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Route Optimization Strategy for Delivery Platform

Context and Evolution

Technical Design Workshop Insights

During our initial technical design workshop, we uncovered critical limitations in our original route optimization approach. What began as a simple geospatial distance calculation evolved into a comprehensive routing strategy.

Initial Approach Limitations

  • Relied on basic 2D distance calculations
  • Did not account for actual road networks
  • Ignored real-world travel constraints
  • Provided mathematically accurate but practically irrelevant routes

Strategic Transformation

We recognized that true delivery route optimization requires: - Actual road network routing - Consideration of real travel distances - Handling of complex geographic constraints - Efficient, scalable routing algorithms

Why Traditional Distance Calculation Falls Short

  1. Euclidean Distance Misconception
  2. Assumes straight-line travel
  3. Ignores road infrastructure

  4. Unrealistic for urban and rural environments

  5. Real-World Routing Complexity

  6. Roads aren't straight lines
  7. Traffic conditions vary
  8. Terrain and infrastructure impact routes

Routing Strategy Evolution

Our approach transformed from a simple geometric calculation to a sophisticated, road-aware routing system that considers: - Actual road networks - Traffic conditions - Vehicle-specific routing - Computational efficiency

Core Challenge

Develop an intelligent routing system that: - Calculates actual road-based routes - Minimizes total travel distance - Handles multiple delivery points - Provides cost-effective routing

Routing Approach Options

1. External Routing Services

Pros

  • Accurate road network data
  • Real-time traffic considerations
  • Comprehensive routing algorithms

Cons

  • Potential high costs
  • Dependency on third-party services
  • API rate limitations

2. Open-Source Routing Solutions

OSRM (Open Source Routing Machine)

  • Detailed road network routing
  • Supports multiple vehicle profiles
  • Self-hostable
  • Highly performant

GraphHopper

  • Flexible routing engine
  • Multiple transportation modes
  • Java-based with API interfaces

3. Google Maps Integration

Strategic Advantages

  • Real-time traffic data
  • Comprehensive road network information
  • Dynamic routing capabilities
  • Global coverage

Integration Approach

  • Fallback routing mechanism
  • Selective real-time data enrichment
  • Intelligent API call management

Key Features

  • Traffic condition analysis
  • Alternative route suggestions
  • Time-based routing variations
  • Seasonal and event-related insights

Routing Provider Comparison

Provider Strengths Limitations Use Case
OSRM Open-source, self-hostable, performant Limited real-time data Primary routing
GraphHopper Flexible, multi-modal Java-based complexity Secondary option
Google Maps Comprehensive real-time data High cost, API limitations Real-time enrichment, fallback

Technical Implementation Strategy

Key Components

  1. Route Calculation Engine
  2. Caching Mechanism
  3. Optimization Algorithm
  4. Performance Monitoring

Routing Calculation Workflow

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

Enhanced Routing Strategy

Multi-Provider Routing Approach

  1. Primary Routing: OSRM
  2. Real-time Enrichment: Google Maps
  3. Fallback Mechanism: Intelligent provider switching

Technical Integration Considerations

  • Implement rate limiting
  • Create robust caching mechanism
  • Design cost-effective API usage strategy
  • Develop performance tracking

Performance Optimization Techniques

  • Aggressive result caching
  • Batch route calculations
  • Background processing
  • Intelligent route segment reuse

Cost Management

  • Self-host routing infrastructure
  • Implement intelligent API call minimization
  • Create local routing data cache
  • Use efficient route segment calculations

Cost and Performance Optimization

  • Selective Google Maps API calls
  • Intelligent caching of routing results
  • Performance-based provider selection
  • Continuous routing strategy evaluation

Future Expansion Considerations

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

Technical Risks

  • Routing data staleness
  • Performance overhead
  • Complexity of route optimization
  • Scaling challenges with increasing delivery volumes
  1. OSRM as primary routing engine
  2. Go-based custom routing client
  3. Aggressive caching strategy
  4. Modular design for future enhancements

Success Metrics

  • Route calculation time < 500ms
  • Distance optimization > 15%
  • Routing accuracy > 90%
  • API call reduction > 70%

Technology Stack

  • Backend: Go
  • Routing Engine: OSRM
  • Caching: Redis
  • Database: PostgreSQL
  • Geospatial Library: go-geom

Next Development Phases

  1. Prototype routing engine
  2. Caching mechanism implementation
  3. Performance benchmarking
  4. Incremental optimization