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<p>The Airline Crew Pairing Problem (CPP) is a complex combinatorial optimization challenge that assigns flight sequences to crew members to minimize costs while adhering to regulatory constraints. This paper proposes the Jumping Particle Swarm-based Crew Pairing (JSCP) algorithm, which integrates Jumping Particle Swarm Optimization (JPSO) with two novel local search strategies: cost ratio-based search and column search. JSCP achieves up to 5.8% lower costs and 30% faster execution times compared to state-of-the-art methods (NCG, EA, HBS) on large-scale North American airline datasets. Theoretical analysis proves JPSCP’s correctness and polynomial time complexity (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\text{O}(mn)),\)</EquationSource> </InlineEquation> ensuring scalability for real-time applications. Statistical analyses, including Nemenyi post-hoc tests <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\left(p&lt;0.001\right),\)</EquationSource> </InlineEquation> confirm JSCP’s superior solution quality and computational efficiency, making it a robust and scalable solution for airline crew scheduling.</p>

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A Hybrid Jumping Particle Swarm Optimization with Local Search for Airline Crew Pairing: Theoretical and Empirical Insights

  • 期刊论文

  • 发表日期：

    2026年3月31日

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  • V. Devi,
  • S. Balaji

摘要

The Airline Crew Pairing Problem (CPP) is a complex combinatorial optimization challenge that assigns flight sequences to crew members to minimize costs while adhering to regulatory constraints. This paper proposes the Jumping Particle Swarm-based Crew Pairing (JSCP) algorithm, which integrates Jumping Particle Swarm Optimization (JPSO) with two novel local search strategies: cost ratio-based search and column search. JSCP achieves up to 5.8% lower costs and 30% faster execution times compared to state-of-the-art methods (NCG, EA, HBS) on large-scale North American airline datasets. Theoretical analysis proves JPSCP’s correctness and polynomial time complexity ( \(\text{O}(mn)),\) ensuring scalability for real-time applications. Statistical analyses, including Nemenyi post-hoc tests \(\left(p<0.001\right),\) confirm JSCP’s superior solution quality and computational efficiency, making it a robust and scalable solution for airline crew scheduling.