Iteration final - PROBLEM_DESCRIPTION
Sequence: 5
Timestamp: 2025-07-25 22:26:53

Prompt:
You are a business analyst creating structured optimization problem documentation.

DATA SOURCES EXPLANATION:
- FINAL OR ANALYSIS: Final converged optimization problem from alternating process (iteration 1), contains business context and schema mapping evaluation
- DATABASE SCHEMA: Current database structure after iterative adjustments  
- DATA DICTIONARY: Business meanings and optimization roles of tables and columns
- CURRENT STORED VALUES: Realistic business data generated by triple expert (business + data + optimization)
- BUSINESS CONFIGURATION: Scalar parameters and business logic formulas separated from table data

CRITICAL REQUIREMENTS: 
- Ensure problem description naturally leads to LINEAR or MIXED-INTEGER optimization formulation
- Make business context consistent with the intended decision variables and objectives
- Align constraint descriptions with expected mathematical constraints
- Ensure data descriptions map clearly to expected coefficient sources
- Maintain business authenticity while fixing mathematical consistency issues
- Avoid business scenarios that would naturally require nonlinear relationships (variable products, divisions, etc.)

AUTO-EXTRACTED CONTEXT REQUIREMENTS:
- Business decisions match expected decision variables: x_journalist_event: binary variable indicating if journalist j is assigned to event e
- Operational parameters align with expected linear objective: maximize ∑(Years_working_journalist × Event_Attendance_event × x_journalist_event)
- Business configuration includes: Maximum number of events a journalist can be assigned to (used for Constraint bound for journalist assignments)
- Business logic formulas to express in natural language: Calculation of coverage quality based on journalist experience and event attendance (calculation method for Objective coefficient calculation)
- Use natural language to precisely describe linear mathematical relationships
- NO mathematical formulas, equations, or symbolic notation
- Present data as current operational information
- Focus on precise operational decision-making that leads to linear formulations
- Resource limitations match expected linear constraints
- Avoid scenarios requiring variable products, divisions, or other nonlinear relationships
- Include specific operational parameters that map to expected coefficient sources
- Reference business configuration parameters where appropriate

FINAL OR ANALYSIS:
{
  "database_id": "news_report",
  "iteration": 1,
  "business_context": "A news agency wants to optimize the assignment of journalists to events to maximize the total coverage quality, considering factors like journalist experience and event attendance. The optimization problem is linear, ensuring each event is covered by at least one journalist and each journalist is assigned to no more than a specified number of events.",
  "optimization_problem_description": "Maximize the total coverage quality by assigning journalists to events. The coverage quality is a linear function of the journalist's years of experience and the event's attendance. Constraints include ensuring each event is covered by at least one journalist and each journalist is assigned to no more than a specified number of events.",
  "optimization_formulation": {
    "objective": "maximize \u2211(Years_working_journalist \u00d7 Event_Attendance_event \u00d7 x_journalist_event)",
    "decision_variables": "x_journalist_event: binary variable indicating if journalist j is assigned to event e",
    "constraints": "1. \u2211(x_journalist_event) \u2265 1 for each event e, 2. \u2211(x_journalist_event) \u2264 max_assignments_j for each journalist j"
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "Years_working_journalist": {
        "currently_mapped_to": "journalist.Years_working",
        "mapping_adequacy": "good",
        "description": "Years of experience of the journalist"
      },
      "Event_Attendance_event": {
        "currently_mapped_to": "event.Event_Attendance",
        "mapping_adequacy": "good",
        "description": "Attendance of the event"
      }
    },
    "constraint_bounds": {
      "max_assignments_j": {
        "currently_mapped_to": "journalist.max_assignments",
        "mapping_adequacy": "good",
        "description": "Maximum number of events a journalist can be assigned to"
      }
    },
    "decision_variables": {
      "x_journalist_event": {
        "currently_mapped_to": "journalist_event_assignment.assignment",
        "mapping_adequacy": "good",
        "description": "Binary variable indicating if a journalist is assigned to an event",
        "variable_type": "binary"
      }
    }
  },
  "missing_optimization_requirements": [],
  "iteration_status": {
    "complete": true,
    "confidence": "high",
    "next_focus": "Ready for convergence"
  }
}

FINAL DATABASE SCHEMA:
```sql
-- Iteration 1 Database Schema
-- Objective: Schema changes include creating a new table for journalist-event assignments and adding a column for maximum assignments per journalist. Configuration logic updates include adding scalar parameters for maximum assignments and a formula for coverage quality calculation.

CREATE TABLE journalist (
  Years_working INTEGER,
  max_assignments INTEGER
);

CREATE TABLE event (
  Event_Attendance INTEGER
);

CREATE TABLE journalist_event_assignment (
  assignment BOOLEAN
);


```

CURRENT STORED VALUES:
```sql
-- Iteration 1 Realistic Data
-- Generated by triple expert (business + data + optimization)
-- Values were determined based on realistic business scenarios, considering journalist experience levels, event attendance ranges, and assignment constraints to ensure a meaningful and solvable optimization problem.

-- Realistic data for journalist
INSERT INTO journalist (Years_working, max_assignments) VALUES (5, 3);
INSERT INTO journalist (Years_working, max_assignments) VALUES (10, 4);
INSERT INTO journalist (Years_working, max_assignments) VALUES (15, 5);

-- Realistic data for event
INSERT INTO event (Event_Attendance) VALUES (100);
INSERT INTO event (Event_Attendance) VALUES (200);
INSERT INTO event (Event_Attendance) VALUES (300);

-- Realistic data for journalist_event_assignment
INSERT INTO journalist_event_assignment (assignment) VALUES (0);
INSERT INTO journalist_event_assignment (assignment) VALUES (1);
INSERT INTO journalist_event_assignment (assignment) VALUES (0);


```

DATA DICTIONARY:
{
  "tables": {
    "journalist": {
      "business_purpose": "Stores information about journalists",
      "optimization_role": "business_data",
      "columns": {
        "Years_working": {
          "data_type": "INTEGER",
          "business_meaning": "Years of experience of the journalist",
          "optimization_purpose": "Objective coefficient",
          "sample_values": "5, 10, 15"
        },
        "max_assignments": {
          "data_type": "INTEGER",
          "business_meaning": "Maximum number of events the journalist can be assigned to",
          "optimization_purpose": "Constraint bound",
          "sample_values": "3, 4, 5"
        }
      }
    },
    "event": {
      "business_purpose": "Stores information about events",
      "optimization_role": "business_data",
      "columns": {
        "Event_Attendance": {
          "data_type": "INTEGER",
          "business_meaning": "Attendance of the event",
          "optimization_purpose": "Objective coefficient",
          "sample_values": "100, 200, 300"
        }
      }
    },
    "journalist_event_assignment": {
      "business_purpose": "Stores binary assignments of journalists to events",
      "optimization_role": "decision_variables",
      "columns": {
        "assignment": {
          "data_type": "BOOLEAN",
          "business_meaning": "Binary variable indicating if a journalist is assigned to an event",
          "optimization_purpose": "Decision variable",
          "sample_values": "0, 1"
        }
      }
    }
  }
}


BUSINESS CONFIGURATION:

BUSINESS CONFIGURATION:
{
  "max_assignments": {
    "data_type": "INTEGER",
    "business_meaning": "Maximum number of events a journalist can be assigned to",
    "optimization_role": "Constraint bound for journalist assignments",
    "configuration_type": "scalar_parameter",
    "value": 4,
    "business_justification": "A balanced assignment limit that allows journalists to cover multiple events without overburdening them."
  },
  "coverage_quality_formula": {
    "data_type": "STRING",
    "business_meaning": "Calculation of coverage quality based on journalist experience and event attendance",
    "optimization_role": "Objective coefficient calculation",
    "configuration_type": "business_logic_formula",
    "formula_expression": "Years_working_journalist * Event_Attendance_event"
  }
}

Business Configuration Design: 
Our system separates business logic design from value determination:
- Configuration Logic (business_configuration_logic.json): Templates designed by data engineers with sample_value for scalars and actual formulas for business logic
- Configuration Values (business_configuration.json): Realistic values determined by domain experts for scalar parameters only
- Design Rationale: Ensures business logic consistency while allowing flexible parameter tuning


TASK: Create structured markdown documentation for SECTIONS 1-3 ONLY (Problem Description).

EXACT MARKDOWN STRUCTURE TO FOLLOW:

# Complete Optimization Problem and Solution: news_report

## 1. Problem Context and Goals

### Context  
[Regenerate business context that naturally aligns with LINEAR optimization formulation. Ensure:]
- Business decisions match expected decision variables: x_journalist_event: binary variable indicating if journalist j is assigned to event e
- Operational parameters align with expected linear objective: maximize ∑(Years_working_journalist × Event_Attendance_event × x_journalist_event)
- Business configuration includes: Maximum number of events a journalist can be assigned to (used for Constraint bound for journalist assignments)
- Business logic formulas to express in natural language: Calculation of coverage quality based on journalist experience and event attendance (calculation method for Objective coefficient calculation)
- Use natural language to precisely describe linear mathematical relationships
- NO mathematical formulas, equations, or symbolic notation
- Present data as current operational information
- Focus on precise operational decision-making that leads to linear formulations
- Resource limitations match expected linear constraints
- Avoid scenarios requiring variable products, divisions, or other nonlinear relationships
- Include specific operational parameters that map to expected coefficient sources
- Reference business configuration parameters where appropriate
- CRITICAL: Include ALL business configuration information (scalar parameters AND business logic formulas) in natural business language

### Goals  
[Regenerate goals that clearly lead to LINEAR mathematical objective:]
- Optimization goal: maximize
- Metric to optimize: maximize ∑(Years_working_journalist × Event_Attendance_event × x_journalist_event)
- Success measurement aligned with expected coefficient sources
- Use natural language to precisely describe linear optimization goal
- NO mathematical formulas, equations, or symbolic notation

## 2. Constraints    

[Regenerate constraints that directly match expected LINEAR mathematical constraints:]
- Expected constraint: 1. ∑(x_journalist_event) ≥ 1 for each event e, 2. ∑(x_journalist_event) ≤ max_assignments_j for each journalist j (Form: Standard constraint form based on business requirements)

[Each constraint should be described in business terms that naturally lead to LINEAR mathematical forms (no variable products or divisions)]

## 3. Available Data  

### Database Schema  
```sql
-- Iteration 1 Database Schema
-- Objective: Schema changes include creating a new table for journalist-event assignments and adding a column for maximum assignments per journalist. Configuration logic updates include adding scalar parameters for maximum assignments and a formula for coverage quality calculation.

CREATE TABLE journalist (
  Years_working INTEGER,
  max_assignments INTEGER
);

CREATE TABLE event (
  Event_Attendance INTEGER
);

CREATE TABLE journalist_event_assignment (
  assignment BOOLEAN
);


```

### Data Dictionary  
[Create comprehensive business-oriented data dictionary mapping tables and columns to their business purposes and optimization roles - NOT technical database terms. Base this on the data_dictionary provided but rewrite in business language that clearly connects to the expected linear mathematical formulation]

### Current Stored Values  
```sql
-- Iteration 1 Realistic Data
-- Generated by triple expert (business + data + optimization)
-- Values were determined based on realistic business scenarios, considering journalist experience levels, event attendance ranges, and assignment constraints to ensure a meaningful and solvable optimization problem.

-- Realistic data for journalist
INSERT INTO journalist (Years_working, max_assignments) VALUES (5, 3);
INSERT INTO journalist (Years_working, max_assignments) VALUES (10, 4);
INSERT INTO journalist (Years_working, max_assignments) VALUES (15, 5);

-- Realistic data for event
INSERT INTO event (Event_Attendance) VALUES (100);
INSERT INTO event (Event_Attendance) VALUES (200);
INSERT INTO event (Event_Attendance) VALUES (300);

-- Realistic data for journalist_event_assignment
INSERT INTO journalist_event_assignment (assignment) VALUES (0);
INSERT INTO journalist_event_assignment (assignment) VALUES (1);
INSERT INTO journalist_event_assignment (assignment) VALUES (0);


```

STOP HERE - Do not include section 4 (Mathematical Optimization Formulation).

Response:
# Complete Optimization Problem and Solution: news_report

## 1. Problem Context and Goals

### Context  
A news agency is tasked with optimizing the assignment of journalists to events to maximize the overall quality of coverage. The quality of coverage is determined by the journalist's years of experience and the attendance of the event they are assigned to. Each event must be covered by at least one journalist, and no journalist can be assigned to more than a specified number of events, as defined by the business configuration. The decision to assign a journalist to an event is represented as a binary choice, ensuring a clear and linear relationship between the decision variables and the objective. The coverage quality is calculated as the product of the journalist's years of experience and the event's attendance, which directly contributes to the linear objective function. This setup ensures that the optimization problem remains linear, avoiding any nonlinear relationships such as variable products or divisions.

### Goals  
The primary goal of this optimization problem is to maximize the total coverage quality across all events. This is achieved by strategically assigning journalists to events in a way that leverages their experience and the significance of the event, as measured by attendance. Success is measured by the sum of the coverage quality scores for all assignments, where each score is the product of the journalist's years of experience and the event's attendance. The optimization process ensures that the assignments are made within the constraints of each journalist's maximum allowable number of event assignments, maintaining a balanced and efficient allocation of resources.

## 2. Constraints    

1. **Event Coverage Requirement**: Each event must be covered by at least one journalist. This ensures that all events receive adequate coverage, and no event is left unattended.  
2. **Journalist Assignment Limit**: No journalist can be assigned to more than the maximum number of events specified in the business configuration. This prevents overburdening journalists and ensures a fair distribution of assignments.  

These constraints are designed to maintain operational feasibility while aligning with the linear structure of the optimization problem. They ensure that the assignments are both comprehensive and balanced, without introducing any nonlinear complexities.

## 3. Available Data  

### Database Schema  
```sql
-- Iteration 1 Database Schema
-- Objective: Schema changes include creating a new table for journalist-event assignments and adding a column for maximum assignments per journalist. Configuration logic updates include adding scalar parameters for maximum assignments and a formula for coverage quality calculation.

CREATE TABLE journalist (
  Years_working INTEGER,
  max_assignments INTEGER
);

CREATE TABLE event (
  Event_Attendance INTEGER
);

CREATE TABLE journalist_event_assignment (
  assignment BOOLEAN
);
```

### Data Dictionary  
- **Journalist Table**:  
  - **Years_working**: Represents the number of years a journalist has been working. This value is used to calculate the coverage quality score.  
  - **max_assignments**: Specifies the maximum number of events a journalist can be assigned to, ensuring balanced workloads.  

- **Event Table**:  
  - **Event_Attendance**: Represents the number of attendees at an event. This value is used to calculate the coverage quality score.  

- **Journalist_Event_Assignment Table**:  
  - **assignment**: A binary indicator (0 or 1) that determines whether a journalist is assigned to a specific event. This is the decision variable in the optimization problem.  

### Current Stored Values  
```sql
-- Iteration 1 Realistic Data
-- Generated by triple expert (business + data + optimization)
-- Values were determined based on realistic business scenarios, considering journalist experience levels, event attendance ranges, and assignment constraints to ensure a meaningful and solvable optimization problem.

-- Realistic data for journalist
INSERT INTO journalist (Years_working, max_assignments) VALUES (5, 3);
INSERT INTO journalist (Years_working, max_assignments) VALUES (10, 4);
INSERT INTO journalist (Years_working, max_assignments) VALUES (15, 5);

-- Realistic data for event
INSERT INTO event (Event_Attendance) VALUES (100);
INSERT INTO event (Event_Attendance) VALUES (200);
INSERT INTO event (Event_Attendance) VALUES (300);

-- Realistic data for journalist_event_assignment
INSERT INTO journalist_event_assignment (assignment) VALUES (0);
INSERT INTO journalist_event_assignment (assignment) VALUES (1);
INSERT INTO journalist_event_assignment (assignment) VALUES (0);
```
