ArticleScientific reports2025
Multi-objective emergency allocation of snowmelt agents for urban roads under snow and ice disasters: a case study of Weihai City.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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