A genetic algorithm for public transport driver scheduling
Computers and Operations Research - Special issue on genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Solving Large Airline Crew Scheduling Problems: Random Pairing Generation and Strong Branching
Computational Optimization and Applications
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Branch-And-Price: Column Generation for Solving Huge Integer Programs
Operations Research
A Column Generation Approach for Large-Scale Aircrew Rostering Problems
Operations Research
Improving Crew Scheduling by Incorporating Key Maintenance Routing Decisions
Operations Research
Hybrid Column Generation Approaches for Urban Transit Crew Management Problems
Transportation Science
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
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This paper describes a general hybrid column generation algorithm for crew scheduling problems, using genetic algorithm to speed up the generation of new columns, combined with an integer programming exact method to assure optimality. The subproblem of the column generation must generate a new feasible set of tasks to be assigned to a crew member. It is modeled as a shortest path with resource constraints problem in a graph, which virtually can be applied to all kinds of crew scheduling problems. The genetic algorithm is also general, and knowledge about specific problems may be incorporated. The hybrid algorithm is tested with instances from the literature and also with real instances, and the results show that the genetic algorithm is able to quickly generate most of the columns needed to solve the problem, while the exact method generates the last columns to find the optimal solution. The algorithm can also incorporate other kind of heuristics.