Cooperation in a multi-stage game for modeling distributed task delegation in a supply chain procurement problem

Kaizhi Tang, Soundar R.T. Kumara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

We develop an evolutionary method that combines reinforcement learning and fictitious playing to seek equilibrium solution for a multi-agent and multi-stage game in the context of supply chain procurement. The game is designed to model task delegation among a group of self-interested transportation companies which serve logistic shipment. The game involves more than two agents and multiple stages of matrix games. The integration of reinforcement learning and fictitious play overcomes the weaknesses of each approach and exploits their strengths. This innovative approach performs extraordinarily well on a game with three players, unknown number of stages, and large gaps of payoff values.

Original languageEnglish (US)
Title of host publicationProceedings of the 2005 IEEE Conference on Automation Science and Engineering, IEEE-CASE 2005
Pages93-98
Number of pages6
DOIs
StatePublished - Dec 1 2005
Event2005 IEEE Conference on Automation Science and Engineering, IEEE-CASE 2005 - Edmonton, Canada
Duration: Aug 1 2005Aug 2 2005

Publication series

NameProceedings of the 2005 IEEE Conference on Automation Science and Engineering, IEEE-CASE 2005
Volume2005

Other

Other2005 IEEE Conference on Automation Science and Engineering, IEEE-CASE 2005
CountryCanada
CityEdmonton
Period8/1/058/2/05

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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