A hybrid meta-heuristic algorithm for optimization of crew scheduling

  • Authors:

  • A. Azadeh;M. Hosseinabadi Farahani;H. Eivazy;S. Nazari-Shirkouhi;G. Asadipour

  • Affiliations:

  • Department of Industrial Engineering, Center of Excellence for Intelligent Based Experimental Mechanics, Department of Engineering Optimization Research, College of Engineering, University of Tehr ...;Department of Industrial Engineering, Center of Excellence for Intelligent Based Experimental Mechanics, Department of Engineering Optimization Research, College of Engineering, University of Tehr ...;Department of Civil Engineering, University of Alberta, Canada;Department of Industrial Engineering, Center of Excellence for Intelligent Based Experimental Mechanics, Department of Engineering Optimization Research, College of Engineering, University of Tehr ...;Department of Industrial Engineering, Center of Excellence for Intelligent Based Experimental Mechanics, Department of Engineering Optimization Research, College of Engineering, University of Tehr ...

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

Crew scheduling problem is the problem of assigning crew members to the flights so that total cost is minimized while regulatory and legal restrictions are satisfied. The crew scheduling is an NP-hard constrained combinatorial optimization problem and hence, it cannot be exactly solved in a reasonable computational time. This paper presents a particle swarm optimization (PSO) algorithm synchronized with a local search heuristic for solving the crew scheduling problem. Recent studies use genetic algorithm (GA) or ant colony optimization (ACO) to solve large scale crew scheduling problems. Furthermore, two other hybrid algorithms based on GA and ACO algorithms have been developed to solve the problem. Computational results show the effectiveness and superiority of the proposed hybrid PSO algorithm over other algorithms.