Practical Industrial Engineering in the Documentation, Measurement, and Balance of the Assembly Line

The book deals with two main decision problems which arise when flow-line production systems are installed and operated. The assembly line balancing problem consists of partitioning the work, necessary to assemble the product(s), among different stations of an assembly line. If several models of a product are jointly processed on a line, this medium-term problem is connected with the short-term problem of determining an operating sequence of the models. In Part I balancing and sequencing problems are discussed, classified, and arranged within a hierarchical planning system. In the present second edition special emphasis is given to u-shaped assembly lines which are important components of modern just-in-time production systems. Part II is concerned with exact and heuristic procedures for solving those decision problems. For each problem type considered, a survey of existing procedures is given and new efficient solution methods are developed. Comprehensive numerical investigations showing the effectiveness of the new methods and their superiority over existing approaches are reported.

Efficient assembly line design is a problem of considerable industrial importance. Assembly Line Design will be bought by technical personnel working in design, planning and production departments in industry as well as managers in industry who want to learn more about concurrent engineering. This book will also be purchased by researchers and postgraduate students in mechanical, manufacturing or micro-engineering.

With the rapid growth of technology in society, communication networks have become a heavily researched topic. Implementing these advanced systems is a challenge, however, due to the abundance of optimization problems within these networks. The use of meta-heuristic algorithms and nature-inspired computing has become a prevalent technique among researchers for solving these complex problems within communication networks. Despite its popularity, this specific computing technique lacks the appropriate amount of research that is needed for professionals to grasp a definite understanding. Nature-Inspired Computing Applications in Advanced Communication Networks is a collection of innovative research on the methods and applications of natural computation techniques and algorithms within communication systems such as wireless sensor networks, vehicular adhoc networks, and internet of things. While highlighting topics including mobile sensor deployment, routing optimization, and sleep scheduling, this book is ideally designed for researchers, network professionals, computer scientists, mathematicians, developers, scholars, educators, and students seeking to enhance their understanding of nature-inspired computing and its solutions within various advanced communication networks.

Network models are critical tools in business, management, science and industry. “Network Models and Optimization” presents an insightful, comprehensive, and up-to-date treatment of multiple objective genetic algorithms to network optimization problems in many disciplines, such as engineering, computer science, operations research, transportation, telecommunication, and manufacturing. The book extensively covers algorithms and applications, including shortest path problems, minimum cost flow problems, maximum flow problems, minimum spanning tree problems, traveling salesman and postman problems, location-allocation problems, project scheduling problems, multistage-based scheduling problems, logistics network problems, communication network problem, and network models in assembly line balancing problems, and airline fleet assignment problems. The book can be used both as a student textbook and as a professional reference for practitioners who use network optimization methods to model and solve problems.

​Assembly Line Planning and Control describes the basic fundamentals of assembly lines for single model lines, mixed model make-to-stock lines, mixed model make-to-order lines and for one-station assembly. The book shows how to select the quantity of units to schedule for a shift duration, compute the number of operators needed on a line, set the conveyor speed, coordinate the main line with sub-assembly lines, assign the work elements to the operators on the line, sequence the models down the line, sequence the jobs down the line, calculate the part and component requirements for a line and for each station, determine the replenish needs of the parts and components from the suppliers, compute the similarity between the models being produced and show applications, use learning curves to estimate time and costs of assembly, and measure the efficiency of the line. The material is timeless and the book will never become obsolete. The author presents solutions with easy-to-understand numerical examples that can be applied to real-life applications.​

Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world. Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text. - Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more - Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing - Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods