Research On VRPTM Of Fresh Agricultural Products

Fresh agricultural products are easier to corrode,so the distribution that is not only related to delivery cost,but also related to product quality and customer satisfaction is very important in the cold chain of fresh agricultural products.Therefore,it has great significance to increase competitiveness of enterprises by studying the VRPTW of fresh agricultural products according to their characteristics.First of all,this paper analyzes the factors that affect delivery cost and customer satisfaction.The total cost includes vehicle fixed cost and running cost,freshness loss cost,and time penalty cost.For the customer satisfaction,after consideration of the distribution service time,this paper focuses on the impact of product quality on customer satisfaction.Freshness loss coefficient is introduced to indirectly reflect the effect of product quality on customer satisfaction.This paper establishes the customer satisfaction function which is based on vehicle arrival time and product quality.Then,the optimization model of VRPTW of fresh agricultural products is given in order to minimize total cost and maximize customer satisfaction.Then,the theoretical knowledge and concrete steps of genetic algorithm are analyzed.In order to make up some defects of genetic algorithm,for instance,it is easy to sink into the local optimal solution,adaptive mechanism and catastrophic operator are introduced,and three operators that include selection,crossover,and mutation are redesigned.In other words,an improved genetic algorithm named adaptive genetic algorithm with catastrophic operator is proposed so as to determine the total number of vehicles and the order of customers.Finally,this paper analyzes the model and algorithm with two practical examples in order to validate the feasibility and validity of the model and improved genetic algorithm.At the same time,standard genetic algorithm and the genetic algorithm in a reference are also used to solve these problems.These three algorithms are calculated 30 times,and the optimal results are selected.The results show that this algorithm converges better than the other two algorithms.For the first example,the vehicle scheduling scheme based on the improved genetic algorithm is the same as that of genetic algorithm in the reference,which is better than that of standard genetic algorithm.For the second example,the vehicle scheduling scheme based on the improved genetic algorithm is clearly superior to these of the other two algorithms.These show that the model established in this paper is effective,the convergence of the improved algorithm is better and it has some advantages.Of course,vehicle scheduling is more complex.This model is based on some assumptions and constraints,so it can't fully reflect the reality.For the algorithm,it should be tested several times to get the most excellent solution.So there is still a need for further research to improve them.