New genetic algorithm for parallel machine scheduling with process constraint

被引：0

作者：

Yin, Wen-Jun ^{[1
]}

Liu, Min ^{[1
]}

Wu, Cheng ^{[1
]}

机构：

[1] Dept. of Automat., Tsinghua Univ., Beijing 100084, China

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2001年 / 29卷 / 11期

关键词：

Constraint theory - Decoding - Encoding (symbols) - Genetic algorithms - Mathematical operators - Production control - Scheduling;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

A new genetic algorithm is studied for solving parallel machine scheduling problems with process constraint to minimize the total number of tardy jobs. A so-called vector-group coding method is presented, which shows the quality of coding simply, decoding fast and satisfying process constraints automatically. A new crossover operator named Extended Order Crossover (EOX) is then proposed, which has the merits of automatically satisfying procedure constraints and preserving much genetic information. Mutation, the other genetic operator, is implemented with the combination of bit-mutation and swap-mutation to keep the population diverse. The algorithm behaves more efficiently than others experimentally using various random data and application instance from practical production line.

引用

页码：1482 / 1485