Abstract
With the development of economy, the distribution problem of logistics becomes more and more complex. Based on the traffic network data, this study analyzed the vehicle routing problem (VRP), designed a dynamic vehicle routing problem with time window (DVRPTW) model, and solved it with genetic algorithm (GA). In order to improve the performance of the algorithm, the genetic operation was improved, and the output solution was further optimized by hill climbing algorithm. The analysis of example showed that the improved GA algorithm had better performance in path optimization planning, the total cost of planning results was 31.44 % less than that of GA algorithm, and the total cost of planning results increased by 11.48 % considering the traffic network data. The experimental results show that the improved GA algorithm has good performance and can significantly reduce the cost of distribution and that research on VRP based on the traffic network data is more in line with the actual situation of logistics distribution, which is conducive to the further application of the improved GA algorithm in VRP.
Highlights
With the development of economy, logistics industry, as an auxiliary industry, has been developing very fast, and its service level is constantly improving, but the high cost of logistics is still a problem of great concern
In this study, based on the general Vehicle routing problem (VRP) problem, the time window of customers was considered, a dynamic vehicle routing problem with time window (DVRPTW) model was designed with the traffic network data, the objective function and constraints were analyzed, and an improved genetic algorithm (GA) algorithm was designed to solve the model
The results showed that the total cost of the planning results was less when not considering traffic network data, which was 1026.61 yuan, which showed that traffic network data had an effect on VRP problem
Summary
With the development of economy, logistics industry, as an auxiliary industry, has been developing very fast, and its service level is constantly improving, but the high cost of logistics is still a problem of great concern. Norouzi et al [3] studied the VRP problem in the competition, aiming to minimize the cost, maximize the sales volume and reach the customer point before the competitors They designed an improved particle swarm optimization algorithm and found through experiment that the method had good accuracy. 1. Model establishment When VRP is studied on the basis of traffic network data, there are some uncertainties in the network data, such as changes in customer demand, vehicle number, road condition information, etc., and there is time window demand for service [11]. The VRP problem was solved on the basis of soft time window in this study, and the dynamic vehicle routing problem with time windows (DVRPTW) model was established considering the dynamic traffic network data. X b ijk refers to that if there is vehicle k driving from customer i to customer j at time period b, xb ijk
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