Hybrid simulation-optimization architectures

This technical brief treats hybrid simulation-optimization architectures as a decision system: define its state, objective, feasible actions and evidence before comparing algorithms.

Practitioner lens

A responsible implementation of hybrid simulation-optimization architectures includes a fallback policy, approval thresholds, model-version records and a way to explain infeasible or conflicting recommendations.

First experiment

Document assumptions and version the data, code, parameters and scenario set for reproducibility.

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