This thesis aims to contribute to the literature of mobile clinics operations management. Mobile clinics are often implemented in a humanitarian context. They allow healthcare practitioners to offer their services in underserved areas (i.e., areas that have limited access to healthcare facilities). For example, mobile clinics are used after wars to serve populations returning to their homes. Also, mobile clinics are used to provide family planning services in developing nations. This thesis focuses on the tactical level of mobile clinics operations. As it is common for practitioners to face challenges in the tactical phase after non-for-profit organizations have received directions from donors and boards of directors regarding the strategical plan. However, there is no comprehensive tool or guide addressing these tactical challenges. The methodology developed is based on the principles of operations research (data collection and problem definition, mathematical modeling, development of solution algorithm, and validation of results). By studying the challenges and capturing them in with mathematical models we aim to provide tools and managerial insights for practitioners and researchers. The thesis is divided into three articles.

Article 1. The first article surveys the existing literature pertinent to mobile clinics. In addition, it identifies potential operations management and operations research models and techniques that can be applied to mobile clinic deployments for humanitarian relief. The gaps in the literature as well as future research questions are highlighted to serve as a guide for researchers and practitioners.

Article 2. The second article presents the deterministic tactical planning problem for the deployment of mobile clinics. This problem was defined in collaboration with the international humanitarian organization Première Urgence Internationale (PUI), which deploys mobile clinics around the world. We modeled the deployment of mobile clinics as a multiperiod location routing problem (MLRP) to capture the time dependency nature of mobile clinic deployments for healthcare humanitarian relief. To solve the MLRP, set-packing formulation that relies on the generation of routes has been implemented. The optimization of the proposed model yields the selection of depots and the routes that will be performed at each time period through the planning horizon, i.e., the tactical plans where humanitarian relief is quantified as the benefit of covering locations and serving the population. Results are presented for an application of a mobile clinic deployment in Iraq managed by PUI, including sensitivity analyses on the modeling of covering and continuity of care benefits, and the impacts of some strategic and tactical organizational decisions, e.g., the number of mobile clinics. Moreover, managerial insights are also be presented.

Article 3. The third article presents a tactical plan that considers the uncertainty that arises in the context of mobile clinics. Practitioners strive to deploy clinics that can access populations with the highest needs. However, when planning humanitarian operations, there is uncertainty arises in the travel time, usability of roads, and access to population points. This study models mobile clinic deployment as a two-stage Stochastic Prize Collection Problem that maximizes the benefit offered and minimizing the costs, while considering several sources of uncertainty. The impact of four recourse policies (simple recourse, select-then-route, re-route per time period, and full network recourse) on the mobile clinic deployment plans are also studied. The recourse policies’ performance is evaluated on three different severity levels: moderate, medium, and severe. Results and managerial insights are presented for different real-world instances, that represent different network structures (i.e., sparse/rural and grid/city settings).

Keywords: Mobile Clinics, Disaster Relief, Humanitarian Logistics, Mobile Unit Teams, Multiperiod Location Routing, Stochastic Programing, Travel Time Uncertainty, Accessibility of Locations, Usability of Roads