A Comparison Study of Multi-Objective Metaheuristic Techniques for Continuous Review Stochastic Inventory System

Abstract

Supply chain management which involves managing the flow of material andinformation from sources to customers has been one of the most challenging issuesfacing both the academicians and the practitioners for years. Inventory control is acrucial part of tactical decision level affecting the performance of supply chain indistribution and production. The main focus of this study is to compare theperformance of different multi-objective metaheuristic techniques to optimizeinventory parameters for single-product continuous review stochastic inventorysystem with transportation costs. The simulation-based optimization method is usedto solve the problem by combining the simulation model and metaheuristicalgorithms in order to determine the inventory policy taking into account twoconflicting objectives: customer service level and total inventory cost. We build adiscrete event simulation model to evaluate the objective function of the problem.The Metaheuristic techniques such as the genetic algorithm and particle swarmoptimization are applied to search the solution space. The results obtained by allthese proposed techniques are compared and the effectiveness of each technique hasbeen illustrated.