Start by clarifying the optimization objective (e.g., minimize average wait time) and the physical constraints (speed, capacity, single car). Then describe the controller's state representation, decision logic (e.g., SCAN algorithm), data structures, and a simulation framework to compare strategies like FCFS, SCAN, and LOOK.
Pro tip: Frame the problem as a sequential decision-making task under uncertainty, and emphasize how you would use simulation to evaluate trade-offs between average wait time and worst-case wait time, which is crucial for data science roles.
Clarify what you're optimizing for (e.g., minimize average wait time, minimize max wait time, energy efficiency) and list physical constraints (e.g., speed, acceleration, capacity, single car, four stops).
Specify what the controller tracks: current floor, direction, set of pending requests (up/down per floor), passenger load, and possibly time-based metrics.
Select a scheduling algorithm (e.g., FCFS, SCAN, LOOK, or a custom heuristic) and describe data structures (e.g., queues, sets, priority queues) to manage requests efficiently.
Outline a discrete-event simulation: generate random passenger arrivals, model elevator movement, and collect metrics like average wait time, max wait time, and throughput. Compare policies under different arrival patterns.
Discuss trade-offs between simplicity and optimality, and recommend a policy based on simulation results, considering real-world factors like passenger patience and energy consumption.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.