Optimizing Urban Emergency Facility Allocation Considering the Uncertainties of Road Network Conditions and Human Mobility

Optimizing the number and spatial distribution of emergency facilities is of paramount importance in minimizing rescue response times and thus loss of life and property. This study proposes a novel integrated two-stage stochastic integer model to optimize the allocation of emergency facilities, including hospitals, fire stations, and shelters. The first stage is to minimize expected travel times given the uncertainties of road network conditions and human mobility, where the travel time is computed from the second stage for each given emergency situation and time slot. To address the computational complexities of the model, we prove the integer optimality property of the second stage and employ the L-shaped algorithm to efficiently solve the model, circumventing unnecessary complications by using the integer L-shaped algorithm. The applicability of the proposed model is demonstrated through a case study in Shanghai’s central district, and its significance is evaluated by the expected value of perfect information (EVPI) and the value of the stochastic solution (VSS). Furthermore, a comprehensive sensitivity analysis is performed to assess the model’s robustness and explore the significance of integrated emergency facility allocation solutions. The study comprehensively considers various critical emergency facilities from a system perspective and appropriately captures the uncertainties of road network conditions as well as human mobility. It can contribute to the advancement of a more comprehensive and effective emergency response system, offering valuable insights for decision makers involved in emergency management and resource allocation.