Manoj Tiwari - Evolutionary Computing in Advanced Manufacturing

Evolutionary Computing in Advanced Manufacturing

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ISBN 978-0-470-63924-5

Preface

The increasing availability and use of computers in engineering has significantly changed production and manufacturing domains, since computer-controlled manufacturing systems can greatly improve the quality and pace of production. In the current era of highly competitive, global environments, industries are facing immense pressure to deliver new products more cheaply and quickly, with greater product variety and shorter life cycles. Industry therefore requires effective planning and optimal results in all stages of production, from raw material acquisition to final delivery. Traditional methods are often inappropriate and cannot deal with the planning demands of the advanced technology and requirements in modern manufacturing systems. In the recent years, evolutionary computing has gained popularity for solving manufacturing related problems.

Although many research papers and proceedings exist on evolutionary computing in production and the manufacturing realm, there are hardly any books which coherently present and explain both aspects (i.e. evolutionary computing in the context of manufacturing problems). In most research papers, production and manufacturing problems and evolutionary computation approaches are only loosely coupled which makes it difficult for readers to understand the implementation parts of the algorithms.

In this book, we have addressed the research issues related to evolutionary computing in the manufacturing domain. We have invited contributions from various learned researchers with significant expertise in the field of computational intelligence for advanced manufacturing. Each chapter explains explicitly the research related issues and ways of implementing computational intelligence techniques.

With this composition, we aim to provide readers with a good basis for understanding the development of mathematical models for production and manufacturing related issues. In addition to the mathematical models, various evolutionary algorithms such as Genetic Algorithm (G A), Particle Swarm Optimization (PSO) etc. have been discussed from their fundamentals to implementation aspects. This book will therefore help scholars, researchers and practitioners in understanding both the fundamentals and advanced aspects of computational intelligence in production and manufacturing.

The Structure of the Contributed Chapters

In this book, chapters 1 and 2 deal with the production and process planning issues and describe the basic Genetic Algorithm (GA) and Ant Colony Algorithm (AGO). Chapter 6 and chapter 11 introduce the Immune and Psycho-Clonal Algorithm in operation allocation and fault diagnosis problems respectively. Chapters 3, 4, 5, 7, 8, 9 and 13 provide variants of different algorithms. In chapter 3, the set up and process planning problem is described with a hybrid Genetic Algorithm. Chapter 4 uses cellular particle swarm optimization (CPSO) in the supply chain and product development domain. A variant of GA, Genetic algorithm with chromosome differentiation (GACD) in process plan selection is presented in chapter 5. A description of tool selection and hybrid simulated annealing (SA)-Tabu search algorithm has been explained in the chapter 7. In chapter 8, production planning has been integrated with automated guided vehicles (AGV). In this chapter the use of an enhanced memetic particle swarm optimization (EMPSO) has also been explained. Chapter 9 explains the assembly planning problem in the aircraft industry and uses the self guided ant colony algorithm, which is a variant of the ant colony algorithm (ACO).

Chapters 10, 12 and 13 introduce the most recent research issues in the production and manufacturing field. The benefits of additive manufacturing along with its applications using evolutionary computing have been explained in chapter 10. Chapter 12 deals with the product and platform performance issues in stochastic demand conditions. In the final chapter, a brief introduction to virtual enterprises (VE) has been presented, and the most difficult part in the formation of VE i.e. partner selection problem, has been discussed in the context of a hybrid particle swarm optimization(PSO) and ant colony optimization (ACO) algorithm.

Although there are many variants of evolutionary algorithms which we have not discussed here, the basic ideas behind the algorithms have been provided explicitly. We hope that readers will both enjoy and benefit significantly from this book.

Dr. J.A. Harding & Prof. M.K. Tiwari

List of Contributors

David Ben-Arieh is a Professor of Industrial Engineering at Kansas State University. His industrial experience includes working for AT&T Bell Laboratories and consulting for the aerospace industry and NASA. His research interests concentrate mainly on applications systems design and modeling and holds one patent in this area. In recent years Dr. Ben-Arieh has focused on applications in product development and innovation as well as in Health Care Systems Management, including patients flow, information systems integration, and patient quality and safety improvements.