Applying Deep Reinforcement Learning to VRP and Its Extension

Abstract

In the current home medical care system, the matching of patients and doctors and the scheduling of medical care are done manually, which is inefficient for doctors. In order to make home medical care more general, scheduling must be more efficient and automated. The goal of this research is to develop an efficient algorithm that helps to create such a schedule. As a first step, the authors applied deep reinforcement learning to the vehicle routing problem (VRP), a problem for minimizing the travel costs of multiple vehicles that travel from a starting point to a demanded point with satisfying all demands. Then, the problem was extended to the scheduling problem for visiting patients by adding conditions specific to home medical care, such as time constraints for treating patients in their desired time frame and matching patients and doctors according to symptoms, gender, etc.