article

Bi-Objective Ant Colony Optimization approach to optimize production and maintenance scheduling

Authors:

M. MezghicheAuthors Info & Claims

Computers and Operations Research, Volume 37, Issue 9

Pages 1584 - 1596

https://doi.org/10.1016/j.cor.2009.11.017

Published: 01 September 2010 Publication History

Abstract

This paper presents an algorithm based on Ant Colony Optimization paradigm to solve the joint production and maintenance scheduling problem. This approach is developed to deal with the model previously proposed in [3] for the parallel machine case. This model is formulated according to a bi-objective approach to find trade-off solutions between both objectives of production and maintenance. Reliability models are used to take into account the maintenance aspect. To improve the quality of solutions found in our previous study, an algorithm based on Multi-Objective Ant Colony Optimization (MOACO) approach is developed. The goal is to simultaneously determine the best assignment of production tasks to machines as well as preventive maintenance (PM) periods of the production system, satisfying at best both objectives of production and maintenance. The experimental results show that the proposed method outperforms two well-known Multi-Objective Genetic Algorithms (MOGAs): SPEA 2 and NSGA II.

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Cited By

Cacereño AGreiner DGalván B(2023)Simultaneous optimization of design and maintenance for systems using multi-objective evolutionary algorithms and discrete simulationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-08922-227:24(19213-19246)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s00500-023-08922-2
Ecoretti ACeschia SSchaerf A(2022)Local Search for Integrated Predictive Maintenance and Scheduling in Flow-ShopMetaheuristics10.1007/978-3-031-26504-4_19(260-273)Online publication date: 11-Jul-2022
https://dl.acm.org/doi/10.1007/978-3-031-26504-4_19
Fazel Zarandi MSadat Asl ASotudian SCastillo O(2020)A state of the art review of intelligent schedulingArtificial Intelligence Review10.1007/s10462-018-9667-653:1(501-593)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1007/s10462-018-9667-6
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Bi-Objective Ant Colony Optimization approach to optimize production and maintenance scheduling

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cover image Computers and Operations Research

Computers and Operations Research Volume 37, Issue 9

September, 2010

160 pages

ISSN:0305-0548

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Copyright © Elsevier Ltd © 2009.

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Elsevier Science Ltd.

United Kingdom

Publication History

Published: 01 September 2010

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Cited By

Cacereño AGreiner DGalván B(2023)Simultaneous optimization of design and maintenance for systems using multi-objective evolutionary algorithms and discrete simulationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-08922-227:24(19213-19246)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s00500-023-08922-2
Ecoretti ACeschia SSchaerf A(2022)Local Search for Integrated Predictive Maintenance and Scheduling in Flow-ShopMetaheuristics10.1007/978-3-031-26504-4_19(260-273)Online publication date: 11-Jul-2022
https://dl.acm.org/doi/10.1007/978-3-031-26504-4_19
Fazel Zarandi MSadat Asl ASotudian SCastillo O(2020)A state of the art review of intelligent schedulingArtificial Intelligence Review10.1007/s10462-018-9667-653:1(501-593)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1007/s10462-018-9667-6
Tian JLin ZWang F(2020)Ant Colony Optimization Algorithm for understanding of trade-offs between safety and benefit: a case of Beijing taxi service systemCognition, Technology and Work10.1007/s10111-019-00585-022:3(489-499)Online publication date: 1-Aug-2020
https://dl.acm.org/doi/10.1007/s10111-019-00585-0
Feylizadeh MKarimi NLi D(2018)Multi-stage production planning using fuzzy multi-objective programming with consideration of maintenanceJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-1791634:4(2753-2769)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.3233/JIFS-17916
Zhou CYang YGong BLi MLi JWen R(2018)The Model Design of Mobile Resource Scheduling in Large Scale ActivitiesMobile Networks and Applications10.1007/s11036-018-1023-123:3(382-394)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s11036-018-1023-1
Rajappan SRangasamy D(2018)Estimation of incomplete values in heterogeneous attribute large datasets using discretized Bayesian max---min ant colony optimizationKnowledge and Information Systems10.1007/s10115-017-1123-456:2(309-334)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s10115-017-1123-4
Goyal DPabla BDhami SLachhwani K(2017)Optimization of condition-based maintenance using soft computingNeural Computing and Applications10.5555/3041299.316898528:1(829-844)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.5555/3041299.3168985
Afzalirad MRezaeian J(2017)A realistic variant of bi-objective unrelated parallel machine scheduling problemApplied Soft Computing10.1016/j.asoc.2016.10.03950:C(109-123)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.asoc.2016.10.039
Khatami MZegordi S(2017)Coordinative production and maintenance scheduling problem with flexible maintenance time intervalsJournal of Intelligent Manufacturing10.1007/s10845-014-1001-928:4(857-867)Online publication date: 1-Apr-2017
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