Amazon
Logistics Lab
Route optimization, warehouse management, package tracking
Walkthrough
How Logistics Works
Step-by-step from package arrival to delivery
Packages Arrive at Warehouse
Packages of varying weights and destinations arrive at the central warehouse. Each package has a priority, weight, and destination zone that determines how it should be routed.
Experiments
Visualization
3D Control Center
Interactive logistics network visualization
Analysis
Complexity Analysis
Time and space complexity of logistics algorithms
| Algorithm | Time Complexity | Space Complexity | Use Case |
|---|---|---|---|
| BFS (Route) | O(V + E) | O(V) | Unweighted shortest path |
| Dijkstra | O((V+E) log V) | O(V) | Weighted shortest path |
| A* Search | O(E) | O(V) | Heuristic pathfinding |
| DFS (Tracking) | O(V + E) | O(V) | Path existence |
| Flood Fill | O(V + E) | O(V) | Failure zone isolation |
Architecture
System Design
How Amazon-scale logistics systems are architected
System Architecture
- Microservices-based architecture with separate services for routing, tracking, and warehouse management
- Event-driven communication using message queues for real-time package status updates
- Distributed graph database (Neo4j) for storing and querying the logistics network topology
- Redis caching layer for frequently accessed routes and warehouse inventory data
Data Flow
- Package scan events trigger status updates propagated through Kafka topics
- Route optimization service consumes real-time traffic data and recalculates ETA using Dijkstra/A*
- Warehouse inventory changes are broadcast to all connected delivery stations
- Failure detection via heartbeat monitoring triggers automatic re-routing workflows
Scalability
- Horizontal scaling of routing workers using Kubernetes pods based on queue depth
- Geographic sharding of the graph database by region to reduce query latency
- CDN-based static asset delivery for tracking dashboard with WebSocket live updates
- Read replicas for the inventory DB to handle peak holiday season traffic
Key Algorithms
- Dijkstra & A* for shortest path computation across the delivery network graph
- BFS/DFS for warehouse zone traversal and package location in the sortation center
- Greedy algorithms for bin packing and truck load optimization
- Distributed consensus (Raft) for failover coordination between regional control centers
Practice
Interview Questions
Common system design and DSA questions from Amazon interviews
Design a package tracking system that can handle 1M+ packages in transit at any time.
Consider sharding by region, event sourcing, and WebSocket push for live updates.
How would you implement real-time shortest path re-computation when a road segment fails?
Incremental Dijkstra with a dynamic graph. Cache previous paths and only re-compute affected subgraphs.
Design a warehouse inventory system that optimizes picker travel time.
Model as a traveling salesman problem variant. Use A* with Manhattan distance heuristic per zone.
How would you scale last-mile delivery route optimization across thousands of drivers?
Hierarchical approach: cluster deliveries by region, then solve vehicle routing problem per cluster using greedy + local search.