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A Hybrid Genetic Algorithm for the Job Shop Scheduling Problem
 EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
, 2002
"... 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 ..."
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Cited by 56 (10 self)
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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 of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.
Parallel Grasp With PathRelinking For Job Shop Scheduling
 Parallel Computing
, 2002
"... In the job shop scheduling problem (JSP), a finite set of jobs is processed on a finite set of machines. Each job is required to complete a set of operations in a fixed order. Each operation is processed on a specific machine for a fixed duration. A machine can process no more than one job at a ..."
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Cited by 38 (19 self)
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In the job shop scheduling problem (JSP), a finite set of jobs is processed on a finite set of machines. Each job is required to complete a set of operations in a fixed order. Each operation is processed on a specific machine for a fixed duration. A machine can process no more than one job at a time and once a job initiates processing on a given machine it must complete processing without interruption. A schedule is an assignment of operations to time slots on the machines. The objective of the JSP is to find a schedule that minimizes the maximum completion time, or makespan, of the jobs. In this paper, we describe a parallel greedy randomized adaptive search procedure (GRASP) with pathrelinking for the JSP. A GRASP is a metaheuristic for combinatorial optimization. It usually consists of a construction procedure based on a greedy randomized algorithm and of a local search. Pathrelinking is an intensification strategy that explores trajectories that connect high quality solutions. Independent and cooperative parallelization strategies are described and implemented. Computational experience on a large set of standard test problems indicates that the parallel GRASP with pathrelinking finds goodquality approximate solutions of the job shop scheduling problem.
A greedy randomized adaptive search procedure for job shop scheduling
 IEEE Trans. on Power Systems
, 2001
"... Abstract. In the job shop scheduling problem (JSP), a finite set of jobs is processed on a finite set of machines. Each job is characterized by a fixed order of operations, each of which is to be processed on a specific machine for a specified duration. Each machine can process at most one job at a ..."
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Cited by 24 (2 self)
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Abstract. In the job shop scheduling problem (JSP), a finite set of jobs is processed on a finite set of machines. Each job is characterized by a fixed order of operations, each of which is to be processed on a specific machine for a specified duration. Each machine can process at most one job at a time and once a job initiates processing on a given machine it must complete processing uninterrupted. A schedule is an assignment of operations to time slots on the machines. The objective of the JSP is to find a schedule that minimizes the maximum completion time, or makespan, of the jobs. In this paper, we describe a greedy randomized adaptive search procedure (GRASP) for the JSP. A GRASP is a metaheuristic for combinatorial optimization. Although GRASP is a general procedure, its basic concepts are customized for the problem being solved. We describe in detail our implementation of GRASP for job shop scheduling. Further, we incorporate to the conventional GRASP two new concepts: an intensification strategy and POP (Proximate Optimality Principle) in the construction phase. These two concepts were first proposed by Fleurent & Glover (1999) in the context of the quadratic assignment problem. Computational experience on a large set of standard test problems indicates that GRASP is a competitive algorithm for finding approximate solutions of the job shop scheduling problem. 1.
A GRASP For Job Shop Scheduling
 Essays and Surveys on Metaheuristics
, 2000
"... In the job shop scheduling problem (JSP), a finite set of jobs is processed on a finite set of machines. Each job is characterized by a fixed order of operations, each of which is to be processed on a specific machine for a specified duration. Each machine can process at most one job at a time and o ..."
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Cited by 19 (8 self)
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In the job shop scheduling problem (JSP), a finite set of jobs is processed on a finite set of machines. Each job is characterized by a fixed order of operations, each of which is to be processed on a specific machine for a specified duration. Each machine can process at most one job at a time and once a job initiates processing on a given machine it must complete processing uninterrupted. A schedule is an assignment of operations to time slots on the machines. The objective of the JSP is to find a schedule that minimizes the maximum completion time, or makespan, of the jobs. In this paper, we describe a greedy randomized adaptive search procedure (GRASP) for the JSP. A GRASP is a metaheuristic for combinatorial optimization. Although GRASP is a general procedure, its basic concepts are customized for the problem being solved. We describe in detail our implementation of GRASP for job shop scheduling. Further, we incorporate to the conventional GRASP two new concepts: an ...
Local Search Genetic Algorithms for the Job Shop Scheduling Problem
 Applied Intelligence
"... In previous work, we developed three deadlock removal strategies for the job shop schedul ing problem (JSSP) and proposed a hybridized genetic algorithm for it. While the genetic algorithm (GA) gave promising results, its performance depended greatly on the choice of deadlock removal strategies emp ..."
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Cited by 11 (0 self)
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In previous work, we developed three deadlock removal strategies for the job shop schedul ing problem (JSSP) and proposed a hybridized genetic algorithm for it. While the genetic algorithm (GA) gave promising results, its performance depended greatly on the choice of deadlock removal strategies employed. This paper introduces a genetic algorithm based scheduling scheme that is deadlock free. This is achieved through the choice of chromosome representation and genetic operators. We propose an efficient solution representation for the JSSP in which the job task ordering constraints are easily encoded. Furthermore, a problem specific crossover operator that ensures solutions generated through genetic evo lution are all feasible is also proposed. Hence, both checking of the constraints and repair mechanism can be avoided, thus resulting in increased efficiency. A mutationlike operator geared towards local search is also proposed which further improves the solution quality. Lastly, a hybrid strategy using the genetic algorithm reinforced with a tabu search is de veloped. An empirical study is carried out to test the proposed strategies using benchmark data.
Characterizing the distribution of lowmakespan schedules in the job shop scheduling problem
 In Proc. ICAPS’05
, 2005
"... We characterize the search landscape of the job shop scheduling problem (JSSP), with a focus on schedules whose makespan is optimal or nearoptimal. Building on previous work on the ‘big valley ’ distribution of local optima, we use special branch and bound algorithms to examine in greater detail t ..."
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Cited by 5 (2 self)
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We characterize the search landscape of the job shop scheduling problem (JSSP), with a focus on schedules whose makespan is optimal or nearoptimal. Building on previous work on the ‘big valley ’ distribution of local optima, we use special branch and bound algorithms to examine in greater detail the extent to which JSSP search spaces conform to the intuitive picture conveyed by the words ‘big valley’. We also examine how this changes as a function of the job:machine ratio. We find that for square JSSPs, lowmakespan schedules are tightly clustered in a small region of the search space, and the size of this region decreases as the makespan gets closer to optimality. As the job:machine ratio increases beyond 1, however, lowmakespan schedules become dispersed throughout the search space. We discuss the reasons for this and provide analytical results for two limiting cases. We close with an examination of neighborhood exactness in the JSSP, which illustrates some limitations of the big valley picture for JSSP landscapes.
Evolution In Materio
, 2005
"... This thesis describes a method to program materials directly to perform a computation. The work demonstrates that an evolutionary algorithm can exploit the physical properties of materials such as liquid crystal, enabling them to perform computation. The thesis demonstrates the approach applied to s ..."
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Cited by 4 (3 self)
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This thesis describes a method to program materials directly to perform a computation. The work demonstrates that an evolutionary algorithm can exploit the physical properties of materials such as liquid crystal, enabling them to perform computation. The thesis demonstrates the approach applied to several different problems including signal processing, control and digital logic. In addition to demonstrating the technique on real liquid crystal, simulations are used to show the applicability to cellular automata and a kind of neural network. The thesis also argues that the developed technique may also be suitable for programming systems, such as, bacterial consortia to perform computations.
Using Online Algorithms to Solve NPHard Problems More Efficiently in Practice
, 2007
"... as representing the official policies of the U.S. Government. ..."
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Cited by 4 (2 self)
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as representing the official policies of the U.S. Government.
A REVIEW OF SCHEDULING PROBLEMS IN RADIOTHERAPY
"... This paper describes the radiotherapy patient scheduling problem of minimising waiting times. Like many other service industry problems, radiotherapy patient scheduling may be solved by first modelling and formulating it into a shop scheduling problem. Over the years, these shop scheduling models ha ..."
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Cited by 3 (0 self)
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This paper describes the radiotherapy patient scheduling problem of minimising waiting times. Like many other service industry problems, radiotherapy patient scheduling may be solved by first modelling and formulating it into a shop scheduling problem. Over the years, these shop scheduling models have been researched and solved using various approaches. This paper typifies radiotherapy patient scheduling into a job shop problem. In addition, exact and metaheuristic approaches of solving job shop scheduling problems are also reviewed and comparatively analysed. 1
Applying Scheduling Techniques to Minimize the Number of Late Jobs in Workflow Systems
 In Proc. of the 2004 ACM Symposium on Applied Computing (SAC
, 2004
"... Ordering the cases in a workflow can result in significant decrease on the number of late jobs. But merging workflow and scheduling is not trivial. This paper presents some of the problems of using scheduling results in ordering cases in a workflow and tackles two of them: the uncertainties on the c ..."
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Ordering the cases in a workflow can result in significant decrease on the number of late jobs. But merging workflow and scheduling is not trivial. This paper presents some of the problems of using scheduling results in ordering cases in a workflow and tackles two of them: the uncertainties on the cases ’ processing times and routing. A new approach to modeling these uncertainties is also proposed: the guess and solve technique. It consists of making a guess on the execution times and routes the case will follow, and solving the corresponding deterministic scheduling problem using a suitable technique, in this paper genetic algorithms. Simulation results show that for almost all workloads rules such as earliest due date first, and guess and solve (if the error in guessing is bound by 30%) are statistically significantly better than the commonly used FIFO rule regarding the number of late jobs.