Iteration 2 Summary
Business Context: A university is optimizing the allocation of student advisors to students with pets, ensuring balanced workloads in terms of student numbers and total pet weight. The goal is to minimize the maximum workload across all advisors.
Optimization Problem: Minimize the maximum workload of any advisor, where workload is a linear combination of the number of students and total pet weight assigned to each advisor. Each student is assigned to exactly one advisor, and each advisor has limits on the number of students and total pet weight they can handle.
Objective: minimize max_workload = max(sum(students_assigned[i] + pet_weight_assigned[i] for each advisor i))
Tables Created: 1
Tables Modified: 1
Tables Deleted: 0
Key Change: Schema changes include creating new tables for missing optimization data, modifying existing tables to improve mapping adequacy, and updating business configuration logic for scalar parameters and formulas.
Status: Complete
Confidence: high
Next Focus: Ready for convergence
Mapping Adequacy: mostly_good
Business Configuration Parameters: 1

Triple Expert Data: Values were determined based on typical university advisor workloads and average pet weights, ensuring that constraints are neither too tight nor too loose, allowing for a balanced distribution of students and pets.