航空公司运营与管理Managing Airline Ground Operations.ppt

A total of K activities need to be carried out in this workflow and each activity k has a stochastic operating time, namely the state sojourn time, denoted by . The stochastic operating time of activity k (i.e. ) is modeled by a stochastic function, . Similarly, the workflow of passenger processing is modeled as a Markov Chain. Some activities in aircraft turnaround operations are conducted independently from the two previous major workflows. Four major disrupting events are modeled here, namely disruptions to refueling, engineering check delays, aircraft damage, and aircraft change delays. Model parameters in the TS model were compiled from airline data, including the occurrence probabilities of disrupting events, service time of various turnaround activities, and their corresponding probability distributions. These parameters were used to simulate the major components of the TS model, including the semi-Markov Chain and discrete event simulation. The major advantage of the TS model, when compared with the PERT model, is that the TS model is able to simulate the stochastic and dynamic transition behavior between ground service activities as well as to model the occurrence of disruptions to aircraft turnaround. Compared with the TTA model, the advantage of the TS model is that it is able to simulate on a micro level the operations of individual activities and the occurrence of disruptions. For OTP benchmarking purposes, both TTA and TS models are able to provide benchmarks before schedule implementation. 5. managing passenger flows at airports Apart from facilitating passenger flows and providing high service quality to customers, an important operational goal for airlines is to ensure a smooth process for passengers from check-in to boarding and ensuring there are no delays to flight departures due to passenger handling at airports. The capacity of these service and the capacity of airport terminals are critical in managing p