Multi-objective emergency allocation of snowmelt agents for urban roads under snow and ice disasters: a case study of Weihai City

To address the contradiction between surging demand and resource misallocation in snowmelt agent emergency allocation during snow and ice disasters, this paper proposes a multi-objective collaborative optimization approach. A snowmelt agent demand formula is developed based on snowfall intensity, road characteristics, and spreading standards. By deconstructing the three-tier allocation network of vehicle depots, snowmelt agent reserve points, and snow-covered roads, and embedding mixed time windows and road priorities, an emergency allocation model is established to minimize total timeliness, time penalty costs, and supply rate discrepancies. An improved multi-objective grasshopper optimization algorithm (IMOGOA) is proposed, integrating a population guidance strategy and cosine-adaptive parameters to improve optimization performance. Comparative results show that the Pareto solution set obtained by IMOGOA outperforms those of comparable algorithms. A case study of Weihai City is conducted. Results indicate that under the optimal time penalty plan, 100% of demand on primary roads is satisfied, while secondary roads achieve a 26.7% coverage rate. This research enables precise emergency resource scheduling and offers an effective solution for improving the resilience of urban transportation lifelines.