A Learnheuristic Algorithm for the Capacitated Dispersion Problem under Dynamic Conditions
Gómez González, Juan Francisco (Universitat Politècnica de València)
Rodríguez Uguina, Antonio (Universitat Politècnica de València)
Panadero, Javier (Universitat Autònoma de Barcelona. Departament d'Arquitectura de Computadors i Sistemes Operatius)
Juan, Ángel A. (Universitat Politècnica de València)

Date:	2023
Abstract:	The capacitated dispersion problem, which is a variant of the maximum diversity problem, aims to determine a set of elements within a network. These elements could symbolize, for instance, facilities in a supply chain or transmission nodes in a telecommunication network. While each element typically has a bounded service capacity, in this research, we introduce a twist. The capacity of each node might be influenced by a random Bernoulli component, thereby rendering the possibility of a node having zero capacity, which is contingent upon a black box mechanism that accounts for environmental variables. Recognizing the inherent complexity and the NP-hard nature of the capacitated dispersion problem, heuristic algorithms have become indispensable for handling larger instances. In this paper, we introduce a novel approach by hybridizing a heuristic algorithm with reinforcement learning to address this intricate problem variant.
Grants:	European Commission 101092612 Agencia Estatal de Investigación PID2022-138860NB-I00 Ministerio de Ciencia e Innovación RED2022-134703-T
Rights:	Aquest 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.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Capacitated dispersion problem ; Metaheuristics ; Reinforcement learning ; Supply chains ; Telecommunication networks
Published in:	Algorithms, Vol. 16, Issue 12 (December 2023) , art. 532, ISSN 1999-4893

DOI: 10.3390/a16120532

15 p, 519.9 KB

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