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The non-smooth and bi-objective team orienteering problem with soft constraints

  • Datos identificativos

    Identificador: imarina:8787037
    Autores:
    Estrada-Moreno AFerrer AJuan AAPanadero JBagirov A
    Resumen:
    © 2020 by the authors. In the classical team orienteering problem (TOP), a fixed fleet of vehicles is employed, each of them with a limited driving range. The manager has to decide about the subset of customers to visit, as well as the visiting order (routes). Each customer offers a different reward, which is gathered the first time that it is visited. The goal is then to maximize the total reward collected without exceeding the driving range constraint. This paper analyzes a more realistic version of the TOP in which the driving range limitation is considered as a soft constraint: every time that this range is exceeded, a penalty cost is triggered. This cost is modeled as a piece-wise function, which depends on factors such as the distance of the vehicle to the destination depot. As a result, the traditional reward-maximization objective becomes a non-smooth function. In addition, a second objective, regarding the design of balanced routing plans, is considered as well. A mathematical model for this non-smooth and bi-objective TOP is provided, and a biased-randomized algorithm is proposed as a solving approach.
  • Otros:

    Autor según el artículo: Estrada-Moreno A; Ferrer A; Juan AA; Panadero J; Bagirov A
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Estrada Moreno, Alejandro
    Palabras clave: Team orienteering problem Soft constraints Routing problem Particle swarm optimization Non-smooth optimization Multi-objective optimization Depot Biased-randomized algorithms Algorithm
    Resumen: © 2020 by the authors. In the classical team orienteering problem (TOP), a fixed fleet of vehicles is employed, each of them with a limited driving range. The manager has to decide about the subset of customers to visit, as well as the visiting order (routes). Each customer offers a different reward, which is gathered the first time that it is visited. The goal is then to maximize the total reward collected without exceeding the driving range constraint. This paper analyzes a more realistic version of the TOP in which the driving range limitation is considered as a soft constraint: every time that this range is exceeded, a penalty cost is triggered. This cost is modeled as a piece-wise function, which depends on factors such as the distance of the vehicle to the destination depot. As a result, the traditional reward-maximization objective becomes a non-smooth function. In addition, a second objective, regarding the design of balanced routing plans, is considered as well. A mathematical model for this non-smooth and bi-objective TOP is provided, and a biased-randomized algorithm is proposed as a solving approach.
    Áreas temáticas: Química Mathematics (miscellaneous) Mathematics (all) Mathematics General mathematics Engineering (miscellaneous) Computer science (miscellaneous) Astronomia / física
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: alejandro.estrada@urv.cat
    Identificador del autor: 0000-0001-9767-2177
    Fecha de alta del registro: 2023-04-30
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.mdpi.com/2227-7390/8/9/1461
    Referencia al articulo segun fuente origial: Mathematics. 8 (9):
    Referencia de l'ítem segons les normes APA: Estrada-Moreno A; Ferrer A; Juan AA; Panadero J; Bagirov A (2020). The non-smooth and bi-objective team orienteering problem with soft constraints. Mathematics, 8(9), -. DOI: 10.3390/math8091461
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.3390/math8091461
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2020
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Computer Science (Miscellaneous),Engineering (Miscellaneous),Mathematics,Mathematics (Miscellaneous)
    Team orienteering problem
    Soft constraints
    Routing problem
    Particle swarm optimization
    Non-smooth optimization
    Multi-objective optimization
    Depot
    Biased-randomized algorithms
    Algorithm
    Química
    Mathematics (miscellaneous)
    Mathematics (all)
    Mathematics
    General mathematics
    Engineering (miscellaneous)
    Computer science (miscellaneous)
    Astronomia / física
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