Study Roadmap for Industrial Engineering
My Roadmap for Industrial Engineering
Undergraduate Industrial Engineering
- Introduction of Industrial Engineering and Decision Analytics
- Computing in Industrial Applications
- Product Design
- Engineering Management
- Logistics and Freight Transportation Operations
- Probability for Engineers
- Statistics for Engineers
- Prescriptive Analytics
- Ergonomics and Safety Management
- Manufacturing Processes
- Data-Driven Portfolio Optimization
- Engineering Economics and Accounting
- Stochastic Models
- Data-Driven Quality Technology
- Industrial Data Systems
- E-Commerce Technology and Applications
- Introduction to Financial Engineering
- Routing and Fleet Management
- Demand and Supply Analytics
- Predictive Analytics
- Transportation Systems
- Integrated Production Systems
- System Simulation
- Service Engineering and Management
- Design of Logistics and Manufacturing Systems
- Quantitative Methods in Financial Engineering
- Data Driven Supply Chain Management
- Dynamic Pricing and Revenue Optimization
- Engineering Foundations of FinTech
- Systems Risk Management
- Numerical Methods for Financial Engineering
- Engineering Psychology
Undergraduate Mathematics
- Linear Algebra
Postgraduate Industrial Engineering
- Revenue Management and Pricing Analytics
- Production and Operations Management
- Deterministic Models in Operations Research
- Stochastic Models in Operations Research
- Advanced Quality Technology and Data Analytics
- Engineering Statistics and Data Analytics
- Convex Optimization
Details
Introduction of Industrial Engineering and Decision Analytics
Study material
- Text by author
This course provides an introduction to industrial engineering and decision analytics (IEDA). It comprises of two parts. The first part introduces basic IE analytical tools, such as optimization, game theory, probability and statistics, at a conceptual level. In the second part, many of the IEDA practical concepts, including production and operations management, logistics and supply chain management, financial technology are introduced.
Computing in Industrial Applications
Introduction to microprocessor technologies and computer hardware with industrial applications. Computer systems for industrial control. Digital communication, mobile computing and RFID technology.
Product Design
Fundamentals of product design from an industrial engineering perspective, including market research and communication, process design and evaluation, design for manufacturability/assembly, design for usability and safety, aesthetics design, and design for reuse. Methods and theories of design and case studies are presented.
Engineering Management
Techniques relating to modeling and analysis and management of engineering operations; productivity assessment and improvement, quality assessment and improvement; principles of behavioral science and its application to engineering management.
Logistics and Freight Transportation Operations
Introduction to intermodalism, globalization, third-part logistics, carrier logistics, shipper logistics, manufacturing logistics, supply chain management, and rules, conventions and practices in various transportation modes. Discussion of characteristics, issues, and practices of air cargo systems, surface transportation systems, sea freight operations, and terminal operations.
Probability for Engineers
Study material
- A First Course in Probability by Sheldon M. Ross
This is a systematic introduction to basic probability theory for engineering, including sample space and sampling methods, calculus of probability, conditional probability, joint distribution, moment generating functions, the law of large numbers and central limit theorem. Along the course, students will learn a wide range of discrete and continuous probability distributions, which are important and useful in various applications.
Statistics for Engineers
Study material
- Applied Statistics and Probability for Engineers by Douglas C. Montgomery and George C. Runger
This is a systematic introduction to statistics for engineering, including descriptive statistics, point and interval estimation, hypothesis testing and linear regression analysis. In addition to theories, students will be taught a statistical language (R or Python) and have hands on experience of processing and analyzing data.
Prescriptive Analytics
Study material
- Introduction to Operations Research by F.S. Hillier and G.J. Lieberman
- Data, Models, and Decisions. The Fundamentals of Management Science by D. Bertsimas and R.M. Freund
- Learning Python: Powerful Object-Oriented Programming by M. Lutz
Introduction to optimization methods. Topics include linear programming, integer programming, nonlinear programming, decision-making under uncertainty, and sequential decision-making. Software packages are used to solve data-driven decision-making problems in engineering and business.
Ergonomics and Safety Management
Introduction to ergonomics and safety management. Work environment stressors and their reduction. Technical compliance of Occupational Safety and Health Ordinance and their respective laws in UK, EC, and US. Accident causation models.
Manufacturing Processes
Machine tools, tools and tooling. Machining, fabrication, joining, assembly, and welding. Experiments in cutting tool performance involving tool geometry, speed, surface finish, tool life and production economics associated with those variables. Concepts of NC, CNC.
Data-Driven Portfolio Optimization
Study material
- A Signal Processing Perspective on Financial Engineering by Yiyong Feng and Daniel P. Palomar
- Optimization Methods for Financial Index Tracking: From Theory to Practice. Foundations and Trends® in Optimization by Konstantinos Benidis, Yiyong Feng, and Daniel P. Palomar
- Convex Optimization by S. Boyd and L. Vandenberghe
- Optimization Methods in Finance by G. Cornuejols and R. Tutuncu
- Robust Portfolio Optimization and Management by F. J. Fabozzi, P. N. Kolm, D. A. Pachamanova, and S. M. Focardi
Modern portfolio theory started with Harry Markowitz’s 1952 seminal paper “Portfolio Selection.” He put forth the idea that risk-adverse investors should optimize their portfolio based on a combination of two objectives: expected return and risk. Until today, that idea has remained central in portfolio optimization. However, the vanilla Markowitz portfolio formulation does not seem to behave as expected in practice and most practitioners tend to avoid it. During the past half century, researchers and practitioners have reconsidered the Markowitz portfolio formulation and have proposed countless of improvements and alternatives such as robust optimization methods, alternative measures of risk, regularization via sparsity, improved estimators of the covariance matrix, robust estimators for heavy tails, factor models, volatility clustering models, risk-parity formulations, index tracking, etc. This course will explore the Markowitz portfolio optimization in its many variations and extensions, with special emphasis on Python programming.
Engineering Economics and Accounting
Study material
- Engineering Economy by Sullivan, Wicks, Koelling
Application of microeconomics to engineering and managerial decision making. Basic accounting cash flow analysis of capital investment. Present worth, rate of return, taxes and depreciation, capital budgeting, cost accounting, risk and uncertainty.
Stochastic Models
Study material
- Introduction to Probability Models by Sheldon M. Ross
Poisson process, Markov process, and Markov decision processes; inventory theory, reliability, queuing theory. Application softwares.
Data-Driven Quality Technology
Control charts and statistical on-line quality control methods, off-line quality control and parameter design, modern quality philosophy and Taguchi method.
Industrial Data Systems
Fundamental concepts on database, network, object-oriented methodology, and system integration; design and development of database systems for productions (e.g. MRP), manufacturing (e.g. CAPP), and management (e.g. BPR).
E-Commerce Technology and Applications
A significant portion of modern commercial activity is dependent on electronic commerce. In this course, students will gain familiarity with common e-commerce business models and get an understanding of how and when they are used. The course will cover important enabling technologies, including basics of internet communication, security, clouds, as well as low level technology enabling functions such as localization and tracking. Several important applications in various sectors of industry, including visualization and analysis as well as ELogistics will be introduced.
Introduction to Financial Engineering
Study material
- Investment Science by Luenberger, D. G.
- Financial Markets and Institutions by Frederic S. Mishkin and Stanley G. Eakins
This course is intended to provide an introduction to important aspects of financial engineering. Specifically, this course will primarily cover fundamentals of the financial system, interest rate and term structure, various financial markets, financial derivatives, option pricing and hedging, risk management, and financial modeling.
Routing and Fleet Management
Applications and algorithms for network optimization, vehicle routing, shortest path problems, maximum flow problems, matching models and dynamic vehicle allocation.
Demand and Supply Analytics
This course will introduce students to an array of tools to efficiently manage supply and demand networks. Topics include service and inventory trade offs, stock allocation, pricing, markdown management and contracts, timely product distribution to market while avoiding excess inventory, allocating adequate resources to the most profitable products and selling the right product to the right customer at the right price and at the right time.
Predictive Analytics
This course focuses on how companies identify, evaluate, and capture decision analytic opportunities to create value. Basic analytic methods as well as real corporate cases studies will be covered. The analytical methods include ways to use data to develop insights and predictive capabilities using machine learning, data mining, and forecasting techniques. Some aspects of the use of optimization methods to support decision-making in the presence of a large number of alternatives and business constraints will be covered. The concepts learned in this class should help students identify opportunities in which decision analytics can be used to improve performance and support important decisions.
Transportation Systems
Introduction to transportation systems; characteristics of transportation models; traffic flow fundamentals; transportation economics; traffic demand forecasting including trip generation, trip distribution, modal split and trip assignment; interface between transportation systems and logistics planning/operations.
Integrated Production Systems
Basic concepts and techniques in design and operational control of integrated production systems, including MRP, JIT, forecasting, production planning, inventory control, and shop floor control and scheduling.
System Simulation
Basic concepts and algorithm of discrete-event simulation, generation of random variates, modeling input distributions, statistical analysis of simulation outputs, verification and validation of simulation models, comparisons and optimization via simulation, simple spreadsheet simulation, intermediate modeling and analysis with a commercial simulation package.
Service Engineering and Management
Service system design, service level, quality of service, service product life cycle, measurements, design for serviceability, analysis, productivity in services, client satisfaction, training and services logistics.
Design of Logistics and Manufacturing Systems
Facility location, process and material flow analysis, space allocation and plant layout, computerized layout planning, material handling equipment, material handling system design.
Quantitative Methods in Financial Engineering
Study material
- Stochastic Calculus for Finance I: The Binomial Asset Pricing Model by S. Shreve
- Stochastic Calculus for Finance II: Continuous-Time Models by S. Shreve
The course covers some quantitative methods commonly used in financial engineering for modeling, analyzing, and solving basic financial engineering problems. The course will start with basic concepts in stochastic calculus and stochastic differential equations. These will be used to introduce some advanced stochastic models such as jump diffusion, regime-switching, and stochastic volatility models. In the final part, some numerical methods for derivatives pricing will be introduced.
Data Driven Supply Chain Management
An introduction to the design, development, and management of integrated logistics supply chain systems, including inventory management, distribution channels, and information systems. Emphasis on the impact of e-business on companies and industries, especially how the Internet changes the way in which goods and services flow through the value chain from manufacturers to customers.
Dynamic Pricing and Revenue Optimization
This course focuses on capacity allocation, dynamic pricing and revenue management. It covers pricing implications of revenue management models for perishable and/or products in limited supply. Applications of these models to various industries including service, airlines, hotels etc. will be covered.
Engineering Foundations of FinTech
Study material
- Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction by A. Narayanan, J. Bonneau, E. Felten, A. Miller, and S. Goldfeder
- Credit Scoring and Its Applications by L. Thomas, J. Crook, and D. Edelman
FinTech, short for financial technology, is a remarkably booming industry that aims at improving traditional financial services by applying novel technologies. In this course, students will acquire an understanding of popular financial technologies and learn how they are employed to enhance the effectiveness and efficiency of the existing financial systems. More specifically, this course will cover important financial technologies and innovations, including investment and financing technologies such as P2P lending, crowdfunding, and microloans, payment technologies such as digital wallets and mobile payments, wealth management technologies such as robo‐advisors, and blockchain technologies such as cryptocurrencies (e.g., bitcoin). For DA and RMBI students with approval of the course instructor for enrollment in the course.
Systems Risk Management
Study material
- Risk Management and Financial Institutes by John C. Hull
This course seeks to develop the knowledge and analytical skills for risk management in operations. It covers different technical approaches for systems risk management, such as evaluating and modeling risk from data, making robust operational plans, preparing contingency plans, and generating disruption recovery solutions. These methodologies are introduced with applications in different industries such as services, logistics, and IT.
Numerical Methods for Financial Engineering
Study material
- Options, Futures and other Derivatives by Hull, J
The course aims to introduce various important numerical methods that have been widely applied in financial engineering. More specifically, the topics consist primarily of lattice methods, Monte Carlo simulation, and finite difference methods. Furthermore, broad applications of these numerical methods in financial engineering are also covered.
Engineering Psychology
Introduction to cognitive engineering and human performance. Perception, psychophysics, attention, time-sharing, workload and their implications on human performance.
Linear Algebra
Vector space, matrices and system of linear equations, linear mappings and matrix forms, inner product, orthogonality, eigenvalues and eigenvectors, symmetric matrix.
Revenue Management and Pricing Analytics
Ph.D.-level course covering current topics in Revenue Management and Pricing Analytics. The goal of the course is to provide students with the background and tools required to perform research in the field. The course is divided into two parts. Part 1 goes for ten weeks and consists of a combination of lectures on discrete-choice models, assortment optimization, revenue management with dependent demands, followed by lectures on pricing analytics including basic pricing theory, dynamic pricing and on-line learning. The lectures will be interspersed with paper presentations that reinforce the theory. Students are expected to read the material provided before coming to class. Part 2 will take place during the three last weeks of the course and will be devoted to project presentations. The instructor will provide a list of current research topics from which the students can select a class project, but students are free to propose their own projects. Intended Learning Outcomes
Production and Operations Management
The course introduces concepts, principals and techniques related to the design, planning, management and improvement of both manufacturing and service operations. Topics include demand forecasting and estimation, inventory management, production control and process improvement, queueing systems, procurement and supply chain management.
Deterministic Models in Operations Research
This course focuses on the theory and the use of deterministic optimization models for real life decision making problems. It covers linear, integer, combinatorial and nonlinear programming.
Stochastic Models in Operations Research
Poisson processes, renewal processes, Markov processes. Fundamental concepts and applications of these stochastic processes demonstrated through examples in queueing, inventory and reliability models.
Advanced Quality Technology and Data Analytics
Fundamental principles of quality technology and data analytics, including planning, designing, and analyzing statistical experiments, randomized block, factorial, and fractional factorial experimental designs, and other advanced statistical learning and machine learning techniques with application to quality and industrial engineering.
Engineering Statistics and Data Analytics
The course introduces advanced concepts and mathematical principles in statistical inference (e.g., estimation theory, hypothesis testing, and regression models) and machine learning (e.g. classification and tree-based models, support vector machines, model selection, and unsupervised learning). This course assumes the knowledge of multivariable calculus and probability.
Convex Optimization
Convex optimization theory with applications in signal processing, finance, and machine learning. It covers fundamentals (convex sets/functions/problems, Lagrange duality, algorithms), more advanced optimization techniques (sparsity, low-rank, robust optimization, decomposition methods, distributed algorithms), and specific applications (e.g., portfolio optimization, filter/beamforming design, classification methods, wireless communication systems, circuit design, image processing, data-drived graph learning, discrete MLE, network optimization, Internet protocol design, etc.).For PG students in second year or above.
Reference
- https://prog-crs.hkust.edu.hk/ugcourse
- https://prog-crs.hkust.edu.hk/pgcourse
- http://stellar.mit.edu/index.html
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