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This book provides a complete overview of production systems and describes the best approaches to analyze their performance. Written by experts in the field, this work also presents numerous techniques that can be used to describe, model, and optimize the performance of various types of production lines. The book is intended for researchers, production managers, and graduate students in industrial, mechanical, and systems engineering.
Manufacturing systems rarely perform exactly as expected and predicted. Unexpected events, such as order changes, equipment failures and product defects, affect the performance of the system and complicate decision-making. This volume is devoted to the development of analytical methods aiming at responding to variability in a way that limits its corrupting effects on system performance. The book includes fifteen novel chapters that mostly focus on the development and analysis of performance evaluation models of manufacturing systems using decomposition-based methods, Markovian and queuing analysis, simulation, and inventory control approaches. They are organized into four distinct sections to reflect their shared viewpoints: factory design, unreliable production lines, queuing network models, production planning and assembly.
Analysis and Modeling of Manufacturing Systems is a set of papers on some of the newest research and applications of mathematical and computational techniques to manufacturing systems and supply chains. These papers deal with fundamental questions (how to predict factory performance: how to operate production systems) and explicitly treat the stochastic nature of failures, operation times, demand, and other important events. Analysis and Modeling of Manufacturing Systems will be of interest to readers with a strong background in operations research, including researchers and mathematically sophisticated practitioners.
This book aims at updating the relevant computer science-related research communities, including professors, researchers, scientists, engineers and students, as well as the general reader from other disciplines, on the most recent advances in applications of methods based on Fusing Machine Learning Paradigms. Integrated or Hybrid Machine Learning methodologies combine together two or more Machine Learning approaches achieving higher performance and better efficiency when compared to those of their constituent components and promising major impact in science, technology and the society. The book consists of an editorial note and an additional eight chapters and is organized into two parts, na...
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Comprehensive index to current and retrospective biographical dictionaries and who's whos. Includes biographies on over 3 million people from the beginning of time through the present. It indexes current, readily available reference sources, as well as the most important retrospective and general works that cover both contemporary and historical figures.
This handbook surveys important stochastic problems and models in manufacturing system operations and their stochastic analysis. Using analytical models to design and control manufacturing systems and their operations entail critical stochastic performance analysis as well as integrated optimization models of these systems. Topics deal with the areas of facilities planning, transportation, and material handling systems, logistics and supply chain management, and integrated productivity and quality models covering: • Stochastic modeling and analysis of manufacturing systems • Design, analysis, and optimization of manufacturing systems • Facilities planning, transportation, and material ...