Distributionally robust network design

Advanced planning methods are only useful when they improve a real decision under real constraints. Here, distributionally robust network design is framed as a testable system-design choice.

Practitioner lens

A model for distributionally robust network design should state its objective and feasible set explicitly. Hidden penalty weights or relaxed constraints can make an impressive result operationally impossible.

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.