- Apply the methods and techniques learned in the course to real-world applications and use cases related to various business and technological domains. - Analyze the sustainability of organizations and operations, and evaluate the impact of decisions on company strategies. - Design and develop decision support systems for strategic and tactical applications, considering factors such as policy-making, policy design, and validation. - Use the learning-by-doing paradigm to actively participate in workgroups and apply different methods within realistic settings. - Demonstrate the ability to design effective solutions for real-world combinatorial decision problems, considering the specific constraints and objectives involved. - Apply different solution approaches and algorithms to address specific decision problem situations, considering factors such as complexity, time constraints, and available resources. - Evaluate and analyze the outcomes of a decision support system, considering the effectiveness and efficiency of the implemented solutions. - Present and disseminate the results of decision-making processes and communicate them effectively to stakeholders and decision-makers. - Demonstrate a deep understanding of the principles and techniques of combinatorial optimization, exact methods, heuristics, metaheuristic methods, and Artificial Intelligence. - Collaborate effectively in interdisciplinary workgroups, demonstrating teamwork and communication skills. - Critically evaluate the strengths and limitations of different decision-making approaches and select the most suitable methods based on the problem at hand. - Apply problem-solving and analytical thinking skills to complex decision-making scenarios, considering multiple variables and constraints. By the end of the course, students will have developed a strong foundation in decision-making methodologies and artificial intelligence techniques, enabling them to tackle real-world problems, analyze outcomes, and effectively present their findings to stakeholders.

Basic mathematical skills Usage of computer-based tools

Course Topics: 1. Decision-Making Process: (30 hours) - Understanding the concept of a decision-making process - Modeling problems using Mixed Integer Programming (MIP) models - Formalizing solutions and identifying their key characteristics - Linking solutions to the decision-making environment, including decision-makers, users, and existing technologies 2. Quantitative Methods for Decision Making: (30 hours) - Introduction to Combinatorial Optimization and complexity - Overview of exact methods for optimization problem solving - Exploring heuristics and metaheuristics as problem-solving approaches - Introduction to Artificial Intelligence (AI) and Machine Learning (ML) techniques - Understanding when and how to apply each methodology based on problem structure, decision type (strategic, tactical, operational, real-time), and available resources (time, cost, human resources) 3. Workgroup and Laboratories: (20 hours) - Hands-on experience in designing a decision support system - Following the entire process of problem environment definition, solution definition, problem formalization, implementation, and testing - Usage of the decision support system for policy validation and suggestion - Analyzing real projects described in technical reports/papers - Introduction to industry tools such as IBM Cplex for MIP models and Orange 3 for AI/ML algorithms Note: The distribution of hours may slightly vary depending on the specific course structure and requirements and the students' background.

For more details, see the video https://youtu.be/4l17hTl_R-k Participation in the classes is strongly recommended. However, there are some classes where participation is compulsory. These classes are normally defined at the beginning of the course. These classes include: - Workgroup presentations: the presence of all the students of the group is mandatory; - Experts' workgroup and classes. During the course, students will be asked to participate in some workshops conducted by experts and professionals. The presence at these workshops is also mandatory.

The course employs a "learning-by-doing" approach, encouraging active participation in workgroups and the application of various methods in realistic scenarios. The exercise classes directly relate to the course topics, offering interactive lab sessions facilitated by instructors. These sessions focus on fostering collaborative workgroups, where real-life decisional problems provided by the instructors are addressed using the methodologies covered in the initial part of the course. Each workgroup is tasked with designing and implementing a solution for a specific real-life problem, thereby yielding practical outcomes.

Slides will be provided directly by the instructors. Reference textbooks that contain part of the topics presented in the course are among others - F. Glover , G. Kochenberger , 2003, Handbook of metaheurictics , Kluwer academic publishers , pp 227 263 - V.Th Paschos (Editor) Concepts of Combinatorial Optimization, 2nd Edition, Wiley-ISTE, 2014.

Lecture slides; Lab exercises; Multimedia materials; Self-assessment tools;

Exam: Written test; Group project;

The assessment in this course comprises an individual written test, a workgroup assignment addressing a real-world problem, and periodic assessments throughout the duration of the course. The final grade is calculated based on the following components (minimum grade to pass 18, maximum 30 with laude, corresponding to 31 points): - Individual Examination: (Written, 1 hour) Maximum of 11 points Minimum passing grade: 4 points Usage of notes, slides, and books is not allowed -Workgroup Assignment: Written report to be submitted in the designated "Elaborati" section of the course website by a specified deadline Maximum of 14 points Usage of notes, slides, and books is allowed -Workgroup Presentation: Oral presentation lasting 20 minutes. Maximum of 6 points Usage of notes, slides, and books is not allowed The date of the presentation is scheduled at the beginning of the course, and the presence of all the students of the group is mandatory.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.