A Decision-Making Tool for Flood Victim Evacuation Planning: A Case Study of Flooded Areas in Chiang Mai Province, Thailand During Typhoon Yagi

Severe flooding displaces populations and disrupts access to essential supplies, so relief operations must evacuate victims and deliver survival kits under tight time pressure. This study formulates flood relief route planning as a vehicle routing problem with mixed pickup and delivery (1-M-1/P-D) featuring a heterogeneous multi-trip fleet, node priorities, and a minimum-makespan objective, and develops a mixed integer linear programming (MILP) model together with a customized Differential Evolution (DE) metaheuristic. The methods are evaluated on 24 problem classes (five generated instances each) and on a real case drawn from the 2024 Typhoon Yagi floods in Chiang Mai Province, Thailand (100 demand nodes, 8 vehicles, 3 trips). For the 12 small classes, the MILP model returns proven optima, and DE attains an average heuristic performance of 94.94% with a mean optimality gap of 5.50% (Wilcoxon signed-rank test, p = 0.0005; Cohen’s dz = 1.01). Beyond 30 nodes, the exact model no longer proves optimality within 28,800 s, whereas DE returns feasible solutions for every instance, reaching 81–89% of the corresponding lower bound on the generated large instances and 73% on the real case. The framework is implemented as an intranet decision-support prototype, DisasterthApp; its field validation and comparison with other metaheuristics remain future work.