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Applied stochastic processes. Queuing models and their use. Discrete event simulation using commercial packages. A brief introduction to continuous-systems and Monte Carlo simulation. Dynamic Programming including stochastic dynamic programming. A mainstream course for MSCI/OR majors.
About one-third of MS/OR techniques and models can be classified as stochastic or probabilistic. Here we seek to cover a range of the most popular topics from an MS/OR practitioner's viewpoint, so that you are aware of them, know the basic theory, and can apply them to practical situations, including the collection and analysis of the necessary data. The techniques include Markov chain and queueing models, simulation (especially discrete event simulation, but also continuous and Monte Carlo simulation) and dynamic programming.
By the end of this course you should be able to:· Collect and measure appropriate data in order to fit a range of stochastic models· Fit a theoretical queueing model to real data· Select appropriate simulation software for a particular simulation situation· Write simple simulation programs· Set up and run a simulation experiment· Apply dynamic programming techniques to solve practical problems
(1) MSCI202 or MSCI204; (2) MSCI210 or MSCI280 or MGMT280 or MKTG280 or 30 points of 200-level courses in STAT.
MSCI310, MSCI311, MSCI312
Don McNickle
Course Notes and PoliciesWhat I will do:I will try to give you at least three weeks to complete the assignments. It is normal practice to return assignments by placing them out in boxes or bringing them along to class, so that you can get them as soon as possible. If you object to your work being returned in this way you should indicate this in writing and your work will be returned separately by the departmental secretary.What you must do:Unless there are unexpected circumstances beyond your control the assignments must be handed in by the due date if you want them to count towards your grade. Since they will count towards your final grade they must also be entirely your own work, except for the pairs provision in Assignment 1 i.e. no plagiarism, collusion or copying. You should do about 11 hours of study per week. (This course is half a normal semester load, assuming you work 30 hours per week, and we have 4 contact hours. That leaves 11 hours).TOPICSThis course aims to provide students majoring in Management Science with an appropriate background in the methods and theory of stochastic processes, queueing theory, computer simulation and dynamic programming. We will cover the following material.1. Stochastic ProcessesElementary stochastic processes, probability distributions, expectations, Poisson processes, Markov chains2. Queueing Theory Markovian queueing models. How to derive steady-state characteristics of these models. M/G/1 queues, phase-type approximations. The assignment is to fit a theoretical model to an actual queue. This has benefits beyond this section since the analysis of time-dependent data is common to simulation and other O.R. techniques as well.3. Computer SimulationComputer simulation is one of the most widely used Management Science techniques. The object is to present simulation from a skilled practitioner's viewpoint. The principal simulation approaches to discrete-event simulation programming, generation of random variates, strategies for running simulations to obtain valid results, statistical analysis of simulation output. A survey of simulation packages. Short introductions to continuous systems simulation and Monte-Carlo methods. Two simulation packages, GPSS/H and Simul8. You have studied Simul8 in some detail in MSCI202, but GPSS/H will be covered extensively here so you see both the GUI and language approaches to simulation programming. 4. Dynamic ProgrammingDynamic Programming exploits the structure of ‘finite state’ systems to create extremely efficient optimisation algorithms for some systems. The ‘curse of dimensionality’ places limits on the types of systems efficiently solved by dynamic programming. We cover the structure dynamic programming models, stochastic dynamic programming, a variety of standard formulations, and dynamic programming modelling.Departmental Academic Policies The Department assumes that you have read this document.You should also read the General Course and Examination Regulations Dishonest PracticeThe University of Canterbury considers cheating and plagiarism to be serious acts of dishonesty. All assessed work must be your own individual work unless specifically stated otherwise in the assessment guidelines. Material quoted from any other source must be clearly acknowledged. You must not copy the work of another person (student or published work) in any assessment including examinations, tests and assignments. Any person, who is found to have copied someone else's work, or to have allowed their work to be copied, will receive a fail grade for that piece of assessment and may face disciplinary action which may lead to a fine, community service or exclusion from the university.IMPORTANT: Where there are concerns regarding the authorship of written course work, a student can be required to provide a formal, oral explanation of the content of their work.Coversheets - Group and Individual
Domestic fee $1,311.00
International fee $5,775.00
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
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