Verification and validation of simulation models

This paper surveys verification and validation of models, especially simulation models in operations research. For verification it discusses (1) general good programming practice (such as modular programming, (2) checking intermediate simulation outputs through tracing and statistical testing per module, (3) statistical testing of final simulation outputs against analytical results, and (4) animation. For validation it discusses (1) obtaining real-world data, (2) comparing simulated and real data through simple tests such as graphical, Schruben–Turing, and t tests, (3) testing whether simulated and real responses are positively correlated and moreover have the same mean, using two new statistical procedures based on regression analysis, (4) sensitivity analysis based on design of experiments and regression analysis, and risk or uncertainty analysis based on Monte Carlo sampling, and (5) white versus black box simulation models. Both verification and validation require good documentation, and are crucial parts of assessment, credibility, and accreditation. A bibliography with 61 references is included.