Emergency material scheduling model for flood disaster management in river basins
The"23.7"super flood in the Haihe River Basin underscores the critical importance of flood emergency response efforts.In this paper,we develop a scheduling model for basin floods to create an effective scheme for the allocation of emergency materials,which is then solved using an algorithm.First,we conduct an in-depth analysis of the flood characteristics within the basin to identify the demand patterns for various types of emergency materials.This analysis helps clarify the focus of the material scheduling optimization and establishes a framework for a model that incorporates multiple supply and demand points.Next,we introduce a time window function that accounts for the urgent timing requirements of flood control and emergency rescue operations,based on the identified needs for effective response.Additionally,we construct a psychological perception function based on the material satisfaction rates in the disaster area.We also determine a task differentiation function that reflects the capabilities of the rescue team.These elements culminate in the establishment of a multi-objective scheduling model grounded in cost functions.Finally,we construct a new simulation case based on existing disaster scenarios and utilize a multi-objective genetic algorithm to solve the model.The results demonstrate that the model developed in this study effectively identifies optimal solutions for multi-objective trade-offs,thereby better addressing the needs of emergency dispatching during basin floods.In the scenario presented in this article,when material shortages arise,the model coordinates and optimizes dispatching accordingly.As a result,the satisfaction rate for all types of materials at each disaster site exceeds 66.6％.The overall satisfaction rate,after weighting,can reach 74.63％,demonstrating the model's ability to achieve optimal solutions for multi-objective problems.When an actual flood occurs in the basin,this model offers improved scheduling decisions by accurately predicting the types and quantities of materials needed.
public safetybasin floodemergency material schedulingtime windowmaterial satisfaction ratenon-dominated sorting genetic algorithm Ⅱ
万方数据
维普
