In recent days, the usage of learning algorithm to improve optimization methods have become increasingly interesting. For example, the Vehicle Routing Problem (VRP) that logistic companies might face daily. The main problem is arising whenever the delivery routes could not be optimal, which causes an increase in delivery costs. To reduce these costs, we need optimize the delivery route. However, most optimization algorithm still solves the problem from scratch, even for the same problem type, and nothing useful is extracted from prior solutions. Meanwhile, the historical data could be useful to gain solutions efficiently and effectively. In term of optimization algorithm, the use of artificial intelligence (AI) for solving VRP promise to learn from past solutions or in real-time and then to guide the algorithm to solve the problem. Moreover, the optimization algorithm could learn from its own decisions and adjust its behavior accordingly to gain better behavior. Therefore, the objective of this research is divided into two goals: (1) understand the connection between the quality of the solutions, their features, and the associated problem instances, and (2) construct an efficient learning process consolidated with a powerful optimization algorithm to solve the problems quickly and effectively.