Multi-Pickup and Delivery of Restaurant Orders: A Graph-Aware Reinforcement Learning Approach

The Multi-Pickup and Delivery Problem with Time Window (MPDPTW) is an essential optimization problem in transportation and logistics, with significant real-world applications in today’s fast-paced environment. This research addresses the challenging task of optimizing multiple restaurant order pickups and deliveries, leveraging the power of graph-aware reinforcement learning (RL). By integrating advanced artificial intelligence techniques, this study aims to address the dynamic and complex logistics within the food service industry. The primary goal is to enhance service delivery efficiency, mitigate time lags, and reduce operational expenses. Through the application of a graph-aware RL model, we aim to create a resilient, adaptable system that continually learns and improves from environmental interactions, thereby refining the decision-making process in the context of multiple restaurant order pickups and deliveries. This study provides a fresh perspective on the role of deep learning in revolutionizing food service logistics, making a significant contribution to the growing domain of intelligent and automated delivery systems. We explore graph representation learning to tackle the MPDPTW complexity and to improve the efficiency and effectiveness of transportation and logistics systems.