拖延
渡线
数学优化
可重入
帕累托原理
作业车间调度
遗传算法
流水车间调度
缩小
计算机科学
算法
数学
调度(生产过程)
人工智能
地铁列车时刻表
操作系统
程序设计语言
作者
Hang-Min Cho,Suk Joo Bae,Jungwuk Kim,In‐Jae Jeong
标识
DOI:10.1016/j.cie.2011.04.008
摘要
This paper deals with a scheduling problem for reentrant hybrid flowshop with serial stages where each stage consists of identical parallel machines. In a reentrant flowshop, a job may revisit any stage several times. Local-search based Pareto genetic algorithms with Minkowski distance-based crossover operator is proposed to approximate the Pareto optimal solutions for the minimization of makespan and total tardiness in a reentrant hybrid flowshop. The Pareto genetic algorithms are compared with existing multi-objective genetic algorithm, NSGA-II in terms of the convergence to optimal solution, the diversity of solution and the dominance of solution. Experimental results show that the proposed crossover operator and local search are effective and the proposed algorithm outperforms NSGA-II by statistical analysis.
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