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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: A Multi-Agent Based Cooperative Approach to Scheduling and Routing
Author(s): Martin, Simon
Ouelhadj, Djamila
Beullens, Patrick
Ozcan, Ender
Juan, Angel A
Burke, Edmund K
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Keywords: combinatorial optimization
multi-agent systems
vehicle routing
cooperative search
reinforcement learning
Issue Date: 1-Oct-2016
Citation: Martin S, Ouelhadj D, Beullens P, Ozcan E, Juan AA & Burke EK (2016) A Multi-Agent Based Cooperative Approach to Scheduling and Routing, European Journal of Operational Research, 254 (1), pp. 169-178.
Abstract: In this study, we propose a general agent-based distributed framework where each agent is implementing a different metaheuristic/local search combination. Moreover, an agent continuously adapts itself during the search process using a direct cooperation protocol based on reinforcement learning and pattern matching. Good patterns that make up improving solutions are identified and shared by the agents. This agent-based system aims to provide a modular flexible framework to deal with a variety of different problem domains. We have evaluated the performance of this approach using the proposed framework which embodies a set of well known metaheuristics with different configurations as agents on two problem domains, Permutation Flow-shop Scheduling and Capacitated Vehicle Routing. The results show the success of the approach yielding three new best known results of the Capacitated Vehicle Routing benchmarks tested, while the results for Permutation Flow-shop Scheduling are commensurate with the best known values for all the benchmarks tested.
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Rights: This article is open-access. Open access publishing allows free access to and distribution of published articles where the author retains copyright of their work by employing a Creative Commons attribution licence. Proper attribution of authorship and correct citation details should be given.

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