A deep reinforcement learning approach for solving the Traveling Salesman Problem with Drone

Reinforcement learning has recently shown promise in learning quality solutions in many combinatorial optimization problems. In particular, the attention-based encoder-decoder models show high effectiveness on various routing problems, including the Traveling Salesman Problem (TSP). Unfortunately, they perform poorly for the TSP with Drone (TSP-D), requiring routing a heterogeneous fleet of vehicles in coordination—a truck and a drone. In TSP-D, the two vehicles are moving in tandem and may need to wait at a node for the other vehicle to join. State-less attention-based decoder fails to make such coordination between vehicles. The authors propose a hybrid model that uses an attention encoder and a Long Short-Term Memory (LSTM) network decoder, in which the decoder’s hidden state can represent the sequence of actions made. The authors empirically demonstrate that such a hybrid model improves upon a purely attention-based model for both solution quality and computational efficiency. The authors' experiments on the min-max Capacitated Vehicle Routing Problem (mmCVRP) also confirm that the hybrid model is more suitable for the coordinated routing of multiple vehicles than the attention-based model. The proposed model demonstrates comparable results as the operations research baseline methods.

• Record URL:
  https://doi.org/10.1016/j.trc.2022.103981
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X22003941
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Bogyrbayeva, Aigerim
  • Yoon, Taehyun
  • 0000-0002-2435-1521
  • Ko, Hanbum
  • Lim, Sungbin
  • 0000-0003-2684-2022
  • Yun, Hyokun
  • 0000-0001-6169-4761
  • Kwon, Changhyun
  • 0000-0001-8455-6396
• Publication Date: 2023-3

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 103981
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 148
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Drones; Machine learning; Neural networks; Routing; Traveling salesman problem; Trucks; Vehicle fleets
• Subject Areas: Aviation; Freight Transportation; Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01875034
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 28 2023 9:19AM