Complements of Operations Research
Instance: 2006/2007 - 2S
Cycles of Study/Courses
Teaching language
Portuguese
Objectives
Specific objectives:
Kowledge of operational research techniques. Applicability of the Operational Research techniques to real-world problems.
At the end of the semester the students are supposed to be able to:
1. Know different Operational Research techniques
2. Construct models to represent real-word problems
3. Solve those problems using the thecniques presentes in this course.
Program
MARKOV CHAINS: Characterisation of stochastic processes and Markov chains. Classification of states in a Markov Chain. Transition matrix of a Markov Chain. Analysis of ergodic chains and absolving chains. Generalisations.
QUEUING THEORY: Characterisation and classification of queuing processes. The M/M/1: (GD,+00 ) queuing system. Queuing systems with more than one server. Finite source models and models “blocked customers cleared” models. Priority queuing models. Generalisations.
SIMULATION: Objectives and limitations. Event and process-based approaches to discrete simulation. Discrete simulation software. Design, test and validation of a simulation model. Analysis of simulation output. Application of the simulation method to case-studies.
SEPARABLE PROGRAMMING: Separability of the objective function and constraints. Linearization of the original problem. The use of the Simplex algorithm to obtain the solution to the approximating approximating problem. Conditions of optimality.
INTEGER PROGRAMMING: Formulating the problem. Solving IP problems: The Branch-and-Bound method, the Implicit Enumeration method, The Cutting Plane algorithm. Obtaining the optimal solution of IP models with Excel.
NONLINEAR PROGRAMMING:
Analytical solutions to nonlinear optimisation problems. Unconstrained maximisation and minimisation problems with one variable and with several variables. Methods for solving NonLinear Problems (NLPs ) with constraints: Lagrange Multiplers and the Kuhn-Tucker Conditions.
Numerical solutions to unconstrained NLPs with one variable and with several variables.
Teaching methods and learning activities
The course combines lectures covering the OR techniques, and tutorials to apply these techniques to problems and discuss simuation case studies.
Software
Microsoft Excel
Evaluation Type
Distributed evaluation without final exam
Assessment Components
Description |
Type |
Time (hours) |
Weight (%) |
End date |
Subject Classes |
Participação presencial |
|
|
|
|
Teste |
25,00 |
|
|
|
Trabalho escrito |
25,00 |
|
|
|
Total: |
- |
0,00 |
|
Amount of time allocated to each course unit
Description |
Type |
Time (hours) |
End date |
|
Estudo autónomo |
56 |
|
|
Total: |
56,00 |
|
Eligibility for exams
Not excedding the number of absences allowed;
Do 2 tests;
Make a group project using Simulation technique.
Calculation formula of final grade
The final classification results of the weighted average of the classification obtained in the two tests made during the semester (weight of 40% each one in the final classification) and the classification of the Simulation project. The weight given to the simulation project is 20%.
Examinations or Special Assignments
Simulation Project (Group work)
Special assessment (TE, DA, ...)
Students must attend to both tests of the distributed evaluation, and make the Simulation project.
Classification improvement
Students can repeat one (and only one) of the tests.