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How do you choose the right OR model validation technique?

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Operations research (OR) is the science of applying mathematical models and methods to solve complex problems in various fields, such as engineering, management, logistics, and healthcare. OR models can help you optimize decisions, improve performance, and reduce costs and risks. But how do you know if your OR model is accurate, reliable, and useful? That's where OR model validation comes in. In this article, you'll learn how to choose the right OR model validation technique for your problem and purpose.

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Adam J. Hepworth, PhD

Director | Global Military AI Expert | Visiting Fellow | Tech Innovation | Artificial Intelligence | Decision Science |…

1 What is OR model validation?

OR model validation is the process of checking and evaluating how well your OR model represents the real system or situation that you are trying to analyze or improve. OR model validation can help you verify that your model assumptions, data, parameters, structure, and outputs are correct, consistent, and relevant. OR model validation can also help you assess the sensitivity, robustness, and generalizability of your model results and recommendations.

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Cristian Camilo Bulla Murcia

Analista de información | Optimización | Power apps | VBA | Power BI | R | Python | Cadena de Suministro
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Un análisis de sensibilidad es clave para detectar cuales son los límites de cada variable con las que estas representando el problema que vas a solucionar. Podrías utilizar el precio sombra: Un cambio en de X afecta en Z el costo total del objetivo.

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2 Why is OR model validation important?

OR model validation is important because it can increase your confidence and credibility in your OR model and its solutions. OR model validation can also help you avoid errors, biases, and misunderstandings that can affect your model quality and performance. OR model validation can also help you communicate and justify your model findings and implications to your stakeholders, clients, or users. OR model validation can also help you identify and address any limitations, gaps, or improvements in your model design and implementation.

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3 What are the types of OR model validation techniques?

When validating an OR model, there are several techniques you can use depending on your model objectives, characteristics, and context. Face validity is the simplest and most subjective type of OR model validation; it involves checking if your model looks reasonable and realistic to experts or practitioners familiar with the problem or system. Internal validity checks if your model is logically and mathematically sound and consistent, while external validity checks if your model matches or predicts the observed or expected outcomes of the real system or situation. Lastly, cross validity checks if your model is robust and generalizable to different scenarios, conditions, or settings. Each technique helps to ensure the accuracy, reliability, and usefulness of your model for the problem and purpose.

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4 How do you choose the right OR model validation technique?

When selecting the right OR model validation technique, there is no one-size-fits-all solution. You need to take into account the complexity and uncertainty of your problem or system, the availability and quality of data, the purpose and scope of your model, and the stakeholders and users of your model. If your problem or system is complex and uncertain, you may need to use multiple types and levels of OR model validation techniques. The availability and quality of data can determine whether you should use quantitative and objective OR model validation techniques or qualitative and subjective OR model validation techniques. The purpose and scope of your model can also influence which type of technique you should use; exploratory or descriptive models may require less rigorous techniques while prescriptive or predictive models may require more stringent ones. Finally, it is important to consider the expectations, preferences, and feedback of stakeholders and users when choosing a technique to ensure that your model is valid and valuable.

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Andrés Acevedo Ojeda

I help you find your best self. Professor at TEC de Monterrey. Follow me for insights on learning, mindful living & personal growth. Meditate with me: todomente.com
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Match the validation technique with your model's specific goal. For predictive models, focus on accuracy through statistical tests. For optimization models, emphasize solution feasibility and efficiency. View model validation as an ongoing process rather than a one-time event. Start with basic checks for internal consistency. As the model evolves, introduce more sophisticated methods like sensitivity analysis to test against different scenarios and data sets.

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5 What are the best practices for OR model validation?

OR model validation is not a one-time or final step in OR model development and application, but an ongoing and iterative process that requires constant monitoring and evaluation. To ensure your OR model is valid from different perspectives and dimensions, it is important to plan and document your OR model validation process. This should include defining objectives, criteria, methods, data, and results. Additionally, you should use a combination of subjective and objective, qualitative and quantitative, internal and external OR model validation techniques. It is also beneficial to involve experts and practitioners knowledgeable in the problem or system domain throughout the process. Finally, it is essential to test and update your OR model regularly to remain valid and relevant over time. By following these best practices for OR model validation, you can improve the quality and performance of your OR model.

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6 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?

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Adam J. Hepworth, PhD

Director | Global Military AI Expert | Visiting Fellow | Tech Innovation | Artificial Intelligence | Decision Science | Operations Research | Robotics & Autonomous Systems
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Before considering the analysis methodology or how to assess the selected OR models effectively, analysts must first be able to answer the question: what is the purpose of this decision? Understanding the specific business decision will drive the OR methodology and the evaluation outcomes. Careful consideration of the data needed and the nature of assessments ensure that the model's outputs are relevant and actionable for decision-making. Subsequently, the selected validation techniques should test the model against real-world scenarios and data, ensuring reliability to inform business strategies.

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How do you choose the right OR model validation technique?

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1 What is OR model validation?

2 Why is OR model validation important?

3 What are the types of OR model validation techniques?

4 How do you choose the right OR model validation technique?

5 What are the best practices for OR model validation?

6 Here’s what else to consider

Operations Research

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How do you choose the right OR model validation technique?

1

2

3

4

5

6

1 What is OR model validation?

2 Why is OR model validation important?

3 What are the types of OR model validation techniques?

4 How do you choose the right OR model validation technique?

5 What are the best practices for OR model validation?

6 Here’s what else to consider

Operations Research

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