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