|| MAM8120 |
|| STOCHASTIC OPERATIONAL RESEARCH |
|| 2007/2008 |
|| Mr Alan Jones |
|| Semester 1 |
|| Dr John A Lane |
|| MA11310 |
| Course delivery
|| Lecture || 18 X 1 HOUR |
|| Seminars / Tutorials || 6 X 1 HOUR |
|| Practical || 8 X 2 HOUR |
|| Workload Breakdown || (EVERY 10 CREDITS CARRIES A NOTIONAL STUDENT WORKLOAD OF 100 HOURS)
Lectures and tutorials 24 hours
Worksheets (6 x 5 hours) 30 hours
Practical classes 16 hours
Practical report writing 16 hours
Private study 112 hours
Examination 2 hours
LECTURES AND TUTORIALS |
|Assessment Type||Assessment Length/Details||Proportion|
|Semester Assessment||2 Hours CONVENTIONAL 2 HOUR EXAMINATION ||75%|
|Semester Assessment|| PRACTICAL REPORTS ||25%|
|Supplementary Assessment||2 Hours CONVENTIONAL 2 HOUR EXAMINATION ||100%|
Learning outcomesOn successful completion of this module students should be able to:
1. formulate and solve simple problems in random arrival modelling;
2. set up and solve the equations governing a simple queue;
3. formulate a given real situation as a queueing model;
4. appreciate the failings of simple models and what modified models are trying to cover;
5. design algorithms to simulate from a given distribution using different methods;
6. design a queue simulation and interpret the results of running it.
7. evaluate and use the reliability function, hazard function, mean time to failure and reliable lifetime of lifetime distributions in common use;
8. evaluate the reliability of systems of independent components;
9. calculate and interpret simple bounds on reliability;
10. explain and use the notation used in fault trees and be aware of their uses;
11. in life testing, appropriately use censoring and acceleration; and estimate exponential and Weibull parameters in such life tests.
IMAPS has for some years been able to offer a very limited range of modules for final year students. With the imminent arrival of more staff, it is now proposed to rectify this by introducing Level 3 modules that can be extended to cater for Level M students also. This module introduces the important area of stochastic Operational Research, covering Queueing and Reliability Theory, and also the technique of Simulation; this latter topic greatly extends the area of application and is a technique that has much wider application than the two particular areas covered. It is intended to offer this module in alternate years.
Queueing Theory is a classic application of probability that falls within the umbrella of stochastic operational research, motivated by practical problems. The module covers the basic models of Queueing Theory and the formulation of real scenarios in their terms.
The module also introduces students to the properties of lifetime distributions, to study the reliability of systems of components and gain an appreciation of how high levels of reliability and safety may be achieved in practice. The fitting of models to data is also covered.
The statistical technique of Simulation has extensive applications both in Queueing and Reliability. It also greatly extends the range of problems that can be studied and solved; the module introduces the simulation of queues as an example of its scope.
Modelling of Markovian arrival processes. Queues and their characteristics. Queue model notation. The Simple Queue
Simple Markovian (M/M/1) Queue; basic theory; steady state solutions. Waiting times and their distribution. The output process.
Generalised Markovian systems
Queues where arrival and service rates are dependent on system size. Examples will include limited waiting rooms, multiple server queues, self-service queues, telephone exchange design, machine minding, server fatigue, etc.
The method of stages; Erlangian distributions. Pollaczek Khintchine theory. Priority queues.
Introduction to Simulation
Random numbers. Inverse transform theorem. Composition and Acceptance-rejection techniques. Simulating a queue.
Statistical Failure Models:
Reliability and hazard functions, mean time to failure, reliable lifetime; distributions (Exponential, Weibull, Gamma, Gumbel, Log Normal) competing risks; simple bounds on reliability.
Fitting Models to Reliability Data
Life tests: type 1 and 2 censoring, progressive censoring; accelerated life tests. Kaplan-Meier estimator. Maximum likelihood estimation for exponential and Weibull with censoring; reliability function, reliable lifetime. Arrhenius and power law models.
Series, parallel, k out of n systems. Path and cut sets, monotonic systems, modules. Bounds on system reliability. Fault trees.
|| All situations considered are problem-based to a greater or lesser degree. |
|| Students will be encouraged to consult various books and journals for examples of application. |
|| Students will be required to communicate their findings and results in writing (in practical classes) and to submit worksheets (for tutorials). |
|Improving own Learning and Performance
|| N/A |
|| Students will be encouraged to work together in practical classes |
|| Use of spreadsheets and Statistical Packages. |
|Application of Number
|| Throughout the module |
|Personal Development and Career planning
|| Students will be exposed to an area of application that they have not previously encountered. |
|Subject Specific Skills
|| Modelling of practical situations in stochastic terms |
** General Text
(1991.) Statistical analysis of reliability data /M.J.Crowder ... [et al.].
Chapman & Hall 0412305607
Andrews, J. D. (2002.) Reliability and risk assessment /by JD Andrews and TR Moss.
2nd ed.. Professional Engineering Pub 1860582907
Bedford, T. (2001.) Probabilistic risk analysis :foundations and methods /Tim Bedford, Roger Cooke.
Cambridge University Press 0521773202
Grosh, Doris Lloyd. (c1989.) A primer of reliability theory /Doris Lloyd Grosh.
Taha, H A (2003) Operations Research, an Introduction
7th Edn. Prentice-Hall 0130488089
Winstor, W L (2004) Introduction to Probability Models: Operations Research
Vol 2. Thomson 0534423990
This module is at CQFW Level 7