Joint Optimization of Dynamic Pricing and Pre-sale Period Division for High-speed Trains
Based on the need to enhance high-speed rail revenue and implement a flexible market ticket pricing system,this paper focuses on the joint optimization of dynamic pricing and pre-sale period division considering the demand fluctuations and differences on each day during the booking horizon,as well as the impact of the pre-sale period division on railway revenue.Separate elastic demand functions are constructed for each day.A large-scale nonlinear model is developed to optimize the dynamic pricing and pre-sale period division for high-speed trains in consideration of the train capacity constraints,demand constraints,and price-related constraints.To solve the optimization problem,a bi-level genetic-simulated annealing algorithm is designed according to the model's properties.The optimization problem is divided into an outer-level pre-sale period division problem and an inner-level dynamic pricing and seat allocation problem,which are solved by genetic algorithm and simulated annealing algorithm,respectively.At last,a numerical instance is provided to evaluate the effectiveness of the optimization model and solution algorithm,and the results for different numbers of pre-sale period are discussed.The results indicate that as the number of period increases,the division of the booking horizon primarily concentrates on the latter half.For a case with five periods,the optimized revenue increased by approximately 1.21%.
railway transportationdynamic pricingbi-level genetic-simulated annealing algorithmhigh-speed trainspre-sale period divisionseat allocation