A Simulation-Optimization Approach for Inventory Management in a Multi-Echelon Supply Chain Network

Under pressures from fierce market competition, many enterprises establish flexible multiechelon supply chain networks to shorten the delivery time and realize quick responses to customers. Rational inventory management strategies are essential to manage inventory levels correctly to serve customers on time, reduce costs, and improve the overall efficiency of the supply chain. Considering that a complex supply chain network with multiple echelons and nodes in each echelon can be ordered from different higher nodes, this paper proposes a simulation optimization method for inventory control policy optimization based on a genetic algorithm. This method encodes the parameters of inventory strategies of multiple warehouses as a chromosome. It evaluates the fitness through simulation, thereby achieving joint optimization of inventory strategies across supply chain networks. Finally, this paper conducts numerical experiments using real data from a home appliance enterprise and performs sensitivity analysis on some parameters to validate the effectiveness of the proposed method. The experimental results demonstrate that the simulation optimization method can reduce the total cost of the supply chain by optimizing the inventory control policies.