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Predicting optimal facility location without customer locations
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Yilmaz, Emre, Elbasi, Sanem and Ferhatosmanoglu, Hakan (2017) Predicting optimal facility location without customer locations. In: 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada , 13-17 Aug 2017. Published in: KDD '17 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining pp. 2121-2130. ISBN 9781450348874. doi:10.1145/3097983.3098198
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Official URL: http://dx.doi.org/10.1145/3097983.3098198
Abstract
Deriving meaningful insights from location data helps businesses make better decisions. One critical decision made by a business is choosing a location for its new facility. Optimal location queries ask for a location to build a new facility that optimizes an objective function. Most of the existing works on optimal location queries propose solutions to return best location when the set of existing facilities and the set of customers are given. However, most businesses do not know the locations of their customers. In this paper, we introduce a new problem setting for optimal location queries by removing the assumption that the customer locations are known. We propose an optimal location predictor which accepts partial information about customer locations and returns a location for the new facility. The predictor generates synthetic customer locations by using given partial information and it runs optimal location queries with generated location data. Experiments with real data show that the predictor can find the optimal location when sufficient information is provided.
Item Type: | Conference Item (Paper) | ||||||
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Subjects: | H Social Sciences > HF Commerce Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Store location -- Mathematical models, Geospatial data , Data mining | ||||||
Journal or Publication Title: | KDD '17 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining | ||||||
Publisher: | ACM | ||||||
ISBN: | 9781450348874 | ||||||
Book Title: | Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17 | ||||||
Official Date: | 13 August 2017 | ||||||
Dates: |
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Page Range: | pp. 2121-2130 | ||||||
DOI: | 10.1145/3097983.3098198 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Date of first compliant deposit: | 25 September 2017 | ||||||
Date of first compliant Open Access: | 25 September 2017 | ||||||
Funder: | Alexander von Humboldt-Stiftung (AvHS) | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Halifax, NS, Canada | ||||||
Date(s) of Event: | 13-17 Aug 2017 |
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