Optimization Research On Distribution Of Emergency Perishable Materials With Fuzzy Perishability Rate

The distribution of emergency perishable materials after a disaster is a crucial part of emergency resource dispatching,which is of great significance to reducing disaster losses and casualties,improving rescue work efficiency,and reducing the psychological pressure on victims.However,in the actual rescue,the demand information of the disaster site is often difficult to determine at the first time.The demand for emergency perishable materials is often vague,coupled with the influence of force majeure factors such as climate,the change of corruption rate also becomes vague,and the traditional distribution cannot meet the demand of the disaster site.Therefore,to effectively improve rescue work efficiency,this paper takes the distribution delay penalty and corruption cost minimization into consideration.The paper constructs the vehicle path distribution optimization model and designs the algorithm to solve it with the dual objectives of minimizing the distribution delay penalty and corruption cost and minimizing the demand unsatisfied degree,considering two scenarios of material sufficiency and material shortage.The following list includes the paper’s main points and ground-breaking findings.This paper investigates the optimization of emergency perishable material distribution with sufficient material under the ambiguity of corruption rate.Under the situation of sufficient materials in a distribution center,the vehicle path distribution optimization model of emergency perishable materials is constructed with the dual objectives of minimizing the distribution delay penalty and corruption cost and minimizing the unsatisfied demand with considering the situation of fuzzy corruption rate and demand.For the fuzzy demand and corruption rate of emergency perishable materials at the affected points included in the model,the triangular fuzzy number and its ideas are used to represent the vague demand and corruption rate by applying the most probable value weight method,and then the improved differential evolutionary whale optimization algorithm(DE-WOA)is designed to solve the model by using the ideas of differential evolutionary algorithm and combining with the whale optimization algorithm,which is an emerging algorithm for solving the vehicle path This is an emerging algorithm for solving vehicle path problems.Finally,the feasibility and practicality of the proposed model in post-disaster emergency perishable material distribution are demonstrated by numerical examples.The comparison of the solution results with the standard whale optimization algorithm shows that the improved whale optimization algorithm converges faster and the overall optimization effect is improved by 23%,which further verifies the effectiveness of the designed modified algorithm.This paper investigates the optimization of emergency perishable material distribution with insufficient material under the ambiguity of corruption rate.In the case of a shortage of materials in distribution centers,the optimization model of vehicle path distribution of emergency perishable materials is constructed with the dual objectives of minimum distribution delay penalty and corruption cost and minimum unsatisfied demand,considering the cases of fuzzy corruption rate and demand.The fuzzy number in the model is processed by using the triangular fuzzy number and the most probable value weight method,and the area where the affected points are located is often cross-regional due to the wide range of disasters.Then,the improved differential evolutionary whale optimization algorithm is used to optimize the distribution of emergency perishable materials in different clustering areas.Finally,as an illustration,the earthquake in Jiuzhaigou County,Aba Prefecture,northern Sichuan Province is examined to assess the model’s and algorithm’s efficacy.Finally,the feasibility and practicality of the model in post-disaster emergency perishable goods distribution are demonstrated by numerical arithmetic examples.A comparison with the single-stage whale optimization algorithm shows that the two-stage improved differential evolutionary whale optimization algorithm converges faster and solves better,and the overall optimization effect is improved by33%,further verifying the effectiveness of the designed improved algorithm.