Flowshop with additional resources during setups: Mathematical models and a GRASP algorithm

Machine scheduling problems arise in many production processes, and are something that needs to be consider when optimizing the supply chain. Among them, flowshop scheduling problems happen when a number of jobs have to be sequentially processed by a number of machines. This paper addressees, for th...

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Authors: Yepes-Borrero, Juan C., Perea Rojas-Marcos, Federico, Villa Julià, Fulgencia, Vallada Regalada, Eva
Format: article
Status:Published version
Publication Date:2023
Country:España
Institution:Universidad de Sevilla (US)
Repository:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/146680
Online Access:https://hdl.handle.net/11441/146680
https://doi.org/10.1016/j.cor.2023.106192
Access Level:Open access
Keyword:Scheduling
Flowshop
Mathematical programming
GRASP
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spelling Flowshop with additional resources during setups: Mathematical models and a GRASP algorithmYepes-Borrero, Juan C.Perea Rojas-Marcos, FedericoVilla Julià, FulgenciaVallada Regalada, EvaSchedulingFlowshopMathematical programmingGRASPMachine scheduling problems arise in many production processes, and are something that needs to be consider when optimizing the supply chain. Among them, flowshop scheduling problems happen when a number of jobs have to be sequentially processed by a number of machines. This paper addressees, for the first time, the Permutation Flowshop Scheduling problem with additional Resources during Setups (PFSR-S). In this problem, in addition to the standard permutation flowshop constraints, each machine requires a setup between the processing of two consecutive jobs. A number of additional and scarce resources, e.g. operators, are needed to carry out each setup. Two Mixed Integer Linear Programming formulations and an exact algorithm are proposed to solve the PFSR-S. Due to its complexity, these approaches can only solve instances of small size to optimality. Therefore, a GRASP metaheuristic is also proposed which provides solutions for much larger instances. All the methods designed for the PFSR-S in this paper are computationally tested over a benchmark of instances adapted from the literature. The results obtained show that the GRASP metaheuristic finds good quality solutions in short computational times.ElsevierMatemática Aplicada IIFQM241: Grupo de Investigación en LocalizaciónAgencia Estatal de Investigación (AEI) and the European Regional Development’s fund (ERDF): PID2020-114594GB-C21Regional Government of Andalusia: project FEDER-US-1256951Regional Government of Andalusia: project AT 21_00032Regional Government of Andalusia: project P18-FR-1422Spanish Ministry of Science and Innovation under the project “OPRES-Realistic Optimization in Problems in Public Health” and FEDER No. PID2021-124975OB-I002023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/146680https://doi.org/10.1016/j.cor.2023.106192reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésPID2020-114594GB-C21FEDER-US-1256951AT 21_00032P18-FR-1422PID2021-124975OB-I00https://www.sciencedirect.com/science/article/pii/S0305054823000564info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1466802026-06-17T12:51:07Z
dc.title.none.fl_str_mv Flowshop with additional resources during setups: Mathematical models and a GRASP algorithm
title Flowshop with additional resources during setups: Mathematical models and a GRASP algorithm
spellingShingle Flowshop with additional resources during setups: Mathematical models and a GRASP algorithm
Yepes-Borrero, Juan C.
Scheduling
Flowshop
Mathematical programming
GRASP
title_short Flowshop with additional resources during setups: Mathematical models and a GRASP algorithm
title_full Flowshop with additional resources during setups: Mathematical models and a GRASP algorithm
title_fullStr Flowshop with additional resources during setups: Mathematical models and a GRASP algorithm
title_full_unstemmed Flowshop with additional resources during setups: Mathematical models and a GRASP algorithm
title_sort Flowshop with additional resources during setups: Mathematical models and a GRASP algorithm
dc.creator.none.fl_str_mv Yepes-Borrero, Juan C.
Perea Rojas-Marcos, Federico
Villa Julià, Fulgencia
Vallada Regalada, Eva
author Yepes-Borrero, Juan C.
author_facet Yepes-Borrero, Juan C.
Perea Rojas-Marcos, Federico
Villa Julià, Fulgencia
Vallada Regalada, Eva
author_role author
author2 Perea Rojas-Marcos, Federico
Villa Julià, Fulgencia
Vallada Regalada, Eva
author2_role author
author
author
dc.contributor.none.fl_str_mv Matemática Aplicada II
FQM241: Grupo de Investigación en Localización
Agencia Estatal de Investigación (AEI) and the European Regional Development’s fund (ERDF): PID2020-114594GB-C21
Regional Government of Andalusia: project FEDER-US-1256951
Regional Government of Andalusia: project AT 21_00032
Regional Government of Andalusia: project P18-FR-1422
Spanish Ministry of Science and Innovation under the project “OPRES-Realistic Optimization in Problems in Public Health” and FEDER No. PID2021-124975OB-I00
dc.subject.none.fl_str_mv Scheduling
Flowshop
Mathematical programming
GRASP
topic Scheduling
Flowshop
Mathematical programming
GRASP
description Machine scheduling problems arise in many production processes, and are something that needs to be consider when optimizing the supply chain. Among them, flowshop scheduling problems happen when a number of jobs have to be sequentially processed by a number of machines. This paper addressees, for the first time, the Permutation Flowshop Scheduling problem with additional Resources during Setups (PFSR-S). In this problem, in addition to the standard permutation flowshop constraints, each machine requires a setup between the processing of two consecutive jobs. A number of additional and scarce resources, e.g. operators, are needed to carry out each setup. Two Mixed Integer Linear Programming formulations and an exact algorithm are proposed to solve the PFSR-S. Due to its complexity, these approaches can only solve instances of small size to optimality. Therefore, a GRASP metaheuristic is also proposed which provides solutions for much larger instances. All the methods designed for the PFSR-S in this paper are computationally tested over a benchmark of instances adapted from the literature. The results obtained show that the GRASP metaheuristic finds good quality solutions in short computational times.
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/146680
https://doi.org/10.1016/j.cor.2023.106192
url https://hdl.handle.net/11441/146680
https://doi.org/10.1016/j.cor.2023.106192
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv PID2020-114594GB-C21
FEDER-US-1256951
AT 21_00032
P18-FR-1422
PID2021-124975OB-I00
https://www.sciencedirect.com/science/article/pii/S0305054823000564
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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