Airline planning benchmark problems—Part II: Passenger groups, utility and demand allocation

Abstract

This paper is the second of two papers entitled "Airline Planning Benchmark Problems", aimed at developing benchmark data that can be used to stimulate innovation in airline planning, in particular, in flight schedule design and fleet assignment. The former has, to date, been under-represented in the optimization literature, due in part to the difficulty of obtaining data that adequately reflects passenger choice, and hence schedule revenue. Revenue models in airline planning optimization only roughly approximate the passenger decision process. However there is a growing body of literature giving empirical insights into airline passenger choice. Here we propose a new paradigm for passenger modelling, that enriches our representation of passenger revenue, in a form designed to be useful for optimization. We divide the market demand into market segments, or passenger groups, according to characteristics that differentiate behaviour in terms of airline product selection. Each passenger group has an origin, destination, size (number of passengers), departure time window, and departure time utility curve, indicating willingness to pay for departure in time sub-windows. Taking as input market demand for each origin-destination pair, we describe a process by which we construct realistic passenger group data, based on analysis of empirical airline data collected by our industry partner. We give the results of that analysis, and describe 33 benchmark instances produced.

Airline planning benchmark problems—Part I:

“…The methodology behind the first two steps in the framework, namely, characterising airline networks and OD-pair demand using limited data, were presented in this paper. The methodology of the second two steps, namely, characterising passenger groups and the allocating the OD-pair demand, is presented in the second paper (Akartunalı et al, 2010a).…”

“…The second paper (Akartunalı, Boland, Evans, Wallace, and Waterer, 2010a) presents the methodology behind the third and fourth steps in this framework. The third step partitions the market demand into passenger groups, according to characteristics that differentiate behaviour in terms of airline product selection.…”

A study on the dynamic refund fee model of air tickets based on win-to-win mechanism

“…Yaser E. Hawas found the factors that influence the effectiveness of passenger travel and the derivation of passenger utility (Hawas, 2004). Liu Wei proposed the effective arrival rate of passengers to consider the expected value of airline tickets, and constructed the airline ticket overbooking model (Wei, 2005), Akartunal et al studied the choice behavior of air passengers and constructed the passenger utility curve (Akartunalı, Boland, Evans, Wallace & Waterer, 2013). Gao Jin Min built the expected utility function of passengers, and established the ticket pricing model ( Jin-Ming, Mei-Long & Chi-Lin, 2016).…”

Airline delay prediction by machine learning algorithms

“…In the robust approach, the problems occurring in one or more steps of the planning phase are solved in relation to the operational phase. More information on this method is provided in [7] and [8]. Disruption management involves the the consideration of the operational problems that occur during flight planning.…”