Handling uncertainty in the quay crane scheduling problem: a unified distributionally robust decision model

Detalhes bibliográficos
Autor(a) principal: Rodrigues, Filipe
Data de Publicação: 2024
Outros Autores: Agra, Agostinho
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/39950
Resumo: Quay cranes are among the most important resources in port terminals, and their efficient use is crucial for port terminals to remain competitive in the market. The problem of determining the sequence of tasks performed by the quay cranes that minimizes the turnaround time of a vessel is an NP-hard problem known as the quay crane scheduling problem (QCSP). In this paper, we consider the unidirectional QCSP under uncertain processing times, where the cranes are only allowed to move in a specific direction. We start by presenting the first distributionally robust optimization (DRO) model for this problem and an exact decomposition algorithm to solve it. The proposed DRO model makes it possible to derive a stochastic programming model and a robust optimization model for the unidirectional QCSP by an appropriate choice of the risk-averse parameter of the model. Through extensive numerical results, we compare these three approaches—stochastic programming, robust optimization, and DRO—to investigate whether significant differences among the solutions obtained exist. Finally, we propose a new method for helping practitioners to determine a representative set of different DRO solutions.
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spelling Handling uncertainty in the quay crane scheduling problem: a unified distributionally robust decision modelQuay crane schedulingDistributionally robust optimizationWasserstein distanceUncertaintyUnidirectionalQuay cranes are among the most important resources in port terminals, and their efficient use is crucial for port terminals to remain competitive in the market. The problem of determining the sequence of tasks performed by the quay cranes that minimizes the turnaround time of a vessel is an NP-hard problem known as the quay crane scheduling problem (QCSP). In this paper, we consider the unidirectional QCSP under uncertain processing times, where the cranes are only allowed to move in a specific direction. We start by presenting the first distributionally robust optimization (DRO) model for this problem and an exact decomposition algorithm to solve it. The proposed DRO model makes it possible to derive a stochastic programming model and a robust optimization model for the unidirectional QCSP by an appropriate choice of the risk-averse parameter of the model. Through extensive numerical results, we compare these three approaches—stochastic programming, robust optimization, and DRO—to investigate whether significant differences among the solutions obtained exist. Finally, we propose a new method for helping practitioners to determine a representative set of different DRO solutions.Wiley2024-01-04T15:35:38Z2024-03-01T00:00:00Z2024-03info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/39950eng0969-601610.1111/itor.13325Rodrigues, FilipeAgra, Agostinhoinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T12:17:57Zoai:ria.ua.pt:10773/39950Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:09:58.018382Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Handling uncertainty in the quay crane scheduling problem: a unified distributionally robust decision model
title Handling uncertainty in the quay crane scheduling problem: a unified distributionally robust decision model
spellingShingle Handling uncertainty in the quay crane scheduling problem: a unified distributionally robust decision model
Rodrigues, Filipe
Quay crane scheduling
Distributionally robust optimization
Wasserstein distance
Uncertainty
Unidirectional
title_short Handling uncertainty in the quay crane scheduling problem: a unified distributionally robust decision model
title_full Handling uncertainty in the quay crane scheduling problem: a unified distributionally robust decision model
title_fullStr Handling uncertainty in the quay crane scheduling problem: a unified distributionally robust decision model
title_full_unstemmed Handling uncertainty in the quay crane scheduling problem: a unified distributionally robust decision model
title_sort Handling uncertainty in the quay crane scheduling problem: a unified distributionally robust decision model
author Rodrigues, Filipe
author_facet Rodrigues, Filipe
Agra, Agostinho
author_role author
author2 Agra, Agostinho
author2_role author
dc.contributor.author.fl_str_mv Rodrigues, Filipe
Agra, Agostinho
dc.subject.por.fl_str_mv Quay crane scheduling
Distributionally robust optimization
Wasserstein distance
Uncertainty
Unidirectional
topic Quay crane scheduling
Distributionally robust optimization
Wasserstein distance
Uncertainty
Unidirectional
description Quay cranes are among the most important resources in port terminals, and their efficient use is crucial for port terminals to remain competitive in the market. The problem of determining the sequence of tasks performed by the quay cranes that minimizes the turnaround time of a vessel is an NP-hard problem known as the quay crane scheduling problem (QCSP). In this paper, we consider the unidirectional QCSP under uncertain processing times, where the cranes are only allowed to move in a specific direction. We start by presenting the first distributionally robust optimization (DRO) model for this problem and an exact decomposition algorithm to solve it. The proposed DRO model makes it possible to derive a stochastic programming model and a robust optimization model for the unidirectional QCSP by an appropriate choice of the risk-averse parameter of the model. Through extensive numerical results, we compare these three approaches—stochastic programming, robust optimization, and DRO—to investigate whether significant differences among the solutions obtained exist. Finally, we propose a new method for helping practitioners to determine a representative set of different DRO solutions.
publishDate 2024
dc.date.none.fl_str_mv 2024-01-04T15:35:38Z
2024-03-01T00:00:00Z
2024-03
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10773/39950
url http://hdl.handle.net/10773/39950
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0969-6016
10.1111/itor.13325
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dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
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instacron:RCAAP
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