Bulk Upload and Route Optimization - Engineering Specification¶

1. Project Context¶

Background¶

Initial Partner: Online Florist Delivery Service
Daily Delivery Volume: 100-300 orders
Current Pain Point: Manual route planning consuming 3-4 hours daily

2. System Requirements¶

2.1 Bulk Upload Specifications¶

Input Constraints¶

Supported File Format: CSV
Maximum File Size:
Soft Limit: 1 MB
Hard Limit: 5 MB
Recommended Row Count: Up to 200 deliveries per upload

Required Columns¶

Delivery ID (unique, alphanumeric)
Pickup Latitude
Pickup Longitude
Dropoff Latitude
Dropoff Longitude

2.2 Data Validation Rules¶

Delivery ID Validation
Must be unique
Non-empty
Alphanumeric characters only
Coordinate Validation
Latitude Range: -90 to 90
Longitude Range: -180 to 180
Must be valid decimal numbers

2.3 Error Handling¶

Provide detailed error messages
Highlight specific rows with issues
Option to download error report
Support partial upload of valid rows

3. Route Optimization Requirements¶

3.1 Performance Targets¶

Processing Time: < 5 seconds for 200 entries
Route Calculation Time: < 500ms
Routing Accuracy: > 90%
Distance Optimization: Significant reduction

3.2 Routing Approach¶

Primary Routing Engine: OSRM (Open Source Routing Machine)
Real-time Enrichment: Google Maps API
Fallback Mechanism: Intelligent provider switching

3.3 Routing Workflow¶

[Delivery Points] 
    → Geocoding Validation 
    → Route Permutation Generation 
    → OSRM Route Calculation 
    → Optimization Selection 
    → Cached Route Storage

4. Technical Architecture¶

4.1 Technology Stack¶

Backend: Go (Gin/Echo framework)
Routing Engine: OSRM
Database: PostgreSQL with PostGIS
Caching: Redis
Frontend: React with TypeScript

4.2 Infrastructure Requirements¶

Compute: 4 vCPU, 16GB RAM
Storage: 250GB SSD
Estimated Monthly Cost: $250

5. Key Challenges¶

Efficient CSV parsing
Geospatial data validation
Real-time route optimization
Handling variable delivery constraints

6. Optimization Complexity Level¶

Intermediate Route Optimization
Time window constraints
Priority management
Basic capacity planning
Multiple vehicle considerations

7. Development Phases¶

CSV Parsing Module
Geospatial Validation
Initial Routing Algorithm
Caching Mechanism
Performance Benchmarking
Incremental Optimization

8. Success Metrics¶

Reduce route planning time by 75%
Minimize delivery planning complexity
Provide actionable routing suggestions
Support incremental route optimization

9. Ethical Considerations¶

Transparent algorithmic decision-making
Fair treatment of delivery personnel
Minimizing environmental impact
Protecting individual privacy

10. Future Expansion¶

Machine learning route prediction
Real-time traffic integration
Dynamic routing adjustments
Multi-vehicle type support

Appendix: Potential Risks¶

Routing data staleness
Performance overhead
Scaling challenges with increasing delivery volumes
API dependency and rate limiting