We propose a manager-worker framework based on deep reinforcement learning to
tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP),
\ie~multiple-vehicle TSP with time window and rejections (mTSPTWR), where
customers who cannot be served before the deadline are subject to rejections.
Particularly, in the proposed framework, a manager agent learns to divide
mTSPTWR into sub-routing tasks by assigning customers to each vehicle via a
Graph Isomorphism Network (GIN) based policy network. A worker agent learns to
solve sub-routing tasks by minimizing the cost in terms of both tour length and
rejection rate for each vehicle, the maximum of which is then fed back to the
manager agent to learn better assignments. Experimental results demonstrate
that the proposed framework outperforms strong baselines in terms of higher
solution quality and shorter computation time. More importantly, the trained
agents also achieve competitive performance for solving unseen larger
instances.

提出一种基于深度强化学习的经理 - 工作人员框架来解决一种复杂但非常重要的旅行商问题的变体（即具有时间窗口和拒绝的多车 TSP），其中不能在截止日期前提供服务的客户可能被拒绝。经过实验结果证明，该框架在解决大型问题时具有高质量的解决方案和更短的计算时间。