Author/Judge's Commentary 371 teams started directly with the binomial distribution without loss of continu ity. Some teams went on to use the normal approximation to the binomial Revenues were generally calculated using some sort of"expected value"equa tion. Some teams built nonlinear optimization models which was a nice and different approach Teams usually started with a simple example: a single plane with a fixed cost and capacity, one ticket price, and a reasonable value for no-shows based on historical data. This then became a model from which teams could build refinements (not only to their parameters)but also to include the changes based on post-9-11 Teams often simulated these results using the computer and then made sense of the simulation by summarizing the results Nake Forest had two Outstanding papers. Team 69, with their paper entitled ACE is High, was the INFORMS winner because of its superior analysis Both papers began using a binomial approach as their base model. Team 273 developed a single-plane model, a 2-plane model, and generalized to an n- plane model. Team 69 did a superb job in maximizing revenue after examining alternatives and varying their parameters The Harvey Mudd team, the MAA winner, had-by far-the best literature search. They used it to discuss existing models to determine if any could be used for post 9-11. Their research examined many of the current overbooking models that could be adapted to the situation. The University of Colorado team used Frontier Airlines as their airlines They began with the binomial random variable approach, with revenues be ing expected values. They modeled both linear and nonlinear compensation plans for bumped passengers. They developed an auction-style model using Chebyshevs weighting distribution. They also consider time-dependency in their model The Duke University team, the SIaM winner, had an excellent mix of liter- ature search material and development of their own models. They too began with a basic binomial model. They considered multiple fares and related each post-9-11 issue to parameters in their model. They varied their parameters and provided many key insights to the overbooking problem. This paper was the first paper in a long time to receive an Outstanding from judges who had read distribution as their probability distribution and then put together an expected mean Gh their pap Bill. whai The Bethel College team built a risk assessment model. They used a normal value model for revenue. Their analysis of Vanguard Airlines with a plane capacity of 130 passengers was done well Most papers found an"optimal"overbooking strategy to be to overbook between 9% and 15%, and they used these numbers to find"optimal"revenu for the airlines. Many teams tried alternative strategies for compensation, and some even considered the different classes of seats on an airplane All teams and their advisors are commended for the efforts on the airline Overbooking ProblemAuthor/Judge’s Commentary 371 teams started directly with the binomial distribution without loss of continu￾ity. Some teams went on to use the normal approximation to the binomial. Revenues were generally calculated using some sort of “expected value” equa￾tion. Some teams built nonlinear optimization models, which was a nice and different approach. Teams usually started with a simple example: a single plane with a fixed cost and capacity, one ticket price, and a reasonable value for no-shows based on historical data. This then became a model from which teams could build refinements (not only to their parameters) but also to include the changes based on post-9-11. Teams often simulated these results using the computer and then made sense of the simulation by summarizing the results. Wake Forest had two Outstanding papers. Team 69, with their paper entitled “ACE is High,” was the INFORMS winner because of its superior analysis. Both papers began using a binomial approach as their base model. Team 273 developed a single-plane model, a 2-plane model, and generalized to an n￾plane model. Team 69 did a superb job in maximizing revenue after examining alternatives and varying their parameters. The Harvey Mudd team, the MAA winner, had—by far—the best literature search. They used it to discuss existing models to determine if any could be used for post 9-11. Their research examined many of the current overbooking models that could be adapted to the situation. The University of Colorado team used Frontier Airlines as their airlines. They began with the binomial random variable approach, with revenues be￾ing expected values. They modeled both linear and nonlinear compensation plans for bumped passengers. They developed an auction-style model using Chebyshev’s weighting distribution. They also consider time-dependency in their model. The Duke University team, the SIAM winner, had an excellent mix of liter￾ature search material and development of their own models. They too began with a basic binomial model. They considered multiple fares and related each post-9-11 issue to parameters in their model. They varied their parameters and provided many key insights to the overbooking problem. This paper was the first paper in a long time to receive an Outstanding from judges who had read their paper. Bill, what does this mean?? The Bethel College team built a risk assessment model. They used a normal distribution as their probability distribution and then put together an expected value model for revenue. Their analysis of Vanguard Airlines with a plane capacity of 130 passengers was done well. Most papers found an “optimal” overbooking strategy to be to overbook between 9% and 15%, and they used these numbers to find “optimal” revenues for the airlines. Many teams tried alternative strategies for compensation, and some even considered the different classes of seats on an airplane. All teams and their advisors are commended for the efforts on the Airline Overbooking Problem