A Local Search Deep Q-Network to Optimize On-Demand Tranportation Problems

This paper presents a new Deep Q-Network (DQN) method to improve the efficiency of solving the Dial-a-Ride Problem (DARP). We focus on the solution quality of DARPs, aiming to enhance the Total Travel Cost. Our approach begins by generating initial solutions through an insertion heuristic, which are then represented within a Markov Decision Process (MDP). Based on the DQN's ability to learn from experience, the system refines its decisions over time. A local search is integrated through the DQN process improving upon the baseline solutions. The results of our experiments demonstrate that the DQN leads to more efficient vehicle routes by improving the overall system performance for DARPs.