Optimizing Last-Mile Deliveries : Addressing Customer Absence Through Genetic Algorithm

Last-mile delivery logistics face significant challenges, particularly regarding customer absences during scheduled delivery times. This issue not only frustrates customers but also imposes substantial economic costs on delivery companies, estimated at up to 15 euros per failed delivery. This resear...

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Detalles Bibliográficos
Autores: Sánchez-Soriano, Javier, Sánchez Soriano, Javier, Verdín-Urgal, Guillermo, Gordo-Herrera, Natalia
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad de Málaga
Repositorio:DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria
Idioma:inglés
OAI Identifier:oai:ddfv.ufv.es:10641/6316
Acceso en línea:https://hdl.handle.net/10641/6316
Access Level:acceso abierto
Palabra clave:genetic algorithm
last mile
logistics
optimization
routing
simulated annealing
Computer Science (miscellaneous)
Yes
yes
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repository_id_str
spelling Optimizing Last-Mile Deliveries : Addressing Customer Absence Through Genetic AlgorithmSánchez-Soriano, JavierSánchez Soriano, JavierVerdín-Urgal, GuillermoGordo-Herrera, Nataliagenetic algorithmlast milelogisticsoptimizationroutingsimulated annealingComputer Science (miscellaneous)YesyesLast-mile delivery logistics face significant challenges, particularly regarding customer absences during scheduled delivery times. This issue not only frustrates customers but also imposes substantial economic costs on delivery companies, estimated at up to 15 euros per failed delivery. This research aims to address this problem by optimizing last-mile delivery processes using a genetic algorithm (GA) designed to minimize rerouting costs while respecting customer time preferences. The study compares the performance of the proposed GA with a Simulated Annealing (SA) algorithm, assessing their efficiency in route optimization. Through detailed simulations, GA reduces operational costs by over 35,000 euros annually by considering customer preferences. It significantly outperforms the SA algorithm in scenarios with high customer variability, highlighting its potential for cost-efficient last-mile delivery solutions. Additionally, the GA consistently respected 4–7 more customer preferences per route compared to traditional methods, leading to enhanced customer satisfaction. This work contributes to the field by providing a robust methodology for balancing cost efficiency and user satisfaction in last-mile deliveries, offering actionable insights for logistics optimization.Escuela Politécnica Superior20252025-03-0120252025-03-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10641/6316reponame:DDFV. Repositorio Institucional de la Universidad Francisco de Vitoriainstname:Universidad de MálagaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ddfv.ufv.es:10641/63162026-06-11T12:44:57Z
dc.title.none.fl_str_mv Optimizing Last-Mile Deliveries : Addressing Customer Absence Through Genetic Algorithm
title Optimizing Last-Mile Deliveries : Addressing Customer Absence Through Genetic Algorithm
spellingShingle Optimizing Last-Mile Deliveries : Addressing Customer Absence Through Genetic Algorithm
Sánchez-Soriano, Javier
genetic algorithm
last mile
logistics
optimization
routing
simulated annealing
Computer Science (miscellaneous)
Yes
yes
title_short Optimizing Last-Mile Deliveries : Addressing Customer Absence Through Genetic Algorithm
title_full Optimizing Last-Mile Deliveries : Addressing Customer Absence Through Genetic Algorithm
title_fullStr Optimizing Last-Mile Deliveries : Addressing Customer Absence Through Genetic Algorithm
title_full_unstemmed Optimizing Last-Mile Deliveries : Addressing Customer Absence Through Genetic Algorithm
title_sort Optimizing Last-Mile Deliveries : Addressing Customer Absence Through Genetic Algorithm
dc.creator.none.fl_str_mv Sánchez-Soriano, Javier
Sánchez Soriano, Javier
Verdín-Urgal, Guillermo
Gordo-Herrera, Natalia
author Sánchez-Soriano, Javier
author_facet Sánchez-Soriano, Javier
Sánchez Soriano, Javier
Verdín-Urgal, Guillermo
Gordo-Herrera, Natalia
author_role author
author2 Sánchez Soriano, Javier
Verdín-Urgal, Guillermo
Gordo-Herrera, Natalia
author2_role author
author
author
dc.contributor.none.fl_str_mv Escuela Politécnica Superior
dc.subject.none.fl_str_mv genetic algorithm
last mile
logistics
optimization
routing
simulated annealing
Computer Science (miscellaneous)
Yes
yes
topic genetic algorithm
last mile
logistics
optimization
routing
simulated annealing
Computer Science (miscellaneous)
Yes
yes
description Last-mile delivery logistics face significant challenges, particularly regarding customer absences during scheduled delivery times. This issue not only frustrates customers but also imposes substantial economic costs on delivery companies, estimated at up to 15 euros per failed delivery. This research aims to address this problem by optimizing last-mile delivery processes using a genetic algorithm (GA) designed to minimize rerouting costs while respecting customer time preferences. The study compares the performance of the proposed GA with a Simulated Annealing (SA) algorithm, assessing their efficiency in route optimization. Through detailed simulations, GA reduces operational costs by over 35,000 euros annually by considering customer preferences. It significantly outperforms the SA algorithm in scenarios with high customer variability, highlighting its potential for cost-efficient last-mile delivery solutions. Additionally, the GA consistently respected 4–7 more customer preferences per route compared to traditional methods, leading to enhanced customer satisfaction. This work contributes to the field by providing a robust methodology for balancing cost efficiency and user satisfaction in last-mile deliveries, offering actionable insights for logistics optimization.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-03-01
2025
2025-03-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10641/6316
url https://hdl.handle.net/10641/6316
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

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

http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria
instname:Universidad de Málaga
instname_str Universidad de Málaga
reponame_str DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria
collection DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812455

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