Electric Vehicle Routing Model for Last-mile Logistics in Cities with Steep Streets

Abstract

The Quito local government aims to establish a low-emission zone in the city’s historic center. A key focus is the shift to eco-friendly transportation for last-mile logistics, including electric cargo bikes and other types of light electric vehicles (LEVs). Our research delves into integer programming models to optimize the vehicle routes. We address a variation of the electric vehicle routing problem (EVRP), factoring in vehicle load and street slope for battery consumption and travel times. Moreover, we consider the existence of multiple paths between each pair of customers, which vary in distance and slope, yielding different travel times and battery consumption values. For instance, some paths may have small travel times but require high battery consumption, while other paths may have longer travel times and require less battery consumption. The problem is formulated on a customer multigraph that has one node for each customer and depot, and where parallel arcs are used to represent efficient paths in the original network. Road selection is carried out as part of the vehicle routing. This talk highlights findings on modeling strategies and reports some computational results to examine the impact of some model parameters upon the optimal solutions.