Explainability for constrained optimization

This technical brief treats explainability for constrained optimization as a decision system: define its state, objective, feasible actions and evidence before comparing algorithms.

Practitioner lens

Stress-test explainability for constrained optimization beyond the average case. Tail events, correlated shortages and delayed information often determine whether a policy is robust enough to use.

First experiment

Separate model-generated proposals from approved actions; preserve a safe fallback when inputs are missing.

This is an original educational brief. It is a starting point for discussion, not a claim that one technique fits every supply chain.