A Learnheuristic Algorithm for a Max-Sum Capacitated Dispersion Problem with Dynamic Costs

Elnaz Ghorbani (), Juan F. Gomez (), Javier Panadero () and Angel A. Juan ()
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Elnaz Ghorbani: Universitat Oberta de Catalunya
Juan F. Gomez: Universitat Politècnica de València
Javier Panadero: Universitat Autònoma de Barcelona
Angel A. Juan: Universitat Politècnica de València

A chapter in Operations Research Proceedings 2024, 2025, pp 99-105 from Springer

Abstract: Abstract The capacitated dispersion problem (CDP) presents a challenging optimization scenario where the objective is maximizing node dispersion while respecting a capacity constraint. This paper proposes a novel variant of CDP that incorporates a cost constraint and dynamic facility costs as well as a max-sum objective function. To address these complexities, a learnheuristic framework integrated with machine learning and a metaheuristic method is proposed. The operational cost of each facility can be affected by a Bernoulli distribution function, introducing the possibility that a facility might have zero operational cost. This uncertainty is addressed by a black-box mechanism that takes into account the utilization and energy consumption rate of each facility. The proposed methodology is evaluated using a set of benchmark instances. Results demonstrate the efficiency of our learnheuristic approach in achieving near-optimal solutions under dynamic cost conditions, specifying its potential for real-world applications in logistics, telecommunications, and beyond.

Keywords: capacitated dispersion problem; metaheuristics; machine learning; dynamic optimization problem (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-92575-7_14

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DOI: 10.1007/978-3-031-92575-7_14

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