rai-prescriptive-problem

Prescriptive Problem

Requires relationalai>=1.11.0. The dual-guided multi-objective methods here read solver sensitivity via solve("highs", sensitivity=True); earlier versions reject the request. See rai-setup.

Summary

What: Everything before the solve runs — decision variables, constraints, objectives, problem-type classification, solver selection, and pre-solve validation. Assumes a problem has already been selected via discovery.

When to use:

• Formulating variables, constraints, and objectives for a selected problem; reviewing or validating an existing formulation
• Translating business requirements into mathematical formulation (and eliciting the constraints users can't articulate upfront)
• Debugging formulations that would produce trivial or infeasible solutions (missing constraints, conflicting bounds, wrong aggregation scope), or relaxing an over-constrained formulation to resolve a reported conflict / IIS
• Classifying the problem type (LP / MILP / QP / QCP / NLP / CSP) and choosing a solver
• Designing multi-concept coordination (flow networks, selection + quantity)

When NOT to use:

• Executing the solve, reading status/duals/IIS, quality assessment, or explaining results — see rai-prescriptive-results
• Question discovery (what can this ontology answer) — see rai-discovery
• PyRel syntax — see rai-pyrel; ontology modeling or enrichment — see rai-ontology

Overview:

1. Ground in the base ontology via inspect.schema(model)
2. Define decision variables (type, bounds, scope, naming)
3. Define constraints (forcing, capacity, balance, linking)
4. Define objective(s) (direction, coefficients, multi-component)
5. Validate the complete formulation — including the pre-solver audit that variables/constraints/objectives registered, bound, and grounded
6. Classify the problem type, select the solver, solve and refine (targeted display(ref) diagnosis)
7. Post-solve refinement — present results, surface reactions, iterate on the formulation