Sunday, November 26, 2023

Engineering Optimization - Industrial Engineering Optimization (IEOR) - Introduction

Lesson 373  of  Industrial Engineering ONLINE Course -  #Optimization Module.



Optimization - Principle of Industrial Engineering


TAYLOR - NARAYANA RAO PRINCIPLES OF INDUSTRIAL ENGINEERING

https://www.proquest.com/docview/1951119980


Optimization: Maximize the benefit. Minimize the cost. Maximize the difference.

Each engineering system design idea needs to be optimized to get the best desired output and then only alternatives are to be compared for selection of the best alternative.


IE alternatives, that is alternative engineering solutions, need to be optimized. The discrete or continuous values which are possible due to various engineering elements used have to be operated at values that give maximum desired benefit.


Industrial engineers develop engineering modifications to existing facilities and processes to increase productivity while maintaining current effectiveness intact. In the process chart based process improvement, they identify operations - material processing, inspection, material handling, and storage to improve engineering of each of them to improve performance.


Industrial Engineering Optimization


Engineering optimization is development of mathematical models of engineering decisions and mathematically determining desired maximum or minimum values of objective functions. Industrial engineers have to convert their engineering change ideas into mathematical models and find the best solution or optimal solution. But in industrial engineering studies, the first investigation is to find engineering changes that are possible. Then both the existing configuration and new possible configuration are subjected to engineering optimization procedure to find the best result and then a decision is taken to stick to the current solution as optimized or new solution as optimized.



"The ever-increasing demand on engineers to lower production costs to withstand competition has prompted engineers to look for rigorous methods of decision making, such as optimization methods, to design and produce products both economically and efficiently. Optimization techniques, having reached a degree of maturity over the past several years, are being used in a wide spectrum of industries, including aerospace, automotive, chemical, electrical, and manufacturing industries. With rapidly advancing computer technology, computers are becoming more powerful, and correspondingly, the size and the complexity of the problems being solved using optimization techniques are also increasing. Optimization methods, coupled with modern tools of computer-aided design, are also being used to enhance the creative process of conceptual and detailed design of engineering systems." S.S. Rao in Preface to the book - Engineering Optimization: Theory and Practice, 3rd Edition, Wiley Interscience Publication, New York, 1996.


Table of Contents 


Ch. 1 and  2 in detail


1. Introduction to Optimization .......................................... 1 

1.1 Introduction ......................................................................... 1 

1.2 Historical Development ....................................................... 3 

1.3 Engineering Applications of Optimization .......................... 4 

1.4 Statement of an Optimization Problem .............................. 5 

1.4.1 Design Vector .................................................. 6 

1.4.2 Design Constraints .......................................... 7 

1.4.3 Constraint Surface ........................................... 8 

1.4.4 Objective Function ........................................... 9 

1.4.5 Objective Function Surfaces ............................ 10 

1.5 Classification of Optimization Problems ............................. 15 

1.5.1 Classification Based on the Existence of Constraints ...................................................... 15 

1.5.2 Classification Based on the Nature of the Design Variables .............................................. 15 

1.5.3 Classification Based on the Physical Structure of the Problem .................................. 17 

1.5.4 Classification Based on the Nature of the Equations Involved .......................................... 20 

1.5.5 Classification Based on the Permissible Values of the Design Variables ........................ 31 

1.5.6 Classification Based on the Deterministic Nature of the Variables .................................... 32 

1.5.7 Classification Based on the Separability of the Functions ................................................... 34 

1.5.8 Classification Based on the Number of Objective Functions ......................................... 36 

1.6 Optimization Techniques .................................................... 38 

1.7 Engineering Optimization Literature ................................... 39 

References and Bibliography ........................................................ 40 

Review Questions ......................................................................... 44 

Problems ....................................................................................... 46 



2. Classical Optimization Techniques ............................... 65 

2.1 Introduction ......................................................................... 65 

2.2 Single-Variable Optimization .............................................. 65 

2.3 Multivariable Optimization with No Constraints ................. 71 

2.3.1 Semidefinite Case ............................................ 77 

2.3.2 Saddle Point .................................................... 77 

2.4 Multivariable Optimization with Equality Constraints ......... 80 

2.4.1 Solution by Direct Substitution ......................... 80 

2.4.2 Solution by the Method of Constrained Variation .......................................................... 82 

2.4.3. Solution by the Method of Lagrange Multipliers ........................................................ 91 

2.5 Multivariable Optimization with Inequality Constraints ................................................................ 100 

2.5.1 Kuhn-Tucker Conditions .................................. 105 

2.5.2 Constraint Qualification .................................... 105 

2.6 Convex Programming Problem .......................................... 112 

References and Bibliography ........................................................ 112 

Review Questions ......................................................................... 113 

Problems ................................




Play List

https://www.youtube.com/watch?v=QTi0Mv7DGDs&list=PL7XCYAQpq_DPkyrj-LEi5Gn73Xx_u-J1Q

Introduction to numerical methods to solve single objective non-linear optimization problems. 


(Lecture delivered by Dr. Saroj Kumar Patel, Professor, Mechanical Engineering Department, NIT Rourkela to its postgraduate  students on the subject ME601: Optimization Methods in Engineering Design  and video made with support of A.N. Khosla Centre for Technology Enabled Learning (ANKCTEL), NIT Rourkela)


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https://www.youtube.com/watch?v=QTi0Mv7DGDs    

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Optimization methods are extensively used in those engineering design problems where the emphasis is on maximizing or minimizing a certain goal. Optimization algorithms are routinely used in aerospace design activities to minimize the overall weight. The minimization of the weight of aircraft components is of major concern to aerospace designers. Chemical engineers, on the other hand, are interested in designing and operating a process plant for least cost of operation for the required rate of production. Mechanical engineers design mechanical components for the purpose of achieving either a minimum manufacturing cost or a maximum component life.  Civil engineers are involved in designing buildings, bridges, dams, and other structures in order to achieve a minimum overall cost or maximum safety or both. Communication   engineers are interested in designing communication networks so as to achieve minimum time for communication from one node to another.

All the above-mentioned tasks involve either minimization or maximization (collectively known as optimization) of an objective. 

This lesson is part of Product Industrial Engineering.

The major techniques that constitute  Product Industrial Engineering. 

1. Value Analysis and Engineering

2. Design for Manufacturing

3. Design for Assembly

4. Design for Additive Manufacturing

5. Design to Cost

6. Design to Value

7. Design to Target Cost

8. Engineering Product Design Optimization

9. Six Sigma for Design Improvement - Robust Design (Video)

10. Life Cycle Cost Analysis based redesign

11. Design analysis done during Process Industrial Engineering

12. Lean Product Design Concept


Product Redesign to Reduce Cost - Product Industrial Engineering. 

PRODUCT INDUSTRIAL ENGINEERING. MODERN INDUSTRIAL ENGINEERING.

https://nraoiekc.blogspot.com/2012/09/product-design-industrial-engineering.html


Professors - Engineering Optimization

Panos Y. Papalambros

Panos Y. Papalambros is the James B. Angell Distinguished University Professor Emeritus and the Donald C. Graham Professor Emeritus of Engineering; Professor Emeritus of Mechanical Engineering; Professor Emeritus of Integrative Systems and Design; Professor Emeritus of Architecture; and Professor Emeritus of Art and Design -- all at the University of Michigan. His primary interest is in mathematical design optimization for product development and complex systems design with emphasis on sustainability, including automotive systems,  electric and hybrid powertrains, structural design, modularity and product platforms, and multi-vehicle systems -- linking design decisions with defense, commercial, and regulatory decisions to derive business and government policies. His research in design preference elicitation, including machine learning and crowdsourcing, has linked engineering design with computing, marketing, and behavioral and social sciences models. 


With D. J. Wilde, he co-authored the standard textbook Principles of Optimal Design: Modeling and Computation.


https://sites.google.com/umich.edu/pyp/biosketch?authuser=0


CMU - Prof. Conrad Tucker - Mech Engg.

Prof. Tucker has IE degree

http://meche-test.engineering.cmu.edu/faculty/aipex.html

https://scholar.google.com/citations?user=8N6uFIkAAAAJ&hl=en

Special Issue: Machine Learning for Engineering Design

https://ideal.umd.edu/assets/pdfs/2019_ml-eng-design-editorial.pdf






Interesting search results are available for "Engineering Optimization - Introduction"


Ud. 26.11.2023, 13.3.2022

Pub: 3.2.2022

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