Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning
This work addresses a combinatorial optimization problem with applications in logistics and scheduling, but it is incremental as it extends existing methods to a variant with constraints.
The authors tackled the Traveling Salesperson Problem with precedence constraints (TSPPC) by adapting deep reinforcement learning methods, achieving competitive results on benchmark instances.
This work presents solutions to the Traveling Salesperson Problem with precedence constraints (TSPPC) using Deep Reinforcement Learning (DRL) by adapting recent approaches that work well for regular TSPs. Common to these approaches is the use of graph models based on multi-head attention (MHA) layers. One idea for solving the pickup and delivery problem (PDP) is using heterogeneous attentions to embed the different possible roles each node can take. In this work, we generalize this concept of heterogeneous attentions to the TSPPC. Furthermore, we adapt recent ideas to sparsify attentions for better scalability. Overall, we contribute to the research community through the application and evaluation of recent DRL methods in solving the TSPPC.