A steady-state genetic algorithm for multi-product supply chain network design

被引：151

作者：

Altiparmak, Fulya ^{[1
]}

Gen, Mitsuo ^{[2
]}

Lin, Lin ^{[2
]}

Karaoglan, Ismail ^{[3
]}

机构：

[1] Gazi Univ, Dept Ind Engn, Ankara, Turkey

[2] Waseda Univ, Grad Sch Informat Product & Syst, Tokyo, Japan

[3] Selcuk Univ, Dept Ind Engn, Konya, Turkey

来源：

COMPUTERS & INDUSTRIAL ENGINEERING | 2009年 / 56卷 / 02期

关键词：

Supply chain network design; Genetic algorithms; Simulated annealing; Lagrangean heuristic; TRANSPORTATION PROBLEM; MODEL;

D O I：

10.1016/j.cie.2007.05.012

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Supply chain network (SCN) design is to provide an optimal platform for efficient and effective supply chain management (SCM). The problem is often an important and strategic operations management problem in SCM. The design task involves the choice of facilities (plants and distribution centers (DCs)) to be opened and the distribution network design to satisfy the customer demand with minimum cost. This paper presents it solution procedure based on steady-state genetic algorithms (ssGA) with it new encoding structure for the design of a single-source, multi-product, multi-stage SCN. The effectiveness of the ssGA has been investigated by comparing its results with those obtained by CPLEX, Lagrangean heuristic, hyrid GA and simulated annealing on a set of SCN design problems with different sizes. (C) 2007 Elsevier Ltd. All rights reserved.

引用

页码：521 / 537

页数：17

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