Multi-objective flexible job shop scheduling problem using differential evolution algorithm

被引：0
|
作者：
Cao, Yang [1 ,2 ,3 ,4 ,5 ]
Shi, Haibo [2 ,3 ,4 ]
Han, Zhonghua [2 ,3 ,4 ,5 ]
机构：
[1] Northeastern Univ, Coll Informat Sci & Engn, CO-110819 Shenyang, Liaoning, Peoples R China
[2] Chinese Acad Sci, Dept Digital Factory, Shenyang Inst Automat, CO-110016 Shenyang, Liaoning, Peoples R China
[3] Univ Chinese Acad Sci, CO-100049 Beijing, Peoples R China
[4] Chinese Acad Sci, Key Lab Networked Control, CO-110016 Shenyang, Liaoning, Peoples R China
[5] Shenyang Jianzhu Univ, Fac Informat & Control Engn, CO-110168 Shenyang, Liaoning, Peoples R China
来源：
2017 9TH INTERNATIONAL CONFERENCE ON MODELLING, IDENTIFICATION AND CONTROL (ICMIC 2017) | 2017年
关键词：
flexible job shop scheduling problem; differential evolution algorithm; multi-objective optimization; OPTIMIZATION;
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：
Flexible job shop scheduling problem (FJSP) is very complex to be controlled, and it is a problem which inherits job shop scheduling problem (JSP) characteristics. FJSP has two sub-problems: routing sub-problem and scheduling sub-problem. In this paper, improved differential evolution (DE) algorithm is presented for multi-objective FJSP. Minimization of three objective functions includes maximum completion time, workload of the most loaded machine and total workload of all machines. The improved algorithm has a well-designed mutation and crossover operator, and uses a Pareto non-dominated sorting method. Computational simulations and comparisons demonstrate the effectiveness of the proposed improved DE algorithm.
引用
收藏
页码：521 / 526
页数：6
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