Engineering Optimization - Journal - Current Issue
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Source: http://www.tandfonline.com/toc/geno20/current - downloaded on 17 June 2015
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https://www.witpress.com/elibrary/wit-transactions-on-the-built-environment/80 Index to Engineering Optimization articles.
Engineering Optimization: Applications, Methods and Analysis
R. Russell Rhinehart
John Wiley & Sons, 26-Mar-2018 - Technology & Engineering - 776 pages
An Application-Oriented Introduction to Essential Optimization Concepts and Best Practices
Optimization is an inherent human tendency that gained new life after the advent of calculus; now, as the world grows increasingly reliant on complex systems, optimization has become both more important and more challenging than ever before. Engineering Optimization provides a practically-focused introduction to modern engineering optimization best practices, covering fundamental analytical and numerical techniques throughout each stage of the optimization process.
Although essential algorithms are explained in detail, the focus lies more in the human function: how to create an appropriate objective function, choose decision variables, identify and incorporate constraints, define convergence, and other critical issues that define the success or failure of an optimization project.
Examples, exercises, and homework throughout reinforce the author’s “do, not study” approach to learning, underscoring the application-oriented discussion that provides a deep, generic understanding of the optimization process that can be applied to any field.
Providing excellent reference for students or professionals, Engineering Optimization:
Describes and develops a variety of algorithms, including gradient based (such as Newton’s, and Levenberg-Marquardt), direct search (such as Hooke-Jeeves, Leapfrogging, and Particle Swarm), along with surrogate functions for surface characterization
Provides guidance on optimizer choice by application, and explains how to determine appropriate optimizer parameter values
Details current best practices for critical stages of specifying an optimization procedure, including decision variables, defining constraints, and relationship modeling
Provides access to software and Visual Basic macros for Excel on the companion website, along with solutions to examples presented in the book
Clear explanations, explicit equation derivations, and practical examples make this book ideal for use as part of a class or self-study, assuming a basic understanding of statistics, calculus, computer programming, and engineering models. Anyone seeking best practices for “making the best choices” will find value in this introductory resource.
https://books.google.co.in/books?id=3FpTDwAAQBAJ
See Case Studies chapters 36 to 45 in the above book.
Meta-heuristic and Evolutionary Algorithms for Engineering Optimization
Front Cover
Omid Bozorg-Haddad, Mohammad Solgi, Hugo A. Loáiciga
John Wiley & Sons, 09-Oct-2017 - Mathematics - 304 pages
A detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner and how they relate to engineering optimization problems
This book introduces the main metaheuristic algorithms and their applications in optimization. It describes 20 leading meta-heuristic and evolutionary algorithms and presents discussions and assessments of their performance in solving optimization problems from several fields of engineering. The book features clear and concise principles and presents detailed descriptions of leading methods such as the pattern search (PS) algorithm, the genetic algorithm (GA), the simulated annealing (SA) algorithm, the Tabu search (TS) algorithm, the ant colony optimization (ACO), and the particle swarm optimization (PSO) technique.
Chapter 1 of Meta-heuristic and Evolutionary Algorithms for Engineering Optimization provides an overview of optimization and defines it by presenting examples of optimization problems in different engineering domains. Chapter 2 presents an introduction to meta-heuristic and evolutionary algorithms and links them to engineering problems. Chapters 3 to 22 are each devoted to a separate algorithm— and they each start with a brief literature review of the development of the algorithm, and its applications to engineering problems. The principles, steps, and execution of the algorithms are described in detail, and a pseudo code of the algorithm is presented, which serves as a guideline for coding the algorithm to solve specific applications. This book:
Introduces state-of-the-art metaheuristic algorithms and their applications to engineering optimization;
Fills a gap in the current literature by compiling and explaining the various meta-heuristic and evolutionary algorithms in a clear and systematic manner;
Provides a step-by-step presentation of each algorithm and guidelines for practical implementation and coding of algorithms;
Discusses and assesses the performance of metaheuristic algorithms in multiple problems from many fields of engineering;
Relates optimization algorithms to engineering problems employing a unifying approach.
Meta-heuristic and Evolutionary Algorithms for Engineering Optimization is a reference intended for students, engineers, researchers, and instructors in the fields of industrial engineering, operations research, optimization/mathematics, engineering optimization, and computer science.
OMID BOZORG-HADDAD, PhD, is Professor in the Department of Irrigation and Reclamation Engineering at the University of Tehran, Iran.
MOHAMMAD SOLGI, M.Sc., is Teacher Assistant for M.Sc. courses at the University of Tehran, Iran.
HUGO A. LOÁICIGA, PhD, is Professor in the Department of Geography at the University of California, Santa Barbara, United States of America.
https://books.google.co.in/books?id=FmozDwAAQBAJ
Seminar Papers of Altair
https://www.altairatc.com/india/previous-events/atc/2017/2017-Technical-Papers.htm
https://www.altairatc.com/india/previous-events/atc/index.htm
Northwestern Engineering Course
http://www.mech.northwestern.edu/courses/descriptions/441-engineering-optimization-for-product-design-and-manufacturing.html
NPTEL Course
http://nptel.iitm.ac.in/courses/112106064/
Kelvin's Law of Economic Size of Conductor (Engineering Economics by Taylor)
Optimization of Helical Spring - Gear - 2002 MS Thesis
http://etd.ohiolink.edu/send-pdf.cgi/DESHMUKH%20DINAR%20VIVEK.pdf?ucin1028738547
University of Maryland Course
http://www.enme.umd.edu/undergrad/courses/enme489D.html
2013
Seco Tools India article on metal cutting optimization
http://www.secotools.com/CorpWeb/india/pdf/Production%20economy1.%20Seco2.pdf
2012 Paper
On the Economics of Computer Assisted Numerically Controlled Cylindrical Turning Operations - Optimized Conditions for Maximum Rate of Profit.- - T.K. Jack and O.M.U. Etubu
http://www.ijetae.com/files/Volume2Issue4/IJETAE_0412_75.pdf
A review of optimization techniques in metal cutting processes
Indrajit Mukherjee , Pradip Kumar Ray,
a Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur 721 302, India
Computers & Industrial Engineering
Volume 50, Issues 1–2, May 2006, Pages 15–34
http://www.sciencedirect.com/science/article/pii/S0360835205001403
OPTIMIZATION OF CUTTING PARAMETERS FOR CNC TURNED
PARTS USING TAGUCHI’S TECHNIQUE
http://annals.fih.upt.ro/pdf-full/2012/ANNALS-2012-3-86.pdf
Cutting Speed and Feed Rate Optimization for Minimizing
Production Time of Turning Process
S. S. K. Deepak
(Department of Mechanical Engineering, Rungta Engineering College, Raipur, Chhattisgarh, India)
International Journal of Modern Engineering Research (IJMER)
Vol.2, Issue.5, Sep-Oct. 2012 pp-3398-3401
http://www.ijmer.com/papers/Vol2_Issue5/BQ2533983401.pdf
2010
Advanced Modeling and Optimization of Manufacturing Processes: International Research and Development
R. Venkata Rao
Springer Science & Business Media, 01-Dec-2010 - Technology & Engineering - 380 pages
Advanced Modeling and Optimization of Manufacturing Processes presents a comprehensive review of the latest international research and development trends in the modeling and optimization of manufacturing processes, with a focus on machining. It uses examples of various manufacturing processes to demonstrate advanced modeling and optimization techniques. Both basic and advanced concepts are presented for various manufacturing processes, mathematical models, traditional and non-traditional optimization techniques, and real case studies. The results of the application of the proposed methods are also covered and the book highlights the most useful modeling and optimization strategies for achieving best process performance. In addition to covering the advanced modeling, optimization and environmental aspects of machining processes, Advanced Modeling and Optimization of Manufacturing Processes also covers the latest technological advances, including rapid prototyping and tooling, micromachining, and nano-finishing. Advanced Modeling and Optimization of Manufacturing Processes is written for designers and manufacturing engineers who are responsible for the technical aspects of product realization, as it presents new models and optimization techniques to make their work easier, more efficient, and more effective. It is also a useful text for practitioners, researchers, and advanced students in mechanical, industrial, and manufacturing engineering.
https://books.google.co.in/books?id=fUaRXbrhnAIC
2001
Multi-objective optimization of industrial hydrogen plants
J. K. Rajesh!, S. K. Gupta",1, G. P. Rangaiah!, A. K. Ray!,*
!Department of Chemical and Environmental Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore
"Department of Chemical Engineering, University of Wisconsin, Madison, WI 53706, USA
Chemical Engineering Science 56 (2001) 999-1010
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Genetic Algorithms and Engineering Optimization
Mitsuo Gen, Runwei Cheng
John Wiley & Sons, 2000 - Computers - 495 pages
A comprehensive guide to a powerful new analytical tool by two of its foremost innovators
The past decade has witnessed many exciting advances in the use of genetic algorithms (GAs) to solve optimization problems in everything from product design to scheduling and client/server networking. Aided by GAs, analysts and designers now routinely evolve solutions to complex combinatorial and multiobjective optimization problems with an ease and rapidity unthinkable withconventional methods. Despite the continued growth and refinement of this powerful analytical tool, there continues to be a lack of up-to-date guides to contemporary GA optimization principles and practices. Written by two of the world's leading experts in the field, this book fills that gap in the literature.
Taking an intuitive approach, Mitsuo Gen and Runwei Cheng employ numerous illustrations and real-world examples to help readers gain a thorough understanding of basic GA concepts-including encoding, adaptation, and genetic optimizations-and to show how GAs can be used to solve an array of constrained, combinatorial, multiobjective, and fuzzy optimization problems. Focusing on problems commonly encountered in industry-especially in manufacturing-Professors Gen and Cheng provide in-depth coverage of advanced GA techniques for:
* Reliability design
* Manufacturing cell design
* Scheduling
* Advanced transportation problems
* Network design and routing
Genetic Algorithms and Engineering Optimization is an indispensable working resource for industrial engineers and designers, as well as systems analysts, operations researchers, and management scientists working in manufacturing and related industries. It also makes an excellent primary or supplementary text for advanced courses in industrial engineering, management science, operations research, computer science, and artificial intelligence.
Google book link with preview facility
http://books.google.co.in/books?hl=en&lr=&id=U7MuV1q6P1oC
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Optimal Engineering Design: Principles and Applications
James N. Siddall
CRC Press, 22-Jun-1982 - Science - 536 pages
https://books.google.co.in/books?hl=en&lr=&id=i2hyniQpecYC
Updated 2.2.2022, 22 December 2019
23 August 2018
18 June, 23 May 2015
First published 29 6 2013
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