Iteration 2 Summary
Business Context: A university is optimizing the allocation of students' time between playing video games and participating in sports to maximize their overall engagement while ensuring academic responsibilities are not compromised.
Optimization Problem: The goal is to maximize the total engagement score of students by allocating their available hours between playing video games and participating in sports, subject to constraints on maximum hours available, minimum academic hours required, and scholarship requirements.
Objective: maximize total_engagement = sum(GameEngagementScore[StuID, GameID] * x[StuID, GameID] + SportEngagementScore[StuID, SportName] * y[StuID, SportName])
Tables Created: 2
Tables Modified: 1
Tables Deleted: 0
Key Change: Schema changes include creating new tables for decision variables, modifying existing tables to improve mapping adequacy, and moving scalar parameters to configuration logic.
Status: Complete
Confidence: high
Next Focus: Ready for convergence
Mapping Adequacy: mostly_good
Business Configuration Parameters: 3

Triple Expert Data: Values were determined based on typical student schedules, engagement levels, and scholarship requirements to ensure a balanced allocation between games and sports.