Research On Demand Forecasting And Scheduling Of Urban Fresh Product Emergency Logistics In The Context Of Public Health Emergencies

Recent years have seen frequent public health emergencies,such as the COVID-19 epidemic,influenza A(H1N1)and norovirus,all of which have affected people’s work and life.When these events break out in a large area,there will be a large number of urgent material needs,including medical supplies and daily necessities,which require reasonable rescue within an effective time to reduce the loss of life and property.Urban emergency logistics system assumes the responsibility of logistics planning and transportation,and its role is essential.Many Chinese cities have a certain emergency capacity and have established some emergency logistics procedures,but there are still weaknesses in the demand prediction and scheduling distribution of emergency materials,especially for the easy corruption,difficult to store fresh products is a great challenge.Therefore,it is imperative to study the demand prediction and scheduling of urban fresh product emergency logistics under the background of public health events,so as to ensure reasonable supply and distribution,meet people’s normal life and reduce costs and waste.This thesis mainly studies the demand prediction and scheduling optimization of urban fresh product emergency logistics under public health emergencies.First of all,the background and significance of the topic are expounded,and relevant researches at home and abroad on urban emergency logistics demand forecasting and material scheduling are reviewed and reviewed.The relevant concepts of emergency logistics are defined and the theoretical basis of emergency logistics demand forecasting and scheduling is summarized.Secondly,this thesis divided into two steps to establish the demand prediction model of urban fresh product emergency logistics.The first step improved the grey prediction model,using new data instead of the old data dynamic model to predict the number of affected people under public health emergencies.The second step is to establish the target model of fresh product demand based on the number of people and inventory theory to predict the required quantity in each region.Thirdly,based on the multi-objective optimization theory,a city fresh product emergency logistics scheduling model was constructed which considered both the maximum time satisfaction and the minimum total cost.Through comparative analysis,particle swarm optimization algorithm was selected as the solution method of the scheduling model,and finally a material scheduling scheme satisfying the demand of fresh products in various regions was obtained.Finally,taking Shanghai as the empirical object,empirical simulation and sensitivity test were conducted on the established model based on the real data of the novel coronavirus outbreak event in April 2022.The empirical test results show that the improved dynamic demand forecasting model has higher accuracy and smaller error than the general grey forecasting model,and is suitable for the demand forecasting with public health emergencies as the background.The scheduling model of emergency logistics considers different objectives and takes into account the efficiency and fairness of emergency logistics.The application of particle swarm optimization algorithm has a fast convergence speed,and the result has a high sensitivity,which can solve the emergency logistics scheduling problem of urban fresh products according to the actual situation.Compared with general logistics problems,this thesis focuses on the coordination and integrity of emergency logistics,and establishes a demand prediction and material scheduling model with high efficiency,which can be applied to the practical problems of urban fresh product emergency logistics,and provides theoretical basis and practical reference for future response to public health emergencies.