CO2 Emissions of Innovative Last Mile Delivery Solutions

Abstract

This work studies the impact of innovative last mile delivery solutions on the amount of produced CO2 emissions. An innovative last mile delivery solution is defined as a truck that carries a drone and together in tandem the truck and drone deliver parcels to the set of customers. The problem of finding the optimal route to serve customers by the truck and drone tandem is known as Travelling Salesman Problem with Drone (TSPD). However, all previously developed approaches for solving TSP-D do not consider the time dependent nature of the problem when the speed of the truck is affected by traffic congestion, infrastructure constraints, etc. To address this task, the genetic algorithm for solving a time dependent TSP-D was developed. The input information for the time dependent TSP-D algorithm is derived through “Simulation of Urban Mobility" (SUMO) software. SUMO allows realistic simulation of the various infrastructure constraints and calculation of the information required for the time dependent TSP-D algorithm. The computational experiments show that the truck and drone tandem can significantly (more than twice) shorten the delivery time in congested urban areas. The sensitivity analysis reveals that drone speed does not considerably affect delivery time or the amount of produced CO2 emissions. Ultimately this study demonstrates that using the truck and drone tandem contributes to shorter delivery time and less CO2 emissions and provides the model for assessing these benefits.