Monte Carlo experiment obtains and displays a collection of simulation outputs for a stochastic model or for a model with stochastically varied parameter(s).
You can find the detailed description of the Monte-Carlo method in Wikipedia.
The experiment allows you to run simulation a number of times, obtain the collection of outputs and view them as a histogram. If the model itself is stochastic, then each run would produce a different output even if you do not change the input parameters. Alternatively, you may generate a random parameter value for each simulation run. Both regular and 2D histograms may be used to display simulation outputs.
You can control the Monte Carlo experiment with Java code. Refer to the functions section for details.
To create a Monte Carlo experiment
- In the Projects view, right-click (macOS: Ctrl + click) the model item and choose New >
Experiment from the popup menu. The New Experiment wizard is displayed. - Choose
Monte Carlo option in the Experiment Type list. - Type the experiment name in the Name edit box.
- Choose the top-level agent of the experiment from the Top-level agent drop-down list.
- If you want to apply model time settings from another experiment, leave the Copy model time settings from check box selected and choose the experiment in the drop-down list to the right.
- Click Next to go to the Parameters page of the wizard. Here you specify the number of iterations and how you wish to setup parameters before each simulation run.
- Type the number of iterations you want to run in the Number of iterations edit box.
- In the Parameter Variation table, specify how you wish to setup parameters before each simulation run. Each parameter of the top level agent is defined in individual row of the table. For example, write uniform( 0, 10 ) in the Expression field of the parameter, whose value you wish to be drawn from a uniform distribution between 0 and 10. Blank field means the default value will be used.
- Here you specify how the parameters of the root agent type should be varied. All parameters are listed in the table. You can specify any stochastic expression for a parameter in the corresponding Expression table cell.
- If you want to display the distributions of the simulation output of single-value type using histograms, click Next. Histograms page of the wizard will be opened. Here you define properties of histograms that will visualize experiment results. In the Expression field use root to refer to the top level agent, e.g. root.myOutputValue.
- If you want to display the distributions of the simulation output of dataset type using 2D histograms, click Next. 2D Histograms page of the wizard will be opened. Here you define properties of 2D histograms that will visualize experiment results. In the Expression field use root to refer to the top level agent, e.g. root.myDataset.
- Click Finish.
- General
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Name — The name of the experiment.
Since AnyLogic generates Java class for each experiment, please follow Java naming guidelines and start the name with an uppercase letter.Ignore — If selected, the experiment is excluded from the model.
Top-level agent — Using the drop-down list, choose the top-level agent type for the experiment. The agent of this type will play a role of a root for the hierarchical tree of agents in your model.
Maximum available memory — The maximum size of Java heap allocated for the model.
Create default UI — The button creates the default UI for the experiment.
Do not press this button since it will delete the experiment UI created by the wizard and will create the default UI for the experiment that may not correspond to your task. - Parameters
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Parameters — Here you can select how the parameters will be varied: in the specified range, or freely.
Varied in range — If selected, you should explicitly define the range of values for each parameter you want to vary.
This option should be selected if you need parameter variation experiment.Freeform — If selected, model is run for the fixed number of iterations you specify below in the Number of runs field and the parameter values are calculated according to the expressions you define.
Exactly this option should be selected for the Monte Carlo experiment.Number of runs — [Applied if the Parameters: Freeform option is selected above] Defines how many times the experiment will run the model with different parameter values.
The table located here displays all the parameters of the top-level agent. For each parameter in the Expression column, you can specify a (usually stochastic) expression, according to which the value of this parameter will be calculated during the next “run” of the model by this experiment. Using the index keyword in the expression you can refer to the current model run number.
If the Parameters: Varied in range option is selected above, then instead of the Expression column, the table will display Type and Value (Min, Max, Step) columns, but this is required for another experiment - parameter variation.
- Model time
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Stop — Defines, whether the model will Stop at specified time, Stop at specified date, or it will Never stop. In the first two cases, the stop time is specified using the Stop time/Stop date controls.
Start time — The initial time for the simulation time horizon.
Start date — The initial calendar date for the simulation time horizon.
Stop time — The final time for the simulation time horizon (the number of model time units for model to run before it will be stopped).
Stop date — The initial calendar date for the simulation time horizon.
Additional experiment stop conditions — Here you can define any number of additional experiment stop conditions. When any of these conditions will become true, experiment will be stopped. Condition can include checks of dataset mean confidence, variable values, etc. The top-level agent of the experiment can be accessed here as root, so if you want e.g. to stop the experiment when the variable plainVar of the experiment’s top-level agent steps over the threshold, type here, say, root.plainVar>11. To make the condition active, select the checkbox in the corresponding row of the table.
- Randomness
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Random number generator — Here you specify, whether you want to initialize random number generator for this model randomly or with some fixed seed. This makes sense for stochastic models. Stochastic models require a random seed value for the pseudorandom number generator. In this case model runs cannot be reproduced since the model random number generator is initialized with different values for each model run. Specifying the fixed seed value, you initialize the model random number generator with the same value for each model run, thus the model runs are reproducible. Moreover, here you can substitute AnyLogic default RNG with your own RNG.
- Random seed (unique simulation runs) — If selected, the seed value of the random number generator is random. In this case random number generator is initialized with the same value for each model run, and the model runs are unique (non-reproducible).
- Fixed seed (reproducible simulation runs) — If selected, the seed value of the random number generator is fixed (specify it in the Seed value field). In this case random number generator is initialized with the same value for each model run, and the model runs are reproducible.
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Custom generator (subclass of Random) — If for any reason you are not satisfied with the quality of the default random number generator Random, you can substitute it with your own one. Just prepare your custom RNG (it should be a subclass of the Java class Random, e.g. MyRandom), choose this particular option and type the expression returning an instance of your RNG in the field on the right, for example: new MyRandom() or new MyRandom( 1234 ).
You can find more information in Custom number generator.
- Replications
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Use replications — if selected, the engine will run several replications per one simulation. You need this when your model contains stochastics. In such cases, the results of the simulation runs are unique and the values of the optimized function which are obtained for the simulation runs executed with the same values of optimization parameters most likely differ from each other. We cannot execute only one simulation run, accept its results as the current iteration result and proceed with optimization by checking other parameter values. To obtain reliable representative data, we need to execute several runs (called "replications" here) for a single set of parameter values and accept the mean of all replications results as the values of the objective.
Fixed number of replications — if selected, fixed number of replications will be run per each simulation.
- Replications per iteration — [enabled if Fixed number of replications is set] the fixed number of replications, which will be run per each simulation.
Varying number of replications (stop replications after minimum replications, when confidence level is reached) — if selected, varying number of replications will be run per each simulation. When running a varying number of replications, you will specify minimum and maximum number of replications to be run. The engine will always run the minimum number of replications for a solution. Then, the optimization engine determines if more replications are needed. The engine stops evaluating a solution when one of the following occurs:
- The true objective value is within a given percentage of the mean of the replications to date.
- The current replication objective value is not converging.
- The maximum number of replications has been run.
For this property, the following options are available:
- Minimum replications — [enabled if Varying number of replications is set] The minimum number of replications the engine will always run per one simulation.
- Maximum replications — [enabled if Varying number of replications is set] The maximum number of replications the engine can run per one simulation.
- Confidence level — [enabled if Varying number of replications is set] The confidence level to be evaluated for the objective.
- Error percent — [enabled if Varying number of replications is set] The percent of the objective for which the confidence level is determined.
- Window
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Window properties define the appearance of the model window that will be shown when the user starts the experiment.
The size of the experiment window is defined using the model frame and applies to all experiments and agent types of the model.Title — The title of the model window.
Enable zoom and panning — If selected, the user will be allowed to pan and zoom the model window.
Enable developer panel — Select/clear the checkbox to enable/disable the developer panel in the model window.
Show developer panel on start — [Enabled only if the Enable developer panel checkbox is selected] If selected, the developer panel will be shown by default in the model window every time you run the experiment.
- Java actions
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Initial experiment setup — The code executed on experiment setup.
Before each experiment run — The code executed before each simulation run.
Before simulation run — The code executed before simulation run. This code is run on setup of the model. At this moment the top-level agent of the model is already created, but the model is not started yet. You may perform here some actions with elements of the top-level agent, e.g assign actual parameter values here.
After simulation run — The code executed after simulation run. This code is executed when simulation engine finishes the model execution (Engine.finished() function is called). This code is not executed when you stop your model by clicking the Terminate execution button.
After iteration — The code executed after iteration run.
After experiment — The code executed after experiment run.
- Advanced Java
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Imports section — import statements needed for correct compilation of the experiment class’ code. When Java code is generated, these statements are inserted before definition of the Java class.
Additional class code — Arbitrary member variables, nested classes, constants and methods are defined here. This code will be inserted into the experiment class definition. You can access these class data members anywhere within this experiment.
Java machine arguments — Specify here Java machine arguments you want to apply on launching your model. You can find the detailed information on possible arguments at Java Sun Microsystems website: http://java.sun.com/j2se/1.5.0/docs/tooldocs/windows/java.html.
Command-line arguments — Here you can specify command line arguments you want to pass to your model. You can get the values of passed argument values using the String[] getCommandLineArguments() method from any code field of your choice. The only exception is values of static variables since these are initialized before the experiment class itself.
- Advanced
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Allow parallel evaluations — If the option is selected and the processor has several cores, AnyLogic will run several experiment iterations in parallel on different processor cores. Thereby performance is multiply increased and the experiment is performed significantly quicker. This feature is made controllable because in some rare cases parallel evaluations may affect the optimizer strategy so that more iterations are required to find the optimal solution.
Do not use static variables, collections, table functions, and custom distributions (check that their advanced option Static is deselected), if you turn on parallel evaluations here.Load top-level agent from snapshot — If selected, the experiment will load the model state from the snapshot file specified in the control to the right. The experiment will be started from the time when the model state was saved.
You can use the following functions to control the experiment, retrieve the data on its execution status, and use it as a framework for creating custom experiment UI.
- Controlling execution
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Function Description void run() Starts the experiment execution from the current state.
If the model does not exist yet, the function resets the experiment, creates and starts the model.void pause() Pauses the experiment execution. void step() Performs one step of experiment execution.
If the model does not exist yet, the function resets the experiment, creates and starts the model.void stop() Terminates the experiment execution. void close() This function returns immediately and performs the following actions in a separate thread: - Stops experiment if it is not stopped,
- Destroys the model,
- Closes the experiment window (only if the model is started in the application mode).
Experiment.State getState() Returns the current state of the experiment: IDLE, PAUSED, RUNNING, FINISHED, ERROR, or PLEASE_WAIT. double getRunTimeSeconds() Returns the duration of the experiment execution in seconds, excluding pause times. int getRunCount() Returns the number of the current simulation run, i.e., the number of times the model was destroyed. double getProgress() Returns the progress of the experiment: a number between 0 and 1 corresponding to the currently completed part of the experiment (a proportion of completed iterations of the total number of iterations), or -1 if the progress cannot be calculated. int getParallelEvaluatorsCount() Returns the number of parallel evaluators used in this experiment.
On multicore / multiprocessor systems that allow parallel execution this number may be greater than 1. - Iterations
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Function Description int getCurrentIteration() Returns the current value of iteration counter. int getMaximumIterations() Returns the total number of iterations. int getNumberOfCompletedIterations() Returns the number of completed iterations. - Replications
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Before calling the Monte Carlo experiment functions you may need to ensure that replications are used (call the isUseReplications() function).
Function Description boolean isUseReplications()
Returns true if the experiment uses replications; returns false otherwise. int getCurrentReplication() Returns the number of replications run so far for the current iteration. - Accessing the model
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Function Description Engine getEngine() Returns the engine executing the model. To access the model’s top-level agent (typically, Main), call getEngine().getRoot();. IExperimentHost getExperimentHost() Returns the experiment host object of the model, or some dummy object without functionality if the host object does not exist. - Restoring the model state from snapshot
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Function Description void setLoadRootFromSnapshot(String snapshotFileName) Tells the simulation experiment to load the top-level agent from AnyLogic snapshot file. This function is only available in AnyLogic Professional.
snapshotFileName — the name of the AnyLogic snapshot file, for example: "C:\My Model.als"boolean isLoadRootFromSnapshot() Returns true if the experiment is configured to start the simulation from the state loaded from the snapshot file; returns false otherwise. String getSnapshotFileName() Returns the name of the snapshot file, from which this experiment is configured to start the simulation. - Error handling
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Function Description RuntimeException error(Throwable cause, String errorText) Signals an error during the model run by throwing a RuntimeException with errorText preceded by the agent full name.
This function never returns, it throws runtime exception by itself. The return type is defined for the cases when you would like to use the following form of call:
throw error("my message");
cause — the cause (which will be saved for more detailed message), may be null.
errorText — the text describing the error that will be displayed.RuntimeException errorInModel(Throwable cause, String errorText) Signals a model logic error during the model run by throwing a ModelException with specified error text preceded by the agent full name.
This function never returns, it throws runtime exception by itself. The return type is defined for the cases when you would like to use the following form of call:
throw errorInModel("my message");
This function differs from error() in the way of displaying error message: model logic errors are “softer” than other errors, they use to happen in the models and signal the modeler that model might need some parameters adjustments.
Examples: “agent was unable to leave flowchart block because subsequent block was busy”, “insufficient capacity of pallet rack”, etc.
cause — the cause (which will be saved for more detailed message), may be null.
errorText — the text describing the error that will be displayed.void onError(Throwable error) This function may be overridden to perform custom handling of the errors that occurred during the model execution (i.e., errors in the action code of events, dynamic events, transitions, entry/exit codes of states, formulas, etc.).
By default, this function does nothing as its definition is empty. To override it, you can add a function to the experiment, name it onError and define a single argument of the java.lang.Throwable for it.
error — an error which has occurred during event execution.void onError(Throwable error, Agent root) Similar to onError(Throwable error) function except that it provides one more argument to access the top-level (root) agent of the model.
error — an error which has occurred during event execution.
root — the top-level (root) agent of the model. Useful for experiments with multiple runs executed in parallel. May be null in some cases (e.g. on errors during top-level agent creation). - Command-line arguments
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Function Description String[] getCommandLineArguments() Returns an array of command-line arguments passed to this experiment on model start. Never returns null: if no arguments are passed, an empty array is returned.
Cannot be called from within a value of a static variable: these are initialized before the experiment class itself.
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