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.

This book describes methods to improve software performance and safety using advanced mathematical and computational analytics. The main focus is laid on the increase of software reliability by preventive and predictive maintenance with efficient usage of modern testing resources. The editors collect contributions from international researchers in the field.

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.

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