Learning Node-Pair Insertion for the Pickup and Delivery Problem with Time Windows

Pickup and Delivery Problem with Time Windows (PDPTW) is a prevalent research direction in modern logistics transportation. In this challenging problem, customers are divided into pickup nodes and delivery nodes, and vehicles must first serve each pickup node before proceeding to its corresponding delivery node. Moreover, the hard time window constraint presents an obstacle for the existing learning-to-construct methods. Hence, this paper proposes a novel learning-to-construct approach based on node-pair insertion to address the complex time window constraint. It involves predicting the insertion point for the next node pair within the current partial solution and ensuring constraint adherence. We enhance the context information for the decoder to produce better solutions. The experimental results verify that the proposed approach can construct high-quality solutions in a very short period of time.