How can you incorporate risk and uncertainty into operations research models?

1 Define risk and uncertainty

Risk and uncertainty are two related but distinct concepts in OR. Risk refers to the variability or unpredictability of outcomes that can be measured by probabilities or frequencies. Uncertainty refers to the lack of knowledge or information about the outcomes or the probabilities. For example, you may know the probability of a coin landing heads or tails (risk), but you may not know the probability of a new product being successful in the market (uncertainty).

2 Identify sources of risk and uncertainty

Before you build your OR model, you need to identify the sources of risk and uncertainty that may affect your system or process. These can include external factors, such as market demand, customer preferences, competitor actions, weather, regulations, etc., or internal factors, such as production capacity, inventory levels, quality, costs, etc. You also need to assess the impact and likelihood of each source on your objectives and constraints.

3 Choose appropriate modeling techniques

When dealing with risk and uncertainty, you may need to choose different modeling techniques to capture them in your OR model. Scenario analysis involves creating and comparing different cases that represent possible outcomes, assigning probabilities or weights to each one. Sensitivity analysis changes one or more parameters or variables in the model and observes how the results change. Stochastic programming incorporates random variables or distributions into the model to represent uncertain parameters or outcomes. Robust optimization designs the model to perform well under a range of possible scenarios, rather than optimizing for a single one. All of these techniques can help reduce the sensitivity of your model to uncertainty and ensure feasibility or stability.

4 Validate and update your model

Once you have incorporated risk and uncertainty into your OR model, you need to validate and update your model regularly to ensure its accuracy and relevance. You can use various methods to validate your model, such as comparing it with historical data, expert opinions, simulations, experiments, etc. You also need to update your model with new data, information, or feedback as the environment changes and new risks or uncertainties emerge.

Incorporating risk and uncertainty into your OR models can help you improve your decision making and cope with complex and dynamic environments. However, it also requires careful analysis, judgment, and communication. You need to understand the limitations and assumptions of your model, communicate the results and implications clearly and transparently, and be ready to adapt and revise your model as needed.

5 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?