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Research Article

An integer programming model for the deployment of mobile health clinics

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Abstract

Facility location and resource allocation play an essential role in the operational management of mobile health clinics (MHCs) as optimization of both can help medical service providers improve their service coverage under limited resources. In this paper, we consider the issue of maximizing the coverage of immunization services provided by MHCs in vulnerable communities such as school zones and census tracts. Since demand in communities overlaps with neighboring communities, this leads to the so-called mobile facility location with overlapped demand (MFL-OD). To capture the overlapped demand in different communities, we introduce a novel integer programming model with linear and bilinear constraints (BLCIP) and show that the BLCIP model is NP-hard. A polynomial-time approximation algorithm is proposed for the BLCIP, and the approximation ratio of the obtained solution is estimated. We implement the proposed model and approximation algorithm for a case study in the Houston Independent School District and its associated communities. Preliminary experiments demonstrate the efficacy of the proposed model and the efficiency of the proposed approximation method.

Acknowledgments

The authors thank the area editor, the associate editor, and two anonymous referees for their careful reading and many useful suggestions that helped to improve the presentation of this work.

Disclosure statement

The authors report there are no competing interests to declare

Consent and approval statement

Any patient, service user, or participant (or that person’s parent or legal guardian) in any type of qualitative or quantitative research, has given informed consent to participate in the research.

Notes

1 For illustration, we can use the Texas Children’s Hospital Mobile Clinic Program a prototype, which provides immunization service to Houston Independent School District schools and their associated census tract.

Additional information

Funding

This work is supported by National Science Foundation, NSF CNS 1637347.

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