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Optimization methods for decision making

Semestre 1-2 · 27511 · Corso di laurea magistrale in Data Analytics for Economics and Management · 12CFU · EN


Module 1 deals with:
• Linear optimization techniques
• Nonlinear optimization techniques
• Combinatorial optimization techniques
• Multicriteria optimization and decision making
• Decision making under uncertainty

Module 2 focuses on the application of data science techniques to optimize resources, evaluate risks, and support sustainable decision-making in business and economic contexts. Students will work with spatio-temporal data, applying models for trend-surface estimation, spatial and temporal correlation, and prediction. The course also introduces robust statistical methods and outlier detection techniques to ensure reliability under data contamination and heavy-tailed distributions. Additional topics include tail dependence, extreme value modeling, and multivariate risk assessment, with real-world applications in finance, environmental planning, and policy evaluation. Emphasis is placed on interpreting results from empirical analyses and implementing solutions using modern statistical software.

Docenti: Andreas Heinrich Hamel, Davide Ferrari, Giulia Bertagnolli

Ore didattica frontale: M1: - 24 hours of in-person lectures - 12 hours of video lectures (counted as 24 hours to account for re-watching) M2: - 24 hours of in-person lectures 12 hours of video lectures (counted as 24 hours to account for re-watching)
Ore di laboratorio: -
Obbligo di frequenza: Recommended, but not required.

Argomenti dell'insegnamento
M1: • Linear optimization techniques • Nonlinear optimization techniques • Discussion of combinatorial optimization problems • Multicriteria optimization and decision making • Decision making under uncertainty M2: Spatio-Temporal Data Analysis: Trend-surface estimation, spatial and temporal correlation, forecasting methods Robust Statistics & Outlier Detection: Data contamination and heavy tails, robust estimation and outlier analysis. Risk Modeling & Dependence Structures: Extreme value methods, multivariate risk assessment Applications: Finance and risk evaluation, environmental planning, policy and resource optimization

Modalità di insegnamento
The course adopts a blended, student-centered approach that emphasises problem-based learning and active engagement. A portion of the lecture content is made available online in advance, allowing students to explore key concepts independently and at their own pace before attending class. This preparatory work enables in-person sessions to focus on the application of knowledge through real-world problems, collaborative activities, and guided discussions — fostering critical thinking and deeper learning. The course is fully aligned with the principles of the Italian Universities Digital Hub (EDUNEXT) initiative (https://edunext.eu), which promotes the integration of digital resources and active learning strategies within university teaching.

Modalità d'esame
The overall exam mark will be determined by the assessment of the two modules (M1+M2) M1: A written exam and a project presentation including an oral presentation. M2: Written exam: combination of multiple choice and essay questions. Project work: development of an individual project related to the methodologies studied, their implementation in statistical software, and their applications to empirical data.

Criteri di valutazione
M1: The written exam of 1 hour counts 50%, the project 50% towards the final grade. Evaluation criteria are understanding of modeling features, capability of applying solution methods (only small scale for the written exam) problems and the capability to interpret/discuss the results w.r.t. economic/managerial decision making. M2: To pass the M2 module exam students must obtain a positive evaluation on both final exam (50% of the grade) and project (50% of the grade).

Bibliografia obbligatoria

M1:

Video lectures and slides provided during the course.

M2:

Lecture notes and selected readings from the following books:

Wikle, Christopher K., Andrew Zammit-Mangion, and Noel Cressie. Spatio-temporal statistics with R. Chapman and Hall/CRC, 2019.

Kolaczyk, Eric D., and Gábor Csárdi. Statistical analysis of network data with R. Vol. 65. New York: Springer, 2014.



Bibliografia facoltativa

M1:

Boyd/Vandenberghe, Convex Optimization,

Wright/Recht, Optimization for Data Analysis,

Sundaram, A First Course in Optimization Theory.




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Modules

Semestre 1 · 27511A · Corso di laurea magistrale in Data Analytics for Economics and Management · 6CFU · EN

Module A — M1 - Optimization methods for economics and business

The module deals with:
• Linear optimization techniques
• Nonlinear optimization techniques
• Combinatorial optimization techniques
• Multicriteria optimization and decision making
• Decision making under uncertainty

Docenti: Andreas Heinrich Hamel

Ore didattica frontale: - 24 hours of in-person lectures - 12 hours of video lectures (counted as 24 hours to account for re-watching)
Ore di laboratorio: -

Argomenti dell'insegnamento
• Linear optimization techniques • Nonlinear optimization techniques • Discussion of combinatorial optimization problems • Multicriteria optimization and decision making • Decision making under uncertainty

Modalità di insegnamento
The module adopts a blended, student-centered approach that emphasizes problem-based learning and active engagement. A portion of the lecture content is made available online in advance, allowing students to explore key concepts independently and at their own pace before attending class. This preparatory work enables in-person sessions to focus on the application of knowledge through real-world problems, collaborative activities, and guided discussions — fostering critical thinking and deeper learning. The course is fully aligned with the principles of the Italian Universities Digital Hub (EDUNEXT) initiative (https://edunext.eu), which promotes the integration of digital resources and active learning strategies within university teaching.

Bibliografia obbligatoria

Video lectures and slides provided during the course.



Bibliografia facoltativa

Boyd/Vandenberghe, Convex Optimization,

Wright/Recht, Optimization for Data Analysis,

Sundaram, A First Course in Optimization Theory.



Semestre 2 · 27511B · Corso di laurea magistrale in Data Analytics for Economics and Management · 6CFU · EN

Module B — M2 - Data science applications for resource optimization, risk evaluation and sustainability

This module focuses on the application of data science techniques to optimize resources, evaluate risks, and support sustainable decision-making in business and economic contexts. Students will work with spatio-temporal data, applying models for trend-surface estimation, spatial and temporal correlation, and prediction. The course also introduces robust statistical methods and outlier detection techniques to ensure reliability under data contamination and heavy-tailed distributions. Additional topics include tail dependence, extreme value modeling, and multivariate risk assessment, with real-world applications in finance, environmental planning, and policy evaluation. Emphasis is placed on interpreting results from empirical analyses and implementing solutions using modern statistical software.

Docenti: Davide Ferrari, Giulia Bertagnolli

Ore didattica frontale: - 24 hours of in-person lectures - 12 hours of video lectures (counted as 24 hours to account for re-watching)
Ore di laboratorio: -

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