Solving airline crew scheduling problems by branch-and-cut
Management Science
Management Science
A column-generation technique for the long-haul crew-assignment problem
Optimization in industry 2
Application of a hybrid genetic algorithm to airline crew scheduling
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
Flight graph based genetic algorithm for crew scheduling in airlines
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on evolutionary algorithms
Solving Large Airline Crew Scheduling Problems: Random Pairing Generation and Strong Branching
Computational Optimization and Applications
Crew Pairing Optimization with Genetic Algorithms
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Branch-And-Price: Column Generation for Solving Huge Integer Programs
Operations Research
The Operational Airline Crew Scheduling Problem
Transportation Science
An integer programming approach to generating airline crew pairings
Computers and Operations Research
Local search genetic algorithm for optimal design of reliablenetworks
IEEE Transactions on Evolutionary Computation
Multi-objective optimization of stochastic disassembly line balancing with station paralleling
Computers and Industrial Engineering
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The crew pairing problem (CPP) deals with generating crew pairings due to law and restrictions and selecting a set of crew pairings with minimal cost that covers all the flight legs. In this study, we present three different algorithms to solve CPP. The knowledge based random algorithm (KBRA) and the hybrid algorithm (HA) both combine heuristics and exact methods. While KBRA generates a reduced solution space by using the knowledge received from the past, HA starts to generate a reduced search space including high quality legal pairings by using some mechanisms in components of genetic algorithm (GA). Zero-one integer programming model of the set covering problem (SCP) which is an NP-hard problem is then used to select the minimal cost pairings among solutions in the reduced search space. Column generation (CG) which is the most commonly used technique in the CPP literature is used as the third solution technique. While the master problem is formulated as SCP, legal pairings are generated in the pricing problem by solving a shortest path problem on a structured network. In addition, the performance of CG integrated by KBRA (CG_KBRA) and HA (CG_HA) is investigated on randomly generated test problems. Computational results show that HA and CG_HA can be considered as effective and efficient solution algorithms for solving CPP in terms of the computational cost and solution quality.