Multi-objective genetic algorithm for integrated process planning and scheduling with fuzzy processing time

被引：0

作者：

Wen, Xiaoyu ^{[1
]}

Li, Xinyu ^{[1
]}

Gao, Liang ^{[1
]}

Wan, Liang ^{[1
]}

Wang, Wenwen ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China

来源：

2013 SIXTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI) | 2013年

关键词：

Integrated process planning and scheduling; multi-objective genetic algorithm; fuzzy processing time;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Integrated process planning and scheduling is a significant research focus in recent years, which could improve the performance of manufacturing system. In real manufacturing environment, multi-objectives should be taken into consideration simultaneously during the machining process. Meanwhile, the processing time for each job is often imprecise in many real applications. Therefore, multi-objective integrated process planning and scheduling (IPPS) problem with fuzzy processing time is addressed in this paper. The processing time is described as triangular fuzzy number. A multi-objective genetic algorithm (MOGA) is designed to search for the Pareto solutions of multiobjective IPPS problem with fuzzy processing time. An instance has been designed to test the performance of proposed algorithm. The experiment result shows that the proposed MOGA could obtain satisfactory Pareto solutions for the multi-objective IPPS problem with fuzzy processing time.

引用

页码：293 / 298

页数：6

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