A Learnheuristic Algorithm for the Capacitated Dispersion Problem under Dynamic Conditions

Juan F. Gomez, Antonio R. Uguina, Javier Panadero, Angel A. Juan*

*Autor correspondiente de este trabajo

Producción científica: Contribución a una revistaArtículoInvestigaciónrevisión exhaustiva


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.

Idioma originalInglés
Número de artículo532
Número de páginas15
EstadoPublicada - dic 2023


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