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

被引：3

作者：

Zhang, Dongmei ^{[1
]}

Xu, Dachuan ^{[2
]}

Wang, Yishui ^{[3
]}

Zhang, Peng ^{[4
]}

Zhang, Zhenning ^{[3
]}

机构：

[1] Shandong Jianzhu Univ, Sch Comp Sci & Technol, Jinan 250101, Shandong, Peoples R China

[2] Beijing Univ Technol, Beijing Inst Sci & Engn Comp, 100 Pingleyuan, Beijing 100124, Peoples R China

[3] Beijing Univ Technol, Dept Informat & Operat Res, Coll Appl Sci, 100 Pingleyuan, Beijing 100124, Peoples R China

[4] Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Shandong, Peoples R China

来源：

JOURNAL OF COMBINATORIAL OPTIMIZATION | 2018年 / 35卷 / 04期

关键词：

Approximation algorithm; Facility location; Local search;

D O I：

10.1007/s10878-018-0261-2

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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 and a facility set from a metric space, a facility opening cost for each , and an integer k. The goal is to open a facility subset with 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.

引用

页码：1168 / 1184

页数：17

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