74972 - Decision Support Systems

Academic Year 2018/2019

  • Teaching Mode: Traditional lectures
  • Campus: Cesena
  • Corso: Second cycle degree programme (LM) in Computer Science and Engineering (cod. 8614)

Learning outcomes

Business analytics, con terminologia corrente. Al termine del corso, che adotta un approccio orientato alle applicazioni, lo studente acquisisce competenze scientifiche e tecnologiche necessarie alla progettazione, sviluppo e messa in uso di un sistema di supporto alle decisioni in contesti aziendali reali. Questo comporta: - lavorare su dati aziendali reali, analizzando quali siano i problemi gestionali coinvolti e come risolverli. - lo studio dei dati per mezzo di statistica e analisi operazionale, - la applicazione di tecniche di ottimizzazione con l’obiettivo di orientare pianificazione e processo decisionale, - lo studio di algoritmi di ottimizzazione euristici e metauristici, - la formazione di modelli predittivi, - la comunicazione dei risultati ottenuti (a clienti, colleghi o partner) tramite piattaforme diverse. Verrà progettato e sviluppato assieme codice specifico, con attenzione alla possibilità di deploy su piattaforme mobile e web.

Course contents

The learning outcomes should be the following, but for some reasons I am not entitled to edit them:

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 delpoy 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:

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 and dynamic or robust optimization (hints)

- metaheuristics solution techniques: simulated annealing, tabu search, iterated local search, variable neighborhood search, grasp

- 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, 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. There will be a competition to determine which project will show the best computational efficiency.

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 c++. 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