How do you validate an operations research model?

1 Define your objectives

Before you build and validate your OR model, you need to have a clear and specific idea of what you want to achieve with it. What is the problem you are trying to solve? What are the criteria and constraints you have to consider? What are the inputs and outputs of your model? What are the expected benefits and outcomes of your model? By defining your objectives, you can set the scope and boundaries of your model, and also establish the standards and metrics for evaluating its validity.

2 Choose your modeling approach

Depending on your objectives, data, and resources, you can choose different types of OR models, such as linear programming, integer programming, network analysis, simulation, or game theory. Each type of model has its own advantages and limitations, and requires different methods and tools for validation. For example, linear programming models are easier to validate than simulation models, but they may not capture the complexity and uncertainty of real-world situations. You should choose the modeling approach that best suits your problem and data, and also consider the trade-off between simplicity and accuracy.

3 Verify your model

Verification is the process of checking if your model is correctly implemented and free of errors. You can verify your model by testing its logic, syntax, and functionality, using methods such as debugging, code review, unit testing, or peer review. You can also compare your model with other existing or alternative models, and see if they produce consistent and reasonable results. Verification ensures that your model works as intended, and that it does not contain any mistakes or bugs that could affect its validity.

4 Validate your model

Validation is the process of checking if your model accurately represents the real-world system or phenomenon that you are modeling. You can validate your model by comparing its outputs with historical or experimental data, using methods such as calibration, sensitivity analysis, scenario analysis, or cross-validation. You can also consult with experts, stakeholders, or users, and get their feedback and opinions on your model. Validation ensures that your model is relevant, reliable, and robust, and that it can capture the essential features and behaviors of the system or phenomenon.

5 Refine your model

Based on the results of verification and validation, you may need to refine your model to improve its quality and performance. You can refine your model by adjusting its parameters, assumptions, or structure, using methods such as optimization, tuning, or revision. You can also add or remove variables, constraints, or components, to make your model more realistic or efficient. Refining your model can help you enhance its accuracy, precision, or flexibility, and also address any issues or limitations that you identified during verification and validation.

6 Document your model

Documentation is the process of recording and communicating the details and results of your model. You can document your model by writing a report, a paper, or a presentation, using methods such as description, explanation, or visualization. You can also use code comments, annotations, or metadata, to provide additional information or context for your model. Documentation can help you share your model with others, and also support its transparency, reproducibility, and credibility.

7 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?