  In response to carbon-neutral policies in developed countries, electric
vehicles route optimization has gained importance for logistics companies. With
the increasing focus on customer expectations and the shift towards more
customer-oriented business models, the integration of delivery time-windows has
become essential in logistics operations. Recognizing the critical nature of
these developments, this article studies the heterogeneous electric vehicle
routing problem with time-window constraints (HEVRPTW). To solve this variant
of vehicle routing problem (VRP), we propose a DRL-based approach, named
Edge-enhanced Dual attentIon encoderR and feature-EnhanCed dual aTtention
decoder (Edge-DIRECT). Edge-DIRECT features an extra graph representation, the
node connectivity of which is based on the overlap of customer time-windows.
Edge-DIRECT's self-attention encoding mechanism is enhanced by exploiting the
energy consumption and travel time between the locations. To effectively
account for the heterogeneity of the EVs' fleet, a dual attention decoder has
been introduced. Experimental results based on two real-world datasets reveal
that Edge-DIRECT outperforms a state-of-the-art DRL-based method and a
well-established heuristic approach in solution quality and execution time.
Furthermore, it exhibits competitive performance when compared to another
leading heuristic method.
