An Improved Genetic Algorithm For Vehicle Routing Problem With Fresh Food Delivery

With the rapid development of economy today,with the continuous improvement of people’s living standards,the rise of e-commerce,logistics distribution is an important part of online shopping,the vehicle routing problem(VRP)is born.Minimizing transportation cost,transportation time and improving customer satisfaction are the core of vehicle routing problem.In the distribution of fresh agricultural products,it is necessary to use the cold chain logistics vehicles for distribution.Adding the cold chain transportation cost to the mathematical model of the traditional vehicle routing problem constitutes the subject studied in this paper:the optimization of the distribution path of fresh agricultural products.Due to the limitations of using traditional methods to solve VRP problems,many scholars at home and abroad have adopted fusion algorithm to solve VRP problems and made some progress.However,there are still some problems,such as poor solution quality and easy to fall into local optimal defects during operation.Genetic algorithm is a classical algorithm,its advantage is a strong global search ability,but its shortcoming is also obvious,easy to fall into the local optimal and precocious problem in the running.Fireworks algorithm has explosiveness and diversity and can jump out of local optimum.In order to solve the above problems,this paper uses genetic algorithm and fireworks algorithm to solve VRP.This paper proposes two algorithms: Fireworks-Genetic Algorithm and Improved Fireworks-Genetic Algorithm To solve the problem of vehicle routing for fresh produce distribution.There are two innovative points in this paper:(1)FWGA: Combining genetic algorithm and fireworks algorithm,fireworks algorithm is implemented for the optimal and worst solution of each generation of genetic algorithm,which makes up for the defect of premature convergence of genetic algorithm.(2)IFWGA: On the basis of FWGA,the mutation operator is improved.In order to give consideration to the global search ability and local search ability,two kinds of dynamic mutation operators of step size are set up,and the mutation operator with larger step size is used in the early stage,The mutation operator with short step size is used in the later stage.The dynamic switching of mutation operator is controlled according to the number of iterations,which speeds up the convergence speed and improves the searching precision of the algorithm.Finally,it is found through experiments that IFWGA is superior to FWGA,genetic algorithm and fireworks algorithm in solving quality.