Appling an improved genetic algorithm for solving the production scheduling problem of steelmaking and continuous casting

被引：0

作者：

Wang, Hong-Bing ^{[1
]}

Xu, An-Jun ^{[2
]}

Yao, Lin ^{[1
]}

Tian, Nai-Yuan ^{[2
]}

Du, Xi ^{[3
]}

机构：

[1] School of Information Engineering, University of Science and Technology Beijing, Beijing 100083, China

[2] School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China

[3] School of Computer Science and Technology, Anhui University, Hefei 230601, China

来源：

Beijing Keji Daxue Xuebao/Journal of University of Science and Technology Beijing | 2010年 / 32卷 / 09期

关键词：

Genetic algorithms - Job shop scheduling - Problem solving - Production control - Steelmaking - Exponential functions;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

The manufacturing flow of steelmaking and continuous casting is a complex multiple-phase and multiple-product production process. The production scheduling problem in this manufacturing flow can be seen as a job shop scheduling problem. An improved genetic algorithm for solving this problem was proposed and the improved aspects were as follows: rank-based fitness assignment, job filter order-based crossover operator, and mutation rate according to an exponential function relation. Twenty-four benchmarks were comparatively investigated and the result shows that the improved genetic algorithm has a better capacity of seeking optimum than a traditional genetic algorithm. The production scheduling problem of steelmaking and continuous casting with sixteen plans and six procedures was computed using the improved genetic algorithm. It is shown that the algorithm is effective.

引用

页码：1232 / 1237