Abstract
Crew planning with complicated constraints is decomposed into two sequential phases: crew scheduling phase, crew rostering phase. Setting a dynamic model based on set covering model, Genetic Algorithm is adopted based on feasible solution range in search of optimal scheduling set with minimum time. Constructing a node-arc TSP network, it adopts Genetic Algorithm and Simulated Annealing Algorithm to create a work roster. Based on Wuhan-Guangzhou High-Speed Railway in China, the balance degree of crew planning is measured by crew working time entropy. The proposed model proves strong practical application.
Full Text
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call