Evolutionary Computing in Advanced Manufacturing E-Book

Manoj Tiwari

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Wiley-Scrivener
• Sprache: Englisch

Seitenzahl: 434

Veröffentlichungsjahr: 2011

Beschreibung

This cutting-edge book covers emerging, evolutionary and nature inspired optimization techniques in the field of advanced manufacturing. The complexity of real life advanced manufacturing problems often cannot be solved by traditional engineering or computational methods. Hence, in recent years researchers and practitioners have proposed and developed new strands of advanced, intelligent techniques and methodologies. Evolutionary computing approaches are introduced in the context of a wide range of manufacturing activities, and through the examination of practical problems and their solutions, readers will gain confidence to apply these powerful computing solutions. The initial chapters introduce and discuss the well established evolutionary algorithm, to help readers to understand the basic building blocks and steps required to successfully implement their own solutions to real life advanced manufacturing problems. In the later chapters, modified and improved versions of evolutionary algorithms are discussed. The book concludes with appendices which provide general descriptions of several evolutionary algorithms.

Leseprobe

Contents

Cover

Half Title page

Title page

Copyright page

Preface

List of Contributors

Chapter 1: Production Planning Using Genetic Algorithm

1.1 Introduction

1.2 Production Planning Models

1.3 Genetic Algorithm

1.4 Implementation of GA

1.5 Summary

Further Reading

Chapter 2: Process Planning through Ant Colony Optimization

2.1 Introduction

2.2 Ant Colony Optimization (ACO)

References

Chapter 3: Introducing a Hybrid Genetic Algorithm for Integration of Set Up and Process Planning

3.1 Introduction

3.2 Process Planning

3.3 Machine Set-up Time

3.4 Chromosome Representation

3.5 Fitness Value Evaluation

3.6 Selection Operation

3.7 Crossover Operations

3.8 Mutation Operations (k-opt Exchange)

3.9 Conclusion

References

Chapter 4: Design for Supply Chain with Product Development Issues Using Cellular Particle Swarm Optimization (CPSO) Technique

4.1 Introduction

4.2 Problem Formulation

4.3 Computational Analysis and Result

4.4 Conclusions

References

Chapter 5: Genetic Algorithms with Chromosome Differentiation (GACD) Based Approach for Process Plan Selection Problems

5.1 Introduction

5.2 Problem Formulation

5.3 Genetic Algorithm with Chromosome Differentiation

5.4 GACD Based Solution Methodology to Process Plan Selection Problem

5.5 Numerical Experiments

5.6 Conclusions

References

Chapter 6: Operation Allocation in Flexible Manufacturing System Using Immune Algorithm

6.1 Introduction

6.2 Machine Loading Problem

6.3 Solution Methodology

6.4 Implementing Immune Algorithm for Machine Loading Problem

6.5 Computational Result

6.6 Conclusion

References

Chapter 7: Tool Selection in FMS A Hybrid SA-Tabu Algorithm Based Approach

7.1 Introduction

7.2 Literature Survey

7.3 Problem Formulation

7.4 Background on SA-Tabu Heuristic

7.5 Implementation of Tabu-Simulated Annealing

7.6 Test Cases

7.7 Conclusion

References

Chapter 8: Integrating AGVs and Production Planning with Memetic Particle Swarm Optimization

8.1 Introduction

8.2 Literature Review

8.3 Mathematical Model

8.4 PSO and EMPSO

8.5 Example

8.6 Recombination (Local Search)

8.7 Summary

References

Chapter 9: Simulation-Based Aircraft Assembly Planning Using a Self-Guided Ant Colony Algorithm

9.1 Introduction

9.2 Background and Literature Survey

9.3 Specifications of the Considered Aircraft Assembly

9.4 Proposed Simulation-Based Assembly Planning Framework

9.5 Experiment and Results

9.6 Conclusion and Future Work

References

Chapter 10: Applications of Evolutionary Computing to Additive Manufacturing

10.1 Introduction

10.2 Design for Additive Manufacturing

10.3 Data Handling

10.4 Process Planning

10.5 Concluding Remarks

References

Chapter 11: Multiple Fault Diagnosis Using Psycho-Clonal Algorithms

11.1 Introduction

11.2 Multiple Fault Diagnosis Problems

11.3 Background of Psychoclonal Algorithm

11.4 Numerical Experiments

11.5 Conclusion

References

Chapter 12: Platform Formation Under Stochastic Demand

12.1 Introduction

12.2 Background

12.3 Problem Description

12.4 Evolutionary Solution Approaches

12.5 Example Problem - Results and Discussions

12.6. Conclusion and Recommendations for Future Research

References

Chapter 13: A Hybrid Particle Swarm and Ant Colony Optimizer for Multi-attribute Partnership Selection in Virtual Enterprises

13.1 Introduction

13.2 Literature Review

13.3 Partner Selection Problem Formation

13.4 Solution Methodology

13.5 Experimental Analysis

13.6 Conclusion

References

Index

Evolutionary Computing in Advanced Manufacturing

Scrivener Publishing3 Winter Street, Suite 3Salem, MA 01970

Scrivener Publishing Collections Editors

James E. R. CouperRichard ErdlacPradip KhaladkarNorman LiebermanW. Kent MuhlbauerS. A. Sherif

Ken DragoonRafiq IslamVitthal KulkarniPeter MartinAndrew Y. C. NeeJames G. Speight

Publishers at Scrivener

Martin Scrivener ([email protected])

Phillip Carmical ([email protected])

Copyright © 2011 by Scrivener Publishing LLC. All rights reserved.

Co-published by John Wiley & Sons, Inc. Hoboken, New Jersey, and Scrivener Publishing LLC, Salem, Massachusetts.Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data:

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.

Puneet Bhardwaj completed his degree in Industrial Engineering and Management in the year 2010 from Indian Institute of Technology, Kharagpur, India. He is currently a first year PhD candidate at the Department of Systems and Industrial Engineering at the University of Arizona, USA, and currently working as a Research Assistant as well as Teaching Assistant for manufacturing courses.

Nurcin Celik is an Assistant Professor in the Department of Industrial Engineering at the University of Miami. Her research interests are in the areas of architectural design and development of adaptive simulations for large scale and complex systems, and structural and functional analysis of social networks. She has received several awards such as the UM Provost Research Award (2011), International Association for Management of Technology (IAMOT) Research Project Award (2011), and Institute of Industrial Engineers (HE) Outstanding Graduate Research Award (2009).

Felix Chan received his MSc and PhD in Manufacturing Engineering from the Imperial College of Science and Technology, University of London, UK. Dr Chan is an Associate Professor at the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University. His current research interests are Logistics and Supply Chain Management, Operations Management, Distribution Coordination, Systems Modelling and Simulation, Supplier Selection. To date, he has published 10 book chapters, over 200 articles in SCI journals and 200 peer reviewed conference papers.

Anand Mohan Choubey is Operations and Strategy Analytics professional currently working at Bank of America. His industrial experience, prior to Bank of America, includes working for H & R Block Inc. on various operational improvement projects and strategy initiatives. Anand has a Master of Science degree in Industrial Engineering from Kansas State University. His professional and research interests include operations research, process improvement, risk management, operations and strategy analytics and consulting.

Jenny Harding joined Loughborough University in 1992 after working in industry for many years. Her industrial experience includes textile production and engineering, and immediately prior to joining Loughborough University, she spent 7 years working in R&D at Rank Taylor Hobson Ltd., manufacturers of metrology instruments. Her experience is mostly in the areas of mathematics and computing for manufacturing.

Nitesh Khilwani is a post-doc researcher at Nano-tera.ch, EPFL (Switzerland) doing research on semantic technologies for social networking, knowledge management and community development. He completed his PhD from Loughborough University (UK) in 2010. His research interests are industrial engineering and management, semantic web and ontologies, text analysis and mining, programming language and database, algorithms and AI techniques.

Sri Krishna Kumar is pursuing his PhD in the Wolfson School of Mechanical and Manufacturing Engineering department of Loughborough University, UK. He received his bachelors degree in Marine Engineering from Jadavpur University, India. His research interests include knowledge management, mathematical modelling and artificial intelligence.

Vikas Kumar received PhD in Management Studies from Exeter Business School, University of Exeter, UK. Dr. Kumar is a lecturer in the Management Department at the Dublin City University Business School. Previously he has held the position of Research Assistant at Exeter Business School and The University of Hong Kong. His research interests include Supply Chain Management, Lean and Agile Systems, and Service Operations Management. To date he has published 4 book chapters and more than 30 peer reviewed articles in leading international journals and conferences.

Roberto Lu is the Vice President in Advanced Manufacturing of the TE Connectivity (formerly Tyco Electronics). He co-authored this paper while he was a Technical Fellow at The Boeing Company. He taught part-time Industrial and Systems Engineering at the University of Washington as an Affiliate Assistant Professor. His research focuses on advanced manufacturing, decision analysis, discrete event simulation, analytical process optimization, global logistics, large scale production systems integration, lean manufacturing, robotic and machine vision applications, and mass customization.

Candice Majewski is a post-doctoral research associate at Loughborough University, UK. She obtained her PhD in a polymer materials-related subject in 2007. She has spent over ten years working in the area of Additive Manufacturing with a particular focus on polymer materials and processes. She has published over twenty refereed journal and conference publications, and is a member of several committees including the ASTM task-force charged with the production of standards for use throughout the Additive Manufacturing industry.

Nishikant Mishra is lecturer in School of Management and Business, Aberystwyth University. His PhD research focused on the development of a decision support system for radiotherapy planning. He has worked in collaboration with NHS (National Health Service) and developed several software systems. He has published more than 25 articles in leading international journals and conferences. His research interests include development of decision support systems, mathematical modeling, heuristics and algorithms development for a variety of real world manufacturing, healthcare, supply chain and fault diagnosis problems.

Sai Srinivas Nageshwaraniyer is a PhD student in the Department of Systems and Industrial Engineering at The University of Arizona. His research interests are in the areas of Coal mining and transport logistics, distributed simulations of complex systems, and Meta-heuristics.

A.Y.C. Nee is Professor in the Department of Mechanical Engineering, and currently the Director of Research Administration, National University of Singapore. He received his PhD and DEng from Manchester and UMIST respectively. He is a Fellow of the International Academy for Production Engineering (CIRP) and the Society of Manufacturing Engineers (SME). He has published over 350 refereed journal papers and 10 books. He received the National Technology Award in 2002, and National Day Award in 2007. Other awards include: Kayamori Award from IEEE, Norman Dudley Award from IJPR, Joseph Whitworth Award from the IMechE, UK

Niu Sihong received a B.Eng. degree in Mechanical Engineering and Automation from Xi’ an Jiaotong University, PR. China in July 2006. She is a PhD candidate in the Digital Manufacturing Group in Mechanical Engineering Department, National University of Singapore since Jan. 2007. Her current research interests include solving partnership selection in Virtual Enterprise and single/multiple objective scheduling using Meta-heuristics, as well as multi-agent based systems.

S.K. Ong is currently lecturing in the Mechanical Engineering Department at the National University of Singapore. Her research interests are virtual and augmented reality applications in manufacturing and assistive technology and distributed digital manufacturing. She has published over 170 international refereed journals and conference papers. She has received many accolades including the 2002 Norman Dudley Award, the 2004 Outstanding Young Manufacturing Engineer Award and the 2009 Emerging Leaders Award in Academia by the US Society for Women Engineers. In 2005, she was elected an Associate Member of CIRP

Mayank Kumar Pandey is pursuing his PhD in the Mechanical Engineering department of University of Alberta, Canada. He received his M.Tech. degree in Manufacturing Engineering from National Institute of Foundry and Forge Technology, India. His research interests include reliability and maintenance of systems, condition monitoring for prognosis and diagnosis, flexible manufacturing systems and artificial intelligence.

PKS Prakash received his PhD in Industrial Engineering from University of Wisconsin-Madison, WI, US, in 2010. His other educational background includes a B. Tech. (Metallurgy and Materials Engineering) which he gained in 2005 from the National Institute of Foundry and Forge Technology, Ranchi, India, and an M.S. (Industrial and Systems Engineering) from the University of Wisconsin-Madison, WI, US awarded in 2006. He is currently working as a Researcher at University of Warwick, UK and is supporting Warwick Analytics Limited, UK with the development of advance in-database analytics. His current research focuses on the advance process control with focus toward developing fundamentals of self-resilient systems for fault isolation and adjustment of NDF (No-defect found)

Nagesh Shukla has joined the Digital Lab under the Warwick Manufacturing Group (WMG), University of Warwick, UK as a PhD scholar in October 2007. He received the prestigious Dorothy Hodgkin Postgraduate Award, from UK Research Councils. He has worked on various collaborative projects with GE-Healthcare and University Hospitals for systems modelling & simulations. He has published various journal papers, peer reviewed conferences, book chapters and patents. Currently, he is a member of Digital Lifecycle Management Lab in WMG. His primary research interests are operational research, business process modelling, operations management, evolutionary computation, and fault diagnosis. Other interests involve the development of evolutionary algorithms, and engineering applications of evolutionary algorithms.

M. K. Tiwari is Professor at the Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur, India. He is associate editor of the Journal of Intelligent Manufacturing and International Journal of System Science. He has published over 140 articles in leading international journals. His research interests lies in the area of Evolutionary Computing, Modeling and Simulation of Manufacturing Systems, Supply Chain Management, Planning and Scheduling of Automated Manufacturing System.

Young-Jun Son is Professor of Systems and Industrial Engineering and Director of Advanced Integration of Manufacturing Systems and Technologies Center at the University of Arizona. His research focuses on the coordination of a multi-scale, networked-federated simulation and decision model needed for design and control in manufacturing enterprise, renewable energy network, homeland security, and social network. He has received several research awards such as the SME 2004 Outstanding Young Manufacturing Engineer Award, the HE 2005 Outstanding Young Industrial Engineer Award, the IERC Conference Best Paper Awards (2005, 2008, 2009), and Best Paper of the Year Award in 2007 from IJIE.

Chapter 1

Production Planning Using Genetic Algorithm

S.K. Kumar1 and M.K. Tiwari2

1 Wolfson School of Mechanical and Manufacturing Engineering, UK

2Indian Institute of Technology Kharagpur, India

Abstract

Production planning and control, in manufacturing industries, generally addresses the issues of acquisition, utilization and allocation of resources to satisfy customer requirements in the most efficient and effective way. Therefore efficient management of the production function is of the utmost importance to achieve this objective. The production function includes production level, inventory level, work force level, assignment of overtime and transhipments. Mathematical models developed in this context are widely accepted and can act as decision support systems. This chapter initially focuses on developing the optimization model in different scenarios such as multi-item, multi-period, un-capacitated, capacitated, backorder, fixed and variable workforce. Improving the decision quality in those fields gives rise to complex combinatorial optimization problems, which mostly fall into the class of NP-hard problems. Finding a satisfactory solution in an acceptable time is also an important factor. Genetic Algorithms (GA) provide potent methods for solving such optimization problems and steps for GA implementation, and an example optimization problem solved by using GA, are also included in this chapter.

Keywords: Production planning, inventory, work force, genetic algorithm

1.1 Introduction

Production planning is a set of functions to undertake the efficient and effective utilization of available resources over a time horizon, with the motivation of satisfying customer needs whilst creating profits for the organization. In manufacturing industries these functions can be defined as acquisition, utilization and allocation of resources such as raw material, man power, machines, cash flow, information etc. In today’s globalized environment, with rapid technological advancement, highly customized demand, short life cycles and high competitiveness, market demand is highly volatile. Production planning therefore plays a crucial tool for any organization trying to satisfy highly customized demand and to remain competitive in the market. Poor production planning can be devastating for any organization and can lead to excessive inventory, high operational cost, poor cash flow, stock outs or lost sales and their subsequent effects. Therefore, managers must utilize their resources efficiently to avoid such consequences.