Conference Agenda

The increasing demands of last-mile delivery in a diverse range of logistical scenarios, ranging from small communities with specific needs, to dynamic urban environments where demand fluctuates significantly, highlight the necessity for innovative and efficient logistics solutions. This study introduces a novel routing strategy leveraging modular vehicle (MV) technology, which are capable of dynamic assembly and disassembly in motion. This capability facilitates platooning during transit, significantly reducing energy consumption and optimizing route efficiency. We develop a mixed-integer linear programming (MILP) model to address this 'Modular Pickup and Delivery Problem' for cargo, incorporating both in-motion transhipment and platooning to minimize delivery times and operational costs. Given that the MVs are anticipated to be predominantly electric, the model uniquely incorporates charging strategies within the operational framework to ensure continuous vehicle readiness without compromising delivery efficiency. The proposed model allows MVs to perform transhipment operations not only on arcs while platooning but also at nodes, which offers enhanced flexibility in logistics operations. The MILP formulation captures essential aspects such as vehicle platoon status, cargo en-route transhipment, variable vehicle capacity, and electric vehicle charging requirements, with constraints tailored to handle the spatial-temporal dynamics of delivery networks. The contributions extend beyond theoretical modeling to offer a scalable solution for real-world application, potentially paving the way for the improvement of last-mile delivery operations through the adoption of advanced MV technology and platooning.

Last-mile logistics has gained increasing attention in the last few years.

Driven by the rise of e-commerce and the growing demand for fast deliveries,

companies are exploring several strategies to optimize last-mile operations.

One of the most promising options in city logistics is the Truck-and-Robot

(TnR) concept that involves a truck transporting parcels and autonomous

robots. This delivery system exploits a combination of drop-off points and

robot depots. When a truck arrives at a drop-off point, robots are released

directly from the truck to autonomously deliver goods. On the contrary, if a

depot is reached, each driver unloads parcels for subsequent robot deliveries.

In this paper, three delivery models have been simulated and optimized in

order to determine the most effective approach. The compared methodolo-

gies are: the TnR method previously described, which enables parallelized

deliveries, a standard approach, where the driver directly visits customers,

and a mixed strategy where a portion of clients is served by robots and the

other one by trucks.

The obtained results on literature’s instances and on a real use case show

that the collaboration between trucks and robots (mixed strategy) is more

convenient than the traditional truck service. However, an increase in robot

costs strongly influences the optimal tours configuration, in favor of trucks’

use.

Self-driving robots are particularly suitable for simultaneous deliveries as

they reduce times and costs, respecting time windows constraints. Neverthe-

less, traditional logistics remain faster and more efficient in scenarios where

delivery parallelization is not feasible.

The ongoing growth of e-commerce has led to a significant increase in last-mile deliveries. New technologies are being investigated to provide these deliveries efficiently and in a customer-friendly manner. Mobile parcel lockers demonstrate an interesting extension of conventional stationary parcel lockers and attended home delivery services. During the preparation of deliveries, it is essential to consider the perspectives of both the service provider and the customer. On the one hand, service providers have many orders to fulfil and attempt to schedule each customer optimally. On the other hand, each customer has their individual availabilities and preferences. We need to consider firstly, whether the customer prefers to be delivered at home or from a parcel locker; secondly, within what time period; and thirdly, how far the customer is willing to travel for pickup. To ensure optimal routing, these requirements must be aligned with the current and incoming order situation. Consequently, providers offer customers a selection of possible delivery slots from which they then choose the one that suits them best. It is important which slots are offered to which customer, as the selection has a strong impact on if and how subsequent customers can be served. We therefore present a customer acceptance procedure in which customers can be served with mobile parcel locker or attended home delivery services and evaluate preliminary offering strategies in terms of performance (accepted customers) and service quality (travel distance and service provision period).

In times of skilled labor shortages, it becomes essential for delivery companies to stand out from their competitors. Creating a supportive work environment where employees feel valued is key to achieve this goal. For example, recent studies have shown that delivery drivers have different preferences when it comes to parking and how far they are willing to walk. These preferences can vary for each driver based on factors like age or experience. For instance, experienced drivers are skilled to approach difficult parking spots but might not want to walk long distances. The challenges of parking and walking also vary depending on the area of the city, e.g., in commercial, downtown, or rural areas.

Consequently, delivery companies may want to integrate individual driver preferences in their operational planning of delivery routes. To show the impact of such integration, we present a multi-objective vehicle routing problem. Using Berlin as a case study, we examine the balance between driver convenience and travel cost. Our findings demonstrate that integrating driver preferences into planning models can be achieved at relatively low cost.