Predicting optimal facility location without customer locations
Author
Yilmaz, Emre
Elbaşı, Sanem
Ferhatosmanoğlu, Hakan
Date
2017-08Source Title
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher
ACM
Pages
2121 - 2130
Language
English
Type
Conference PaperItem Usage Stats
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Metadata
Show full item recordAbstract
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 generationLocation 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/11693/37557Published Version (Please cite this version)
http://dx.doi.org/10.1145/3097983.3098198Collections
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