Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/13446
Title: An innovation approach for achieving cost optimization in supply chain management
Authors: Lau, HCW
Ho, GTS
Chan, TM
Tsui, WT
Keywords: Fuzzy logic
Genetic algorithms
Integer programming
Lateral transshipment
Optimization
Supplier selection
Supply chain network
Vehicle routing
Issue Date: 2014
Publisher: Ios Press
Source: Journal of intelligent and fuzzy systems, 2014, v. 26, no. 1, p. 173-192 How to cite?
Journal: Journal of Intelligent and Fuzzy Systems 
Abstract: This paper presents a joint optimization of the supply chain network in which supplier selection, lateral transshipment, and vehicle routing are involved. Separate consideration of these decisions involved probably offers only poor-quality local optimal solutions. The contribution of this paper is to study the cost minimization of the supply chain network involving the three decisions simultaneously, using both vertical and preventive lateral transshipment, and considering both single objective and multi-objective approach with the following objectives: (a) minimize the total ordering cost incurred by the wholesaler, (b) maximize the amount of savings on the different products, and (c) find the best sequence for delivering various kinds of products to different retailers. A stochastic search technique called fuzzy logic guided genetic algorithms (FLGA) is proposed to solve the problems. In order to demonstrate the effectiveness of the FLGA, several search methods are compared with the FLGA through simulations in the single objective approach. In the multi-objective approach, two multi-objective evolutionary algorithms entitled Nondominated Sorting Genetic Algorithms 2 (NSGA2) and Strength Pareto Evolutionary Algorithm 2 (SPEA2) are adopted for comparison with the FLGA. Results show that the FLGA outperforms others in all three considered scenarios for both single objective and multi-objective approaches.
URI: http://hdl.handle.net/10397/13446
ISSN: 1064-1246
DOI: 10.3233/IFS-120725
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