Optimization is a discipline included in decision science and management science. The course introduces the fundamentals of the discipline and the role of the Python programming language in the context of optimization.
The course provides students the basics of Optimization, which are most relevant to the operational planning of enterprises. The course aims to develop optimization models and provide mathematical programming methods, both exact and heuristic, for decision-makers. Students are also provided with the necessary knowledge to understand the structure of an optimization algorithm and to implement it with Python. By the end of the course, students will have the skills necessary to identify the methodological approach needed to address a problem and the ability to implement in Python that method to determine solutions.
At the end of the teaching unit, the student will be able to: - Apply the main optimization methods (content) to real decision problems (condition), selecting the most appropriate technique according to the context (criterion); - Develop basic optimization algorithms in Python (content) for solving structured problems (condition), verifying the correctness of the obtained results (criterion); - Design linear and integer mathematical models (content) for optimization problems on graphs and with binary variables (condition), respecting the specific constraints of the problem (criterion); - Analyze and compare solutions obtained through exact, heuristic, and meta-heuristic methods (content) on proposed case studies (condition), evaluating the effectiveness and efficiency of the solutions (criterion).
Recommended:
Algebra, Analytic geometry, Operations Research, Programming.
The teaching unit includes frontal lessons held in the computer classroom, giving students the opportunity to formulate, solve, and analyze the proposed problems together with the teachers. If it is not possible to carry out activities in class, the teaching methods decided by the University will be adopted. For updates, please refer to Aulaweb. Attendance is not compulsory. For students with learning disabilities or special needs, personalized methods are provided according to University regulations.
Students who have valid certification of physical or learning disabilities and who wish to discuss possible accommodations or other circumstances regarding lectures, coursework and exams, should speak both with the instructor and with Professor Elena Lagomarsino elena.lagomarsino@unige.it, the Department's disability liaison.
Consistent with the objectives previously illustrated in the course, the following topics are covered
The following books, articles and link are suggested.
Ricevimento: Office hours by appointment, in person or via Teams, to be arranged by email with the professor.
First Semester
https://easyacademy.unige.it/portalestudenti/index.php?view=easycourse&_lang=it&include=attivita
The timetable for this course is available here: EasyAcademy
The exam consists of a written test comprising short-answer questions, multiple-choice questions, and brief exercises. Students who achieve a minimum grade of 18/30 will pass the exam.
Knowledge and skills will be assessed through online tests and exercises during class, evaluating the ability to apply optimization methods to proposed problems, the correctness of solutions implemented in Python, and clarity of exposition. Assessment criteria include: correctness of answers, appropriate use of terminology, analytical and synthesis skills.
Please contact the instructor for further information not included in the teaching unit description.
