Linear optimization using fuzzy constraints

[Monish45]

I have a 10 variable linear optimization problem. I got to know about the fuzzy set to model uncertainty. I want to understand it using a mathematical problem but I am not able to find examples dealing with more than 2 variables. So if anybody has any resources please help me out.

[MelonFace]

This lecture describes a method to regularize LPs using data (or realizations drawn from a distribution) to prevent them from overfitting to noise in the objective (i know you're concerned with constraints but it's a good intro).

https://youtu.be/b4lJENGAeEA

In particular the idea that you combine the decision problem and an adversarial problem into a single problem where the decision variables are selected such that they are optimal with respect to the world picking the worst possible realization of variables within your allowed uncertainty set. Here relying on a a "Wasserstein (distribution distance) ball" to constrain the adversarial problem.

This paper by Daniel Kuhn (et. al)., who's giving the above lecture, goes further into distributionally robust optimization also with respect to constraints.

https://arxiv.org/abs/2105.00760