Bypassing Waypoint Optimization

Bypassing Waypoint Optimization is an open-source project designed to optimize delivery routes efficiently by leveraging geographic and time-based data. This tool provides a framework for determining the best routes among multiple waypoints while considering store locations, opening hours, and API constraints.

Features

• Data Collection: Gathers location coordinates and opening hours for stores (currently configured for Kyiv's Epicenter locations).
• Dynamic Grouping: Groups stores into clusters based on geographic proximity with a customizable coordinate deviation.
• Combination Analysis: Generates all possible route combinations within clusters to identify optimal paths.
• API Integration: Interacts with a Waypoint Optimization API to evaluate route efficiency for various time slots.
• Error Handling: Includes random delays to prevent "Too Many Requests" errors and gracefully manages complex-route API limitations.
• Result Storage: Outputs the best routes and travel times to JSON files for easy reuse and further analysis.

How It Works

• Data Initialization: Extracts and processes store data, grouping them into clusters. For example, 15 locations can be grouped into 12 clusters with a coordinate deviation of 0.035.
• Route Simulation: Runs simulations for all possible time slots (e.g., every 30 minutes from 7:30 AM to 6:00 PM) and evaluates combinations using the Waypoint Optimization API.
• Optimal Route Selection: Identifies the route with the lowest travel time, ensuring practical delivery schedules (e.g., delivery by the latest store closing time of 10:00 PM).
• Result Compilation: Stores results in best_data_points.json and best_results.json for further analysis. Provides formatted output via result.md.

Getting Started

Prerequisites

• Python 3.x
• A valid API key for the Waypoint Optimization API (insert in constants.py).

Setup Instructions

Clone this repository:

git clone https://github.com/yourusername/bypassing-waypoint-optimization.git
cd bypassing-waypoint-optimization

Install dependencies:

pip install -r requirements.txt

1. Insert your API key in constants.py.
2. Running the Program
3. Prepare your data:
4. Modify the data source if needed (e.g., for a different city or dataset).
5. Update coordinate deviations or time slot ranges in main.py.

Execute the main logic:

Copy code
python main.py

View results:

JSON outputs: best_data_points.json and best_results.json. Formatted summary: result.md.

Contributing

Contributions are welcome! If you'd like to add new features, fix bugs, or expand the project's compatibility, please fork the repository and submit a pull request. Make sure to follow the existing coding conventions.

License

This project is licensed under the MIT License. See LICENSE for details.