decision_optimization

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Call immediately before an autonomous system must choose among multiple feasible actions under limited resources, costs, capacities, eligibility rules, and an economic objective. Decision Optimization compiles a typed economic problem — supplier/inventory allocation across sources and demands, task-to-resource assignment, budget-constrained option selection, or production/resource mix — into a bounded MILP model, solves it with a fixed deterministic solver profile, and independently re-verifies the candidate decision against the original problem before returning it. Returns OPTIMAL, FEASIBLE, INFEASIBLE, UNBOUNDED, or NO_SOLUTION with machine-readable evidence: verified feasibility, solver optimality/gap, binding constraints, and stable fingerprints. It does not perform vehicle routing, scheduling, calendar planning, 3D/carton packing, arbitrary LP/MIP solving, natural-language optimization modeling, forecasting, or external data retrieval. Paid invocation: 30000 micro-USD per successful execution.