12 votes

Modeling the uncertainty of the input parameters

In reference to the first question, I think it often comes down to the information you have about the underlying uncertainty. If you only have intervals or ranges, robust is the way to go. If you have ...
  • 1,307
10 votes

Modeling the uncertainty of the input parameters

The following papers discuss this extensively with numerical experiments, but they tackle specific examples. Emphasis is mine. Kazamzadeh et al. (2017) This is a comparison of the two techniques using ...
  • 5,199
9 votes

Safety stock when there is uncertainty in order completion

What you’re describing is known as inventory optimization under yield uncertainty. There is quite a bit of literature on it. Two relevant literature reviews are Yano and Lee (OR 1995) and Grosfeld-Nir ...
9 votes
Accepted

How is optimization under uncertainty done in real world applications?

The following is purely personal opinion. I would say a (substantial) majority of non-academic optimization problems do not involve any of the methods you listed, for a number of reasons. "...
  • 32.2k
9 votes
Accepted

Modeling the uncertainty of the input parameters

Regarding your first question, I think other answers have summed it up pretty good. Two things I would add are as follows: Stochastic programming models (besides chance constraint/probabilistic ...
  • 2,410
6 votes

How to cope with the rigidity of solutions?

There are many ways to approach your problem and there is a lot of literature on similar problems. You could look at the following article: Gorissen, Bram L., et al. “A Practical Guide to Robust ...
  • 1,057
6 votes
Accepted

Families of methods to deal with criterion uncertainties in multicriteria decision analysis

There are many applications of different MCDM (Multi-Criteria Decision Making) method families when there is some kind of uncertainty in weights or amount of objectives or criteria. Mosadeghi et al. (...
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4 votes

Model or State Uncertainty in Queueing Model due to uncertain arrival rate

After having read Chapter 5.3 of Decision Making Under Uncertainty by Mykel J. Kochenderfer, I have come to some conclusions. We are dealing with model uncertainty, in which case we can formulate a ...
3 votes

How to generate correlated samples?

How to generate a multivariate Gaussian? It must be answered somewhere on Cross Validated, but I cannot find it now, some comments at https://stats.stackexchange.com/questions/341805/are-mvrnorm-in-...
2 votes
Accepted

How to generate correlated samples?

The issue you are describing has to do with the necessity of accounting for both short- and long-term dynamics in a decision problem under uncertainty, or in general uncertainty at different levels of ...
  • 1,493
2 votes

Modeling the uncertainty of the input parameters

Stochastic Optimization (SO) requires the probability distributions (PDF) of the uncertain variables which are usually hard to fit. Then, a large number of scenarios are required to be sampled from ...

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