Logistics

Different innovative concepts 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 costs 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 system in the last leg of the delivery in dense urban areas.

Logistics play a vital role in the prosperity of today’s cities, but current urban
logistics delivery practices have proven problematic and to be causing various negative
effects in cities. This study proposes an alternative method for delivering cargo with the
leasing of a network of logistics hubs within urban areas for designated daily time intervals
and handcart last-mile deliveries. The objective of the study is the development of a
mathematical programming model for identifying the optimal number and locations of
hubs for serving demand with the minimum cost, as well as the optimal times during the
day for leasing the facilities, while also allocating hubs to customers. The problem is
effectively solved by applying a Lagrangian relaxation and subgradient optimization
approach. Numerical examples and a sensitivity analysis provide evidence of the
robustness of the model and its ability to be effectively applied to address real problems.