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Cost Optimization of the Supply Chain Network Using Genetic Algorithms

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4 Author(s)
Henry C. W. Lau ; The Hong Kong Polytechnic University, Kowloon ; T. M. Chan ; Wan Ting Tsui ; G. T. S. Ho

This paper presents a joint optimization of supply chain network in which supplier selection, lateral transshipment, and vehicle routing are involved. The objective regarded in this problem is to: (a) select one or more suppliers to order and replenish different types of products in order to minimize the total ordering cost spent by a wholesaler, (b) maximize the sum of maximum savings of different products, and (c) find the best sequence of delivering various kinds of products to different retailers in order to minimize the total cost due to the total traveled distance of a vehicle and due to the total time required for a vehicle to serve retailers. We propose to use a stochastic search technique called fuzzy logic guided genetic algorithms (FLGA) to solve the problem. The role of fuzzy logic is to dynamically adjust the crossover rate and mutation rate after each ten consecutive generations instead of two used in the past. In order to demonstrate the effectiveness of the FLGA, several search methods, branch and bound, standard GA, simulated annealing, and tabu search, are utilized to compare with the FLGA through simulations. Results show that the FLGA outperforms other search methods in all of three considered scenarios.

Note: NOTICE TO THE READER The authors and the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (TKDE) agree that the following article: "Cost Optimization of the Supply Chain Network Using Genetic Algorithms??? by H. C. W. Lau, T. M. Chan, W. T. Tsui, and G. T. S. Ho, DOI: 10.1109/TKDE.2009.20, is withdrawn from TKDE???s publication queue and should not be considered for research and citation use. Editor-in-Chief, IEEE Transactions on Knowledge and Data Engineering.  

Published in:

IEEE Transactions on Knowledge and Data Engineering  (Volume:PP ,  Issue: 99 )