PORTALE DELLA DIDATTICA

PORTALE DELLA DIDATTICA

PORTALE DELLA DIDATTICA

Elenco notifiche



Decision Making and AI for business change

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A.A. 2024/25

Course Language

Inglese

Degree programme(s)

Master of science-level of the Bologna process in Ingegneria Gestionale (Engineering And Management) - Torino
Master of science-level of the Bologna process in Ingegneria Gestionale - Torino

Course structure
Teaching Hours
Lezioni 60
Esercitazioni in aula 20
Lecturers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Perboli Guido Professore Ordinario MATH-06/A 60 0 0 0 3
Co-lectures
Espandi

Context
SSD CFU Activities Area context
MAT/09 8 D - A scelta dello studente A scelta dello studente
2024/25
Technology has revolutionized the way companies conduct business. But this shift is not just technological. It affects companies and markets in the way the business is developed and the decisions are taken. Moreover, the data explosion and the need for real-time decision support means that advanced automated solutions are necessary for humans to deal with data and problems that are increasingly complex and decision-making algorithms (the rules, predictions, constraints, and logic that determine how a decision is made) are changing as well to answer to this challenge. The objective of the course is to deepen the student's skills in designing decision-making processes, with a specific focus on how to design and deliver decision-making solutions based on quantitative methods. In more detail, the course will give, in the first part, the basics of how to identify with a measurable and repeatable methodology the key points of a solution (the decision-makers, the users, the key resources and technologies, the objectives, and the constraints), share them with the decision-makers and the users, and finally formalize them by a model. In particular, the course will stress the difference in solution design when different types of solutions must be designed (strategic, tactical, operational, real-time). In the second part, the course will focus more on quantitative-based methods. In detail, the basics of combinatorial optimization, exact methods, heuristic, metaheuristic methods, and Artificial Intelligence will be given. Moreover, the basics of the usage of a commercial solver will be presented.
Introduction: Decision Making and Artificial Intelligence Course In today's rapidly evolving business landscape, technology has brought about a revolution, transforming the way companies operate. This shift encompasses not only the technological aspects but also has a profound impact on the decision-making processes within organizations and their markets. As businesses face the challenges posed by data explosion and the need for real-time decision support, advanced automated solutions have become essential for managing increasingly complex problems. Consequently, decision-making algorithms, which encompass the rules, predictions, constraints, and logic guiding decision-making, are also evolving to meet these challenges. The primary objective of the "Decision Making and Artificial Intelligence" course is to enhance students' skills in designing effective decision-making processes, with a specific focus on utilizing quantitative methods to develop and implement decision-making solutions. Throughout the course, students will gain a comprehensive understanding of the principles and techniques involved in designing and delivering decision-making solutions. The course begins by introducing students to the fundamentals of identifying key elements of a solution using measurable and repeatable methodologies. This involves identifying decision-makers, users, crucial resources and technologies, objectives, and constraints. Students will learn how to effectively communicate these elements with stakeholders and subsequently formalize them through a structured model. Notably, the course emphasizes the differences in solution design when addressing various types of decisions, including strategic, tactical, operational, and real-time scenarios. In the second part of the course, the focus shifts to quantitative-based methods. Students will delve into the essentials of combinatorial optimization, exact methods, heuristics, metaheuristic methods, and Artificial Intelligence. Additionally, the course will introduce students to the practical usage of commercial solvers. By the end of the course, students will have gained a comprehensive skill set enabling them to effectively design and implement decision-making processes using quantitative methods. They will be equipped to tackle complex problems and leverage Artificial Intelligence techniques to deliver optimal solutions. Through practical exercises and real-world case studies, students will develop the necessary proficiency to navigate the evolving landscape of decision-making in the digital era.
This course, structured according to the learning-by-doing paradigm, will let the students apply the methods presented in the class to real applications and use cases related to different topics of business and technological change, including sustainability of organizations and operations, the impact of the decisions on the company strategies, decision support systems for strategic and tactical applications, policy-making, policy design and validation. The students will be asked to apply different methods in a workgroup based on a realistic setting.
- 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
Basic mathematical skills Usage of computer-based tools
Decision-making process: 30 hours. In this part of the course, the students will learn • what is a decision-making process • how to model a problem (MIP models) • how to formalize a solution • identify the main characteristics of a solution • how to link the solution to the decision-making environment (decision-makers, users, existing technologies) Quantitative methods for decision making: 30 hours. The main topics are: • Combinatorial Optimization and complexity • Exact methods • Heuristics and metaheuristics • Artificial Intelligence/Machine Learning The topics will be covered to give the students the basics of each methodology and when to use it in terms of problem structure, decision type (strategic, tactical, operational, real-time), resources (time, cost, human resources) Workgroup and laboratories: 20 hours. The students will be asked to follow the entire process of the design of a decision support system, from the definition of the problem environment to the solution definition, the problem formalization, its implementation and testing, as well as its usage for the validation/suggestion of a policy. Concerning the decision-making process, the students will be asked to analyze real projects described by technical reports/papers, considering in particular the ASP projects. Concerning the quantitative methods tools, they will be introduced to the usage of two industrial products: IBM Cplex for the MIP models and Orange 3 for the AI/ML algorithms.
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.
The exercise classes are related to the topics presented in the course. Some classes will be devoted to developing workgroups through interactive lab classes with the instructors. Workgroups are such that real-life decisional problems provided by the instructors will be approached by means of the methodologies presented in the first part of the course. The output of each workgroup will be the design and the implementation of a solution for the considered real-life problem.
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.
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.
Slides; Dispense; Esercizi; Esercizi risolti; Esercitazioni di laboratorio; Esercitazioni di laboratorio risolte;
Lecture slides; Lecture notes; Exercises; Exercise with solutions ; Lab exercises; Lab exercises with solutions;
Modalità di esame: Prova scritta (in aula); Prova orale facoltativa; Elaborato scritto prodotto in gruppo; Elaborato progettuale in gruppo;
Exam: Written test; Optional oral exam; Group essay; Group project;
... The assessment is composed of an individual written test, a workgroup covering the solution of a real problem and periodical assessments during the course. The groups are made of 3 students at most. In detail, the final grade is computed as follows: - Individual oral examination or individual test (written, 1h, usage of notes, slides and books not allowed): max 11 points, minimum to pass 4 points, usage of notes, slides and books not allowed) - Workgroup (written report to be submitted in the "Elaborati" section of the course website by a deadline given at the beginning of the course, usage of notes, slides and books allowed): max 10 points - Workgroup presentation (oral presentation, 20 minutes, usage of notes, slides and books not allowed): max 6 points - Mid-term assessment (written, 2 hours, usage of notes, slides and books allowed): max 2 points - Review of a scientific paper (written report to be submitted in the "Elaborati" section of the course website by a deadline given at the beginning of the course, usage of notes, slides and books allowed): max 2 points
Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam: Written test; Optional oral exam; Group essay; 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. Workgroups consist of a maximum of three students. The final grade is calculated based on the following components: - Individual Examination: (Oral or 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 10 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 -Mid-term Assessment: Written examination with a duration of 2 hours Maximum of 2 points Usage of notes, slides, and books is allowed -Scientific Paper Review: Written report to be submitted in the designated "Elaborati" section of the course website by a specified deadline Maximum of 2 points Usage of notes, slides, and books is allowed
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.
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