While the airline industry has benefited from advancements made in advanced analytical OR methods, most products used in operation stem from the frictionless environment of the planning stage. With 22% of all flights being delayed and 3% being canceled in the U.S. since 2001, schedule perturbations are inevitable. The complexity of the operational environment is exacerbated by the need for obtaining a solution in as close to real-time as possible. Given some time horizon, the recovery process seeks to repair the flight schedule, aircraft rotations, crew schedule, and passenger itineraries in a tractable manner. Each component individually can be difficult to solve, so early research on irregular operations has studied these problems in isolation leading to a sequential process by which the recovery process is conducted. Recent work has integrated a subset of these four components, usually abstracting from crew recovery. We present an optimization-based approach to solve the airline integrated recovery problem. After our solution methodology is presented, it is tested using data from an actual U.S. carrier with a dense flight network. It is shown that in several instances an integrated solution is delivered in a reasonable runtime. Moreover we present results showing the integrated approach can substantially improve the solution quality over the incumbent sequential approach. To the best of our knowledge, are the first to present computational results on the fully integrated airline recovery problem.
Runway scheduling deals with the sequencing of arriving and departing aircraft at airports such that a predefined objective is optimized subject to several operational constraints. Different from the existing deterministic approaches in the literature, we consider a new approach to the stochastic version of this problem within the general context of machine scheduling problems. As part of our analysis, we first show that a restricted version of the stochastic runway-scheduling problem is equivalent to a machine-scheduling problem on a single machine with sequence-dependent setup times and stochastic due dates. We then extend this restricted model by considering characteristics specific to the runway-scheduling problem and present two different stochastic integer programming models. We derive some tight valid inequalities for these formulations and propose a solution methodology based on sample average approximation and Lagrangian-based scenario decomposition. Realistic data sets are then used to perform a detailed computational study involving implementations and analyses of several different configurations of the models. The results from the computational tests indicate that truncated versions of the proposed solution algorithm, where the best solution is reported after short run times, almost always produce very high-quality solutions, implying that the proposed stochastic approach to runway scheduling is likely to be practically implementable with potential value over current practice or deterministic models. The online appendix is available at https://doi.org/10.1287/trsc.2017.0784 .
A Runway Planning optimization model has been developed as a part of a comprehensive suite of models for the optimization of airport surface traffic in the presence of uncertainty. The runway planning problem is formulated as a two-stage stochastic program where the first stage determines an aircraft weight class sequence and the second stage assigns individual aircraft to the sequence. Stochastic attributes include pushback delay, time spent on taxiway, and deviation from estimated arrival time. The computational study shows that, if the schedule is dense enough, there is a potential benefit of using the stochastic runway planner over first-come-first-serve planning policy or a deterministic runway planner.
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