Scheduling Optimization Of Emergency Supplies Based On Improved Genetic Algorithm

In recent years,emergencies have occurred frequently,posing a huge threat to the safety of human life and property.The optimization of multi-objective scheduling of emergency supplies is a key step in disaster emer gency rescue,and it is a very challenging topic in the field of disaster emergency decision-making.How to realize timely and efficient emergency scheduling and ensure the reasonable distribution of emergency supplies has become a problem that all mankind is paying more and more attention to and urgently to be solved.Based on the research work of scholars at home and abroad,this paper puts forward the optimization problem of emergency supplies scheduling in different situations,namely,single supply point,multiple supply points,and multi-modal transportation.The solutions are given from different research perspectives,and the corresponding solutions are obtained,and the effectiveness of the model and solution algorithm proposed in this paper is verified by comparison with previous work.The details are as follows:(1)An optimization model for single supply point emergency supplies scheduling based on hybrid niche genetic algorithm is constructed.From the model point of view,with the COVID-19 epidemic situation as the background,based on the predecessors’ consideration of the time window of the demand points and the distribution cost,a single supply point emergency supplies scheduling optimization model considering the life safety of the distributor is proposed.From the perspective of the solution method,an adaptive hybrid niche strategy is designed to improve the problems of insufficient population diversity and premature convergence that may occur in the traditional genetic algorithm when solving problems.(2)An emergency supplies scheduling optimization model with multiple supply points based on the idea of a complete feasible region of recovery problem is proposed.Considering that when most scholars solve the weak economic problem of multiple supply points and the model contains multiple different targets,they use the distance-based clustering method in the first stage of the traditional "two-stage" algorithm to reduce the original feasible region of the problem and reduce the problem complexity.Taking into account the scientific question of the above approach,a fully feasible region idea of recovery problem with strong generalization ability for all multi-supply point scheduling optimization problems is proposed,and a class of variable-length genotypes improved genetic algorithm considering the idea of fully feasible region is designed.Tests are carried out on the benchmark test examples of the multi-depot vehicle routing problem and emergency supplies scheduling optimization model with multiple supply points proposed in this paper.The results confirm the effectiveness of the proposed ideas and improved algorithms.(3)A model of emergency supplies scheduling with multiple supply points and multiple targets considering multimodal transportation is constructed.In terms of model construction,considering that multimodal transportation has the characteristics of high transportation efficiency and strong comprehensive transportation capacity,based on the emergency supplies scheduling optimization model with multiple supply points considering the fully feasible domain idea in chapter 4,the multimodal transportation mode is introduced to improve the comprehensive transportation efficiency of the post-disaster transportation network.In terms of algorithm design,linear weighting is no longer used to deal with multi-objective optimization problems,and a more intuitive and effective variable-length genotype improved non-dominated sorting genetic algorithm II(VINSGA-II)is proposed.The results of the calculation example show that multimodal transportation can effectively solve the problem of large demand differences between demand points after the disaster,and through comparison with the non-dominated sorting genetic algorithm II(NSGA-II),it provides decision-makers with better decisions.