Facility Location Optimization App

An interactive Streamlit application for exploring five classic facility location optimization problems using integer programming.

Features

• Interactive Parameter Control: Adjust number of demand points, facilities, coverage radius, and random seed
• Five Optimization Problems: LSCP, MCLP, P-Median, P-Center, and SPLP
• Optimal Solutions: Uses integer programming with PuLP/CBC solver for guaranteed optimal solutions
• Real-time Visualization: Interactive plots showing optimal facility locations and assignments
• Solution Metrics: Detailed performance metrics for each problem type
• Data Export: Download results as CSV files

Installation

1. Install required packages:

pip install -r requirements.txt

2. Run the Streamlit app:

streamlit run facility_location_app.py

3. Open your browser to http://localhost:8501

Usage

Parameter Settings

• Number of Demand Points: 10-50 (default: 25)
• Number of Potential Facilities: 8-25 (default: 15)
• Random Seed: 1-10000 (default: 1234)
• Coverage Radius: 1.0-4.0 (default: 2.0)
• P-Median/P-Center Facilities: 2-8 (default: 3)
• MCLP Facilities: 2-8 (default: 4)

Problem Types

1. LSCP (Location Set Covering Problem)

   • Minimize facilities to cover all demand points
   • Each demand point must be within coverage radius

2. MCLP (Maximum Covering Location Problem)

   • Maximize coverage with fixed number of facilities
   • Budget constraint on number of facilities

3. P-Median Problem

   • Minimize total weighted distance
   • Exactly p facilities must be located

4. P-Center Problem

   • Minimize maximum distance from any demand point
   • Exactly p facilities must be located

5. SPLP (Simple Plant Location Problem)

   • Minimize total cost (facility opening + transportation)
   • No limit on facilities, but each has opening cost

Visualization

The app displays:

• Base Data Panel: Shows demand points (size = weight) and potential facilities (size = cost)
• Coverage Problems: LSCP and MCLP show coverage circles around selected facilities
• Distance Problems: P-Median, P-Center, and SPLP show assignment lines from demand to facilities
• Color Coding: Each problem type uses a distinct color scheme

Solution Metrics

• LSCP: Number of facilities, coverage percentage
• MCLP: Number of facilities, coverage percentage
• P-Median: Number of facilities, total weighted distance
• P-Center: Number of facilities, maximum distance
• SPLP: Number of facilities, total cost

Technical Details

• Solver: PuLP with CBC (Coin-or Branch and Cut) for optimal solutions
• Problem Size: Designed for 10-50 demand points and 8-25 facilities
• Random Generation: Uses numpy with configurable seed for reproducible results
• Spatial Data: Demand and facility locations generated in 10×8 coordinate space

Files

• facility_location_app.py: Main Streamlit application
• facility_location_solver.py: Optimization functions (imported by app)
• requirements.txt: Python package dependencies
• multi_panel_figure.ipynb: Jupyter notebook version

Educational Use

This app is designed for educational purposes in operations research and facility location courses. It demonstrates:

• Different optimization objectives and their trade-offs
• Impact of problem parameters on solutions
• Visual interpretation of optimization results
• Comparison between different facility location models

Troubleshooting

• Import Error: Ensure facility_location_solver.py is in the same directory
• Solver Issues: PuLP requires CBC solver (usually installed automatically)
• Memory Issues: Reduce problem size for very large instances
• Slow Performance: Integer programming can be slow for large problems

License

Educational use - OMG411 Course Material