Emergency logistics hub optimization through multi-dimensional risk assessment

Efficient and priority-based logistics hub placement during large-scale disasters requires a multi-dimensional approach that considers disaster risk and social vulnerability. This study proposes a Two-Stage Hazard Coverage Optimization model (TSHCO) that integrates multi-dimensional risk assessment with facility location optimization. In Stage I, the Maximal Covering Location Problem (MCLP) maximizes coverage of high-risk areas with existing hubs. In Stage II, the Set Covering Location Problem (SCLP) covers the remaining high-risk areas with the minimum number of additional hubs, solved exactly via integer linear programming Using open government data from Shizuoka Prefecture, Japan, the empirical analysis identified 912 high-risk areas (7.9% of inhabited areas) and demonstrated that 14 heliports (24.6% of available sites) achieve 83.2% coverage, while 28 heliports achieve complete coverage. Virtual simulation experiments over 100 trials demonstrated computational feasibility and stable hub requirements, with an average of 14.7 additional hubs required to satisfy the SCLP full-coverage constraint. The proposed approach enables rapid and priority-based planning for supporting high-risk areas under limited resources while maintaining consistency with official emergency response plans.