Sentence classification
| Sentence | Label | Note |
|---|---|---|
| "Operates 2 ovens" | Parameter | Both ovens are available. This is not an oven-selection problem. |
| "Plans to bake 300 loaves this week" | Constraint (implicit) | Committed phrasing, so bread = 300 exactly. |
| "May also bake cakes" | Decision | The number of cakes is chosen. |
| "Cakes earn $12, cost $5; bread earns $4, costs $1.50" | Parameters | Net per unit is $7 per cake and $2.50 per loaf. |
| "Each oven runs at most 40 hours" | Parameter (40) and constraint | Oven time used is at most 2 × 40 = 80 hours. |
| "A loaf takes 0.1 oven-hours, a cake 0.4" | Parameters | They set the oven-time usage. |
| "Determine the weekly baking plan" | Objective (implicit) | Revenue and costs are both given, so maximize total profit. |
Formulation
- Decision: c = number of cakes (≥ 0). Integer in practice, though the LP optimum is already whole.
- Fixed by commitment: loaves b = 300.
- Objective: maximize 2.5·b + 7·c, which is profit = revenue − ingredient cost.
- Constraint: 0.1·b + 0.4·c ≤ 80 oven-hours.
- Problem type: LP, or a trivial MILP if cakes must be integers.
Solution
- Bread uses 300 × 0.1 = 30 oven-hours, leaving 50 hours.
- Each cake nets a positive $7, so the oven time left over goes to cakes: c = 50 / 0.4 = 125 cakes.
- The plan splits across the two ovens without trouble. For example, oven 1 bakes all 300 loaves and 25 cakes (30 + 10 = 40 h), and oven 2 bakes 100 cakes (40 h).
Plan: 300 loaves and 125 cakes. Profit = 300 × 2.50 + 125 × 7 = $750 + $875 = $1,625. Revenue is $2,700 and ingredient cost is $1,075. Both ovens are fully used at 80 of 80 hours.
Ambiguities
- "Plans to bake 300 loaves": I read this as exactly 300. If it meant at least 300, the answer changes. Bread earns $25 per oven-hour against $17.50 for cakes, so the plan would become 800 loaves and 0 cakes, with a profit of $2,000. Tell me if that is what you meant.
- "Earn": I read this as revenue, with cost subtracted. If $12 and $4 were already profits, the plan stays the same (300 loaves and 125 cakes) and the profit would be $2,700.
Concepts-only skill, so the trial gave the model only the Skill and Read tools. The model's arithmetic was checked by hand (300 loaves and 125 cakes, profit 1,625; 800 loaves if the commitment were a minimum). Nothing was compared against a no-skill run.
Qué hace
Enseña al modelo a formular programas lineales (LP), programas lineales enteros mixtos (MILP) y programas cuadráticos (QP) a partir de un problema escrito. Solo cubre conceptos: no incluye API de resolución ni código.
Cómo funciona
- Clasifica cada frase como parámetro, restricción, decisión u objetivo y distingue compromisos («planea», «opera») de posibilidades («puede», «puede elegir»).
- Identifica objetivos implícitos: «determinar el plan» con solo costes significa minimizar el coste; con costes e ingresos, maximizar el beneficio.
- Indica al modelo que pregunte o resuelva todas las interpretaciones plausibles y comunique sus resultados, en lugar de elegir una sin decirlo.
- Cubre patrones y errores: beneficio lineal por tramos con totales enteros, desperdicio en corte de materiales, programación por metas, inventario de varios períodos y mezclas con una etapa compartida.
Para qué sirve
Ejercicios de investigación operativa y problemas reales de planificación: producción, programación, mezclas, carteras y asignación.
Límites
Formula, pero no resuelve. Tú decides cómo combinarla con un solucionador. El informe incluido de NVIDIA —su evaluación, no la nuestra— describe la compatibilidad QP de cuOpt como beta.
Riesgo bajo: solo contiene archivos de instrucciones del repositorio oficial de Skills de NVIDIA (Apache-2.0). Sin scripts, acceso a la red ni escritura de archivos; no necesita cuenta ni clave. El paquete incluye también el informe de evaluación de NVIDIA y un archivo de firma: se incluyeron sin cambios y no verificamos la firma. El modelo genera los resultados; comprueba la formulación y las cifras antes de usarlas en decisiones reales.