An effective new island model genetic algorithm for job shop scheduling problem

Kurdi, Mohamed Zakaria

doi:10.1016/j.cor.2015.10.005

Cited by 91 publications

(53 citation statements)

References 31 publications

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“…However, the best value which is selected from lots of tests needs 11 generations by traditional GA. The simulation result indicates that the convergence rate and stability of AMGA are remarkably better than those of the traditional GA. Table 4 summarizes the best results obtained by AMGA algorithm and previous approaches including GA [32], SA [33], tabu search (TS) [34], modified genetic algorithm (MGA) [8], parallel agent-based genetic algorithm (PAGA) [28], and new island model genetic algorithm (NIMGA) [27] for MT benchmark problems. From Table 4, it can be concluded that the basic GA, TS, SA and AMGA proposed in this paper can reach the best value of the problem for simple MT06 benchmark problem.…”

Section: Comparison For Mt Benchmark Problemsmentioning

confidence: 98%

“…AMGA is also used to solve some complicated typical scheduling LA benchmark problems [31], and the improved performance is observed in comparison with GA [32], SA [33], TS [34], MGA [8], PAGA [28] and NIMGA [27]. Table 5 shows the experimental results on LA benchmark problems.…”

Section: Comparison For Some La Benchmark Problemsmentioning

confidence: 99%

“…A new hybrid island model GA for JSP was proposed by Kurdi [26]. A new naturally inspired migration selection mechanism and a new naturally inspired evolution model were proposed in order to avoid falling into the premature convergence and improve the search diversification [27]. An agent-based parallel GA for JSP was proposed to create the initial population, and the simulation results on benchmark instances indicated that the proposed algorithm could improve the efficiency [28].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An adaptive multi-population genetic algorithm for job-shop scheduling problem

Wang

Cai

2016

Adv. Manuf.

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Job-shop scheduling problem (JSP) is a typical NP-hard combinatorial optimization problem and has a broad background for engineering application. Nowadays, the effective approach for JSP is a hot topic in related research area of manufacturing system. However, some JSPs, even for moderate size instances, are very difficult to find an optimal solution within a reasonable time because of the process constraints and the complex large solution space. In this paper, an adaptive multi-population genetic algorithm (AMGA) has been proposed to solve this problem. Firstly, using multi-populations and adaptive crossover probability can enlarge search scope and improve search performance. Secondly, using adaptive mutation probability and elite replacing mechanism can accelerate convergence speed. The approach is tested for some classical benchmark JSPs taken from the literature and compared with some other approaches. The computational results show that the proposed AMGA can produce optimal or near-optimal values on almost all tested benchmark instances. Therefore, we can believe that AMGA can be considered as an effective method for solving JSP.

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Section: Comparison For Mt Benchmark Problemsmentioning

confidence: 98%

Section: Comparison For Some La Benchmark Problemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An adaptive multi-population genetic algorithm for job-shop scheduling problem

Wang

Cai

2016

Adv. Manuf.

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“…Figure 3 shows the concept of the Fab-level backward capacity-filtering procedure. The production and Sadowski 1995Sadowski , 1997Pritsker and Snyder 1997;Ho and Li 2004), genetic algorithm-based approaches (Kurz and Askin 2004;Huang and Suer 2015;Kurdi 2016), and time-bucketing approaches (Shimoyashiro, Isoda, and Awane 1984;Ragatz and Mabert 1988;Bobrowski 1989;Cigolini and Portioli-Staudacher 2002;Saad, Pickett, and Kittiaram 2004). Although the use of simulations is a powerful technique for the evaluation of various alternative solutions, it has limitations regarding the generation of suitable solutions on the first step.…”

Section: Introductionmentioning

confidence: 99%

Backward capacity-filtering for electronics Fabs

Seo

Chung

Kim³

et al. 2016

Production Planning & Control

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In electronics Fabs such as semiconductor Fabs and liquid crystal display (LCD) Fabs, which are capitalintensive, finite-capacity planning is critical to achieving full-capacity production and on-time production. However, existing finite-capacity planning methods do not adequately reflect the actual capacity profiles of an electronics Fab. In this paper, we propose a new Fab-level capacity-filtering procedure to generate finite-capacity plan: a backward capacity-filtering procedure for generating finite-capacity release plan. When developing the Fab-level filtering procedures, we use stage-level capacity-filtering algorithms as a key building block. In this study, we enhance the previous capacity-filtering algorithms proposed by one of the authors in order to facilitate the practical requirements of electronics Fabs. We apply the proposed capacity-filtering method to a modern LCD-TFT Fab in Korea. Performance analyses demonstrate that the proposed method is superior to existing methods. ARTICLE HISTORYFinite-capacity planning; flexible flow line; electronic Fab; lcd and semiconductor Fab

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“…During the last years, the parallel island model has been successfully used, showing its robustness and applicability in different problems and areas of knowledge such as: Unequal Area Facility 22 , Job Shop Scheduling 23 , Cluster Geometry Optimization 15 , Parallel Harmony Search 24 , Multi-Objective problems 25 and problems of the molecular biology domain 26 .…”

Section: Related Workmentioning

confidence: 99%

A New Efficient Entropy Population-Merging Parallel Model for Evolutionary Algorithms

Arellano‐Verdejo¹,

Godoy-Calderón²,

Alonso-Pecina³

et al. 2017

IJCIS

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In this paper a coarse-grain execution model for evolutionary algorithms is proposed and used for solving numerical and combinatorial optimization problems. This model does not use migration as the solution dispersion mechanism, in its place a more efficient population-merging mechanism is used that dynamically reduces the population size as well as the total number of parallel evolving populations. Even more relevant is the fact that the proposed model incorporates an entropy measure to determine how to merge the populations such that no valuable information is lost during the evolutionary process. Extensive experimentation, using genetic algorithms over a well-known set of classical problems, shows the proposed model to be faster and more accurate than the traditional one.

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An effective new island model genetic algorithm for job shop scheduling problem

Cited by 91 publications

References 31 publications

An adaptive multi-population genetic algorithm for job-shop scheduling problem

An adaptive multi-population genetic algorithm for job-shop scheduling problem

Backward capacity-filtering for electronics Fabs

A New Efficient Entropy Population-Merging Parallel Model for Evolutionary Algorithms

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