Predicting optimal facility location without customer locations

dc.citation.epage2130en_US
dc.citation.spage2121en_US
dc.contributor.authorYilmaz, Emreen_US
dc.contributor.authorElbaşı, Sanemen_US
dc.contributor.authorFerhatosmanoğlu, Hakanen_US
dc.coverage.spatialHalifax, NS, Canada
dc.date.accessioned2018-04-12T11:43:52Z
dc.date.available2018-04-12T11:43:52Z
dc.date.issued2017-08en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionDate of Conference: 13-17 August, 2017
dc.descriptionConference name: KDD '17 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
dc.description.abstractDeriving 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).en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T11:43:52Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2017en
dc.identifier.doi10.1145/3097983.3098198en_US
dc.identifier.urihttp://hdl.handle.net/11693/37557en_US
dc.language.isoEnglishen_US
dc.publisherACMen_US
dc.relation.isversionofhttp://dx.doi.org/10.1145/3097983.3098198en_US
dc.source.titleProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Miningen_US
dc.subjectData generationen_US
dc.subjectLocation analyticsen_US
dc.subjectOptimal location queriesen_US
dc.subjectPredictionen_US
dc.subjectUncertaintyen_US
dc.subjectData miningen_US
dc.subjectForecastingen_US
dc.subjectSalesen_US
dc.subjectObjective functionsen_US
dc.subjectOptimal facility locationen_US
dc.subjectOptimal locationsen_US
dc.subjectOptimal-location queryen_US
dc.subjectPartial informationen_US
dc.subjectSet of customersen_US
dc.subjectUncertaintyen_US
dc.subjectLocationen_US
dc.titlePredicting optimal facility location without customer locationsen_US
dc.typeConference Paperen_US

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