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

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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 \(r\in {\mathcal {R}}^+\). 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 number of locations needed to solve the problem. We propose a grid partitioning-based strategy to refine the initial solution and obtain the final solution. We further extend our algorithms to road networks. We perform extensive experiments on the proposed algorithms. The results show that the proposed algorithms can solve the problem effectively and efficiently in both Euclidean spaces and road networks.

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Notes

  1. http://www.dis.uniroma1.it/challenge9/download.shtml.

  2. http://www.cs.utah.edu/~lifeifei/SpatialDataset.htm.

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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.

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Authors and Affiliations

  1. The College of Computer Science and Information Engineering, Shanghai Polytechnic University, Shanghai, 201209, China

    Fangshu Chen

  2. School of Computing and Information Systems, University of Melbourne, Melbourne, 3000, Australia

    Jianzhong Qi

  3. The College of Computer Science and Technology, Zhejiang University, Hangzhou, 310000, China

    Huaizhong Lin, Yunjun Gao & Dongming Lu

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  1. Fangshu Chen
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Correspondence to Huaizhong Lin.

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This work is partially done when Fangshu is visiting the University of Melbourne.

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Chen, F., Qi, J., Lin, H. et al. GOAL: a clustering-based method for the group optimal location problem. Knowl Inf Syst 61, 873–903 (2019). https://doi.org/10.1007/s10115-018-1307-6

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