
Course unit
OPERATIONS RESEARCH
SCP4065562, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2017/18
ECTS: details
Type 
ScientificDisciplinary Sector 
Credits allocated 
Educational activities in elective or integrative disciplines 
MAT/09 
Operational Research 
7.0 
Course unit organization
Period 
First semester 
Year 
3rd Year 
Teaching method 
frontal 
Type of hours 
Credits 
Teaching hours 
Hours of Individual study 
Shifts 
Practice 
1.5 
12 
25.5 
No turn 
Laboratory 
1.0 
12 
13.0 
No turn 
Lecture 
4.5 
36 
76.5 
No turn 
Examination board
Board 
From 
To 
Members of the board 
4 a.a 2019/2020 
01/10/2019 
28/02/2021 
DE GIOVANNI
LUIGI
(Presidente)
RINALDI
FRANCESCO
(Membro Effettivo)
CONFORTI
MICHELANGELO
(Supplente)
DE FRANCESCO
CARLA
(Supplente)
DI SUMMA
MARCO
(Supplente)

3 a.a 2018/2019 
01/10/2018 
28/02/2020 
DE GIOVANNI
LUIGI
(Presidente)
RINALDI
FRANCESCO
(Membro Effettivo)
CONFORTI
MICHELANGELO
(Supplente)
DE FRANCESCO
CARLA
(Supplente)
DI SUMMA
MARCO
(Supplente)

Prerequisites:

Basic elements of Calculus.
Formal prerequisite courses: "Algebra and Discrete Mathematics". 
Target skills and knowledge:

Use of mathematical models as decision support tools, and related solution algorithms. Focus on continuous and discrete linear
programming, and on graph optimization. Use of software packages for the solution of optimization problems. 
Examination methods:

The exam is written and it includes a problem to be formulated with a linear programming model, exercises and theory questions. If needed, an oral discussion can be required. The candidate may also prepare an optional miniproject. 
Assessment criteria:

The examination evaluates to what extent the student has learned the notions presented (e.g., linear programming modeling, application of the simplex algorithm, application of network optimization algorithms, application of the duality theory, application of the BranchandBound algorithm, tests on all presented notions etc.) 
Course unit contents:

1. Optimization problems and models: creating models and using software solvers.
2. Linear programming: simplex theory and method, duality theory and applications.
3. Graph optimization: models and algorithms for optimal spanning trees, shortest paths (Dijkstra and BellmanFord algorithms), max flow (FordFulkerson algorithm), min cost flow.
4. Introduction to Integer Linear Programming and Combinatorial Optimization: exact methods (BranchandBound), introduction to heuristics and metaheuristics (local search based). 
Planned learning activities and teaching methods:

Classes and labs. During labs, some basic linear programminf models will be implemented, using an algebraic modeling language. 
Additional notes about suggested reading:

The teacher will provide lecture notes and/or slides containing all the notions requested.
The interested student can find further details on the following references:
 D. Bertsimas, J. Tsitsiklis, Introduction to linear optimization, 1996, Athena Scientific.
 R. K.Ahuja, T. L. Magnanti, J. B. Orlin "Network flows. Theory, algorithms, and applications", 1993, Prentice Hall.
 L. A. Wolsey: "Integer programming", 1998, Wiley. 
Textbooks (and optional supplementary readings) 

Innovative teaching methods: Teaching and learning strategies
 Lecturing
 Laboratory
 Problem based learning
 Case study
 Interactive lecturing
 Questioning
 Problem solving
 Use of online videos
Innovative teaching methods: Software or applications used
 Moodle (files, quizzes, workshops, ...)
 One Note (digital ink)
 Kaltura (desktop video shooting, file loading on MyMedia Unipd)
 Camtasia (video editing)
 Latex
 AMPL, Excel Spreadsheet and Solver
Sustainable Development Goals (SDGs)

