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Simulation

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Simulation

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  1. Simulation Michael F. Gorman, Ph.D. MBA 691

  2. Presentation Outline • Introduction to Simulation • Tools of the trade: • Tables, RAND, VLOOKUP, TABLES, NORMDIST, STDEV, Confidence Intervals • Example Problems: • Simulation of decision trees • Cell phone minutes optimization/simulation • Simulation of inventory decision • Simulation of operations • Simulation of regression curves

  3. Why Simulation? • Quick and easy to use. • Versatile enough to model any system. • Captures system dynamics and variation. • Shows system behavior over time. • Force one to think through the operational details of a system.

  4. The Flaw of Averages Avg Depth 10 ft.

  5. Spreadsheets • Spreadsheets are usually used to capture numerous, complex mathematical relations • Very effective for communicating numerical models • However, typically they do not capture dynamically changing interactions • they offer static relations • Usually, randomness and unpredictability within the system are not represented • often, inadequate “scenarios” are presented • Often, averages or end-states are used which can misrepresent interim states of the system

  6. Simulation maximizes performance in the least amount of time Potential Simulation System Performance Trial and error SystemLife Time

  7. Variability • Uncertainty, randomness, unpredictability, fluctuations all exist and create variability • Variability has uncertain impact on system performance • Especially when considered in tandem with interdependencies - variability destroys predictability. • Why not get rid of variability? • We just can’t. • Deming: always better to reduce sources of variability where ever possible (including our own reactions to inherent variability).

  8. Simulation in Systems Management • Production/Customer Scheduling • Resource Scheduling • Maintenance Scheduling • Work Prioritization • Flow Management • Delay/Inventory management • Quality Management

  9. Reason for Increased Use of Simulation • Increased awareness of the technology. • Increased availability, capability and ease-of-use of simulation software. • Increased computer memory and processing speeds (especially at the PC level). • Declining computer and software costs.

  10. When to Simulate • If the system is stochastic -- at least one random variable, even the statistical average results are never 100% certain. • If system has interdependencies and variability. • If interested in observing behavior over time. • If the importance of the decision warrants the time and effort of a simulation.

  11. Typical Random Variables • Durations -- Operation times, repair times, setup times, move times • Outcomes -- The success of an operation, the decision of which activity to do next • Quantities -- Lot sizes, arrival quantities, number of workers absent. • Eventintervals -- Time between arrivals, time between equipment failures • Attributes -- Customer type, part size, skill level

  12. Tools of the trade • We will use Excel in this class – it is everywhere • As a general-purpose tool, it is useful, but not nicely customized to simulation • Decision Tree Simulation: • Use @Risk (or Crystal Ball) add-on to Excel • Operations Simulation: • Use Promodel or similar animated simulation software

  13. Excel Tools • RAND() Generates uniform random variables between 0 and 1 • NORMDIST uses RAND seed to generate normal random variables • VLOOKUP() • Database function in Excel to lookup values based on a key • In this case, can lookup based on probability of an event • TABLE() • Allows replication of a model with stochastic components to see the variability of the results after a large number of • STDEV(result list)

  14. Random Number Generation An algorithm for producing numbers between 0 and 1. 0 £ x < 1 Excel: “=RAND()” These numbers are generated uniformly – an equal probability of obtaining a number in any range over the interval. (rectangular distribution) Alternative distributions are obtained by plugging the random number into other distribution functions, for example: For example, to get a normal distribution: Excel: =NORMINV(RAND(), 0,1) Pr(x) 0 1

  15. Converting RAND to various ranges • RAND() generates numbers between 0 and 1. • What if you want a different range? (still with Uniform distribution) • Easy: • MIN + RANGE * RAND() • E.g. • Probability ranges from .6 to .8 • .6 + .2*RAND() • Payoff ranges from 1000 to 1500 • 1000 + 500*rand() • Expected time is 8 minutes, plus or minus two minutes • Min in this case is 6, max is 10

  16. VLOOKUP and Custom /Discrete Probability Distributions • We may have a discrete distribution which is relatively well-known • We want to map from a uniform random to a custom distribution • VLOOKUP allows us to lookup a random value in a list of cumulative probabilities and return the variable value. • For example, say that demand level probabilities are known to be: Lookup Table: • We could look VLOOKUP to randomly generate demand levels given this distribution: • VLOOKUP(RAND(), LookupTable, 2) • This tells Excel to generate random number between 0 and 1 (which we interpret as a probability), then lookup the first value in the lookup table equal to that value, and return the value in the second column. • If no equal value is found, Excel returns the highest value less than the lookup value • For example, a random value of .15 returns 100, .5 returns 200, .99 returns 300

  17. Data Tables Command • Used to replicate basic Excel model with random variation of some contributing variables • Build a standard Excel model that captures the key attributes of the model (perhaps with static values) • Allow for some element of randomness (RAND) in the model for the stochastic variables • One-way data table: • “Anchor cell” is the value or values you want to capture in the replication • Top left cell, or top row of Data Table • Select data range: • Rows – R – number of replications • Columns – C – number of captured statistics for each replication • Data – Tables – • Column Input Cell – any BLANK cell in spreadsheet • Excel replicates your base model with R replications • Create summary statistics of the captured key measures

  18. Data Tables Continued • Two-way Data tables • If you want to test the benefits of various policy/decision variables in the face of uncertainty • Anchor Cell – top left cell of data table • Policy variable values – top row of data table • Replication Count – Left hand column of data table • Data – tables • Row Input Cell – The cell in your BASE MODEL that you want to replace with the values in the top row of your data table • Column Input Cell – any BLANK CELL in your spreadsheet • Result – values of your anchor cell in each replication (row) for each policy variable value (column) you tested • Take sample statistics on each column to evaluate the performance of each policy in the face of volatility.

  19. Spreadsheet running slowly? • Data Tables are incredibly memory and CPU intensive. • RAND() recalculates every time you change a cell • Between the tow, you can spend a lot of time waiting. • To fix this, choose Tools -> Options-> Calculations • Change it from “Automatic” to “Automatic Except Tables” • Then you hit F9 to recalculate tables, only when you want to.

  20. Basic Statistics • The range of outcomes depends on the min and the max observations, which are often outliers • We may want to know what the interval is that profit will end up in, say, 95% of the time • This depends on the standard deviation of the variable you are measuring • The standard deviation of the mean of a random variable is: • Std Dev (mean) = std dev / Sqrt (count of obs) • The confidence interval is given by the z-score, where, for example, • 95% of the sample means will lie within 1.96 standard deviations of the true mean • We can estimate the 95% confidence interval as 1.96 standard deviations of the sample mean • To compare the difference of two means with unknown variance, we use a t-test • Tools – data analysis – t-test with unequal variances

  21. Simulation of Inventory with Uncertain Demand • Newsvendor Problem • Given a mean demand of 100 papers per day, normally distributed with a standard deviation of 12 • The cost per paper is $.10; The Revenue per paper is $.30. The paper is worthless if it isn’t sold. • What is the optimal level of inventory to hold? • What is the 95% confidence interval of the mean profitability?

  22. Operations Simulation • Say you run a bakery and want to establish your customer service levels. • You have a single employee who works the counter • You know on average a customer arrives every 7 minutes with a standard deviation of 2 minutes • You know that on average it takes 6 minutes to serve a customer with a standard deviation of 2 minutes • Assume a normal distribution of inter-arrival times and processing times (there are better assumptions than this) • Will your customers ever wait? • If so, how long? What is the worst case? • What is the overall utilization of your employee (time spent serving customers over total time)

  23. Demand Simulation and Optimal Pricing • We make optimal pricing decisions based on estimated regression estimates and cost curves • Estimate the optimal price given demand and total cost data • Regression: Quantity = Intercept + a*Price • Regression: TC = Intercept + b*Quantity • What is the optimal price and quantity • Non-linear (quadratic) optimization: = P*(Intercept + a*Price) • Objective: Profit = TR – TC; • Decision Variable is Price; TR • Unconstrained • Test the robustness of this optimal solution, given • Random variation in the estimated demand parameters • Assume Normal distribution of price coefficient estimate (a) • Note: Intercept = average(Quantity) – a*average(Price) • Standard Error of Estimate indicates • What is the 95% confidence interval on this solution?

  24. P P ijk ijk p1 p p D’ D D” D’ D D” high Q low high low Q Figure 2b. Demand Shift Sampling (ijk) Figure 2a. Demand Elasticity Sampling (ijk). Allowing for Uncertainty:Sampling due to Demand Curve Uncertainty

  25. General Steps to Spreadsheet Simulation • Build “base” model – without random elements • Decision tree • Optimization problem • Make sure your base model works – verify and validate • Add random elements • Probabilities : RAND, NORMINV, VLOOKUP • Competitor behavior • Customer reaction (demand curve) • Simulate results with randomness • Replicate using data tables • Compare results statistically • Test proposed solutions – range, standard deviation of outcomes • Compare with other solutions • T-test comparison of two means

  26. A good model is one that... • includes ONLY relevant information. • includes ALL relevant information. • is a valid representation of the system. • provides meaningful & intelligible results. • is quick and inexpensive to build. • can be easily modified and expanded. • is credible and convincing.