An intelligent solution system for a vehicle routing problem in urban distribution

In this paper, we present an intelligent solution system for a vehicle routing problem (VRP) with rigid time window in urban distribution. The solution has three stages. The first stage uses Clustering Analysis in Data Mining to classify all customers by a number of attributes, such as distance, demand level, the density of customer, and city layout. The second stage introduces how to generate feasible routing schemes for each vehicle type. Specifically, a depth-first search algorithm with control rules is presented to generate feasible routing schemes. In the last stage, an integer programming model is constructed to identify the optimal routing schemes. Finally, we present a real VRP case to show that the approach and the system are efficient and provide a new way to solve the VRP problems with time-windows.