This course introduces students to the theory, algorithms, and applications of optimization for management and decision-making. It provides a broad overview of the main modeling paradigms used in management science, including linear programming, network optimization, integer programming, and decision analysis. The course emphasizes the formulation and interpretation of models in business contexts, while exposing students to additional tools such as dynamic programming, nonlinear programming, game theory, and queueing models.

Knowledge and understanding: Students will acquire the fundamental concepts and methodological foundations of optimization and decision models used in management science. Applying knowledge and understanding: Students will be able to formulate real-world business problems as optimization or decision models, select appropriate solution approaches, and interpret results to support managerial decisions. Making judgements: Students will develop the ability to compare alternative modeling approaches, evaluate assumptions and trade-offs, and select the most appropriate tool depending on the decision context. Communication skills: Students will be able to clearly present models, assumptions, and results using appropriate technical terminology, and communicate insights to both technical and non-technical audiences. Learning skills: Students will be able to independently deepen their knowledge of optimization methods and adapt them to new decision-making contexts.

Introduction to optimization and modeling. The first part of the course will be dedicated to linear programming before investigating the main models.

Main text book: - Frederick S. Hillier and Gerald J. Lieberman, Introduction to Operations Research, McGraw-Hill. - Lecture slides and additional teaching materials available on Luiss Learn. Other text book : - Wayne L. Winston, Operations Research: Applications and Algorithms, Cengage Learning.

The course combines plenary lectures with guided problem solving and exercise sessions. Teaching emphasizes model formulation, intuition, interpretation of results, and applications to management problems.

For attending students, continuous assessment counts for 10/30 of the final grade and consists of a midterm exam worth 9/30 plus an attendance component worth 1/30 for attendance above 80 percent. The final exam counts for 20/30 and includes a general section on the second part of the course and a recovery section on the first part for students without a valid midterm. It will contain several exercises. For non-attending or non-compliant students, the final exam counts for 30/30. Exams are closed-book; an official formula sheet may be provided. Grades cannot be refused.

Students interested in writing a final thesis in optimization or management science are invited to contact the instructor to discuss topic feasibility, methodological requirements, and coherence with the program objectives.