OVERVIEW

The course illustrates different methodologies for the optimization and optimal control of complex systems, with specific reference to problems of interest for management engineering.

AIMS AND CONTENT

LEARNING OUTCOMES

To formalize an optimization problem for planning and managing complex systems; to identify the most suitable optimization methods; to implement the optimization algorithms with specific software; to discuss the results and make sensitivity analyses on the solutions

AIMS AND LEARNING OUTCOMES

At the end of the course, the student will be able:

• to formalize a multi-objective optimization problem for complex systems, with specific reference to manufacturing and logistics systems, project management;
• to formalize an optimal control problem for complex systems, with specific reference to manufacturing and logistics systems, project management;
• to identify the most suitable optimization and control methods for the considered problem;
• to design and implement the optimization and control algorithms with specific software;
• to discuss the results and make sensitivity analyses on the solutions.

Besides these learning outcomes, the student who will actively participate to lessons and team working activities can acquire the following  soft skills:

• social skills – basic level: capacity to manage social interactions, collaborative behaviour, constructive communication in different environments;
• capacity of learning to learn – basic level: awareness of the preferred learning stretegies, organization and evaluation of personal learning.

PREREQUISITES

Operations research, systems theory.

TEACHING METHODS

The lessons will consist in lectures given by the Professor and team working activities with specific software.

The team working activities will allow the students who will actively participate to acquire social skills (basic level) and the capacity of learning to learn (basic level).

SYLLABUS/CONTENT

• Introduction to optimization
• Multi-objective optimization methods
• Multi-criteria analysis
• Introduction to control
• Optimal control
• LQ problem
• Model Predictive Control

RECOMMENDED READING/BIBLIOGRAPHY

B.S. Blanchard, W.J. Fabrycky, "Systems Engineering and Analysis", Prentice Hall International Series in Industrial & Systems Engineering, 2010

R.T. Clemen, T. Reilly, "Making Hard Decisions with Decision Tools", South-Western Cengage Learning, 2013

P.P. Parlos, “Multi-criteria decision making methods: a comparative study”, Kluwer, 2000

P. Bolzern, R. Scattolini, N. Schiavoni, Fondamenti di controlli automatici, McGraw-Hill

D.P. Bertsekas, Dynamic Programming and Optimal Control, Athena Scientific, 2000

J.M. Maciejowski, Predictive Control with Constraints, Prenctice Hall, 2002

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

https://corsi.unige.it/en/corsi/8734/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The exam is oral and consists in the presentation of a project implemented with specific software and in some open-ended questions on the contents of the course.

Students with learning disorders will be allowed to use specific modalities and supports that will be determined on a case-by-case basis in agreement with the delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities.

ASSESSMENT METHODS

The exam aims to verify the following aspects about the knowledge of the student:

- knowledge regarding optimization and control methods for complex systems

- ability to design optimization and control algorithms for specific numerical examples and real systems

- ability to analyse the impact of the obtained solution on the systems and to make sensitivity analyses

The evaluation of the exam will consider not only the knowledge of the contents addressed in the course but also the reasoning and critical analysis skills of the student, as well as his/her capability to use a technical lexicon appropriate for the context.