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Automating the Analysis of Simulation Output Data Katy Hoad (kathryn.hoad@wbs.ac.uk), Stewart Robinson, Ruth Davies, Mark Elder Funded by EPSRC and SIMUL8 Corporation

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INTRODUCTION Appropriate use of a simulation model requires accurate measures of model performance. This in turn, requires decisions concerning three key areas: warm-up, run-length and number of replications. These decisions require specific skills in statistics. Most Simulation software provides little or no guidance to users on making these important decisions. The AutoSimOA Project is investigating the development of a methodology for automatically advising a simulation user on these three key decisions: Project Web Site: http://www.wbs.ac.uk/go/autosimoa

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How long a warm-up is required? How long a run length is required? How many replications should be run? PROJECT OBJECTIVES To determine the most appropriate methods for automating simulation output analysis To determine the effectiveness of the analysis methods To revise the methods where necessary in order to improve their effectiveness and capacity for automation To propose a procedure for automated output analysis of warm-up, replications and run-length (Only looking at analysis of a single scenario)

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Simulation model Warm-up analysis Run-length analysis Replications analysis Use replications or long-run? Recommendation possible? Recommendation Output data Analyser Obtain more output data

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Task 1: MODEL CLASSIFICATION Creating A Standard Set of Model Outputs At the beginning of this project it was decided that a standard set of model outputs was required for testing the output analysis methods. As this was not readily available in the literature it was proposed to create a representative and sufficient set of models / data output that could be used in discrete event simulation research by this project and other researchers. A set of artificial data sets were developed and a range of ‘real’ simulation models gathered together.

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Cash et al 1992: AR(1); M/M/1; Markov Chain. Robinson 2007: AR(1); M/M/1. Goldsman et al. 1994: AR(1); M/M/1. White, Cobb & Spratt 2000: AR(2). Ockerman & Goldsman 1997: Random Walk; AR(1); MA(1). Kelton & Law 1983: M/M/1 (FIFO); M/M/1 (LIFO); M/M/1(SIRO); M/M/1 (initialized with 10 customers); E4/M/1; M/H2/1; M/M/2; M/M/4; M/M/1/M/1/M/1. Hsieh et al 2004: M/M/1/199; M/G/1/199; M/M/1/19; Number-in-stock process single item inventory management system. 22 artificial models located in literature – All steady state outputs with or without a warm-up period. LITERATURE REVIEW: Artificial Models

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There are three main methods for creating artificial models/output data sets: Create simple simulation models where theoretical value of some output / response is known. –E.g. Model: M/M/1. Output: mean waiting time. Create simple simulation models where the value of some output / response is estimated but model characteristics can be controlled. –E.g. Model: Single item inventory management system. Output: Number-in-stock. Create data sets from known equations, which closely resemble real model output, with known value for some specific output / response. –E.g. AR(1) with Normal(0,1) errors

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Our aim was to collect a wide range of real models and artificial models/output such that the collection would cover each general type of model and output encountered in real life modeling. Real models are defined as discrete event simulation models of real existing systems: ModelOutput / response Call Centre% of calls answered within 30 secs Production Line in Manufacturing Plant Through-put Fast Food StoreTime in Queue HospitalTime in system For example:

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9 other characteristics of models and output data sets were chosen to be used to categorize the models/output within these two main groups: METHODOLOGY: It was determined that model output fell into two main categories or groups: Transient (including out-of-control trend) Steady-state (including steady-state cycle)

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Model characteristics Deterministic or Stochastic (random) model Significant pre- determined model changes (by time) Dynamic internal changes i.e. ‘feed- back’ Non-Statistical Empty-to-empty pattern Initial transient (warm-up) Out of control trend ρ≥1 Cycle Statistical Auto-correlation Statistical distribution Output data characteristics

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After collection or creation of model / output the data output sets were identified as one of 5 types: Steady state; Steady state cycle; Transient; Out-of-Control Each type was statistically analysed as follows: 1.Steady State: Subtract mean from output data. Test residuals for Auto-correlation and Normality. 2. Steady State Cycle:Run model for many cycles. Take mean of each cycle to create a new time series. Subtract mean from this new output data. Test residuals for Auto-correlation and Normality. 3. Transient:Test for Auto-correlation on output data. Run many replications (1000) Take mean of each replication to create new (non auto-correlated) data series. Test for what type of statistical distribution best fits this new data series. 4.Out-Of-Control:Plot data

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RESULTS: The following distributions were found to be a ‘good’ fit to the various transient data output. Normal, Beta, Pearson5, LogNormal, Weibull, Gamma, Pearson6, Erlang, Chi squared No fits could be found for two of the transient data sets. Classification tables were drawn up

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DISCUSSION: YOUR COMMENTS APPRECIATED Using our chosen classification criteria, we have classified acomplete set of possible models / output: But are these criteria sufficient? Main model/output types missing from our collection: Transient with warm-up. Deterministic transient. Cycle with warm-up Are these missing model criteria feasible?

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Justification of selection of model results / output e.g. through-put etc… Picked most likely output result for each model, using already programmed results collection when feasible. Future intentions: To create artificial data sets for each category that is missing a real model example. To test chosen (automatic) simulation output analysis methods on each category of model / output.

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Task 2: NUMBER OF REPLICATIONS

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Precision, d, ½ width of Confidence Limit expressed as % of the mean: Inner Precision Limits (IPL): Where is described as a % of REPLICATION DEFINITIONS

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While Criteria not met Run n replications of model. Calculate cumulative mean If required precision d is reached: Let Nsol be the number of replications required to reach required d. Check kLimit replications ahead that Criteria is met: d stays below required limit is stable: i.e. stays within Inner Precision Limits. Let n = n+1 Loop Recommend Nsol replications. REPLICATIONS ALGORITHM

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For Example: Precision, d <= 5% kLimit Nsol Nsol + kLimit

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FUTURE WORK To determine the most appropriate methods for automating warm-up and run-length analysis To determine the effectiveness of the analysis methods To revise the methods where necessary in order to improve their effectiveness and capacity for automation To propose a procedure for automated output analysis of warm-up and run-length

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