Optimizing Warehouse Efficiency with Python 🚀

Welcome to the Optimizing Warehouse Efficiency via Python-Based Order Batching project! This repository contains a powerful tool designed to help warehouses reduce walking time by simulating picking routes. With this tool, users can test various strategies, such as wave picking and clustering, on a 2D layout to discover the most efficient methods for order picking.

Table of Contents

1. Introduction
2. Features
3. Installation
4. Usage
5. Strategies
   • Wave Picking
   • Clustering
6. Data Visualization
7. Contributing
8. License
9. Contact

Introduction

In modern logistics, optimizing warehouse operations is crucial. Walking time can significantly impact efficiency and productivity. This project provides a Python tool that simulates different order-picking strategies. By using this tool, warehouse managers can make informed decisions to enhance their operations.

Features

• Simulation of Picking Routes: Visualize and analyze various picking strategies.
• 2D Layout Support: Test strategies on a realistic 2D warehouse layout.
• User-Friendly Interface: Built with Streamlit for easy interaction.
• Data Visualization: Gain insights through clear visual representations of data.
• Customizable Parameters: Adjust settings to fit specific warehouse needs.

Installation

To get started with this project, follow these steps:

1. Clone the repository:

   git clone https://github.com/shaniggj/Optimizing-Warehouse-Efficiency-via-Python-Based-Order-Batching.git

2. Navigate to the project directory:

   cd Optimizing-Warehouse-Efficiency-via-Python-Based-Order-Batching

3. Install the required packages:

   pip install -r requirements.txt

Usage

To run the application, execute the following command:

streamlit run app.py

Once the application is running, open your web browser and navigate to http://localhost:8501 to access the tool.

Strategies

Wave Picking

Wave picking is a method where orders are grouped into waves. Each wave is processed separately, allowing for better organization and efficiency. This strategy minimizes the distance traveled by pickers, leading to faster order fulfillment.

Clustering

Clustering groups items based on their locations within the warehouse. This method reduces the time spent traveling between different sections. By implementing clustering, warehouses can optimize their picking routes and enhance overall productivity.

Data Visualization

Data visualization plays a key role in understanding warehouse operations. The tool provides various visual outputs, including:

• Heatmaps: Identify high-traffic areas within the warehouse.
• Route Maps: Visualize the paths taken by pickers.
• Performance Metrics: Track the efficiency of different strategies.

These visualizations help warehouse managers make data-driven decisions.

Contributing

We welcome contributions from the community! If you have ideas for improvements or new features, please fork the repository and submit a pull request. Before contributing, ensure that your code adheres to the project's coding standards.

License

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

Contact

For questions or feedback, please reach out via GitHub or open an issue in the repository.

For the latest updates and releases, visit our Releases section. You can download the latest version and execute the tool to start optimizing your warehouse efficiency.

Thank you for your interest in optimizing warehouse operations with Python! We hope this tool helps you achieve greater efficiency in your logistics processes.