Managing genetic search in job shop scheduling

被引：36

作者：

Uckum, Serdar ^{[1
]}

Bagchi, Sugato ^{[1
]}

Kawamura, Kazuhiko ^{[1
]}

Miyabe, Yutaka ^{[1
]}

机构：

[1] Nippon Steel Corp, Stanford, United States

来源：

IEEE expert | 1993年 / 8卷 / 05期

关键词：

Artificial intelligence - Operations research - Optimization - Scheduling;

D O I：

10.1109/64.236477

中图分类号：

学科分类号：

摘要：

Genetic algorithms can serves as the search methodology for job shop scheduling problems. VSOP also uses domain-specific chromosome representations, recombination operators, and local enumerative search to increase efficiency. The Vanderbilt Schedule Optimizer Prototype (VSOP) takes the third approach. Our project's goals were to develop a knowledge representation formalism for a job shop scheduling, explore GAs as a viable search method, and develop a prototype problem solver for proof-of-principle.

引用

页码：15 / 24