The main problem facing both amusement park customers and owners such as Disney World is customer satisfaction and efficiency, which are both negatively effected by high wait times. As a result, these parks have spent significant time and money to implement methods which reduce wait times to both increase customer satisfaction and efficiency. We explored the implementation of an reservation-dependent priority queuing system to devise how to best reduce average customer wait times for Expedition Everest, a popular ride at Disney World. Using third-party data, we first built constant-rate and time-dependent-rate queuing systems to model current behavior, followed by implementing an Express Queue into the system. We found a decrease in average wait time of 18.31% through simulating 30 days of typical customer behavior with the improvement strategy. Finally, we performed sensitivity analysis to optimize the parameters of our improvements, finding an ultimate optimal decrease in wait times of 44.42%.
zhaosongyi / amusement-park-queuing-optimization Goto Github PK
View Code? Open in Web Editor NEWThis project forked from clandgrebe/amusement-park-queuing-optimization
The main problem facing both amusement park customers and owners such as Disney World is customer satisfaction and efficiency, which are both negatively effected by high wait times. As a result, these parks have spent significant time and money to implement methods which reduce wait times to both increase customer satisfaction and efficiency. We explored the implementation of an reservation-dependent priority queuing system to devise how to best reduce average customer wait times for Expedition Everest, a popular ride at Disney World. Using third-party data, we first built constant-rate and time-dependent-rate queuing systems to model current behavior, followed by implementing an Express Queue into the system. We found a decrease in average wait time of 18.31% through simulating 30 days of typical customer behavior with the improvement strategy. Finally, we performed sensitivity analysis to optimize the parameters of our improvements, finding an ultimate optimal decrease in wait times of 44.42%.