Research For Heterogeneous Vehicle Routing Problem Of Company Y

In recent years,China's logistics industry has developed rapidly.The logistics industry plays a role in promoting production and stimulating consumption in the national economy,but the total cost of China's logistics industry is still high.How to reduce the logistics distribution cost is one of the major problems faced by logistics enterprises in reducing cost and increasing efficiency,improving enterprise competitiveness and customer satisfaction.Therefore,the optimization research of urban distribution vehicle routing problem is particularly important.According to the operation status of Y logistics company,this paper analyzes the distribution scheme of the logistics company,and finds many problems in the design and implementation of the distribution scheme:(1)the distribution path is unreasonable and the distribution distance is too long;(2)Unreasonable vehicle arrangement,low loading rate and waste of transport capacity;(3)There are great differences in working hours between drivers,and they often work overtime.Then it briefly summarizes the vehicle routing problem of Y logistics company,analyzes the objective function and constraints in the vehicle routing problem of Y logistics company,and then establishes a multi-objective multi vehicle type vehicle routing problem model aiming at the lowest total cost and balanced working time,comprehensively considering the constraints of vehicle capacity,working hours and node access.Based on the traditional genetic algorithm,this paper improves its selection operator,crossover operator and mutation operator.Among them,the combination of roulette and optimal preservation strategy is used to replace the original selection operator,two-point crossover is used to replace the original single point crossover,catastrophe operation is added to the mutation operator,and reversal operation is added,Greatly increase the local search ability and form an improved genetic algorithm.In order to verify the effectiveness and feasibility of the improved genetic algorithm,the algorithm is tested.A series of standard examples with different scales are used to test the algorithm,and compared with the traditional genetic algorithm.The results show that the error between the improved genetic algorithm designed in this paper and the optimal solution of the standard example is smaller,and the solution is better than the traditional genetic algorithm,especially in large-scale examples.According to the actual distribution demand of Y logistics company on a certain day,the distribution scheme is optimized,and the optimization scheme is obtained through algorithm solution.Compared with the distribution scheme before optimization,the total driving distance is reduced by about 15% in terms of transportation distance,and the carbon emission is reduced by nearly 16% in terms of carbon emission.In terms of working hours,the gap between working hours is greatly shortened,and the maximum gap is no more than 1 hour,The overtime hours are controlled within 1 hour.From the perspective of distribution cost,the vehicle driving cost is reduced,the overtime cost is greatly reduced,and the total cost is reduced by about 30%.In general,the optimized distribution scheme is significantly better than the optimized distribution scheme in many aspects.