Daily aircraft routing and scheduling
Management Science
Branch-And-Price: Column Generation for Solving Huge Integer Programs
Operations Research
Flight String Models for Aircraft Fleeting and Routing
Transportation Science
Airline Fleet Assignment with Time Windows
Transportation Science
Benders Decomposition for Simultaneous Aircraft Routing and Crew Scheduling
Transportation Science
Airline Crew Scheduling with Time Windows and Plane-Count Constraints
Transportation Science
A Robust Fleet-Assignment Model with Hub Isolation and Short Cycles
Transportation Science
An integrated aircraft routing, crew scheduling and flight retiming model
Computers and Operations Research
Robust Airline Fleet Assignment: Imposing Station Purity Using Station Decomposition
Transportation Science
Computers and Operations Research
Airline Crew Scheduling Under Uncertainty
Transportation Science
A Stochastic Programming Approach to the Airline Crew Scheduling Problem
Transportation Science
Robust Airline Crew Pairing: Move-up Crews
Transportation Science
Computers and Operations Research
Integrated Airline Fleeting and Crew-Pairing Decisions
Operations Research
An iterative approach to robust and integrated aircraft routing and crew scheduling
Computers and Operations Research
An integrated scenario-based approach for robust aircraft routing, crew pairing and re-timing
Computers and Operations Research
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For reasons of tractability, the airline scheduling problem has traditionally been sequentially decomposed into various stages (e.g., schedule generation, fleet assignment, aircraft routing, and crew pairing), with the decisions from one stage imposed upon the decision making process in subsequent stages. Although this approach greatly simplifies the solution process, it unfortunately fails to capture the many dependencies between the various stages, most notably between those of aircraft routing and crew pairing, and how these dependencies affect the propagation of delays through the flight network. Because delays are commonly transferred between late running aircraft and crew, it is important that aircraft routing and crew pairing decisions are made together. The propagated delay may then be accurately estimated to minimize the overall propagated delay for the network and produce a robust solution for both aircraft and crew. In this paper we introduce a new approach to accurately calculate and minimize the cost of propagated delay in a framework that integrates aircraft routing and crew pairing.