Optimisation of the beer distribution game with complex customer demand patterns

Authors:
Hongliang Liu;Enda Howley;Jim Duggan
Affiliations:
Department of Information Technology, National University of Ireland, Galway;Department of Information Technology, National University of Ireland, Galway;Department of Information Technology, National University of Ireland, Galway
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

Citing 4
Cited 0

Modeling managerial behavior: misperceptions of feedback in a dynamic decision making experiment

Management Science
Information Sharing in a Supply Chain with Dynamic Consumer Demand Pattern

HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 3 - Volume 3
Beer game order policy optimization under changing customer demand

Decision Support Systems
Behavioral Causes of the Bullwhip Effect and the Observed Value of Inventory Information

Management Science

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper examines a simulation of the Beer Distribution Game and a number of optimisation approaches to this game. This well known game was developed at MIT in the 1960s and has been widely used to educate graduate students and business managers on the dynamics of supply chains. This game offers a complex simulation environment involving multidimensional constrained parameters. In this research we have examined a traditional genetic algorithm approach to optimising this game, while also for the first time examining a particle swarm optimisation approach. Optimisation is used to determine the best ordering policies across an entire supply chain. This paper will present experimental results for four complex customer demand patterns. We will examine the efficacy of our optimisation approaches and analyse the implications of the results on the Beer Distribution Game. Our experimental results clearly demonstrate the advantages of both genetic algorithm and particle swarm approaches to this complex problem. We will outline a direct comparison of these results, and present a series of conclusions relating to the Beer Distribution Game.