Knowledge-Based Learning for Solving Vehicle Routing Problem

In this study, we have developed a method that applies machine learning in combination with an optimization heuristic algorithm such as a genetic algorithm (GA) for solving the vehicle routing problem (VRP). Further, we developed a knowledge-based algorithm for a knowledge learning system. The algorithm learns to classify coordinates (unlabeled) into regions. Consequently, dividing routing calculations into regions (clusters) provides many benefits over traditional methods, and can result in an improvement in routing cost over the traditional company method by up to 25.68% and over the classical GA by up to 8.10%. It is also shown that our proposed method can reduce traveling distance compared to previous methods. Finally, the prediction of future customer regions has an accuracy of up to 0.72 for the predicted unlabeled customer coordinates. This study can contribute toward creation of more efficient and environmentally friendly urban freight transportation systems.