Solving binary cutting stock problems by column generation and branch-and-bound
Computational Optimization and Applications
A Note on Branch-and-Price Algorithms for the One-Dimensional Cutting Stock Problems
Computational Optimization and Applications
Branch-And-Price: Column Generation for Solving Huge Integer Programs
Operations Research
Drive: Dynamic Routing of Independent Vehicles
Operations Research
Transportation Science
A New Pricing Scheme for Airline Crew Scheduling
INFORMS Journal on Computing
A Robust Fleet-Assignment Model with Hub Isolation and Short Cycles
Transportation Science
Robust Airline Fleet Assignment: Imposing Station Purity Using Station Decomposition
Transportation Science
Selected Topics in Column Generation
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
Robust crew pairing for managing extra flights
Computers and Operations Research
Disruption management in the airline industry-Concepts, models and methods
Computers and Operations Research
An iterative approach to robust and integrated aircraft routing and crew scheduling
Computers and Operations Research
An exact algorithm for IP column generation
Operations Research Letters
On an exact method for the constrained shortest path problem
Computers and Operations Research
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In this study, we solve a robust version of the airline crew pairing problem. Our concept of robustness was partially shaped during our discussions with small local airlines in Turkey which may have to add a set of extra flights into their schedule at short notice during operation. Thus, robustness in this case is related to the ability of accommodating these extra flights at the time of operation by disrupting the original plans as minimally as possible. We focus on the crew pairing aspect of robustness and prescribe that the planned crew pairings incorporate a number of predefined recovery solutions for each potential extra flight. These solutions are implemented only if necessary for recovery purposes and involve either inserting an extra flight into an existing pairing or partially swapping the flights in two existing pairings in order to cover an extra flight. The resulting mathematical programming model follows the conventional set covering formulation of the airline crew pairing problem typically solved by column generation with an additional complication. The model includes constraints that depend on the columns due to the robustness consideration and grows not only column-wise but also row-wise as new columns are generated. To solve this difficult model, we propose a row and column generation approach. This approach requires a set of modifications to the multi-label shortest path problem for pricing out new columns (pairings) and various mechanisms to handle the simultaneous increase in the number of rows and columns in the restricted master problem during column generation. We conduct computational experiments on a set of real instances compiled from local airlines in Turkey.