The text is designed for engineering students at the senior undergraduate level and first-year students at graduate level, and professionals (R&D engineers in the industry and factory managers). The authors offer a unique effort in presenting a unified and systematic treatment of various modeling methodologies and analysis techniques for performance evaluation of automated manufacturing systems. The text begins with an overview of automated manufacturing systems, and then provides a clear and comprehensive discussion of three principal analytical modeling paradigms: Markov Chains, Queues and Queuing Networks, and Petri Nets. Salient Features • Present the first ever treatment of the mathematical modeling of manufacturing systems. • Offers a unified study of principal analytical modeling paradigms for automated manufacturing systems. • Discusses many recent research contributions in the area of modeling of automated manufacturing systems. • Discusses many recent research contributions in the area of modeling of automated manufacturing systems, including deadlock modeling, transient analysis, queuing network approximations, Petri Net modeling, and integrated analytical modeling. • Provides a large number of exercises and problems.

Control and Dynamic Systems: Advances in Theory and Applications, Volume 47: Manufacturing and Automation Systems: Techniques and Technologies, Part 3 of 5 deals with techniques and technologies in manufacturing and automation systems. This book discusses techniques in modeling and control policies for production networks; effective planning and control of day-to-day operations; evaluation of automated manufacturing systems; the use of Petri Nets in modeling, control and performance analysis of automated manufacturing systems; and concurrent engineering and evaluation of concurrency in engineering design. The final chapter discusses the algorithm for solving allocation problems. This book will provide a uniquely significant reference source for practitioners in the field who want a comprehensive source of techniques with significant applied implications.

There is perhaps no facet of modern society where the influence of computer automation has not been felt. Flight management systems for pilots, diagnostic and surgical aids for physicians, navigational displays for drivers, and decision-aiding systems for air-traffic controllers, represent only a few of the numerous domains in which powerful new automation technologies have been introduced. The benefits that have been reaped from this technological revolution have been many. At the same time, automation has not always worked as planned by designers, and many problems have arisen--from minor inefficiencies of operation to large-scale, catastrophic accidents. Understanding how humans interact with automation is vital for the successful design of new automated systems that are both safe and efficient. The influence of automation technology on human performance has often been investigated in a fragmentary, isolated manner, with investigators conducting disconnected studies in different domains. There has been little contact between these endeavors, although principles gleaned from one domain may have implications for another. Also, with a few exceptions, the research has tended to be empirical and only theory-driven. In recent years, however, various groups of investigators have begun to examine human performance in automated systems in general and to develop theories of human interaction with automation technology. This book presents the current theories and assesses the impact of automation on different aspects of human performance. Both basic and applied research is presented to highlight the general principles of human-computer interaction in several domains where automation technologies are widely implemented. The major premise is that a broad-based, theory-driven approach will have significant implications for the effective design of both current and future automation technologies. This volume will be of considerable value to researchers in human

Designing and scaling complex, networked automation systems is a challenging task. Developers must consider different kinds of influence factors that impact the system performance already in the early stages of the development. If performance bottlenecks are detected too late, costly corrections may follow and the commissioning can be significantly delayed.There already exist several approaches to predict the performance of a system under development. However, developers must identify and decide themselves which factors must be regarded for a performance analysis and to what detail. Therefore, developers usually require in-depth knowledge of the system which is -- at least to this level of detail -- not available until the discipline specific development starts. In particular it is not clear, which factors influence the performance of a PLC and how these can be used for an early validation of a system model.The first contribution of this thesis is the identification and creation of a list of influence factors that impact one or more quality of service attributes of an automation system. For each factor, a decision is made, whether it is available in the early development stages, what assumptions must be made by the developer, and what impact it has on the overall system performance.The second contribution is a method that enables developers of automated systems to define the identified influence factors in systems engineering models and to carry out a subsequent automatic performance analysis. The method includes a model for the specification of influence factors within an existing Systems Engineering model as well as an accompanying process. Based on these models, an analysis can be performed to predict the utilization of a selected PLC. The results can be used to evaluate the design of the automation in the early phases and, therefore, avoid costly and ... ; eng

Air traffic controllers need advanced information and automated systems to provide a safe environment for everyone traveling by plane. One of the primary challenges in developing training for automated systems is to determine how much a trainee will need to know about the underlying technologies to use automation safely and efficiently. To ensure safety and success, task analysis techniques should be used as the basis of the design for training in automated systems in the aviation and aerospace industries. Automated Systems in the Aviation and Aerospace Industries is a pivotal reference source that provides vital research on the application of underlying technologies used to enforce automation safety and efficiency. While highlighting topics such as expert systems, text mining, and human-machine interface, this publication explores the concept of constructing navigation algorithms, based on the use of video information and the methods of the estimation of the availability and accuracy parameters of satellite navigation. This book is ideal for aviation professionals, researchers, and managers seeking current research on information technology used to reduce the risk involved in aviation.

Automation has the benefit of reducing human operators' workload. By leveraging the power of computers and information technology, the work of human operators is becoming easier. However, when the workload is too low but the human is required to be present either by regulation or due to limitations of automation, human performance can be negatively affected. Negative consequences such as distraction, mind wandering, and inattention have been reported across many high risk settings including unmanned aerial vehicle operation, process control plant supervision, train engineers, and anesthesiologists. Because of the move towards more automated systems in the future, a better understanding is needed to enable intervention and mitigation of possible negative impacts. The objectives of this research are to systematically investigate the attention and performance of human operators when they interact with automated systems under low task load, build a dynamic model and use it to facilitate system design. A systems-based framework, called the Boredom Influence Diagram, was proposed to better understand the relationships between the various influences and outcomes of low task loading. A System Dynamics model, named the Performance and Attention with Low-task-loading (PAL) Model, was built based on this framework. The PAL model captures the dynamic changes of task load, attention, and performance over time in long duration low task loading automated environments. In order to evaluate the replication and prediction capability of the model, three dynamic hypotheses were proposed and tested using data from three experiments. The first hypothesis stated that attention decreases under low task load. This was supported by comparing model outputs with data from an experiment of target searching using unmanned vehicles. Building on Hypothesis 1, the second and third hypotheses examined the impact of decreased attention on performance in responding to an emergency event. Hypothesis 2 was examined by comparing model outputs with data from an experiment of accident response in nuclear power plant monitoring. Results showed that performance is worse with lower attention levels. Hypothesis 3 was tested by comparing model outputs with data from an experiment of defensive target tracking. The results showed that the impact of decreased attention on performance was larger when the task was difficult. The process of testing these three hypotheses shows that the PAL model is a generalized theory that could explain behaviors under low task load in different supervisory control settings. Finally, benefits, limitations, generalizability and applications of the PAL model were evaluated. Further research is needed to improve and extend the PAL model, investigate individual differences to facilitate personnel selection, and develop system and task designs to mitigate negative consequences.