✈️ Optimization of Airplane Seating ✈️

This repository contains a comprehensive analysis and implementation of various algorithms to optimize airplane boarding strategies. The project compares traditional boarding methods with advanced optimization algorithms to reduce boarding times and improve passenger experience.

📂 Repository Structure

• AirplaneSeating.R: Main R script containing the implementation of various optimization algorithms.
• boarding_results.csv: Summary of the boarding time results for different strategies and algorithms.
• boarding_optimization_results.csv: Detailed output of the optimization runs.
• boarding_comparison.png: Visualization comparing boarding times across strategies.
• boarding_times_comparison.png: Chart highlighting the performance of optimization methods.

🚀 Getting Started

Prerequisites

To run the code and reproduce the results, ensure you have the following installed:

• R (version 4.0 or newer)
• The following R libraries:
  • dplyr
  • ggplot2
  • genalg
  • DEoptim
  • pso
  • tabuSearch
  • tidyr
  • scales

You can install the required R packages with:

install.packages(c("dplyr", "ggplot2", "genalg", "DEoptim", "pso", "tabuSearch", "tidyr", "scales"))

Running the Code

1. Clone the repository:

   git clone https://github.com/DanteSc03/Optimization-of-Airplane-Seating.git

2. Open the R script AirplaneSeating.R in your preferred R IDE (e.g., RStudio).

3. Run the script to simulate and compare various boarding strategies and optimization algorithms.

Output

• A CSV file summarizing the results (boarding_results.csv).
• Visualization plots (boarding_comparison.png, boarding_times_comparison.png) highlighting the performance of boarding methods.

🔍 Project Overview

The project addresses the following:

1. Aircraft Configuration:

   • A single-aisle aircraft with 30 rows and 6 seats per row (total: 180 passengers).
   • Passengers have varying luggage quantities, affecting boarding times.

2. Optimization Algorithms:

   • Genetic Algorithms (GA)
   • Simulated Annealing (SA)
   • Differential Evolution (DE)
   • Particle Swarm Optimization (PSO)
   • Tabu Search
   • Hill Climbing
   • Blind Search
   • Monte Carlo Search

3. Objective:

   • Minimize total boarding time.
   • Compare traditional boarding methods (e.g., front-to-back, random) with algorithm-optimized strategies.

4. Seatmap

• This project was based on the seating configuration of an all economy class Airbus A320
• This plane has 30 rows in a 3 x 3 configuration
• This code can be adapted to all types of aircraft

📊 Key Results

Fixed Strategies

The traditional boarding methods such as front-to-back and random boarding were evaluated. The results show significant variation in boarding times.

Optimization Results

Optimization algorithms significantly outperformed traditional methods, with the best results achieved by methods like Genetic Algorithms and Simulated Annealing.

📈 Visualizations

Boarding Time Comparison

🤝 Contributions

Contributions and suggestions are welcome! Feel free to fork this repository, open issues, or submit pull requests.

📄 License

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

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Authored by Dante Schranz.

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