A sim-learnheuristic algorithm for solving a capacitated dispersion problem under stochastic and non-static conditions

[EN] A fundamental assumption in addressing real-world problems is acknowledging the presence of uncertainty and dynamism. Dismissing these factors can lead to the formulation of an optimal solution for an entirely different problem. This paper presents a novel variant of the capacitated dispersion...

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Autores: Ghorbani, Elnaz, Gómez González, Juan Francisco, Panadero, Javier, 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:10251/221706
Acceso en línea:https://riunet.upv.es/handle/10251/221706
Access Level:acceso abierto
Palabra clave:Capacitated dispersion problem
Sim-learnheuristic
Machine learning
Simulation
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spelling A sim-learnheuristic algorithm for solving a capacitated dispersion problem under stochastic and non-static conditionsGhorbani, ElnazGómez González, Juan FranciscoPanadero, JavierJuan, Angel A.|||0000-0003-1392-1776Capacitated dispersion problemSim-learnheuristicMachine learningSimulation[EN] A fundamental assumption in addressing real-world problems is acknowledging the presence of uncertainty and dynamism. Dismissing these factors can lead to the formulation of an optimal solution for an entirely different problem. This paper presents a novel variant of the capacitated dispersion problem (CDP) referred to as the stochastic and non-static CDP. The main objective of this problem is to strategically position facilities to achieve maximum dispersion while meeting the capacity demand constraint. The proposed approach combines stochastic and non-static elements, introducing a new paradigm to address the problem. This innovation allows us to consider more realistic and flexible environments. To solve this challenging problem, a novel sim-learnheuristic algorithm is proposed. This algorithm combines a biased-randomized metaheuristic (optimization component) with a simulation component (to model the uncertainty) and a machine learning component (to model nonstatic behavior). The non-static part works by using black box and white box mechanisms to learn the uncertainty with some related facilities¿ variables. Based on an extended set of traditional benchmarks for the CDP, a series of computational experiments were carried out. The results demonstrate the effectiveness of the proposed sim-learnheuristic approach for solving the CDP under non-static and stochastic scenarios.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 Generalitat Valenciana (PROMETEO/2021/065).American Institute of Mathematical SciencesDepartamento de Estadística e Investigación Operativa Aplicadas y CalidadCentro de Investigación en Gestión e Ingeniería de ProducciónEscuela Politécnica Superior de AlcoyGeneralitat ValencianaAgencia Estatal de InvestigaciónMinisterio de Ciencia e InnovaciónRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-08-16journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/221706reponame: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 ELECTRICOSGeneralitat Valenciana https://doi.org/10.13039/501100003359 PROMETEO%2F2021%2F065 Industrial Production and Logistics Optimization in Industry 4.0 (i4OPT)Ministerio 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:10251/2217062026-06-13T07:49:27Z
dc.title.none.fl_str_mv A sim-learnheuristic algorithm for solving a capacitated dispersion problem under stochastic and non-static conditions
title A sim-learnheuristic algorithm for solving a capacitated dispersion problem under stochastic and non-static conditions
spellingShingle A sim-learnheuristic algorithm for solving a capacitated dispersion problem under stochastic and non-static conditions
Ghorbani, Elnaz
Capacitated dispersion problem
Sim-learnheuristic
Machine learning
Simulation
title_short A sim-learnheuristic algorithm for solving a capacitated dispersion problem under stochastic and non-static conditions
title_full A sim-learnheuristic algorithm for solving a capacitated dispersion problem under stochastic and non-static conditions
title_fullStr A sim-learnheuristic algorithm for solving a capacitated dispersion problem under stochastic and non-static conditions
title_full_unstemmed A sim-learnheuristic algorithm for solving a capacitated dispersion problem under stochastic and non-static conditions
title_sort A sim-learnheuristic algorithm for solving a capacitated dispersion problem under stochastic and non-static conditions
dc.creator.none.fl_str_mv Ghorbani, Elnaz
Gómez González, Juan Francisco
Panadero, Javier
Juan, Angel A.|||0000-0003-1392-1776
author Ghorbani, Elnaz
author_facet Ghorbani, Elnaz
Gómez González, Juan Francisco
Panadero, Javier
Juan, Angel A.|||0000-0003-1392-1776
author_role author
author2 Gómez González, Juan Francisco
Panadero, Javier
Juan, Angel A.|||0000-0003-1392-1776
author2_role 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
Generalitat Valenciana
Agencia Estatal de Investigación
Ministerio de Ciencia e Innovación
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Capacitated dispersion problem
Sim-learnheuristic
Machine learning
Simulation
topic Capacitated dispersion problem
Sim-learnheuristic
Machine learning
Simulation
description [EN] A fundamental assumption in addressing real-world problems is acknowledging the presence of uncertainty and dynamism. Dismissing these factors can lead to the formulation of an optimal solution for an entirely different problem. This paper presents a novel variant of the capacitated dispersion problem (CDP) referred to as the stochastic and non-static CDP. The main objective of this problem is to strategically position facilities to achieve maximum dispersion while meeting the capacity demand constraint. The proposed approach combines stochastic and non-static elements, introducing a new paradigm to address the problem. This innovation allows us to consider more realistic and flexible environments. To solve this challenging problem, a novel sim-learnheuristic algorithm is proposed. This algorithm combines a biased-randomized metaheuristic (optimization component) with a simulation component (to model the uncertainty) and a machine learning component (to model nonstatic behavior). The non-static part works by using black box and white box mechanisms to learn the uncertainty with some related facilities¿ variables. Based on an extended set of traditional benchmarks for the CDP, a series of computational experiments were carried out. The results demonstrate the effectiveness of the proposed sim-learnheuristic approach for solving the CDP under non-static and stochastic scenarios.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-08-16
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/10251/221706
url https://riunet.upv.es/handle/10251/221706
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
Generalitat Valenciana https://doi.org/10.13039/501100003359 PROMETEO%2F2021%2F065 Industrial Production and Logistics Optimization in Industry 4.0 (i4OPT)
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 American Institute of Mathematical Sciences
publisher.none.fl_str_mv American Institute of Mathematical Sciences
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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