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A multi-agent based cooperative approach to scheduling and routing

A multi-agent based cooperative approach to scheduling and routing
A multi-agent based cooperative approach to scheduling and routing
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.
combinatorial optimization, multi-agent systems, scheduling, vehicle routing, metaheuristics, cooperative search, reinforcement learning
0377-2217
169-178
Martin, Simon
4b1e35de-d834-40eb-9294-9c98f4b98fe8
Ouelhadj, Djamila
a1deec2b-b1f1-49ef-a1a6-1a5e367b4fd8
Beullens, Patrick
893ad2e2-0617-47d6-910b-3d5f81964a9c
Ozcan, Ender
ca7b403e-702e-4759-abc0-705c912e53d5
Juan, Angel A.
a08d6aac-1e9b-4537-81a7-29a1ba791f26
Burke, Edmund K.
b7c774d9-9736-4550-87d8-3ae0b04c8d56
Martin, Simon
4b1e35de-d834-40eb-9294-9c98f4b98fe8
Ouelhadj, Djamila
a1deec2b-b1f1-49ef-a1a6-1a5e367b4fd8
Beullens, Patrick
893ad2e2-0617-47d6-910b-3d5f81964a9c
Ozcan, Ender
ca7b403e-702e-4759-abc0-705c912e53d5
Juan, Angel A.
a08d6aac-1e9b-4537-81a7-29a1ba791f26
Burke, Edmund K.
b7c774d9-9736-4550-87d8-3ae0b04c8d56

Martin, Simon, Ouelhadj, Djamila, Beullens, Patrick, Ozcan, Ender, Juan, Angel A. and Burke, Edmund K. (2016) A multi-agent based cooperative approach to scheduling and routing. European Journal of Operational Research, 254 (1), 169-178. (doi:10.1016/j.ejor.2016.02.045).

Record type: Article

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.

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More information

Accepted/In Press date: 28 February 2016
e-pub ahead of print date: 4 March 2016
Published date: 1 October 2016
Keywords: combinatorial optimization, multi-agent systems, scheduling, vehicle routing, metaheuristics, cooperative search, reinforcement learning
Organisations: Operational Research

Identifiers

Local EPrints ID: 389560
URI: https://eprints.soton.ac.uk/id/eprint/389560
ISSN: 0377-2217
PURE UUID: 3f8a1589-b376-4f9d-8aa5-e738d615385f
ORCID for Patrick Beullens: ORCID iD orcid.org/0000-0001-6156-3550

Catalogue record

Date deposited: 09 Mar 2016 11:23
Last modified: 03 Dec 2019 06:47

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