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
169-178
Martin, Simon
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Ouelhadj, Djamila
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Beullens, Patrick
893ad2e2-0617-47d6-910b-3d5f81964a9c
Ozcan, Ender
ca7b403e-702e-4759-abc0-705c912e53d5
Juan, Angel A.
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Burke, Edmund K.
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1 October 2016
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), .
(doi:10.1016/j.ejor.2016.02.045).
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.
Text
Martin et al 2016 EJOR accepted manuscript
- Accepted Manuscript
Text
1-s2.0-S0377221716300984-main
- Version of Record
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: http://eprints.soton.ac.uk/id/eprint/389560
ISSN: 0377-2217
PURE UUID: 3f8a1589-b376-4f9d-8aa5-e738d615385f
Catalogue record
Date deposited: 09 Mar 2016 11:23
Last modified: 15 Mar 2024 05:25
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Contributors
Author:
Simon Martin
Author:
Djamila Ouelhadj
Author:
Ender Ozcan
Author:
Angel A. Juan
Author:
Edmund K. Burke
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