A hybrid meta-heuristic for the flexible flow shop scheduling with blocking

被引:8
|
作者
Wang, Yan
Jia, Zhao-hong [1 ]
Zhang, Xing-yi
机构
[1] Anhui Univ, Sch Comp Sci & Technol, Hefei 230039, Anhui, Peoples R China
关键词
Blocking flow shop scheduling; Batch processing machine; Ant colony optimization; Genetic algorithm; Bi-objective optimization; ANT COLONY OPTIMIZATION; ALGORITHM; MACHINES; TIME; ACO;
D O I
10.1016/j.swevo.2022.101195
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper investigates a multi-stage flexible flow shop scheduling problem with blocking on batch processing machines. The objective is to minimize the makespan and the total energy consumption of machines, simultaneously. We first describe the problem and formulate its mathematical model. Based on problem characteristics, we design a hybrid meta-heuristic algorithm based on ant colony optimization and genetic algorithms to solve the problem. In order to solve this problem efficiently, the proposed algorithm consists of two components, which are used to construct batch and achieve overall scheduling, respectively. The search strategy based on ant colony optimization algorithm is designed to construct batches to improve the convergence of the algorithm. Based on this, a global scheduling strategy based on genetic algorithm is proposed to intensify the diversity. Finally, extensive simulation experiments are conducted to verify the effectiveness and efficiency of the proposed algorithm.
引用
收藏
页数:12
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