A significant drawback of the usual sequential airline scheduling approach is the long lead time between solution of the aircraft routing and aircrew planning problems, and the day of operation. We consider a new approach to airline planning, in which aircraft routes and crew pairings are reoptimized close to the day of operations, via solution of an integrated aircraft routing, crew pairing, and tail assignment problem. Instead of scheduling routes for generic aircraft, we generate routes for each, individual, aircraft given its current location, maintenance, and flying history, while also respecting its individual maintenance requirements. New pairings for crews are planned so as to lie within the work periods given in their roster. This allows aircraft routes and pairings to be designed based on more up-to-date information. By solving an integrated problem, the option of increasing robustness of the resulting schedule by keeping crews and aircraft on the same connections when the connection time is not long can be included in the optimization objective. The problem is formulated as a branch-and-price model with a pricing problem (PP) for each aircraft and each group of crews having the same work period availability and base. We develop two strategies to address the challenge of solving the large number of PPs that result. The feasibility of this approach is demonstrated using real airline data from an Australian domestic airline.
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.
This paper is the first 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. While optimisation has made an enormous contribution to airline planning in general, the area suffers from a lack of standardised data and benchmark problems. Current research typically tackles problems unique to a given carrier, with associated specification and data unavailable to the broader research community. This limits direct comparison of alternative approaches, and creates barriers of entry for the research community. Furthermore, flight schedule design 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. This is Part I of two papers taking first steps to address these issues. It does so by providing a framework and methodology for generating realistic airline demand data, controlled by scalable parameters. First, a characterisation of flight network topologies and network capacity distributions is deduced, based on analysis of airline data. Then a bi-objective optimisation model is proposed to solve the inverse problem of inferring OD-pair demands from passenger loads on arcs. These two elements are combined to yield a methodology for generating realistic flight network topologies and OD-pair demand data, according to specified parameters. This methodology is used to produce 33 benchmark instances exhibiting a range of characteristics. Part II will extend this work by partitioning the demand in each market (OD pair) into market segments, each with its own utility function and set of preferences for alternative airline products. The resulting demand data will better reflect recent empirical research on passenger preference, and is expected to facilitate passenger choice modelling in flight schedule optimisation.
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