Assessing sustainability of smart last mile delivery: a simulation-based decision support tool

The increasing demand of e-commerce is forcing economic and environmental inefficiency in last mile logistics (LML). The adoption of smart and autonomous technologies, such as Unmanned Aerial Vehicles (UAVs) and Autonomous Delivery Robots (ADRs), is being evaluated in LML in order to increase its effectiveness. UAVs offer advantages such as faster delivery times and reduced traffic congestion, but face challenges like weather sensitivity and the need for dedicated take-off and landing infrastructure. ADRs can reduce emissions and operational costs compared to traditional LML systems, but their full application is limited mainly due to slower speeds and complex interactions with pedestrians. Despite their limitations, in future years these technologies could be fully applied for LML: thus, evaluating their environmental impact during LML service is necessary to plan their full-scale application. This study proposes a simulation-based decision support tool for assessing the performance of traditional and smart LML technologies according to economic and environmental points of view. By leveraging advanced simulation models, the proposed tool allows to estimate these impacts under varying operational conditions, providing a comprehensive framework for decision-making the LML field by comparing traditional versus innovative LML services. The tool was validated through a case study application in an urban context, demonstrating its ability to highlight the potential benefits and challenges of applying UAVs and ADRs into LML networks. Results indicate that unmanned delivery vehicles allow for a substantial reduction in carbon emissions in the operational phase, confirming their potential as a more environmentally sustainable solution for urban last mile logistics. In addition, the total cost associated with unmanned systems is found to be comparable to that of conventional vehicles, particularly when these latter operate under medium-to-high traffic conditions. Researchers and logistic companies can use this tool to evaluate and optimize the impact of their innovative LML services strategies and achieve improved economic and environmental sustainability levels.