Balancing setup workers' load of flexible job shop scheduling using hybrid genetic algorithm with tabu search strategy

2016 ◽  
Vol 2 (1/2/3) ◽  
pp. 71 ◽  
Author(s):  
Yasuhiko Morinaga ◽  
Masahiro Nagao ◽  
Mitsuru Sano
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Search Strategy ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling

Research on Bi-Objective Scheduling of Dual-Resource Constrained Flexible Job Shop

2011 ◽  
Vol 211-212 ◽  
pp. 1091-1095 ◽  
Author(s):  
Xiao Xia Liu ◽  
Chun Bo Liu ◽  
Ze Tao
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Pareto Ranking ◽  
Ranking Strategy

A hybrid genetic algorithm based on Pareto was proposed and applied to the flexible job shop scheduling problem (FJSP) with bi-objective, and the bi-objective FJSP optimization model was built, where the make-span and the production cost were concerned. The algorithm embeds Pareto ranking strategy into Pareto competition method, and the niche technology and four kinds of crossover operations are used in order to promote solution diversity. Pareto filter saves the optimum individual occurring in the course of evolution, which avoids losing the optimum solutions. This hybrid genetic algorithm reasonably assigns the resources of machines and workers to jobs and achieves optimum on some performance. In this paper, the influence of the proportion of workers and machines on the scheduling result is researched on the basis of the hybrid genetic algorithm and the result is in accord with other researchers. In conclusion, the algorithm proposed in this paper is available and efficient.

scholarly journals Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm

2018 ◽  
Vol 10 (10) ◽  
pp. 168781401880409 ◽  
Author(s):  
Rui Wu ◽  
Yibing Li ◽  
Shunsheng Guo ◽  
Wenxiang Xu
Keyword(s):  
Genetic Algorithm ◽  
Variable Neighborhood Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Neighborhood Search ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

In this article, we investigate a novel dual-resource constrained flexible job shop scheduling problem with consideration of worker’s learning ability and develop an efficient hybrid genetic algorithm to solve the problem. To begin with, a comprehensive mathematical model with the objective of minimizing the makespan is formulated. Then, a hybrid algorithm which hybridizes genetic algorithm and variable neighborhood search is developed. In the proposed algorithm, a three-dimensional chromosome coding scheme is employed to represent the individuals, a mixed population initialization method is designed for yielding the initial population, and advanced crossover and mutation operators are proposed according to the problem characteristic. Moreover, variable neighborhood search is integrated to improve the local search ability. Finally, to evaluate the effectiveness of the proposed algorithm, computational experiments are performed. The results demonstrate that the proposed algorithm can solve the problem effectively and efficiently.

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