Status - based routing in baggage handling systems : searching verses learning

This study contributes to work in baggage handling system (BHS) control, specifically dynamic bag routing. Although studies in BHS agent-based control have examined the need for intelligent control, but there has not been an effort to explore the dynamic routing problem. As such, this study provides additional insight into how agents can learn to route in a BHS. This study describes a BHS status-based routing algorithm that applies learning methods to select criteria based on routing decisions. Although numerous studies have identified the need for dynamic routing, little analytic attention has been paid to intelligent agents for learning routing tables rather than manual creation of routing rules. We address this issue by demonstrating the ability of agents to learn how to route based on bag status, a robust method that is able to function in a variety of different BHS designs.<br /

A heuristic algorithm for carton to pallet loading problem

This paper presents an algorithm used to solve a carton to pallet packing problem in a drink manufacturing firm. The aim was to determine the cartons loading sequence and the number pallets required, prior to dispatch by truck. The algorithm consists of a series of nine parts to solve this industrial application problem. The pallet loading solution relatively computationally efficient and reduces the number pallets required, compared to other \u27trail and error\u27 or manual spreadsheet calculation methods.<br /

Interpreting and modeling baggage handling systems as a system of systems

The topic of systems of systems has been one of the most challenging areas in science and engineering due to its multidisciplinary scope and inherent complexity. Despite all attempts carried out so far in both academia and industry, real world applications are far remote. The purpose of this paper is to modify and adopt a recently developed modeling paradigm for system of systems and then employ it to model a generic baggage handling system of an airport complex. In a top-down design approach, we start modeling process by definition of some modeling goals that guide us in selection of some high level attributes. Then functional attributes are defined which act as ties between high level attributes (the first level of abstraction) and low level metrics/measurements. Since the most challenging issues in developing models for system of systems are identification and representation of dependencies amongst constituent entities, a machine learning technique is adopted for addressing these issues.<br /

Estimating performance indexes of a baggage handling system using metamodels

In this study, we develop some deterministic metamodels to quickly and precisely predict the future of a technically complex system. The underlying system is essentially a stochastic, discrete event simulation model of a big baggage handling system. The highly detailed simulation model of this is used for conducting some experiments and logging data which are then used for training artificial neural network metamodels. Demonstrated results show that the developed metamodels are well able to predict different performance measures related to the travel time of bags within this system. In contrast to the simulation models which are computationally expensive and expertise extensive to be developed, run, and maintained, the artificial neural network metamodels could serve as real time decision aiding tools which are considerably fast, precise, simple to use, and reliable.<br /

Construction of optimal prediction intervals for load forecasting problems

Short-term load forecasting is fundamental for the reliable and efficient operation of power systems. Despite its importance, accurate prediction of loads is problematic and far remote. Often uncertainties significantly degrade performance of load forecasting models. Besides, there is no index available indicating reliability of predicted values. The objective of this study is to construct prediction intervals for future loads instead of forecasting their exact values. The delta technique is applied for constructing prediction intervals for outcomes of neural network models. Some statistical measures are developed for quantitative and comprehensive evaluation of prediction intervals. According to these measures, a new cost function is designed for shortening length of prediction intervals without compromising their coverage probability. Simulated annealing is used for minimization of this cost function and adjustment of neural network parameters. Demonstrated results clearly show that the proposed methods for constructing prediction interval outperforms the traditional delta technique. Besides, it yields prediction intervals that are practically more reliable and useful than exact point predictions. <br /

Simulation-based input loading condition optimisation of airport baggage handling systems

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.<br /

An empirical examination of feedback : user control and performance in a hapto-audio-visual training environment

Utilising advanced technologies, such as virtual environments (VEs), is of importance to training and education. The need to develop and effectively apply interactive, immersive 3D VEs continues to grow. As with any emerging technology, user acceptance of new software and hardware devices is often difficult to measure and guidelines to introduce and ensure adequate and correct usage of such technologies are lacking. It is therefore imperative to obtain a solid understanding of the important elements that play a role in effective learning through VEs. In particular, 3D VEs may present unusual and varied interaction and adoption considerations. The major contribution of this study is to investigate a complex set of interrelated factors in the relatively new sphere of VEs for training and education. Although many of the factors appears to be important from past research, researcher have not explicitly studied a comprehensive set of inter-dependant, empirically validated factors in order to understand how VEs aid complex procedural knowledge and motor skill learning. By integrating theory from research on training, human computer interaction (HCI), ergonomics and cognitive psychology, this research proposes and validates a model that contributes to application-specific VE efficacy formation. The findings of this study show visual feedback has a significant effect on performance. For tactile/force feedback and auditory feedback, no significant effect were found. For satisfaction, user control is salient for performance. Other factors such as interactivity and system comfort, as well as level of task difficulty, also showed effects on performance.<br /