithms (Johnson, SPTCH and Palmer are proposed. Numerical experiments of different sizes are implemented to evaluate the performance of presented mathematical programming model and the designed GA in compare to heuristic algorithms and a benchmark algorithm. Computational results indicate that the designed GA can produce near optimal solutions in a short computational time for different size problems.
Rash, James
2014-01-01
NASA's space data-communications infrastructure-the Space Network and the Ground Network-provide scheduled (as well as some limited types of unscheduled) data-communications services to user spacecraft. The Space Network operates several orbiting geostationary platforms (the Tracking and Data Relay Satellite System (TDRSS)), each with its own servicedelivery antennas onboard. The Ground Network operates service-delivery antennas at ground stations located around the world. Together, these networks enable data transfer between user spacecraft and their mission control centers on Earth. Scheduling data-communications events for spacecraft that use the NASA communications infrastructure-the relay satellites and the ground stations-can be accomplished today with software having an operational heritage dating from the 1980s or earlier. An implementation of the scheduling methods and algorithms disclosed and formally specified herein will produce globally optimized schedules with not only optimized service delivery by the space data-communications infrastructure but also optimized satisfaction of all user requirements and prescribed constraints, including radio frequency interference (RFI) constraints. Evolutionary algorithms, a class of probabilistic strategies for searching large solution spaces, is the essential technology invoked and exploited in this disclosure. Also disclosed are secondary methods and algorithms for optimizing the execution efficiency of the schedule-generation algorithms themselves. The scheduling methods and algorithms as presented are adaptable to accommodate the complexity of scheduling the civilian and/or military data-communications infrastructure within the expected range of future users and space- or ground-based service-delivery assets. Finally, the problem itself, and the methods and algorithms, are generalized and specified formally. The generalized methods and algorithms are applicable to a very broad class of combinatorial
Reinforcement learning in scheduling
Dietterich, Tom G.; Ok, Dokyeong; Zhang, Wei; Tadepalli, Prasad
1994-01-01
The goal of this research is to apply reinforcement learning methods to real-world problems like scheduling. In this preliminary paper, we show that learning to solve scheduling problems such as the Space Shuttle Payload Processing and the Automatic Guided Vehicle (AGV) scheduling can be usefully studied in the reinforcement learning framework. We discuss some of the special challenges posed by the scheduling domain to these methods and propose some possible solutions we plan to implement.
Wei, Xiu; Zhang, Wenqiang; Weng, Wei; Fujimura, Shigeru
This paper proposed a multi-objective local search procedure (MOLS). It is combined with NSGA-II for solving bi-criteria PFSP with the objectives of minimizing makespan and maximum tardiness. Utilizing the properties of active blocks for flow shop scheduling problem, neighborhood structures MOINS (multi-objective insertion) and MOEXC (multi-objective exchange) are designed in order to improve efficiency of perturbation. Any perturbation based on MOINS and MOEXC takes effect on different criteria simultaneously. The original idea of MOLS is systematic change neighborhoods in the local search procedure. The search direction of MOLS on an individual is naturally guided by interaction of MOINS and MOEXC. Moreover, there is no need to set parameters in MOLS. The MOLS combined with popular multi-objective evolutionary algorithm NSGA-II (Non-dominated Sorting Genetic Algorithm-II) is called as “NSGA-II-MOLS”. To illustrate the efficacy of proposed approach, four different scaled problems are used to test performance of NSGA-II-MOLS. The numerous comparisons show efficacy of NSGA-II-MOLS is better than most of algorithms even with the same number of individual evaluations and parameters setting.
Noori-Darvish, Samaneh; Tavakkoli-Moghaddam, Reza
2012-10-01
We consider an open shop scheduling problem with setup and processing times separately such that not only the setup times are dependent on the machines, but also they are dependent on the sequence of jobs that should be processed on a machine. A novel bi-objective mathematical programming is designed in order to minimize the total tardiness and the makespan. Among several multi-objective decision making (MODM) methods, an interactive one, called the TH method is applied for solving small-sized instances optimally and obtaining Pareto-optimal solutions by the Lingo software. To achieve Pareto-optimal sets for medium to large-sized problems, an improved non-dominated sorting genetic algorithm II (NSGA-II) is presented that consists of a heuristic method for obtaining a good initial population. In addition, by using the design of experiments (DOE), the efficiency of the proposed improved NSGA-II is compared with the efficiency of a well-known multi-objective genetic algorithm, namely SPEA-II. Finally, the performance of the improved NSGA-II is examined in a comparison with the performance of the traditional NSGA-II.
Special Relativity as a Simple Geometry Problem
de Abreu, Rodrigo; Guerra, Vasco
2009-01-01
The null result of the Michelson-Morley experiment and the constancy of the one-way speed of light in the "rest system" are used to formulate a simple problem, to be solved by elementary geometry techniques using a pair of compasses and non-graduated rulers. The solution consists of a drawing allowing a direct visualization of all the fundamental…
Special Relativity as a Simple Geometry Problem
de Abreu, Rodrigo; Guerra, Vasco
2009-01-01
The null result of the Michelson-Morley experiment and the constancy of the one-way speed of light in the "rest system" are used to formulate a simple problem, to be solved by elementary geometry techniques using a pair of compasses and non-graduated rulers. The solution consists of a drawing allowing a direct visualization of all the fundamental…
Water Quality Considerations and Related Dishwashing Problems.
McClelland, Nina I.
A number of the chemical and physical factors which cause dishwashing problems are presented in a series of charts. Water quality considerations are vital, but the importance of good housekeeping and proper operating practices cannot and must not be minimized. Topics discussed include--(1) dissolved minerals, (2) dissolved gases, (3) detergents,…
The initial value problem as it relates to numerical relativity.
Tichy, Wolfgang
2017-02-01
Spacetime is foliated by spatial hypersurfaces in the 3+1 split of general relativity. The initial value problem then consists of specifying initial data for all fields on one such a spatial hypersurface, such that the subsequent evolution forward in time is fully determined. On each hypersurface the 3-metric and extrinsic curvature describe the geometry. Together with matter fields such as fluid velocity, energy density and rest mass density, the 3-metric and extrinsic curvature then constitute the initial data. There is a lot of freedom in choosing such initial data. This freedom corresponds to the physical state of the system at the initial time. At the same time the initial data have to satisfy the Hamiltonian and momentum constraint equations of general relativity and can thus not be chosen completely freely. We discuss the conformal transverse traceless and conformal thin sandwich decompositions that are commonly used in the construction of constraint satisfying initial data. These decompositions allow us to specify certain free data that describe the physical nature of the system. The remaining metric fields are then determined by solving elliptic equations derived from the constraint equations. We describe initial data for single black holes and single neutron stars, and how we can use conformal decompositions to construct initial data for binaries made up of black holes or neutron stars. Orbiting binaries will emit gravitational radiation and thus lose energy. Since the emitted radiation tends to circularize the orbits over time, one can thus expect that the objects in a typical binary move on almost circular orbits with slowly shrinking radii. This leads us to the concept of quasi-equilibrium, which essentially assumes that time derivatives are negligible in corotating coordinates for binaries on almost circular orbits. We review how quasi-equilibrium assumptions can be used to make physically well motivated approximations that simplify the elliptic
The initial value problem as it relates to numerical relativity
Tichy, Wolfgang
2017-02-01
Spacetime is foliated by spatial hypersurfaces in the 3+1 split of general relativity. The initial value problem then consists of specifying initial data for all fields on one such a spatial hypersurface, such that the subsequent evolution forward in time is fully determined. On each hypersurface the 3-metric and extrinsic curvature describe the geometry. Together with matter fields such as fluid velocity, energy density and rest mass density, the 3-metric and extrinsic curvature then constitute the initial data. There is a lot of freedom in choosing such initial data. This freedom corresponds to the physical state of the system at the initial time. At the same time the initial data have to satisfy the Hamiltonian and momentum constraint equations of general relativity and can thus not be chosen completely freely. We discuss the conformal transverse traceless and conformal thin sandwich decompositions that are commonly used in the construction of constraint satisfying initial data. These decompositions allow us to specify certain free data that describe the physical nature of the system. The remaining metric fields are then determined by solving elliptic equations derived from the constraint equations. We describe initial data for single black holes and single neutron stars, and how we can use conformal decompositions to construct initial data for binaries made up of black holes or neutron stars. Orbiting binaries will emit gravitational radiation and thus lose energy. Since the emitted radiation tends to circularize the orbits over time, one can thus expect that the objects in a typical binary move on almost circular orbits with slowly shrinking radii. This leads us to the concept of quasi-equilibrium, which essentially assumes that time derivatives are negligible in corotating coordinates for binaries on almost circular orbits. We review how quasi-equilibrium assumptions can be used to make physically well motivated approximations that simplify the elliptic
Relative-Residual-Based Dynamic Schedule for Belief Propagation Decoding of LDPC Codes
Huang Jie; Zhang Lijun
2011-01-01
Two Relative-Residual-based Dynamic Schedules (RRDS) for Belief Propagation (BP) decoding of Low-Density Parity-Check (LDPC) codes are proposed,in which the Variable code-RRDS (VN-RRDS) is a greediness-reduced version of the Check code-RRDS (CN-RRDS).The RRDS only processes the variable (or check) node,which has the maximum relative residual among all the variable (or check) nodes in each decoding iteration,thus keeping less greediness and decreased complexity in comparison with the edge-based Variable-to-Check Residual Belief Propagation (VC-RBP) algorithm.Moreover,VN-RRDS propagates first the message which has the largest residual based on all check equations.For different types of LDPC codes,simulation results show that the convergence rate of RRDS is higher than that of VC-RBP while keeping very low computational complexity.Furthermore,VN-RRDS achieves faster convergence as well as better performance than CN-RRDS.
王冰; 席裕庚
2006-01-01
This paper addresses the single-machine scheduling problem with release times minimizing the total completion time. Under the circumstance of incomplete global information at each decision time, a two-level rolling scheduling strategy (TRSS) is presented to create the global schedule step by step. The estimated global schedules are established based on a dummy schedule of unknown jobs. The first level is the preliminary scheduling based on the predictive window and the second level is the local scheduling for sub-problems based on the rolling window. Performance analysis demonstrates that TRSS can improve the global schedules. Computational results show that the solution quality of TRSS outperforms that of the existing rolling procedure in most cases.
利用猫群算法求解流水车间调度问题%The research of flow-shop scheduling problem based on cat swarm optimization
马邦雄; 叶春明
2014-01-01
Flow-shop Scheduling Problem ( FSP) is a kind of traditional production scheduling problem ,which has been shown to be NP-hard problem ,swarm intelligence algorithm showed excellent performance in solving such problems .Cat swarm optimization is a relatively new swarm intelligence algorithm ,the patterns of behavior of cats is divided into search mode and tracking mode to achieve the purpose of optimization performed by a certain percentage of the number of cats in two different modes .By the results of the comparison with standard particle swarm optimization and bats algorithm for solving the flow shop scheduling problem ,indi-cating that the basic cat swarm optimization scheduling problem in a well-optimized performance and application prospects .%流水车间调度问题是一类传统的生产调度问题，其已被证明是NP-hard问题，而群体智能算法在求解此类问题中表现出优秀的性能。猫群算法是一种较新颖的群体智能算法，将猫群的行为模式分为搜寻模式和跟踪模式，通过一定比例的猫群数量执行两种不同的模式来达到优化的目的。通过将猫群算法与标准粒子群算法和蝙蝠算法在求解流水车间调度问题的结果进行比较，表明了猫群算法在调度问题中的良好优化性能以及应用前景。
Hyperbolic differential operators and related problems
Ancona, Vincenzo
2003-01-01
Presenting research from more than 30 international authorities, this reference provides a complete arsenal of tools and theorems to analyze systems of hyperbolic partial differential equations. The authors investigate a wide variety of problems in areas such as thermodynamics, electromagnetics, fluid dynamics, differential geometry, and topology. Renewing thought in the field of mathematical physics, Hyperbolic Differential Operators defines the notion of pseudosymmetry for matrix symbols of order zero as well as the notion of time function. Surpassing previously published material on the top
Optimization problems related to zigzag pocket machining
Arkin, E.M.; Held, M.; Smith, C.L. [State Univ. of New York, Stony Brook, NY (United States)
1996-12-31
A fundamental problem of manufacturing is to produce mechanical parts from billets by clearing areas within specified boundaries from the material. Based on a graph-theoretical formulation, the algorithmic handling of one particular machining problem {open_quote}zigzag pocket machining{close_quote} is investigated. We present a linear-time algorithm that ensures that no region of the pocket is machined repeatedly, thereby attempting to minimize the number of tool retractions required. This problem is shown to be NP-hard for pockets with holes. Our algorithm is a provable good in the sense that the machining path generated for a pocket with h holes requires at most 5. OPT+ 6 - h retractions, where OPT is the (unknown) minimum number of retractions required by any algorithm. The algorithm has been implemented, and practical tests for pockets without holes clearly showed that one can expect an approximation factor of about 1.5 for practical examples, rather than the factor 5 as proved by our analysis.
Miyake, Hideaki; Harada, Ken-ichi; Miyazaki, Akira; Fujisawa, Masato
2015-03-01
The objective of this study was to investigate the significance of changes from the standard dosing schedule of sunitinib, which is 4 weeks of treatment and 2 weeks off (schedule 4/2), to an alternative schedule with 2 weeks of treatment and 1 week off (schedule 2/1), after encountering dose-limiting toxicity in 45 consecutive Japanese patients with metastatic renal cell carcinoma (mRCC). Despite a definitively improved relative dose intensity of sunitinib by changing from schedule 4/2 to 2/1, this difference was not significant. Adverse events (AEs) occurred in all patients on both schedules 4/2 and 2/1; however, the proportion of patients experiencing AEs ≥ grade 3 on schedule 2/1 was significantly lower than that on schedule 4/2. Quality of life (QOL) analysis using SF-36 revealed that all eight scores during schedule 2/1 were more favorable than those during schedule 4/2, and there were significant differences in 2 of the 8 scores between these two schedules. Furthermore, multivariate analyses, which were performed to evaluate the contribution of several AEs on schedule 2/1 to the improvement of each score in SF-36, revealed that fatigue had independent impacts on two scores, despite the lack of an independent association between any scores and the remaining AEs examined. These findings suggest that schedule 2/1 is the optimal dosing schedule of sunitinib against mRCC that balances efficacy and toxicity, since treatment on schedule 2/1 resulted in a markedly improved QOL compared with that on schedule 4/2 by relieving the profile of sunitinib-related AEs.
Parallel scheduling algorithms
Dekel, E.; Sahni, S.
1983-01-01
Parallel algorithms are given for scheduling problems such as scheduling to minimize the number of tardy jobs, job sequencing with deadlines, scheduling to minimize earliness and tardiness penalties, channel assignment, and minimizing the mean finish time. The shared memory model of parallel computers is used to obtain fast algorithms. 26 references.
S. Dousthaghi
2012-08-01
Full Text Available This paper considers an economic lot and delivery scheduling problem (ELDSP in a fuzzy environment with the fuzzy shelf life for each product. This problem is formulated in a flexible job shop with unrelated parallel machines, when the planning horizon is finite and it determines lot sizing, scheduling and sequencing, simultaneously. The proposed model of this paper is based on the basic period (BP approach. In this paper, a mixed-integer nonlinear programming (MINLP model is presented and then it is changed into two models in the fuzzy shelf life. The main model is dependent to the multiple basic periods and it is difficult to solve the resulted proposed model for large-scale problems in reasonable amount of time; thus, an efficient heuristic method is proposed to solve the problem. The performance of the proposed model is demonstrated using some numerical examples.
Chen Ming
2017-01-01
Full Text Available To solve the Flexible Job-shop Scheduling Problem (FJSP with different varieties and small batches, a modified meta-heuristic algorithm based on Genetic Algorithm (GA is proposed in which gene encoding is divided into process encoding and machine encoding, and according to the encoding mode, the machine gene fragment is connected with the process gene fragment and can be changed with the alteration of process genes. In order to get the global optimal solutions, the crossover and mutation operation of the process gene fragment and machine gene fragment are carried out respectively. In the initialization operation, the machines with shorter manufacturing time are more likely to be chosen to accelerate the convergence speed and then the tournament selection strategy is applied due to the minimum optimization objective. Meanwhile, a judgment condition of the crossover point quantity is introduced to speed up the population evolution and as an important interaction bridge between the current machine and alternative machines in the incidence matrix, a novel mutation operation of machine genes is proposed to achieve the replacement of manufacturing machines. The benchmark test shows the correctness of proposed algorithm and the case simulation proves the proposed algorithm has better performance compared with existing algorithms.
Current problems in Russian-Latvian relations
Mezhevich Nikolay
2013-09-01
Full Text Available Current relations between Russia and Latvia are still influenced by a series of mutual claims that appeared after the demise of the USSR. Latvia — as well as Estonia and Lithuania — is both an EU and NATO member state. However, unlike the above mentioned countries, its relations with Russia are developing at a more pragmatic level. Numerous political differences often result in economic losses both for Latvia and Russia. Despite the fact that Latvia has been an independent state for more than 20 years, there are still some unresolved issues in its relations with Russia. Today, relations between the two countries are often viewed through the prism of EU — Russia relations. Nonetheless, they often do not fit this context. Settling differences between Latvia and Russia will contribute to trade relations, which are increasingly important for both parties. In order to prevent and localise emerging conflicts, diplomats, politicians, and experts should interpret Russian-Latvian relations in view of the national features without referring to theoretical models based on the mythological “unity” of the three Baltic States.
2015-07-01
management , acquisition, supply chain , and logistics.11 However, progress in making system and process improvements has been slow, and weaknesses in...military services nearly 100 percent of the consumable items they need to operate, including food , fuel and energy, uniforms, medical supplies , and...DOD FINANCIAL MANAGEMENT Actions Are Needed on Audit Issues Related to the Marine Corps’ 2012 Schedule of Budgetary
2010-07-01
... creation (see also 36 CFR part 1235). See § 1235.42 of this subchapter for specifications and standards for... scheduling requirements for audiovisual, cartographic, and related records? 1237.14 Section 1237.14 Parks, Forests, and Public Property NATIONAL ARCHIVES AND RECORDS ADMINISTRATION RECORDS MANAGEMENT...
Game injuries in relation to game schedules in the National Basketball Association.
Teramoto, Masaru; Cross, Chad L; Cushman, Daniel M; Maak, Travis G; Petron, David J; Willick, Stuart E
2017-03-01
Injury management is critical in the National Basketball Association (NBA), as players experience a wide variety of injuries. Recently, it has been suggested that game schedules, such as back-to-back games and four games in five days, increase the risk of injuries in the NBA. The aim of this study was to examine the association between game schedules and player injuries in the NBA. Descriptive epidemiology study. The present study analyzed game injuries and game schedules in the 2012-13 through 2014-15 regular seasons. Game injuries by game schedules and players' profiles were examined using an exact binomial test, the Fisher's exact test and the Mann-Whitney-Wilcoxon test. A Poisson regression analysis was performed to predict the number of game injuries sustained by each player from game schedules and injured players' profiles. There were a total of 681 cases of game injuries sustained by 280 different players during the three years (total N=1443 players). Playing back-to-back games or playing four games in five days alone was not associated with an increased rate of game injuries, whereas a significant positive association was found between game injuries and playing away from home (pgames and away games were significant predictors of frequent game injuries (pGame schedules could be one factor that impacts the risk of game injuries in the NBA. The findings could be useful for designing optimal game schedules in the NBA as well as helping NBA teams make adjustments to minimize game injuries. Copyright © 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
Controlling alcohol-related global health problems.
Lam, Tai Hing; Chim, David
2010-07-01
Alcohol's adverse public health impact includes disease, injury, violence, disability, social problems, psychiatric illness, drunk driving, drug use, unsafe sex, and premature death. Furthermore, alcohol is a confirmed human carcinogen. The International Agency for Research on Cancer concluded that alcohol causes cancer of the oral cavity, pharynx, larynx, esophagus, liver, colon-rectum, and breast. World Cancer Research Fund/American Institute for Cancer Research concluded that the evidence justifies recommending avoidance of consuming any alcohol, even in small quantities. Despite being responsible for 3.8% of global deaths (2,255,000 deaths) and 4.6% of global disability-adjusted life years in 2004, alcohol consumption is increasing rapidly in China and Asia. Contrary to the World Health Assembly's call for global control action, Hong Kong has reduced wine and beer taxes to zero since 2008. An International Framework Convention on Alcohol Control is urgently needed. Increasing alcohol taxation and banning alcohol advertisement and promotion are among the most effective policies.
Imen Chaieb Memmi
2013-09-01
Full Text Available Purpose: We aim to examine the capacitated multi-item lot sizing problem which is a typical example of a large bucket model, where many different items can be produced on the same machine in one time period. We propose a new approach to determine the production sequence and lot sizes that minimize the sum of start up and setup costs, inventory and production costs over all periods.Design/methodology/approach: The approach is composed of three steps. First, we compute a lower bound on total cost. Then we propose a three sub-steps iteration procedure. We solve optimally the lot sizing problem without considering products sequencing and their cost. Then, we determine products quantities to produce each period while minimizing the storage and variable production costs. Given the products to manufacture each period, we determine its correspondent optimal products sequencing, by using a Branch and Bound algorithm. Given the sequences of products within each period, we evaluate the total start up and setup cost. We compare then the total cost obtained to the lower bound of the total cost. If this value riches a prefixed value, we stop. Otherwise, we modify the results of lot sizing problem.Findings and Originality/value: We show using an illustrative example, that the difference between the total cost and its lower bound is only 10%. This gap depends on the significance of the inventory and production costs and the machine’s capacity. Comparing the approach we develop with a traditional one, we show that we manage to reduce the total cost by 30%.Research limitations/implications: Our model fits better to real-world situations where production systems run continuously. This model is applied for limited number of part types and periods.Practical implications: Our approach determines the products to manufacture each time period, their economic amounts, and their scheduling within each period. This outcome should help decision makers bearing expensive
ON-LINE PREEMPTIVE SCHEDULING ON UNIFORM MACHINES
ZHANG Yuzhong; WANG Shouyang; Bo Chen; ZHANG Shuxia
2001-01-01
We address the problem of preemptively schedule on-line jobs on arbitrary muniformly related machines with the objective of minimizing the schedule length. We provide the first on-line algorithm for this general problem, and show that the algorithm being the speeds of the m machines.
Scheduling Algorithm for Complex Product Development
LIUMin; ZHANGLong; WUCheng
2004-01-01
This paper describes the Complex product development project scheduling problem (CPDPSP) with a great number of activities, complicated resource, precedence and calendar constraints. By the conversion of precedence constraint relations, the CPDPSP is simplified. Then, according to the predictive control principle, we propose a new scheduling algorithm Based on prediction (BoP-procedure). In order to get the problem characteristics coming from resource status and precedence constraints of the scheduling problem at the scheduling time, a sub-project is constructed on the basis of a sub-AoN (Activity on node) graph of the project. Then, we use the modified GDH-procedure to solve the sub-project scheduling problem and to obtain the maximum feasible active subset for determining the activity group which satisfies resource, precedence and calendar constraints and has the highest scheduling priority at the scheduling time. Additionaily, we make a great number of numerical computations and compare the performance of BoP-procedure algorithm with those of other scheduling algorithms. Computation results show that the BoP-procedure algorithm is more suitable for the CPDPSP. At last, we discuss briefly future research work in the CPDPSP.
When greediness fails: examples from stochastic scheduling
Uetz, Marc Jochen
The purpose of this paper is to present examples for the sometimes surprisingly different behavior of deterministic and stochastic scheduling problems. In particular, it demonstrates some seemingly counterintuitive properties of optimal scheduling policies for stochastic machine scheduling problems.
When greediness fails: examples from stochastic scheduling
Uetz, Marc
2003-01-01
The purpose of this paper is to present examples for the sometimes surprisingly different behavior of deterministic and stochastic scheduling problems. In particular, it demonstrates some seemingly counterintuitive properties of optimal scheduling policies for stochastic machine scheduling problems.
Accounting for Cache Related Pre-emption Delays in Hierarchical Scheduling
Lunniss, W.; Altmeyer, S.; Lipari, G.; Davis, R.I.
2014-01-01
Hierarchical scheduling provides a means of composing multiple real-time applications onto a single processor such that the temporal requirements of each application are met. This has become a popular technique in industry as it allows applications from multiple vendors as well as legacy application
Kadda Zerrouki
2013-01-01
Full Text Available The metaheuristics are approximation methods which deal with difficult optimization problems. The Work that we present in this paper has primarily as an objective the adaptation and the implementation of two advanced metaheuristics which are the Memetic Algorithms (MA and the Electromagnetism Metaheuristic (EM applied in the production systems of Hybrid Flow Shop (HFS type for the problem of scheduling. The Memetic Algorithms or hybrid genetic algorithms are advanced metaheuristic ones introduced by Moscato in 1989. Electromagnetism Metaheuristic (EM draws its inspiration in the electromagnetic law of Coulomb on the particles charged. We will propose an adaptation of two methods to the discrete case on the problems of scheduling with the production systems (HFS. We present then a comparison between the Memetic Algorithms (MA, the Parallel Memetic Algorithms with Migration (PMA_MIG and then we present a comparison between Electromagnetism Metaheuristic (EM and Parallel Electromagnetism Metaheuristic with migration (PEM_MIG. Finally we give the results obtained by its algorithms applied to HFSs (HFS4: FH3 (P4, P2, P3 | | Cmax and HFS4: FH2 (P3, P2 | | Cmax for the two problems: scheduling and assignment.
Dynamic Fractional Resource Scheduling vs. Batch Scheduling
Casanova, Henri; Vivien, Frédéric
2011-01-01
We propose a novel job scheduling approach for homogeneous cluster computing platforms. Its key feature is the use of virtual machine technology to share fractional node resources in a precise and controlled manner. Other VM-based scheduling approaches have focused primarily on technical issues or on extensions to existing batch scheduling systems, while we take a more aggressive approach and seek to find heuristics that maximize an objective metric correlated with job performance. We derive absolute performance bounds and develop algorithms for the online, non-clairvoyant version of our scheduling problem. We further evaluate these algorithms in simulation against both synthetic and real-world HPC workloads and compare our algorithms to standard batch scheduling approaches. We find that our approach improves over batch scheduling by orders of magnitude in terms of job stretch, while leading to comparable or better resource utilization. Our results demonstrate that virtualization technology coupled with light...
Ricardo Ferrari Pacheco
1999-04-01
Full Text Available Este artigo propõe uma classificação hierarquizada dos modelos utilizados na solução do problema de programação da produção intermitente do tipo job shop, incluindo tanto os que fornecem solução ótima, quanto os modelos heurísticos mais recentes baseados em métodos de busca estendida. Por meio dessa classificação obteve-se um painel amplo dos modelos existentes, evidenciando as diferentes abordagens do problema e suas soluções, com o objetivo de proporcionar uma orientação preliminar na escolha do modelo de job shop scheduling mais adequado.This paper proposes a hierarchical model classification used in the job shop scheduling problem, including those that provide an optimal solution and the more recent ones based on heuristics, called extended search methods. A panel with the existing models is obtained by this classification, and solutions and approach differences are highlighted with the aim of a preliminary orientation on the choice of a more adequate job shop scheduling model.
帅旗; 姚锡凡; 杨莹
2012-01-01
对于多目标job-shop柔性制造系统调度决策问题,在实际调度过程中不同优化目标之间存在层次关系,同时在实际生产环境中所调度追求目标并非固定常常处于变动之中.为此提出了基于Agent多层次目标任务调度规划模型,将复杂的求解问题按层次分解为具有相对独立性的各求解子问题单元并建立各单元间的联系机制,采用交互式策略对复杂调度问题进行求解.通过对不同调度算例的求解并得到满意调度方案,并验证此方法的合理性及可行性.%To solve the problem of flexible job-shop scheduling with multi- objective optimization, it is impossible and difficult to get a global optimized result because of the complexity of the optimization and the related hierarchy of multi- objective group. This paper proposes a scheduling algorithm based on multi - agent which is used to decompose the complex problem into some simple problem units and establish the hierarchical relation between those problem units. Using the algorithm with interactive strategy, some job- shop scheduling problems wit h multi- objective optimization are solved, and the proposed algorithm is proved to be reasonable and feasible.
A Beam Search-based Algorithm for Flexible Manufacturing System Scheduling
ZHOU Bing-hai; ZHOU Xiao-jun; CAI Jian-guo; FENG Kun
2002-01-01
A new algorithm is proposed for the flexible manufacturing system (FMS) scheduling problem in this paper. The proposed algorithm is a heuristic based on filtered beam search. It considers the machines and automated guided vehicle (AGV) as the primary resources, It utilizes system constraints and related manufacturing and processing information to generate machines and AGV schedules. The generated schedules can be an entire scheduling horizon as well as various lengths of scheduling periods. The proposed algorithm is also compared with other well-known dispatching rulesbased FMS scheduling. The results indicate that the beam search algorithm is a simple, valid and promising algorithm that deserves further research in FMS scheduling field.
Optimal randomized scheduling by replacement
Saias, I.
1996-05-01
In the replacement scheduling problem, a system is composed of n processors drawn from a pool of p. The processors can become faulty while in operation and faulty processors never recover. A report is issued whenever a fault occurs. This report states only the existence of a fault but does not indicate its location. Based on this report, the scheduler can reconfigure the system and choose another set of n processors. The system operates satisfactorily as long as, upon report of a fault, the scheduler chooses n non-faulty processors. We provide a randomized protocol maximizing the expected number of faults the system can sustain before the occurrence of a crash. The optimality of the protocol is established by considering a closely related dual optimization problem. The game-theoretic technical difficulties that we solve in this paper are very general and encountered whenever proving the optimality of a randomized algorithm in parallel and distributed computation.
The management of alcohol-related problems in general practice in north India.
Varma, V K; Malhotra, A K
1988-07-01
Twenty-seven general medical practitioners (GPs) were administered WHO semi-structured schedule enquiring "The Management of Alcohol-Related Problems in General Practice". Majority of the GPs had some involvement in each one of the specified alcohol-related problems. The involvement in alcohol and health education had been modest. Involvement in the control and regulatory activities was minimal. None of them felt that they had any role in the development of health and alcohol policy. Treatment response lo three typical situations appeared to be quite appropriate. To regulate production, to market less potent drinks at cheaper rates, to organize public health education programme through mass media were the suggestions made by them. It is suggested that GPs can and should be encouraged in leadership roles in policy decisions regarding the delivery of services, control and regulation of alcohol and research.
Mathematical programming and financial objectives for scheduling projects
Kimms, Alf
2001-01-01
Mathematical Programming and Financial Objectives for Scheduling Projects focuses on decision problems where the performance is measured in terms of money. As the title suggests, special attention is paid to financial objectives and the relationship of financial objectives to project schedules and scheduling. In addition, how schedules relate to other decisions is treated in detail. The book demonstrates that scheduling must be combined with project selection and financing, and that scheduling helps to give an answer to the planning issue of the amount of resources required for a project. The author makes clear the relevance of scheduling to cutting budget costs. The book is divided into six parts. The first part gives a brief introduction to project management. Part two examines scheduling projects in order to maximize their net present value. Part three considers capital rationing. Many decisions on selecting or rejecting a project cannot be made in isolation and multiple projects must be taken fully into a...
Relative Equilibria in the Spherical, Finite Density 3-Body Problem
Scheeres, D J
2016-01-01
The relative equilibria for the spherical, finite density 3 body problem are identified. Specifically, there are 28 distinct relative equilibria in this problem which include the classical 5 relative equilibria for the point-mass 3-body problem. None of the identified relative equilibria exist or are stable over all values of angular momentum. The stability and bifurcation pathways of these relative equilibria are mapped out as the angular momentum of the system is increased. This is done under the assumption that they have equal and constant densities and that the entire system rotates about its maximum moment of inertia. The transition to finite density greatly increases the number of relative equilibria in the 3-body problem and ensures that minimum energy configurations exist for all values of angular momentum.
基于缓冲区的同型机物资调度优化%Material Scheduling Optimization Problem of Two Parallel Machines Based on Buffer
李鹏举
2011-01-01
Methods of scheduling vehicles is complex in modern logistics transport. A buffer redundancy is introduced in logistics scheduling. In this paper, we consider a semi on-line logistics scheduling problem on two parallel machine system with a buffer, where the objective is to minimized makespan of the schedule. We propose a semi on-line algorithm with the competitive ratio, which is not less than 2/3.%现代物流运输车辆调度方法复杂多变.将缓冲区引入物流调度中,解决物资冗余的问题;分析了带缓冲区的两台同型机半在线调度优化问题,目标为最大化最小机器加工时间,给出了一个竞争比至少为2/3的半在线算法.
Extraction of a group-pair relation: problem-solving relation from web-board documents
Pechsiri, Chaveevan; Piriyakul, Rapepun
2016-01-01
This paper aims to extract a group-pair relation as a Problem-Solving relation, for example a DiseaseSymptom-Treatment relation and a CarProblem-Repair relation, between two event-explanation groups, a problem-concept group as a symptom/CarProblem-concept group and a solving-concept group as a treatment-concept/repair concept group from hospital-web-board and car-repair-guru-web-board documents. The Problem-Solving relation (particularly Symptom-Treatment relation) including the graphical rep...
Mohamad_Bagher Fakhrzad
2012-06-01
Full Text Available In this paper, a non linear mathematical model has been proposed for solving a single machine scheduling problem with a linear earliness and quadratic tardiness cost, where machine idle time and preemptions are allowed. As the model is complex and cannot be solved in polynomial time, it has been assumed to be a NP hard problem, so the known optimal solution methods may not be applicable for its solution. A Genetic Algorithm approach has been developed for solving the model and numerical examples has been presented, which imply that the proposed method is efficient and effective.
Takeuchi, Tomoka; Fukuda, Kazuhiko; Sasaki, Yuka; Inugami, Maki; Murphy, Timothy I
2002-02-01
To further investigate mechanisms of isolated sleep paralysis (ISP) in normal individuals, we experimentally elicited ISPs by facilitating sleep onset REM periods (SOREMP), a prerequisite of ISPs, and examined behavioral and psychological measurements relating to ISP appearances. The multi-phasic sleep/wake schedule (MPS) began at approximately midnight and ended when net sleep reached 7.5 hours. Participants were awakened after every 5 min of REM sleep to obtain a maximum number of SOREMPs. Upon each awakening, mentation reports and subjective measurements were collected. Performance tests were then assigned. Sleep lab, Tokyo Metropolitan Institute for Neurosciences, Japan. Thirteen healthy Japanese students (10 males) with high self-reported frequencies of ISPs but no other narcolepsy-related symptoms. From 184 sleep interruptions, 8 ISP episodes were obtained. In within participant comparisons between episodes with and without ISPs, the vigilance task (VT) reaction times were elevated before SOREMPs with ISPs. In between analyses (ISP vs non-ISP), the ISP group showed poorer performance, more complaints of physical, mental, and neurotic symptoms, increased subjective fatigue and increased stage 1 throughout the entire schedule. VT hit rates remained constant in the non-ISP group, but dropped in the later part of schedule in the ISP group. Subjective sleepiness dropped over time in the non-ISP group while it slightly increased in the ISP group. ISP is likely to appear as a phenotype of REM dissociation during SOREMP when participants with low tolerance for disrupted sleep-wake rhythms are placed in this type of schedule.
Variables Related to Sleep Problems in Children with Autism
Mayes, Susan Dickerson; Calhoun, Susan L.
2009-01-01
Our study of 477 children with autism (1-15 years, IQs 9-146) showed that parent reported sleep problems are found in most children with autism and are not significantly related to age, IQ, gender, race, parent occupation, neuropsychological functioning, and learning ability. However, sleep problems increased with severity of autistic symptoms and…
1988-06-01
relationship of the rise in stock prices and the National Football Conference winning the Superbowl is an example of apparent relationship with no causal...0.0 1.0 0.0 1.0 SEP 0.0 1.0 0.0 0.0 0.0 1.0 0.0 1.0 MONTHLY EMPLOYMENT SCHEDULE DATA USS STERET (CG-31) MaroI RSA [MEL RtK IDM 21ADP 0VHLP I? URPK
Automated Scheduling Via Artificial Intelligence
Biefeld, Eric W.; Cooper, Lynne P.
1991-01-01
Artificial-intelligence software that automates scheduling developed in Operations Mission Planner (OMP) research project. Software used in both generation of new schedules and modification of existing schedules in view of changes in tasks and/or available resources. Approach based on iterative refinement. Although project focused upon scheduling of operations of scientific instruments and other equipment aboard spacecraft, also applicable to such terrestrial problems as scheduling production in factory.
SOLVING FLEXIBLE JOB SHOP SCHEDULING PROBLEM BY GENETIC ALGORITHM%用遗传算法求解柔性作业车间调度问题
乔兵; 孙志峻; 朱剑英
2001-01-01
古典作业车间调度问题已经被研究了几十年并证明为NP-hard问题。柔性作业车间调度是古典作业车间调度问题的扩展，它允许工序由一个机床集合中的任意一台加工，调度的目的是将工序分配给各机床，并对各机床上的工序进行排序以使完成所有工序的时间最小化。本文采用遗传算法进行柔性作业车间调度研究，针对柔性作业车间问题提出了一种新颖直观的基因编码方法，从而取消了运用遗传算法求解作业车间问题时为使基因合法化而进行的基因修复过程，仿真结果表明用该遗传算法解决柔性作业车间调度问题是有效的。%The job shop scheduling problem has been studied for decades and known as an NP-hard problem. The flexible job shop scheduling problem is a generalization of the classical job scheduling problem that allows an operation to be processed on one machine out of a set of machines. The problem is to assign each operation to a machine and find a sequence for the operations on the machine in order that the maximal completion time of all operations is minimized. A genetic algorithm is used to solve the flexible job shop scheduling problem. A novel gene coding method aiming at job shop problem is introduced which is intuitive and does not need repairing process to validate the gene. Computer simulations are carried out and the results show the effectiveness of the proposed algorithm.
'Eye Freckles' May Predict Sun-Related Problems
... page: https://medlineplus.gov/news/fullstory_167479.html 'Eye Freckles' May Predict Sun-Related Problems The spots ... on the iris -- the colored part of the eye -- aren't cancerous, but these "eye freckles" could ...
Pharmacist intervention in drug-related problems for patients with ...
Purpose: To investigate the role of the community pharmacist in identifying, preventing and ... drug related problems (DRPs) encountered by patients, with particular ..... patients since 2009, documentation of .... medication safety review clinics.
Scheduling Problem of JIT Purchasing Based 3PL Transportation%基于JIT采购的3PL运输调度问题研究
王菲; 宋志刚
2011-01-01
研究Just in Time(JIT)背景下制造商主导的第三方物流(Third Party Logistics,3PL)运输调度问题.制造商根据其生产计划的要求进行采购.使用C-W路线优化算法,在线路规划中考虑了车辆载重量、容积以及车辆到达时间的影响,实现以最小的成本达到JIT采购的目的,并用一个实例验证了修正的C-W算法对解决采购物流中运输调度问题的适用性.%The paper studies the vehicle scheduling problem for manufacturer-led 3PL systems under JIT condition using C-W algorithm. Vehicle carriage load, capacity and arriving time as constraint conditions, ehe paper works on the route schedule to minimize the cost of JIT purchase. Then an example is given to confirm the applicability of the modified C-W algorithm in solving vehicle scheduling problems.
Lusby, Richard Martin; Muller, Laurent Flindt; Petersen, Bjørn
2013-01-01
This paper describes a Benders decomposition-based framework for solving the large scale energy management problem that was posed for the ROADEF 2010 challenge. The problem was taken from the power industry and entailed scheduling the outage dates for a set of nuclear power plants, which need...... to be regularly taken down for refueling and maintenance, in such away that the expected cost of meeting the power demand in a number of potential scenarios is minimized. We show that the problem structure naturally lends itself to Benders decomposition; however, not all constraints can be included in the mixed...... integer programming model. We present a two phase approach that first uses Benders decomposition to solve the linear programming relaxation of a relaxed version of the problem. In the second phase, integer solutions are enumerated and a procedure is applied to make them satisfy constraints not included...
张沙清; 杨海东; 赵洁
2015-01-01
Flexible Job Shop scheduling problem of robotic manufacturing cell is a new kind of scheduling problem with wide engineering application background and tough challenges,which attracts much attention of academia and industry. The content and features of flexible Job Shop scheduling problem of robotic manufacturing cell were analyzed,a scheduling model based on molds manufacturing with the op-timizing targets to minimal makespan was proposed in this paper. A Chaotic Quantum-behaved Particle Swarm Optimization algorithm (CQPSO) was put forward to solve the model. Based on the Quantum-behaved Particle Swarm Optimization (QPSO),the improved Tend chaotic mapping mechanism was introduced. The shortcoming of easily falling into local minimum for QPSO could be avoided, meanwhile,the fast convergence speed of QPSO could be kept in this algorithm. So the effectiveness of algorithm was improved. The sim-ulation results show that CQPSO could effectively solved the flexible Job Shop scheduling problem of robotic manufacturing cell.%柔性Job Shop类型机器人制造单元调度问题是一类新的具有广泛工程应用背景而又极富挑战的调度问题,引起了学术界和工业界的极大关注。文中分析了柔性Job Shop类型机器人单元调度问题的内容与特点,并以模具生产为背景,构建了一种以工件组最大完工时间最小为目标的Job Shop类型机器人单元调度模型,进而提出了一种混沌量子粒子群算法( CQPSO)用于模型求解。该算法在量子粒子群算法( QPSO)基础上,引入改进的Tent混沌映射机制,在保持QPSO算法收敛速度快的同时,克服了其易陷入局部极小值的缺点,提高了算法求解效率。仿真实验结果表明,CQPSO算法在求解柔性Job Shop类型机器人调度问题方面具有较大的应用优势。
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.
Whitley, L. Darrell (Colorado State University, Fort Collins, CO); Watson, Jean-Paul; Howe, Adele E. (Colorado State University, Fort Collins, CO)
2005-06-01
Over the last decade and a half, tabu search algorithms for machine scheduling have gained a near-mythical reputation by consistently equaling or establishing state-of-the-art performance levels on a range of academic and real-world problems. Yet, despite these successes, remarkably little research has been devoted to developing an understanding of why tabu search is so effective on this problem class. In this paper, we report results that provide significant progress in this direction. We consider Nowicki and Smutnicki's i-TSAB tabu search algorithm, which represents the current state-of-the-art for the makespan-minimization form of the classical jobshop scheduling problem. Via a series of controlled experiments, we identify those components of i-TSAB that enable it to achieve state-of-the-art performance levels. In doing so, we expose a number of misconceptions regarding the behavior and/or benefits of tabu search and other local search metaheuristics for the job-shop problem. Our results also serve to focus future research, by identifying those specific directions that are most likely to yield further improvements in performance.
Work-Related Health Problems among Nursing Personnel.
Umesh, Sasikala R; David, Shirley; Segaran, Florence; Venkatesh, K
2014-01-01
Work-related injuries among nursing personnel are quite frequent and costly problems in terms of both workers'pain and suffering as well as medical expenses, and lost work for organisations. A descriptive study was conducted in Christian Medical College, Vellore to assess the prevalence of selected work-related health problems among nursing personnel. Total of 500 Nursing personnel were included in the study. The instruments used were Modified Cornell Musculoskeletal discomfort questionnaire to assess and score the musculoskeletal discomfort and CEAP (C-clinical, E-Etiologic, A-Anatomic, P- Pathophysiologic) classification to assess the presence and grade the varicose veins. Results demonstrated that 84.4 percent of the participants had musculoskeletal discomfort and 29.6 percent of the participants had varicose veins. Findings of the study demonstrated that there is a need to increase the awareness among nurses regarding the problems and to follow specific strategies to prevent work-related health problems.
Alcohol-related Problems in Vagrant People in Havana
Beatriz Almaguer Barroso
2014-04-01
Full Text Available Background: irresponsible alcohol consumption is one of the most common problems in vagrant people.Objective: to identify alcohol-related problems in residents of the Care Center for People with Vagrant Behavior in Havana.Methods: a descriptive cross-sectional study was conducted. A questionnaire for identifying alcohol-related disorders was administered to 80 vagrants admitted to the center between June and August 2012.Results: it was demonstrated that alcohol consumption in subjects who participated in the research is quite common. Only 21.25% of these people do not suffer from alcohol-related problems, just a similar percent are at-risk drinkers and 57.5 % has physical and physiological problems and probable alcohol dependence.Conclusion: consumption of alcoholic beverages is common in the study population; hence strategies to reduce its negative effects on personal, professional, family and social life of these people must be implemented.
Dental Student Study Strategies: Are Self-Testing and Scheduling Related to Academic Performance?
McAndrew, Maureen; Morrow, Christina S; Atiyeh, Lindsey; Pierre, Gaëlle C
2016-05-01
Self-testing, a strategy wherein a student actively engages in creating questions and answers from study materials to assist with studying, has been found to be especially advantageous because it enhances future retrieval of information. Studies have found correlations among students' grade point averages (GPAs), self-testing, and rereading study strategies, as well as the spacing of study sessions over time. The aim of this study was to assess relationships among dental students' study strategies, scheduling of study time, and academic achievement. A 16-item survey requesting information on study habits, study schedules, and GPAs was distributed to 358 second-year dental students at New York University College of Dentistry. Additionally, the survey asked students to report the average number of hours per week they devoted to studying for didactic courses and preparing for hands-on preclinical courses. Of the 358 students, 94 (26%) responded to the survey. The vast majority of the respondents reported utilizing self-testing and rereading study strategies. High performers (with higher GPAs) were more likely to use self-testing, especially with flashcards, and to space their studying over multiple sessions. Lower performing students were more likely to highlight or underline their notes and to mass their study sessions or cram. Longer hours devoted to studying and practicing for simulation courses were associated with stronger performance; lower performers reported spending significantly fewer hours practicing for simulation courses. Half of the dental students surveyed said that they felt their studying would be more productive in the morning, although 84% reported doing most of their studying in the evening or late night. Sound study decisions depend on accurate regulation of ongoing learning and appropriate use and timing of evidence-based study strategies, so these results suggest that dental students may require guidance in these areas.
Simultaneous scheduling of machines and mobile robots
Dang, Vinh Quang; Nielsen, Izabela Ewa
2013-01-01
This paper deals with the problem of simultaneously scheduling machines and a number of autonomous mobile robots in a flexible manufacturing system (FMS). Besides capability of transporting materials between machines, the considered mobile robots are different from other material handling devices...... in terms of their advanced ability to perform tasks at machines by using their manipulation arms. The mobile robots thus have to be scheduled in relation to scheduling of machines so as to increase the efficiency of the overall system. The performance criterion is to minimize time required to complete all...
Rinto Yusriski
2015-09-01
Full Text Available This research discusses an integer batch scheduling problems for a single-machine with position-dependent batch processing time due to the simultaneous effect of learning and forgetting. The decision variables are the number of batches, batch sizes, and the sequence of the resulting batches. The objective is to minimize total actual flow time, defined as total interval time between the arrival times of parts in all respective batches and their common due date. There are two proposed algorithms to solve the problems. The first is developed by using the Integer Composition method, and it produces an optimal solution. Since the problems can be solved by the first algorithm in a worst-case time complexity O(n2n-1, this research proposes the second algorithm. It is a heuristic algorithm based on the Lagrange Relaxation method. Numerical experiments show that the heuristic algorithm gives outstanding results.
Scheduling Linearly Indexed Assignment Codes
Kailath, Thomas; Roychowdhury, Vwani P.
1989-05-01
It has been recently shown that linearly indexed Assignment Codes can be efficiently used for coding several problems especially in signal processing and matrix algebra. In fact, mathematical expressions for many algorithms are directly in the form of linearly indexed codes, and examples include the formulas for matrix multiplication, any m-dimensional convolution/correlation, matrix transposition, and solving matrix Lyapunov's equation. Systematic procedures for converting linearly indexed Assignment Codes to localized algorithms that are closely related to Regular Iterative Algorithms (RIAs) have also been developed. These localized algorithms can be often efficiently scheduled by modeling them as RIAs; however, it is not always efficient to do so. In this paper we shall analyze and develop systematic procedures for determining efficient schedules directly for the linearly indexed ACs and the localized algorithms. We shall also illustrate our procedures by determining schedules for examples such as matrix transposition and Gauss-Jordan elimination algorithm.
Employers’ View on Problems Related to Workforce Skills and Qualification
Klimplová Lenka
2012-01-01
The aim of this exploratory study is to reveal employers’ views on problems related to workforce human capital (skills and qualification). Where do employers themselves view the core of difficulties with ensuring adequately skilled workforce? Do they assign them to technological and organizational changes (a functional concept of job-specific human capital obsolescence), or do they see these problems as a result of other circumstances, such as macro-structural conditions or institutional sett...
Classical and Quantum Two-Body Problem in General Relativity
Maheshwari, Amar; Todorov, Ivan
2016-01-01
The two-body problem in general relativity is reduced to the problem of an effective particle (with an energy-dependent relativistic reduced mass) in an external field. The effective potential is evaluated from the Born diagram of the linearized quantum theory of gravity. It reduces to a Schwarzschild-like potential with two different `Schwarzschild radii'. The results derived in a weak field approximation are expected to be relevant for relativistic velocities.
Huixin Tian
2016-01-01
Full Text Available Different from most researches focused on the single objective hybrid flowshop scheduling (HFS problem, this paper investigates a biobjective HFS problem with sequence dependent setup time. The two objectives are the minimization of total weighted tardiness and the total setup time. To efficiently solve this problem, a Pareto-based adaptive biobjective variable neighborhood search (PABOVNS is developed. In the proposed PABOVNS, a solution is denoted as a sequence of all jobs and a decoding procedure is presented to obtain the corresponding complete schedule. In addition, the proposed PABOVNS has three major features that can guarantee a good balance of exploration and exploitation. First, an adaptive selection strategy of neighborhoods is proposed to automatically select the most promising neighborhood instead of the sequential selection strategy of canonical VNS. Second, a two phase multiobjective local search based on neighborhood search and path relinking is designed for each selected neighborhood. Third, an external archive with diversity maintenance is adopted to store the nondominated solutions and at the same time provide initial solutions for the local search. Computational results based on randomly generated instances show that the PABOVNS is efficient and even superior to some other powerful multiobjective algorithms in the literature.
Schedule Matters: Understanding the Relationship between Schedule Delays and Costs on Overruns
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.
Gain scheduling using the Youla parameterization
Niemann, Hans Henrik; Stoustrup, Jakob
1999-01-01
Gain scheduling controllers are considered in this paper. The gain scheduling problem where the scheduling parameter vector cannot be measured directly, but needs to be estimated is considered. An estimation of the scheduling vector has been derived by using the Youla parameterization. The use...... in connection with H_inf gain scheduling controllers....
排序问题Pm，ai|on-line|Cmax的LPT算法%LPT Algorithm for Scheduling Problem Pm，ai|on-line|Cmax
赵传立; 唐恒永
2000-01-01
In this paper we discuss the on-line parallel processors scheduling problem where tasks arrive over time. Chen and Vestjens proved that the bound of LPT is 3/2. We generalize the result to the case where the processors have ready time.%讨论了任务实时到达的平行机在线排序问题。Chen和Vestjens证明了LPT算法的界为3／2。将这一结论推广到了处理机具有准备时间的情况。
Single Machine Scheduling Problem with Fuzzy Due Dates and Fuzzy Precedence%模糊交货期和模糊优先下的单机调度问题
谢源; 谢剑英; 黄芹华
2005-01-01
A single machine scheduling problem involving fuzzy due dates and fuzzy precedence constraints is investigated. The fuzzy precedence reflects the satisfaction level with respect to precedence between two jobs. A membership function is associated with each job Ji, which describes the degree of satisfaction with respect to completion time of Ji. For the bi-criteria scheduling problem, an O ( n3 ) algorithm is proposed for finding nondominated solutions.
Relationship Between Alcohol Drinking and Alcohol-related Health Problems
JIA-FANG ZHANG; YUN-XIA LU; XIAO-XIA QIU; YA FANG
2004-01-01
Objective To study the relationship between drinking environment, attitudes and situation and alcohol-related health problems. Methods A sample of 2327 respondents was randomly collected from Wuhan, Hubei Province in China by a face-to-face interview. The structural equation modeling analysis was performed for the data collected. Results Both parents' drinking behaviors and respondents' drinking situation strongly impacted the alcohol-related problems and diseases. Friends' or peers' drinking behaviors influenced the respondents' drinking attitudes and behaviors. Males experienced more alcohol-related problems and diseases than females. Conclusions Comparatively, parents' drinking behaviors exert the most significant influence on drinkers. Therefore, it is beneficial to restrict parents' drinking behaviors for the offsprings and the whole society, and an intensive professional education in early motherhood is also necessary for Chinese women.
Healthcare Scheduling by Data Mining: Literature Review and Future Directions
Maria M. Rinder
2012-01-01
Full Text Available This article presents a systematic literature review of the application of industrial engineering methods in healthcare scheduling, with a focus on the role of patient behavior in scheduling. Nine articles that used mathematical programming, data mining, genetic algorithms, and local searches for optimum schedules were obtained from an extensive search of literature. These methods are new approaches to solve the problems in healthcare scheduling. Some are adapted from areas such as manufacturing and transportation. Key findings from these studies include reduced time for scheduling, capability of solving more complex problems, and incorporation of more variables and constraints simultaneously than traditional scheduling methods. However, none of these methods modeled no-show and walk-ins patient behavior. Future research should include more variables related to patient and/or environment.
Special relativity. An introduction with 200 problems and solutions
Tsamparlis, Michael [Athens Univ., Zografos (Greece). Dept. of Astrophysics, Astronomy and Mechanics
2010-07-01
This textbook develops Special Relativity in a systematic way assuming no prior knowledge of Relativity; however the student is assumed to be familiar with the basics of the standard vector calculus. The approach is structural in the sense that it develops Special Relativity in Minkowski space following the same steps with the development of Newtonian Physics in Euclidian space. A second characteristic of the book is that it discusses the mathematics of the theory independently of the physical principles, so that the reader will appreciate its role in the development of the physical theory. The book is intended to be used both as a text-book for a teaching course in Special Relativity but also as a reference book for the future. In that respect it is linked to an online repository with more than 500 problems, carefully classified according to subject area and solved in detail, providing an independent problem book on Special Relativity. (orig.)
Extraction of a group-pair relation: problem-solving relation from web-board documents.
Pechsiri, Chaveevan; Piriyakul, Rapepun
2016-01-01
This paper aims to extract a group-pair relation as a Problem-Solving relation, for example a DiseaseSymptom-Treatment relation and a CarProblem-Repair relation, between two event-explanation groups, a problem-concept group as a symptom/CarProblem-concept group and a solving-concept group as a treatment-concept/repair concept group from hospital-web-board and car-repair-guru-web-board documents. The Problem-Solving relation (particularly Symptom-Treatment relation) including the graphical representation benefits non-professional persons by supporting knowledge of primarily solving problems. The research contains three problems: how to identify an EDU (an Elementary Discourse Unit, which is a simple sentence) with the event concept of either a problem or a solution; how to determine a problem-concept EDU boundary and a solving-concept EDU boundary as two event-explanation groups, and how to determine the Problem-Solving relation between these two event-explanation groups. Therefore, we apply word co-occurrence to identify a problem-concept EDU and a solving-concept EDU, and machine-learning techniques to solve a problem-concept EDU boundary and a solving-concept EDU boundary. We propose using k-mean and Naïve Bayes to determine the Problem-Solving relation between the two event-explanation groups involved with clustering features. In contrast to previous works, the proposed approach enables group-pair relation extraction with high accuracy.
Compact Formulations of the Steiner Traveling Salesman Problem and Related Problems
Letchford, Adam N; Theis, Dirk Oliver
2012-01-01
The Steiner Traveling Salesman Problem (STSP) is a variant of the Traveling Salesman Problem (TSP) that is particularly suitable when dealing with sparse networks, such as road networks. The standard integer programming formulation of the STSP has an exponential number of constraints, just like the standard formulation of the TSP. On the other hand, there exist several known {\\em compact} formulations of the TSP, i.e., formulations with a polynomial number of both variables and constraints. In this paper, we show that some of these compact formulations can be adapted to the STSP. We also briefly discuss the adaptation of our formulations to some closely-related problems.
Anxiety Sensitivity and Sleep-Related Problems in Anxious Youth
Weiner, Courtney L.; Elkins, Meredith; Pincus, Donna; Comer, Jonathan
2015-01-01
Anxiety disorders constitute the most common mental health disturbance experienced by youth. Sleep-related problems (SRPs) are highly prevalent among anxious youth and encompass a variety of problems including nighttime fears, insomnia, and refusal to sleep alone. Given that chronic sleep disturbance is associated with a range of behavioral and physical problems in youth and predicts future psychopathology, it is important to elucidate the nature of SRPs in anxious youth. The present study investigated the relationship between sleep problems and anxiety sensitivity in a sample of 101 anxious youth, ages 6–17. Heightened anxiety sensitivity significantly predicted prolonged sleep onset latency across the sample, even after accounting for severity of anxiety, depression, and age. Results support previous research indicating that SRPs are common among anxious youth and suggest that anxiety sensitivity may play a particularly important role in sleep onset latency. PMID:25863826
Mixed Tabu Search Algorithm for Dynamic Vehicle Scheduling Problem%基于TS的动态车辆调度问题的混合算法研究
袁建清
2011-01-01
对带时间窗的动态车辆调度问题进行分析,采用实时再优化方法进行研究,引入时间轴概念,建立动态车辆调度模型,并给出求解的混合禁忌搜索算法.该算法先用C-K节约算法求得初始解,然后用禁忌搜索进行优化,得到全局最优解.禁忌搜索算法中采用动态邻域移动方法构造候选解和动态禁忌长度选取策略设置紧急长度,提高算法的收敛速度.最后用实例证明该混合算法的可行性和有效性.%On the basis of studying dynamic vehicle scheduling problem with time windows, a dynamic vehicle scheduling mathematical model is established through introducing time axis concept and adopting real-time optimizing research methods. Then a mixture algorithm based on a C-K economical method and Tabu Search algorithm is designed to solve dynamic vehicle scheduling problem.This algorithm improves convergence speed by using dynamic candidate solutions constructor method and dynamic length selection strategy. Finally computational results are provided to show that the mixed Tabu Search algorithm is feasible and efficient.
MEDICAL STAFF SCHEDULING USING SIMULATED ANNEALING
Ladislav Rosocha
2015-07-01
Full Text Available Purpose: The efficiency of medical staff is a fundamental feature of healthcare facilities quality. Therefore the better implementation of their preferences into the scheduling problem might not only rise the work-life balance of doctors and nurses, but also may result into better patient care. This paper focuses on optimization of medical staff preferences considering the scheduling problem.Methodology/Approach: We propose a medical staff scheduling algorithm based on simulated annealing, a well-known method from statistical thermodynamics. We define hard constraints, which are linked to legal and working regulations, and minimize the violations of soft constraints, which are related to the quality of work, psychic, and work-life balance of staff.Findings: On a sample of 60 physicians and nurses from gynecology department we generated monthly schedules and optimized their preferences in terms of soft constraints. Our results indicate that the final value of objective function optimized by proposed algorithm is more than 18-times better in violations of soft constraints than initially generated random schedule that satisfied hard constraints.Research Limitation/implication: Even though the global optimality of final outcome is not guaranteed, desirable solutionwas obtained in reasonable time. Originality/Value of paper: We show that designed algorithm is able to successfully generate schedules regarding hard and soft constraints. Moreover, presented method is significantly faster than standard schedule generation and is able to effectively reschedule due to the local neighborhood search characteristics of simulated annealing.
Use of Data Mining in Scheduler Optimization
Anderson, George; Nelwamondo, Fulufhelo V
2010-01-01
The operating system's role in a computer system is to manage the various resources. One of these resources is the Central Processing Unit. It is managed by a component of the operating system called the CPU scheduler. Schedulers are optimized for typical workloads expected to run on the platform. However, a single scheduler may not be appropriate for all workloads. That is, a scheduler may schedule a workload such that the completion time is minimized, but when another type of workload is run on the platform, scheduling and therefore completion time will not be optimal; a different scheduling algorithm, or a different set of parameters, may work better. Several approaches to solving this problem have been proposed. The objective of this survey is to summarize the approaches based on data mining, which are available in the literature. In addition to solutions that can be directly utilized for solving this problem, we are interested in data mining research in related areas that have potential for use in operat...
Mansooreh Madani-Isfahani
2013-04-01
Full Text Available In this paper, we present a new Imperialist Competitive Algorithm (ICA to solve a bi-objective scheduling of parallel-unrelated machines where setup times are sequence dependent. The objectives include mean completion tasks and mean squares of deviations from machines workload from their averages. The performance of the proposed ICA (PICA method is examined using some randomly generated data and they are compared with three alternative methods including particle swarm optimization (PSO, original version of imperialist competitive algorithm (OICA and genetic algorithm (GA in terms of the objective function values. The preliminary results indicate that the proposed study outperforms other alternative methods. In addition, while OICA performs the worst as alternative solution strategy, PSO and GA seem to perform better.
Drug-related problems in patients with osteoporosis
Ilić Darko
2016-01-01
Full Text Available Background/Aim. Drug-related problems are especially frequent among patients suffering from non-communicable diseases, like osteoporosis, leading to suboptimal treatment response. The aim of this study was to identify drug-related problems in patients with osteoporosis. Methods. This cross-sectional prospective study was conducted in January 2014 on outpatients with osteoporosis from three health facilities in Belgrade, Serbia. The patients included in the study were older than 50 years, and they were offered an anonymous questionnaire with open-ended questions. Results. There were 355 study participants, 329 (92.7% females and 26 (7.3% males. The patients who experienced at least one osteoporotic fracture (n = 208 were significantly less adherent to the therapy, less engaged in sports and regular physical activities, and more prone to nutrition with inadequate intake of calcium and vitamin D than patients without fractures (n = 147. Conclusion. The effectiveness of osteoporosis treatment is decreased by several drug-related problems encountered by both physicians and patients. However, the majority of the drug-related problems could be greatly influenced by appropriate educational programs. [Projekat Ministarstva nauke Republike Srbije, br. 175007
Some Recent Advances on Ice Related Problems in Offshore Engineering
段梦兰; 刘杰鸣; 樊晓东; 朱守铭; 赵秀菊
2000-01-01
This paper deals with several hot topics in ice related problems. In recent years, advances have been made on ice breaking modes, dynamic ice loads on offshore structures, ice-induced structural vibrations, fatigue and fracture by ice-structure interaction, and design of jackets in the Bohai Gulf.
Issues related to topology optimization of snap-through problems
Lindgaard, Esben; Dahl, Jonas
2012-01-01
This work focuses on issues related to topology optimization of static geometrically nonlinear structures experiencing snap-through behaviour. Different compliance and buckling criterion functions are studied and applied to topology optimization of a point loaded curved beam problem with the aim ...
The relation between problem areas and stages of computer implementation
Brummelhuis, ten Alfons; Plomp, Tjeerd
1991-01-01
Using data from an international comparative study on the use of new technologies in education in about 22 countries, an assessment of the relation between problem areas and stages of computer implementation was undertaken. The study--"Computers in Education" (COMPED)--has been conducted since 1987
Using the internet: skill related problems in users’ online behavior
van Deursen, Alexander Johannes Aloysius Maria; van Dijk, Johannes A.G.M.
2009-01-01
This study extends the conventional and superficial notion of measuring digital skills by proposing definitions for operational, formal, information and strategic skills. The main purpose was to identify individual skill related problems that users experience when navigating the Internet. In particu
Lipschitz Properties in Variable Exponent Problems via Relative Rearrangement
Jean-Michel RAKOTOSON
2010-01-01
The author first studies the Lipschitz properties of the monotone and relative rearrangement mappings in variable exponent Lebesgue spaces completing the result given in[9].This paper is ended by establishing the Lipschitz properties for quasilinear problems with variable exponent when the right-hand side is in some dual spaces of a suitable Sobolev space associated to variable exponent.
Min Dai
2013-01-01
Full Text Available A flexible flow-shop scheduling (FFS with nonidentical parallel machines for minimizing the maximum completion time or makespan is a well-known combinational problem. Since the problem is known to be strongly NP-hard, optimization can either be the subject of optimization approaches or be implemented for some approximated cases. In this paper, an improved genetic-simulated annealing algorithm (IGAA, which combines genetic algorithm (GA based on an encoding matrix with simulated annealing algorithm (SAA based on a hormone modulation mechanism, is proposed to achieve the optimal or near-optimal solution. The novel hybrid algorithm tries to overcome the local optimum and further to explore the solution space. To evaluate the performance of IGAA, computational experiments are conducted and compared with results generated by different algorithms. Experimental results clearly demonstrate that the improved metaheuristic algorithm performs considerably well in terms of solution quality, and it outperforms several other algorithms.
张宏远; 席裕庚; 谷寒雨
2005-01-01
There are many flow shop problems of throughput (denoted by FSPT) with constraints of due date in real production planning and scheduling. In this paper, a decomposition and coordination algorithm is proposed based on the analysis of FSPT and under the support of TOC (theory of constraint). A flow shop is at first decomposed into two subsystems named PULL and PUSH by means of bottleneck. Then the subsystem is decomposed into single machine scheduling problems,so the original NP-HARD problem can be transferred into a serial of single machine optimization problems finally. This method reduces the computational complexity, and has been used in a real project successfully.
Koichu, Boris
2010-01-01
This article discusses an issue of inserting mathematical knowledge within the problem-solving processes. Relatively advanced mathematical knowledge is defined in terms of "three mathematical worlds"; relatively advanced problem-solving behaviours are defined in terms of taxonomies of "proof schemes" and "heuristic behaviours". The relationships…
Koichu, Boris
2010-01-01
This article discusses an issue of inserting mathematical knowledge within the problem-solving processes. Relatively advanced mathematical knowledge is defined in terms of "three mathematical worlds"; relatively advanced problem-solving behaviours are defined in terms of taxonomies of "proof schemes" and "heuristic behaviours". The relationships…
王旭坪; 吴绪; 王征
2011-01-01
For solving the vehicle routing problem with disruption that may be vehicle breakdowns or traffic accidents in the logistics distribution system, this paper builds a mixed integer programming disruption management model. For the attributes of the vehicle routing problem with disruption, a series of solving-simplify strategies are given to simplify the solution space. On the basis of the characteristic of the model, the improved saving algorithm based on disturbed value is designed based on the disruption value. Numerical experiments from multiple wheel-driven vehicle scheduling problem and vehicle scheduling interference management problem are given to prove the efficiently of the disruption management model and algorithm.%为解决物流配送系统中因运输车辆毁坏而产生的干扰问题,建立混合整数规划干扰管理模型.针对多车场车辆调度干扰管理问题的特有属性,设计一系列求解简化策略,简化问题的求解空间.结合干扰管理模型的特点,使用基于扰动值的改进节约算法进行求解.数值实验从多车场车辆调度问题和车辆调度干扰管理问题2个角度验证干扰管理模型及改进算法的有效性.
Employers’ View on Problems Related to Workforce Skills and Qualification
Klimplová Lenka
2012-12-01
Full Text Available The aim of this exploratory study is to reveal employers’ views on problems related to workforce human capital (skills and qualification. Where do employers themselves view the core of difficulties with ensuring adequately skilled workforce? Do they assign them to technological and organizational changes (a functional concept of job-specific human capital obsolescence, or do they see these problems as a result of other circumstances, such as macro-structural conditions or institutional settings? To answer these questions selected employers in mechanical engineering and information technology sectors in the Czech Republic were interviewed. The results show that the employers see the problems: 1 on the side of workforce – insufficient abilities and skills, exaggerated demands and low motivation; 2 as inadequate capacities and capabilities of the organization itself; 3 at macro-level as institutional shortcomings in the initial educational system and social benefits system. The problems related to workforce skills and qualification cannot be, thus, interpreted only in the functionalist view as job-specific human capital obsolescence, but the formulation of the problems is significantly affected by the institutional framework.
Sun, Yan; Lang, Maoxiang; Wang, Danzhu
2016-07-28
The transportation of hazardous materials is always accompanied by considerable risk that will impact public and environment security. As an efficient and reliable transportation organization, a multimodal service should participate in the transportation of hazardous materials. In this study, we focus on transporting hazardous materials through the multimodal service network and explore the hazardous materials multimodal routing problem from the operational level of network planning. To formulate this problem more practicably, minimizing the total generalized costs of transporting the hazardous materials and the social risk along the planned routes are set as the optimization objectives. Meanwhile, the following formulation characteristics will be comprehensively modelled: (1) specific customer demands; (2) multiple hazardous material flows; (3) capacitated schedule-based rail service and uncapacitated time-flexible road service; and (4) environmental risk constraint. A bi-objective mixed integer nonlinear programming model is first built to formulate the routing problem that combines the formulation characteristics above. Then linear reformations are developed to linearize and improve the initial model so that it can be effectively solved by exact solution algorithms on standard mathematical programming software. By utilizing the normalized weighted sum method, we can generate the Pareto solutions to the bi-objective optimization problem for a specific case. Finally, a large-scale empirical case study from the Beijing-Tianjin-Hebei Region in China is presented to demonstrate the feasibility of the proposed methods in dealing with the practical problem. Various scenarios are also discussed in the case study.
Sun, Yan; Lang, Maoxiang; Wang, Danzhu
2016-01-01
The transportation of hazardous materials is always accompanied by considerable risk that will impact public and environment security. As an efficient and reliable transportation organization, a multimodal service should participate in the transportation of hazardous materials. In this study, we focus on transporting hazardous materials through the multimodal service network and explore the hazardous materials multimodal routing problem from the operational level of network planning. To formulate this problem more practicably, minimizing the total generalized costs of transporting the hazardous materials and the social risk along the planned routes are set as the optimization objectives. Meanwhile, the following formulation characteristics will be comprehensively modelled: (1) specific customer demands; (2) multiple hazardous material flows; (3) capacitated schedule-based rail service and uncapacitated time-flexible road service; and (4) environmental risk constraint. A bi-objective mixed integer nonlinear programming model is first built to formulate the routing problem that combines the formulation characteristics above. Then linear reformations are developed to linearize and improve the initial model so that it can be effectively solved by exact solution algorithms on standard mathematical programming software. By utilizing the normalized weighted sum method, we can generate the Pareto solutions to the bi-objective optimization problem for a specific case. Finally, a large-scale empirical case study from the Beijing–Tianjin–Hebei Region in China is presented to demonstrate the feasibility of the proposed methods in dealing with the practical problem. Various scenarios are also discussed in the case study. PMID:27483294
Novel Cloud Architecture to Decrease Problems Related to Big Data
Entesar Althagafy
2017-02-01
Full Text Available IT companies are facing many difficulties and challenges when dealing with big data. These difficulties have surfaced due to the ever-expanding amount of data generated via personal computer, mobile devices, and social network sites. The significant increase in big data has created challenges for IT companies that need to extract necessary information and knowledge. Cloud computing, with its virtualized resources usage and dynamic scalability, is broadly used in organizations to address challenges related to big data and has an important influence on business in organizations. Furthermore, big data is changing the way organizations do business. This paper proposes novel cloud architecture to decrease problems related to big data. The proposed architecture is a combination of many big data infrastructures in the creation of a service. This architecture minimizes problems related to big data by improving performance and quality of service.
Statistics and Corporate Environmental Management: Relations and Problems
Madsen, Henning; Ulhøi, John Parm
1997-01-01
Statistical methods have long been used to analyse the macroeconomic consequences of environmentally damaging activities, political actions to control, prevent, or reduce these damages, and environmental problems in the natural environment. Up to now, however, they have had a limited and not very...... specific use in corporate environmental management systems. This paper will address some of the special problems related to the use of statistical techniques in corporate environmental management systems. One important aspect of this is the interaction of internal decisions and activities with conditions...
On some lattice computations related to moduli problems
Peterson, A
2010-01-01
We show how to solve computationally a combinatorial problem about the possible number of roots orthogonal to a vector of given length in $E_8$. We show that the moduli space of K3 surfaces with polarisation of degree 2d is also of general type for d=52. This case was omitted from the earlier work of Gritsenko, Hulek and the second author. We also apply this method to some related problems. In Appendix A, V. Gritsenko shows how to arrive at the case d=52 and some others directly.
Glowworm Swarm Optimization for cross dock scheduling problem%萤火虫群优化算法在越库调度问题中的应用
吴斌; 钱存华; 倪卫红
2013-01-01
萤火虫群优化算法是一种新兴的群体智能优化算法,目前在组合优化领域中的应用比较少.提出萤火虫群优化算法(Glowworm Swarm Optimization,GSO)求解越库调度问题的优化方法.越库调度问题是一类极为复杂的NP难题,是影响越库配送效率的关键问题.依据算法和问题特点,设计基于随机键的两段式最大顺序值编码方法.为了解决GSO算法优化精度低、收敛速度慢等问题,提出逐维移动,贪婪接受的搜索策略.基于社会心理学原理,对位置更新公式进行改进.通过实验仿真,结果表明改进的GSO算法是求解越库调度问题的有效方法.%Glowworm Swarm Optimization(GSO) is a new swarm intelligence optimization algorithm, but now it has few applications in the field of combinatorial optimization. GSO is presented to solve the cross dock scheduling problem in the paper. Cross dock scheduling problem is a kind of highly complex optimization problem, and it is the critical issue for the cross docking logistics. The two-stage Largest Order Value (LOV) based on random key encoding method is designed for the solution. In order to enhance accuracy and convergence rate of the GSO, the best acceptance and moving by dimensional strategies are proposed. Based on the principle of social psychology, the position update equation is improved. Simulation results show that the proposed GSO algorithm is efficient for the cross dock scheduling problem.
Ju, Suquan; Clausen, Jens
2004-01-01
The ELDSP problem is a combined lot sizing and sequencing problem. A supplier produces and delivers components of different component types to a consumer in batches. The task is to determine the cycle time, i.e. that time between deliveries, which minimizes the total cost per time unit. This incl......The ELDSP problem is a combined lot sizing and sequencing problem. A supplier produces and delivers components of different component types to a consumer in batches. The task is to determine the cycle time, i.e. that time between deliveries, which minimizes the total cost per time unit....... This includes the determination of the production sequence of the component types within each cycle. We investigate the computational behavior of two published algorithms, a heuristic and an optimal algorithm. With large number of component types, the optimal algorithm has long running times. We devise a hybrid...
Analysis of modern problems and state of land relations
Т. В. Козлова
2013-07-01
Full Text Available Problems of the current situation of land relations and public land policy in Ukraine are investigated. Key factors that cause inhibition of land reform are identified. It was noted that public land policy today does not correspond to the full European and world standards and requirements of effective land management, so creating modern public land management is the main task, which will create a clear mechanism for land relations regulation. It was found that land issues can not be seen in isolation from the complex related to social, economic, environmental and legal issues. The measures to be implemented at this stage of land reform are proposed.
Clausen, Jens; Ju, S.
2006-01-01
The ELDSP problem is a combined lot sizing and sequencing problem. A supplier produces and delivers components of different types to a consumer in batches. The task is to determine the cycle time, i.e., the time between deliveries, which minimizes the total cost per time unit. This includes the d...... is both optimal and efficient. (c) 2005 Elsevier B.V. All rights reserved....
李凯; 史烨; 马英
2013-01-01
analyzed. Two kinds of machines, including key machine and non-key machine, are defined. There is at least one non-key machine in a non-optimal solution. To minimize the makespan, the algorithm must aim at the sub-schedule with a key-machine and a non-key machine. Minimizing workflow differences between key machine and non-key machine can help obtain the optimal solutions effectively. A lower bound is also designed to evaluate the accuracy of solutions and build the condition to stop the algorithm. Based on the above analysis, this paper designs a simulated annealing algorithm. To improve the efficiency of the simulated annealing algorithm, the optimization process aims at the sub-scheduling with a key machine and a non-key machine. In addition, the sub-scheduling changes during the iteration. The simulated annealing algorithm performance is tested on 20, 000 random instances. The computational results indicate that the proposed simulated annealing algorithm can solve problems with 1000 jobs within 0. 1 seconds, and the relative error is controlled within 0.01%. Therefore, the proposed simulated annealing algorithm has high accuracy and efficiency. In summary, this paper solves the parallel machine scheduling problem with changeable job processing times. The proposed optimization algorithm can provide reference values for practical scheduling problems such as preheating furnace scheduling in the steel industry and scheduling active manufacturers in the pull supply chain.
Moral Problem of Suicide and its Relation to Human Freedom
Pavel Liliana Lacramioara
2010-06-01
Full Text Available The thesis treats the problem of suicide by putting it in relation to the individual freedom of human. I will concentrate on just a few problems which target to attempt to offer an answer to the themeabove mentioned. To achieve the purpose I use different opinions and theories regarding theme in question which demonstrates that the theme has been treated thoroughly and also by prominent authors like G. Minois, E. Cioran, A. Schopenhauer or Paul-Ludwig Landsberg. We were asking ourselves whether suicide is a moral problem. How about a solution to escape from the existential anguish? People have never understood completely the acts of suicide showing almost constantly contempt, fear, pity or indifference to these.
基于现金流均衡目标的多模式项目调度问题研究%Multi-mode Project Scheduling Problems with Cash Flow Balanced Objectives
何正文; 刘人境; 徐渝
2011-01-01
A contractor needs to spend capital on project-related resources before receiving payment from customers for services delivered at each milestone. However, contractors have varying capital availabilities when implementing different projects. Thus,balancing cash inflows and outflows throughout the project life cycle is critical to project success for contractors.This paper studies multi-mode project scheduling problems with the goal of balancing cash inflows and outflows. We use the event-based method to represent a contractor's project schedule in the activity-on-node mode, and distribute project expenses to their beginning and ending activities. The objective of this exercise is to minimize the gap of a contractor's cumulative capital flows given the constraint of a fixed project deadline.We define two groups of decision variables in order to determine the modes of performing activities and the actual delivery date of events. A0-1 programming model is established to address cash flow problems. A simulated annealing heuristic algorithm with two nested loops is developed to solve cash flow problems. These two nested loops include inner and outer loops. The inner loop searches for a desirable vector of an event's completion time under a given activity's performing mode. The outer loop seeks the desirable vector of an event's completion time established by the inner loop. The simulation results show desirable solutions for cash problems in inner and outer loops. A contracted superhighway construction project is used as a case study to illustrate the use of our proposed simulated algorithm. This project is able to develop a satisfactory schedule. We compare the developed schedule with the actual schedule regarding key cash flow parameters, including project deadline, payment proportion, advance payment proportion, deposit rate of security payment, and objective function values.A contractor can maximize its cumulative capital gap by reducing the procrastination of
State Confessional Relations: Problem of the Subject Structure
Alexandra A. Dorskaya
2014-06-01
Full Text Available In the article various existing definitions of the concept "state and confessional relations" are analyzed, also author's definition is offered. Three levels of the state and confessional relations are revealed: conceptual, legislative and administrative-managerial. In the article it is shown that in Russia a tradition of only two subjects of the state and confessional relations – government bodies and the religious organizations allocation exists. It is revealed that at the present stage many researchers are dissatisfied with such situation. Scientific sources of the problem of the state and church relations within the psychological school of the law, which are addressed to the personality and experiences in the legal sphere are studied and revealed. Special attention is paid to scientific heritage of the M.A. Reysner, who was one of the first to begin study of this problem. In the article the question of the school of three subjects of the state and confessional relations allocation formation, what adds the faithful or faithless personality in addition to two traditional subjects is analyzed. The state and confessional relations are considered in the context of the human rights development. The question of new type of the believer possessing high education level and knowledge formation is considered. In the article it is shown that at the present stage relations of any regulation between the state and religious organizations is based on the basis of international legal standards, domestic legislation and norms of canon law.
Yu Zhang
2014-01-01
Full Text Available We consider an ad hoc Floyd-A∗ algorithm to determine the a priori least-time itinerary from an origin to a destination given an initial time in an urban scheduled public transport (USPT network. The network is bimodal (i.e., USPT lines and walking and time dependent. The modified USPT network model results in more reasonable itinerary results. An itinerary is connected through a sequence of time-label arcs. The proposed Floyd-A∗ algorithm is composed of two procedures designated as Itinerary Finder and Cost Estimator. The A∗-based Itinerary Finder determines the time-dependent, least-time itinerary in real time, aided by the heuristic information precomputed by the Floyd-based Cost Estimator, where a strategy is formed to preestimate the time-dependent arc travel time as an associated static lower bound. The Floyd-A∗ algorithm is proven to guarantee optimality in theory and, demonstrated through a real-world example in Shenyang City USPT network to be more efficient than previous procedures. The computational experiments also reveal the time-dependent nature of the least-time itinerary. In the premise that lines run punctually, “just boarding” and “just missing” cases are identified.
The problem of time quantum mechanics versus general relativity
Anderson, Edward
2017-01-01
This book is a treatise on time and on background independence in physics. It first considers how time is conceived of in each accepted paradigm of physics: Newtonian, special relativity, quantum mechanics (QM) and general relativity (GR). Substantial differences are moreover uncovered between what is meant by time in QM and in GR. These differences jointly source the Problem of Time: Nine interlinked facets which arise upon attempting concurrent treatment of the QM and GR paradigms, as is required in particular for a background independent theory of quantum gravity. A sizeable proportion of current quantum gravity programs - e.g. geometrodynamical and loop quantum gravity approaches to quantum GR, quantum cosmology, supergravity and M-theory - are background independent in this sense. This book's foundational topic is thus furthermore of practical relevance in the ongoing development of quantum gravity programs. This book shows moreover that eight of the nine facets of the Problem of Time already occur upon ...
B. Yuce
2015-01-01
Full Text Available This paper focuses on improvements to the Bees Algorithm (BA with slope angle computation and Hill Climbing Algorithm (SACHCA during the local search process. First, the SAC was employed to determine the inclination of the current sites. Second, according to the slope angle, the HCA was utilised to guide the algorithm to converge to the local optima. This enabled the global optimum of the given problem to be found faster and more precisely by focusing on finding the available local optima first before turning the attention on the global optimum. The proposed enhancements to the BA have been tested on continuous-type benchmark functions and compared with other optimisation techniques. The results show that the proposed algorithm performed better than other algorithms on most of the benchmark functions. The enhanced BA performs better than the basic BA, in particular on higher dimensional and complex optimisation problems. Finally, the proposed algorithm has been used to solve the single machine scheduling problem and the results show that the proposed SAC and HCA-BA outperformed the basic BA in almost all the considered instances, in particular when the complexity of the problem increases.
HE Long-min; SUN Shi-jie; CHENG Ming-bao
2008-01-01
This paper considers a hybrid two-stage flow-shop scheduling problem with m identical parallel machineson one stage and a batch processor on the other stage.The processing time of job Jj on any of m identical parallel machines is aj≡a(j∈N),and the processing time of job Jj is bj(j∈N)on a batch processor M.We take makespan(Cmax)as our minimization objective.In this paper,for the problem of FSMP-BI(m identical parallel machines on the first stage and a batch processor on the second stage),based on the algorithm given by Sung and Choung for the problem of l I rj,BI I Cmax under the constraint of the given processing sequence,we develop an optimal dynamic programming Algorithm H1 for it in max{O(nlogn),O(nB)} time.A max{O(nlogn),O(nB)} time symmetric Algorithm H2 is given then for the problem of BI-FSMP(a batch processor on the first stage and m identical parallel machines on the second stage).
V K MANUPATI; G RAJYALAKSHMI; FELIX T S CHAN; J J THAKKAR
2017-03-01
This paper addresses a fuzzy mixed-integer non-linear programming (FMINLP) model by considering machine-dependent and job-sequence-dependent set-up times that minimize the total completion time,the number of tardy jobs, the total flow time and the machine load variation in the context of unrelated parallel machine scheduling (UPMS) problem. The above-mentioned multi-objectives were considered based on nonzero ready times, machine- and sequence-dependent set-up times and secondary resource constraints for jobs.The proposed approach considers unrelated parallel machines with inherent uncertainty in processing times and due dates. Since the problem is shown to be NP-hard in nature, it is a challenging task to find the optimal/nearoptimal solutions for conflicting objectives simultaneously in a reasonable time. Therefore, we introduced a new multi-objective-based evolutionary artificial immune non-dominated sorting genetic algorithm (AI-NSGA-II) to resolve the above-mentioned complex problem. The performance of the proposed multi-objective AI-NSGA-II algorithm has been compared to that of multi-objective particle swarm optimization (MOPSO) and conventionalnon-dominated sorting genetic algorithm (CNSGA-II), and it is found that the proposed multi-objective-based hybrid meta-heuristic produces high-quality solutions. Finally, the results obtained from benchmark instances and randomly generated instances as test problems evince the robust performance of the proposed multiobjective algorithm.
Pei, Jun; Liu, Xinbao; Pardalos, Panos M.; Fan, Wenjuan; Wang, Ling; Yang, Shanlin
2016-03-01
Motivated by applications in manufacturing industry, we consider a supply chain scheduling problem, where each job is characterised by non-identical sizes, different release times and unequal processing times. The objective is to minimise the makespan by making batching and sequencing decisions. The problem is formalised as a mixed integer programming model and proved to be strongly NP-hard. Some structural properties are presented for both the general case and a special case. Based on these properties, a lower bound is derived, and a novel two-phase heuristic (TP-H) is developed to solve the problem, which guarantees to obtain a worst case performance ratio of ?. Computational experiments with a set of different sizes of random instances are conducted to evaluate the proposed approach TP-H, which is superior to another two heuristics proposed in the literature. Furthermore, the experimental results indicate that TP-H can effectively and efficiently solve large-size problems in a reasonable time.
[Problems of psychiatrization, medicalization and related social phenomena].
Opalić, Petar
2009-01-01
The introduction contains definitions of the terms psychiatrization, medicalization, psychotherapeutization and psychologization of the society, i.e. social problems. Different aspects of the above phenomena are analyzed, their origin, relation with the professions they originate from, and, finally, their social significance, i.e. social function. In conclusion, the article points to different possibilities to prevent the above phenomena, undesirable both for the society and the objectives and activities of the professions they originate from.
A RELATIVE BENEFIT ALGORITHM FOR BASIC ECONOMIC LOT SIZE PROBLEM
马辉民; 张子刚; 周少甫; 黄卫来
2001-01-01
The paper develops an algorithm that solves economic lot size problem in O(n2) time in the Wagner-Whitin case. The algorithm is based on the standard dynamic programming approach which requires the computation of the maximal relative benefit for some possible subplans of the production plan. In this algorithm the authors have studied the forward property and decomposition properties which can make computation easy. The proposed algorithm appears to perform quite reasonably for practical application.
Relations Between Toddler Sleep Characteristics, Sleep Problems, and Temperament
Molfese, Victoria J.; Rudasill, Kathleen M.; Prokasky, Amanda; Champagne, Carly; Holmes, Molly; Molfese, Dennis; Bates, Jack
2015-01-01
Two sources of information (parent reported sleep diaries and actigraph records) were used to investigate how toddler sleep characteristics (bed time/sleep onset, wake time/sleep offset, total nighttime sleep and total sleep time) are related to sleep problems and temperament. There were 64 toddler participants in the study. Consistent with studies of older children, parent reports differed from actigraph based records. The findings that parent reported and actigraph recorded sleep characteri...
Xu, Jiuping; Feng, Cuiying
2014-01-01
This paper presents an extension of the multimode resource-constrained project scheduling problem for a large scale construction project where multiple parallel projects and a fuzzy random environment are considered. By taking into account the most typical goals in project management, a cost/weighted makespan/quality trade-off optimization model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform the fuzzy random parameters into fuzzy variables that are subsequently defuzzified using an expected value operator with an optimistic-pessimistic index. Then a combinatorial-priority-based hybrid particle swarm optimization algorithm is developed to solve the proposed model, where the combinatorial particle swarm optimization and priority-based particle swarm optimization are designed to assign modes to activities and to schedule activities, respectively. Finally, the results and analysis of a practical example at a large scale hydropower construction project are presented to demonstrate the practicality and efficiency of the proposed model and optimization method.
Angela Hsiang-Ling Chen
2016-09-01
Full Text Available Modeling and optimizing organizational processes, such as the one represented by the Resource-Constrained Project Scheduling Problem (RCPSP, improve outcomes. Based on assumptions and simplification, this model tackles the allocation of resources so that organizations can continue to generate profits and reinvest in future growth. Nonetheless, despite all of the research dedicated to solving the RCPSP and its multi-mode variations, there is no standardized procedure that can guide project management practitioners in their scheduling tasks. This is mainly because many of the proposed approaches are either based on unrealistic/oversimplified scenarios or they propose solution procedures not easily applicable or even feasible in real-life situations. In this study, we solve a more true-to-life and complex model, Multimode RCPSP with minimal and maximal time lags (MRCPSP/max. The complexity of the model solved is presented, and the practicality of the proposed approach is justified depending on only information that is available for every project regardless of its industrial context. The results confirm that it is possible to determine a robust makespan and to calculate an execution time-frame with gaps lower than 11% between their lower and upper bounds. In addition, in many instances, the solved lower bound obtained was equal to the best-known optimum.
University Professors' Stress and Perceived State of Health in Relation to Teaching Schedules
Cladellas, Ramon; Castello, Antoni
2011-01-01
Introduction: The aim of this research is to analyze the influence of time management, particularly in connection with university teachers' assigned class hours, on psychosocial factors relating to perceived health and stress symptoms. Special attention is given to the effect of very early and very late class hours. Method: The sample comprised…
University Professors' Stress and Perceived State of Health in Relation to Teaching Schedules
Cladellas, Ramon; Castello, Antoni
2011-01-01
Introduction: The aim of this research is to analyze the influence of time management, particularly in connection with university teachers' assigned class hours, on psychosocial factors relating to perceived health and stress symptoms. Special attention is given to the effect of very early and very late class hours. Method: The sample comprised…
Terrorism-related trauma in Africa, an increasing problem.
Alfa-Wali, Maryam; Sritharan, Kaji; Mehes, Mira; Abdullah, Fizan; Rasheed, Shahnawaz
2015-06-01
Global terrorist activities have increased significantly over the past decade. The impact of terrorism-related trauma on the health of individuals in low- and middle-income countries is under-reported. Trauma management in African countries in particular is uncoordinated, with little or no infrastructure to cater for emergency surgical needs. This article highlights the need for education, training and research to mitigate the problems related to terrorism and surgical public health. Copyright © 2014 Ministry of Health, Saudi Arabia. Published by Elsevier Ltd. All rights reserved.
Toptal, Ayşegül
1999-01-01
Ankara : Department of Industrial Engineering and the Institute of Engineering and Science of Bilkent Univ., 1999. Thesis (Master's) -- Bilkent University, 1999. Includes bibliographical references. Distributed Scheduling (DS) is a new paradigm that enables the local decisionmakers make their own schedules by considering local objectives and constraints within the boundaries and the overall objective of the whole system. Local schedules from different parts of the system are...
Optimal scheduling of logistical support for an emergency roadway repair work schedule
Yan, S.; Lin, C. K.; Chen, S. Y.
2012-09-01
The completion of every disaster rescue task performed by repair work teams relies on the in-time supply of materials to the rescue workers. Up to now, logistical support planning for emergency repair work in Taiwan has been done manually, which is neither effective nor efficient. To remedy the problem, this study presents a logistical support scheduling model for the given emergency repair work schedule. The objective is to minimize the short-term operating cost subject to time constraints and other related operating constraints. This model is formulated as an integer multiple-commodity network flow problem which is characterized as NP-hard. A heuristic algorithm, based on the problem decomposition and variable fixing techniques, is also proposed to efficiently solve this problem. Computational tests are performed using data from Taiwan's 1999 Chi-Chi earthquake. The results show that the model and the solution algorithm would be useful for the logistical support scheduling.
李华; 韩宝明; 赵鹏
2012-01-01
从广义和狭义两个角度阐述了我国动车组运用问题的内涵,指出当前关注的重点是以动车组运用计划编制为核心的狭义范畴.从我国动车组产品系列、列车开行特征、动车组检修与管理体制等方面对动车组运用计划相关影响因素的现状进行了说明；将动车组运用计划编制过程划分为交路计划和检修计划两个阶段,从我国铁路生产实际需求出发,分析了计划编制过程中考虑的约束条件和评价指标；最后总结提出了亟待解决的具体问题,对后续相关研究具有借鉴意义.%This paper explains the connotations of the train-set operation issue from both broad sense and narrow sense, and points out that the connotation from narrow sense with the train-set scheduling problem at its core is the current focus of concern. First, the current situations of several influencing factors of train-set routing schedule are illustrated, in terms of the train-set product series, train operation characteristics, train-set maintenance system and management system in China. Then, the process of train-set scheduling is divided into circulation planning and maintenance routing stages, and the constraints and evaluation indices of both stages are analyzed from the practical production demands of railway in China. Finally, several key points of the train-set scheduling problem are proposed to support the related studies in the future.
用于作业车间调度的模拟退火算法%A simulated annealing algorithm on solving job shop scheduling problem
赵良辉; 邓飞其
2006-01-01
作业车间调度问题(Job Shop Scheduling Problem,JSP)是一类NP完全问题,解决此类问题较常使用非数值算法,而模拟退火算法是其中较为突出的而且应用广泛的一种算法.本文结合车间调度问题的特点阐述了模拟退火算法在解决车间调度问题上的应用,提出了基于模拟退火算法的车间调度问题模型,并以Matlab为工具进行了仿真实验.
免疫算法在物流车辆优化调度中的应用%Application of Immune Algorithm in Vehicle Scheduling Problem
武亚丽; 段富
2007-01-01
车辆调度(Vehicle scheduling Problem,简称VSP)是物流配送中广泛存在的一类问题,也是各大型企业部门的一项日常性工作,物资的合理运输直接关系到一个企业的经济效益.本文将免疫算法应用到车辆调度的实际系统中,针对受容量和时间窗限制的运输问题确立了带有惩罚项的目标函数.
Personnel Scheduling in Laboratories
Franses, Philip; Post, Gerhard; Burke, Edmund; De Causmaecker, Patrick
2003-01-01
We describe an assignment problem particular to the personnel scheduling of organisations such as laboratories. Here we have to assign tasks to employees. We focus on the situation where this assignment problem reduces to constructing maximal matchings in a set of interrelated bipartite graphs. We d
芦鹏宇; 孙文俊; 井瑞
2012-01-01
针对多个IT项目的人力资源调度问题,根据其在时间和人员方面的特殊要求,可以将项目的演进时间划分为相等的时间片段,然后根据特定时间片段内的活动与可选人员之间的关系,建立相应的人员调度方案搜索树.将所有时间片段内的搜索树按顺序连接后,可以得到总的方案树,并列出所有的方案,然后根据不同方案下得到的平均提前完工率和提前完工率方差,找到最优解.又为该搜索算法添加了启发规则,使搜索空间急剧收缩,极大提高了搜索效率.基于该算法的特殊性,可以将其用于求解许多具有与上述问题类似条件的资源调度问题.本研究通过实例发现,根据运算中的特殊规律,该算法还具有进一步提取启发规则的潜力.%This paper deals with the human resource scheduling problem of multiple IT projects. According to its specific requirements on time and personnel, the duration of the whole development process can be divided into equal-length time slices. Based on the relations among activities and personnel in a certain time slice, the search tree of personnel scheduling solutions can be constructed. By connecting all the search trees sequentially from all the time slices, the solution tree for the whole problem can be generated and all the solutions can be listed. Then, based on the average of advanced completion rate and the variance of advanced completion rate, the optimal solution can be found. By adding heuristic rules to this algorithm, the searching space can contract rapidly, and the searching efficiency can be enhanced greatly. In view of the particularities of this algorithm, it can be applied to many generalized resource scheduling problems that possess similar conditions with the problem described above. After applying this algorithm to a mock example, some regular patterns emerged, showing the potential that further heuristic rules can be extracted.
A multidisciplinary approach to solving computer related vision problems.
Long, Jennifer; Helland, Magne
2012-09-01
This paper proposes a multidisciplinary approach to solving computer related vision issues by including optometry as a part of the problem-solving team. Computer workstation design is increasing in complexity. There are at least ten different professions who contribute to workstation design or who provide advice to improve worker comfort, safety and efficiency. Optometrists have a role identifying and solving computer-related vision issues and in prescribing appropriate optical devices. However, it is possible that advice given by optometrists to improve visual comfort may conflict with other requirements and demands within the workplace. A multidisciplinary approach has been advocated for solving computer related vision issues. There are opportunities for optometrists to collaborate with ergonomists, who coordinate information from physical, cognitive and organisational disciplines to enact holistic solutions to problems. This paper proposes a model of collaboration and examples of successful partnerships at a number of professional levels including individual relationships between optometrists and ergonomists when they have mutual clients/patients, in undergraduate and postgraduate education and in research. There is also scope for dialogue between optometry and ergonomics professional associations. A multidisciplinary approach offers the opportunity to solve vision related computer issues in a cohesive, rather than fragmented way. Further exploration is required to understand the barriers to these professional relationships. © 2012 The College of Optometrists.
Bermejo, Alfonso; Iglesias, Carlos; Ruiz-Alonso, María; Blesa, David; Simón, Carlos; Pellicer, Antonio; García-Velasco, Juan
2014-06-01
Does the combined oral contraceptive pill (COCP) change endometrial gene expression when used for cycle programming? COCP used for scheduling purposes does not have a significant impact on endometrial gene expression related to endometrial receptivity. Controversy exists around COCP pretreatment for IVF cycle programming, as some authors claim that it might be detrimental to the live birth rate. Microarray technology applied to the study of tissue gene expression has previously revealed the behavior of genes related to endometrial receptivity under different conditions. Proof-of-concept study of 10 young healthy oocyte donors undergoing controlled ovarian stimulation (COS) recruited between June 2012 and February 2013. Microarray data were obtained from endometrial biopsies from 10 young healthy oocyte donors undergoing COS with GnRH antagonists and recombinant FSH. In group A (n = 5), COCP pretreatment was used for 12-16 days, and stimulation began after a 5-day pill-free interval. Stimulation in group B (n = 5) was initiated on cycle day 3 after a spontaneous menses. Endometrial biopsies were collected 7 days after triggering with hCG. No individual genes exhibited increased or decreased expression (fold change (FC) >2) in patients with prior COCP treatment (group A) compared with controls (group B). However, the results of the functional analysis showed a total of 11 biological processes that were significantly enriched in group A compared with group B (non-COCP). The Endometrial Receptivity Array (ERA) has only been validated on endometrial samples obtained in natural cycles and after hormonal replacement treatment (HRT). Therefore, it was not possible in this study to classify the endometrial samples as receptive or non-receptive. We used the ERA to focus on 238 genes that are intimately related to endometrial receptivity, thus simplifying the analysis and understanding of the data. Cycle scheduling is common in IVF units and is used to avoid weekend
Supply Chain Scheduling with Open-shop Problem%自由作业环境下的供应链排序问题
陈荣军; 唐国春
2009-01-01
In this paper, we study an integrated scheduling model of production and dis-tribution operations. In this model, a set of jobs (i.e., customer orders) are first processed in the processing facility of open-shop machine and then delivered to the manufacturers di-rectly without intermediate inventory. The problem is to find a joint schedule of production and distribution such that an objective function that takes into account both production cost and distribution cost is optimized, where production cost is measured by the maximum delivery time and the distribution cost of a delivery shipment consists of a fixed charge and a variable cost proportional to the total distance of the route taken by the shipment. For the problem under this integrated scheduling model, we use dynamic programming to provide a heuristic algorithm with worst-case performance analysis. Finally some special cases are also introduced.%本文研究自由作业环境下的供应链排序问题,研究供应链的上游如何安排工件在自由作业机器上加工,把加工完毕的工件分批发送给下游,使得生产排序费用和发送费用总和最少.这里,生产排序费用是用工件送到时间的函数来表示;发送费用是由发送的固定费用和与运输路径有关的变化费用所组成.本文研究以工件最大送到时间为生产排序费用的自由作业供应链排序问题,在指出问题的NP困难性后,用动态规划算法构造多项式时间近似算法,并分析算法的性能比.本文最后还对特殊情形进行了讨论.
Compressed Sensing with Nonlinear Observations and Related Nonlinear Optimisation Problems
Blumensath, Thomas
2012-01-01
Non-convex constraints have recently proven a valuable tool in many optimisation problems. In particular sparsity constraints have had a significant impact on sampling theory, where they are used in Compressed Sensing and allow structured signals to be sampled far below the rate traditionally prescribed. Nearly all of the theory developed for Compressed Sensing signal recovery assumes that samples are taken using linear measurements. In this paper we instead address the Compressed Sensing recovery problem in a setting where the observations are non-linear. We show that, under conditions similar to those required in the linear setting, the Iterative Hard Thresholding algorithm can be used to accurately recover sparse or structured signals from few non-linear observations. Similar ideas can also be developed in a more general non-linear optimisation framework. In the second part of this paper we therefore present related result that show how this can be done under sparsity and union of subspaces constraints, wh...
Information-related complexity: a problem-oriented approach
Perevalov, Eugene
2013-01-01
A general notion of information-related complexity applicable to both natural and man-made systems is proposed. The overall approach is to explicitly consider a rational agent performing a certain task with a quantifiable degree of success. The complexity is defined as the minimum (quasi-)quantity of information that's necessary to complete the task to the given extent -- measured by the corresponding loss. The complexity so defined is shown to generalize the existing notion of statistical complexity when the system in question can be described by a discrete-time stochastic process. The proposed definition also applies, in particular, to optimization and decision making problems under uncertainty in which case it gives the agent a useful measure of the problem's "susceptibility" to additional information and allows for an estimation of the potential value of the latter.
1983-02-01
no task is scheduled with overlap. Let numpi be the total number of preemptions and idle slots of size at most to that are introduced. We see that if...no usable block remains on Qm-*, then numpi < m-k. Otherwise, numpi ! m-k-1. If j>n when this procedure terminates, then all tasks have been scheduled
Stochastic partial differential equations in turbulence related problems
Chow, P.-L.
1978-01-01
The theory of stochastic partial differential equations (PDEs) and problems relating to turbulence are discussed by employing the theories of Brownian motion and diffusion in infinite dimensions, functional differential equations, and functional integration. Relevant results in probablistic analysis, especially Gaussian measures in function spaces and the theory of stochastic PDEs of Ito type, are taken into account. Linear stochastic PDEs are analyzed through linearized Navier-Stokes equations with a random forcing. Stochastic equations for waves in random media as well as model equations in turbulent transport theory are considered. Markovian models in fully developed turbulence are discussed from a stochastic equation viewpoint.
Theoretical and observational problems related to solar eclipses. Proceedings.
Mouradian, Z.; Stavinschi, M.
The contributions to this book are based on the current knowledge of solar corona physics and on the prospects for future total eclipse observations, focusing on the eclipse of August 11, 1999, which forecasters believe will occur at precisely the maximum of solar activity. The results of past eclipse observations are reviewed, including coronal hot and cold structures, coronal heating, public education and instrumental problems. The relation of the corona to the Sun is discussed, viz., the energy and mass transfer between the chromosphere and the corona, including the formation of prominences by coronal condensation in coronal cavities and the supply of mass to the corona by spicules.
The problem of friction in two-dimensional relative motion
Grech, D K; Grech, Dariusz; Mazur, Zygmunt
2000-01-01
We analyse a mechanical system in two-dimensional relative motion with friction. Although the system is simple, the peculiar interplay between two kinetic friction forces and gravity leads to the wide range of admissible solutions exceeding most intuitive expectations. In particular, the strong qualitative dependence between behaviour of the system, boundary conditions and parameters involved in its description is emphasised. The problem is intended to be discussed in theoretical framework and might be of interest for physics and mechanics students as well as for physics teachers.