An Optimization Model for Determining Optimal Fleet Size for a Robot-Sharing System

Abstract

Intelligent transportation technologies are aiming to improve last-mile urban logistics and reduce traffic congestion. Congested metropolitan cities are implementing last-mile delivery robots to make the delivery cheaper and faster. A key factor for the success of automated delivery robots (ADRs) in the last-mile is its ability to meet the fluctuating demand for robots at each micro-hub. Delivery companies rent robots from micro-hubs scattered around the city, use them for deliveries, and return them at micro-hubs. This paper studies the dynamic assignment of the robots to satisfy their demands between the micro-hubs. A mixed integer linear programming (MILP) model is developed, which minimizes the total transportation cost by determining the optimum required fleet size. The result determines the number of robots required for each planning period to meet all the demands. It provides algorithms to operate and schedule the robot-sharing program efficiently with the same approximation guarantee for all related problems.