A multi-agent based cooperative approach to scheduling and routing
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 p...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Published: |
Elsevier
2016
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/32191/ |
| _version_ | 1848794354174394368 |
|---|---|
| author | Martin, Simon Ouelhadj, Djamila Beullens, Patrick Özcan, Ender Juan, Angel A. Burke, Edmund |
| author_facet | Martin, Simon Ouelhadj, Djamila Beullens, Patrick Özcan, Ender Juan, Angel A. Burke, Edmund |
| author_sort | Martin, Simon |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:14:51Z |
| format | Article |
| id | nottingham-32191 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:14:51Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-321912020-05-04T17:42:51Z https://eprints.nottingham.ac.uk/32191/ A multi-agent based cooperative approach to scheduling and routing Martin, Simon Ouelhadj, Djamila Beullens, Patrick Özcan, Ender Juan, Angel A. Burke, Edmund 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. Elsevier 2016-03-04 Article PeerReviewed Martin, Simon, Ouelhadj, Djamila, Beullens, Patrick, Özcan, Ender, Juan, Angel A. and Burke, Edmund (2016) A multi-agent based cooperative approach to scheduling and routing. European Journal of Operational Research . ISSN 0377-2217 (In Press) Combinatorial optimization Multi-agent systems Scheduling 2 vehicle routing Metaheuristics Cooperative search Reinforcement learning http://www.sciencedirect.com/science/article/pii/S0377221716300984 doi:10.1016/j.ejor.2016.02.045 doi:10.1016/j.ejor.2016.02.045 |
| spellingShingle | Combinatorial optimization Multi-agent systems Scheduling 2 vehicle routing Metaheuristics Cooperative search Reinforcement learning Martin, Simon Ouelhadj, Djamila Beullens, Patrick Özcan, Ender Juan, Angel A. Burke, Edmund A multi-agent based cooperative approach to scheduling and routing |
| title | A multi-agent based cooperative approach to scheduling and routing |
| title_full | A multi-agent based cooperative approach to scheduling and routing |
| title_fullStr | A multi-agent based cooperative approach to scheduling and routing |
| title_full_unstemmed | A multi-agent based cooperative approach to scheduling and routing |
| title_short | A multi-agent based cooperative approach to scheduling and routing |
| title_sort | multi-agent based cooperative approach to scheduling and routing |
| topic | Combinatorial optimization Multi-agent systems Scheduling 2 vehicle routing Metaheuristics Cooperative search Reinforcement learning |
| url | https://eprints.nottingham.ac.uk/32191/ https://eprints.nottingham.ac.uk/32191/ https://eprints.nottingham.ac.uk/32191/ |