Computers play the beer game: can artificial agents manage supply chains?

Authors:
Steven O. Kimbrough;D. J. Wu;Fang Zhong
Affiliations:
The Wharton School, University of Pennsylvania, 3620 Locust Walk, Philadelphia, PA;LeBow College of Business, Drexel University, Philadelphia, PA;LeBow College of Business, Drexel University, Philadelphia, PA
Venue:
Decision Support Systems - Special issue: Formal modeling and electronic commerce
Year:
2002

Citing 9
Cited 30

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

Management Science
Information distortion in a supply chain: the bullwhip effect

Management Science - Special issue on frontier research in manufacturing and logistics
A multi-agent framework for the coordination and integration of information systems

Management Science
Decentralized Multi-Echelon Supply Chains: Incentives and Information

Management Science
Decentralized supply chains subject to information delays

Management Science
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence

Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Quantitative Models for Supply Chain Management

Quantitative Models for Supply Chain Management
Supply Chain Process and Agent Design for E-Commerce

HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 6 - Volume 6
A New Approach to Solve Supply Chain Management Problem by Integrating Multi-Agent Technology and Constraint Network

HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 5 - Volume 5

Cooperation in multi-agent bidding

Decision Support Systems - Special issue: Formal modeling and electronic commerce
Multi-Agent coordination based on tokens: reduction of the bullwhip effect in a forest supply chain

AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Supporting Learning in Evolving Dynamic Environments

Computational & Mathematical Organization Theory
Formulating ordering policies in a supply chain by genetic algorithm
Agent-based demand forecast in multi-echelon supply chain

Decision Support Systems
MACE-SCM: A multi-agent and case-based reasoning collaboration mechanism for supply chain management under supply and demand uncertainties

Expert Systems with Applications: An International Journal
A Fuzzy agent-based model for reduction of bullwhip effect in supply chain systems

Expert Systems with Applications: An International Journal
Reengineering, simulation and data analysis of an RFID system

Journal of Theoretical and Applied Electronic Commerce Research
Demonstrating supply chain parameter optimization through beer game simulation

Information-Knowledge-Systems Management
Agent-Based Modeling of Supply Chains in Critical Situations

ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
A reinforcement learning model for supply chain ordering management: An application to the beer game

Decision Support Systems
Learning bidding strategies with autonomous agents in environments with unstable equilibrium

Decision Support Systems
Optimal reorder decision-making in the agent-based apparel supply chain

Expert Systems with Applications: An International Journal
Reducing the negative effects of sales promotions in supply chains using genetic algorithms

Expert Systems with Applications: An International Journal
Learning and practising supply chain management strategies from a business simulation game: a comprehensive supply chain simulation

Proceedings of the 40th Conference on Winter Simulation
Altering the Information System for a plywood supply chain in order to tame the Bullwhip effect

International Journal of Intelligent Systems Technologies and Applications
Constraint-directed business simulation for supporting game-based problem-based learning

MTDL '09 Proceedings of the first ACM international workshop on Multimedia technologies for distance learning
Evolution and incremental learning in the iterated prisoner's dilemma

IEEE Transactions on Evolutionary Computation
Machine learning for dynamic multi-product supply chain formation

Expert Systems with Applications: An International Journal
Multi-agents based protocols for negotiation in a crisis management supply chain

CIMMACS'09 Proceedings of the 8th WSEAS International Conference on Computational intelligence, man-machine systems and cybernetics
Review article: A review of soft computing applications in supply chain management

Applied Soft Computing
Behaviour adaptation in the multi-agent, multi-objective and multi-role supply chain

Computers in Industry
Fuzzy inventory control of a supply chain

ICS'06 Proceedings of the 10th WSEAS international conference on Systems
Incorporating software agents into supply chains: experimental investigation with a procurement task

MIS Quarterly
A hybrid system based on multi-agent systems in case of e-weddingthailand

ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction
Improvement of supply chain efficiency with a computer learning game

AMERICAN-MATH'12/CEA'12 Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics
e-Supply network coordination: the design of intelligent agents for buyer-supplier dynamic negotiations

Journal of Intelligent Manufacturing
Multiagent based simulation as a supply chain analysis workbench

Transactions on Compuational Collective Intelligence VI
A pragmatic stochastic decision model for supporting goods trans-shipments in a supply chain environment

Decision Support Systems
Agent-assisted supply chain management: Analysis and lessons learned

Decision Support Systems

Quantified Score

Hi-index	0.01

Visualization

Abstract

We model an electronic supply chain managed by artificial agents. We investigate whether artificial agents do better than humans when playing the MIT Beer Game. Can the artificial agents discover good and effective business strategies in supply chains both in stationary and non-stationary environments? Can the artificial agents discover policies that mitigate the Bullwhip effect? In particular, we study the following questions: Can agents learn reasonably good policies in the face of deterministic demand with fixed lead time? Can agents cope reasonably well in the face of stochastic demand with stochastic lead time? Can agents learn and adapt in various contexts to play the game? Can agents cooperate across the supply chain?