Registry
Module Specifications
Archived Version 2019 - 2020
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Description The purpose of this module is to introduce the fundamentals of the field of engineering and scientific practice of Discrete Event Simulation. In this module students will develop knowledge and skills in developing and analysing simulation models with particular emphasis on manufacturing systems problems. Students will participate in the following learning activities: they will attend weekly lectures and laboratory, participate in assignments and present for end of semester examination This module can be delivered to distance learning students but may require the students to complete alternative assignments as software is not available off-campus | |||||||||||||||||||||||||||||||||||||||||
Learning Outcomes 1. develop conceptual, paper based, models of manufacturing systems problems as a prior step to developing a discrete event simulation model 2. develop a discrete event simulation model. 3. distinguish between the concepts of model verification, validation and credibility and make recommendations on how to best accomplish each of these in a manufacturing environment 4. assess the goodness of fit of a theoretical probability distribution to a dataset of observations 5. analyse the outputs of discrete event simulation models to determine appropriate simulation model run lenghts, identify the warm-up period, and determine the appropriate number of runs required to achieve a desired level of confidence in the estimate of a performance measure 6. identify and implement an appropriate variance reduction technique when designing simulation experiments 7. analyse the output of simulation models to compare the results of two or more alternative system configurations and/or operating policies | |||||||||||||||||||||||||||||||||||||||||
All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml |
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Indicative Content and
Learning Activities Types of simulationdiscrete, continuous, combined.Stochastic simulation.Discrete simulationentities, activities, events, queues, sets, states.Overview of simulation software.Simulation Input Data Analysisgoodness of fit testsSimulation Output AnalysisDetermining the appropriate number of runs, run lenght and Warm-up periodVariance Reduction Techniquesthe method of Common Random Numbers and the Antithetic Variates VRTComparing Alternative ScenariosComparing the output data from simulation models in order to determine the best alternative | |||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List
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