Modeling Multi-Product Multi-Stage Supply Chain Network Design

Abstract Supply chain network makes it possible to create an effective and helpful context for managing supply chain. This network is a series of equipments that play roles in the supply chain development. In this network exist producers of raw materials and product-making factories, centers of distributing products and customers. The aim of the network is minimizing the total cost so that customer's demands might be answered. In this paper, three-phase multi-product supply chain network model is presented. The super-innovative method of genetic algorithm is used to solve these problems since they are classified into NP-Hard problems. Encoding of this presented. The super-innovative method of genetic algorithm is used to solve these problems since they are classified into NP-Hard problems. Encoding of this genetic algorithm is based on priority-centered encoding. In this method, the network nodes are developed according to their priority. Some types of problems are posed that are solved by means of genetic algorithm and mathematic programming problem solving software (LINGO) and then the results are compared. Moreover, this algorithm is shown to give acceptable answers and is therefore suitable for solving the problems in three-phase multi-product supply chain network.

[1]  Ali Amiri,et al.  Production , Manufacturing and Logistics Designing a distribution network in a supply chain system : Formulation and efficient solution procedure , 2005 .

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

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

[4]  Mitsuo Gen,et al.  Genetic algorithms and engineering design , 1997 .

[5]  Mitsuo Gen,et al.  Network Models and Optimization: Multiobjective Genetic Algorithm Approach , 2008 .

[6]  Sai Ho Chung,et al.  A heuristic methodology for order distribution in a demand driven collaborative supply chain , 2004 .

[7]  Rizauddin Ramli,et al.  A genetic algorithm for optimizing defective goods supply chain costs using JIT logistics and each-cycle lengths , 2014 .

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

[9]  Charles C. Palmer,et al.  An approach to a problem in network design using genetic algorithms , 1994, Networks.

[10]  G. Celano,et al.  A new efficient encoding/decoding procedure for the design of a supply chain network with genetic algorithms , 2010, Comput. Ind. Eng..

[11]  P. Tsiakis,et al.  OPTIMAL PRODUCTION ALLOCATION AND DISTRIBUTION SUPPLY CHAIN NETWORKS , 2008 .

[12]  Romeo M. Marian,et al.  Optimization of Multi-commodities Consumer supply Chains for-Part I-Modeling , 2013, J. Comput. Sci..

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

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

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