Increasing air travel by about 5.3% per year and increasing freight transportation by more than 4% are demanding sophisticated approaches in order to use existing airport resources and to predict travel and freight demand precisely. We want to address two problems.
The baggage handling system of airports is a crucial resource, in particular at hub airports. It consists of several sub-systems (such as check-in counters, infeed stations, baggage warehouses, claim carousels, loading carousels) that are connected by a conveyor belt network. The sub-systems are operated by workers (e.g. loading bags into containers at the loading carousel). All baggage traversing at airport is routed through the baggage handling system. Within the airport industry, the baggage handling systems are mainly manually planned and controlled by human dispatchers with the help of IT-based planning systems. These systems provide information but do not apply optimization techniques. Based on existing work (see Frey et al. 2017) we want to develop models and methods in order to optimize the operations of the baggage handling system.
Accurate prediction of air cargo is required in order to plan required capacity accordingly. Prediction is particularly important as freight is paid for shipped and not for reserved cargo, leading to deviations between reserved and shipped cargo. Furthermore, relevant parameters (such as size, weight, category or type of cargo) are of different data types (continuous vs. discrete, ordered vs. unordered) that need to be unified. Based on previous work (see Brieden and Gritzmann 2012) and its subsequent applications we want to develop appropriate transformations of parameters into a unified space and develop predictive algorithms based on appropriately clustered homogenous substructures.