A simulation-based inventory management with genetic algorithm for uncertain demand for third-party logistics provider

This research aims to study and apply inventory management system for Third party logistics provider. Currently, the company uses economic order quantity to control inventory. The analysis of historical demand data shows that the demand is not deterministic. Hence, assumptions of using economic order quantity are violated. In this research, the simulation-based technique is applied to solve for optimal order quantity and reorder point. Since there are numerous items in the considered warehouse, ABC analysis is utilized to select important items to analyze. Then simulation and genetic algorithm are applied to find the optimal solution. Design of experiment with full factorial design is used to determine the best parameter setting of genetic algorithm. The performance measures are the average total inventory cost which composes of average ordering cost, average inventory holding cost and average lost sale cost. The results show that the average total cost for product code G2654, G2581, G0706, G2791 can be reduced by 73.43%, 49.86%, 28.50% and 13.38% respectively. For product code G2654, the average lost sale cost can be reduced by 85.30%. In summary, the solution from simulation and genetic algorithm provides better results than the one from economic order quantity method.