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Soft Computing Techniques for Engineering Optimization
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Book Description
This book covers the issues related to optimization of engineering and management problems using soft computing techniques with an industrial outlook. It covers a broad area related to real life complex decision making problems using a heuristics approach. It also explores a wide perspective and future directions in industrial engineering research on a global platform/scenario. The book highlights the concept of optimization, presents various soft computing techniques, offers sample problems, and discusses related software programs complete with illustrations.
Features
- Explains the concept of optimization and relevance to soft computing techniques towards optimal solution in engineering and management
- Presents various soft computing techniques
- Offers problems and their optimization using various soft computing techniques
- Discusses related software programs, with illustrations
- Provides a step-by-step tutorial on how to handle relevant software for obtaining the optimal solution to various engineering problems
Table of Contents
Section I. Introduction to Soft Computing Techniques. Chapter 1. Introduction to Optimization and Relevance of Soft Computing towards Optimal Solution. Chapter 2. Various Soft Computing Techniques and Their Description. Section II. Various Case Studies Comprising Industrial Problems and Their Optimal Solutions Using Different Techniques. Chapter 3. Single-Objective Optimization Using Response Surface Methodology. Chapter 4. Multi-Objective Oprimization Using Weighted Principal Component Analysis. Chapter 5. Single-Objective Optimization Using Taguchi Technique (Maximization). Chapter 6. .Single-Objective Optimization Using Taguchi Technique (Minimization). Chapter 7. Multi-Objective Optimization Using Grey-Taguchi Technique. Chapter 8. Multi-Objective Optimization Using Taguchi-Coupled Fuzzy Logic. Chapter 9. Multi-Objective Optimization Using Non-Dominated Sorting Genetic Algorithm. Chaper 10. Single-Objective Optimization Using Artificial Bee Colony Algorithm. Section III. Hands-On Training on Various Software Dedicated for the Usage of Techniques. Chapter 11. Working with Minitab for Optimization Using Taguchi Technique. Chapter 12. Workign with MATLAB for Optimization Using Artificial Neural Network. Chapter 13. Working with MATLAB for Optimization Using Genetic Algorithm.
Author(s)
Biography
Kaushik Kumar, B.Tech (Mechanical Engineering, REC (Now NIT), Warangal), MBA (Marketing, IGNOU) and Ph.D (Engineering, Jadavpur University), is presently an Associate Professor in the Department of Mechanical Engineering, Birla Institute of Technology, Mesra, Ranchi, India. He has 14 years of Teaching & Research and over 11 years of industrial experience in a manufacturing unit of Global repute. His areas of teaching and research interest are Quality Management Systems, Optimization, Non-conventional machining, CAD / CAM, Rapid Prototyping and Composites. He has 9 Patents, 14 Book, 6 Edited Book, 30 Book Chapters, 120 international Journal publications, 18 International and 8 National Conference publications to his credit. He is on the editorial board and review panel of 7 International and 1 National Journals of repute. He has been felicitated with many awards and honours.
Dr. Supriyo Roy, M.Sc (Statistics), M. Tech (ORI&BM, Ph.D. (Business Model Optimization) has been working as Associate Professor, Birla Institute of Technology – Mesra, Ranchi. A Post-Doctoral Fellow from Airbus Group Endowed (Formerly EADS-SMI) Center for Sourcing and Management, IIM – Bangalore. Dr. Roy has a rich experience of more than 5 years in Industry and 15 years of Post Graduate Teaching. His teaching interest comprises interdisciplinary areas of Management Science, Production and Operations Management, Decision Science, Supply Chain Management, Soft Computing for Manufacturing, etc. He has to his credit published 1 book, 10 International book chapters, 12 Conference papers and more than 45 research papers in leading International as well as National Journals. Dr. Supriyo is associated as impaneled reviewer of many International and National Journals of repute. He has been facilitated with award of "Outstanding Contribution to Teaching" in Management Science.
Dr. J. Paulo Davim received the Ph.D. degree in Mechanical Engineering in 1997, the M.Sc. degree in Mechanical Engineering (materials and manufacturing processes) in 1991, the Mechanical Engineer degree (MEng-5 years) in 1986, from the University of Porto (FEUP), the Aggregate title (Full Habilitation) from the University of Coimbra in 2005 and the D.Sc. from London Metropolitan University in 2013. He is Eur Ing by FEANI-Brussels and Senior Chartered Engineer by the Portuguese Institution of Engineers with a MBA and Specialist title in Engineering and Industrial Management. Currently, he is Professor at the Department of Mechanical Engineering of the University of Aveiro, Portugal. He has more than 30 years of teaching and research experience in Manufacturing, Materials and Mechanical Engineering with special emphasis in Machining & Tribology. He has also interest in Management & Industrial Engineering and Higher Education for Sustainability & Engineering Education. He has guided large numbers of postdoc, Ph.D. and masters students. He has received several scientific awards. He has worked as evaluator of projects for international research agencies as well as examiner of Ph.D. thesis for many universities. He is the Editor in Chief of several international journals, Guest Editor of journals, books Editor, book Series Editor and Scientific Advisory for many international journals and conferences. Presently, he is an Editorial Board member of 25 international journals and acts as reviewer for more than 80 prestigious Web of Science journals. In addition, he has also published as editor (and co-editor) more than 100 books and as author (and co-author) more than 10 books, 70 book chapters and 400 articles in journals and conferences (more than 200 articles in journals indexed in Web of Science core collection/h-index 43+/5500+ citations and SCOPUS/h-index 52+/7500+ citations).