Simulation CSCI 526
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This 20 page Class Notes was uploaded by Aliza Ruecker on Thursday October 29, 2015. The Class Notes belongs to CSCI 526 at College of William and Mary taught by Evgenia Smirni in Fall. Since its upload, it has received 17 views. For similar materials see /class/231162/csci-526-college-of-william-and-mary in ComputerScienence at College of William and Mary.
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Date Created: 10/29/15
Discrete Event Simulation A First Course Steve Park and Larry Leemis College of William and Mary Technical Attractions of Simulation 0 Ability to compress time expand time 0 Ability to control sources of variation 0 Avoids errors in measurement 0 Ability to stop and review 0 Ability to restore system state 0 Facilitates replication 0 Modeler can control level of detail gt DiscreteEvent Simulation Modeling Programming and Analysis by G Fishman 2001 pp 26 27 Ways To Study A System Experiment with a model of actual system Physical Malhematical model model Analytical Solution Slmulanon Simulation Modeling amp Analysis 36 by Law and Kellen 2000 p 4 Figure 11 Introduction What is discrete event simulation Modeling simulating and analyzing systems Computational and mathematical techniques Model construct a conceptual framework that describes a system Simulate perform experiments using computer implementation of the model Analyze draw conclusions from output that assist in decision making process We Will first focus on the model Characterizing a Model 0 Deterministic or Stochastic Does the model contain stochastic components Randomness is easy to add to a DES 0 Static or Dynamic Is time a significant variable 0 Continuous or Discrete Does the system state evolve continuously or only at discrete points in time Continuous classical mechanics Discrete queuing inventory machine shop models Definitions 0 Discrete Event Simulation Model Stochastic some state variables are random Dynamic time evolution is important DiscreteEvent significant changes occur at discrete time instances 0 Monte Carlo Simulation Model Stochastic Static time evolution is not important Model Taxonomy system modei J deterministic T static i idynemici I static i ldynemicl Monte Carlo simuiationquot continuous discrete Eontinuoue iscrete discreteevent simuiation DES Model Development Algorithm 11 How to develop a model 1 Determine the goals and objectives 2 Build a conceptual model 3 Convert into a speci cation model 4 Convert into a computational model 5 Verify 6 Validate Typically an iterative process Three Model Levels 0 Conceptual Very high level How comprehensive should the model be What are the state variables Which are dynamic and Which are important 0 Specification On paper May involve equations pseudocode etc How Will the model receive input 0 Computational A computer program General purpose PL or simulation language Verification vs Validation 0 Veri cation Computational model should be consistent With specification model Did we build the model right 0 Validation Computational model should be consistent With the system being analyzed Did we build the right model Can an expert distinguish simulation output from system output 0 Interactive graphics can prove valuable A Machine Shop Model 150 identical machines Operate continuously 8 hrday 250 daysyr Operate independently Repaired in the order of failure Income 20hr of operation 0 Service technicians 2 year contract at 52000yr Each works 230 8 hr daysyr 0 How many service technicians should be hired System Diagram 0000000000 00 as oo no as oo oo oo no 00 0000000000 oooooo service technicians Algorithm 111 Applied 1 Goals and Objectives Find number of technicians for maX profit Extremes one techie one techie per machine 2 Conceptual Model State of each machine failed operational State of each techie busy idle Provides a high level description of the system at any time 3 Specification Model What is known about time between failures What is the distribution of the repair times How Will time evolution be simulated Algorithm 11 Applied 4 Computational Model Simulation clock data structure Queue of failed machines Queue of available techies 5 Verify Software engineering activity Usually done via extensive testing 6 Validate Is the computational model a good approximation of the actual machine shop If operational compare against the real thing Otherwise use consistency Checks Observations 0 Make each model as simple as possible Never simpler Do not ignore relevant characteristics Do not include extraneous characteristics 0 Model development is not sequential Steps are often iterated In a team setting some steps Will be in parallel Do not merge verification and validation 0 Develop models at three levels Do not jump immediately to computational level Think a little program a lot and poorly Think a lot program a little and well Simulation Studies Algorithm 112 Using the resulting model 7 Design simulation experiments What parameters should be varied Perhaps many combinatoric possibilities 8 Make production runs Record initial conditions input parameters Record statistical output 9 Analyze the output Use common statistical analysis tools Ch 4 10 Make decisions 11 Document the results Algorithm 112 Applied 7 Design Experiments Vary the number of technicians What are the initial conditions How many replications are required 8 Make Production Runs Manage output Wisely Must be able to reproduce results exactly 9 Analyze Output Observations are often correlated not independent Take care not to derive erroneous conclusions Algorithm 112 Applied 10 Make Decisions Graphical display gives optimal number of technicians and sensitivity Implement the policy subject to external conditions 11 Document Results System diagram Assumptions about failure and repair rates Description of specification model Software Tables and figures of output Description of output analysis DES can provide valuable insight about the system Programming Languages 0 General purpose programming languages Flexible and familiar Well suited for learning DES principles and techniques Eg C C Java 0 Special purpose simulation languages Good for building models quickly Provide built in features eg queue structures Graphics and animation provided E g Arena Promodel Terminology 0 Model vs Simulation noun Model can be used WRT conceptual specification or computational levels Simulation is rarely used to describe the conceptual or specification model Simulation is frequently used to refer to the computational model program 0 Model vs Simulate verb To model can refer to development at any of the levels To simulate refers to computational activity 0 Meaning should be obvious from the context
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