Iteration 1 Summary
Business Context: Optimize the allocation of bikes across stations to minimize the number of stations running out of bikes or docks at any given time.
Optimization Problem: Determine the optimal number of bikes to allocate to each station at the start of the day to minimize the likelihood of stations running out of bikes or docks, considering current availability and expected demand.
Objective: minimize ∑(shortage_penalty * shortage[i] + excess_penalty * excess[i])
Tables Created: 1
Tables Modified: 1
Tables Deleted: 0
Key Change: Schema changes include creating new tables for penalty costs and expected demand, modifying existing tables to include missing data, and updating configuration logic for scalar parameters and formulas.
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 urban bike-sharing systems, considering average station sizes, expected demand patterns, and penalty costs that reflect operational priorities.