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).

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In today’s competitive and dynamic market conditions, Supply Chain Risk Management (SCRM) has become a key concern for organizations in order to respond effectively to market uncertainties and disruptions. There are several sources for supply chain risk such as process, control, demand, supply, and environment. Natural disaster and manmade crises have also put negative impact on the performance of supply chains. In addition, risk management in the supply chain is a challenge due to the fact that the time, place, and severity of risks are fairly unpredictable. Controlling and monitoring risk in real-time is critical to providing a quick response to unanticipated events in order to reduce the consequences of these events. Hence, there is an immediate need to incorporate the risk management, computer simulation, and real-time information systems into a framework to assist decision makers in evaluating and managing supply chain risks. This paper develops a framework for the design of a simulation-based decision support system for the real-time management of disruptions and mitigation of risks in supply chains. The agent-based simulation is integrated into the framework in order to analyze the detailed interactions among various actors of the supply chain and evaluate the risk management process. The proposed simulation platform also provides a virtual marketplace that takes into account the interdependencies of the decisions made by costumers and company

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