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

  • Authors:

  • Kerem Akartunalı;Natashia Boland;Ian Evans;Mark Wallace;Hamish Waterer

  • Affiliations:

  • Management Science Department, University of Strathclyde, Glasgow G1 1QE, Scotland, United Kingdom and Department of Mathematics and Statistics, University of Melbourne, Parkville, VIC 3010, Austr ...;School of Mathematical and Physical Sciences, University of Newcastle, Callaghan, NSW 2308, Australia and Department of Mathematics and Statistics, University of Melbourne, Parkville, VIC 3010, Au ...;Constraint Technologies International, Level 7 224 Queen Street, Melbourne, VIC 3000, Australia;Faculty of Information Technology, Monash University, Caulfield, VIC 3145, Australia;School of Mathematical and Physical Sciences, University of Newcastle, Callaghan, NSW 2308, Australia and Faculty of Information Technology, Monash University, Caulfield, VIC 3145, Australia

  • Venue:
  • Computers and Operations Research
  • Year:
  • 2013

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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 optimisation literature, due in part to the difficulty of obtaining data that adequately reflects passenger choice, and hence schedule revenue. Revenue models in airline planning optimisation 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 optimisation. 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 the analysis of empirical airline data collected by our industry partner. We give the results of that analysis, and describe 33 benchmark instances produced.