An Optimization Model for Efficient Relocation of Hazardous Materials and Valuable Assets During Natural Disaster Warning Periods

Background : Natural disasters can trigger hazardous material (Hazmat) releases and damage valuable assets, increasing human, environmental, and economic losses. Effective pre-disaster relocation planning is therefore critical but operationally challenging. Methods : This study develops a mixed-integer programming model for the pre-disaster relocation of Hazmat and valuable assets (HVAs). The model jointly optimizes safe-location activation, fleet allocation, and trip scheduling within a limited warning period, subject to vehicle availability, storage and capacity limits. The objective minimizes total cost, including facility activation, transportation, and penalties for unrelocated inventories. The model is solved using the Gurobi Optimizer. A base scenario and sensitivity analyses on fleet size and safe-location capacity are conducted using data from a cyclone-prone region in Oman. Results : In the base scenario, 73.4% of HVAs are relocated by activating 10 safe locations. Sensitivity analysis shows rapid gains at small fleet sizes, followed by diminishing returns beyond a threshold. Over 95% of HVAs are relocated by doubling safe-location capacities with 80 vehicles or tripling capacities with 65 vehicles. Conclusions : Total vehicle capacity, time-window, and safe-location capacity constraints become binding at different thresholds, highlighting the need for balanced investments. The proposed model provides an analytics-driven decision-support tool for risk-aware, time-bounded disaster relocation planning.