ECO0011 - OPTIMIZATION METHODS IN BUSINESS ANALYTICS

insegnamento
Tipo Insegnamento:
Obbligatoria
Durata (ore):
40
CFU:
6
SSD:
RICERCA OPERATIVA
Sede:
BRESCIA
Url:
Analytics e Data Science for Economics and Management/PERCORSO COMUNE Anno: 2
Anno:
2024
Course Catalogue:
https://permalink.unibs.it/suacds/afcc/2024?corso=...

Dati Generali

Periodo di attività

Secondo Quadrimestre (27/02/2025 - 09/06/2025)

Syllabus

Obiettivi Formativi

Knowledge and understanding:
At the end of the course the student will have acquired: (1) a vision of prescriptive analytics and its objectives through relevant economics and management case studies; (2) awareness of the importance of good practices in mathematical modeling and optimization algorithm design ; (3) knowledge of optimization libraries and heuristic algorithms in Python; (4) awareness of the importance of computational tests and sensitivity analysis in assessing the results of optimization models.

Applied knowledge and understanding:
Through the course, the student will acquire: (1) the ability to use mathematical modeling and to design ad-hoc optimization algorithms to formulate complex decision problems; (2) the ability to use high-level programming languages and spreadsheets to implement optimization algorithms in Python; (3) the ability to test the results of optimization models and algorithms over a complete set of scenarios.

Autonomy of judgment:
The student will be able to evaluate the areas in which the acquired tools are applicable, to use advanced quantitative methods and to evaluate critically the results, suggesting practical policies and action strategies.

Communication skills:
At the end of the training activity, the student will be able to: (1) understand the description of a complex optimization problem; (2) design effective algorithms to solve it; (3) produce a complete report on case studies providing problem description, modeling approach, solution methods, results and practical insights.

Ability to learn:
At the end of the course, the student will be able to: (1) identify and frame a business/managerial problem as an optimization problem, at either strategic, tactical or operational level; (2) identify the quantitative and software tools suitable for dealing with it; (3) autonomously search for innovative approaches and solutions.

Prerequisiti

A priori knowledge of the following topics is reccomended:

- linear programming
- basic notions of computation
- knowledge of data types and structures in Python
- how to define functions in Python
- control structures for the design of algorithms in Python
- import, analysis, and manipulation of datasets to extract and visualize useful information in Python
- basic use of NumPy, Pandas and Jupyter Notebook

Metodi didattici

The educational objectives highlighted here are achieved through lectures, seminars, lab sessions, case studies analysis involving projects and exercises as well as the use of ad-hoc software.

Verifica Apprendimento

The final grade is based on a written exam (50%) and on a project to be developed and discussed in an oral exam (50%).
The final graderanges from 0 to 30 points for both parts and the final grade is the weighted average between the two parts, rounded up. An excellent preparation in both parts could lead to a final grade of 30 points ‘cum laude’.

Testi

These textbooks are meant to be used only for reference. Slides and supplementary materials (packages documentations, tutorials, etc.) will be provided through the course e-learning page.
1) Hillier, Frederick S., and Gerald J. Lieberman. Introduction to Operations Research, 9th (International ed.). McGraw-Hill, Boston (2005).)
2) Basant A. Hands-On Data Structures and Algorithms with Python: Store, manipulate, and access data effectively and boost the performance of your applications, 3rd Edition. Packt, Birghingham-Mumbai (2022)
3) McKinney, W. Python for Data Analysis, 3rd Edition. O'Really (2022).

Contenuti

The aim of this course is to provide theoretical and practical tools to model and solve complex optimization problems arising in business and management such as production planning, manpower planning, facility location, vehicle routing. It provides an overview of the principles of model building, with a focus on linear, integer, and mixed-integer optimization models. A solid knowledge of optimization packages and ad-hoc heuristic algorithms to solve large-scale problems for Python will be provided through lab sessions and case studies. The analysis of the results is focused on good formulations, well-structured algorithms, sensitivity analysis, and managerial insights.

Lingua Insegnamento

English

Corsi

Corsi

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