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      Predicting optimal facility location without customer locations

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      Author
      Yilmaz, Emre
      Elbaşı, Sanem
      Ferhatosmanoğlu, Hakan
      Date
      2017-08
      Source Title
      Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
      Publisher
      ACM
      Pages
      2121 - 2130
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      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. © 2017 Copyright held by the owner/author(s).
      Keywords
      Data generation
      Location analytics
      Optimal location queries
      Prediction
      Uncertainty
      Data mining
      Forecasting
      Sales
      Objective functions
      Optimal facility location
      Optimal locations
      Optimal-location query
      Partial information
      Set of customers
      Uncertainty
      Location
      Permalink
      http://hdl.handle.net/11693/37557
      Published Version (Please cite this version)
      http://dx.doi.org/10.1145/3097983.3098198
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      • Department of Computer Engineering 1411
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