Price of Airline Frequency Competition

Frequency competition influences capacity allocation decisions in airline markets and has important implications to airline profitability and airport congestion. Market share of a competing airline is a function of its frequency share and the relationship between the two is pivotal for understanding the impacts of frequency competition on airline business. Based on the most commonly accepted form of this relationship, we propose a game-theoretic model of airline frequency competition. We characterize the conditions for existence and uniqueness of a Nash equilibrium for the 2-player case. We analyze two different myopic learning dynamics for the non-equilibrium situations and prove their convergence to Nash equilibrium under mild conditions. For the N-player game between identical players, we characterize all the pure strategy equilibria and identify the worst-case equilibrium, i.e. the equilibrium with maximum total cost. We provide an expression for the measure of inefficiency, similar to the price of anarchy, which is the ratio of the total cost of the worst-case equilibrium to the total cost of the cost minimizing solution and investigate its dependence on different parameters of the game

Robust airline schedule design in a dynamic scheduling environment

In the past decade, major airlines in the US have moved from banked hub-and-spoke operations to de-banked hub-and-spoke operations in order to lower operating costs. In Jiang and Barnhart (2009), it is shown that dynamic airline scheduling, an approach that makes minor adjustments to flight schedules in the booking period by re-fleeting and re-timing flight legs, can significantly improve utilization of capacity and hence increase profit. In this paper, we develop robust schedule design models and algorithms to generate schedules that facilitate the application of dynamic scheduling in de-banked hub-and-spoke operations. Such schedule design approaches are robust in the sense that the schedules produced can more easily be manipulated in response to demand variability when embedded in a dynamic scheduling environment. In our robust schedule design model, we maximize the number of potentially connecting itineraries weighted by their respective revenues. We provide two equivalent formulations of the robust schedule design model and develop a decomposition-based solution approach involving a variable reduction technique and a variant of column generation. We demonstrate, through experiments using data from a major U.S. airline that the schedule generated can improve profitability when dynamic scheduling is applied. It is also observed that the greater the demand variability, the more profit our robust schedules achieve when compared to existing ones

OPTIMIZATION APPROACHES TO AIRLINE INDUSTRY CHALLENGES: Airline Schedule Planning and Recovery

The airline industry has a long history of developing and applying optimization approaches to their myriad of scheduling problems, including designing flight schedules that maximize profitability while satisfying rules related to aircraft maintenance; generating cost-minimizing, feasible work schedules for pilots and flight attendants; and identifying implementable, low-cost changes to aircraft and crew schedules as disruptions render the planned schedule inoperable. The complexities associated with these problems are immense, including long-and short-term planning horizons; and multiple resources including aircraft, crews, and passengers, all operating over shared airspace and airport capacity. Optimization approaches have played an important role in overcoming this complexity and providing effective aircraft and crew schedules. Historical optimization-based approaches, however, often involve a sequential process, first generating aircraft schedules and then generating crew schedules. Decisions taken in the first steps of the process limit those that are possible in subsequent steps, resulting in overall plans that, while feasible, are typically sub-optimal. To mitigate the myopic effects of sequential solutions, researchers have developed extended models that begin to integrate som

09261 Abstracts Collection -- Models and Algorithms for Optimization in Logistics

From June 21 to June 26, 2009 the Dagstuhl Seminar Perspectives Workshop 09261 ``Models and Algorithms for Optimization in Logistics \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Testing the Validity of the MIP Approach for Locating Carsharing Stations in One-way Systems

The most relevant problem to manage one-way carsharing systems is the vehicle stock imbalance across the stations. Previous research proposed a mathematical model for choosing the stations’ location as an approach to solve it. However, it does not allow including relocation operations and trip uncertainty. In this paper we develop a simulation model that considers demand variability and one vehicle relocation policy and test the solutions provided by the previous MIP model. We have concluded that these factors influence significantly the company profit and should be considered in future research in one-way carsharing systems planning

A decomposition approach for commodity pickup and delivery with time-windows under uncertainty

We consider a special class of large-scale, network-based, resource allocation problems under uncertainty, namely that of multi-commodity flows with time-windows under uncertainty. In this class, we focus on problems involving commodity pickup and delivery with time-windows. Our work examines methods of proactive planning, that is, robust plan generation to protect against future uncertainty. By a priori modeling uncertainties in data corresponding to service times, resource availability, supplies and demands, we generate solutions that are more robust operationally, that is, more likely to be executed or easier to repair when disrupted. We propose a novel modeling and solution framework involving a decomposition scheme that separates problems into a routing master problem and Scheduling Sub-Problems; and iterates to find the optimal solution. Uncertainty is captured in part by the master problem and in part by the Scheduling Sub-Problem. We present proof-of-concept for our approach using real data involving routing and scheduling for a large shipment carrier’s ground network, and demonstrate the improved robustness of solutions from our approach

Modeling Passenger Travel and Delays in the National Air Transportation System

Many of the existing methods for evaluating an airline's on-time performance are based on flight-centric measures of delay. However, recent research has demonstrated that passenger delays depend on many factors in addition to flight delays. For instance, significant passenger delays result from flight cancellations and missed connections, which themselves depend on a significant number of factors. Unfortunately, lack of publicly available passenger travel data has made it difficult for researchers to explore the nature of these relationships. In this paper, we develop methodologies to model historical travel and delays for U.S. domestic passengers. We develop a multinomial logit model for estimating historical passenger travel and extend a previously developed greedy reaccommodation heuristic for estimating the resulting passenger delays. We report and analyze the estimated passenger delays for calendar year 2007, developing insights into factors that affect the performance of the National Air Transportation System in the United States.United States. Federal Aviation Administration. National Center for Excellence for Aviation Operations Researc