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Heuristic-Augmented Attentions for the Electric Vehicle Routing Problem With Time Windows
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王
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DOI：10.1109/tvt.2026.3665950.png)
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Abstract
En 中文
Recently, the integration of electric vehicles (EVs) into intelligent transportation systems has introduced significant and novel challenges, necessitated by the need to account for additional factors such as battery consumption and charging station characteristics. To achieve the sustainable integration of EVs into these systems, the development of new models, methods, and technologies is crucial. The electric vehicle routing problem with time windows (EVRPTW) represents an innovative challenge in this domain. It involves determining optimal routes for a fleet of EVs to provide freight transportation services, while considering energy consumption, charging times, and customer service times. Although existing methods incorporating attention-based Transformer architecture have achieved remarkable success in solving traditional vehicle routing problems, they encounter substantial difficulties when directly applied to the EVRPTW. This is mainly due to the complex interplay between customer time-window restrictions and battery-capacity limitations. To address these challenges, this paper presents a novel heuristic-augmented attention method that harnesses heuristic information to enhance attention mechanisms. Specifically, an adaptive compatibility module is introduced to leverage time windows information, thereby enhancing the effective connectivity of nodes under various constraints and improving the understanding of the solution space. Furthermore, a dynamic heuristic-enhanced attention is utilized to facilitate a more in-depth understanding of the relationships between different types of nodes. The experimental results demonstrate that the proposed approach outperforms representative attention-based methods. It achieves superior performance in balancing route length and time-window satisfaction.
Keywords:
Electric vehicle routing
optimization
heuristic
attention
constraint
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7.1
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1.7W
Citations:
6.6W
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