Giulia Dotti

Progetto di ricerca
From Industrial Applications to Exact Methods: Decision Support Systems for Manufacturing and Logistics

This PhD thesis investigates quantitative approaches for improving decision-making in manufacturing and logistics systems. The research combines applied studies carries out in real industrial environments with methodological advances to combinatorial optimization. The overall objective is to design and implement Decision Support Systems (DSS) that apply optimization and simulation tools to transform data into actionable insights, thereby improving operational efficiency and resource utilization in industrial practice. 

The first part of the thesis focuses on applications in the ceramic industry, a particularly interesting sector characterized by its high process and product variability. Two complementary studies are presented. The first employs Discrete Event Simulation to analyze and improve material flow management, showing how simulation can evaluate alternative storage and classification strategies to increase production efficiency. The second develops an optimization-based DSS for intra-logistics and order management. The system uses mathematical programming and digital integration to automate complex decisions such as order aggregation, transport allocation and stock utilization. Together, these studies illustrate how data-driven decision systems can enhance flexibility, reduce costs, and support the digital transformation of traditional manufacturing.

The second part of the thesis proposes new exact optimization methods and decision tools for broader industrial applications. Two new exact methods based on branch-and-bound algorithms are proposed. The first addresses parallel machine scheduling with renewable resource constraints, capturing relevant applications in energy-aware production planning where energy usage must not exceed a fixed limit at any time. The second extends this idea to the two-dimensional orthogonal packing problem, introducing a combinatorial exact method that does not rely on linear programming solvers. Taken together, these studies address problems with strong practical relevance and high computational complexity, showing the potential of exact methods to support complex decision-making in industrial contexts. 

Overall, the thesis highlights how simulation, optimization, and digital decision-support tools can effectively guide internal decision-making, providing methodological advances and tangible benefits for modern manufacturing systems and helping bridge the gap between theoretical models and industrial practice.

Pubblicazioni

• M. Taccini, M. Iori, G. Dotti, and A. Subramanian, “Improving buffer storage performance in ceramic tile industry via simulation,” in 2023 Winter Simulation Conference (WSC), pp. 1676–1687, IEEE, 2023 (https://ieeexplore.ieee.org/abstract/document/10407555?casa_token=KyfVl0UeWlMAAAAA:95G4L9rMP_n4EyytQJ-GMCj8NLw92Ar6m9egD9u2o40pskeQTBVe4G4mBPYORlj__bBb2Mw7)

• G. Dotti, M. Iori, A. Subramanian, M. Taccini, et al., “An integrated decision support system for intra-logistics management with peripheral storage and centralized distribution,” in Proceedings of the 26th International Conference on Enterprise Information Systems, vol. 1, pp. 612–619, SciTePress, 2024 (https://www.scitepress.org/Papers/2024/125816/125816.pdf)