A deep reinforcement learning approach for solving the Traveling Salesman Problem with Drone
Bogyrbayeva A. Yoon T. Ko H. Lim S. Yun H. Kwon C.
March 2023Elsevier Ltd
Transportation Research Part C: Emerging Technologies
2023#148
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 decoders 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.
Drones , Neural networks , Reinforcement learning , Traveling salesman problem , Vehicle routing
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Suleyman Demirel University, Kazakhstan
UNIST, South Korea
Korea University, South Korea
Amazon, United States
University of South Florida, United States
Suleyman Demirel University
UNIST
Korea University
Amazon
University of South Florida
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