01FTJPH

A.A. 2023/24

Course Language

Inglese

Course degree

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

Course structure

Teaching | Hours |
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Teachers

Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
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Teaching assistant

Context

SSD | CFU | Activities | Area context |
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MAT/09 | 8 | D - A scelta dello studente | A scelta dello studente |

2022/23

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.

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.

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.

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.
At the end of the course, the student is expected to be able to design a solution for real-world combinatorial decisional problems, to apply the different solution approaches in relation to specific decision problem situations and to analyze the outcomes of a decision support system and present and disseminate the results.

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.

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.

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 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.

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.

...
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.

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

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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Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY

Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY