I will use an example to detail my question but I would like you to keep in mind that I wanted to define:

  • Robustness,
  • Resillience,
  • Reliability

in the most general case within Operations Research.

Let us suppose you want to find the classical shortest path in a graph between two different nodes. However, you know in advance that at most one edge could be unavailable or present a failure. e.g. for rehabilitation works.

You use a model, it's not important which it is, to minimize the cost of your shortest path and for each edge of this shortest path, your objective function will take into account the detour caused by the failure of possibly each edge.

Would you say that your model is resilient, robust, reliable, or a combination of those three adjectives?

  • From what I've search, I have read this question about Robustness Optimization and thus, we will say that the model will have robustness because a part of the data is uncertain: which edge that will fail.
  • For resilience, wikipedia writes:

Resilience is the ability to "provide and maintain an acceptable level of service in the face of faults and challenges to normal operation."

which is quite also appropriate to our model.

  • For reliability, it is sometimes defined as the quality of being trustworthy or of performing consistently well.

With these very similar definitions, I wander which adjectives are appropriate, for my small example first, but in general in Operations Research. Maybe some papers have dealt with such definitions, maybe introducing theirs?


1 Answer 1


I think these terms are all rather vague and imprecise, and different people use them slightly differently. Some papers try to draw clear lines between them—for example, in my dissertation in 2003, I draw a distinction between

robust (i.e., perform well with respect to uncertainties in the data, such as demand) and reliable (i.e., perform well when parts of the system fail).

—but such clear lines should be interpreted as being respected within the scope of the document, and not necessarily interpreted the same way more broadly in other works. And even within a definition (like mine above), there is ambiguity—one could interpret failures in parts of the system as arising from uncertainties in the data.

Having said that, my own opinion is that robust refers to data uncertainty, and resilient and reliable refer to disruptions of some sort (and are nearly synonymous with each other).


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