Optimizing beer distribution game order policy using numerical simulations

Abstract

One of the major challenges in supply chain management is the level of information availability. It is very hard yet important to coordinate each stage in the supply chain when the information is not centralized and the demand is uncertain. In this thesis, I analyzed the bullwhip effect in supply chain management using the MIT Beer Distribution Game. I also proposed heuristics and models to optimize the MIT Beer Distribution Game order policy when the customer's demand is both known and unknown. The proposed model provides each player with an order policy based on how many weeks of inventory the player needs to keep ahead to minimize the global cost of the supply chain. The optimized order policy is robust, practical, and generated by numerical simulations. The model is applied in a number of experiments involving deterministic and random demand and lead time. The simulation results of my work are compared with two other artificial agent algorithms, and the improvements brought by my results are presented and analyzed.