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

Shu-Zi ZHOU, Zhi-Hui ZHAN, Zong-Gan CHEN, Sam KWONG, Jun ZHANG

Research output: Journal PublicationsJournal Article (refereed)peer-review

63 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)6784-6798
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number11
Early online date8 Jun 2020
Publication statusPublished - Nov 2021
Externally publishedYes


  • ant colony system (ACS)
  • Crew rostering problem (CRP)
  • multi-objective optimization
  • multiple populations for multiple objectives (MPMO)


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