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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao zbMATH Openarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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Article
Data sources: zbMATH Open
Transportation Science
Article . 1997 . Peer-reviewed
Data sources: Crossref
DBLP
Article . 1997
Data sources: DBLP
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Advances in the Optimization of Airline Fleet Assignment

Advances in the optimization of airline fleet assignment
Authors: Russell A. Rushmeier; Spyridon A. Kontogiorgis;

Advances in the Optimization of Airline Fleet Assignment

Abstract

We present an advanced model for the formulation and solution of large scale fleet assignment problems that arise in the scheduling of air transportation. Fleet assignment determines the type of aircraft to operate each flight in a given schedule, subject to a variety of side constraints, due to marketing, operational, maintenance and crew restrictions. We model the problem as mixed-integer multicommodity flow on networks encoding activities linking flight departures. We focus on fully representing flight connection possibilities, while accurately capturing complex operational rules. We also provide a unified framework for the expression of resource constraints via piecewise linear penalties, which permits a profitability-based tradeoff between operational goals and revenue. Computational results on actual schedules show that high quality assignments for one-day problems can be obtained within an hour of computation. The use of the model at USAir results in an annual benefit of at least $15 million.

Keywords

Transportation, logistics and supply chain management, Mixed integer programming, mixed-integer multicommodity flow on networks, Deterministic network models in operations research, large scale fleet assignment, Case-oriented studies in operations research, scheduling of air transportation, computational results

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    81
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
81
Top 10%
Top 1%
Average
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