Parallel hybrid metaheuristics for the flexible job shop problem

Boejko, Wojciech; Uchroński, Mariusz; Wodecki, Mieczysław

doi:10.1016/j.cie.2010.05.004

Cited by 82 publications

(34 citation statements)

References 15 publications

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“…The detail computational results are reported and compared with the CDDS and the HDE-N 1 of Yuan et al 43 in Table 7. As can be seen from Table 8 lists the best makespan and the of AFSA-ED, TSBM 2 h by Bozejko et al 13 , CDDS 37 , IFS by Oddi et al 44 , HDE-N 1 and HDE-N 2 by Yuan et al 43 . For the IFS algorithm, its performance depends on the relaxing factor , the table lists the results obtained by running IFS with ranging from 0.2 to 0.7, respectively.…”

Section: Results Of Bcdata Problemsmentioning

confidence: 91%

“…More recently, with the emergence of new techniques from the field of artificial intelligence, much attention has been devoted to meta-heuristics. The tabu search (TS) has been widely used, such as Brandimart 11 , Mastrolilli and Gambardella 12 , Bozejko et al 13 and Li et al 14 , while the genetic algorithm (GA) has also been examined to be an efficient method such as in Chen et al 15 , Kacem et al 16 , Pezzella et al 17 and Gao et al 18 Besides, some other meta-heuristics have been employed for this problem such as simulated annealing (SA) [19][20][21] , particle swarm optimization (PSO) [22][23][24] , ant colony optimization (ACO) 25 , artificial neural network (ANN) 26 , and artificial immune system (AIS) 27 .…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Artificial Fish Swarm Model with Estimation of Distribution for Flexible Job Shop Scheduling

Ge¹,

Sun²,

Chen³

et al. 2016

IJCIS

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The flexible job shop scheduling problem (FJSP) is one of the most important problems in the field of production scheduling, which is the abstract of some practical production processes. It is a complex combinatorial optimization problem due to the consideration of both machine assignment and operation sequence. In this paper, an efficient artificial fish swarm model with estimation of distribution (AFSA-ED) is proposed for the FJSP with the objective of minimizing the makespan. Firstly, a pre-principle and a post-principle arranging mechanism that operate by adjusting machine assignment and operation sequence with different orders are designed to enhance the diversity of population. Following this, the population is divided into two sub-populations and then two arranging mechanisms are applied. In AFSA-ED, a preying behavior based on estimation of distribution is proposed to improve the performance of algorithm. Moreover, an attracting behavior is proposed to improve the global exploration ability and a public factor based critical path search strategy is proposed to enhance the local exploitation ability. Simulated experiments are carried on BRdata, BCdata and HUdata benchmark sets. The computational results validate the performance of the proposed algorithm in solving the FJSP, as compared with some other state of the art algorithms.

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Section: Results Of Bcdata Problemsmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

An Efficient Artificial Fish Swarm Model with Estimation of Distribution for Flexible Job Shop Scheduling

Ge¹,

Sun²,

Chen³

et al. 2016

IJCIS

View full text Add to dashboard Cite

show abstract

“…Dokeroglu ve Cosar yeni melez sezgisel kutulama algoritmaları geliştirerek paralel ortamda 1BKP'ni iyi çözen yöntemler buldular [2]. İki ve üç boyutlu kutulama problemleri için paralel ve bazı hiper-sezgisel algoritmalar geliştirilmiş olduğu bilinmesine rağmen, bir boyutlu kutulama problemleri için bizim önerdiğimiz gibi hiper-sezgisel paralel bir algoritmanın geliştirilmemiş olduğu görülmektedir [13].…”

Section: öNceki çAlışmalarunclassified

Bir boyutlu kutulama probleminin eniyilenmesi için hiper-sezgisel paralel bir algoritma

Dökeroğlu

2017

Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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“…Mastrolilli and Gambardella (2000) used tabu search techniques and presented two neighborhood functions allowing an approximate resolution for the FJSP. Bozejko et al (2010a) presented a tabu search approach based on a new golf neighborhood for the FJSP, and in the same year, Bozejko et al (2010b) proposed another new model of a distributed tabu search algorithm for the FJSP, using a cluster architecture consisting of nodes equipped with the GPU units (multi-GPU) with distributed memory. A novel hybrid tabu search algorithm with a fast Public Critical Block neighborhood structure (TSPCB) was proposed by Li et al (2011) to solve the FJSP.…”

Section: Introductionmentioning

confidence: 99%

Solving the flexible job shop problem by hybrid metaheuristics-based multiagent model

2017

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The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem that allows to process operations on one machine out of a set of alternative machines. The FJSP is an NPhard problem consisting of two sub-problems, which are the assignment and the scheduling problems. In this paper, we propose how to solve the FJSP by hybrid metaheuristics-based clustered holonic multiagent model. First, a neighborhood-based genetic algorithm (NGA) is applied by a scheduler agent for a global exploration of the search space. Second, a local search technique is used by a set of cluster agents to guide the research in promising regions of the search space and to improve the quality of the NGA final population. The efficiency of our approach is explained by the flexible selection of the promising parts of the search space by the clustering operator after the genetic algorithm process, and by applying the intensification technique of the tabu search allowing to restart the search from a set of elite solutions to attain new dominant scheduling solutions. Computational results are presented using four sets of well-known benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

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Parallel hybrid metaheuristics for the flexible job shop problem

Cited by 82 publications

References 15 publications

An Efficient Artificial Fish Swarm Model with Estimation of Distribution for Flexible Job Shop Scheduling

An Efficient Artificial Fish Swarm Model with Estimation of Distribution for Flexible Job Shop Scheduling

Bir boyutlu kutulama probleminin eniyilenmesi için hiper-sezgisel paralel bir algoritma

Solving the flexible job shop problem by hybrid metaheuristics-based multiagent model

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