A game theoretic approach to decentralized multi-project scheduling

Authors:
Tony Wauters;Katja Verbeeck;Greet Vanden Berghe;Patrick De Causmaecker
Affiliations:
CODes, KaHo Sint-Lieven, Gent, Belgium;CODes, KaHo Sint-Lieven, Gent, Belgium;CODes, KaHo Sint-Lieven, Gent, Belgium;CODes, K. U. Leuven Campus Kortrijk, Kortrijk, Belgium
Venue:
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Year:
2010

Citing 2
Cited 3

Networks of Learning Automata: Techniques for Online Stochastic Optimization

Networks of Learning Automata: Techniques for Online Stochastic Optimization
A (μ, λ)-coordination mechanism for agent-based multi-project scheduling

OR Spectrum

A multi-agent system for distributed multi-project scheduling: An auction-based negotiation approach

Engineering Applications of Artificial Intelligence
Fast permutation learning

LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Schema and solutions for decentralised multi-project scheduling problem

International Journal of Computer Applications in Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we demonstrate how decentralized multi-project scheduling problems can be solved efficiently by a group of project manager agents playing a simple sequence learning game. In the multi-project scheduling problem, multiple projects, each having a number of activities, must be scheduled. A set of local and global resources are available for carrying out the activities of the projects. It is shown that the sequence learning game improves the best objective function value found (minimal average project delay). In fact, the combination of local reinforcement learning, the sequence learning game and a smart forward-backward implementation of the serial scheduler realizes, on average over all MPSPLIB benchmark instances, a 25% improvement on the best published results.