Research On Optimization Of Logistics Distribution Paths Under The Impact Of The Epidemic

In the context of the spread of pneumonia caused by the new coronavirus,competition in the logistics and distribution industry has become increasingly fierce.In the face of the increasing material needs of customers and the delivery of epidemic prevention materials,enterprises need to obtain greater profits while improving their service quality In order to survive in the competition of many companies.During the epidemic,all cities have introduced policies to prevent and control the epidemic.Logistics vehicles must be strictly inspected to prevent the spread of the epidemic.Companies must carry out effective logistics planning to meet customer needs under numerous traffic restrictions between cities.It is very necessary to study the optimization of logistics distribution path under the chemical prevention and control mechanism.In order to study better optimization of logistics distribution paths,this article applies the traveling salesman problem to the optimization problem of logistics distribution paths,and proposes the generalized coverage traveling salesman problem,and establishes a Generalized Coverage Traveling Salesman Problem(GCTSP)model,and proposes an improved genetic algorithm with feasible recovery mechanism(IGAFRM)to solve it.Genetic algorithm has better global search capabilities and is easier to implement,so it has many applications in optimization problems,but it has the shortcomings of easy access to local optima and poor convergence.Therefore,this paper introduces four kinds of initialization populations Rules are used to increase the quality of the initial population,and random selection is performed proportionally to avoid the solution falling into the local optimum.Three different rules are proposed to select individuals to increase the diversity of the population.At the same time,two methods are set up to screen the parents in the crossover operation.Keep the good genes of the parents,determine the good genes of the individual by introducing different crossover operators and mutation operators,and add a feasibility recovery mechanism between the crossover operation and the mutation method to prove the feasibility of the solution.Finally,in order to solve the problem of poor convergence in genetic algorithms,a population update strategy based on population similarity is added.This paper selects a set of 115 examples from the TSP library for simulation experiment verification,and compares the test results with other accurate algorithms and heuristic algorithms.At the same time,the convergence analysis is carried out.After comparative analysis,it is found that IGAFRM has better performance in terms of solution quality and speed.So the algorithm is very reliable.