Beer game order policy optimization under changing customer demand

Authors:
F. Strozzi;J. Bosch;J. M. Zaldívar
Affiliations:
Carlo Cattaneo University, Engineering Faculty, Quantitative Methods Institute 21053 Castellanza (VA), Italy;Carlo Cattaneo University, Engineering Faculty, Quantitative Methods Institute 21053 Castellanza (VA), Italy;European Commission, Joint Research Centre, Institute for Environment and Sustainability, 21020 Ispra (VA), Italy
Venue:
Decision Support Systems
Year:
2007

Citing 3
Cited 4

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

Management Science
Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information

Management Science

A reinforcement learning model for supply chain ordering management: An application to the beer game

Decision Support Systems
Optimisation of the beer distribution game with complex customer demand patterns

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Review article: A review of soft computing applications in supply chain management

Applied Soft Computing
A supply chain as a network of auctions

Decision Support Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

The present work analyses the optimal Beer Game order policy when customers demand increases. The optimal policy is found by means of a Genetic Algorithms (GAs) technique. GAs are specially suited for this problem because of the high dimension of the search space, and because the objective function i.e. the global score of the chain, has many local minima. Our results show that the best performance of the chain is obtained when the sectors have different order policies. The advantage increases with the increasing change in the customer demand.