Show simple item record

dc.contributor.authorPara, Jesus
dc.contributor.authorDel Ser, Javier
dc.contributor.authorNebro, Antonio J.
dc.date.accessioned2022-03-20T22:21:22Z
dc.date.available2022-03-20T22:21:22Z
dc.date.issued2022-01-29
dc.identifier.citationPara, Jesus, Javier Del Ser, and Antonio J. Nebro. “Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives.” Applied Sciences 12, no. 3 (January 29, 2022): 1491. doi:10.3390/app12031491.en
dc.identifier.urihttp://hdl.handle.net/11556/1292
dc.description.abstractIn recent years, the application of artificial intelligence has been revolutionizing the manufacturing industry, becoming one of the key pillars of what has been called Industry 4.0. In this context, we focus on the job shop scheduling problem (JSP), which aims at productions orders to be carried out, but considering the reduction of energy consumption as a key objective to fulfill. Finding the best combination of machines and jobs to be performed is not a trivial problem and becomes even more involved when several objectives are taken into account. Among them, the improvement of energy savings may conflict with other objectives, such as the minimization of the makespan. In this paper, we provide an in-depth review of the existing literature on multi-objective job shop scheduling optimization with metaheuristics, in which one of the objectives is the minimization of energy consumption. We systematically reviewed and critically analyzed the most relevant features of both problem formulations and algorithms to solve them effectively. The manuscript also informs with empirical results the main findings of our bibliographic critique with a performance comparison among representative multi-objective evolutionary solvers applied to a diversity of synthetic test instances. The ultimate goal of this article is to carry out a critical analysis, finding good practices and opportunities for further improvement that stem from current knowledge in this vibrant research area.en
dc.description.sponsorshipJavier Del Ser acknowledges funding support from the Basque Government (consolidated research group MATHMODE, Ref. IT1294-19). Antonio J. Nebro is supported by the Spanish Ministry of Science and Innovation via Grant PID2020-112540RB-C41 (AEI/FEDER, UE) and the Andalusian PAIDI program with Grant P18-RT-2799.en
dc.language.isoengen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleEnergy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectivesen
dc.typearticleen
dc.identifier.doi10.3390/app12031491en
dc.rights.accessRightsopenAccessen
dc.subject.keywordsJob shop schedulingen
dc.subject.keywordsEnergy efficiencyen
dc.subject.keywordsMetaheuristicsen
dc.subject.keywordsMulti-objective optimizationen
dc.identifier.essn2076-3417en
dc.issue.number3en
dc.journal.titleApplied Sciencesen
dc.page.initial1491en
dc.volume.number12en


Files in this item

Thumbnail

    Show simple item record

    Attribution 4.0 InternationalExcept where otherwise noted, this item's license is described as Attribution 4.0 International