A Sim-Learnheuristic for the Team Orienteering Problem

In this paper, we introduce a novel sim-learnheuristic method designed to address the team orienteering problem (TOP) with a particular focus on its application in the context of unmanned aerial vehicles (UAVs). Unlike most prior research, which primarily focuses on the deterministic and stochastic...

Descripción completa

Detalles Bibliográficos
Autores: Peyman, Mohammad|||0000-0003-4734-2414, Martin, Xabier A.|||0000-0003-4182-0120, Panadero, Javier|||0000-0002-3793-3328, Juan, Ángel A.|||0000-0003-1392-1776
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:304472
Acceso en línea:https://ddd.uab.cat/record/304472
https://dx.doi.org/urn:doi:10.3390/a17050200
Access Level:acceso abierto
Palabra clave:Biased randomization
Learnheuristic
Simheuristic
Team orienteering problem
SDG 9 - Industry, Innovation, and Infrastructure
id ES_4f4e637f03bf93649799bb8ecab3c820
oai_identifier_str oai:ddd.uab.cat:304472
network_acronym_str ES
network_name_str España
repository_id_str
spelling A Sim-Learnheuristic for the Team Orienteering ProblemApplications to Unmanned Aerial VehiclesPeyman, Mohammad|||0000-0003-4734-2414Martin, Xabier A.|||0000-0003-4182-0120Panadero, Javier|||0000-0002-3793-3328Juan, Ángel A.|||0000-0003-1392-1776Biased randomizationLearnheuristicSimheuristicTeam orienteering problemSDG 9 - Industry, Innovation, and InfrastructureIn this paper, we introduce a novel sim-learnheuristic method designed to address the team orienteering problem (TOP) with a particular focus on its application in the context of unmanned aerial vehicles (UAVs). Unlike most prior research, which primarily focuses on the deterministic and stochastic versions of the TOP, our approach considers a hybrid scenario, which combines deterministic, stochastic, and dynamic characteristics. The TOP involves visiting a set of customers using a team of vehicles to maximize the total collected reward. However, this hybrid version becomes notably complex due to the presence of uncertain travel times with dynamically changing factors. Some travel times are stochastic, while others are subject to dynamic factors such as weather conditions and traffic congestion. Our novel approach combines a savings-based heuristic algorithm, Monte Carlo simulations, and a multiple regression model. This integration incorporates the stochastic and dynamic nature of travel times, considering various dynamic conditions, and generates high-quality solutions in short computational times for the presented problem.Universitat Autònoma de Barcelona. Departament d'Arquitectura de Computadors i Sistemes Operatius 22024-01-0120242024-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/304472https://dx.doi.org/urn:doi:10.3390/a17050200reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengAgencia Estatal de Investigación https://doi.org/10.13039/501100011033 PRE2020-091842Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2022-138860NB-I00Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 RED2022-134703-TEuropean Commission https://doi.org/10.13039/501100000780 101092612open accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:3044722026-06-06T12:50:31Z
dc.title.none.fl_str_mv A Sim-Learnheuristic for the Team Orienteering Problem
Applications to Unmanned Aerial Vehicles
title A Sim-Learnheuristic for the Team Orienteering Problem
spellingShingle A Sim-Learnheuristic for the Team Orienteering Problem
Peyman, Mohammad|||0000-0003-4734-2414
Biased randomization
Learnheuristic
Simheuristic
Team orienteering problem
SDG 9 - Industry, Innovation, and Infrastructure
title_short A Sim-Learnheuristic for the Team Orienteering Problem
title_full A Sim-Learnheuristic for the Team Orienteering Problem
title_fullStr A Sim-Learnheuristic for the Team Orienteering Problem
title_full_unstemmed A Sim-Learnheuristic for the Team Orienteering Problem
title_sort A Sim-Learnheuristic for the Team Orienteering Problem
dc.creator.none.fl_str_mv Peyman, Mohammad|||0000-0003-4734-2414
Martin, Xabier A.|||0000-0003-4182-0120
Panadero, Javier|||0000-0002-3793-3328
Juan, Ángel A.|||0000-0003-1392-1776
author Peyman, Mohammad|||0000-0003-4734-2414
author_facet Peyman, Mohammad|||0000-0003-4734-2414
Martin, Xabier A.|||0000-0003-4182-0120
Panadero, Javier|||0000-0002-3793-3328
Juan, Ángel A.|||0000-0003-1392-1776
author_role author
author2 Martin, Xabier A.|||0000-0003-4182-0120
Panadero, Javier|||0000-0002-3793-3328
Juan, Ángel A.|||0000-0003-1392-1776
author2_role author
author
author
dc.contributor.none.fl_str_mv Universitat Autònoma de Barcelona. Departament d'Arquitectura de Computadors i Sistemes Operatius
dc.subject.none.fl_str_mv Biased randomization
Learnheuristic
Simheuristic
Team orienteering problem
SDG 9 - Industry, Innovation, and Infrastructure
topic Biased randomization
Learnheuristic
Simheuristic
Team orienteering problem
SDG 9 - Industry, Innovation, and Infrastructure
description In this paper, we introduce a novel sim-learnheuristic method designed to address the team orienteering problem (TOP) with a particular focus on its application in the context of unmanned aerial vehicles (UAVs). Unlike most prior research, which primarily focuses on the deterministic and stochastic versions of the TOP, our approach considers a hybrid scenario, which combines deterministic, stochastic, and dynamic characteristics. The TOP involves visiting a set of customers using a team of vehicles to maximize the total collected reward. However, this hybrid version becomes notably complex due to the presence of uncertain travel times with dynamically changing factors. Some travel times are stochastic, while others are subject to dynamic factors such as weather conditions and traffic congestion. Our novel approach combines a savings-based heuristic algorithm, Monte Carlo simulations, and a multiple regression model. This integration incorporates the stochastic and dynamic nature of travel times, considering various dynamic conditions, and generates high-quality solutions in short computational times for the presented problem.
publishDate 2024
dc.date.none.fl_str_mv 2
2024-01-01
2024
2024-01-01
dc.type.none.fl_str_mv 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://ddd.uab.cat/record/304472
https://dx.doi.org/urn:doi:10.3390/a17050200
url https://ddd.uab.cat/record/304472
https://dx.doi.org/urn:doi:10.3390/a17050200
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 https://doi.org/10.13039/501100011033 PRE2020-091842
Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2022-138860NB-I00
Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 RED2022-134703-T
European Commission https://doi.org/10.13039/501100000780 101092612
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/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
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869407811763437568
score 15,811543

Similares en LA Referencia