Start by clarifying requirements and defining objectives (e.g., minimize delivery time, balance driver load) and constraints (real-time, partial information, scale). Then propose a high-level architecture with data structures (e.g., geospatial index, priority queue) and algorithms (e.g., greedy matching with rebalancing), and discuss trade-offs and scaling strategies.
Pro tip: Emphasize that perfect assignment is impossible with partial information; instead, focus on a robust, incremental approach that handles uncertainty and adapts as new data arrives, which mirrors real-world systems.
Ask questions to understand scale (orders/drivers per second), latency requirements, and business goals (e.g., minimize delivery time, maximize driver utilization). Define clear objectives and constraints.
Propose data structures to efficiently store and query driver locations (e.g., geohash, quadtree) and orders (e.g., priority queue by time). Consider real-time updates and spatial indexing.
Describe a matching algorithm (e.g., greedy nearest driver, Hungarian algorithm for batch) and justify choice based on trade-offs (optimality vs. latency). Discuss handling partial information (e.g., driver availability, traffic).
Explain how to scale horizontally (sharding by region, using message queues) and ensure fault tolerance (replication, fallbacks). Discuss monitoring and dynamic rebalancing.
Summarize key trade-offs (e.g., optimality vs. speed, centralization vs. decentralization) and potential improvements (e.g., machine learning for demand prediction).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.