Constraint programming for sequence-dependent setups

A method that performs well on average may still fail at the tail. This note examines constraint programming for sequence-dependent setups with attention to stress cases, feasibility and operational control.

Practitioner lens

Separate prediction from action in constraint programming for sequence-dependent setups: identify what is estimated, what is optimized and which business rules must remain binding regardless of model confidence.

First experiment

Report feasibility, tail outcomes, runtime and sensitivity—not only the best-case objective value.

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