A data-driven optimization framework for routing mobile medical facilities

Eda Yücel, F. Sibel Salman (), Burçin Bozkaya and Cemre Gökalp
Additional contact information
Eda Yücel: TOBB University of Economics and Technology
F. Sibel Salman: Koç University
Burçin Bozkaya: Sabancı University
Cemre Gökalp: Sabancı University

Annals of Operations Research, 2020, vol. 291, issue 1, No 42, 1077-1102

Abstract: 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.

Keywords: Mobile health care; Team orienteering; Partial coverage; Vehicle routing; Data analytics (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://link.springer.com/10.1007/s10479-018-3058-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:291:y:2020:i:1:d:10.1007_s10479-018-3058-x

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-018-3058-x

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().