Multi-objective enhanced memetic algorithm for green job shop scheduling with uncertain times

The quest for sustainability has arrived to the manufacturing world, with the emergence of a research field known as green scheduling. Traditional performance objectives now co-exist with energy-saving ones. In this work, we tackle a job shop scheduling problem with the double goal of minimising ene...

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Detalles Bibliográficos
Autores: Afsar, Sezin, Palacios, Juan José, Puente, Jorge, Vela, Camino R., González Rodríguez, Inés|||0000-0003-3266-009X
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad de Cantabria (UC)
Repositorio:UCrea Repositorio Abierto de la Universidad de Cantabria
Idioma:inglés
OAI Identifier:oai:repositorio.unican.es:10902/28236
Acceso en línea:https://hdl.handle.net/10902/28236
Access Level:acceso abierto
Palabra clave:Job shop scheduling
Fuzzy durations
Multi-objective
Makespan
Non-processing energy
Memetic algorithm
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spelling Multi-objective enhanced memetic algorithm for green job shop scheduling with uncertain timesAfsar, SezinPalacios, Juan JoséPuente, JorgeVela, Camino R.González Rodríguez, Inés|||0000-0003-3266-009XJob shop schedulingFuzzy durationsMulti-objectiveMakespanNon-processing energyMemetic algorithmThe quest for sustainability has arrived to the manufacturing world, with the emergence of a research field known as green scheduling. Traditional performance objectives now co-exist with energy-saving ones. In this work, we tackle a job shop scheduling problem with the double goal of minimising energy consumption during machine idle time and minimising the project’s makespan. We also consider uncertainty in processing times, modelled with fuzzy numbers. We present a multi-objective optimisation model of the problem and we propose a new enhanced memetic algorithm that combines a multiobjective evolutionary algorithm with three procedures that exploit the problem-specific available knowledge. Experimental results validate the proposed method with respect to hypervolume, -indicator and empirical attaintment functions.ElsevierUniversidad de Cantabria20222022-01-01journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articlehttps://hdl.handle.net/10902/28236Swarm and Evolutionary Computation, 2022, 68, 101016reponame:UCrea Repositorio Abierto de la Universidad de Cantabriainstname:Universidad de Cantabria (UC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositorio.unican.es:10902/282362026-06-02T12:39:31Z
dc.title.none.fl_str_mv Multi-objective enhanced memetic algorithm for green job shop scheduling with uncertain times
title Multi-objective enhanced memetic algorithm for green job shop scheduling with uncertain times
spellingShingle Multi-objective enhanced memetic algorithm for green job shop scheduling with uncertain times
Afsar, Sezin
Job shop scheduling
Fuzzy durations
Multi-objective
Makespan
Non-processing energy
Memetic algorithm
title_short Multi-objective enhanced memetic algorithm for green job shop scheduling with uncertain times
title_full Multi-objective enhanced memetic algorithm for green job shop scheduling with uncertain times
title_fullStr Multi-objective enhanced memetic algorithm for green job shop scheduling with uncertain times
title_full_unstemmed Multi-objective enhanced memetic algorithm for green job shop scheduling with uncertain times
title_sort Multi-objective enhanced memetic algorithm for green job shop scheduling with uncertain times
dc.creator.none.fl_str_mv Afsar, Sezin
Palacios, Juan José
Puente, Jorge
Vela, Camino R.
González Rodríguez, Inés|||0000-0003-3266-009X
author Afsar, Sezin
author_facet Afsar, Sezin
Palacios, Juan José
Puente, Jorge
Vela, Camino R.
González Rodríguez, Inés|||0000-0003-3266-009X
author_role author
author2 Palacios, Juan José
Puente, Jorge
Vela, Camino R.
González Rodríguez, Inés|||0000-0003-3266-009X
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidad de Cantabria
dc.subject.none.fl_str_mv Job shop scheduling
Fuzzy durations
Multi-objective
Makespan
Non-processing energy
Memetic algorithm
topic Job shop scheduling
Fuzzy durations
Multi-objective
Makespan
Non-processing energy
Memetic algorithm
description The quest for sustainability has arrived to the manufacturing world, with the emergence of a research field known as green scheduling. Traditional performance objectives now co-exist with energy-saving ones. In this work, we tackle a job shop scheduling problem with the double goal of minimising energy consumption during machine idle time and minimising the project’s makespan. We also consider uncertainty in processing times, modelled with fuzzy numbers. We present a multi-objective optimisation model of the problem and we propose a new enhanced memetic algorithm that combines a multiobjective evolutionary algorithm with three procedures that exploit the problem-specific available knowledge. Experimental results validate the proposed method with respect to hypervolume, -indicator and empirical attaintment functions.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10902/28236
url https://hdl.handle.net/10902/28236
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Swarm and Evolutionary Computation, 2022, 68, 101016
reponame:UCrea Repositorio Abierto de la Universidad de Cantabria
instname:Universidad de Cantabria (UC)
instname_str Universidad de Cantabria (UC)
reponame_str UCrea Repositorio Abierto de la Universidad de Cantabria
collection UCrea Repositorio Abierto de la Universidad de Cantabria
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869406416628875264
score 15,301603

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