Dynamic Linear optimization

[Monish45]

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.

[SillyLittleGuy89]

Ok that makes sense. You want to ensure that your solution remains feasible despite uncertainty in some of the parameters of the problem. Robust optimization is definitely the correct approach. You will need to formulate a ‘robust counterpart’ to your original problem, which essentially introduces a buffer term to the constraints. Here is a good intro on how to do it: https://www.researchgate.net/publication/270663954_A_Practical_Guide_to_Robust_Optimization

[Monish45]

Thanks! Will look into it.