模糊逻辑
调度(生产过程)
作业车间调度
人口
遗传算法
流水车间调度
计算机科学
算法
数学优化
理论计算机科学
人工智能
数学
地铁列车时刻表
操作系统
社会学
人口学
标识
DOI:10.1080/00207540902814348
摘要
This paper presents a flexible job shop scheduling problem with fuzzy processing time. An efficient decomposition-integration genetic algorithm (DIGA) is developed for the problem to minimise the maximum fuzzy completion time. DIGA uses a two-string representation, an effective decoding method and a main population. In each generation, DIGA decomposes the chromosomes of the main population into a job sequencing part and a machine assigning part and independently evolves the populations of these parts. Some instances are designed and DIGA is tested and compared with other algorithms. Computational results show the effectiveness of DIGA.
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