Journal article

GOAL: a clustering-based method for the group optimal location problem

Fangshu Chen, Jianzhong Qi, Huaizhong Lin, Yunjun Gao, Dongming Lu

Knowledge and Information Systems | Springer Verlag | Published : 2019

Abstract

Optimal location problems are important problems and are particularly useful for strategic planning of resources. However, existing studies mainly focus on computing one or k optimal locations. We study the Group OptimAl Location (GOAL) problem, which computes a minimum set of locations such that establishing facilities at these locations guarantees that every facility user can access at least one facility within a given distance r∈ R + r∈R+. We propose two algorithms, GOAL-Greedy and GOAL-DP, to first solve the problem in the Euclidean space. These two algorithms are supported by a clustering-based method to compute an initial solution of the problem, which yields an upper bound of the numb..

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Grants

Awarded by Key Disciplines of Computer Science and Technology of Shanghai Polytechnic University


Awarded by Research Project of Shanghai Polytechnic University


Awarded by Australian Research Council (ARC)


Awarded by NSFC


Funding Acknowledgements

This work was supported in part by the Key Disciplines of Computer Science and Technology of Shanghai Polytechnic University (No. XXKZD1604), the Research Project of Shanghai Polytechnic University (project number EGD18XQD02), Australian Research Council (ARC) Discovery project (project number DP180103332), the Cultural Relic Protection Science and Technology project of Zhejiang Province, the Key Research and Development Program of Zhejiang Province, the NSFC under Grants (project number 61522208), and the ZJU-Hikvision Joint Project. Huaizhong Lin is the corresponding author.