rai-prescriptive-results

Prescriptive Results

Requires relationalai>=1.11.0. Sensitivity (shadow prices, reduced costs, basis status) and conflict / IIS diagnosis depend on the dual-bearing solver; earlier versions reject solve("highs", sensitivity=True) and omit the dual fields on solve_info(). See rai-setup.

Summary

What: The solve and everything it produces — execution and parameters, status interpretation, solution extraction, quality assessment, sensitivity analysis, infeasibility diagnosis, and result explanation.

When to use:

• Executing a formulated problem: solve() parameters, time limits, gap tolerances, diagnostics requests, parametric sweeps
• Extracting solution values; communicating any status (OPTIMAL, INFEASIBLE, DUAL_INFEASIBLE, TIME_LIMIT) to stakeholders
• Diagnosing why a solved-OPTIMAL result is trivial or wrong (all zeros, all at bounds, concentrated on one entity), or why a model is infeasible (conflict / IIS)
• Reading duals and marginals; running sensitivity / what-if analysis; explaining results in business language

When NOT to use:

• Designing or fixing the formulation (adding constraints, changing variables), classifying the problem type, or selecting a solver — see rai-prescriptive-problem. In particular, an OPTIMAL result the user rejects on preference grounds ("too much X") signals latent constraints, not a bug — route to rai-prescriptive-problem > Step 7 / constraint elicitation.
• PyRel and query syntax — see rai-pyrel

Overview:

1. Execute the solve to completion — parameters set, diagnostics requested up front
2. Check termination status; recall the optimization goal the formulation encodes
3. Extract solution values (model queries or Variable.values())
4. Assess quality against the goal (trivial-solution detection, reasonableness)
5. Explain to stakeholders (decisions, drivers, business impact)
6. Explore what-if (marginals to rank, scenarios to quantify)

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Solve Execution

problem.solve(solver_name, time_limit_sec=60)      # returns None — never assign it
model.require(problem.termination_status() == "OPTIMAL")   # engine-side check
si = problem.solve_info()                           # Python-side summary
si.display()
if si.termination_status != "OPTIMAL":
    print(si.error)                                 # solver-level error details