SUMMARY

Airports as major transportation hubs need to transport a large number of travelers every day. Effectively managing such a large transportation system without compromising safety has always been a huge challenge. One of the keys to enhancing overall airport management is improving operational efficiency which includes optimizing the allocation and scheduling of limited resources, such as workforce and equipment.
To address the joint job scheduling problem for both the GSS and GSE in the airport apron area, this thesis proposed an intelligent decision support system, which integrates the use of smarting sensing, predictive learning for maintenance scheduling, and algorithm and simulation for job schedule optimization. First, the demands, information flow, interaction between the stakeholders and the system, and the functionalities of the proposed system have been analyzed using various design tools. Second, a real-time GSE status monitoring solution using non-invasive sensor hardware and smart sensing has been proposed; this method enables status displaying both locally on each GSE and remotely on a cloud-based platform. Third, by processing and analyzing raw status data collected by sensor hardware, the time range and duration of GSE maintenance could be predicted using recurrent neural networks. Next, optimal job schedules will be generated for GSE and GSS using a 2-dimensional genetic algorithm and validated in a discrete event simulation model. Finally, the system architecture and process flow of the integrated decision support system are analyzed. Moreover, a sample mobile app has been created and the functionalities of the app are introduced in this thesis.