Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems

Kacem, Imed; Hammadi, Slim; Borne, Pierre

doi:10.1109/tsmcc.2002.1009117

Cited by 638 publications

(400 citation statements)

References 9 publications

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“…The comparison is made among our algorithm and the other two well known algorithms, that is, Kacem et al [10]. and Ho et al [11].…”

Section: Test Sample ιmentioning

confidence: 99%

“…The first test sample comes from [10]. The comparison is made among our algorithm and the other two well known algorithms, that is, Kacem et al [10].…”

Section: Test Sample ιmentioning

confidence: 99%

“…COMPUTATIONAL RESULT In our experiment, two sets of sample problems are used to analyze this instantiation. Test sample Ι includes the T-FJSP instances taken from [10]. Test sample Π includes the P-FJSP instances taken from [3].…”

mentioning

confidence: 99%

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A Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Flexible Job-Shop Scheduling Problem

Li¹,

Pan²,

Xie³

et al. 2010

IJCTE

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Abstract-Flexible job-shop scheduling problem (FJSP) is very important in many research fields such as production management and combinatorial optimization. The FJSP problems cover two difficulties namely machine assignment problem and operation sequencing problem. In this paper, a hybrid of particle swarm optimization (PSO) algorithm and tabu search (TS) algorithm are presented to solve the FJSP with the criterion to minimize the maximum completion time (makespan). In the novel hybrid algorithm, PSO was used to produce a swarm of high quality candidate solutions, while TS was used to obtain a near optimal solution around the given good solution. The computational results have proved that the proposed hybrid algorithm is efficient and effective for solving FJSP, especially for the problems with large scale.Index Terms -Flexible job shop scheduling problem; Particle swarm optimization; Tabu search algorithm; makespan I. INTRODUCTIONFlexible job-shop problem (FJSP) is harder than the classical job-shop problem. In FJSP, one operation can be operated on a set of machines [1][2][3][4][5]. Therefore, FJSP should solve two problems: first, assign a proper machine from a set of machines to operation each operation; second, sequence each operation on every given machine. The former problem can be seen as a parallel machine problem, which is also a NP-hard problem. The latter is equal to a classical job-shop problem.The FJSP recently captured the interests of many researchers. The first paper about FJSP was proposed by Brucker and Schlie (Brucker & Schlie, 1990) discuss a simple FJSP model with two jobs and operations performed on any machines with the same processing time. To solve more genera FJSP problems with more than two jobs and machines, many researchers proposed hierarchical approaches, i.e., decomposing the problem into two stages: machine assignment sub-problem and job shop sub-problem. The first author to use the hierarchical idea was Brandimarte (Brandimarte, 1993) [3], who solved the first stage with some existing dispatching rules and the second stage with tabu search heuristic algorithms. Mati (Mati Rezg & Xie, 2001) [4] proposed a greedy heuristic for simultaneously dealing with the two stages. Liouane (Liouane, 2007) [5] illustrated a combined ant system optimization with local search methods, including tabu search for solving the FJSP problems. Saidi-mehrabad (Saidi-mehrabad, 2007) [6] gave a detailed solution for solving FJSP with tabu search method.In this paper, we give a new chromosome representation for the FJSP solutions, and propose some novel crossover and mutation functions for the particle swarm optimization algorithm. In each generation, we use tabu search algorithm to find near optimum solutions for the given best solution. After a detailed experiment, the result verifies that our novel method can get better solutions in very short period. The paper is organized as follows: in section 2, we introduce the problem definition; in section 3, some related algorithms such as particle swarm opt...

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“…The comparison is made among our algorithm and the other two well known algorithms, that is, Kacem et al [10]. and Ho et al [11].…”

Section: Test Sample ιmentioning

confidence: 99%

“…The first test sample comes from [10]. The comparison is made among our algorithm and the other two well known algorithms, that is, Kacem et al [10].…”

Section: Test Sample ιmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Flexible Job-Shop Scheduling Problem

Li¹,

Pan²,

Xie³

et al. 2010

IJCTE

View full text Add to dashboard Cite

show abstract

“…The multiple resource constrained JSSP or multiple processor task JSSP, in turn, requires that an operation be processed simultaneously on two or more resources (Brucker andKramer 1996 andFredendall et al 1996). Finally, the flexible job shop scheduling problem (FJSP) incorporates the allocation of operations to resources into the optimization process (Gao et al 2006;Kacem et al 2002a;and Xia and Wu 2005).…”

Section: The Problem Contextmentioning

confidence: 99%

“…Firstly, apart from the PSO-SA hybrid of Xia and Wu (2005), all the algorithms make use of a two-part particle representation and resource allocation is addressed by means of rounding off the continuous PSO particle dimensions to the nearest integer value representing a resource index. Secondly, the largest problem attempted by any of these PSO-based algorithms only consider the scheduling of 56 operations on at most 15 resources, which is currently considered to be a problem of "great size" in FJSP scheduling literature (Kacem et al 2002a). This paper, on the other hand considers a multi-objective FJSP where the scheduling of up to 256 operations on 216 resources need to be considered.…”

Section: Addressing More Complex Scheduling Problemsmentioning

confidence: 99%

Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time

Grobler

Engelbrecht²,

Kok³

et al. 2009

Ann Oper Res

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This paper investigates the application of particle swarm optimization (PSO) to the multi-objective flexible job shop scheduling problem with sequence-dependent set-up times, auxiliary resources and machine down time. To achieve this goal, alternative particle representations and problem mapping mechanisms were implemented within the PSO paradigm. This resulted in the development of four PSO-based heuristics. Benchmarking on real customer data indicated that using the priority-based representation resulted in a significant performance improvement over the existing rule-based algorithms commonly used to solve this problem. Additional investigation into algorithm scalability led to the development of a priority-based differential evolution algorithm. Apart from the academic significance of the paper, the benefit of an improved production schedule can be generalized to include cost reduction, customer satisfaction, improved profitability, and overall competitive advantage.

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Genetic Algorithms for Solving Flexible Job Shop Scheduling Problems

Kacem¹

2013

Metaheuristics for Production Scheduling

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Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems

Cited by 638 publications

References 9 publications

A Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Flexible Job-Shop Scheduling Problem

A Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Flexible Job-Shop Scheduling Problem

Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time

Genetic Algorithms for Solving Flexible Job Shop Scheduling Problems

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