Simulation-based Input Loading Condition Optimisation of Airport Baggage Handling Systems

Abstract

Abstract⎯ Scheduling check-in station operations are a challenging problem within airport systems. Prior to determining check-in resource schedules, an important step is to estimate the Baggage Handling System (BHS) operating capacity under non-stationary conditions. This ensures that check-in stations are not overloaded with bags, which would adversely affect the system and cause cascade stops and blockages. Cascading blockages can potentially lead to a poor level of service and in worst scenario a customer may depart without their bags. This paper presents an empirical study of a multiobjective problem within a BHS system. The goal is to estimate near optimal input operating conditions, such that no blockages occurs at check-in stations, while minimising the baggage travel time and maximising the throughput performance measures. We provide a practical hybrid simulation and binary search technique to determine a near optimal input throughput operating condition. The algorithm generates capacity constraint information that may be used by a scheduler to plan check-in operations based on flight arrival schedules.

A dynamic time warped clustering technique for discrete event simulation-based system analysis

“…1. For an extended description of a BHS we refer to earlier work (Johnstone, Creighton, & Nahavandi, 2010) and for an alternative analysis of this particular system see Le, Creighton, and Nahavandi (2007).…”

A prediction interval-based approach to determine optimal structures of neural network metamodels

“…Therefore, precise modeling and analysis of BHS is a vital task for optimization and planning purposes. For these purposes, a highly detailed discrete event simulation model was developed based on real data from the underlying BHS (Khosravi, Nahavandi, & Creighton, 2009;Le, Creighton, & Nahavandi, 2007). Quest Ò package from Delmia Inc. was used for 3D representation of this real BHS.…”

“…Stochastic process times, machine breakdowns, failure to complete a task, and need to rework are among factors that contribute to the uncertainty accumulation within BHS. Further information about BHS and its internal operation can be found in (Johnstone, Creighton, & Nahavandi, 2007;Khosravi et al, 2009;Le et al, 2007) and within references.…”

Constructing prediction intervals for neural network metamodels of complex systems

“…In this study Quest® from Delmia was used to develop a highly detailed model of an international Baggage Handling System (BHS) [25] [26]. A BHS includes many processes and operations that are completed by either machines (screening, tag reading, diverting, merging and routing) or humans (picture analysis, manual encoding, unloading bags from laterals and makeup loops and manual search).…”

“…Events such as cascade stops or route blockages are due to these physical or operational dependencies. For further information about BHSs and issues related to their internal operation, we refer interested readers to [25] [26] [27] and references therein. A snapshot of a BHS simulation model, including labels for different components, is shown in Fig.…”