Application of a hybrid genetic algorithm to airline crew scheduling☆
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Cited by (89)
An adaptive greedy heuristic for large scale airline crew pairing problems
2024, Journal of Air Transport ManagementAirline crew scheduling: Models and algorithms
2021, Transportation Research Part E: Logistics and Transportation ReviewCitation Excerpt :Then, a set-covering or set-partitioning problem is applied to identify a sub-set of pairings with a minimum cost through using integer programming techniques like branch-and-bound, cutting planes, and sub-gradient optimization. However, due to the high problem complexity, many studies solve the problem heuristically (Azadeh et al., 2013; Cohn and Barnhart, 2003; Levine, 1996). For example, to deal with the airline crew scheduling, Azadeh et al. (2013) developed a hybrid particle swarm optimization (PSO) algorithm synchronized with a local search heuristic and compared the performance of the hybrid PSO algorithm with other heuristic algorithms developed based on genetic algorithm and ant colony optimization.
Allocating and scheduling resources for a mobile photo enforcement program
2021, Transportation Research Part C: Emerging TechnologiesCitation Excerpt :These mainly include branch-and-cut (B&C) and column generation (CG) based decomposition methods (Desaulniers, 2010; Desaulniers et al., 1997; Hoffman and Padberg, 1993; Lavoie et al., 1988; Nishi et al., 2011; Vance et al., 1997). Although metaheuristics such as SA (Emden-Weinert and Proksch, 1999) and some hybrid algorithms (Azadeh et al., 2013; Ke and Feng, 2013; Levine, 1996) have been employed to solve large vehicle routing and airline crew scheduling problems, Emden-Weinert and Proksch concede that feasible solutions were difficult to find due to a number of reasons. A combined approach has primarily been used to solve the largest airline crew scheduling problems: one first decomposes a schedule using the Dantzig-Wolfe algorithm, and then solves the reformulation using a column generation algorithm (Desaulniers et al., 2002; Ernst et al., 2004).
A combinatorial social group whale optimization algorithm for numerical and engineering optimization problems
2021, Applied Soft ComputingA survey of the literature on airline crew scheduling
2018, Engineering Applications of Artificial IntelligenceTwo-level decomposition-based matheuristic for airline crew rostering problems with fair working time
2018, European Journal of Operational Research
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This work was supported by the Mathematical, Information, and Computational Sciences Division subprogram of the Office of Computational and Technology Research, U.S. Department of Energy, under Contract W-31-109-ENG-38.
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David Levine is a member of the research staff of the Mathematics and Computer Science Division at Argonne National Laboratory. He received an M.Sc. degree in Industrial Engineering from the University of Arizona and Ph.D. degree in Computer Science from the Illinois Institute of Technology. He is the developer of the PGAPack parallel genetic algorithm library. His current research interests are computational biophysics, genetic algorithms, parallel computing and virtual reality visualization.