Iteration 1 Summary
Business Context: A theme park chain aims to optimize the distribution of roller coasters across its parks globally to maximize visitor satisfaction, while adhering to budget, space, and maximum roller coaster constraints per park.
Optimization Problem: The objective is to maximize the total visitor satisfaction score across all parks by determining the number of roller coasters of each type to install in each park, subject to budget, space, and maximum roller coaster constraints.
Objective: maximize ∑(satisfaction_score[park_id, coaster_type] × num_coasters[park_id, coaster_type])
Tables Created: 4
Tables Modified: 0
Tables Deleted: 0
Key Change: Schema changes include creating new tables for visitor satisfaction scores, budget limits, available space, and maximum roller coasters per park. Configuration logic updates include scalar parameters for cost and space requirements of roller coaster types.
Status: Complete
Confidence: high
Next Focus: Ready for convergence
Mapping Adequacy: mostly_good
Business Configuration Parameters: 2

Triple Expert Data: Values were determined based on industry standards, realistic theme park budgets, and typical roller coaster costs and space requirements. The data ensures that the optimization problem is meaningful and solvable by providing a balance between visitor satisfaction, budget constraints, and space limitations.