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Bulk Route Optimizer Full Engineering Spec

Here's a software engineering specification draft for your auto bulk route optimizer:

SECTION 01 78 00

BULK ROUTE OPTIMIZER - SOFTWARE ENGINEERING SPECIFICATIONS

1.0 GENERAL

1.1 OVERVIEW

The Bulk Route Optimizer is a Python-based application that generates optimized delivery routes from a user-uploaded CSV file containing destination coordinates. Each route minimizes deviations from the most efficient path (e.g., straight-line trajectory) while grouping destinations efficiently under constraints. Future iterations will integrate real-time traffic and weather data using machine learning, specifically graph neural networks (GNNs) and related architectures.

1.2 GOALS AND DELIVERABLES

  • Provide optimized delivery routes with minimal detours.
  • Enable uploading and parsing of destination data in CSV format.
  • Allow configuration of routing parameters via a user-friendly UI.
  • Use Open Source Routing Machine (OSRM) for classical routing calculations.
  • Generate foundational data for training advanced machine learning models.

2.0 FUNCTIONAL REQUIREMENTS

2.1 INPUT SPECIFICATIONS

  • CSV Input: The uploaded CSV file should contain:
  • Column 1: Destination_ID (Unique identifier for each location).
  • Column 2: Latitude.
  • Column 3: Longitude.
  • Pickup Location: Defined statically (e.g., warehouse coordinates) but customizable later.

2.2 ROUTE GENERATION

  • Base Algorithm:
  • Use OSRM API to compute travel times and distances between destinations and from the pickup point.
  • Group destinations into clusters based on straight-line distance to the final drop-off point.
  • Generate routes that minimize the total distance or detour from a straight trajectory.
  • Constraints:
  • Each route must contain no more than N destinations, configurable by the user.
  • Allow configurations for max deviation thresholds in meters or percentage.

2.3 OUTPUT SPECIFICATIONS

  • Route Output:
  • CSV file with:
    • Route_ID (Unique identifier for the route).
    • Destination_Sequence (Ordered list of destinations).
    • Total Distance (Meters or miles).
  • Visualization in the UI using a map API (e.g., Leaflet, Google Maps).

2.4 USER CONFIGURATIONS

  • Parameters via UI:
  • Maximum number of destinations per route.
  • Tolerance for deviations (percentage or absolute).
  • Route prioritization (e.g., minimize distance, balance load).

3.0 SYSTEM DESIGN

3.1 ARCHITECTURE

  • Backend: Python with Flask/Django for API and routing logic.
  • Frontend: React.js or similar for an interactive UI.
  • Routing Engine:
  • OSRM hosted locally for low-latency requests.
  • Modular integration for switching between classical and ML-based optimizers.
  • Database: SQLite/PostgreSQL for storing user preferences, routes, and historical data.

3.2 WORKFLOW

  1. User uploads a CSV file.
  2. System parses and validates the data.
  3. Optimizer computes routes using OSRM and clustering logic.
  4. Results are visualized and downloadable in CSV format.

4.0 FUTURE EXPANSIONS

4.1 MACHINE LEARNING INTEGRATION

  • Develop a dataset from classical routing results for supervised learning.
  • Train a GNN to predict routing paths given constraints (traffic, weather, etc.).
  • Allow dynamic re-routing with real-time data feeds (traffic APIs, weather).

4.2 SCALABILITY

  • Cloud deployment for handling large-scale datasets.
  • Parallel processing for multiple routing tasks.

5.0 TESTING AND VALIDATION

5.1 FUNCTIONAL TESTING

  • Verify routing accuracy with sample datasets.
  • Test clustering logic for varying N and deviation tolerances.

5.2 PERFORMANCE TESTING

  • Measure API response times for OSRM requests under different loads.

5.3 USER ACCEPTANCE TESTING

  • Ensure UI intuitiveness with feedback from test users.

6.0 NON-FUNCTIONAL REQUIREMENTS

6.1 RELIABILITY

  • Ensure 99.9% uptime for hosted OSRM service.

6.2 PERFORMANCE

  • Optimize response time to <2 seconds per route generation for datasets with 100+ points.

6.3 SECURITY

  • Sanitize user inputs to prevent injection attacks.
  • Encrypt CSV uploads during transmission.

6.4 MAINTAINABILITY

  • Modular design for routing logic and ML integration.
  • Clear documentation for API and algorithmic components.

Does this align with your vision? Let me know if you'd like refinements or further elaboration on any section.