Title: Scalable decentralised approaches for job shop scheduling
Authors: De Causmaecker, Patrick
Verbeeck, Katja
Wauters, Tony
Martinez, Yailen
Issue Date: 11-Jul-2010
Host Document: Proceedings of EUROXXIV pages:203-203
Conference: EUROXXIV edition:24 location:Lisbon date:11-14 July 2010
Article number: TF-28-4
Abstract: We present two Reinforcement Learning approaches for the Parallel Machines
Job Shop Scheduling Problem. The objective used is the minimization of the
schedule makespan. We study two approaches, one where resources are modeled as intelligent agents and have to choose what operation to process next,
and an other where operations themselves are seen as the agents that have to
choose their mutual scheduling order. We use a value iteration method (QLearning) and a policy iteration method (Learning Automata). The results of
both approaches improve on recently published results from the literature and
we argue that they exhibit better scaling behaviour. We validate our approaches
by applying them to the flexible job shop scheduling problem where operations
can be executed on any of a number of available machines
Publication status: published
KU Leuven publication type: IMa
Appears in Collections:Computer Science, Campus Kulak Kortrijk
Informatics Section
Computer Science Technology TC, Technology Campuses Ghent and Aalst
Technologiecluster Computerwetenschappen

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