考虑三级调度网络的应急物资多资源动态调度问题研究  

Research on dynamic scheduling of emergency materials with multiple resources considering the three-level dispatching network

作　　者：王付宇[1,2] 贺昕 王欣蕊 周鑫鑫 李艳[1] WANG Fuyu;HE Xin;WANG Xinrui;ZHOU Xinxin;LI Yan(School of Management Science and Engineering,Anhui University of Technology,Maanshan 243032,Anhui,China;Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes,Anhui University of Technology,Maanshan 243002,Anhui,China)

机构地区：[1]安徽工业大学管理科学与工程学院,安徽马鞍山243032 [2]复杂系统多学科管理与控制安徽普通高校重点实验室(安徽工业大学),安徽马鞍山243002

出　　处：《安全与环境学报》2024年第8期3180-3190,共11页Journal of Safety and Environment

基　　金：国家自然科学基金项目(72274001,71872002)。

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。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 findin

关 键 词：公共安全 应急物资调度 需求预测 改进灰狼优化算法 多目标优化 

分 类 号：X956[环境科学与工程—安全科学]