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 several productive network analysis techniques properly
- Properly formulate and solve production problems through dynamic programming
Alexandra Maria Batista Ramos Tenera, António Carlos Bárbara Grilo
Weekly - 5
Total - 70
Is advised that students have some expertise in Statistics and Operations Research
- 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.
Lectures are carried out combining theoretical classes and applied classe
The course grading will be based on the following:
- Individual Evaluation: Exam (EX) or Tests (Test in the middle (T1) and at the end of the semester (T2): if the average of the test Ts >= 9,5 then the student can skip the exam.
-Group Evaluation: Course Assignments (GAs) -Course frequency + Report Assignments (GRs)
In order to obtain an UC approval, a minimum grade of 9.5 is required.
FINAL Grade (CF) = 0,1GAs + 0,2GRs + 0,7(Ts ou Ex)
with Final Exam (EX) ≥ 9,5 if applied
if CF>= 17 /20 => Grade check required
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 where the course is taught: