A simulation-based genetic algorithm for inventory optimization in a serial supply chain

One of the important aspects of supply chain management is inventory management because the cost of inventories in a supply chain accounts for about 30% of the value of the product. The main focus of this work is to study the performance of a single-product serial supply chain operating with a base-stock policy and to optimize the inventory (i.e. base stock) levels in the supply chain so as to minimize the total supply chain cost (TSCC), comprising holding and shortage costs at all the installations in the supply chain. A genetic algorithm (GA) is proposed to optimize the base-stock levels with the objective of minimizing the sum of holding and shortage costs in the entire supply chain. Simulation is used to evaluate the base-stock levels generated by the GA. The proposed GA is evaluated with the consideration of a variety of supply chain settings in order to test for its robustness of performance across different supply chain scenarios. The effectiveness of the proposed GA (in terms of generating base-stock levels with minimum TSCC) is compared with that of a random search procedure. In addition, optimal base-stock levels are obtained through complete enumeration of the solution space and compared with those yielded by the GA. It is found that the solutions generated by the proposed GA do not significantly differ from the optimal solution obtained through complete enumeration for different supply chain settings, thereby showing the effectiveness of the proposed GA.