Monday, September 30, 2019

September - Industrial Engineering Knowledge Revision Plan

Productivity Engineering - Product Design Based - Process Design Based

Productivity Engineering is driven by productivity science and productivity analysis



New Study Plan for September

Revision of Production Technology and Product Design

September 1st Week

Production Technology for Industrial Engineers - Knowledge Base for Industrial Engineers
Introduction - Product Design and Development

Engineering Materials for Product Design and Fabrication

Product Development Process
Metal Casting

Identifying Customer Needs for Product Development
Metal Forming - Hot Working - Cold Working

Product - Part Concept Generation, Selection and Testing - Product Architecture

September 2nd  Week

Contents of NPTEL - Manufacturing Processes II

Machining - Cutting Tools and Cutting Speeds

Lathe and Milling

Shaping, Planing and Slotting

Drilling, Boring, Reaming

Grinding - Surface Finishing

CNC Machines

Contents of NPTEL - Manufacturing Processes II

September Third Week

Additive Manufacturing

3D Printing - Additive Manufacturing Industrial Engineering - Productivity Science and Engineering

Design for 3D Printing - Additive Manufacturing - Product Industrial Engineering

3D Printing Materials

3D Printing - Production Applications

Additive Manufacturing - 3D Printing - Human Effort Industrial Engineering

September Fourth Week

Energy Management and Energy Industrial Engineering

Heat Treatment

New Machining Processes

Plastic Components Processing

Robots - Manufacturing Applications

Material Handling and Transport

Earlier Plan - Now to be Shifted to Further Months

September 1st Week

Industrial Engineering Optimization

Mathematical optimization was used by F.W. Taylor. As operations research was developed and more optimization techniques were developed, industrial engineers advocated the use of them in companies to improve productivity, reduce costs, and increase profits. All industrial engineering redesigns are to be optimized and industrial engineers use various optimization techniques to optimize their engineering redesigns to increase productivity.

Complete Course in OR/Optimization -


Operations Research - An Efficiency Improvement Tool for Industrial Engineers

(from the perspective of an industrial engineer)
(From Maynard's Industrial Engineering Handbook, 5th Edition, pp. 11.27-11.44)
Jayant Rajgopal (From Rajgopal's website)


What is mathematical programming?
Examples of Mathematical Programming.


Simplex Method

4.  Transportation Problem

5. Queing Models

September 2nd  Week

8. Simulation

9. An Overview of Optimization Techniques for CNC Milling Machine

10. New Technology and Optimization of Mobile Phone Battery

11. Combustion Optimization in PF Boilers

12. Application of Optimization Techniques in the Power System Control

September Third Week

Industrial Engineering Statistics

F.W. Taylor himself advocated maintaining of records and data for decision making. The other industrial engineering pioneers also promoted record keeping and data analysis. As sampling based  decision making became more robust, industrial engineers promoted it as a productivity improvement initiative and imperative. One of the prominent areas of application is statistical quality control. Now six sigma, a statistics based technique is being promoted by the IE profession.

15.  Basics of Statistics

16.  Statistical Process Control

Evaluation Improvement of Production Productivity Performance using Statistical Process Control, Overall Equipment Efficiency, and Autonomous Maintenance,
Amir Azizi
Procedia Manufacturing
Volume 2, 2015, Pages 186-190
open access

17. Statistical Quality Control

18. Calculation of Sample Sizes in Work Measurement and Work Sampling  (WorK measurement full chapter - Includes sample size calculation for time study and work sampling)

19. Test of Hypothesis

Test of hypothesis is to be used by industrial engineers to confirm or validate that their redesign or a process has resulted in the increase of productivity. This becomes useful when there is variation in the output from various workstations or persons.  We can also visualize activities in different places. In such case we test the hypothesis that productivity has improved in the workstations where redesign is is implemented.

HYPOTHESIS TESTING FOR THE PROCESS CAPABILITY RATIO - 2002 MS Thesis!etd.send_file%3Faccession%3Dohiou1040054409%26disposition%3Dinline

One More presentation

September Fourth Week

22. Design of Experiments

23. Six Sigma

24. Application of Six Sigma

25. Application of Six Sigma

26. Application of Six Sigma


One Year Industrial Engineering Knowledge Revision Plan

January - February - March - April - May - June

July - August - September - October - November - December

Updated 27 August 2019,   5 September 2017,  23 August 2017, 11 September 2016,  30 September 2014


67 likes for the post in Industrial Engineering Network Group

Updated on 24 August 2019, 30 September 2017

Thursday, September 26, 2019

Milling - Method Study - Process Industrial Engineering Exercises

Use Operation Analysis Method after preparing process chart - Operation Process Chart and Flow Process Chart.

See how a milling operation was analyzed using operation analysis sheet - Using Operation Analysis Sheet for Milling Operation

Cutting Keyways - 1941
Museum of Our Industrial Heritage
Published on 11 Apr 2018



Milling slots on the Bridgeport
TJS Welding and Fabrication
Published on 16 Jun 2013



Cnc Router cutting aluminium - Test high speed
6,355,306 views•28 Jul 2017


Updated on 27 September 2019, 10 July 2019.

Tuesday, September 24, 2019

Attributes of Industrial Engineering and Research

Attributes of Industrial Engineering 

Research Possibilities are there in relation to them.
Think of a sentence that links industrial engineering with each term.

#Activity #adjustment #advantage  #Adoption  #Agreement  #Aptitude #Arrangement
#Best #Big  #Better  #Bound #Benchmarking
#Comfort #care #communication #cooperation  #clear #concept #cost #costreduction #costing #costcontrol #Casestudy #Creativity
#Decision  #Development #Distribution  #Design #Decrease #Dance #Danger #Dream #Dignity #Demonstration #Desire
#Engine #Engineering #Employee #Employment #Effectiveness  #Efficiency  #Engagement #Excitement  #Excellence #Eager #eagerness #Extreme #Extrovert #Extroversion #Extension #Existence #Extra #Estimate
#Fatigue #Forecasting #Finance #Fitness  #Future
#Goal #Game  #Going  #Gain #Guess
#Hope #Heat #Harness #Hard #
#Increase  #Involve #Insure #Improve #Improvement

Research Relating Attributes and Industrial Engineering

#Increase  #Involve #Insure #Improve #Improvement

Productivity improvement using industrial engineering tools
H A Salaam, S B How and M F Faisae
IOP Conference Series: Materials Science and Engineering, Volume 36, conference 1

Fundamentals of Supply Chain Theory - Snyder and Shen - Book Information

Fundamentals of Supply Chain Theory

Lawrence V. Snyder, Zuo-Jun Max Shen
John Wiley & Sons, 11-Jul-2019 - Business & Economics - 784 pages

Comprehensively teaches the fundamentals of supply chain theory

This book presents the methodology and foundations of supply chain management and also demonstrates how recent developments build upon classic models. The authors focus on strategic, tactical, and operational aspects of supply chain management and cover a broad range of topics from forecasting, inventory management, and facility location to transportation, process flexibility, and auctions. Key mathematical models and quantitative approaches for optimizing the design, operation, and evaluation of supply chains are presented as well as models currently emerging from the research frontier.

Fundamentals of Supply Chain Theory, Second Edition contains new chapters on transportation, integrated supply chain models, and applications of supply chain theory. New sections have also been added throughout, on topics including machine learning models for forecasting, conic optimization for facility location, a multi-supplier model for supply uncertainty, and a game-theoretic analysis of auctions. The second edition also contains case studies for each chapter that illustrate the real-world implementation of the models presented. This edition also contains nearly 200 new homework problems, over 60 new worked examples, and over 140 new illustrative figures.

Plentiful teaching supplements are available, including an Instructor’s Manual and PowerPoint slides, as well as MATLAB programming assignments that require students to code algorithms in an effort to provide a deeper understanding of the material.

Ideal as a textbook for upper-undergraduate and graduate-level courses in supply chain management in engineering and business schools.

List of Figures xxi

List of Tables xxvii

List of Algorithms xxix

Preface xxxi

1 Introduction 1

1.1 The Evolution of Supply Chain Theory 1

1.2 Definitions and Scope 2

1.3 Levels of Decision Making in Supply Chain Management 4

2 Forecasting and Demand Modeling 5

2.1 Introduction 5

2.2 Classical Demand Forecasting Methods 6

2.3 Forecast Accuracy 15

2.4 Machine Learning in Demand Forecasting 17

2.5 Demand Modeling Techniques 23

2.6 Bass Diffusion Model 24

2.7 Leading Indicator Approach 30

2.8 Discrete Choice Models 33

Case Study: Semiconductor Demand Forecasting at Intel 38

Problems 39

3 Deterministic Inventory Models 45

3.1 Introduction to Inventory Modeling 45

3.2 Continuous Review: The Economic Order Quantity Problem 51

3.3 Power of Two Policies 57

3.4 The EOQ with Quantity Discounts 60

3.5 The EOQ with Planned Backorders 67

3.6 The Economic Production Quantity Model 70

3.7 Periodic Review: The Wagner–Whitin Model 72

Case Study: Ice Cream Production and Inventory at Scotsburn Dairy Group 76

Problems 77

4 Stochastic Inventory Models: Periodic Review 87

4.1 Inventory Policies 87

4.2 Demand Processes 89

4.3 Periodic Review with Zero Fixed Costs: Base-Stock Policies 89

4.4 Periodic Review with Nonzero Fixed Costs: (s; S) Policies 114

4.5 Policy Optimality 123

4.6 Lost Sales 136

Case Study: Optimization of Warranty Inventory at Hitachi 138

Problems 140

5 Stochastic Inventory Models: Continuous Review 155

5.1 (r; Q) Policies 155

5.2 Exact (r; Q) Problem with Continuous Demand Distribution 156

5.3 Approximations for (r; Q) Problem with Continuous Distribution 161

5.4 Exact (r; Q) Problem with Continuous Distribution: Properties of Optimal r and Q 170

5.5 Exact (r; Q) Problem with Discrete Distribution 177

Case Study: (r; Q) Inventory Optimization at Dell 180

Problems 182

6 Multiechelon Inventory Models 187

6.1 Introduction 187

6.2 Stochastic-Service Models 191

6.3 Guaranteed-Service Models 203

6.4 Closing Thoughts 217

Case Study: Multiechelon Inventory Optimization at Procter & Gamble 222

Problems 223

7 Pooling and Flexibility 229

7.1 Introduction 229

7.2 The Risk-Pooling Effect 230

7.3 Postponement 236

7.4 Transshipments 237

7.5 Process Flexibility 243

7.6 A Process Flexibility Optimization Model 253

Case Study: Risk Pooling and Inventory Management at Yedioth Group 257

Problems 259

8 Facility Location Models 267

8.1 Introduction 267

8.2 The Uncapacitated Fixed-Charge Location Problem 269

8.3 Other Minisum Models 295

8.4 Covering Models 305

8.5 Other Facility Location Problems 314

8.6 Stochastic and Robust Location Models 317

8.7 Supply Chain Network Design 321

Case Study: Locating Fire Stations in Istanbul 332

Problems 335

9 Supply Uncertainty 355

9.1 Introduction to Supply Uncertainty 355

9.2 Inventory Models with Disruptions 356

9.3 Inventory Models with Yield Uncertainty 365

9.4 A Multisupplier Model 372

9.5 The Risk-Diversification Effect 384

9.6 A Facility Location Model with Disruptions 387

Case Study: Disruption Management at Ford 395

Problems 396

10 The Traveling Salesman Problem 403

10.1 Supply Chain Transportation 403

10.2 Introduction to the TSP 404

10.3 Exact Algorithms for the TSP 408

10.4 Construction Heuristics for the TSP 416

10.5 Improvement Heuristics for the TSP 436

10.6 Bounds and Approximations for the TSP 442

10.7 World Records 452

Case Study: Routing Meals on Wheels Deliveries 453

Problems 455

11 The Vehicle Routing Problem 463

11.1 Introduction to the VRP 463

11.2 Exact Algorithms for the VRP 468

11.3 Heuristics for the VRP 475

11.4 Bounds and Approximations for the VRP 495

11.5 Extensions of the VRP 498

Case Study: ORION: Optimizing Delivery Routes at UPS 501

Problems 502

12 Integrated Supply Chain Models 511

12.1 Introduction 511

12.2 A Location–Inventory Model 512

12.3 A Location–Routing Model 529

12.4 An Inventory–Routing Model 531

Case Study: Inventory–Routing at Frito-Lay 534

Problems 535

13 The Bullwhip Effect 539

13.1 Introduction 539

13.2 Proving the Existence of the Bullwhip Effect 541

13.3 Reducing the Bullwhip Effect 552

13.4 Centralizing Demand Information 555

Case Study: Reducing the Bullwhip Effect at Philips Electronics 556

Problems 559

14 Supply Chain Contracts 563

14.1 Introduction 563

14.2 Introduction to Game Theory 564

14.3 Notation 565

14.4 Preliminary Analysis 566

14.5 The Wholesale Price Contract 568

14.6 The Buyback Contract 574

14.7 The Revenue Sharing Contract 578

14.8 The Quantity Flexibility Contract 581

Case Study: Designing a Shared-Savings Contract at McGriff Treading Company 584

Problems 586

15 Auctions 591

15.1 Introduction 591

15.2 The English Auction 593

15.3 Combinatorial Auctions 595

15.4 The Vickrey–Clarke–Groves Auction 599

Case Study: Procurement Auctions for Mars 608

Problems 610

16 Applications of Supply Chain Theory 615

16.1 Introduction 615

16.2 Electricity Systems 615

16.3 Health Care 625

16.4 Public Sector Operations 632

Case Study: Optimization of the Natural Gas Supply Chain in China 639

Problems 641

Appendix A: Multiple-Chapter Problems 643

Problems 643

Appendix B: How to Write Proofs: A Short Guide 651

B.1 How to Prove Anything 651

B.2 Types of Things You May Be Asked to Prove 653

B.3 Proof Techniques 655

B.4 Other Advice 657

Appendix C: Helpful Formulas 661

C.1 Positive and Negative Parts 661

C.2 Standard Normal Random Variables 662

C.3 Loss Functions 662

C.4 Differentiation of Integrals 665

C.5 Geometric Series 666

C.6 Normal Distributions in Excel and MATLAB 666

C.7 Partial Expectations 667

Appendix D: Integer Optimization Techniques 669

D.1 Lagrangian Relaxation 669

D.2 Column Generation 675

References 681

Subject Index 712

Author Index 725

Monday, September 23, 2019

Wichita State University - Industrial Engineering Programs

65. Wichita State University

Industrial, Systems, and Manufacturing Engineering Department

Department Chair:
Dr. krishna Krishnan
Email: krishna.krishnan
Phone: (316) 978-5903

Graduate Coordinator:
Dr. Deepak Gupta
Email: deepak.gupta
Phone: (316) 978-7758

Cindi Mason (Undergraduate Program Coordinator)

Industrial Engineering Graduates are expected, within 3 to 5 years after graduation, to meet the following Program Educational Objectives (PEOs):

PEO1: Be employed in jobs related to designing, modeling, analyzing, and managing modern complex systems, implementing and improving systems in manufacturing and service sectors at local, regional, national and global levels.

The Bachelor of Science in INDUSTRIAL ENGINEERING (BSIE) degree program curriculum has been developed to provide its graduates with a set of comprehensive engineering skills to solve problems in manufacturing and service industries, businesses, and institutions, with the objective of productivity improvement through better use of human resources, financial resources, natural resources and man-made structures and equipment.  

Programs Offered under Industrial, Systems, and Manufacturing Engineering

BS in Industrial Engineering -
BS in Product Design and Manufacturing Engineering-

MS in Industrial Engineering

Productivity Management Course -  No

Enkhsaikhan Boldsaikhan
Laila Cure
Deepak Gupta (Associate Chair and Graduate Program Coordinator)
Mike Jorgensen (Biomedical Egineering Department Chair)
Krishna Krishnan (Chair)
Vis Madhavan (Sam Bloomfield Chair in Engineering Innovation)
Don Malzahn (Emeritus Faculty)
Cindi Mason (Undergraduate Program Coordinator)
Abu Masud (Emeritus Faculty)
Wilfredo Moscoso-Kingsley
Saideep Nannapaneni
Ehsan Salari
Wujun Si
Janet Twomey (Assoc. Dean, College of Engineering)
Gamal Weheba
Bayram Yildirim

Industrial Engineering - Lehigh University


Lawrence V. Snyder

Home Department: Industrial and Systems Engineering
Position: Professor
Address: Harold S. Mohler Laboratory
200 West Packer Avenue
Bethlehem, PA 18015

Areas of Research
Supply Chain Management, Logistics, Engineering Decision Making, Applied Optimization
Integer Programming Algorithms and Heuristics, Energy Applications

Faculty specialising in systems related to various branches of engineering are there in the department.

Energy grids and energy systems

Updated on 24 September 2019, 11 September 2016

Sunday, September 22, 2019




100 cobots were deployed across India by Encon Systems International.

Advertisement by Universal Robts (India) Pvt. Ltd., Bengaluru, India


Case Studies
Bajaj Auto India
Being the first company in India to implement collaborative robots has enabled Bajaj to improve its production capabilities and evolve its multi-modelling offerings.

Columbia University in the City of New York - Industrial Engineering Programs

MS in Industrial Engineering (MSIE)
The Master of Science in Industrial Engineering is a 30-credit program for students with an undergraduate engineering degree who want to enhance their training in special fields, such as scheduling, production planning, inventory control, and industrial economics.

Faculty Directory

Columbia | Engineering. The Fu Foundation School of Engineering and Applied Science
500 W. 120th Street #315
New York, NY 10027
Tel (212)-854-2942

Columbia Engineering Magazine
Columbia University

Supply Chain and Sales Engineering Technology - Purdue University - Purdue Polytechnic Institute

Supply Chain and Sales Engineering Technology
A major in the Industrial Engineering Technology Program
in the School of Engineering Technology

Virtually all corporations are dependent upon their supply chains to manage the flow of goods, services and information to help customers. You will study the entire supply chain enterprise, yet have the flexibility to select courses for your chosen career path.

The top ERP (Enterprise Resource Planning) software in the industry, SAP ERP, is embedded throughout the curriculum. The latest technology and software is also used to help graduates become career-ready.

Core courses
ENGT 18000 - Engineering Technology Foundations
ENGT 18100 - Engineering Technology Applications
TLI 21400 - Introduction To Supply Chain Management Technology
TLI 31300 - Technology Innovation And Integration: Bar Codes To Biometrics
TLI 31600 - Statistical Quality Control
TLI 34200 - Warehouse And Inventory Management
TLI 34300 - Technical And Service Selling
TLI 34350 - Business To Business Sales Management
TLI 41400 - Financial Analysis For Technology Systems
TLI 43530 - Operations Planning And Management
TLI 43630 - Design Of Experiments
TLI 43640 - Lean Six Sigma
TLI 44275 - Global Transportation And Logistics Management
IET 44500 - Strategic Supply Chain Management
TLI 48390 - Industrial Engineering Technology Capstone I: Problem Identification And Analysis
TLI 48395 - Industrial Engineering Technology Capstone II: Project Design

Ohio University-Main Campus - Industrial Engineering Programs

20) Ohio State University - Main Campus Columbus, Ohio

ISE: Integrated Systems Engineering

Curriculum & ISE Elective Tracks
ISE Technical Elective Tracks

In addition to the core curriculum, the program requires students to select a track of electives across a wide variety of topic areas within the discipline. These elective tracks give students the opportunity to gain greater depth in one of the following five areas:

Data Analytics and Optimization
Supply Chain Management and Logistics
Management Systems and Operations Research
Human Systems Integration and Design
ISE Capstone options
Students have the ability to select among several options for their capstone experience in their final academic year in one of the following:

ISE 4900
Multidisciplinary Capstone
ISE 5811 & ISE 5812 (Green Belt Certification program in Integrated Lean Six Sigma)
prerequisite: ISE 5810 (Black Belt Foundation Course Certificate)
Industrial and Systems Engineering Curriculum

In order to keep up with the industry and adequately prepare students for becoming Industrial & Systems Engineers, our department continuously updates the curriculum;  however, we allow students to begin and end their time in our department with the same requirements they were given during the academic year they were admitted to the University.

BS Curriculum: -

MS Curriculum:-
Productivity Management: - Not in Curriculum
Amy Shaw (Professor) shaw.229

Farhang Pourboghrat

Scott Sink
Executive in Residence/
Senior Lecturer

ISE Graduate Curriculum
The ISE Department offers both an M.S. and Ph.D. in Industrial and Systems Engineering.  These degrees offer students with an opportunity to specialize in one of several concentration areas, opening doors to new jobs and career paths. These concentration areas include:

Data Analytics and Optimization (M.S. only)
Human Systems Integration (M.S. and Ph.D.)
Occupational Safety and Ergonomics (M.S.)
Integrated Lean Six Sigma (M.S. only)
Manufacturing Engineering (M.S. and Ph.D.)
Operations Research (M.S. and Ph.D.)
Supply Chain Management and Production Systems (M.S. only)
Master of Business Logistics Engineering (M.S. only)

Manufacturing Processes.  At the graduate level, ISE Department offers both a M.S. concentration and Ph.D. focusing on its excellent program in advanced manufacturing processes.  This includes education and research concerned with:

Composites and nanocomposites manufacturing
Forming of lightweight materials
Light metal alloy development
Light metal casting processes
Micromachining and precision engineering
Polymer processing

Supply Chain Management and Production Systems.  Logistics is the science of design, control, and maintenance of the effective and efficient flow of resources, service, manufactured goods, personnel, and information. The Industrial Engineer typically focuses on certain aspects of logistics in the supply chain. These include:

Transportation of raw materials to the manufacturer.
Handling and storage of raw materials.
Production and storage of goods.
Scheduling and control of jobs and activities.
Management of the labor force.
Warehousing of finished goods.
Delivery of product to the customer.
Design of various portions of the supply chain that include raw material and distribution networks, production and warehouse facilities.
Design and control of information flow.

Each of these areas requires specialized methods for developing and creating effective and efficient solutions, which are usually based on mathematical theories and methods.

Stanford University CA - Management Science and Engineering

Emeriti: (Professors) James L. Adams, Stephen R. Barley, Richard W. Cottle, B. Curtis Eaves, Warren H. Hausman, Frederick S. Hillier, Ronald A. Howard, Donald L. Iglehart, David G. Luenberger, Michael M. May, William J. Perry, David A. Thompson; (Associate Professor) Samuel S. Chiu; (Professors, Research) Siegfried S. Hecker, Walter Murray, Michael A. Saunders; (Professor, Teaching) Robert E. McGinn

Chair: Nicholas Bambos

Director of Graduate Studies: Kay Giesecke

Director of Undergraduate Studies: Ross D. Shachter

Professors: Nicholas Bambos, Margaret L. Brandeau, Kathleen M. Eisenhardt, Kay Giesecke, Peter W. Glynn, Ashish Goel, Pamela J. Hinds, Ramesh Johari, Riitta Katila, M. Elisabeth Paté-Cornell, Robert I. Sutton, James L. Sweeney, Benjamin Van Roy, Yinyu Ye

Associate Professors: Itai Ashlagi, Jose Blanchet, Charles E. Eesley, Amin Saberi, Ross D. Shachter, Edison T. S. Tse

Assistant Professors: Guillaume W. Basse, Sharad Goel, Irene Y. Lo, Markus Pelger, Aaron Sidford, Johan Ugander, Melissa A. Valentine

Professor (Research): John P. Weyant

Professor (Teaching): Thomas H. Byers

Professor of the Practice: Tina L. Seelig

Courtesy Professors: Stephen P. Boyd, Paul Milgrom, Douglas K. Owens, Alvin Roth

Saturday, September 21, 2019

Top Industrial Engineering Programs USA

Top Industrial Engineering Programs USA

The ranks are 2014 related

Rank School Name - Industrial Engineering Program at

1 Georgia Institute of Technology-Main Campus

2 University of Michigan-Ann Arbor

University of California Berkeley CA
Stanford University CA  - Management Science and Engineering
Northwestern University (McCormick) IL

3 Texas A & M University-College Station

4 Pennsylvania State University-Main Campus

5 Virginia Polytechnic Institute and State University

6 Purdue University-Main Campus - MSIE Online Program

Cornell University NY
Texas A&M University College Station TX
7 University of Wisconsin-Madison
Columbia University (Fu Foundation) NY

8 University of Southern California (Viterbi)
9 University of Central Florida
10 Ohio State University-Main Campus
11 Auburn University
12 Wichita State University
13 University at Buffalo
14 Northwestern University

Lehigh University (Rossin)

University of Illinois Urbana Champagne

15 Arizona State University
16 SUNY at Binghamton
17 North Carolina State University at Raleigh
18 West Virginia University
19 University of Puerto Rico-Mayaguez
20 University of Pittsburgh-Pittsburgh Campus
21 Iowa State University
22 Rochester Institute of Technology
23 The University of Texas at Arlington
24 Clemson University
25 Oklahoma State University-Main Campus

26 University of South Florida-Main Campus
27 University of Houston
28 Columbia University in the City of New York - Already included at the top.
29 California Polytechnic State University-San Luis Obispo
30 University of Arkansas
31 New Mexico State University-Main Campus
32 South Dakota School of Mines and Technology
33 University of Oklahoma Norman Campus
34 Texas Tech University
35 Northeastern University
36 Northern Illinois University
37 Rensselaer Polytechnic Institute
38 University of Louisville
39 New Jersey Institute of Technology
40 University of Washington-Seattle Campus
41 University of Miami
42 Oregon State University
43 Lehigh University
44 Kansas State University
45 University of Missouri-Columbia
46 University of Illinois at Urbana-Champaign
47 Ohio University-Main Campus
48 The University of Texas at El Paso
49 Wayne State University
50 Indiana Institute of Technology

Morgan State University Youngstown State University

Louisiana State University - Engineering Minors for IE Program

Lamar University

Mississippi State University

Western Michigan University

Read more: Most Popular Schools for Industrial Engineering Major and Degree Program -

Above information is combined with Info in

Updated on 22 September 2019,  3 September 2019,   17 August 2019
15 July 2018
Earlier update 16 November 2018

Virginia Polytechnic Institute and State University - Industrial Engineering Programs

Virginia Tech, Blacksburg, Virginia

BSE  Curriculum:-

MS Curriculum:-

Productivity Management:- Not in Curriculum.
Dr. Eileen M. Van Aken (Department Head and Professor)
Email:- evanaken@vt.edud  Good information is provided in this link for many courses.