Marcial Lapp

Decreasing airline delay propagation by re-allocating scheduled slack

Passenger airline delays have received increasing attention over the past several years as air space congestion, severe weather, mechanical problems, and other sources cause substantial disruptions to a planned flight schedule. Adding to this challenge is the fact that each flight delay can propagate to disrupt subsequent downstream flights that await the delayed flights aircraft and crew. This potential for delays to propagate is exacerbated by a fundamental conflict: slack in the planned schedule is often viewed as undesirable, as it implies missed opportunities to utilize costly perishable resources, whereas slack is critical in operations as a means for absorbing disruption. This article shows how delay propagation can be reduced by redistributing existing slack in the planning process, making minor modifications to the flight schedule while leaving the original fleeting and crew scheduling decisions unchanged. Computational results based on data from a major U.S. carrier are presented that show that significant improvements in operational performance can be achieved without increasing planned costs.

Modifying lines-of-flight in the planning process for improved maintenance robustness

As a part of their planning process, airlines construct lines-of-flight (LOFs ) - daily repeating sequences of flights, each of which will be flown by a single aircraft. In the week leading up to the actual day-of-operations, these LOFs are then assigned to specific aircraft (tails), forming multi-day aircraft routings that in turn enable the scheduling of routine maintenance checks. Operational disruptions, however, can lead to deviations from these routings, which in turn disrupt the maintenance plan. The goal of our research is to improve the construction of LOFs so as to increase the likelihood of being able to recover from maintenance disruptions without costly over-the-day aircraft swaps. We present a new metric, maintenance reachability (MR), which measures the robustness of a planned set of LOFs, and develop a mathematical programming approach to improving the MR of a given set of LOFs. We provide computational results based on data from a major U.S. carrier demonstrating that significant improvements in MR can be achieved with only a small number of changes to the original set of LOFs. Finally, we conclude by showing that even under imperfect input data, MR can be improved relative to a planned set of LOFs.

A recursion-based approach to simulating airline schedule robustness

Flight disruptions due to events such as inclement weather or mechanical failure are an increasing occurrence in today¿s air travel. It is important to develop flight schedules that are not only economically feasible, but also provide opportunities to absorb these disruptions so as to reduce downstream delays. In this paper, we present a simulation algorithm to evaluate a flight schedule¿s ability to mitigate disruptions by analyzing propagation effects on the flight network. This task is challenging for two reasons: the interdependence of flights, due to shared resources (e.g. cockpit/flight crews, aircraft), and the cyclic nature of the schedule, which repeats on a daily basis. We show how a recursion-based approach to the simulation enables us to overcome these challenges.

Characterizing an effective hospital admissions scheduling and control management system: a genetic algorithm approach

Proper management of hospital inpatient admissions involves a large number of decisions that have complex and uncertain consequences for hospital resource utilization and patient flow. Further, inpatient admissions has a significant impact on the hospitals profitability, access, and quality of care. Making effective decisions to drive high quality, efficient hospital behavior is difficult, if not impossible, without the aid of sophisticated decision support. Hancock and Walter (1983) developed such a management system with documented implementation success, but for each hospital the system parameters are “optimized” manually. We present a framework for valuing instances of this management system via simulation and optimizing the system parameters using a genetic algorithm based search. This approach reduces the manual overhead in designing a hospital management system and enables the creation of Pareto efficiency curves to better inform management of the trade-offs between critical hospital metrics when designing a new control system.

A simulation framework to evaluate airport gate allocation policies under extreme delay conditions

Severe weather can lead to significant runway capacity reductions. Runway priority is typically given to inbound flights, thus fewer flights depart and fewer gates become available for arriving aircraft, leading to delays on the tarmac. We provide a simulation-based framework for evaluating gate allocation policies under reduced runway capacity. We first analyze a simple example, demonstrating the complexity of the problem and some key insights into different operating policies. Having shown that even simple scenarios can be difficult (if not impossible) to evaluate in closed form, we turn to simulation. We model the impact of reduced runway capacity on inbound and outbound flights by considering a major U.S. airport and its legacy carrier, focusing on the impact of delays on passengers. The contributions of this work are to highlight the challenges of accurately modeling the impact of runway capacity reductions and to present a simulation-based framework for evaluating operational policies.