Application of Meta-Heuristic Algorithms in Solving a Supply Chain Model

Abstract

Over the last three decades, the integrated optimization approach to logistics systems has become one of the most important aspects of supply chain management optimization. This approach simultaneously examines the relationships between facility locations, supplier / customer allocation to facilities, transportation route structure, and inventory planning and control. One of the most important issues in logistics decisions is location-routing. In this case, the number and location of facilities, the size of the transport fleet, and the structure of the routes are determined according to the location and characteristics of suppliers and customers. In this paper, a mathematical model of the problem with consideration of product decay time constraints and solving with efficient meta-heuristic methods based on ant colony optimization (ACO) and particle swarm optimization (PSO) is presented. The comparison of the results of the two algorithms shows that the ant colony optimization (ACO) is faster in terms of convergence rate and the number of solution iterations is less than the particle swarm algorithm.