Crew Planning Optimization Model of High-Speed Railway

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.