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Optimizing airport slot scheduling problem using optimization algorithms.

Mohd Khaled Shambour1, Muhannad A Abu-Hashem2

  • 1The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research, Umm Al-Qura University, Makkah, Saudi Arabia.

Soft Computing
|May 19, 2023
PubMed
Summary
This summary is machine-generated.

Airport optimization enhances passenger flow at King Abdulaziz International Airport. The Flower Pollination Algorithm (FPA) proved most effective in reducing overall passenger waiting times, improving operational efficiency.

Keywords:
Flight schedulingKing Abdulaziz International AirportOptimization methods

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Area of Science:

  • Operations Research
  • Transportation Management
  • Computational Intelligence

Background:

  • Efficient airport management is crucial for global transportation services, focusing on minimizing passenger latency through optimized checkpoint and lobby movements.
  • King Abdulaziz International Airport, a major global terminal and Hajj pilgrimage hub, requires enhanced traveler movement strategies.

Purpose of the Study:

  • To optimize traveler movement within the King Abdulaziz International Airport's pilgrimage station.
  • To evaluate various optimization algorithms for scheduling airport terminal phases and flight-to-portal assignments.

Main Methods:

  • Comparative analysis of optimization algorithms: Differential Evolution Algorithm (DEA), Harmony Search, Genetic Algorithm (GA), Flower Pollination Algorithm (FPA), and Black Widow Optimization.
  • Simulation-based evaluation of algorithm performance for scheduling and assignment tasks.

Main Results:

  • Genetic Algorithm (GA) demonstrated superior solution quality and convergence rates for small population sizes.
  • Differential Evolution Algorithm (DEA) showed better performance with larger population sizes.
  • Flower Pollination Algorithm (FPA) excelled in minimizing overall passenger waiting time.

Conclusions:

  • Optimization algorithms can significantly improve operational efficiency in large-scale airport terminals.
  • Algorithm selection (e.g., GA, DEA, FPA) depends on specific operational parameters like population size and desired outcome (e.g., solution quality, convergence, waiting time).