First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
STATISTICAL SCIENCES
Course unit
STOCHASTIC OPTIMIZATION
SCP4063217, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2019/20

Information on the course unit
Degree course Second cycle degree in
STATISTICAL SCIENCES
SS1736, Degree course structure A.Y. 2014/15, A.Y. 2019/20
N0
bring this page
with you
Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination STOCHASTIC OPTIMIZATION
Website of the academic structure http://www.stat.unipd.it/studiare/ammissione-laurea-magistrale
Department of reference Department of Statistical Sciences
E-Learning website https://elearning.unipd.it/stat/course/view.php?idnumber=2019-SS1736-000ZZ-2019-SCP4063217-N0
Mandatory attendance No
Language of instruction Italian
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge LUIGI DE GIOVANNI MAT/09

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines MAT/09 Operational Research 9.0

Course unit organization
Period Second semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Laboratory 1.5 14 23.5 No turn
Lecture 7.5 50 137.5 No turn

Calendar
Start of activities 02/03/2020
End of activities 12/06/2020
Show course schedule 2019/20 Reg.2014 course timetable

Examination board
Examination board not defined

Syllabus
Prerequisites: Elementary Probability Theory.
Target skills and knowledge: The objective of the course is to provide a range of tools that may help in finding the best decisions when the information available is not deterministic and complete but given under uncertainty.
Examination methods: The grading will be based on a written test (plus a possible oral examination) and the presentation of a small project (2-3 people working team) agreed with the Instructor.
The exam may be taken in italian or English
Assessment criteria: The examination evaluates to what extent the student has learned the notions presented and her/his ability to apply them to the solution of decision-making problems under uncertainty.
Course unit contents: The course program includes the following topics:
- Queuing Theory
- Decision Analysis
- Robust Optimization
- Markovian Decision processes
- Stochastic Optimization
- Revenue Management
- Discrete Event Simulation
- Simulation of Dynamic Systems
- Use of Simulation Packages
Planned learning activities and teaching methods: Lectures in the classroom and practical experimentation in the computer laboratory. The teaching activities are supported by electronic devices like slides, optimization software, spreadsheets, simulation software.
Additional notes about suggested reading: Besides the textbook (in Italian), further material will be made available in the web page dedicated to the course.
Textbooks (and optional supplementary readings)
  • G. Ghiani e R. Musmanno, Modelli e metodi decisionali in condizioni di incertezza e rischio. Milano: McGraw Hill, 2009. ISBN 978-88-386-6636-0 Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Case study
  • Working in group
  • Questioning
  • Problem solving
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • One Note (digital ink)
  • Latex
  • AnyLogic, IBM Cplex Optimization Studio, Excel Spreadsheet and Solver

Sustainable Development Goals (SDGs)
Quality Education Decent Work and Economic Growth Industry, Innovation and Infrastructure Responsible Consumption and Production