摘要
为解决重大公共卫生事件发生后的物资调度问题,运用SEIR模型预测需求点的各类受灾人群,构建需求预测模型;以综合物资分配满意度最大、综合运输时间满意度最大和综合救援成本最小为目标,采用多供应点、多配送中心、多需求点的三级调度网络,实现多周期、多资源的动态调度;引入混沌反向学习、非线性收敛因子、随机差分变异和贪婪选择策略改进灰狼优化算法,并对模型进行求解.结果表明,该模型可有效平衡物资调度的满意度与经济性,改进灰狼优化算法可得到更优越的调度方案,解决灾后多周期应急物资调度问题.
Abstract
This paper addresses the issue of material dispatch following major public health crises,offering a scientifically sound and efficient scheduling scheme aimed at mitigating disaster spread and enhancing the satisfaction of affected individuals.Recognizing the potential discrepancy between predetermined material quantities at each demand point and the actual requirements,this study employs the SEIR model to predict the impact of various disasters on population numbers.Subsequently,it develops a multi-resource demand forecasting model,leveraging population data to accurately estimate the quantity of materials needed at each demand point.Given the challenge of insufficient material reserves to meet the diverse needs of every demand point during major public health crises,this paper proposes a solution.It constructs a three-tiered dispatching network comprising supply points,distribution centers,and demand points.This network facilitates cross-regional,multi-cycle dynamic scheduling aimed at maximizing satisfaction with comprehensive material distribution,optimizing transportation time,and minimizing overall rescue costs.Acknowledging the limitations of the grey wolf optimization algorithm in addressing material scheduling challenges,this paper presents an Improved Grey Wolf Optimization(IGWO)algorithm.Firstly,it incorporates the chaotic reverse learning strategy to initialize the population,thereby enhancing population diversity and quality.Secondly,it adopts the global search and local search processes of the nonlinear convergence factor balance algorithm.Lastly,the random difference mutation technique is applied to mutate individuals,while a greedy selection strategy aids in escaping local optima and identifying global optimal solutions.The experimental results demonstrate that the IGWO algorithm exhibits improved convergence ability and accuracy,enabling better exploration of solution spaces beyond local optima.Consequently,this paper utilizes the IGWO algorithm to address the material scheduling model.The findings indicate that the scheduling model proposed herein adeptly balances the satisfaction of disaster victims with the cost of material scheduling.Through the enhancement of the GWO algorithm,a fairer and more economical material scheduling scheme is achievable,offering a scientific and effective solution to the multi-cycle material scheduling challenge following major public health events.
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