Technology
Subject: DECISION SUPPORT SYSTEMS (A.A. 2023/2024)
degree course in TECHNOLOGIES FOR THE SMART INDUSTRY
| Course year | 2 |
|---|---|
| CFU | 6 |
| Teaching units |
Unit SISTEMI DI SUPPORTO ALLE DECISIONI
Basic ICT, mathematics, and statistics training (lesson)
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Unit Laboratorio di Sistemi di Supporto alle Decisioni
Other Skills Required for Access to the Job Market (laboratory)
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| Moodle portal | |
| Exam type | oral |
| Evaluation | final vote |
| Teaching language | Italiano |
Teachers
Overview
Aim of the course is to introduce some fundamental mathematical models for decision support and complex systems management.
The course deals with linear and mixed/integer programming, including combinatorial optimization, with focus on the use of an algebraic modeling language and of its programming environment.
For a more detailed analysis the reader is referred to the section about expected learning outcomes.
Admission requirements
Basic linear algebra and programming languages.
Course contents
Linear (LP) and mixed/integer (ILP/MILP) programming, combinatorial optimization (CO): definition, properties, solution methods. (2 ECTS)
Introduction to the algebraic coding language Xpress and its development environment. (1 ECTS)
Classics in optimization problems: formulation, coding, solution analysis. Exact and heuristic methods. (3 ECTS)
W.r.t. the above credit partition, there may be some small fluctuations in the number of hours per credit.
Teaching methods
The course is given in Italian, and includes theoretical lectures and lab activities. Attendance is not mandatory, but participation is strongly recommended considering the characteristics and methodologies of the lessons.
Assessment methods
Team projects and exercises (that include writing a short report) on the main topics addressed in the course. The final grade is obtained as follows: 30-50%: exercises on lab activities performed throughout the course 50-70%: final project. Each assignment comes with all the necessary support material. Available time varies according to the complexity of the project. The grading of each completed test will be discussed on request.
Learning outcomes
1) Knowledge and Understanding
- understand the theoretical background of LP, ILP/MILP and CO;
- understand the meaning of a mathematical programming model;
- identify the most suitable solution techniques for the problem under examination.
2) Applying knowledge and understanding
- code mathematical programming problems by means of an algebraic coding language;
- manage the whole solution process (from input data to solution output) within an integrated development environment;
- apply and integrate different solution methods;
- summarize and explain the results.
3) Making judgements
- assess correctness and quality of one's own work based on the coherence of the results.
4) Communication
- explain and support one's personal (tehoretical and practical) contribution within a working team;
- summarize performed activities and obtained results in a short report.
5) Lifelong learning skills
- classify new and more complex mathematical models and solution techniques within the theoretical and methodological frame learned during the course.
Readings
Tutto il materiale necessario è fornito direttamente dal docente sui canali istituzionali, o reperibile in rete. Resoconti delle attività di laboratorio sono resi disponibili di norma entro la settimana successiva allo svolgimento.