Information magazine of the Department of Industrial Engineering
Università di Trento
Information magazine of the Department of Industrial Engineering
Università di Trento
How can engineering help build more efficient, digitalized logistics systems that also prioritize human well-being? This is the central question of a PhD project from the Department of Industrial Engineering, which explored logistics in complex real-world settings (from public healthcare to e-commerce and social transport) with a clear objective: to combine operational efficiency with social sustainability.
The research followed three main lines of investigation, all sharing a common approach based on advanced mathematical models, metaheuristic algorithms, and digital simulations, always applied to concrete case studies. The goal wasn’t just to “optimize,” but to rethink logistics as a driver for social impact.
The first project emerged during the COVID-19 pandemic, when healthcare logistics became an emergency priority. The goal was to design and digitalize mass testing and vaccination centers in South Tyrol. A discrete-event simulation model was built to represent all phases of the process (form completion, registration, testing, result entry) and quantify real times based on field data collection.
This data enabled accurate sizing of layouts, flows, and resources for each center. The model was replicated across 184 healthcare facilities (often school gyms), enabling over 360,000 people to be tested in just three days.
With the arrival of vaccines, the project evolved to include a digital twin of the vaccination logistics system, using NFC technology. The virtual model simulated scenarios of crowding and saturation, while the digital infrastructure collected real-time data from physical sites. Key Performance Indicators (KPIs) were displayed on a dedicated web platform accessible to healthcare managers, helping monitor queue lengths, station productivity, and staff usage. This continuous feedback loop between the virtual and real systems allowed for rapid layout adjustments, completing the digital cycle and improving efficiency, especially in Bolzano Fiera, the largest and most complex site.
The second research stream focused on the fast-growing e-commerce sector. The project addressed the Few-to-Many Vehicle Routing Problem with Pickup and Delivery (F-M VRPPD), typical of platforms where a few suppliers deliver to many end customers.
A multi-objective optimization model was used to minimize:
The mathematical model, with three objective functions and about 30 constraints, was solved using a metaheuristic algorithm called Multi-Objective Simulated Annealing (MOSA), implemented in Python. The output was a 3D Pareto frontier (a set of optimal solutions, each excelling in at least one goal) allowing decision-makers to tailor their strategies to different priorities.
The case study involved a platform for local Trentino products delivered in mountainous areas with significant elevation changes. Results included optimized route maps for each driver and automated delivery scheduling files, later integrated into the e-commerce platform interface. The project demonstrated how logistics performance can be improved while respecting workers and the environment, without sacrificing competitiveness.
The final PhD project bridged the previous two themes, focusing on a particularly sensitive case: optimizing transport for non-autonomous patients to hospitals or healthcare facilities. Known as the Dial-a-Ride Problem (DARP), this challenge involves time windows for each passenger and strict planning constraints.
The developed model aimed to minimize:
Due to the complexity, a dedicated metaheuristic algorithm called Adaptive Large Neighbourhood Search (ALNS) was used. After benchmarking, it was applied to real data provided by the Austrian Red Cross to optimize shifts, fleets, and routes.
Tests showed that allowing flexibility in driving shifts could cut overall costs by 10%, while diversifying the fleet based on daily needs yielded a further 3% savings.
This research journey shows that logistics engineering can (and must) engage with major issues of social sustainability, alongside economic and environmental concerns. All projects were tested in real-world contexts, proving that mathematical models can drive practical decisions and create positive impact for people.
Through design, digitalization, and optimization, it’s possible to improve service quality, reduce waste, ease the burden on workers, and increase efficiency, while helping build a fairer and more sustainable future.
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