Uncertainty analysis of autonomous delivery robot operations for last-mile logistics in European cities

Abstract

Although autonomous delivery robots (ADRs) are widely anticipated to significantly enhance the efficiency of last-mile logistics operations in dense urban environments in the coming years, their impact on logistics service providers’ supply chains has yet to be accurately assessed on a large scale. The primary objective of this article is to quantify the uncertainty in the cost of ADR operations as a function of the stochastic behavior of given input variables. First, ADR operations are modeled using the continuous approximation methodology. The mathematical formulations proposed in this article relate certain ADR and service area input parameters to provide an estimate of the carrier’s last-mile operating costs. An uncertainty analysis based on the Monte Carlo approach is then performed. The numerical results indicate that the implementation of a two-echelon delivery scheme with heavy-duty vehicles cooperating with ADRs through an urban logistics micro-hub would, on average, reduce last-mile operating costs by more than 10% in small and medium-sized European cities, given adequate operational conditions.