Iteration 2 Summary
Business Context: A global logistics company is optimizing the distribution of goods from a central warehouse to various cities to minimize transportation costs while meeting each city's demand and capacity constraints.
Optimization Problem: The company aims to minimize the total transportation cost of distributing goods from a central warehouse to various cities. The objective is to determine the optimal quantity of goods to be shipped to each city, considering the demand in each city, the transportation cost per unit, the maximum capacity that can be shipped to each city, and the total available goods.
Objective: minimize total_transportation_cost = sum(TransportationCosts.cost_per_unit[i] * QuantityShipped.quantity[i])
Tables Created: 1
Tables Modified: 0
Tables Deleted: 0
Key Change: Added a new table for decision variables and updated business configuration logic for scalar parameters and formulas based on OR expert mapping analysis.
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 typical logistics scenarios, ensuring that transportation costs, city demands, and capacities are realistic and align with the total available goods.