An improved genetic algorithm for optimizing total supply chain cost in inventory location routing problem

Abstract

Integrating inventory, location, and routing decisions in supply chains may considerably impact their performance. In this paper, the Inventory Location Routing Problem (ILRP) is considered while adopting the Vendor Managed Inventory (VMI) strategy. A mathematical model is formulated to minimize the total supply chain cost. Being NP-hard, an Improved Genetic Algorithm (IGA) is designed and used to solve the problem. Two instances (10 and 30 customers) are solved; to study the effect of the total vehicles capacity (number of available vehicles per depot and vehicle capacity), on the total supply chain cost. The results show that, the IGA outperforms the GA in reaching lower cost, especially for high number of customers. The superiority of the obtained solution performance is basically achieved on the expense of computational time. For the considered problem, the total cost decreases with the increase of vehicle capacity due to the usage of fewer depots. Keywords: Inventory location routing problem, Genetic algorithms, Vehicle capacity, Supply chain cost