W hen air traffic demand is projected to exceed capacity, the Federal Aviation Administration implements traffic flow management (TFM) programs. Independently, these programs maintain a first-scheduled, firstserved invariant, which is the accepted standard of fairness within the industry. Coordinating conflicting programs requires a careful balance between equity and efficiency. In our work, we first develop a fairness metric to measure deviation from first-scheduled, first-served in the presence of conflicts. Next, we develop an integer programming formulation that attempts to directly minimize this metric. We further develop an exponential penalty approach and show that its computational performance is far superior and its tradeoff between delay and fairness compares favorably. In our results, we demonstrate the effectiveness of these models using historical and hypothetical scenarios. Additionally, we demonstrate that the exponential penalty approach exhibits exceptional computational performance, implying practical viability. Our results suggest that this approach could lead to system-wide savings on the order of $25 to $50 million per year.

This paper summarizes research trends and opportunities in the area of managing air transportation demand and capacity. Capacity constraints and resulting congestion and low schedule reliability currently impose large costs on airlines and their passengers. Significant capacity increases that would solve these problems are not expected in the near-or medium-term. This paper outlines first a number of directions for effecting improvement through marginal capacity increases and better management of demand and available capacity. It then describes strategic initiatives that airlines and civil aviation authorities might undertake over time horizons of months to years as well as tactical measures that may be adopted on a daily basis in response to dynamic, ''real-time'' developments like poor weather or schedule disruptions. Research challenges in these areas are identified and classified in terms of specifying, allocating, and utilizing capacity. The first two categories reflect challenges faced by infrastructure providers, the last category challenges faced by airlines.

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 discrete choice 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.Draft completed August 2 nd , 2010. IntroductionOver the past two years, flight and passenger delays have been on the decline due to reduced demand for air travel as a result of the recent economic crisis. As the economy rebounds, demand for air travel in the United States is also expected to recover (Tomer & Puentes, 2009). Thus, after a brief reprieve, the U.S.will once again face a looming transportation crisis due to air traffic congestion. In calendar year 2007, the last year of peak air travel demand before the economic downturn, flight delays were estimated to methodologies, the huge discrepancy between these estimates suggests the need for a more transparent and rigorous approach to measuring passenger delays. Accurately estimating passenger delays is important not only as a means to understand system performance, but also to motivate policy and investment decisions for the National Air Transportation System.Another important consideration is that neither of the passenger delay cost estimates listed above includes the delays associated with itinerary disruptions, such as missed connections or cancellations. Analysis performed by Bratu and Barnhart (2005) suggests that itinerary disruptions and the associated delays represent a significant component of passenger delays. Their analysis was performed using one month of proprietary passenger booking data from a legacy carrier. The challenge in extending this analysis system-wide is that publicly available data sources do not contain passenger itinerary flows. For example, on a given day, there is no way to determine how many passengers planned to take the 7:05amAmerican Airlines flight from Boston Logan (BOS) to Chicago O'Hare (ORD) followed by the 11:15amflight from Chicago O'Hare (ORD) to Los Angeles (LAX), or even the number of non-stop passengers on each of these flights. Instead, the passenger flow data that is publicly available is aggregated over time, either monthly or quarterly, and reports flows based only on...

This paper studies sourcing decisions of firms in a multitier supply chain when procurement is subject to disruption risk. We argue that features of the production process that are commonly encountered in practice (including differential production technologies and financial constraints) may result in the formation of inefficient supply chains, owing to the misalignment of the sourcing incentives of firms at different tiers. We provide a characterization of the conditions under which upstream suppliers adopt sourcing strategies that are suboptimal from the perspective of firms further downstream. Our analysis highlights that a focus on optimizing procurement decisions in each tier of the supply chain in isolation may not be sufficient for mitigating risks at an aggregate level. Rather, we argue that a holistic view of the entire supply network is necessary to properly assess and secure against disruptive events. Importantly, the misalignment we identify does not originate from cost or reliability asymmetries. Rather, firms' sourcing decisions are driven by the interplay of the firms' risk considerations with nonconvexities in the production process. This implies that bilateral contracts that could involve under-delivery penalties may be insufficient to align incentives.

Existing performance metrics utilized by the PGA TOUR have biases towards specific styles of play, which make relative player comparisons challenging. Our goal is to evaluate golfers in a way that eliminates these biases and to better understand how the best players maintain their advantage.Through a working agreement with the PGA TOUR, we have obtained access to proprietary "ShotLink" data that pinpoints the location of every shot taken on the PGA TOUR. Using these data, we develop distance-based models for two components of putting performance: the probability of making the putt and the remaining distance to the pin conditioned on missing. The first is modeled through a logistic regression, the second through a gamma regression. Both models fit the data well and provide interesting insights into the game. Additionally, by describing the act of putting using a simple Markov chain, we are able to combine these two models to characterize the putts-to-go for the field from any distance on the green for the PGA TOUR. The results of this Markov model match both the empirical expectation and variance of putts-to-go.We use our models to evaluate putting performance in terms of the strokes or putts gained per round relative to the field. Using this metric, we can determine what portion of a player's overall performance is due to advantage (or loss) gained through putting, and conversely, what portion of the player's performance is derived off the green. We demonstrate with examples how our metric eliminates significant biases that exist in the PGA TOUR's Putting Average statistic. Lastly, extending the concept of putts gained to evaluate player-specific performance, we show how our models can be used to quickly test situational hypotheses, such as differences between putting for par and birdie and performance under pressure.