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1 Simulation Modeling and Analysis Verification and Validation.

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Presentation on theme: "1 Simulation Modeling and Analysis Verification and Validation."— Presentation transcript:

1 1 Simulation Modeling and Analysis Verification and Validation

2 2 Outline Model Building, Verification and Validation Verification Calibration

3 3 Objectives of Verification and Validation To produce a representative model of the system under study To increase the model credibility To gradually refine the model during the development process

4 4 Verification and Validation Verification –Building the model right Validation –Building the right model Verification and Validation must be conducted simultaneously throughout the model development process

5 5 Model Building, Verification and Validation Steps in Model Building 1.- Observe the real system 2.- Construct conceptual model and perform conceptual validation 3.- Translate conceptual model into a computer model and perform verification 4.- Calibrate, verify and validate at every step

6 6 Verification Verification checks that the computer model accurately represent the conceptual model.

7 7 Verification Strategies Peer review Flow diagrams of all possible actions Detailed output examination Final check of input parameters Make model self- documenting Use the Interactive Run Controller Use the Graphical User Interface Examine current contents, total count and traces Compare against baselines

8 8 Validation and Calibration Validation compares the model to the real system. Calibration adjusts the model to make it more representative of the real system. Validation and Calibration must be performed all the time and until the very last minute.

9 9 Steps in Validation 1.- Build a model with high face validity. 2.- Validate model assumptions. 3.- Compare the model’s input-output transformations against those in the real system.

10 10 Face Validity Face validity is concerned with the reasonableness of the model to knowledgeable peers. Sensitivity analysis can help checking for face validity.

11 11 Validating Model Assumptions Types of Assumptions –Structural –Data Structural assumptions must be checked against the real system. Data assumptions must be checked by statistical testing.

12 12 Validating Transformations The range of outputs of the model for a given range of inputs must resemble the one observed in the real system. Use historical data. Validate on the main response variables. What to do if the model represents a non- existing system?

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