Title: Simulation-based evolutionary multi-objective optimisation approach for integrated decision-making in supplier selection
Authors: Hongwei Ding, Lyes Benyoucef, Xiaolan Xie
Addresses: IBM China Research Lab, ZGC Software Park, 100094 Beijing, PR China. ' INRIA-COSTEAM Project, ISGMP Bat. A, Ile du Saulcy, 57000 Metz, France. ' Ecole Nationale Superieure des Mines de Saint-Etienne (ENSM.SE), 158, Cours Fauriel, 42023 Saint-Etienne, Cedex 2, France
Abstract: In the design of modern supply chains, integrating supplier selection, order splitting, transportation allocation and inventory control is a challenging issue. Existing optimisation approaches handle the different problems separately and for the sake of solvability, neglect impact of strategic decisions on operational decisions and do not take into account uncertainties. In this paper, a simulation-based evolutionary multi-objective optimisation approach is proposed to deal with this problem. The approach consists of an optimiser and a simulator. The optimiser, based on a multi-objective genetic algorithm, is used to find best-compromised solutions with respect to various criteria, such as the total cost and customer service level. Candidate solutions are evaluated through simulation, which enables realistic evaluation taking into account uncertainties and dynamics along the whole supply chain. A simple case study from the textile industry is presented to illustrate the applicability of the proposed approach for the real-world applications.
Keywords: multi-objective genetic algorithms; GAs; simulation; optimisation; supplier selection; textile industry; integrated decision making; supply chain management; SCM; supply chain design; order splitting; transportation allocation; inventory control.
International Journal of Computer Applications in Technology, 2008 Vol.31 No.3/4, pp.144 - 157
Published online: 05 May 2008 *Full-text access for editors Access for subscribers Purchase this article Comment on this article