Simulation of stochastic demand data streams for network revenue management problems

Dynamic control mechanisms for revenue management with flexible products

“…As with network 1, we define three problem classes demands, can be found in the appendix of [17], from which the simulation setting has been adopted. In order to obtain simulation runs for both networks, we generate booking requests for the different products according to independent, nonhomogeneous Poisson processes, a common assumption in revenue management (see [28]). The corresponding arrival rates ( ) i t λ for all i ∈ I and ( ) j t λ for all j ∈ J , which are called booking curves in the context of airline revenue management, distribute the given total expected demand over time.…”

Median split, k-group split, and optimality in continuous populations

“…10, which is obviously minimal at r. Since the beta distribution is a very flexible distribution (compare the left panels of Fig. 9), which can adopt a positively or negatively skewed form, or a symmetric form, in practice it is frequently applied in many fields of research for different data sets (Kimms and Müller-Bungart 2007). Another aspect is that a median split is often chosen for categorization in applications.…”

Identifying and Responding to Outlier Demand in Revenue Management

“…Additionally, simulation studies enable a priori knowledge about the true demand generation process, which can never be known in a real-world setting. Frank et al (2008) discuss the use of simulation for RM and provide guidelines; in a related effort, Kimms and Müller-Bungart (2007) consider demand modelling for RM simulations. The paper at hand follows these contributions in establishing a simulation-based framework to generate outlier observations.…”