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Management Engineering

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Systems modelling and optimization

Academic Year 2025/2026 - Teacher: CARLO FAMOSO

Expected Learning Outcomes

  • Knowledge and understanding

Knowledge on modeling of linear systems and optimal and robust control techniques. Nonlinear modeling based on neural algorithms. Linear and nonlinear programming.

  • Applied knowledge and understanding

Software tools to solve problems of modeling and optimization.

  • Making judgements

The student will be able to autonomously determine the modeling technique more suitable on the basis of the features of the process under consideration. The student will be able to model problems of resource management in terms of liner and nonlinear programming.

  • Communication skills

The student will develop the capability of interfacing with process engineers and with non-engineer personnel to model and solve resource management problems.

  • Learning skills

The student will be able to discriminate among different programming and optimization problems. The student will be able to select the proper methods for their resolution. 

Course Structure

Frontal lectures and Matlab based laboratory.

Required Prerequisites

Basic knowledge on linear systems, and linear algebra.

Attendance of Lessons

The student must attend at least the 70 % of course lectures, as stated in point 3.4 of the Regolamento Didattico of the CdLM in Ingegneria Gestionale, the enrollment through studium.unict.it is mandatory.

Detailed Course Content

The course aim at providing basic knowledge on modeling and control of dynamical systems. In particular, optimal and robust control techniques will be discussed. Moreover, neural network based modeling strategies will be presented. Furthermore, linear and nonlinear programming problems will be considered, providing knowledge about the most used algorithms to solve them.


Textbook Information

  • 1.  L. Fortuna, M. Frasca, A. Buscarino, Optimal and Robust Control - Advanced topics with MATLAB, CRC Press, 2021.
  • 2. F. S. Hillier, G.J. Liebermann, Introduction to Operations Research, Ed. McGraw Hill, 11th edition, 2021.
  • 3. S. Haykin, Neural Networks and Learning Machines, Pearson, 2016.


AuthorTitlePublisherYearISBN
L. Fortuna, M. Frasca, A. BuscarinoOptimal and Robust Control - Advanced topics with MATLABCRC Press20219781032053004
F. S. Hillier, G.J. LiebermannIntroduction to Operations ResearchEd. McGraw Hill, 11th edition20219781259872990
S. HaykinNeural Networks and Learning MachinesPearson20169789332570313

Course Planning

 SubjectsText References
1Introduzione: Richiami di teoria dei sistemi (Prof. Buscarino)Testo 1: Cap.1-2
2Concetti fondamentali e terminologia (Prof. Buscarino)Testo 1: Cap 1-2
3Decomposizione ai valori singolari (Prof. Buscarino)Testo 1: Cap 4
4Analisi alle componenti principali e Realizzazione bilanciata a catena aperta (Prof. Buscarino)Testo 1: Cap 5
5Controllo Ottimo (Prof. Buscarino)Testo 1: Cap 8
6Metodi di risoluzione di problemi di programmazione lineare (Prof. Famoso)Testo 2: Cap 4-5
7Metodi di risoluzione di problemi di programmazione lineare (Prof. Famoso)Testo 2: Cap 4-5
8Metodi di risoluzione di problemi di programmazione binaria e non lineare (Prof. Famoso)Testo 2: Cap. 12-13
9Modellistica mediante reti neurali (Prof. Buscarino)Testo 3

Learning Assessment

Learning Assessment Procedures

The exam consists in a single examination during which a modeling procedure performed in MATLAB, a theoretical aspect and a practical exercise will be discussed.


Examples of frequently asked questions and / or exercises

Proposing a model from time-series based on neural networks: open/closed loop balancing; optimal control; fundamental theoreme of linear programming; desing of the optimal control law ensuring given Hankel singular values and/or characteristic values; setting an optimization problem and solving it using the simplex method.