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

ALTIPARMAK BAYKOÇ F., Gen M., Lin L., KARAOĞLAN İ.

COMPUTERS & INDUSTRIAL ENGINEERING, cilt.56, sa.2, ss.521-537, 2009 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 56 Sayı: 2
  • Basım Tarihi: 2009
  • Doi Numarası: 10.1016/j.cie.2007.05.012
  • Dergi Adı: COMPUTERS & INDUSTRIAL ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.521-537
  • Anahtar Kelimeler: Supply chain network design, Genetic algorithms, Simulated annealing, Lagrangean heuristic, TRANSPORTATION PROBLEM, MODEL
  • Gazi Üniversitesi Adresli: Evet

Özet

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.