A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems

Gao, Jie; Gen, Mitsuo; Sun, Lei; Zhao, Xiaoxue

doi:10.1016/j.cie.2007.04.010

Cited by 178 publications

(67 citation statements)

References 12 publications

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“…al. 97 addresses the fJSP problem with three objectives: min makespan, min maximal machine workload and min total workload and develop a new GA hybridized with an innovative local search procedure (bottleneck shifting) for the problem. The GA uses two representation methods to depict solution candidates of the fJSP problem.…”

Section: Background On Multi-criteria Scheduling (Job-shop)mentioning

confidence: 99%

Review on Job-Shop and Flow-Shop Scheduling Using

Parveen

Ullah

2011

J. mech. eng.

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Section: Background On Multi-criteria Scheduling (Job-shop)mentioning

confidence: 99%

Review on Job-Shop and Flow-Shop Scheduling Using

Parveen

Ullah

2011

J. mech. eng.

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“…As for a priori approach, Xia and Wu [17] discussed a hybrid algorithm, where particle swarm optimization (PSO) and simulated annealing (SA) were employed in global search and local search, respectively. A bottleneck shifting-based local search was incorporated into genetic global search by Gao et al [18]. Zhang et al [19] introduced an effective hybrid PSO algorithm combined with tabu search (TS).…”

Section: Related Workmentioning

confidence: 99%

A Novel Memetic Algorithm Based on Decomposition for Multiobjective Flexible Job Shop Scheduling Problem

Wang

2017

Mathematical Problems in Engineering

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A novel multiobjective memetic algorithm based on decomposition (MOMAD) is proposed to solve multiobjective flexible job shop scheduling problem (MOFJSP), which simultaneously minimizes makespan, total workload, and critical workload. Firstly, a population is initialized by employing an integration of different machine assignment and operation sequencing strategies. Secondly, multiobjective memetic algorithm based on decomposition is presented by introducing a local search to MOEA/D. The Tchebycheff approach of MOEA/D converts the three-objective optimization problem to several single-objective optimization subproblems, and the weight vectors are grouped by K-means clustering. Some good individuals corresponding to different weight vectors are selected by the tournament mechanism of a local search. In the experiments, the influence of three different aggregation functions is first studied. Moreover, the effect of the proposed local search is investigated. Finally, MOMAD is compared with eight state-of-the-art algorithms on a series of well-known benchmark instances and the experimental results show that the proposed algorithm outperforms or at least has comparative performance to the other algorithms.

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“…Developed on the basis of conventional job-shop scheduling, flexible job-shop scheduling breaks through the limitation that each type of resource must be used for a unique purpose, and designs the optimal process chain to improve the production efficiency of enterprises. Hence, the new dispatching mode has important theoretical and practical significance for the research of flexible job-shop [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Scheduling Simulation of Flexible Job-Shop Based on Multi-Population Genetic Algorithm

Zhang¹,

Wen²,

Zhu³

et al. 2017

Int. j. simul. model.

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In view of the difficulty of obtaining the optimal solution to the multi-objective scheduling of flexible job-shop by the general genetic algorithm, this paper takes into account the shortest processing time and the balanced use of machines, and puts forward the multi-population genetic algorithm based on the multi-objective scheduling of flexible job-shop. The method attempts to minimize the longest make-span of workpieces, the load of each machine, and the total machine load through the overall process scheduling of the job-shop. Research results reveal that the proposed method is highly efficient in seeking the optimal machine allocation chain, and effective in avoiding the complex process of intermediate assignment, making it easier to obtain the said optimal solution. The feasibility and effectiveness of the proposed method are also validated by two instances. Compared with the conventional flexible job-shop scheduling algorithms, the proposed algorithm boasts better population quality, algorithm starting point, and initial expression. Besides, it is far better than other algorithms in terms of the initial solution quality and the convergence rate. Despite the local fluctuations in the early phase of the genetic process, the total machine load and the machine load variance are gradually declining and the curves start to converge after the 50 th generation.

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A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems

Cited by 178 publications

References 12 publications

Review on Job-Shop and Flow-Shop Scheduling Using

Review on Job-Shop and Flow-Shop Scheduling Using

A Novel Memetic Algorithm Based on Decomposition for Multiobjective Flexible Job Shop Scheduling Problem

Multi-Objective Scheduling Simulation of Flexible Job-Shop Based on Multi-Population Genetic Algorithm

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