Team 2056 Page 8 of 50 tances along the line. Each passenger is modeled as an agent, and moves along the line until reaching his seat. Each agent has a speed and is constrained by the slowest person in front of him. This sim plest model is merely a prototype, and is not used to derive experi- mental results Our basic model takes into account the topology of the airplane Each row of the plane is broken into a discrete unit. We call these units'processors' since they determine the rate that an individual moves through the system. Each processor has a queue, a list of people waiting to be processed by it (and hence moved to the next node of the system). Each agent has a particular destination pro- cessor. the row where his seat is assigned The extended model adds additional parameters into the simula- tion. For the first time, there is a one-to-one mapping of passengers to seats. This layer accounts for passengers bringing baggage onto the plane. We call a scenario where a passenger is waiting on an- ger to stow his baggage a baggage collision. W model seat collisions. A seat collision occurs when a passenger is sitting between another passenger and his seat(e. g, the passenger with an assigned window seat must move around a passenger who is sitting next to the aisle) Our next model attempts to optimize boarding time based on the order that passengers enter the plane. This is implemented using a genetic algorithm over the search space of all possible orderings Crossovers and mutations occur. with the restriction that each al teration of seat ordering must preserve the property that every seat is represented Our final model is a Markov chain used to model passenger pref erences in an open seating environment. This model simulates a boarding process such as is used by Southwest airlines We combine our models holistically, and each model interacts benefi cially with the other models described. The extended model is com- bined with the genetic model and the passenger preference model to analyze certain test scenarios. All of our results have the extendedTeam 2056 Page 8 of 50 tances along the line. Each passenger is modeled as an agent, and moves along the line until reaching his seat. Each agent has a speed, and is constrained by the slowest person in front of him. This sim￾plest model is merely a prototype, and is not used to derive experi￾mental results. Our basic model takes into account the topology of the airplane. Each row of the plane is broken into a discrete unit. We call these units ‘processors’ since they determine the rate that an individual moves through the system. Each processor has a queue, a list of people waiting to be processed by it (and hence moved to the next node of the system). Each agent has a particular destination pro￾cessor, the row where his seat is assigned. The extended model adds additional parameters into the simula￾tion. For the first time, there is a one-to-one mapping of passengers to seats. This layer accounts for passengers bringing baggage onto the plane. We call a scenario where a passenger is waiting on an￾other passenger to stow his baggage a baggage collision. We also model seat collisions. A seat collision occurs when a passenger is sitting between another passenger and his seat (e.g., the passenger with an assigned window seat must move around a passenger who is sitting next to the aisle). Our next model attempts to optimize boarding time based on the order that passengers enter the plane. This is implemented using a genetic algorithm over the search space of all possible orderings. Crossovers and mutations occur, with the restriction that each al￾teration of seat ordering must preserve the property that every seat is represented. Our final model is a Markov chain used to model passenger pref￾erences in an open seating environment. This model simulates a boarding process such as is used by Southwest Airlines. We combine our models holistically, and each model interacts benefi- cially with the other models described. The extended model is com￾bined with the genetic model and the passenger preference model to analyze certain test scenarios. All of our results have the extended