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Automating the Analysis of Simulation Output Data Stewart Robinson, Katy Ho
Baker College of Port Huron, US has reference to this Academic Journal, Automating the Analysis of Simulation Output Data Stewart Robinson, Katy Hoad, Ruth Davies OR48, September 2006 Outline The problem A prototype automated output Analyser Findings from prototype Analyser The AutoSimOA Project Current work – Collecting in addition to characterising real in addition to artificial models The Problem Prevalence of simulation software: ?easy-to-develop? models in addition to use by non-experts. Simulation software generally have very limited facilities in consideration of directing/advising on simulation experiments. Main exception is directing scenario selection through ?optimisers?. With a lack of the necessary skills in addition to support, it is highly likely that simulation users are using their models poorly.
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The Problem Despite continued theoretical developments in simulation output analysis, little is being put into practical use. There are 3 factors that seem so that inhibit the adoption of output analysis methods: Limited testing of methods Requirement in consideration of detailed statistical knowledge Methods generally not implemented in simulation software (AutoMod/AutoStat is an exception) A solution would be so that provide an automated output ?Analyser?. A Prototype Analyser Masters Project (3 students). The Analyser looked at: Warm-up Run-length Number of replications Scenario analysis could be added. A Prototype Analyser A prototype Analyser has been developed in Microsoft Excel. At present it links so that the SIMUL8 software, but it could be used alongside any software that can be controlled from Excel VBA.
Illustration: Warm-up Load Analyser into Excel. Enter name of SIMUL8 model. Specify initial number of replications in addition to run-length so that use. Illustration: Warm-up Illustration: Replications
Lecture 4 Activation of Adaptive Immunity Overview Clonal Selection Theory (F. Macfarlane Burnet) Postulates of the Clonal Selection Hypothesis First a Word About ?Cluster of Differentiation/Designation? (CD) Antigens Major Lymphocyte Populations B lymphocytes in addition to plasma cells T lymphocytes T Cell Recognition of Antigen Major Histocompatibility Complex (MHC) Gene Products Cellular Cooperation in addition to Antigen Recognition B Cell Antigen Recognition Antigen Presenting Cells Macrophages/monocytes Dendritic cells (e.g., Langerhan?s cells) B cells Cellular Cooperation Effector Mechanisms The Bottom Line
Findings from Prototype Analyser It is possible so that link an Automated Analyser in Excel so that a simulation software tool. This was just a proof of concept. Key issues so that address: More thorough testing of output analysis methods in consideration of their accuracy in addition to their generality. Adaptation of methods so that sequential procedures in addition to so that minimise the need in consideration of user intervention. A 3 year, EPSRC funded project (GR EP/D033640/1) in collaboration alongside SIMUL8 Corporation. The AutoSimOA Project Objectives To determine the most appropriate methods in consideration of automating simulation output analysis To determine the effectiveness of the analysis methods To revise the methods where necessary in order so that improve their effectiveness in addition to capacity in consideration of automation To propose a procedure in consideration of automated output analysis of warm-up, replications in addition to run-length Only looking at analysis of a single scenario The AutoSimOA Project CURRENT WORK: Literature review of warm-up, replications in addition to run-length methods. Development of artificial data sets (Auto-Regressive; Moving average; M/M/n/p Queues?) Collection of ?real? simulation models.
Use models / data sets: Provide a representative in addition to sufficient set of models / data output in consideration of use in discrete event simulation research. Use models / data sets so that test the chosen simulation output analysis methods in the AutoSimOA Project. Group B Auto Correlation Spread round mean Normality Trend Cycling/Seasonality Terminating Non-terminating Steady state In/out of control Transient
ARTIFICIAL MODELS Create simple models where theoretical value of some attribute is known. E.g. M/M/1: mean waiting time. Create simple models where value of some attribute is estimated but model characteristics can be controlled. E.g. Single item inventory management system: Number-in-stock. Construct output, which closely resembles real model output, alongside known value in consideration of some specific attribute. E.g. AR(1) alongside Normal errors Create different output types Transient Steady state Steady state cycle Trend + Initial transient (warm-up) Example artificial models: 1. Auto-Regressive (2) series
Example artificial models: mean 1.8 2. E4 ~ Erlang(4) / M / 1 Queue Traffic Intensity = 0.8 REAL MODELS Models created in ?real circumstances? that cover each general type of model in addition to output encountered in real life modeling. e.g. Call centre: percentage of calls answered within 30 secs e.g. Production Line Manufacturing Plant: through-put / hour e.g. Fast Food Store: average queuing time e.g. Swimming Pool complex: average number in system Transient Steady State Cycle Steady State With or without warm-up Trend Example ? real ? models: 1. Argos ? Number of customers in queue so that pay Stochastic model alongside changing arrival rates. Empty so that empty; transient; autocorrelated; non-normal output.
Example ? real ? models: 2. Leggings Manufacturing Plant ? Through-put / hour Stochastic model. Steady state alongside warm-up; not autocorrelated; normal output. Example ? real ? models: 3. Sanitory Towel Packing Plant ? Through-put / hour Stochastic model alongside changing productivity in work stations. Steady state daily cycle. Series of means of each cycle: autocorrelated; non-normal output. Use this representative in addition to sufficient set of models/output when The AutoSimOA Project determining the most appropriate methods in consideration of automating simulation output analysis determining the effectiveness of the analysis methods revising the methods where necessary in order so that improve their effectiveness in addition to capacity in consideration of automation In order so that propose a procedure in consideration of automated output analysis of warm-up, replications in addition to run-length.
Automating the Analysis of Simulation Output Data Stewart Robinson, Katy Hoad, Ruth Davies OR48, September 2006
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