Rigorous establishment of the feasibility of the trained policies in SDDP.jl using constraint violation penalties in the objective function

Engr_Moiz_Ahmad
Wed, 23 Jul 2025 01:05:09 +0000
Optimization (Mathematical)

I am working with a multi-stage mixed-integer stochastic global supply chain optimization problem.

In this regard, under realistic parameter settings, some constraints are making the instances violate the relatively complete recourse assumption. To circumvent the resulting infeasibilities during training, I am enforcing these constraints using binary penalty terms in the objective function.

However, I want to rigorously establish the feasibility of the resulting trained policies. Can I use some statistical test on the binary penalty terms to achieve this? Is it acceptable to use this strategy to establish policy feasibility? If yes, what statistical test should I use?

11 posts - 3 participants