Title: Learning agents for the multi-mode project scheduling problem
Authors: Wauters, Tony ×
Verbeeck, Katja
Vanden Berghe, Greet
De Causmaecker, Patrick #
Issue Date: Feb-2011
Publisher: Published by Pergamon Press for Operational Research Society
Series Title: Journal of the Operational Research Society vol:62 issue:2 pages:281-290
Abstract: Intelligent optimisation refers to the promising technique of integrating learning mechanisms into (meta-)heuristic search. In this paper we use multi-agent reinforcement learning for building high quality solutions for the multi-mode resource-constrained project scheduling problem. We
use a network of distributed reinforcement learning agents that cooperate to jointly learn a well performing constructive heuristic. Each agent, being responsible for one activity, uses two simple learning devices, called learning automata, that learn to select a successor activity order and a mode, respectively. By coupling the reward signals for both learning tasks, we can clearly show
the advantage of using reinforcement learning in search. We present some comparative results, to show that our method can compete with the best performing algorithms for the multi-mode resource-constrained project scheduling problem, yet using only simple learning schemes without the burden of complex finetuning.
ISSN: 0160-5682
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Informatics Section
Computer Science, Campus Kulak Kortrijk
Faculty of Science, Campus Kulak Kortrijk
Computer Science Technology TC, Technology Campuses Ghent and Aalst
Technologiecluster Computerwetenschappen
× corresponding author
# (joint) last author

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