Iteration 1 - OR_EXPERT_REFINEMENT
Sequence: 3
Timestamp: 2025-07-25 22:28:28

Prompt:
You are an Operations Research (OR) expert in iteration 1 of an alternating optimization process. The algorithm alternates between OR expert analysis and data engineering implementation until convergence.

CRITICAL MATHEMATICAL CONSTRAINTS FOR LINEAR/MIXED-INTEGER PROGRAMMING:
- The optimization problem MUST remain Linear Programming (LP) or Mixed-Integer Programming (MIP)
- Objective function MUST be linear: minimize/maximize ∑(coefficient × variable)
- All constraints MUST be linear: ∑(coefficient × variable) ≤/≥/= constant
- Decision variables can be continuous (LP) or mixed continuous/integer (MIP)
- NO variable products, divisions, or other nonlinear relationships
- If previous iteration introduced nonlinear elements, redesign as linear formulation
- Maintain between 2 and 20 constraints for optimization feasibility

YOUR SCOPE: Focus exclusively on optimization modeling and mapping analysis. Do NOT propose database changes.
ROW COUNT AWARENESS: Understand that data engineer applies 3-row minimum rule - insufficient table data gets moved to business_configuration_logic.json.


DATA AVAILABILITY CHECK: 
Before listing missing requirements, verify:
- Check current schema for required data columns
- Check business configuration logic for required parameters  
- Only list as "missing" if data is truly unavailable
- If all mappings are "good", missing_requirements should be []

CONSISTENCY RULES:
- IF all mapping_adequacy == "good" THEN missing_optimization_requirements = []
- IF missing_optimization_requirements = [] THEN complete CAN be true
- IF complete == true THEN confidence should be "high"

SELF-CHECK: Before responding, verify:
1. Does current schema contain the data I claim is missing?
2. Are my mapping assessments consistent with missing requirements?
3. Is my complete status consistent with missing requirements?

MAPPING COMPLETENESS CHECK: Ensure logical consistency between:
- All objective coefficients mapped with adequacy evaluation
- All constraint bounds mapped with adequacy evaluation  
- All decision variables mapped with adequacy evaluation
- Missing requirements list matches inadequate mappings only


CRITICAL: Respond with ONLY a valid JSON object. No explanations, no markdown, no extra text.



CURRENT STATE (iteration 0):
{
  "iteration": 1,
  "converged": false,
  "business_context": "A university wants to optimize the allocation of students to restaurants based on their preferences and spending habits, aiming to maximize student satisfaction while minimizing total spending.",
  "optimization_problem": "The goal is to maximize student satisfaction by allocating students to restaurants they prefer, while ensuring that the total spending across all students does not exceed a predefined budget. The satisfaction is modeled as a linear function of the time spent and the rating of the restaurant.",
  "objective": "maximize \u2211(satisfaction_score[StuID, ResID] * x[StuID, ResID])",
  "table_count": 2,
  "key_changes": [
    "Schema changes include creating tables for satisfaction scores and restaurant capacities, modifying the Visits_Restaurant table to better map decision variables, and adding business configuration logic for budget and satisfaction score calculation."
  ],
  "math_consistency": "high",
  "next_iteration_focus": "Define satisfaction score calculation and gather budget and capacity data",
  "mapping_adequacy_summary": "needs_improvement"
}

CURRENT SCHEMA:
```sql
-- Iteration 1 Database Schema
-- Objective: Schema changes include creating tables for satisfaction scores and restaurant capacities, modifying the Visits_Restaurant table to better map decision variables, and adding business configuration logic for budget and satisfaction score calculation.

CREATE TABLE Satisfaction_Scores (
  StuID INTEGER,
  ResID INTEGER,
  score FLOAT
);

CREATE TABLE Restaurant_Capacities (
  ResID INTEGER,
  capacity INTEGER
);

CREATE TABLE Visits_Restaurant (
  StuID INTEGER,
  ResID INTEGER,
  visited BOOLEAN
);


```

CURRENT DATA DICTIONARY:
{
  "tables": {
    "Satisfaction_Scores": {
      "business_purpose": "Satisfaction score based on time spent and restaurant rating",
      "optimization_role": "objective_coefficients",
      "columns": {
        "StuID": {
          "data_type": "INTEGER",
          "business_meaning": "Student ID",
          "optimization_purpose": "Identifies the student",
          "sample_values": "1, 2, 3"
        },
        "ResID": {
          "data_type": "INTEGER",
          "business_meaning": "Restaurant ID",
          "optimization_purpose": "Identifies the restaurant",
          "sample_values": "1, 2, 3"
        },
        "score": {
          "data_type": "FLOAT",
          "business_meaning": "Satisfaction score",
          "optimization_purpose": "Used in the objective function",
          "sample_values": "4.5, 3.8, 5.0"
        }
      }
    },
    "Restaurant_Capacities": {
      "business_purpose": "Maximum number of students a restaurant can accommodate",
      "optimization_role": "constraint_bounds",
      "columns": {
        "ResID": {
          "data_type": "INTEGER",
          "business_meaning": "Restaurant ID",
          "optimization_purpose": "Identifies the restaurant",
          "sample_values": "1, 2, 3"
        },
        "capacity": {
          "data_type": "INTEGER",
          "business_meaning": "Maximum capacity",
          "optimization_purpose": "Used in the capacity constraint",
          "sample_values": "50, 75, 100"
        }
      }
    },
    "Visits_Restaurant": {
      "business_purpose": "Allocation of students to restaurants",
      "optimization_role": "decision_variables",
      "columns": {
        "StuID": {
          "data_type": "INTEGER",
          "business_meaning": "Student ID",
          "optimization_purpose": "Identifies the student",
          "sample_values": "1, 2, 3"
        },
        "ResID": {
          "data_type": "INTEGER",
          "business_meaning": "Restaurant ID",
          "optimization_purpose": "Identifies the restaurant",
          "sample_values": "1, 2, 3"
        },
        "visited": {
          "data_type": "BOOLEAN",
          "business_meaning": "Whether the student visited the restaurant",
          "optimization_purpose": "Binary decision variable",
          "sample_values": "true, false"
        }
      }
    }
  }
}


CURRENT BUSINESS CONFIGURATION LOGIC:
{
  "Budget": {
    "sample_value": 10000,
    "data_type": "INTEGER",
    "business_meaning": "Total budget for student spending",
    "optimization_role": "Total spending constraint",
    "configuration_type": "scalar_parameter"
  },
  "Satisfaction_Score_Formula": {
    "formula_expression": "time_spent * restaurant_rating",
    "data_type": "STRING",
    "business_meaning": "Satisfaction score based on time spent and restaurant rating",
    "optimization_role": "Objective coefficient calculation",
    "configuration_type": "business_logic_formula"
  }
}


TASK: Refine the optimization problem formulation by analyzing current data schema mapping and identifying requirements while maintaining LINEAR structure.

JSON STRUCTURE REQUIRED:

{
  "database_id": "restaurant_1",
  "iteration": 1,
  "business_context": "Updated realistic business scenario description that supports linear optimization",
  "optimization_problem_description": "Refined description of LINEAR optimization problem", 
  "optimization_formulation": {
    "objective": "refined linear minimize/maximize with mathematical precision (sum of weighted variables only)",
    "decision_variables": "clearly defined controllable linear variables (continuous or integer)",
    "constraints": "mathematically precise LINEAR constraint definitions (no variable products or divisions) - maintain 2 to 20 constraints"
  },
  
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "coefficient_name[indices]": {
        "currently_mapped_to": "table.column OR business_configuration_logic.key OR missing",
        "mapping_adequacy": "good/missing/redundant/partial/inaccurate",
        "description": "what this coefficient represents in the optimization model"
      }
    },
    "constraint_bounds": {
      "constraint_name[indices]": {
        "currently_mapped_to": "table.column OR business_configuration_logic.key OR missing",
        "mapping_adequacy": "good/missing/redundant/partial/inaccurate", 
        "description": "what this constraint bound represents"
      }
    },
    "decision_variables": {
      "variable_name[indices]": {
        "currently_mapped_to": "table.column OR business_configuration_logic.key OR missing",
        "mapping_adequacy": "good/missing/redundant/partial/inaccurate",
        "description": "what this decision variable represents",
        "variable_type": "continuous/integer/binary"
      }
    }
  },
  
  "missing_optimization_requirements": [
    "List what data/parameters are still needed for linear formulation (should be [] if all mappings are good)"
  ],
  
  "iteration_status": {
    "complete": true/false based on linear formulation completeness,
    "confidence": "updated confidence level",
    "next_focus": "What needs refinement for linear optimization OR 'Ready for convergence'"
  }
}

Response:
{
  "database_id": "restaurant_1",
  "iteration": 1,
  "business_context": "A university aims to optimize student allocation to restaurants based on preferences and spending habits, maximizing satisfaction while keeping total spending within a predefined budget. Satisfaction is modeled as a linear function of restaurant ratings and time spent.",
  "optimization_problem_description": "Maximize student satisfaction by allocating students to preferred restaurants, ensuring total spending does not exceed the budget and restaurant capacities are not exceeded. The problem is formulated as a linear optimization model.",
  "optimization_formulation": {
    "objective": "maximize \u2211(satisfaction_score[StuID, ResID] * x[StuID, ResID])",
    "decision_variables": "x[StuID, ResID] \u2208 {0, 1} (binary decision variable indicating whether student StuID visits restaurant ResID)",
    "constraints": [
      "\u2211(spending[StuID, ResID] * x[StuID, ResID]) \u2264 Budget (total spending constraint)",
      "\u2211(x[StuID, ResID]) \u2264 capacity[ResID] for all ResID (restaurant capacity constraint)",
      "\u2211(x[StuID, ResID]) = 1 for all StuID (each student visits exactly one restaurant)"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "satisfaction_score[StuID, ResID]": {
        "currently_mapped_to": "Satisfaction_Scores.score",
        "mapping_adequacy": "good",
        "description": "Satisfaction score for student StuID at restaurant ResID"
      }
    },
    "constraint_bounds": {
      "Budget": {
        "currently_mapped_to": "business_configuration_logic.Budget",
        "mapping_adequacy": "good",
        "description": "Total budget for student spending"
      },
      "capacity[ResID]": {
        "currently_mapped_to": "Restaurant_Capacities.capacity",
        "mapping_adequacy": "good",
        "description": "Maximum capacity of restaurant ResID"
      }
    },
    "decision_variables": {
      "x[StuID, ResID]": {
        "currently_mapped_to": "Visits_Restaurant.visited",
        "mapping_adequacy": "good",
        "description": "Binary decision variable indicating whether student StuID visits restaurant ResID",
        "variable_type": "binary"
      }
    }
  },
  "missing_optimization_requirements": [],
  "iteration_status": {
    "complete": true,
    "confidence": "high",
    "next_focus": "Ready for convergence"
  }
}
