Quantitative Methods

Objectives

The course seeks to introduce several key analytical methods and tools useful in the analysis of productive systems (in manufacturing and services). At the end of the course, students should be able to:


- Correctly analyze  queueing systems (with and without limitations of capacity and population),

-Apply graph theory and network analysis

- Properly formulate and solve production problems through dynamic programming

General characterization

Code

10580

Credits

6.0

Responsible teacher

Alexandra Maria Batista Ramos Tenera, António Carlos Bárbara Grilo

Hours

Weekly - 5

Total - 70

Teaching language

Português

Prerequisites

Is advised that students have some expertise in Statistics and Operations Research

Bibliography

- Hillier, F. & Lieberman, G. (2010). Introduction to Operations Research (9th ed.). USA, Mcgraw-Hill. 
Taha, H. (2010). Operations Research: An Introduction (9th ed.) Englewood Cliffs, Prentice Hall.

- Evans, J. & Minieka, E. (1992). Optimization Algorithms for Networks and Graphs (2nd ed.). USA, Marcel Dekker, Inc.

- Lapin, L.(1994). Quantitative Methods for Business Decisions with Cases (6nd ed.). USA, Dryden Press.

- Chang, Y-L (2003) WinQSB: Decision Support Software for MS/OM Version 2.0. USA, John Wiley & Sons.

- Bronson, R & Naadimuthu, G. (2001). Investigação Operacional (2ª ed.). Trad. Ruy Costa. Alfragide, Mcgraw-Hill de Portugal, Lda.

Teaching method

Lectures are carried out combining theoretical classes and applied classe

Evaluation method

The course grading will be based on the following:

- Individual Evaluation: Exam (EX)

- Group Evaluation: Course  workgroup Assignments (GAs)  required to obtain course frequency

In order to obtain an UC approval, a minimum grade of 9.5 is required.

FINAL Grade (CF) = 0,3GAs  + 0,7Ex

with  Final Exam (EX) ≥ 9 v

if CF>= 18 /20 => Grade check required

Subject matter

1.Queueing Theory: Basic Structures; Terminology and Notation; Main Performance Measures; Little’s Equations; Deterministic and Probabilistic Models with Exponential distributions and FIFO discipline; Multiple-server;  Finite queue and finite calling population variation; Data Analysis and Goodness Fit Tests

2.Graphs and Network Analysis: Minimum Spanning Tree; Shortest-Path; Maximum Flow; Transportation;   Assignment and Transshipment Problems

3.Dynamic Programming: Graph Formulation; Main Characteristics; Contributions Types: additive, multiplicative, additive-multiplicative, max-min e min-max; Applications

Programs

Programs where the course is taught: