Solving a robust airline crew pairing problem with column generation

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 dicult 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 a local airline in Turkey

Development Challenges of Secondary and Small Airports in California, Research Report 11-21

This study investigates the development of secondary and smaller airports in California. Low-Cost Carrier (LCC) business is growing at these airports because they offer reduced operating costs, and they have adequate capacity to help LCCs avoid battling with incumbent airlines at the large hubs for limited resources, such as gates. However, increased LCC aircraft operations at the secondary airports have led to significant noise impacts on the surrounding communities and this has been a challenge for the secondary airport operators. They have imposed operational curfews to limit the noise impacts, but this approach constrains the resident airlines that want to increase their traffic. As a result, some LCCs have begun to initiate flights out of the large hubs. Statistics from this study show that the LCCs have replaced the legacy airlines as the dominant air provider in the state. With their growing dominance, the LCCs will become more attractive to the large hub airports, and the secondary airports will face increased competition in retaining them. To retain those LCCs, the secondary airports must better understand how LCCs make investment decisions related to airport development. At the same time, they must better educate the LCCs about their airport needs

Using Ground Transportation for Aviation System Disruption Alleviation

An investigation was made into whether passenger delays and airline costs due to disruptive events affecting European airports could be reduced by a coordinated strategy of using alternative flights and ground transportation to help stranded passengers reach their final destination using airport collaborative decision-making concepts. Optimizing for airline cost for hypothetical disruptive events suggests that, for airport closures of up to 10 h, airlines could benefit from up to a 20% reduction in passenger delay-related costs. The mean passenger delay could be reduced by up to 70%, mainly via a reduction in very long delays

New approaches to airline recovery problems

Air traffic disruptions result in fight delays, cancellations, passenger misconnections, creating high costs to aviation stakeholders. This dissertation studies two directions in the area of airline disruption management – an area of significant focus in reducing airlines’ operating costs. These directions are: (i) a joint proactive and reactive approach to airline disruption management, and (ii) a dynamic aircraft and passenger recovery approach to evaluate the long-term effects of climate change on airline network recoverability. Our first direction proposes a joint proactive and reactive approach to airline disruption management, which optimizes recovery decisions in response to realized disruptions and in anticipation of future disruptions. Specifically, it forecasts future disruptions partially and probabilistically by estimating systemic delays at hub airports (and the uncertainty thereof) and ignoring other contingent disruption sources. It formulates a dynamic stochastic integer programming framework to minimize network-wide expected disruption recovery costs. Specifically, our Stochastic Reactive and Proactive Disruption Management (SRPDM) model combines a stochastic queuing model of airport congestion, a fight planning tool from Boeing/Jeppesen and an integer programming model of airline disruption recovery. We develop an online solution procedure based on look-ahead approximation and sample average approximation, which enables the model's implementation in short computational times. Experimental results show that leveraging partial and probabilistic estimates of future disruptions can reduce expected recovery costs by 1-2%, as compared to a baseline myopic approach that uses realized disruptions alone. These benefits are mainly driven by the deliberate introduction of departure holds to reduce expected fuel costs, fight cancellations and aircraft swaps. Our next direction studies the impact of climate change-imposed constraints on the recoverability of airline networks. We first use models that capture the modified payload-range curves for different aircraft types under multiple climate change scenarios, and the associated (reduced) aircraft capacities. We next construct a modeling and algorithmic framework that allows for simultaneous and integrated aircraft and passenger recovery that explicitly capture the above-mentioned capacity changes in aircraft at different times of day. Our computational results using the climate model on a worst-case, medium-case, and mild-case climate change scenarios project that daily total airline recovery costs increase on average, by 25% to 55.9% on average ; and by 10.6% to 156% over individual disrupted days. Aircraft-related costs are driven by a huge increase in aircraft swaps and cancelations; and passenger-related costs are driven by increases in disrupted passengers who need to be rebooked on the same or a different airline. Our work motivates the critical need for airlines to systematically incorporate climate change as a factor in the design of aircraft as well as in the design and operations of airline networks

Air Travel Choices in Multi-Airport Markets

We estimate a conditional logit model to measure the impact of airport and airline supply characteristics on the air travel choices of passengers departing from one of three San Francisco Bay area airports and arriving at one of four airports in greater Los Angeles in October 1995. Non-price characteristics like airport access time, airport delay, flight frequency, the availability of particular airport-airline combinations, and early arrival times are found to strongly affect choice probabilities. Marginal effects and counterfactual scenarios suggest that changes access in times affect travel choices more than changes in travel delays, and that the preferred airport differs by passenger type. In order to examine the robustness of the conditional logit model, we estimate a mixed logit model, and find that the results are similar. We attribute the similarity to our strictly defined travel market and to our distinction between leisure and business travelers, thus controlling for two important sources of consumer heterogeneity.Airports; Airlines; Air travel demand; Discrete choice