74972 - Decision Support Systems

Course Unit Page

SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education Gender equality Decent work and economic growth Industry, innovation and infrastructure

Academic Year 2020/2021

Learning outcomes

Business analytics, using current terminology. At the end of the course, which adopts an application-oriented approach, the student acquires the necessary scientific and technological expertise to design, develop and deploy a decision support system in real business contexts. This involves: - working on real business data, analyzing the management problems involved, and how to solve them. - studying data by means of statistics and operational analysis, - applying optimization techniques with the objective to guide planning and decision-making, - studying heuristic and metaheuristic optimization algorithms, - designing predictive models, - communicating the results obtained (to clients, colleagues or partners) through different platforms. Specialized code will be designed and developed, with attention to the possibility of deployment on mobile and web platforms.

Course contents

Course contents:

The course offers scientific and technological contents, applied to a real-world business case study.

Selected scientific topics will provide the basis for developing business analytics modules operating on data read from a corporate information system, which will build on statistic and optimization competence applied to the modeling of business processes.

In particular, I will discuss:

-stochastic models, random variables. probability distributions

-forecasting models: statistic (ARMA, ARIMA, SARIMA) and neural approaches (feedforward, convolutional, deep learning)

-performance indicators and descriptive statistics

- integer programming models  (hints)

- metaheuristics solution techniques

- matheuristic solution techniques: very large neighborhood search, Lagrangian heuristics

Technological contributions will be necessary for the practical realization of the applied modules, based on a multi-tiered MVC architecture. In the context of the application we will use:

-server side: c#, ado.net, ORM (entity framework), AJAX, JSON

-client side: javascript, HTML5, possibly progressive web apps (PWA).

The analytic modules will be set up in the classroom and completed independently by each student, obtaining the final exam projects.

Readings/Bibliography

Lecture slides.

Teaching methods

I will teach in the lab as much as I will be allowed to, in order to ensure hands-on experience of the proposed methods.

We will also visit a company, which will propose us an actual problem, related to the course contents. We will work together on it, and we will present our results by the end of the course.
The algorithms will be coded, following the students' choice, in c# or python. The web / mobile modules will be implemented in javascript and html5.

Assessment methods

Individual project

Teaching tools

Slides and lecture notes. They will be downloadable before the corresponding lesson.

Links to further information

http://isi-personale.csr.unibo.it/vittorio.maniezzo/didattica/DSS/SistSuppDec.html

Office hours

See the website of Vittorio Maniezzo