  This paper presents a neural network recommender system algorithm for
assigning vehicles to routes based on energy and cost criteria. In this work,
we applied this new approach to efficiently identify the most cost-effective
medium and heavy duty truck (MDHDT) powertrain technology, from a total cost of
ownership (TCO) perspective, for given trips. We employ a machine learning
based approach to efficiently estimate the energy consumption of various
candidate vehicles over given routes, defined as sequences of links (road
segments), with little information known about internal dynamics, i.e using
high level macroscopic route information. A complete recommendation logic is
then developed to allow for real-time optimum assignment for each route,
subject to the operational constraints of the fleet. We show how this framework
can be used to (1) efficiently provide a single trip recommendation with a
top-$k$ vehicles star ranking system, and (2) engage in more general assignment
problems where $n$ vehicles need to be deployed over $m \leq n$ trips. This new
assignment system has been deployed and integrated into the POLARIS
Transportation System Simulation Tool for use in research conducted by the
Department of Energy's Systems and Modeling for Accelerated Research in
Transportation (SMART) Mobility Consortium
