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Answer: Implement a mixed-integer programming model, preprocess data by handling missing values and aggregating by VehicleType.
For optimizing delivery routes, a mixed-integer programming model is suitable as it can handle discrete decisions like route selection. Preprocessing involves handling missing values and aggregating data by VehicleType to ensure each vehicle's data is considered individually.
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You are required to integrate prescriptive analytics into a report for a logistics company aiming to optimize their delivery routes. The dataset includes 'RouteID', 'Distance', 'Time', 'VehicleType', and 'Cost'. Describe how you would use optimization models in Microsoft Fabric to suggest the optimal routes for each vehicle type, considering factors like distance, time, and cost. Include the type of optimization model you would use and the data preprocessing steps necessary.
A
Use a linear programming model, preprocess data by normalizing and splitting into training and test sets.
B
Choose a decision tree model, preprocess data by encoding categorical variables and scaling numerical features.
C
Implement a mixed-integer programming model, preprocess data by handling missing values and aggregating by VehicleType.
D
Select a neural network model, preprocess data by binning and reducing dimensionality.