A local search approximation algorithm for a squared metric k-facility location problem

Dongmei Zhang (), Dachuan Xu (), Yishui Wang (), Peng Zhang () and Zhenning Zhang ()
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Dongmei Zhang: Shandong Jianzhu University
Dachuan Xu: Beijing University of Technology
Yishui Wang: Beijing University of Technology
Peng Zhang: Shandong University
Zhenning Zhang: Beijing University of Technology

Journal of Combinatorial Optimization, 2018, vol. 35, issue 4, No 9, 1168-1184

Abstract: Abstract In this paper, we introduce a squared metric k-facility location problem (SM-k-FLP) which is a generalization of the squared metric facility location problem and k-facility location problem (k-FLP). In the SM-k-FLP, we are given a client set $$\mathcal {C}$$ C and a facility set $$\mathcal {F} $$ F from a metric space, a facility opening cost $$f_i \ge 0$$ f i ≥ 0 for each $$ i \in \mathcal {F}$$ i ∈ F , and an integer k. The goal is to open a facility subset $$F \subseteq \mathcal {F}$$ F ⊆ F with $$ |F| \le k$$ | F | ≤ k and to connect each client to the nearest open facility such that the total cost (including facility opening cost and the sum of squares of distances) is minimized. Using local search and scaling techniques, we offer a constant approximation algorithm for the SM-k-FLP.

Keywords: Approximation algorithm; Facility location; Local search (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10878-018-0261-2

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