Service allocation equity in location coverage analytics

Location covering models are important spatial analytic methods, and have been widely applied to different facility siting problems in order to support decision making processes. It is common to observe facilities that have significantly different workloads when traditional coverage models are relied upon. This can and does impact system efficiency, reliability and service quality. Several approaches have been used to make service allocation more equitable, attempting to better balance facility workloads. However, the relative capabilities of such approaches to address equity remains largely unexplored, particularly in the context of location coverage analytics. This paper studies modeling approaches that can be used to explicitly balance facility workloads, focusing on maximal coverage. Approaches are evaluated comparatively, with completeness, inferiority and maximum gap measures introduced to support this. Empirical results show that if a model does not appropriately reflect workload balancing explicitly, optimality is unlikely. A cost, however, is the need to track explicit variation between sited facilities, which proves to require significantly more computational effort than approximate approaches.