Giulia Caselli

Progetto di ricerca
Mathematical models and exact optimization algorithms for sustainable operations in manufacturing and services

Today more than ever, industrial businesses require new engineering solutions to be more reactive to fast economic changes and to weaken the effects of the climate crisis.
The aim of this research is to merge sustainability principles with Operations Research methodologies and Engineering Economics tools to develop original solutions for local and global enterprises. The research addresses complex decision-making and operational management problems, including staff and room assignment in the educational as well as the medical sector, machine and workforce scheduling in the industrial production context, and facility location for CO2 emissions reduction in the waste management field. The proposed methodology combines a theoretical study of mathematical models and exact optimization algorithms with the analysis of available data and desired economic and environmental objectives of decision-makers, to firstly develop original solution methods, and secondly solve the considered problems in practice providing valuable and replicable decision-making tools.

Pubblicazioni

• Caselli, G., Delorme, M., & Iori, M. (2022). Integer Linear Programming for the Tutor Allocation Problem: A Practical Case in a British University. Expert Systems with Applications, 187, 115967.

• Caselli, G., De Santis, D., Delorme, M., & Iori, M. (2021). A Mathematical Formulation for Reducing Overcrowding in Hospitals’ Waiting Rooms. In 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 297-301). IEEE.

• Caselli G., Columbu G., Iori M., Magni C.A. (2022). Mixed Integer Linear Programming for CO2 emissions minimization in a Waste Transfer
Facility Location Problem. In Proceedings of the 10th International Network Optimization Conference (INOC), Aachen, Germany, June 7–10, 2022
(pp. 51-56).

• Caselli G., Delorme M., Iori M., Magni C. A. (2022). Mixed Integer Linear Programming for a Real-World Parallel Machine Scheduling Problem with Workforce and Precedence Constraints. In Optimization in Artificial Intelligence and Data Sciences (pp. 61-71). Springer, Cham.