Improving Efficiency of Internal Shuttle Service: Approach for Optimization

Approach for Optimizing Internal Shuttle Service

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Question

Your organization wants to make its internal shuttle service route more efficient.

The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am.

The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance.

What approach should you take?

Answers

A. 1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station. 2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction.

B. 1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station. 2. Dispatch an available shuttle and provide the map with the required stops based on the prediction.

C. 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints. 2. Dispatch an appropriately sized shuttle and indicate the required stops on the map.

D. 1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric. 2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.

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Explanations

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A. B. C. D.

The most suitable approach to making the internal shuttle service route more efficient would be option C: defining the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints and dispatching an appropriately sized shuttle and indicating the required stops on the map.

Option A suggests building a tree-based regression model to predict the number of passengers at each shuttle station and dispatching an appropriately sized shuttle based on the prediction. However, this approach does not consider the capacity of the shuttle, which could lead to overloading or underutilization of the shuttle.

Option B suggests building a tree-based classification model to predict whether the shuttle should pick up passengers at each shuttle station and dispatching an available shuttle based on the prediction. However, this approach does not consider the optimal route, which could result in a longer travel time for passengers and inefficient use of the shuttle.

Option D suggests building a reinforcement learning model with tree-based classification models to predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric. This approach may be too complex and resource-intensive for this scenario.

Option C, on the other hand, considers both the optimal route and the capacity constraints of the shuttle. By defining the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints, the shuttle can efficiently transport passengers without overloading or underutilizing the shuttle. Dispatching an appropriately sized shuttle and indicating the required stops on the map further ensures that the shuttle is used efficiently.

Overall, option C is the most appropriate approach to making the internal shuttle service route more efficient in this scenario.

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