Information updated until 30/06/2026 CODE 111883 ACADEMIC YEAR 2026/2027 CREDITS 6 cfu anno 2 INGEGNERIA INFORMATICA 11880 (L-8 R) - GENOVA 6 cfu anno 3 INFORMATICA 8759 (L-31) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR MAT/09 LANGUAGE Italian TEACHING LOCATION GENOVA SEMESTER 2° Semester TEACHING MATERIALS AULAWEB OVERVIEW The Operations Research (OR) classes present a set of mathematical models and methods from Operations Research to solve decision-making and optimization problems. The purpose of this course is to provide the students with skills to model decision-making problems using optimization methods and to apply appropriate algorithms for their solution. In particular, the course primarily focuses on optimization problems addressed by mathematical programming techniques. AIMS AND CONTENT LEARNING OUTCOMES Acquisire familiarità con gli elementi di base della ricerca operativa, con particolare riferimento alla programmazione lineare e alla programmazione lineare intera, apprendendo i principali algoritmi e le loro proprietà. AIMS AND LEARNING OUTCOMES The main objective is to provide students with the skills to define mathematical programming models to solve a variety of decision-making problems by formulating them as optimization problems. Upon successful completion of this course, students will be able to: solve linear programming problems using the simplex method; solve nonlinear programming problems using appropriate algorithms. PREREQUISITES Basic knowledge of linear algebra and calculus. TEACHING METHODS Lectures in class using blackboard or slides. Lab sessions with Python notebooks about some of the proposed algorithms to better understand them. SYLLABUS/CONTENT Introduction to problems and decision-making models. The process of formulating problems using quantitative models. Linear mathematical programming. Graphical formulation and solution of linear programs. The simplex algorithm. Sensitivity analysis and its economic interpretation. Non-linear mathematical programming. Convex programming. First order methods. RECOMMENDED READING/BIBLIOGRAPHY Frederick S Hillier, Gerald J Lieberman, Introduction to Operations Research, 9/e, McGraw-Hill Higher Education, 2010, ISBN: 0073376299 Stephen Boyd, Leuven Vandenberghe, Convex Optimization, Cambridge University Press, 2004 Amir Beck, Introduction to Nonlinear Optimization - Theory, Algorithms and Applications with Python and Matlab MOS-SIAM Series on Optimization. SIAM. 2023 Teachers' notes and material. TEACHERS AND EXAM BOARD SILVIA VILLA Ricevimento: By appointment wich can be fixed in person or via email : silvia.villa@unige.it CESARE MOLINARI LESSONS LESSONS START According to the calendar approved by the Degree Program Board: https://corsi.unige.it/en/corsi/8759/studenti-orario Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION Written exam and oral exam (optional after passing the written part). Guidelines for students with certified Specific Learning Disorders, disabilities, or other special educational needs are available at https://corsi.unige.it/en/corsi/8759/studenti-disabilita-dsa ASSESSMENT METHODS Students will be asked about theoretical concepts related to topic covered in the course. They will be asked to operations research problems using the algorithms introduced in the course and applying theoretical concepts. FURTHER INFORMATION For further information, please refer to the course’s AulaWeb module or contact the instructor.
