Start by clarifying requirements and constraints, then design a modular architecture separating request handling, scheduling, and elevator control. Discuss trade-offs between different scheduling algorithms and multi-elevator allocation strategies, and consider scalability and fault tolerance.
Pro tip: Relate the elevator system to Waymo's autonomous vehicle fleet management, highlighting similarities in resource allocation and real-time scheduling. Emphasize safety and efficiency, as these are critical for both domains.
Ask questions to understand the scope: number of elevators, floors, traffic patterns, real-time constraints, and failure handling. Define functional and non-functional requirements.
Outline the main components: request queue, scheduler, elevator controllers, and state management. Choose a communication model (e.g., event-driven) and data stores.
Propose and compare algorithms like SCAN, LOOK, or destination dispatch. Discuss how to optimize for average wait time, throughput, and fairness.
Design a strategy to assign requests to elevators, considering proximity, direction, load, and future requests. Mention centralized vs. distributed control.
Analyze trade-offs between simplicity and optimality, latency vs. throughput, and discuss how the system scales with more elevators/floors. Address fault tolerance and recovery.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem: what are the goals (minimize wait time, maximize throughput, fairness), constraints (number of elevators, floors, traffic patterns), and assumptions (peak hours, passenger arrival rates). Then propose a simple partitioning strategy (e.g., zoning or sectoring) and analyze its performance. Finally, discuss how the design must evolve at scale, considering dynamic allocation, machine learning for prediction, and trade-offs between complexity and efficiency.
Pro tip: Frame the problem as a resource allocation and scheduling challenge, similar to load balancing in distributed systems. Emphasize that at scale, static partitioning breaks down and you need adaptive, data-driven approaches—this shows you think beyond naive solutions.
Ask about building size, number of elevators, traffic patterns (e.g., morning up-peak, lunch inter-floor), and optimization goals (wait time vs. energy). Establish assumptions to bound the problem.
Suggest a simple static zoning approach: divide floors into contiguous zones, assign each elevator to a zone, and have it serve only those floors. Discuss pros (simplicity, predictability) and cons (inefficient during off-peak).
Evaluate the baseline using metrics like average wait time, throughput, and fairness. Identify bottlenecks (e.g., lobby congestion) and consider alternatives like dynamic sectoring or destination dispatch.
At scale (more floors/elevators), static zoning fails. Introduce dynamic allocation: use real-time data (passenger requests, elevator positions) to assign elevators optimally, possibly with ML for demand prediction. Discuss distributed control and communication overhead.
Conclude with a recommended approach that balances simplicity and scalability, and mention how you would validate it via simulation or A/B testing. Highlight key trade-offs and potential future improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.