SmartChain : A Warehouse Optimization

PickOptima is a powerful simulation tool designed to analyze and optimize order-picking efficiency based on wave size. By providing adjustable parameters and visual insights, this tool helps businesses streamline their picking process to enhance operational productivity.

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Project Structure

PickOptima/
├── app.py  # Main application for visualization using Streamlit
├── utils/
│   ├── simulation/
│   │   ├── simulation_batch.py  # Simulates batch picking processes
│   └── batch/
│       ├── mapping_batch.py  # Handles mapping logic for orders
└── myenv/  # Virtual environment (not included in GitHub)

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Features

• Dynamic Order Line Simulation:

  • Adjust the number of order lines for analysis.
  • Simulate the impact of varying wave sizes on picking efficiency.

• Interactive Visualizations:

  • Provides graphs and charts to visualize the performance metrics.

• Customizable Parameters:

  • Configure the simulation with minimum and maximum wave sizes.

• Scalable Design:

  • Modular architecture for easy customization and extension.

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Installation Guide

1. Prerequisites

• Python 3.8+
• Git

2. Clone the Repository

git clone https://github.com/MalyajNailwal/PickOptima.git
cd PickOptima

3. Set Up Virtual Environment

Create and activate a virtual environment:

python3 -m venv myenv
source myenv/bin/activate  # macOS/Linux
myenv\Scripts\activate  # Windows

4. Install Dependencies

Install required libraries:

pip install -r requirements.txt

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How to Run the Application

1. Activate the virtual environment (if not already active).

   source myenv/bin/activate  # macOS/Linux
   myenv\Scripts\activate  # Windows

2. Launch the Streamlit app:

   streamlit run app.py

3. Open the provided URL in your web browser to access the application.

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Usage

1. Simulation Inputs

• Order Line Scope: Adjust the total number of order lines for analysis.
• Wave Size Range: Configure the minimum and maximum wave sizes.

2. Outputs

• Visualizations showcasing the relationship between wave size and picking efficiency.
• Insights to optimize order-picking workflows.

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File Descriptions

1. app.py

The main Streamlit application that:

• Collects user inputs.
• Displays interactive graphs and outputs.
• Integrates functionalities from utility modules.

2. simulation_batch.py

Simulates batch order-picking processes based on user-defined parameters. Key methods include:

• simulate_batch: Generates simulation data.

3. mapping_batch.py

Handles mapping logic for orders, including:

• Generating mapping data for simulation.

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Technologies Used

• Programming Language: Python 3.8+
• Framework: Streamlit
• Data Visualization: Matplotlib, Plotly

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Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Add feature-name"

4. Push to the branch:

   git push origin feature-name

5. Open a pull request.