Scenario tree airline fleet planning for demand uncertainty

Portfolio-based airline fleet planning under stochastic demand

“…The authors propose a 4-step approach for demand aggregation and demonstrating the application of the model in a case study with a major European airport. In [18] , a scenario tree approach to solve the multi-period airline fleet planning problem. The authors considered that the nodes of the tree represent the decisions points in different stages of the planning horizon and the branches represent the scenario paths with demand variations.…”

“…The fleet optimization models present in the literature usually focus on obtaining a single optimal fleet and do not capture all elements that influence the fleet planning process in practice. -Finally, optimization models and models that explore the evolution of stochastic variables tend to be computationally de-manding (as highlighted by, e.g., [16,18] ). These properties make it challenging to combine these methodologies into one fleet planning modeling framework that provides meaningful results.…”

“…list price) are based on information provided on the internet [27] . Weekly utilization U k , turnaround times TAT k and operating cost C k v ar are based on previous studies done by the authors (e.g., [18] ) and on the assumption that larger aircraft tend to have a higher daily utilization, higher turnaround time and lower unit operating cost [8] . The yearly ownership cost C k f ix are based on the aircraft purchase price and assuming a 20-year linear depreciation period and residual value of 15% at the end of the depreciation period, which is based on an example depreciation scheme provided by Doganis [28] .…”

“…They are replaced by the yearly ownership costs. For the sake of simplicity and following Repko and Santos [18] , we assumed that the ownership costs reflect either the lease costs or the depreciation costs per period.…”

Research on uncertain integrated production planning and scheduling with risk management based on improved collaborative optimization

“…(Huang et al, 2016) proposed a multi-region optimization model based on two-stage stochastic programming framework for demand uncertainty. (Repko and Santos, 2017) proposed an innovative multi-period modeling method to solve the planning problem under demand uncertainty. (Oliver, 2017) conducted case studies on scenario planning under uncertain market conditions.…”