Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/4100
Title: A data-driven optimization framework for routing mobile medical facilities
Authors: Yücel, Eda
Salman, F. Sibel
Bozkaya, Burcin
Gökalp, Cemre
235501
Keywords: Mobile health care
Team orienteering
Partial coverage
Vehicle routing
Data analytics
Issue Date: 2020
Publisher: Springer
Source: Yücel, E., Salman, F. S., Bozkaya, B., & Gökalp, C. (2018). A data-driven optimization framework for routing mobile medical facilities. Annals of Operations Research, 1-26.
Abstract: We study the delivery of mobile medical services and in particular, the optimization of the joint stop location selection and routing of the mobile vehicles over a repetitive schedule consisting of multiple days. Considering the problem from the perspective of a mobile service provider company, we aim to provide the most revenue to the company by bringing the services closer to potential customers. Each customer location is associated with a score, which can be fully or partially covered based on the proximity of the mobile facility during the planning horizon. The problem is a variant of the team orienteering problem with prizes coming from covered scores. In addition to maximizing total covered score, a secondary criterion involves minimizing total travel distance/cost. We propose a data-driven optimization approach for this problem in which data analyses feed a mathematical programming model. We utilize a year-long transaction data originating from the customer banking activities of a major bank in Turkey. We analyze this dataset to first determine the potential service and customer locations in Istanbul by an unsupervised learning approach. We assign a score to each representative potential customer location based on the distances that the residents have taken for their past medical expenses. We set the coverage parameters by a spatial analysis. We formulate a mixed integer linear programming model and solve it to near-optimality using Cplex. We quantify the trade-off between capacity and service level. We also compare the results of several models differing in their coverage parameters to demonstrate the flexibility of our model and show the impact of accounting for full and partial coverage.
URI: https://doi.org/10.1007/s10479-018-3058-x
https://hdl.handle.net/20.500.11851/4100
ISSN: 0254-5330
Appears in Collections:Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
Malzeme Bilimi ve Nanoteknoloji Mühendisliği Bölümü / Department of Material Science & Nanotechnology Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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