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Abstract
One of the most important aspects affecting the performance of a supply chain is the management of inventories. Managing inventory in complex supply chains is typically difficult, and may have a significant impact on the customer service level and system-wide costs. The main challenge of inventory management is that almost every inventory problem involves multiple and conflicting objectives that need to be optimized simultaneously. In this paper, we present an efficient way using simulation-based optimization approach to determine the optimal inventory control parameters of a multi-echelon production-inventory system under a stochastic environment. The Pareto dominance concept is implemented to find a set of near optimal solutions for determining the best trade-off between objectives. The Multi-objective Particle Swarm Optimization (MOPSO) algorithm is used to determine the appropriate inventory control parameters to minimize the total inventory cost and maximize the service level. To evaluate the control parameters generated by the MOPSO, an object-oriented framework for developing the simulation model is presented. Finally, we provide a real-world case study of a major food product supply chain to demonstrate the use of proposed approach and enable decision making at inventory management. The proposed algorithm is compared with existing multi-objective genetic algorithm (NSGA-II).