HELP (1086552), страница 11
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Global_Delays[2,2,2,2,1] = QT$Global_Delays;
END;
/* Aliased Effects in Fractional Factorial Experiment */
EFFECTS(Global_Delays,”I=ABCDE”);
END;
*******************************************************
* Run Procedure *
*******************************************************
PROCEDURE DoTheRun(Run_Number) BEGIN
DoCommand(“CLEAR OFF”); /* Must use OFF to preserve results. */
/* EXPAND THIS RMULT IF YOU HAVE MORE RNGs. */
/* All Random Number Streams must have new seeds. */
TEMPORARY CommandString;
/* Evaluate before passing to DoCommand. */
CommandString = Catenate(“RMULT “,Run_Number#111);
/* DoCommand compiles the string in Global Context. */
DoCommand(CommandString);
/* SET UP YOUR OWN RUN CONDITIONS. */
DoCommand(“START 100,NP”); /* Get past the Startup Period. */
DoCommand(“RESET”); /* Begin the Measurement Period. */
DoCommand(“START 1000,NP”); /* Run the Simulation. */
END;
*******************************************************
Running the Experiment
CONDUCT is the GPSS World Command that is used to invoke an Experiment. The syntax is similar to that of a Procedure Call. But we have a better way. In the Experiment Generation dialog, we can choose to have GPSS World load the F11 Function Key with the appropriate CONDUCT Command. You can see this in the Model Object's Settings. (see Edit / Settings / Function Keys).
To start the execution of the experiment, we must do two things. First we must Translate the Model, thereby creating the Simulation Object. Click on Command / Create Simulation to do this, or just press Ctrl+Alt+S.
Then we press the F11 Function Key. This enters the CONDUCT Command. The Experiment goes to work, reporting status and output to the Journal Window of the newly created Simulation Object. Figure 13-13 shows the results of a Screening Experiment.
Figure 13-13. Results of Screening Experiment.
The Screening Experiment Report shows the strength of each effect and, if it can, calculates an F statistic for those alias groups with main effects as members. From it you can plan future actions such as ignoring insignificant factors, and more fully exploring the important ones.
13.4.3 Optimizing Experiments and Response Surfaces
Optimizationand the quantitative prediction of the behavior of a system are often the primary objectives of a simulation project. Both of these are supported directly by GPSS World's Optimizing Experiment Generator.
Response Surface Methodology is a set of statistical techniques for empirical model building and model exploration. Although most often used to estimate optimum treatment levels, it also can provide important information for general predictions of the yield expected under untested conditions. GPSS World supports several important Response Surface Methodologies in a way intended to make them easy to use. The simulation analyst sets up the initial conditions in a few User Dialogues and GPSS World then automatically creates a response surface experiment that seeks an optimum value. Ideally, you can start the experiment and let it run unattended until it finds the optimum value of the chosen metric. When you fill in the dialog windows you are, in effect, defining a box (actually a "hyperbox") called the Local Experimental Region that wanders over the set of permissible factor levels. If you make this box too small, the experiment will require more runs than necessary to move to the optimum. If you make it too large, the estimates of the optimum treatment levels will be less precise than they could be.
GPSS World applies several different methodologies as it explores the response surface of a GPSS World Simulation Object. The primary concept is that of a Local Experimental Region which is defined by you in the setup dialog. To define it, each of up to 5 factors to be studied are associated with two values defining the extent of the Local Experimental Region. Then, it is the whole Region that moves around the factor space. The goal, which is not always achieved, is to contain the optimum within the Local Region, perform a confirmation run, and then to report the optimum conditions and the mathematical model that was used.
GPSS World uses several experiments of increasing complexity to get the information it needs. As results are available, it attempts to fit either a linear model or a second order model (including two-way interactions) to the data. It begins by using the Method of Steepest Ascent as it leaves its starting point. The direction is determined by the gradient of the first model that fits the data. If the goodness-of-fitness test fails for the first order model, the second order model is used.
Once a gradient is calculated, the center of the Local Experimental Region moves until it encounters either diminishing returns or a limit asserted in the setup dialog. Each step of the movement along the gradient never exceeds the width of the local experimental region, as defined in the Optimizing Experiment Generator Dialog. If the range of treatment levels is too narrow, it will take many unnecessary steps to climb the gradient. Each step in the move requires a simulation at the center of the Local Experimental Region, and its results are reported in the Journal. If, when movement stops, the local experimental region does not then contain an optimum, the experiment attempts to move in a new direction. This redirection can be suppressed by the Redirection Limit in the Optimizing Experiment Generator Dialog.
Figure 13-14. The Optimizing Experiment Generator Dialog.
Figure 13-14 shows the dialog used to generate Optimizing Experiments. Just as with the Screening Experiment Generator it is accessed through the Edit Menu of the Main Window.
To specify the PLUS Experiment that is about to be generated, we simply fill in the fields of the Dialog. The “
Experiment Name” and the “‘Run Procedure’ Name” fields are used as the PROCEDURE names in the generated experiments. The EXPERIMENT calls the Run Procedure repeatedly.
The Factors of the Experiment come next. Each factor name is actually the name of a User Variable and must obey the GPSS World naming conventions. It must begin with an alphabetic character and it must not clash with a keyword, SNA, or SNA class. The two treatment levels of each factor define the Local Experimental Region which moves around searching for the optimum conditions. You must specify the names and two treatment levels for between 1 and 5 factors, inclusively. Factors must be specified consecutively starting with the A factor. The choice of treatment levels is crucial.
Several important trade-offs are implicit in the way you define the Local Experimental Region. First, it forms the starting point of the experiment. You should make it as close to the optimum as you can. Second, making it small usually makes the mathematical model fit better. Third, when the experiment moves one step toward the optimum it moves to an adjacent region. Therefore, a larger experimental region will take fewer steps. As you can see, the issues conflict with each other. It is up to you to choose compromises. Even further, don't overlook the fact that it is easy to modify a generated experiment after it has been inserted into your Model.. It's worthwhile to examine the generated PLUS code to look for ways to improve the experiment. You may want to do some preliminary experimentation so that the choices you make are good ones.
You should apply limits to the Optimizing Experiment. The section of the dialog for Move Limits gives you a chance to restrict the movement of the Local Experimental Region. If possible you should enter limits for each of the factors so that time won't be wasted on infeasible simulations. The Redirection Limit is another way to restrain the search. This value sets a limit to the number of times the experiment can start moving in a new direction in the factor space. This limit can prevent an unending cycle from occurring.
The “Result Expression” is required. You must specify an expression to be evaluated as the metric of the simulation.
Finally, we have two checkboxes that allow us to select additional options. First, you can have GPSS World generate a template Run Procedure to go along with the generated experiment. You can edit this Run Procedure to suit your needs. Second, you can have GPSS World set up a setting which loads the appropriate CONDUCT Command into the setting for Function Key 12. If you choose this, after you create the Simulation Object, you only have to press the F12 Function Key to run the experiment.
The next step is to insert the PLUS code comprising the experiment into your Model Object. When you have filled out all the appropriate items in the Optimizing Experiment Generator Dialog, click Insert Experiment to create the PLUS code and insert it into your model. If you have asked GPSS World to generate a Run Procedure, next we see a Dialog that allows us to personalize it. The details about personalizing the Run Procedure are in the beginning of this section.
In any case, you may want to edit the generated experiment itself in your Model Window. You can change the Run Procedure there, as well. Just click OK and all the new GPSS World Statements comprising the Optimizing Experiment are placed at the bottom of your Model.
Running the Experiment
CONDUCT is the GPSS World Command that is used to invoke an Experiment. The argument to the CONDUCT Command is similar to a Procedure Call. But we have a better way. In the Experiment Generation dialog, we can choose to have GPSS World load the F12 Function Key with the appropriate CONDUCT Command. You can see this in the Model Object's Settings. (see Edit / Settings / Function Keys).
To start the execution of the experiment, we must do two things. First we must Translate the Model, thereby creating the Simulation Object. Click on Command / Create Simulation to do this, or just press Ctrl+Alt+S.
Then we press the F12 Function Key. This enters the CONDUCT Command. The Experiment goes to work, reporting status and output to the Journal Window of the newly created Simulation Object. Figure 13-15 shows the results of an Optimizing Experiment.
Figure 13-15. Results of Optimizing Experiment.
When successful, the Optimizing Experiment reports the results of each run. From this can be seen the results of the sub-experiments and the movement of the local experimental region. The generated PLUS Experiment attempts to move the local experimental region so that it encloses the optimum conditions. The Experiment ends when this occurs, or when the redirection limit or a movement limit is reached.
When movement stops, the results of the goodness of fit test and the equation for the fitted response surface are also reported in the Journal Window. Then, if not already done, the PLUS Experiment attempts to verify the predicted optimum conditions by running a simulation.
13.5 Operating Tips
We end the chapter with some helpful hints that can be useful as well as some common pitfalls to be avoided while you are engaged in an experimentation project.
· GPSS World uses User Variables to take the treatment level of each factor as values. When you create names for your variables, they must not clash with existing keywords, SNAs, or SNA classes. Names with more than two letters, followed by at least one digit or underscore, are safe. The factor names in experiments are simple User Variable names and take on the indicated treatment levels as values during the experiment.
· By default, GPSS World uses the # symbol to denote multiplication and reserves * for SNA indirection. If you prefer to use * for multiplication, you can switch the role of * and # by a checkbox in the first page of the Model Settings. This is accessed through the Edit Menu.
· When you create your own names, they must not be the same as existing keywords or SNA Classes. Names including underscores are safe.
· Settings are inherited from the parent Model Object, including Function Key assignments (see the Edit menu). Optionally, the Experiment Generators load a Function Key with the appropriate CONDUCT command. The Screening Experiment Generator optionally loads F11, the Optimizing Experiment Generator, F12. After creating the Simulation, to run the experiment, just press the Function Key.
· The DoCommand library procedure allows an Experiment, or any Procedure called by an Experiment, to execute any GPSS World Command, except the CONDUCT Command.
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