Abstract

This paper first presents two supply chain design problems: 1) a factory location setting and factory selection problem, and 2) a factory location setting and factory/supplier selection problem. The first involves a number of location known retailers choosing one factory to supply their demands from a number of factories whose locations are to be determined. The goal is to minimize the transportation and manufacturing cost to satisfy the demands. The problem is then augmented into the second problem, where the procurement cost of the raw materials from a chosen material supplier (from a number of suppliers) is considered for each factory. Economic beneficial is taken into account in the cost evaluation. Therefore, the partner selections will influence the cost of the supply chain significantly. To solve these problems, an agent gaming and genetic algorithm hybrid method (AGGAHM) is proposed. The AGGAHM consecutively and alternatively enable and disable the advancement of agent gaming and the evolution of genetic computation. Computation results on solving a number of examples by the AGGAHM were compared with those from methods of a general genetic algorithm and a mutual frozen genetic algorithm. Results showed that the AGGAHM outperforms the methods solely using genetic algorithms. In addition, various parameter settings are tested and discussed to facilitate the supply chain designs.

주제어

#Supply Chain Design   #Agent Gaming   #Two-stage Genetic Operation   #Genetic Algorithm  

참고문헌 (13)

1. Chen, J. M., Chen, Y. S., and Chien, M. C. (2008), Optimal lot-sizing and pricing with markdown for a newsvendor problem, Industrial Engineering and Management Systems, 7(3), 257-265 
2. Holland, H. H. (1975), Adaptation in Natural and Artificial Systems, University of Michigan Press, Detroit, MI 
3. Luss, H. (1982), Operations research and capacity expansion problems: A survey, Operations Research, 30(5), 907-947 
4. Prasertwattana, K. and Chiadamrong, N. (2004), Purchasing and Inventory Policy in a Supply Chain under the Periodic Review: A Single Manufacturer and Multiple Retailer's Case, Industrial Engineering and Management Systems, 3(1), 38-51 
5. Song, S. H. (2006), Multi-Period Integrated Inventory and Distribution Planning with Dynamic Distribution Center Assignment, Industrial Engineering and Management Systems, 5(2), 132-141 
6. Mexixell, M. J. and Gargeya, V. B. (2005), Global supply chain design: A literature review and critique, Transportation Research Part E-logistics and Transportation Review, 41(6), 531-550 
7. Ong, N.-S. and Tan, W.-C. (2002), Sequence placement planning for high-speed PCB assembly machine, Integrated Manufacturing Systems, 13(1), 35-46 
8. Palisade Co. (2001), Evolver, The Genetic Algorithm Super Solver, Palisade Corporation, NY 
9. Ishibuchi, H., Sakamoto, R., and Nakashima, T. (2001), Evolution of unplanned coordination in a market selection game, IEEE Transactions on Evolutionary Computation, 5(5), 524-534 
10. Tesfatsion, L. (2001), Guest editorial: Agent-based Modeling of Evolutionary Economic Systems, IEEE Transactions on Evolutionary Computation, 5(5), 437-441 
11. Beamon, B. M. (1998), Supply chain design and analysis: Models and methods, International Journal of Production Economics, 55(3), 281-294 
12. Chopra, S. and Meindl, P. (2004), Supply Chain Management: Strategy, Planning and Operations, Prentica Hall, Upper Saddle River, NJ 
13. Cohen, M. A. and Moon, S. (1990), Impact of production scale economics, manufacturing complexity, and transportation costs on supply chain facility networks, Journal of Manufacturing and Operations Management, 3, 269-292 

이 논문을 인용한 문헌 (0)

1. 이 논문을 인용한 문헌 없음