Supply and Production/Distribution Planning in Supply Chain with Genetic Algorithm

The present study, using genetic algorithm, tries to improve material flow management in supply chain. Consequently, in this paper, an integrated supply-production and distribution planning (SPDP) is considered despite the fact that in most of the Iranian industrial firms, SPDP is done independently. The effective use of integrated SPDP not only enhances the performance rather decreases inventory cost, holding cost, shortage cost and overall supply chain costs. A quantitative mathematical model is used to the problem articulation, and then it is solved by applying heuristic genetic algorithm (GA) method. The proposed model with genetic algorithm could provide the best satisfactory result with the minimum cost. The reliability test was carried by comparing the model results with that of the amount of variables. DOI: 10.4018/978-1-4666-2625-6.ch078

[1]  Mitsuo Gen,et al.  A steady-state genetic algorithm for multi-product supply chain network design , 2009, Comput. Ind. Eng..

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

[3]  David F. Pyke,et al.  Multiproduct integrated production—distribution systems☆ , 1994 .

[4]  Hasan Pirkul,et al.  A multi-commodity, multi-plant, capacitated facility location problem: formulation and efficient heuristic solution , 1998, Comput. Oper. Res..

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

[6]  Ben Hua,et al.  Supply chain optimization of continuous process industries with sustainability considerations , 2000 .

[7]  Morris A. Cohen,et al.  An integrated plant loading model with economies of scale and scope , 1991 .

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

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

[10]  David Sinriech,et al.  A genetic approach to the pickup/delivery station location problem in segmented flow based material handling systems , 1999 .

[11]  Benita M. Beamon,et al.  A multi-objective approach to simultaneous strategic and operational planning in supply chain design , 2000 .

[12]  Alev Taskin Gumus,et al.  A multi-echelon inventory management framework for stochastic and fuzzy supply chains , 2009, Expert Syst. Appl..

[13]  Young Hae Lee,et al.  Production-distribution planning in supply chain considering capacity constraints , 2002 .

[14]  Felix T.S. Chan,et al.  A hybrid genetic algorithm for production and distribution , 2005 .

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

[16]  FEDERICO DELLA CROCE,et al.  A genetic algorithm for the job shop problem , 1995, Comput. Oper. Res..

[17]  Reza Farzipoor Saen.  Suppliers Selection in Volume Discount Environments in the Presence of Both Cardinal and Ordinal Data , 2009, Int. J. Inf. Syst. Supply Chain Manag..

[18]  Anastasios G. Bakirtzis,et al.  A genetic algorithm solution to the unit commitment problem , 1996 .

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

[20]  Terry P. Harrison,et al.  Global Supply Chain Management at Digital Equipment Corporation , 1995 .

[21]  Marshall L. Fisher,et al.  Coordination of production and distribution planning , 1994 .