# data[].attributes.attachment_link.title
Process for determining wether simulation model is accurate enough for the system (2)

# data[].attributes.attachment_link.title
Process for determining wether simulation model is accurate enough for the system (1)

# data[].attributes.headline
6. CASE STUDY: Forecasting ED Crowding: A Discreet Event Simulation

# data[].attributes.subject
6. CASE STUDY: Forecasting ED Crowding: A Discreet Event Simulation

# data[].attributes.body
[ Sliding Window Validation Technique ]

When validating the model at noon on July 19 2006
(1) Estimate all parameters with 4 weeks of past patient data

From noon June 21 2006 to noon July 19 2006

(2) Initialise the simulation with data from all patients present in ED at noon July 19 2006

(3) Terminate the simulation in the future (at 2pm, 4pm, 6pm and 8pm July 19 2006) to obtain relevant forecast

(4) Repeat the entire process 10 min later, 20 min later and so forth throughout the year.

# data[].attributes.headline
7. Techniques use for validation of models 

# data[].attributes.subject
7. Techniques use for validation of models 

# data[].attributes.body
• Animation: the operational performance of the model is graphically shown as the model evolves over time;

• Validation by events: the events of the simulation model are compared to those of the real system, in order to determine similarities;

• Face-to-face validation: individuals who know the system are asked if the model behavior is reasonable;

• Internal validity: different replicas of a stochastic model are made to determine the amount of variability of the model. A large variability can lead to questioning of the results;

• Operational Graphics: values of several performance measures, such as the queue size and the percentage of busy servers, are shown graphically as the model evolves over time;

• Sensitivity analysis: this technique consists of changing values of inputs and internal parameters of a model to determine the effect on the behavior and output of the model. The same relationships should occur between model and real system;

• Predictive validation: the model is used to predict the behavior of the system, then these predictions are compared with the behavior of the system;

• Turing test: consists in presenting the data generated by the real and the simulated model, randomly, for individuals who know the operations of the system being modeled. These individuals are then asked whether they can distinguish the outputs of both simulated model and actual system;

• Validation by historical data: if there are historical data (data collected in the system), part of the data is used to build the model and the rest of the data is used to determine whether the model behaves like the real system.

# data[].attributes.body
Consider we have to describe a proposed system that doesn't exist at the present nor existed in the past. Therefore, the is no historical data available to compare its performance with. Hence, we have to use a hypothetical system based on assumptions. Following useful pointers will help in making it efficient.

Subsystem Validity − A model itself may not have an existing system to compare it with, but it may consist of a known subsystem. Each of that validity can be tested separately.

Internal Validity − A model with a high degree of internal variance will be rejected as a stochastic system with high variance due to its internal processes that will hide the changes in the output due to input changes.

Sensitivity Analysis − It provides information about the sensitive parameter in the system to which we need to pay higher attention.

Face Validity − When the model performs on opposite logic, then it should be rejected even if it behaves like the real system.

# data[].attributes.body
Use real-world inputs of the model to compare its output with that of the real-world inputs of the real system.

# data[].attributes.body
Verification is the process of checking that the computer model meets the specification.
Validation is the process of checking whether the specification captures the customer's needs.

# data[].attributes.body
•Validation, as defined by Law (2006), is the process for determining whether the simulation model is an accurate enough representation of the system, for a particular purpose of the study. According to the author, a valid model can be used for process depends on the complexity of the system being modeled decision making, and the ease or difficulty of the validation .