Particle swarm optimization algorithm for solving airline crew scheduling problem

Ezzinbi, Omar; Sarhani, Malek; Afia, Abdellatif El; Benadada, Youssef

doi:10.1109/gol.2014.6887447

Cited by 8 publications

(2 citation statements)

References 31 publications

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“…Note that g 1 (X) is a minimal objective, whereas g 2 (X) is a maximal objective. The goal of our model is defined as (6), which is a bi-objective problem.…”

Section: B Objective Formulationmentioning

confidence: 99%

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A Multi-Objective Ant Colony System Algorithm for Airline Crew Rostering Problem With Fairness and Satisfaction

Zhou

Zhan

Chen

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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The airline crew rostering problem (CRP) is significant for balancing the workload of crew and for improving the satisfaction rate of crew's preferences, which is related to the fairness and satisfaction of crew. However, most existing work considers only one objective on fairness or satisfaction. In this study, we propose a new practical model for CRP that takes both fairness and satisfaction into account simultaneously. To solve the multi-objective CRP efficiently, we develop an ant colony system (ACS) algorithm based on the multiple populations for multiple objectives (MPMO) framework, termed multi-objective ACS (MOACS). The main contributions of MOACS lie in three aspects. Firstly, two ant colonies are utilized to optimize fairness and satisfaction objectives, respectively. Secondly, a new hybrid complementary heuristic strategy with three kinds of heuristic information schemes is proposed to avoid ant colonies focusing only on their own objectives. Ant colonies randomly choose one of the three schemes to help explore the Pareto front (PF) sufficiently. Thirdly, a local search strategy with two types of local search respectively for fairness and satisfaction is designed to further approach the global PF. The MOACS is applied to seven real-world monthly CRPs with different sizes from a major North-American airline. Experimental results show that MOACS generally outperforms the greedy algorithm and some other popular multi-objective optimization algorithms, especially on large-scale instances.

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“…Note that g 1 (X) is a minimal objective, whereas g 2 (X) is a maximal objective. The goal of our model is defined as (6), which is a bi-objective problem.…”

Section: B Objective Formulationmentioning

confidence: 99%

“…Santosa et al [5] regarded the variable cost of roster paid by airlines as one of the objective functions, which was represented by actual flying hours. Ezzinbi et al [6] considered minimizing the total cost as the main objective of the crew scheduling problem, and the cost was calculated by the sum of different crew pairing costs.…”

Section: Introductionmentioning

confidence: 99%