The Maximal Covering Location Problem: An Application in Reproductive Health Services

Access to reproductive health care services is of vital concern to a significant portion of the population. The demand for these important services in many communities and regions outstrips the ability of organizations to supply them. Consequently, reproductive health service organizations seeking to optimize decisions regarding resource allocation may need to evaluate numerous alternatives. Proven location or regional science optimization methodologies can be applied to such decisions using geographic information available through the internet and optimization software tools. Population information, distance, and travel time data from the internet along with optimization software installed as add-ins to spreadsheet programs provides organizations an opportunity to evaluate alternative location decision to either minimize patient travel costs to health care services or maximize the total populations served through optimal site locations. This dissertation illustrates how to use these optimization methods within a reproductive health services organization, Planned Parenthood of Orange and San Bernardino Counties. An overview of the services provided by Planned Parenthood and a profile of their patient population is provided as context to the location decision. Finding the optimal solution to objective functions is not the only factor to be considered in making health service location decisions. Consequently, the dissertation also provides information that is helpful in determining health service locations such as demographic, socio-economic, health status, and other provider locations within the San Bernardino County. This information establishes the need for the expansion of services.Two different optimization problems are used in evaluating alternative health service locations for Planned Parenthood in the largest county in the U.S., San Bernardino County. These are the P-Median and Maximal Covering Location Problem. Each of these problems is solved using both miles and travel time as variables. The dissertation compares and contrasts the results of these two approaches and highlights the differences in using miles versus time as the key variable in the problem. Finally, specific location recommendations are made for expansion of services using the results of the analysis