A multi-agent based cooperative approach to scheduling and routing

Martin, Simon; Ouelhadj, Djamila; Beullens, Patrick; Ozcan, Ender; Juan, Angel A.; Burke, Edmund

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.

Citation

Martin, S., Ouelhadj, D., Beullens, P., Ozcan, E., Juan, A. A., & Burke, E. (2016). A multi-agent based cooperative approach to scheduling and routing. European Journal of Operational Research, 254(1), 169-178. https://doi.org/10.1016/j.ejor.2016.02.045

• Journal Article Type: Article
• Acceptance Date: Feb 28, 2016
• Online Publication Date: Mar 4, 2016
• Publication Date: 2016-10
• Deposit Date: Mar 10, 2016
• Publicly Available Date: Mar 10, 2016
• Journal: European Journal of Operational Research
• Print ISSN: 0377-2217
• Electronic ISSN: 0377-2217
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 254
• Issue: 1
• Pages: 169-178
• DOI: https://doi.org/10.1016/j.ejor.2016.02.045
• Keywords: Combinatorial optimization, Multi-agent systems, Scheduling,; 2 vehicle routing, Metaheuristics, Cooperative search, Reinforcement learning
• Public URL: https://nottingham-repository.worktribe.com/output/781413
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S0377221716300984
• Additional Information: This article is maintained by: Elsevier; Article Title: A multi-agent based cooperative approach to scheduling and routing; Journal Title: European Journal of Operational Research; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.ejor.2016.02.045; Content Type: article; Copyright: © 2016 The Authors. Published by Elsevier B.V.