What are the best ways to test simulation models for robustness and sensitivity?

1 Robustness testing

Robustness testing is an evaluation process to determine the range of conditions under which a simulation model produces consistent and acceptable results. It can also help identify potential failures or anomalies. To assess the stability, reliability, and generalizability of your model, some common methods for robustness testing include sensitivity analysis, scenario analysis, and Monte Carlo simulation. Sensitivity analysis involves changing one or more parameters or inputs of the model to determine which factors have the most influence on its performance. Scenario analysis involves creating different scenarios to evaluate how the model adapts to changes in the environment and handles uncertainty or risk. Monte Carlo simulation involves generating random values for the parameters or inputs of the model to estimate the variability and uncertainty of its output. This type of testing can help you avoid overfitting or underfitting, as well as assess the probability and impact of different outcomes.

2 Sensitivity testing

Sensitivity testing is a process used to measure how the output of a simulation model changes when its parameters, inputs, or environment are altered. The objective is to quantify the degree of sensitivity of the model to each factor and identify the critical elements that affect its performance. Through sensitivity testing, you can comprehend the relationships and interactions between the model's components and optimize its design and calibration. Three common methods for sensitivity testing are local sensitivity analysis, global sensitivity analysis, and factorial design. Local sensitivity analysis involves changing one parameter or input of the model by a small amount and calculating the percentage change in the output. This helps gauge the partial derivative or elasticity of the output with respect to each factor and rank the factors by their sensitivity. Global sensitivity analysis involves changing all or a subset of the parameters or inputs of the model by a small amount and calculating the variance or correlation of the output. This allows you to measure the total derivative or sensitivity index of the output with respect to each factor while accounting for interactions and nonlinearities between them. Finally, factorial design involves creating a set of experiments that vary the parameters or inputs of the model according to a predefined plan in order to test main and interaction effects of factors, as well as estimate their coefficients or importance.

3 Why robustness and sensitivity testing are important

Robustness and sensitivity testing are important for operations research, as they can validate and verify the simulation model, improve and refine it, and communicate its credibility to stakeholders and decision makers. This type of testing can help ensure that the simulation model meets the objectives and specifications of the problem, while also eliminating or reducing any sources of bias, error, or uncertainty.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?