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

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.

[1]  Mitsuo Gen,et al.  Genetic algorithms and engineering optimization , 1999 .

[2]  Yang-Byung Park,et al.  A hybrid genetic algorithm for the vehicle scheduling problem with due times and time deadlines , 2001 .

[3]  Rajan Batta,et al.  On the use of genetic algorithms to solve location problems , 2002, Comput. Oper. Res..

[4]  Wei-Chang Yeh.  A hybrid heuristic algorithm for the multistage supply chain network problem , 2005 .

[5]  J. Shapiro.  Modeling the Supply Chain , 2000 .

[6]  Wei-Chang Yeh,et al.  An efficient memetic algorithm for the multi-stage supply chain network problem , 2006 .

[7]  Siddhartha S. Syam.  A model and methodologies for the location problem with logistical components , 2002, Comput. Oper. Res..

[8]  Turan Paksoy,et al.  A genetic algorithm approach for multi-objective optimization of supply chain networks , 2006, Comput. Ind. Eng..

[9]  Hasan Pirkul,et al.  Planning and coordination of production and distribution facilities for multiple commodities , 2001, Eur. J. Oper. Res..

[10]  Mitsuo Gen,et al.  The balanced allocation of customers to multiple distribution centers in the supply chain network: a genetic algorithm approach , 2002 .

[11]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[12]  Maurizio Bielli,et al.  Genetic algorithms in bus network optimization , 2002 .

[13]  I. C. Parmee.  Adaptive Computing in Design and Manufacture , 1998 .

[14]  Vaidyanathan Jayaraman,et al.  Production , Manufacturing and Logistics A simulated annealing methodology to distribution network design and management , 2002 .

[15]  Colin R. Reeves,et al.  A genetic algorithm for flowshop sequencing , 1995, Comput. Oper. Res..

[16]  M. Gen,et al.  Solving Multi-objective Transportation Problems by Spanning Tree-based Genetic Algorithm , 1998 .

[17]  M. Gen,et al.  Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach , 2002 .

[18]  Mitsuo Gen,et al.  A genetic algorithm for two-stage transportation problem using priority-based encoding , 2006, OR Spectr..

[19]  Rubén Ruiz,et al.  TWO NEW ROBUST GENETIC ALGORITHMS FOR THE FLOWSHOP SCHEDULING PROBLEM , 2006 .

[20]  Xiong Chen,et al.  Modeling rolling batch planning as vehicle routing problem with time windows , 1998, Comput. Oper. Res..

[21]  Zbigniew Michalewicz,et al.  A Nonstandard Genetic Algorithm for the Nonlinear Transportation Problem , 1991, INFORMS J. Comput..

[22]  Jinwoo Park,et al.  A combined model of network design and production/distribution planning for a supply network , 2002 .

[23]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[24]  Mitsuo Gen,et al.  Spanning tree-based genetic algorithm for the bicriteria fixed charge transportation problem , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[25]  Alistair I. Mees,et al.  Convergence of an annealing algorithm , 1986, Math. Program..

[26]  Halit Üster,et al.  A scatter search-based heuristic to locate capacitated transshipment points , 2007, Comput. Oper. Res..

[27]  C. Reeves.  Modern heuristic techniques for combinatorial problems , 1993 .

[28]  Mitsuo Gen,et al.  Solving exclusionary side constrained transportation problem by using a hybrid spanning tree-based genetic algorithm , 2003, J. Intell. Manuf..

[29]  Douglas J. Thomas,et al.  Coordinated supply chain management , 1996 .