In the context of our research and innovation activities, we have established a strategic collaboration with the University of Genoa, co-funding a PhD scholarship dedicated to the study and development of new combinatorial optimization models applicable to the public sector.

This collaboration has led to a project focused on the digitalization and long-term preservation of documents within Public Administration. In particular, the work centered on the design of a new optimization problem, the Bin Number Partitioning Problem (BNPP), an innovative variant of the classical Bin Packing Problem, conceived to more effectively address the partitioning of digital documents into archival packages (AIPs). The objective of the model is to optimally balance the content of each package, minimizing space waste and improving storage and information retrieval processes.

The problem has been formalized in a scientific paper currently under submission, presenting both the mathematical formulation and a comparative analysis of different solution approaches. A key aspect of the work involved the use of mathematical solvers, both commercial (such as Gurobi or CPLEX) and open-source (such as LpSolve), providing flexibility for public institutions with limited resources. Although open-source solvers show longer computation times, the model still achieves high-quality solutions, proving robust even in less powerful operational environments.

To further enhance accessibility and real-world applicability for Public Administration and stakeholders, we developed a dedicated heuristic capable of producing high-quality solutions in very short computation times. This heuristic, specifically designed for the BNPP, overcomes the computational limitations of exact solvers and ensures smooth performance even on standard systems, enabling practical adoption of the tool.

To support real-world implementation, we also developed an online platform that provides the service via a web interface designed for direct use by public entities.

This initiative represents a virtuous example of technology transfer, where theoretical research is translated into concrete tools, contributing to the innovation and digital transformation of Public Administration.