Abstract
Recent studies demonstrate the efficacy of machine learning algorithms for learning strategies to solve combinatorial optimization problems. This study presents a novel solution to address the Electric Vehicle Routing Problem with Time Windows (EVRPTW), leveraging deep reinforcement learning (DRL) techniques. Existing DRL approaches frequently encounter challenges when addressing the EVRPTW problem: RNN-based decoders struggle with capturing long-term dependencies, while DDQN models exhibit limited generalization across various problem sizes. To overcome these limitations, we introduce a transformer-based model with a heterogeneous attention mechanism. Transformers excel at capturing long-term dependencies and demonstrate superior generalization across diverse problem instances. We validate the efficacy of our proposed approach through comparative analysis against two state-of-the-art solutions for EVRPTW. The results demonstrated the efficacy of the proposed model in minimizing the distance traveled and robust generalization across varying problem sizes.
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BibTeX (Download)
@conference{Munir2024c,
title = {Route Planning for Electric Vehicles with Charging Constraints},
author = {Aiman Munir, Ramviyas Parasuraman, Jin Ye, WenZhan Song},
url = {https://ieeexplore.ieee.org/abstract/document/10757558},
doi = {10.1109/VTC2024-Fall63153.2024.10757558},
year = {2024},
date = {2024-10-10},
booktitle = {2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall)},
pages = {2577-2465},
abstract = {Recent studies demonstrate the efficacy of machine learning algorithms for learning strategies to solve combinatorial optimization problems. This study presents a novel solution to address the Electric Vehicle Routing Problem with Time Windows (EVRPTW), leveraging deep reinforcement learning (DRL) techniques. Existing DRL approaches frequently encounter challenges when addressing the EVRPTW problem: RNN-based decoders struggle with capturing long-term dependencies, while DDQN models exhibit limited generalization across various problem sizes. To overcome these limitations, we introduce a transformer-based model with a heterogeneous attention mechanism. Transformers excel at capturing long-term dependencies and demonstrate superior generalization across diverse problem instances. We validate the efficacy of our proposed approach through comparative analysis against two state-of-the-art solutions for EVRPTW. The results demonstrated the efficacy of the proposed model in minimizing the distance traveled and robust generalization across varying problem sizes.
},
keywords = {control, learning, multi-robot systems},
pubstate = {published},
tppubtype = {conference}
}