A Learnheuristic Algorithm Based on Thompson Sampling for the Heterogeneous and Dynamic Team Orienteering Problem

[EN] The team orienteering problem (TOP) is a well-studied optimization challenge in the field of Operations Research, where multiple vehicles aim to maximize the total collected rewards within a given time limit by visiting a subset of nodes in a network. With the goal of including dynamic and unce...

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Autores: Uguina, Antonio R., Gomez, Juan F, Panadero, Javier, Martínez-Gavara, Anna, Juan, Angel A.|||0000-0003-1392-1776
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/207466
Acceso en línea:https://riunet.upv.es/handle/10251/207466
Access Level:acceso abierto
Palabra clave:Combinatorial optimization
Team orienteering problem
Reinforcement learning
Learnheuristics
ESTADISTICA E INVESTIGACION OPERATIVA
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repository_id_str
spelling A Learnheuristic Algorithm Based on Thompson Sampling for the Heterogeneous and Dynamic Team Orienteering ProblemUguina, Antonio R.Gomez, Juan FPanadero, JavierMartínez-Gavara, AnnaJuan, Angel A.|||0000-0003-1392-1776Combinatorial optimizationTeam orienteering problemReinforcement learningLearnheuristicsESTADISTICA E INVESTIGACION OPERATIVA[EN] The team orienteering problem (TOP) is a well-studied optimization challenge in the field of Operations Research, where multiple vehicles aim to maximize the total collected rewards within a given time limit by visiting a subset of nodes in a network. With the goal of including dynamic and uncertain conditions inherent in real-world transportation scenarios, we introduce a novel dynamic variant of the TOP that considers real-time changes in environmental conditions affecting reward acquisition at each node. Specifically, we model the dynamic nature of environmental factors-such as traffic congestion, weather conditions, and battery level of each vehicle-to reflect their impact on the probability of obtaining the reward when visiting each type of node in a heterogeneous network. To address this problem, a learnheuristic optimization framework is proposed. It combines a metaheuristic algorithm with Thompson sampling to make informed decisions in dynamic environments. Furthermore, we conduct empirical experiments to assess the impact of varying reward probabilities on resource allocation and route planning within the context of this dynamic TOP, where nodes might offer a different reward behavior depending upon the environmental conditions. Our numerical results indicate that the proposed learnheuristic algorithm outperforms static approaches, achieving up to 25% better performance in highly dynamic scenarios. Our findings highlight the effectiveness of our approach in adapting to dynamic conditions and optimizing decision-making processes in transportation systems.This work has been partially funded by the Spanish Ministry of Science and Innovation (PID2022-138860NB-I00, RED2022-134703-T) as well as by the SUN (HORIZON-CL4-2022-HUMAN01-14-101092612) and AIDEAS (HORIZON-CL4-2021-TWIN-TRANSITION-01-07-101057294) projects of the Horizon Europe program.MDPI AGDepartamento de Estadística e Investigación Operativa Aplicadas y CalidadCentro de Investigación en Gestión e Ingeniería de ProducciónEscuela Politécnica Superior de AlcoyEuropean CommissionAgencia Estatal de InvestigaciónMinisterio de Ciencia e InnovaciónRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-06-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/207466reponame: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 PID2022-138860NB-I00 INTELIGENCIA ARTIFICIAL E INTERNET DE LAS COSAS PARA OPTIMIZAR EL CONSUMO ENERGETICO EN EL TRANSPORTE CON VEHICULOS ELECTRICOSEuropean Commission https://doi.org/10.13039/501100000780 HE 101057294 AI Driven industrial Equipment product life cycle boosting Agility, Sustainability and resilienceEuropean Commission https://doi.org/10.13039/501100000780 HE 101092612 Social and hUman ceNtered XRMinisterio de Ciencia e Innovación http://dx.doi.org/10.13039/501100004837 RED2022-134703-Topen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2074662026-06-13T07:49:27Z
dc.title.none.fl_str_mv A Learnheuristic Algorithm Based on Thompson Sampling for the Heterogeneous and Dynamic Team Orienteering Problem
title A Learnheuristic Algorithm Based on Thompson Sampling for the Heterogeneous and Dynamic Team Orienteering Problem
spellingShingle A Learnheuristic Algorithm Based on Thompson Sampling for the Heterogeneous and Dynamic Team Orienteering Problem
Uguina, Antonio R.
Combinatorial optimization
Team orienteering problem
Reinforcement learning
Learnheuristics
ESTADISTICA E INVESTIGACION OPERATIVA
title_short A Learnheuristic Algorithm Based on Thompson Sampling for the Heterogeneous and Dynamic Team Orienteering Problem
title_full A Learnheuristic Algorithm Based on Thompson Sampling for the Heterogeneous and Dynamic Team Orienteering Problem
title_fullStr A Learnheuristic Algorithm Based on Thompson Sampling for the Heterogeneous and Dynamic Team Orienteering Problem
title_full_unstemmed A Learnheuristic Algorithm Based on Thompson Sampling for the Heterogeneous and Dynamic Team Orienteering Problem
title_sort A Learnheuristic Algorithm Based on Thompson Sampling for the Heterogeneous and Dynamic Team Orienteering Problem
dc.creator.none.fl_str_mv Uguina, Antonio R.
Gomez, Juan F
Panadero, Javier
Martínez-Gavara, Anna
Juan, Angel A.|||0000-0003-1392-1776
author Uguina, Antonio R.
author_facet Uguina, Antonio R.
Gomez, Juan F
Panadero, Javier
Martínez-Gavara, Anna
Juan, Angel A.|||0000-0003-1392-1776
author_role author
author2 Gomez, Juan F
Panadero, Javier
Martínez-Gavara, Anna
Juan, Angel A.|||0000-0003-1392-1776
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Estadística e Investigación Operativa Aplicadas y Calidad
Centro de Investigación en Gestión e Ingeniería de Producción
Escuela Politécnica Superior de Alcoy
European Commission
Agencia Estatal de Investigación
Ministerio de Ciencia e Innovación
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Combinatorial optimization
Team orienteering problem
Reinforcement learning
Learnheuristics
ESTADISTICA E INVESTIGACION OPERATIVA
topic Combinatorial optimization
Team orienteering problem
Reinforcement learning
Learnheuristics
ESTADISTICA E INVESTIGACION OPERATIVA
description [EN] The team orienteering problem (TOP) is a well-studied optimization challenge in the field of Operations Research, where multiple vehicles aim to maximize the total collected rewards within a given time limit by visiting a subset of nodes in a network. With the goal of including dynamic and uncertain conditions inherent in real-world transportation scenarios, we introduce a novel dynamic variant of the TOP that considers real-time changes in environmental conditions affecting reward acquisition at each node. Specifically, we model the dynamic nature of environmental factors-such as traffic congestion, weather conditions, and battery level of each vehicle-to reflect their impact on the probability of obtaining the reward when visiting each type of node in a heterogeneous network. To address this problem, a learnheuristic optimization framework is proposed. It combines a metaheuristic algorithm with Thompson sampling to make informed decisions in dynamic environments. Furthermore, we conduct empirical experiments to assess the impact of varying reward probabilities on resource allocation and route planning within the context of this dynamic TOP, where nodes might offer a different reward behavior depending upon the environmental conditions. Our numerical results indicate that the proposed learnheuristic algorithm outperforms static approaches, achieving up to 25% better performance in highly dynamic scenarios. Our findings highlight the effectiveness of our approach in adapting to dynamic conditions and optimizing decision-making processes in transportation systems.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-06-01
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/10251/207466
url https://riunet.upv.es/handle/10251/207466
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 PID2022-138860NB-I00 INTELIGENCIA ARTIFICIAL E INTERNET DE LAS COSAS PARA OPTIMIZAR EL CONSUMO ENERGETICO EN EL TRANSPORTE CON VEHICULOS ELECTRICOS
European Commission https://doi.org/10.13039/501100000780 HE 101057294 AI Driven industrial Equipment product life cycle boosting Agility, Sustainability and resilience
European Commission https://doi.org/10.13039/501100000780 HE 101092612 Social and hUman ceNtered XR
Ministerio de Ciencia e Innovación http://dx.doi.org/10.13039/501100004837 RED2022-134703-T
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://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
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
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 15.811543

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