Max–min dispersion with capacity and cost for a practical location problem

Lozano-Osorio, Isaac; Martı́nez-Gavara, Anna; Martı́, Rafael; Duarte, Abraham

doi:10.1016/j.eswa.2022.116899

Cited by 11 publications

(2 citation statements)

References 28 publications

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“…This fact may be of interest for other variations of the CDP problem. For instance, in a study by Lozano-Osorio et al [29], a cost is assigned to each node, along with a constraint that the total cost of all opened nodes must not exceed a constant K. This constraint is similar to the capacity constraint with the static algorithm, shown in orange, serving as a comparative baseline.…”

Section: Analysis Of Resultsmentioning

confidence: 99%

A Learnheuristic Algorithm for the Capacitated Dispersion Problem under Dynamic Conditions

Gomez,

Uguina,

Panadero

et al. 2023

Algorithms

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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.

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Section: Analysis Of Resultsmentioning

confidence: 99%

A Learnheuristic Algorithm for the Capacitated Dispersion Problem under Dynamic Conditions

Gomez,

Uguina,

Panadero

et al. 2023

Algorithms

View full text Add to dashboard Cite

show abstract

“…In this context, Lozano-Osorio et al [23] showed a case study in which a real-world application of the CDP was addressed. These authors not only considered a capacity restriction but also a budget constraint.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Start Biased-Randomized Algorithm for the Capacitated Dispersion Problem

et al. 2022

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The capacitated dispersion problem is a variant of the maximum diversity problem in which a set of elements in a network must be determined. These elements might represent, for instance, facilities in a logistics network or transmission devices in a telecommunication network. Usually, it is considered that each element is limited in its servicing capacity. Hence, given a set of possible locations, the capacitated dispersion problem consists of selecting a subset that maximizes the minimum distance between any pair of elements while reaching an aggregated servicing capacity. Since this servicing capacity is a highly usual constraint in real-world problems, the capacitated dispersion problem is often a more realistic approach than is the traditional maximum diversity problem. Given that the capacitated dispersion problem is an NP-hard problem, whenever large-sized instances are considered, we need to use heuristic-based algorithms to obtain high-quality solutions in reasonable computational times. Accordingly, this work proposes a multi-start biased-randomized algorithm to efficiently solve the capacitated dispersion problem. A series of computational experiments is conducted employing small-, medium-, and large-sized instances. Our results are compared with the best-known solutions reported in the literature, some of which have been proven to be optimal. Our proposed approach is proven to be highly competitive, as it achieves either optimal or near-optimal solutions and outperforms the non-optimal best-known solutions in many cases. Finally, a sensitive analysis considering different levels of the minimum aggregate capacity is performed as well to complete our study.

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