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1、Team 20531 of 30 Boarding at the Speed of Flight Team 2053 February 12, 2007 Executive Summary After mathematically analyzing the aircraft boarding problem, our modeling group would like to present our conclusions, strategies, and recommendations to the airline industry. We examined the mathematical
2、 eff ects of waiting in line to board, sending in diff erent groupings of seat assignments, and the interaction between various components of the boarding process to determine the time required to board an aircraft. We developed a detailed simulation methodology to test our ideas and to quantify the
3、 diff erences between boarding strategies. Our simulation models all of the critical factors at play in a boarding scenario, and is easily modifi ed to support diff erent plane dimensions and interior confi gurations as well any as- sortment of passenger characteristics depending on average demograp
4、hics and other statistics. We believe that further collaboration with your company and access to your internal business data would provide us with the capability to more accurately determine results and to tune our parameters specifi c to your airline. Our analysis began by determining what factors
5、impact boarding speed the most across all boarding algorithms. Our conclusions are presented in the list below along with strategies that can be implemented to mitigate their impact: Passenger entry speed: The faster passengersenter the plane, the faster it boards. This means fl ight check-in proced
6、ure (ticket checking) should be optimized to ensure the correct number of gate agents are present. This is particularly important on large planes with multiple aisles or levels. Flight attendants should be stationed at critical junctions (such as entrances to aisles in a multi-aisle plane) to direct
7、 each passenger to the correct row and thereby maintain throughput. Baggage stowage time: The faster passengers put their bags away and sit down, the faster the plane boards. The impact of storage time can be mitigated by changing or enforcing carry-on baggage limits and by having fl ight attendants
8、 assist passengers with particularly large bags that they cannot easily lift.Another possibility to consider is a redesign of the overhead bins to make them more easy to load. For airlines interested in further decreasing average boarding time we have fur- 1 Duke University: Michael Bauer, Kshipra B
9、hawalkar, Matthew Edwards 2007年MCM/ICM特等奖 Team 20532 of 30 ther analyzed the merits of diff erent boarding algorithms. Through our simula- tions we have developed a generic classifi cation of boarding methods: Best No assigned seats Better Outside in boarding Mediocre Back to front We understand tha
10、t the proposition of no assigned seats may be problematic from a customer service perspective. If this is the case outside in boarding (win- dow seats fi rst, in towards the aisle) provides signifi cant advantages over back to front, particularly when our previously mentioned optimizations are incor
11、po- rated into the system. The exact numbers depend on the aircraft dimensions and other factors, but in general outside in boarding provides a 10-30% advan- tage over back to front. Similarly, foregoing assigned seats results in a 10-30% advantage over outside in. We know that for many routes, this
12、 magnitude of improvement could provide the margin necessary for an extra run in the course of a day, resulting in additional revenue. However, our analysis does not stop at determining mere speed increases; we also analyzed the reliability of each boarding method in order to determine the deviation
13、 between the longest and shortest possible delays for each boarding algorithm. In order to schedule an extra fl ight, you have to be sure the tightened timetable will always be met, not just most of the time. We found that the faster methods are also considerably more reliable: outside in has a time
14、 deviation range 50% smaller than back to front. For more specifi c numbers, examples on varying sizes of planes, and in general a more complete description of our work, please refer to our in-depth re- port, attached. With our insights and your business expertise, we can cooperate to benefi t the c
15、ustomer, your business, and your shareholders. 2 Team 20533 of 30 1Introduction Short of a single minor detail the airplane boarding problem would be easily solved using a very simple algorithm. Given his performance in the summer “blockbuster” Snakes on a Plane we know Samuel L. Jackson is an optim
16、al de-boarder of snakes from planes 1. Assuming that he maintains equal eff ec- tiveness with people, simply invert his role and you have an optimal passenger boarding algorithm. We could then simply model people as snakes and play the fi lm in reverse and determine the eff ective boarding time. The only potential challenge would be scaling our results from the Boeing 747 used in the movie to planes of varying sizes. Ignoring the only detail that there is only one Samuel L. Jackson (
