Fixed-Charge Facility Location Problems

Fernández, Elena; Landete, Mercedes

doi:10.1007/978-3-030-32177-2_4

Cited by 6 publications

(3 citation statements)

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“…In the case of the large MDG instances, the values obtained by Gurobi represent only an upper bound attained in 3600 s of computing time. Since we use 10 different seeds, Table 1 shows two columns of results for each heuristic strategy: a column with the average (Avg) result (columns [4] and [6]) when running these 30 seeds, and a column with our bestfound solution (columns [5] and [7]). The last six columns in Table 1 show the percentage gaps between some of the tested solution approaches.…”

Section: Analysis Of Resultsmentioning

confidence: 99%

“…According to Correia and Saldanha-da Gama [3], the operating capacity is a decision that must be made even before knowing the demand. Additionally, one of the biggest difficulties posed whenever a facility must be located is considering that its capacity is insufficient to satisfy customer demand [4]. That is, the capacity of the active facilities must be ideally greater than the total demand that must be serviced.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Analysis Of Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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show abstract

“…The first subproblem consists of determining the number and the locations of WTs to install, and the allocation of the households to the WTs by assigning weights to the number of WTs and to the total user walking distances. This problem is modeled as a Fixed-Charge Facility Location Problem (Fernández and Landete, 2019) which can be solved exactly for the instance sizes considered in this paper.…”

Section: Solution Methodologymentioning

confidence: 99%