Advanced SearchSearch Tips
An Improved Genetic Approach to Optimal Supplier Selection and Order Allocation with Customer Flexibility for Multi-Product Manufacturing
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
An Improved Genetic Approach to Optimal Supplier Selection and Order Allocation with Customer Flexibility for Multi-Product Manufacturing
Mak, Kai-Ling; Cui, Lixin; Su, Wei;
  PDF(new window)
As the global market becomes more competitive, manufacturing industries face relentless pressure caused by a growing tendency of greater varieties of products, shorter manufacturing cycles and more sophisticated customer requirements. Efficient and effective supplier selection and order allocation decisions are, therefore, important decisions for a manufacturer to ensure stable material flows in a highly competitive supply chain, in particular, when customers are willing to accept products with less desirable product attributes (e.g., color, delivery date) for economic reasons. This paper attempts to solve optimally the challenging problem of supplier selection and order allocation, taking into consideration the customer flexibility for a manufacturer producing multi-products to satisfy the customers' demands in a multi period planning horizon. A new mixed integer programming model is developed to describe the behavior of the supply chain. The objective is to maximize the manufacturer's total profit subject to various operating constraints of the supply chain. Due to the complexity and non-deterministic polynomial-time (NP)-hard nature of the problem, an improved genetic approach is proposed to solve the problem optimally. This approach differs from a canonical genetic algorithm in three aspects: a new selection method to reduce the chance of premature convergence and two problem-specific repair heuristics to guarantee feasibility of the solutions. The results of applying the proposed approach to solve a set of randomly generated test problems clearly demonstrate its excellent performance. When compared with applying the canonical genetic algorithm to locate optimal solutions, the average improvement in the solution quality amounts to as high as ten percent.
Customer Flexibility;Genetic Algorithm;Mixed Integer Programming;Order Allocation;Supplier Selection;
 Cited by
An Integrated Mathematical Model for Supplier Selection,;

Industrial Engineering and Management Systems, 2014. vol.13. 1, pp.29-42 crossref(new window)
An Integrated Mathematical Model for Supplier Selection, Industrial Engineering and Management Systems, 2014, 13, 1, 29  crossref(new windwow)
Che, Z. H. and Wang, H. S. (2008), Supplier selection and supply quantity allocation of common and noncommon parts with multiple criteria under multiple products, Computers and Industrial Engineering, 55, 110-133. crossref(new window)

Goldberg, D. E. (1989), Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA.

Gosselin, L., Tye-Gingras, M., and Mathieu-Potvin, F. (2009), Review of utilization of genetic algorithms in heat transfer problems, International Journal of Heat and Mass Transfer, 52, 2169-2188. crossref(new window)

Hodge, B.-M., Pettersson, F., and Chakraborti, N. (2006), Re-evaluation of the optimal operating conditions for the primary end of an integrated steel plant using multi-objective genetic algorithms and Nash equilibrium, Steel Research International, 77, 459-461. crossref(new window)

Holland, J. H. (1975), Adaptation in Natural and Artificial System: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, University of Michigan Press, Ann Arbor, MI.

Jawahar, N. and Balaji, A. N. (2009), A genetic algorithm for the two-stage supply chain distribution problem associated with a fixed charge, European Journal of Operational Research, 194, 496-537. crossref(new window)

Jiao, J., Tseng, M. M., Ma, Q., and Zou, Y. (2000), Generic bill-of-materials-and-operations for high-variety production management, Concurrent Engineering: Research and Applications, 8, 297-321. crossref(new window)

Kim, B., Leung, J. M. Y., Park, K. T., Zang, G., and Lee, S. (2002), Configuring a manufacturing firm's supply network with multiple suppliers, IIE Transactions, 34, 663-677.

Lamothe, J., Hadj-Hamou, K., and Aldanondo, M. (2006), An optimization model for selecting a product family and designing its supply chain, European Journal of Operation Research, 169, 1030-1047. crossref(new window)

Liu, B. (2002), Theory and Practice of Uncertain Programming, Physica-Verlag, Heidelberg, Germany.

Mak, K. L., Wong, Y. S., and Wang, X. X. (2000), An adaptive genetic algorithm for manufacturing cell formation, The International Journal of Advanced Manufacturing Technology, 16, 491-497. crossref(new window)

Mark, K. L., Wong, Y. S., and Chan, F. T. S. (1998), A genetic algorithm for facility layout problems, Computer Integrated Manufacturing Systems, 11, 113-127. crossref(new window)

Michalewicz, Z. (1996), Genetic Algorithms+Data Structures = Evolution Programs, Springer-Verlag, New York, NY.

Salehi, M. and Tavakkoli-Moghaddam, R. (2009), Application of genetic algorithm to computer-aided process planning in preliminary and detailed planning, Engineering Applications of Artificial Intelligence, 22, 1179-1187. crossref(new window)

Singh, S. P. and Sharma, R. R. K. (2006), A review of different approaches to the facility layout problems, The International Journal of Advanced Manufacturing Technology, 30, 425-433. crossref(new window)

Smeltzer, L. R. (1997), The meaning and origin of trust in buyer-supplier relationships, Journal of Supply Chain Management, 33, 40-48. crossref(new window)

Weber, C. A. and Current, J. R. (1993), A multiobjective approach to vendor selection, European Journal of Operational Research, 68, 173-184. crossref(new window)