A multi-agent based cooperative approach to scheduling and routing

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

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In this paper, 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, whilst the results for Permutation Flow-shop Scheduling are commensurate with the best known values for all the benchmarks tested.
Original languageEnglish
Pages (from-to)169-178
Number of pages10
JournalEuropean Journal of Operational Research
Issue number1
Early online date4 Mar 2016
Publication statusPublished - 1 Oct 2016


  • combinational optimization
  • scheduling
  • vehicle routing
  • metaheuristics
  • cooperative search


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