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					 Variance
Reduction
Techniques




   1
              Outline

   Importance of Variance Reduction
   Types of Variance Reduction
    Techniques
   Common Random Numbers
   Example: Common Random Numbers
   Implementing Common Random
    Numbers in Arena
   Antithetic Variates
   Implementing Antithetic Variates in
    Arena
   Control Variates




                  2
      Reasons for Importance

   Associated with an estimate of a
    performance measure (e.g., mean time in
    the emergency room for a patient).
   The variance is a measure of the amount
    of uncertainty in an estimate -- the
    smaller the variance, the less the
    uncertainty.
   The smaller the variance, the easier it is
    to distinguish between/among (i.e., rank,
    or select the best) alternative
    systems/policies.
   To reduce the variance of an estimate,
    one could:
    1. Make more replications.
    2. Use one of several variance reduction
         techniques.
    (The latter is the preferred approach,
    especially if computational time is a
    constraint).

                      3
       Types of Variance
     Reduction Techniques

1. Common Random Numbers (CRN)

2. Antithetic Variates (AV)

3. Control Variates (CV)

4. Others: Indirect Estimation, Conditioning




                      4
Common Random Numbers

   Applies only when comparing two or more
    alternative systems
   Most Popular VRT
   Basic idea: compare alternative system
    configurations using “similar
    experimental conditions” (i.e., the “same”
    set of random numbers)
   A form of “blocking”
   Also called correlated sampling or
    matched streams
   “Synchronization” of random numbers
    across different alternatives is important
   A pilot study may be appropriate, since
    “backfiring” is a possibility
   Formal statistical analysis can be made
    complicated with CRN

                      5
Justification for the Use of
Common Random Numbers
   Let Xij be the observation for the jth
    replication of the ith alternative
    (i=1, 2; j=1, 2, ..., n).
Z j  X1 j  X 2 j
            n
Z (n)   Z j / n, is an unbiased estimator of E( X 1 j )  E( X 2 j ).
           j 1

    Since the Z j ' s are i.i.d:
    Var [ Z (n)] = Var ( Z j ) / n
                      Var ( X 1 j )  Var( X 2 j )  2 Cov( X 1 j , X 2 j )
                  =
                                               n
   If the two sets of replications are done
    independently, then Cov(X1j, X2j)=0,
    but if positive correlation is induced
    with CRN, then Cov(X1j, X2j) >0.



                                   6
       Common Random
          Numbers
   “Synchronization” is an important
    aspect in the use of CRN. For
    example, you would want to
    dedicate particular streams of
    random numbers for particular
    purposes (e.g., for interarrival
    times) for each alternative.




                   7
      Example:
Common Random Numbers
   Suppose we have a queuing situation in
    which two alternative system
    configurations are being considered:
    Alternative 1: 1 server, with a processing
    time that is exponentially distributed,
    with a mean of .9 minute.
    Alternative 2: 2 servers, each with a
    processing time that is exponentially
    distributed, with a mean of 1.8 minute.
   Assume that the interarrival time for
    customers is exponentially distributed,
    with a mean of 27 seconds, and the
    queuing discipline is FIFO.
   Suppose that the performance measure of
    interest is the average time in the queue
    of the first 100 customers.

(This model will be called the ATM model).


                     8
     Example: Common Random
        Numbers (continued)
Running each of these models for 10 replications with no
variance reduction technique gave the following results for
average time in the queue for the first 100 customers.

          j             X  1j           X2j          Z =X -X
                                                       j      1j     2j
          _____________________________________________________________
         1             5.04             1.02            4.02
         2             6.10             3.03            3.07
         3             6.26             1.85             :
         4             1.77             2.81
         5             2.43             3.40
         6             3.20             1.25
         7            11.96             2.63
         8             4.00             2.87
         9            12.58             3.71
        10            11.97             2.18

Sample Mean       6.53                  2.48        4.06
Sample Variance   17.2                   .78       16.56
95% CI Half-Width 2.97                   .63        2.91




                                    9
     Example: Common Random
        Numbers (continued)
Now, implementing Common Random Numbers, through the
use of the SEEDS module (from the Elements panel), we
obtain the following output for average time in the queue for
the first 100 customers. (See the following pages for how this
implementation was accomplished).

         j             X1j           X2j            Zj = X1j - X2j
         _____________________________________________________________
         1             5.71          5.17               .54
         2             2.70          2.29               .41
         3              .53           .41               .12
         4             5.78          5.64               .14
         5             8.77          7.82                :
         6             2.45          2.01
         7             4.18          3.46
         8              .83           .65
         9             5.17          5.00
        10             1.65          1.38

Sample Mean       3.78               3.38           .395
Sample Variance   6.81               6.00           .076
95% CI Half-Width 1.87               1.75           .197


                                   10
        Example: Common
         Random Numbers
   Note how the sample variance for the
    difference was decreased from 16.56
    to 0.076 ( a decrease of 99.5%) and
    how the half-width for the 95%
    confidence interval for the difference
    between the two alternatives was
    decreased from 2.91 to 0.197 ( a
    decrease of 93%).
   In summary, we have:
     95% CI without CRN: (1.15, 6.97)
     95% CI with CRN: (0.319, 0.592)




                    11
     Implementing Common
    Random Numbers in Arena
   By default, Arena uses stream 10 to
    generate all of its random numbers for a
    series of simulation replications. This is
    not what you want for CRN.
   Use the SEEDS module from the Elements
    panel to implement CRN. This allows us to
    name and dedicate different streams for
    different purposes in a model
        Using the Common selection for the
         Initialize Option spaces the seeds for each
         replication within each stream 100,000
         random numbers apart, so that there will
         be no overlap of random number usage
         within a stream across replications.
        Generally, do not use stream 10 when
         using this approach.
        The SEEDS module allows you to dedicate
         the streams 1,2,… in series.




                          12
 Implementing Common
Random Numbers in Arena
   Each location in the model which
    involves generation of random variates
    needs to be modified. For example,
    instead of
              EXPO(5)
    for interarrival time in a Arrive module,
    one would use
             EXPO(5, Stream Interarrivals)
    where Stream Interarrivals is defined as
    a particular stream in the SEEDS
    module.
   To implement CRN, the SEEDS module
    should be employed in all alternative
    model runs.




                     13
     Example: Implementing
     CRN in the Arena model
   For each of the two alternative models,
    1) attach the ELEMENTS panel, and place the
       SEEDS module in the model window.

    2) Name and dedicate streams for Interarrival
       Times and Processing Times, by adding the
       elements:
       i) for stream 1:
         Identifier: Interarrival Times Stream
         Seed Value: (default)
         Initialize Option: Common
       ii) for stream 2:
         Identifier: Processing Times Stream
         Seed Value: (default)
         Initialize Option: Common

    3) In the Arrive module, replace the EXPO(1)
       in the Time Between field with EXPO(1,
       Interarrival Time Stream)



                        14
 Example: Implementing
 CRN in the Arena model
4) In the Server module, replace the Process
   Time in the single server model with
   EXPO(0.9, Processing Time Stream) and in
   the two server model with EXPO(1.8,
   Processing Time Stream).

5) Save the observations for the average value
   of the time in queue for each model to one
   file for the single server model and to
   another file for the two-server model, by
   modifying the Outputs section of the
   Statistics module.




                    15
    Example: Implementing
    CRN in the Arena model
   After running each of the models, we can
    use the output analyzer to compare the
    alternatives:
    1) Select Tools/Output Analyzer.
    2) Select File/New.
    3) Add Data from Files
    4) Select Analyze/Compare Means.
    5) Select
              Data File A
              Replications: Lumped
              Data File B
              Replications: Lumped
   We obtain the following results for a 95%
    paired-t confidence interval:

    Std Dev = 0.275     X 1  X 2  0.395
    CI: [0.198, 0.592]
    Reject Ho: means are not equal
     at the 0.05 level.

                       16
    Antithetic Variates (AV)

   Useful in reducing the variance of an
    estimate for a single alternative.

   Basic idea: make pairs of runs for a single
    alternative so that a “small” observation
    for one run is offset by a large observation
    for the second run of the pair.

   Operationally,
    rn’s for run 1 of a pair: r1, r2, r3, …
    rn’s for run 2 of a pair: 1-r1, 1-r2, 1-r3,…

   Note that both streams are i.i.d., u(0,1)
    rn’s, hence everything is “valid” with
    respect to each run.

   Synchronization must be used.




                      17
    Implementing Antithetic
       Variates in Arena
   This is much like implementing CRN in
    Arena, the only difference is that you need
    to specify Antithetic in the Initialize Option
    field if the SEEDS module. This will give
    you Antithetic pairs in your replications.

   For example, implementing the antithetic
    option in the single-server ATM model
    used for the CRN example would give us
    the following results for average queuing
    time (the AV model is contained in the file
    av1).




                        18
    Implementing Antithetic
       Variates in Arena
Replication No Variance Reduction Antithetic Variates
       1                 5.04             6.83
       2                 6.10             3.15
       3                 6.26             7.16
       4                 1.77             2.51
       5                 2.43             3.41
       6                 3.20             2.68
       7                 11.96            0.58
       8                 4.00             7.25
       9                 12.58            2.43
       10                11.97            1.99

    Sample Mean               6.53        3.8
    Sample Variance           17.2        5.7
    95% CI Half Width:        2.97        1.71

   Note how the sample variance was
    reduced by 67% (from 17.2 to 5.7) and
    how the confidence interval half-length
    was reduced by 42% (from 2.97 to 1.71)
    by using antithetic variates.

                         19
          Control Variates

   The basic idea is to take advantage of
    correlation between certain random
    variates, in order to reduce the
    variance of the output. For example,
    suppose we have a queuing system
    with:
    -- Interarrival Time Mean (specified) = 3 min.
    -- Interarrival Time Mean (actual, from model
          run) = 3.21 min.
    -- Mean Time in system(estimated, from model)
          = 3.4 min.
   Since the mean interarrival time from
    the model (3.21) is greater than it
    should be (3.), one might expect that
    the sample mean time in the system
    (3.4 minutes) is less than it should be.
    We should adjust this time (3.4
    minutes) upwards; the question is, by
    how much?
                       20
    Choosing Control Variates

   Important factors to consider include:

       The correlation between the control
        variate and x should be high,

       The control variate itself should have a
        low variance.




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