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Deep Reinforcement Learning for Solving Vehicle Routing Problems With Backhauls.

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IEEE Transactions on Neural Networks and Learning Systems

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This study introduces a novel deep reinforcement learning (DRL) approach for the vehicle routing problem with backhauls (VRPB). The proposed neural heuristic effectively solves VRPB variants, demonstrating strong performance and generalization capabilities in logistics optimization.

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Area of Science:

Operations Research
Computer Science
Artificial Intelligence

Background:

The vehicle routing problem with backhauls (VRPB) is a complex logistics challenge with significant real-world applications.
Existing methods often struggle with the dual delivery and pickup requirements of VRPB variants.

Purpose of the Study:

To develop a novel neural heuristic for solving traditional and improved VRPB variants.
To leverage deep reinforcement learning (DRL) for efficient route construction in logistics.

Main Methods:

Formulating the VRPB as a Markov decision process (MDP).
Designing a two-stage attention-based encoder within a DRL policy network.
Employing self-attention and heterogeneous attention mechanisms for improved node representation.

Main Results:

The proposed neural heuristic outperforms conventional and existing neural heuristic baselines.
Demonstrated favorable performance on both randomly generated and benchmark VRPB instances.
The model exhibits robust generalization across different problem sizes and distributions.

Conclusions:

The DRL-based neural heuristic offers an effective solution for the VRPB.
The attention-based encoder significantly enhances the quality of routing solutions.
This approach shows promise for practical logistics optimization and planning.