Production Scheduling and Rescheduling with Genetic Algorithms

被引：200
|
作者：
Bierwirth, Christian [1 ]
Mattfeld, Dirk C. [1 ]
机构：
[1] Univ Bremen, Dept Econ, D-28334 Bremen, Germany
来源：
EVOLUTIONARY COMPUTATION | 1999年 / 7卷 / 01期
关键词：
Genetic algorithm; permutation representation; tunable decoding; job shop scheduling problem; dynamic scheduling;
D O I：
10.1162/evco.1999.7.1.1
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed at reasonable runtime costs.
引用
收藏
页码：1 / 17
页数：17
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