An innovation approach for achieving cost optimization in supply chain management

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.