An Improved Genetic Algorithm for Solving Flexible Job shop Scheduling Problem

被引：0

作者：

Zhou Wei ^{[1
]}

Bu Yan-ping ^{[2
]}

Zhou Ye-qing ^{[3
]}

机构：

[1] E China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China

[2] Shanghai Jiao Tong Univ, Sch Technol, Shanghai 201101, Peoples R China

[3] Fudan Univ, Sch Math Sci, Shanghai 200433, Peoples R China

来源：

2013 25TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2013年

关键词：

genetic algorithm; flexible job shop scheduling problem; multi-objective optimization; makespan;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The Flexible Job Shop Scheduling Problem (FJSP) is a very important problem in the modern manufacturing system. It is an extension of the classical job shop scheduling problem. It allows an operation to be processed by any machine from a given set. It is also a NP-hard problem. Since FJSP requires an additional decision of machine allocation during scheduling, therefore it is much more complex problem than JSP. This paper proposed an improved genetic algorithm (IGA) to solve FJSP. We tested the IGA against the GA method. Simulation results demonstrate that it can be superior to the regular GA. We also tested the IGA with the exhaustion method to show the algorithm's efficiency.

引用

页码：4553 / 4558

页数：6

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