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, t...

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Bibliographic Details
Published in:arXiv.org 2022-12
Main Authors: Bogyrbayeva, Aigerim, Yoon, Taehyun, Ko, Hanbum, Lim, Sungbin, Hyokun Yun, Kwon, Changhyun
Format: Article
Language:English
Subjects:
Coders
Combinatorial analysis
Coordination
Deep learning
Drone vehicles
Encoders-Decoders
Optimization
Route planning
Traveling salesman problem
Trucks
Vehicle routing
Vehicles
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Summary: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. We 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. We empirically demonstrate that such a hybrid model improves upon a purely attention-based model for both solution quality and computational efficiency. Our 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.
ISSN:2331-8422