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This paper presents a neural network recommender system algorithm forassigning vehicles to routes based on energy and cost criteria. In this work,we applied this new approach to efficiently identify the most cost-effectivemedium and heavy duty truck (MDHDT) powertrain technology, from a total cost ofownership (TCO) perspective, for given trips. We employ a machine learningbased approach to efficiently estimate the energy consumption of variouscandidate vehicles over given routes, defined as sequences of links (roadsegments), with little information known about internal dynamics, i.e usinghigh level macroscopic route information. A complete recommendation logic isthen developed to allow for real-time optimum assignment for each route,subject to the operational constraints of the fleet. We show how this frameworkcan be used to (1) efficiently provide a single trip recommendation with atop-$k$ vehicles star ranking system, and (2) engage in more general assignmentproblems where $n$ vehicles need to be deployed over $m \leq n$ trips. This newassignment system has been deployed and integrated into the POLARISTransportation System Simulation Tool for use in research conducted by theDepartment of Energy's Systems and Modeling for Accelerated Research inTransportation (SMART) Mobility Consortium

A Real-Time Energy and Cost Efficient Vehicle Route Assignment Neural Recommender System