Optimal Adaptation Process of EMS Systems in a Changing and Uncertain Environment
41 Pages Posted: 12 Aug 2015
Date Written: August 11, 2015
The service quality, which means accessibility of emergency sites, and operating costs of an emergency medical service (EMS) system are directly related to the number of EMS stations and their location. Exogenous changes, like increased traffic volume or structural changes in the underlying urban area cause necessary adaptations of this EMS infrastructure over time. Such EMS system adaptations are made on a strategic level, because the related investments are enormous and modifications are hardly reversible. Due to the high sensitivity of EMS quality to environmental changes (e.g., traffic conditions) an intertemporal smoothing of the EMS system adaptation process is necessary to counteract over-adjustments to short-term disturbances. The complexity of this planning problem increases even more when taking uncertain developments of external influencing factors into consideration. Hence, an optimal dynamic adaptation process for strategic EMS infrastructure planning is presented. Besides optimizing service quality and operating costs, a tempered adaptation process is ensured that respects the initial system state. For this purpose, a deterministic three-objective MILP is developed. To incorporate uncertain developments, three model extensions are suggested. The models are compared in an exemplary application. The developed models overcome the major drawbacks of existing models which neglect the initial system state, environmental changes, or the costs of system adaptation. Thus, the model gives deeper insights to real-world decision situations. By means of model extensions concerning uncertainty, the underlying planning situation is described more realistically, which allows further insights and considers different perspectives of the decision situation.
Keywords: Emergency medical services, Facility location/relocation, Dynamic system adaptation, Uncertainty, Robust optimization, Adaptive planning, Proactive optimization, Decision support
JEL Classification: C00, C60, C61
Suggested Citation: Suggested Citation