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k-most suitable locations selection

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Abstract

Choosing the best location for starting a business or expanding an existing enterprize is an important issue. A number of location selection problems have been discussed in the literature. They often apply the Reverse Nearest Neighbor as the criterion for finding suitable locations. In this paper, we apply the Average Distance as the criterion and propose the so-called k-most suitable locations (k-MSL) selection problem. Given a positive integer k and three datasets: a set of customers, a set of existing facilities, and a set of potential locations. The k-MSL selection problem outputs k locations from the potential location set, such that the average distance between a customer and his nearest facility is minimized. In this paper, we formally define the k-MSL selection problem and show that it is NP-hard. We first propose a greedy algorithm which can quickly find an approximate result for users. Two exact algorithms are then proposed to find the optimal result. Several pruning rules are applied to increase computational efficiency. We evaluate the algorithms’ performance using both synthetic and real datasets. The results show that our algorithms are able to deal with the k-MSL selection problem efficiently.

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Notes

  1. The Pascal’s Formula: \({|P| \choose k} = {|P|-1 \choose k} + {|P|-1 \choose k-1}\).

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Authors and Affiliations

  1. Chang Jung Christian University, Tainan, Taiwan

    Yu-Chi Chung

  2. National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan

    I-Fang Su

  3. National Cheng Kung University, Tainan, Taiwan

    Chiang Lee

Authors
  1. Yu-Chi Chung
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  2. I-Fang Su
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  3. Chiang Lee
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Correspondence to Yu-Chi Chung.

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Chung, YC., Su, IF. & Lee, C. k-most suitable locations selection. Geoinformatica 22, 661–692 (2018). https://doi.org/10.1007/s10707-018-0318-5

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