Title: A unique hybrid particle swarm optimisation algorithm for simulation and improvement of crew scheduling problem

Authors: Ali Azadeh; Ghazal Asadipour; Hesam Eivazy; Salman Nazari-Shirkouhi

Addresses: Department of Industrial Engineering, Center of Excellence for Intelligent Based Experimental Mechanics, College of Engineering, University of Tehran, Iran; Department of Engineering Optimization Research, College of Engineering, University of Tehran, Iran ' Department of Industrial Engineering, Center of Excellence for Intelligent Based Experimental Mechanics, College of Engineering, University of Tehran, Iran; Department of Engineering Optimization Research, College of Engineering, University of Tehran, Iran ' Department of Civil Engineering, University of Alberta, Canada ' Department of Industrial Engineering, Center of Excellence for Intelligent Based Experimental Mechanics, College of Engineering, University of Tehran, Iran; Department of Engineering Optimization Research, College of Engineering, University of Tehran, Iran

Abstract: The crew scheduling problem is a set covering or set partitioning problem. It schedules the crew members so that all flights are covered, while the cost is minimised. The crew scheduling is an non-deterministic polynomial-time hard constrained combinatorial optimisation problem, so it cannot be exactly solved in a reasonable computation time. This paper presents a particle swarm optimisation (PSO) algorithm for simulating and solving the crew scheduling problem. The proposed algorithm is extended from the discrete version of PSO. By applying PSO to the crew scheduling problem, the cost is improved when compared with other well-known algorithms. This is the first study that introduces PSO for simulation and optimisation of the crew scheduling problem.

Keywords: crew scheduling; PSO; particle swarm optimisation; evaluation; simulation; scheduling improvement; flight crews.

DOI: 10.1504/IJOR.2012.046225

International Journal of Operational Research, 2012 Vol.13 No.4, pp.406 - 422

Published online: 04 Mar 2012 *

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