How do I approach System Design interview questions?

System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master system design interviews.

What difficulty level is this interview question?

This is a medium difficulty System Design question, commonly asked during Onsite rounds at PayPal.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at PayPal during technical interviews.

Design elevator scheduling for small building

Quick Overview

This PayPal data-scientist system-design question asks you to design the control policy for a single elevator serving a small building (3 floors plus a basement). A complete answer defines objectives, constraints, inputs, and state; proposes a SCAN/LOOK scheduling strategy with basement/peak priority and anti-starvation; specifies data structures; and lays out a discrete-event simulation to compare policies under varying demand.

Question

Design the control policy for a single elevator serving a small building: 3 floors plus 1 basement (stops at B, 1, 2, 3). The goal is to decide, at each moment, where the elevator should go next so as to minimize passenger waiting and in-car travel time. Cover the following:

Objectives. Define what you are optimizing for (e.g., minimize average wait time and average system time, bound tail latency, avoid starvation, handle peak traffic) and the trade-offs between them.
Constraints. Specify the physical and operational constraints (car capacity / weight limit, floor-to-floor travel time, door open/close and dwell times, safety interlocks, stops only at B/1/2/3).
Inputs and state. Identify the inputs the controller observes (hall up/down calls with direction, in-cab destination calls, current position and direction, door state, load estimate, timers) and the internal state it maintains.
Scheduling strategy. Propose how the elevator decides its next stop. Discuss directional collective control (SCAN/LOOK), basement / peak-traffic priority, anti-starvation, capacity-aware boarding, and optional destination grouping.
Data structures. Describe the supporting data structures and the control state machine.
Simulation plan. Outline how you would compare candidate policies under varying arrival distributions (off-peak, up-peak, down-peak, bursty), which metrics to track, and how to validate the results.

Quick Overview

Question

Objectives. Define what you are optimizing for (e.g., minimize average wait time and average system time, bound tail latency, avoid starvation, handle peak traffic) and the trade-offs between them.
Constraints. Specify the physical and operational constraints (car capacity / weight limit, floor-to-floor travel time, door open/close and dwell times, safety interlocks, stops only at B/1/2/3).
Inputs and state. Identify the inputs the controller observes (hall up/down calls with direction, in-cab destination calls, current position and direction, door state, load estimate, timers) and the internal state it maintains.
Scheduling strategy. Propose how the elevator decides its next stop. Discuss directional collective control (SCAN/LOOK), basement / peak-traffic priority, anti-starvation, capacity-aware boarding, and optional destination grouping.
Data structures. Describe the supporting data structures and the control state machine.
Simulation plan. Outline how you would compare candidate policies under varying arrival distributions (off-peak, up-peak, down-peak, bursty), which metrics to track, and how to validate the results.

Design elevator scheduling for small building

Quick Overview

Question

Solution

Submit Your Answer to Earn 20XP

Design elevator scheduling for small building

Quick Overview

Question

Solution

Submit Your Answer to Earn 20XP