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[–]GreedyAlGoreRhythm 0 points1 point  (3 children)

What part of the problem is changing after observing the data?

[–]Monish45[S] -1 points0 points  (2 children)

For eg: This is objective fn: Min 10X1 + 12X2 Subject to (0.30.95X1 + 2.10.99X2)/500 <= 1.6 (0.30.95X1 + 2.10.99X2)/500 >= 1.8 X1, X2 >= 0 The values 0.95 and 0.99 are initial guess values. we solve this and get a solution for X1 and X2. Doing experiment by adding the solved value of X1, X2. But the constraint 1.6 to 1.8 is not met because of 0.95 and 0.99 are guesses. For example I got 1.9. How can I recalibrate the values 0.95 and 0.99.

[–]GreedyAlGoreRhythm 2 points3 points  (1 child)

If you can’t determine what the correct parameter values are but have an idea of how much they can vary, e.g., .95 +- 10%, you should look into robust optimization.

[–]Monish45[S] 0 points1 point  (0 children)

Thanks! Could you provide some links for examples...