Instruction: Explain your choice of model, how you would handle dynamic data (such as traffic and weather), and how you would measure the model's impact on efficiency.
Context: The question probes the candidate's ability to apply machine learning to solve complex optimization problems, requiring an understanding of dynamic systems and impact measurement.
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I would frame routing as a prediction-plus-optimization problem. The ML part is useful for estimating things like travel time, delay risk, stop duration, or failed-delivery probability, but the actual routing decision also depends on constraints such as capacity, service windows, driver rules, and cost objectives.
So...
medium
hard
hard