Solving Vehicle Routing Problem: A Big Data Analytic Approach - Shaoqing Zheng (IEEE Access, 2019)
In the transport industry, the cost effectiveness relies heavily on the rational design of the transport routes. However, the traditional theories and methods on vehicle routing problem (VRP) cannot describe the dynamic features of travel time accurately. To solve the problem, this paper puts forward a dynamic VRP model based on big data analysis on traffic flow, and solves it by the genetic algorithm (GA). It is assumed that the real-time traffic data are updated every 15mins in the transport network, and the customer demand is updated dynamically from time to time. The example analysis shows that my model and its route adjustment strategy can minimize the total transport cost by routing the vehicles from multiple depots under the soft time window. The research findings help transport enterprises to make effective use of vehicles and receive more profits.