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22
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 35 (18 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 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 29 (8 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.
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 22 (3 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 17 (7 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 8 (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.
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 3 (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
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 2 (2 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.
How the Landscape of Random Job Shop Scheduling Instances Depends on the Ratio of Jobs to Machines
 Problem Difficulty for Tabu Search in JobShop Scheduling, Artificial Intelligence 143(2): 189
, 2003
"... We characterize the search landscape of random instances of the job shop scheduling problem (JSSP). Specifically, we investigate how the expected values of (1) backbone size, (2) distance between nearoptimal schedules, and (3) makespan of random schedules vary as a function of the job to machine ra ..."
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Cited by 2 (0 self)
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We characterize the search landscape of random instances of the job shop scheduling problem (JSSP). Specifically, we investigate how the expected values of (1) backbone size, (2) distance between nearoptimal schedules, and (3) makespan of random schedules vary as a function of the job to machine ratio ( N N N). For the limiting cases → 0 and → ∞ we provide analytical
DISCOVERING DISPATCHING RULES FOR JOB SHOP SCHEDULING PROBLEM THROUGH DATA MINING
, 2010
"... A data mining based approach to discover previously unknown priority dispatching rules for job shop scheduling problem is presented. This approach is based upon seeking the knowledge that is assumed to be embedded in the efficient solutions provided by the optimization module built using tabu searc ..."
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Cited by 1 (0 self)
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A data mining based approach to discover previously unknown priority dispatching rules for job shop scheduling problem is presented. This approach is based upon seeking the knowledge that is assumed to be embedded in the efficient solutions provided by the optimization module built using tabu search. The objective is to discover the scheduling concepts using data mining and hence to obtain a ruleset capable of approximating the efficient solutions in a dynamic job shop scheduling environment. A data mining based scheduling framework is presented.