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A multi-agent based cooperative approach to scheduling and routing

Author

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

Abstract

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.

Suggested Citation

  • Martin, Simon & Ouelhadj, Djamila & Beullens, Patrick & Ozcan, Ender & Juan, Angel A. & Burke, Edmund K., 2016. "A multi-agent based cooperative approach to scheduling and routing," European Journal of Operational Research, Elsevier, vol. 254(1), pages 169-178.
    • Handle: RePEc:eee:ejores:v:254:y:2016:i:1:p:169-178
    DOI: 10.1016/j.ejor.2016.02.045
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    Cited by:

    1. José García & Paola Moraga & Matias Valenzuela & Hernan Pinto, 2020. "A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem," Mathematics, MDPI, vol. 8(4), pages 1-22, April.
    2. José García & Victor Yepes & José V. Martí, 2020. "A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem," Mathematics, MDPI, vol. 8(4), pages 1-22, April.
    3. Selin Çabuk & Rızvan Erol, 2024. "Solving Dynamic Full-Truckload Vehicle Routing Problem Using an Agent-Based Approach," Mathematics, MDPI, vol. 12(13), pages 1-21, July.
    4. José García & José V. Martí & Víctor Yepes, 2020. "The Buttressed Walls Problem: An Application of a Hybrid Clustering Particle Swarm Optimization Algorithm," Mathematics, MDPI, vol. 8(6), pages 1-22, May.
    5. Yan, Yimo & Chow, Andy H.F. & Ho, Chin Pang & Kuo, Yong-Hong & Wu, Qihao & Ying, Chengshuo, 2022. "Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 162(C).
    6. Sana Sahar Guia & Abdelkader Laouid & Mohammad Hammoudeh & Ahcène Bounceur & Mai Alfawair & Amna Eleyan, 2022. "Co-Simulation of Multiple Vehicle Routing Problem Models," Future Internet, MDPI, vol. 14(5), pages 1-16, April.
    7. Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Meyer, Patrick & Karimi-Mamaghan, Amir Mohammad & Talbi, El-Ghazali, 2022. "Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art," European Journal of Operational Research, Elsevier, vol. 296(2), pages 393-422.
    8. Swan, Jerry & Adriaensen, Steven & Brownlee, Alexander E.I. & Hammond, Kevin & Johnson, Colin G. & Kheiri, Ahmed & Krawiec, Faustyna & Merelo, J.J. & Minku, Leandro L. & Özcan, Ender & Pappa, Gisele L, 2022. "Metaheuristics “In the Large”," European Journal of Operational Research, Elsevier, vol. 297(2), pages 393-406.
    9. Nasr Al-Hinai & Chefi Triki, 2020. "A two-level evolutionary algorithm for solving the petrol station replenishment problem with periodicity constraints and service choice," Annals of Operations Research, Springer, vol. 286(1), pages 325-350, March.
    10. Li, Feng & Du, Timon C. & Wei, Ying, 2020. "Enhancing supply chain decisions with consumers’ behavioral factors: An illustration of decoy effect," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).

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