Module Specifications..
Current Academic Year 2023  2024
Please note that this information is subject to change.
 
None Students may avail of a resit opportunity within the DCU resit examination period and resubmit coursework for reassessment before the resit period begins 

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 offcampus  
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 warmup 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 

Indicative Content and Learning Activities
Types of simulation discrete, continuous, combined. Stochastic simulation. Discrete simulation entities, activities, events, queues, sets, states. Overview of simulation software. Simulation Input Data Analysis goodness of fit tests Simulation Output Analysis Determining the appropriate number of runs, run lenght and Warmup period Variance Reduction Techniques the method of Common Random Numbers and the Antithetic Variates VRT Comparing Alternative Scenarios Comparing the output data from simulation models in order to determine the best alternative  
 
Indicative Reading List
 
Other Resources  
Programme or List of Programmes  
Date of Last Revision  16AUG11  
Archives: 
