Team DEDSEC - Optimal Box Packing with Deep Learning (SusHacks'24 Track Prize Winners)

Problem Statement:

Shipping warehouses face the "box problem": identifying the most cost-effective set of boxes to minimize packaging material usage for a vast array of orders, each containing items with varying dimensions and orientations. This project tackles this challenge by harnessing the power of deep learning.

Solution:

We present a novel deep learning-based approach that streamlines the box selection process for warehouses, leading to significant cost savings in packaging materials. This is a very rare and unique problem. sOur solution leverages:

• Synthetic Data Generation: We create a comprehensive dataset of 100,000 simulated historical orders, accurately reflecting real-world order patterns. This dataset includes:
  • Order ID's
  • Number of items per order
  • Item descriptions
  • Item dimensions (x, y, z)
• **Optimization: Taking advantage of existing ILP, LP solvers by identifying how you should frame the problem:
  • we use PuLP module in python which can help us in using the ILP and LP solvers.

We also tried it using other approaches

• Deep Learning Model: We employ a state-of-the-art 3D Convolutional Neural Network (CNN) to effectively capture the spatial relationships between items in different orientations within potential boxes. This model is meticulously trained using the synthetic data to:
  • Analyze order details (number of items, item dimensions)
  • Predict the optimal box configuration, including:
    • Predicted box dimensions (x, y, z)
    • Predicted number of boxes required (if multiple boxes are necessary)

Winning at SusHacks'24 Wild Card Entry (Aegion Dynamics):

We are thrilled to announce that our innovative solution, "Team DEDSEC - Optimal Box Packing with Deep Learning," has emerged victorious as the Wild Card Entry winner at SysHacks'24, the prestigious nationwide hackathon, sponsored by Aeon Dynamics! This recognition is a testament to the effectiveness and ingenuity of our approach.

Key Features and Benefits:

• Data-Driven Optimization: Our deep learning model learns from a massive dataset, enabling it to make increasingly accurate predictions over time.
• Cost Reduction: By recommending the most space-efficient box configurations, our solution helps warehouses minimize packaging material usage, leading to substantial cost savings.
• Scalability and Adaptability: This approach is designed to handle large datasets of orders efficiently and can be readily adapted to accommodate changing order patterns.

Getting Started:

1. Prerequisites: Ensure you have Python (3.x) and the necessary deep learning libraries (TensorFlow, PyTorch, etc.) installed on your system.
2. Clone the Repository: Use git clone https://github.com/your-username/optimal-box-packing.git to clone this repository.
3. Set Up the Environment: Create a virtual environment (recommended) and install the required dependencies using pip install -r requirements.txt.
4. **Run the final phase notebook file in the guthub 'Final_Phase.ipynb'

Further Enhancements:

• Explore alternative deep learning architectures (e.g., Graph Neural Networks) to potentially improve the model's performance.
• Integrate the solution with a warehouse management system for seamless box selection during order processing.
• Implement a continuous learning mechanism to allow the model to adapt to evolving order patterns over time.

Team DEDSEC:

We are a team of passionate developers and problem-solvers who are dedicated to creating innovative solutions. Our team members include:

• Sasi - Core ML and DL Developer
• Vinod - ML Developer
• Rahaman - UI/UX Designer and Data Analyst
• Siddu - Full Stack Developer.

License:

This project is licensed under the MIT License (https://opensource.org/license/mit).

We are confident that this comprehensive and well-structured README will effectively showcase your project's merits, celebrate your SUS HACKS'24 victory, and attract the interest of the developer community.