Supply chain optimisation using evolutionary algorithms

被引：12
|
作者：
Falcone, Marco Aurelio [1 ]
Lopes, Heitor Silverio [2 ]
Coelho, Leandro dos Santos [3 ]
机构：
[1] Tritec Motors, BR-83606360 Campo Largo, PR, Brazil
[2] Fed Technol Univ Parana, CPGEI, BR-80230901 Curitiba, PR, Brazil
[3] Pontificia Univ Catolica Parana, PPGEPS LAS, R Imaculada Conceicao 1155, BR-80215901 Curitiba, PR, Brazil
来源：
INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY | 2008年 / 31卷 / 3-4期
关键词：
evolutionary computation; genetic algorithms; GA; differential evolution; DE; logistics; supply chain;
D O I：
10.1504/IJCAT.2008.018154
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
This paper describes the application of Evolutionary Algorithms (EAs) to the optimisation of a simplified supply chain in an integrated production-inventory-distribution system. The performance of four EAs (Genetic Algorithm (GA), Evolutionary Programming (EP), Evolution Strategies (ES) and Differential Evolution (DE)) was evaluated with numerical simulations. Results were also compared with other similar approaches in the literature. DE was the algorithm that led to better results, outperforming previously published solutions. The robustness of EAs in general, and the efficiency of DE, in particular, suggest their great utility for the supply chain optimisation problem, as well as for other logistics-related problems.
引用
收藏
页码：158 / 167
页数：10
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