A hybrid metaheuristic with learning for a real supply chain scheduling problem

[EN] In recent decades, research on supply chain management (SCM) has enabled companies to improve their environmental, social, and economic performance.This paper presents an industrial application of logistics that can be classified as an inventory-route problem. The problem consists of assigning...

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Detalhes bibliográficos
Autores: Pérez-Bernal, Christian|||0000-0002-9121-7939, Climent Aunes, Laura Isabel, Miguel A. Salido|||0000-0002-4835-4057, Nicoló, Giancarlo, Arbelaez, Alejandro
Formato: artículo
Fecha de publicación:2023
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/205467
Acesso em linha:https://riunet.upv.es/handle/10251/205467
Access Level:acceso abierto
Palavra-chave:Optimization
Metaheuristics
Supply chain management
Hybrid algorithm
GRASP
Meta-learning
Inventor-routing problem
LENGUAJES Y SISTEMAS INFORMATICOS
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
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repository_id_str
spelling A hybrid metaheuristic with learning for a real supply chain scheduling problemPérez-Bernal, Christian|||0000-0002-9121-7939Climent Aunes, Laura IsabelMiguel A. Salido|||0000-0002-4835-4057Nicoló, GiancarloArbelaez, AlejandroOptimizationMetaheuristicsSupply chain managementHybrid algorithmGRASPMeta-learningInventor-routing problemLENGUAJES Y SISTEMAS INFORMATICOS09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación[EN] In recent decades, research on supply chain management (SCM) has enabled companies to improve their environmental, social, and economic performance.This paper presents an industrial application of logistics that can be classified as an inventory-route problem. The problem consists of assigning orders to the available warehouses. The orders are composed of items that must be loaded within a week. The warehouses provide an inventory of the number of items available for each day of the week, so the objective is to minimize the total transportation costs and the costs of producing extra stock to satisfy the weekly demand. To solve this problem a formal mathematical model is proposed. Then a hybrid approach that involves two metaheuristics: a greedy randomized adaptive search procedure (GRASP) and a genetic algorithm (GA) is proposed. Additionally, a meta-learning tuning method is incorporated into our hybridized approach, which yields better results but with a longer computation time. Thus, the trade-off of using it is analyzed.An extensive evaluation was carried out over realistic instances provided by an industrial partner. The proposed technique was evaluated and compared with several complete and incomplete solvers from the state of the art (CP Optimizer, Yuck, OR-Tools, etc.). The results showed that our hybrid metaheuristic outperformed the behavior of these well-known solvers, mainly in large-scale instances (2000 orders per week). This hybrid algorithm provides the company with a powerful tool to solve its supply chain management problem, delivering significant economic benefits every week.The authors gratefully acknowledge the financial support of the European Social Fund (Investing In Your Future) , the Spanish Ministry of Science (project PID2021-125919NB-I00), and valgrAI-Valencian Graduate School and Research Network of Artificial Intelligence and the Generalitat Valenciana, Spain, and co-funded by the European Union. The authors also thank the industrial partner Logifruit for its support in the problem specification and the permission to generate randomized data for evaluating the proposed algorithms.ElsevierDepartamento de Sistemas Informáticos y ComputaciónEscuela Técnica Superior de Ingeniería Aeroespacial y Diseño IndustrialInstituto Universitario de Automática e Informática IndustrialEscuela Técnica Superior de Ingeniería InformáticaEuropean Social FundAgencia Estatal de InvestigaciónUniversitat Politècnica de ValènciaValencian Graduate School and Research Network of Artificial IntelligenceRepositorio Institucional de la Universitat Politècnica de València Riunet20232023-11-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/205467reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-125919NB-I00 BUSQUEDA METAHEURISTICA CON APRENDIZAJE EN PROBLEMAS DE SCHEDULING SOSTENIBLEopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2054672026-06-13T07:49:27Z
dc.title.none.fl_str_mv A hybrid metaheuristic with learning for a real supply chain scheduling problem
title A hybrid metaheuristic with learning for a real supply chain scheduling problem
spellingShingle A hybrid metaheuristic with learning for a real supply chain scheduling problem
Pérez-Bernal, Christian|||0000-0002-9121-7939
Optimization
Metaheuristics
Supply chain management
Hybrid algorithm
GRASP
Meta-learning
Inventor-routing problem
LENGUAJES Y SISTEMAS INFORMATICOS
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
title_short A hybrid metaheuristic with learning for a real supply chain scheduling problem
title_full A hybrid metaheuristic with learning for a real supply chain scheduling problem
title_fullStr A hybrid metaheuristic with learning for a real supply chain scheduling problem
title_full_unstemmed A hybrid metaheuristic with learning for a real supply chain scheduling problem
title_sort A hybrid metaheuristic with learning for a real supply chain scheduling problem
dc.creator.none.fl_str_mv Pérez-Bernal, Christian|||0000-0002-9121-7939
Climent Aunes, Laura Isabel
Miguel A. Salido|||0000-0002-4835-4057
Nicoló, Giancarlo
Arbelaez, Alejandro
author Pérez-Bernal, Christian|||0000-0002-9121-7939
author_facet Pérez-Bernal, Christian|||0000-0002-9121-7939
Climent Aunes, Laura Isabel
Miguel A. Salido|||0000-0002-4835-4057
Nicoló, Giancarlo
Arbelaez, Alejandro
author_role author
author2 Climent Aunes, Laura Isabel
Miguel A. Salido|||0000-0002-4835-4057
Nicoló, Giancarlo
Arbelaez, Alejandro
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Sistemas Informáticos y Computación
Escuela Técnica Superior de Ingeniería Aeroespacial y Diseño Industrial
Instituto Universitario de Automática e Informática Industrial
Escuela Técnica Superior de Ingeniería Informática
European Social Fund
Agencia Estatal de Investigación
Universitat Politècnica de València
Valencian Graduate School and Research Network of Artificial Intelligence
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Optimization
Metaheuristics
Supply chain management
Hybrid algorithm
GRASP
Meta-learning
Inventor-routing problem
LENGUAJES Y SISTEMAS INFORMATICOS
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
topic Optimization
Metaheuristics
Supply chain management
Hybrid algorithm
GRASP
Meta-learning
Inventor-routing problem
LENGUAJES Y SISTEMAS INFORMATICOS
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
description [EN] In recent decades, research on supply chain management (SCM) has enabled companies to improve their environmental, social, and economic performance.This paper presents an industrial application of logistics that can be classified as an inventory-route problem. The problem consists of assigning orders to the available warehouses. The orders are composed of items that must be loaded within a week. The warehouses provide an inventory of the number of items available for each day of the week, so the objective is to minimize the total transportation costs and the costs of producing extra stock to satisfy the weekly demand. To solve this problem a formal mathematical model is proposed. Then a hybrid approach that involves two metaheuristics: a greedy randomized adaptive search procedure (GRASP) and a genetic algorithm (GA) is proposed. Additionally, a meta-learning tuning method is incorporated into our hybridized approach, which yields better results but with a longer computation time. Thus, the trade-off of using it is analyzed.An extensive evaluation was carried out over realistic instances provided by an industrial partner. The proposed technique was evaluated and compared with several complete and incomplete solvers from the state of the art (CP Optimizer, Yuck, OR-Tools, etc.). The results showed that our hybrid metaheuristic outperformed the behavior of these well-known solvers, mainly in large-scale instances (2000 orders per week). This hybrid algorithm provides the company with a powerful tool to solve its supply chain management problem, delivering significant economic benefits every week.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-11-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/205467
url https://riunet.upv.es/handle/10251/205467
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-125919NB-I00 BUSQUEDA METAHEURISTICA CON APRENDIZAJE EN PROBLEMAS DE SCHEDULING SOSTENIBLE
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
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
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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