A flight scheduling model for Taiwan airlines under market competitions

Yan, Shangyao; Tang, Ching Hui; Lee, Ming Chei

doi:10.1016/j.omega.2005.03.002

Cited by 48 publications

(39 citation statements)

References 13 publications

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“…Also, such an optimization tool can help to freight forwarder companies to select and manage the right mix of suppliers and to identify warehousing and distribution facilities best suited to customer needs. Very famous freight forwarders have extensive road and rail feeder network with links to their hubs, sub-hubs and gateways distributed all over the world; see [10,11,12]. The amount of different cargo loads (e.g.…”

Section: Introductionmentioning

confidence: 99%

Efficient heuristic for non-linear transportation problem on the route with multiple ports

Krile

2013

Polish Maritime Research

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Section: Introductionmentioning

confidence: 99%

Efficient heuristic for non-linear transportation problem on the route with multiple ports

Krile

2013

Polish Maritime Research

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“…For more details on recapture rates, see Barnhart et al (2002) and Jiang (2006). Yan et al (2007) develop a flight scheduling model that applies both fleet assignment and flight scheduling under variable market shares estimated by choice models.…”

Section: Previous Studiesmentioning

confidence: 99%

Dynamic airline scheduling: An analysis of the potentials of refleeting and retiming

Warburg¹,

Hansen²,

Larsen³

et al. 2008

Journal of Air Transport Management

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We present a Dynamic Airline Scheduling (DAS) technique which is able to change departure times and reassign aircraft types during the booking process to meet fluctuating passenger demands. The procedure is tested on several different days before departure, resulting in a significant profit increase for a major European airline. The results also indicate that applying DAS close to departure yields the largest potential.

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“…However, as noted in Klabjan (2005), for the most part flight schedule planning is a manual process. Notable exceptions are discussed in two papers, Yan and Tseng (2002) and Yan, Tang, and Lee (2007), on flight scheduling in Taiwan, and in Lohatepanont and Barnhart (2004). Yan and Tseng (2002) formulate schedule design as a kind of network design problem in a time-space network, in which the passengers are represented as explicit multicommodity flows in the network designed by the aircraft variables.…”

Section: Introductionmentioning

confidence: 99%

“…The latter is the only way in which passenger utility with respect to alternate itineraries is considered. Yan et al (2007) extend this work to embed a more sophisticated passenger choice model, in which the presence of competition in the market is modelled by assuming passengers will be unavailable to the airline if they have to wait too long to depart. The passenger loss parameters require a complicated calculation taking into account airline preferences, ticket prices, flight frequency and travel time, which is nonlinear in the decision variables, and seems to be decoupled from subsequent flights in a multi-flight itinerary (only loss at the port of origin is considered).…”

Section: Introductionmentioning

confidence: 99%

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

Akartunalı

Boland

Evans³

et al. 2013

Computers & Operations Research

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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.

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A flight scheduling model for Taiwan airlines under market competitions

Cited by 48 publications

References 13 publications

Efficient heuristic for non-linear transportation problem on the route with multiple ports

Efficient heuristic for non-linear transportation problem on the route with multiple ports

Dynamic airline scheduling: An analysis of the potentials of refleeting and retiming

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

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