Optimising a complex discrete event simulation model using a genetic algorithm

被引：13
|
作者：
Paul, RJ [1 ]
Chanev, TS
机构：
[1] Brunel Univ, Dept Informat Syst & Comp, Uxbridge UB8 3PH, Middx, England
[2] SIGNUM 66 Co, Sofia, Bulgaria
来源：
NEURAL COMPUTING & APPLICATIONS | 1997年 / 6卷 / 04期
关键词：
genetic algorithms; mixed programming; simulation optimisation; simulation model ling; steelworks operation;
D O I：
10.1007/BF01501509
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A steelworks model is selected as representative of the stochastic and unpredictable behaviour of a complex discrete event simulation model. The steelworks has a number of different entity of object types. Using the number of each entity type as parameters, it is possible to find better and worse combinations of parameters for various management objectives. A simple real-coded genetic algorithm is presented that optimises the parameters, demonstrating the versatility that genetic algorithms offer in solving hard inverse problems.
引用
收藏
页码：229 / 237
页数：9
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