Learning to Solve Multiple-TSP with Time Window and Rejections via Deep Reinforcement Learning

1 code implementation • 13 Sep 2022 • Rongkai Zhang, Cong Zhang, Zhiguang Cao, Wen Song, Puay Siew Tan, Jie Zhang, Bihan Wen, Justin Dauwels

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.

Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning

5 code implementations • NeurIPS 2020 • Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, Chi Xu

Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP).

Job Shop Scheduling reinforcement-learning +2

Re-route Package Pickup and Delivery Planning with Random Demands

no code implementations • 24 Jul 2019 • Suttinee Sawadsitang, Dusit Niyato, Kongrath Suankaewmanee, Puay Siew Tan

Recently, a higher competition in logistics business introduces new challenges to the vehicle routing problem (VRP).

Stochastic Optimization