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

Authors:
Fulya Altiparmak;Mitsuo Gen;Lin Lin;Ismail Karaoglan
Affiliations:
Department of Industrial Engineering, Gazi University, Turkey;Graduate School of Information, Production and Systems, Waseda University, Japan;Graduate School of Information, Production and Systems, Waseda University, Japan;Department of Industrial Engineering, Selcuk University, Turkey
Venue:
Computers and Industrial Engineering
Year:
2009

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Abstract

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 a solution procedure based on steady-state genetic algorithms (ssGA) with a 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.