Multi-echelon and multi-period supply chain management network design considering different importance for customers management using a novel meta-heuristic algorithm

Abstract

Supply chain management is an integrated approach to planning and controlling materials and information from suppliers and flows to customers. Given the strategic planning of distribution networks, decisions about it must be optimized. Among the needs for optimization in the distribution network are facility location decisions and inventory control decisions. In this paper, an integrated location-allocation model with inventory control decisions is presented by considering the importance of customers. The proposed model is proposed for a multi-echelon and multi- period supply chain, in which the demand is considered as an uncertain parameter. The main innovation of this research is to consider the distinction between customers because customers with different purchasing volumes in conditions of demand uncertainty have a different level of importance for the supply chain. Moreover, a novel meta-heuristic algorithm as Seeker Evolutionary Algorithm (SEA) is applied to solve the proposed model. The results of comparing SEA with genetic algorithm and GAMS software show that both in small and large dimensions, SEA provides high-quality solutions, and its speed of operation is at an acceptable level.

Keywords

• Supply chain
• Distribution network
• Customer classification
• Seeker evolutionary algorithm
• Genetic algorithm
• Management