Undergraduate Courses 2024-25

Undergraduate courses marked with [BLD] or [SPO] may be offered in the mode of blended learning or self-paced online delivery respectively, subject to different offerings. Students should check the delivery mode of the class section before registration.

• IEDA 1010

  Academic and Professional Development I

  0 Credit(s)
  Description

  A compulsory one-year course for IEDA students. This course aims to provide academic and professional advising to students and to develop their technical and non-technical communication skills. Industrial and academic seminars will be offered. Graded P or F.
• IEDA 1020

  Academic and Professional Development II

  0 Credit(s)
  Description

  A compulsory one-year course for IEDA students, which is a continuation of IEDA 1010. Graded P or F.
• IEDA 1250

  Optimizing Decisions for Personal and Business Development

  3 Credit(s)
  Previous Course Code(s)

  CORE 1251

  Exclusion(s)

  CIVL 2170, IEDA 3010, ISOM 3710

  Description

  This course introduces basic analytical tools that can be used to optimize decisions for personal and business development. Students will learn different tools for scenarios encountered by individuals and companies in real life. For example, how to make decisions when one has limited resources? How to make intelligent decisions for now when one needs to make subsequent decisions later? How to make smart decisions when interacting with other people whose decisions also affect the final outcomes? How to gain insight from data by conducting basic data analytics?
• IEDA 1901

  Industrial Training and Experience

  0 Credit(s)
  Description

  This course is intended to provide UG students with practical hands-on training in the form of either a full-time internship training or industrial training course in an industrial simulated environment. Students who opt for internship training should complete a full-time internship training for a period of at least 4 weeks in an organization or company recognized by the Department for providing qualified internship training relevant to the industrial engineering and decision analytics. Students must also complete the USTSIEB Safety Training module. For IEDA students in their second year of study or above only. Students should seek approval of the internship coordinator for enrollment in the course. Graded P, PP or F.
• IEDA 1990

  Industrial Training

  0 Credit(s)
  Exclusion(s)

  IEDA 1991

  Description

  A practical training course in an industrial simulated environment. For students of the Industrial Engineering and Decision Analytics Department only. Graded P, PP or F.
• IEDA 1991

  Industrial Experience

  0 Credit(s)
  Exclusion(s)

  IEDA 1990

  Description

  Full-time internship training for a period of at least 4 weeks in an organization or company recognized by the Department for providing qualified internship training relevant to the industrial engineering and decision analytics. Students must also complete the USTSIEB Safety Training module. For IEDA students in their second year of study or above only. Students should seek approval of the internship coordinator for enrollment in the course. Graded P or F. May be graded PP.
• IEDA 2010

  Introduction of Industrial Engineering and Decision Analytics

  3 Credit(s)
  Exclusion(s)

  IEDA 2200

  Description

  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.
• IEDA 2100

  Computing in Industrial Applications

  3 Credit(s)
  Description

  Introduction to microprocessor technologies and computer hardware with industrial applications. Computer systems for industrial control. Digital communication, mobile computing and RFID technology.
• IEDA 2150

  Product Design

  3 Credit(s)
  Description

  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.
• IEDA 2200

  Engineering Management

  3 Credit(s)
  Exclusion(s)

  IEDA 2010

  Description

  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.
• IEDA 2410

  Logistics and Freight Transportation Operations

  3 Credit(s)
  Description

  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.
• IEDA 2520

  Probability for Engineers

  3 Credit(s)
  Prerequisite(s)

  MATH 1014 OR MATH 1020 OR MATH 1024

  Exclusion(s)

  ELEC 2600, ELEC 2600H, MATH 2421, MATH 2431

  Description

  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.
• IEDA 2540

  Statistics for Engineers

  3 Credit(s)
  Prerequisite(s)

  IEDA 2520

  Exclusion(s)

  MATH 2411, ISOM 2500, LIFS 3150

  Description

  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.
• IEDA 3010

  Prescriptive Analytics

  3 Credit(s)
  Corequisite(s)

  MATH 2111

  Exclusion(s)

  CIVL 2170, ISOM 3710

  Description

  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.
• IEDA 3130

  Ergonomics and Safety Management

  3 Credit(s)
  Prerequisite(s)

  IEDA 2520 AND IEDA 2540

  Description

  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.
• IEDA 3180

  Data-Driven Portfolio Optimization

  3 Credit(s)
  Alternate code(s)

  ELEC 3180

  Prerequisite(s)

  (IEDA 2520 OR IEDA 2540 OR ELEC 2600) AND (MATH 2111 OR MATH 2121 OR MATH 2131 OR MATH 2350)

  Description

  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.
• IEDA 3230

  Engineering Economics and Accounting

  3 Credit(s)
  Exclusion(s)

  FINA 2303

  Description

  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.
• IEDA 3250

  Stochastic Models

  3 Credit(s)
  Prerequisite(s)

  (IEDA 2520 AND IEDA 2540) OR (MATH 2411 AND MATH 2421)

  Description

  Poisson process, Markov process, and Markov decision processes; inventory theory, reliability, queuing theory. Application softwares.
• IEDA 3270

  Data-Driven Quality Technology

  3 Credit(s)
  Prerequisite(s)

  (IEDA 2520 AND IEDA 2540) OR MATH 2411

  Exclusion(s)

  ISOM 3730

  Mode of Delivery

  [BLD] Blended learning

  Description

  Control charts and statistical on-line quality control methods, off-line quality control and parameter design, modern quality philosophy and Taguchi method.
• IEDA 3300

  Industrial Data Systems

  3 Credit(s)
  Prerequisite(s)

  COMP 1021 OR COMP 1022P OR COMP 1022Q (prior to 2020-21)

  Exclusion(s)

  COMP 3311, ISOM 3260

  Description

  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).
• IEDA 3302

  E-Commerce Technology and Applications

  3 Credit(s)
  Prerequisite(s)

  COMP 1021 OR COMP 1022P OR COMP 1022Q (prior to 2020-21)

  Exclusion(s)

  IEDA 3560

  Description

  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.
• IEDA 3330

  Introduction to Financial Engineering

  3 Credit(s)
  Prerequisite(s)

  CIVL 2160 OR ELEC 2600 OR (IEDA 2520 AND IEDA 2540) OR ISOM 2500 OR MATH 2411 OR MATH 2421 OR MATH 2431

  Exclusion(s)

  FINA 3203, FINA 4303

  Description

  For DA students. 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.
• IEDA 3410

  Routing and Fleet Management

  3 Credit(s)
  Prerequisite(s)

  IEDA 3010

  Description

  Applications and algorithms for network optimization, vehicle routing, shortest path problems, maximum flow problems, matching models and dynamic vehicle allocation.
• IEDA 3460

  Demand and Supply Analytics

  3 Credit(s)
  Prerequisite(s)

  IEDA 2520

  Description

  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.
• IEDA 3560

  Predictive Analytics

  3 Credit(s)
  Prerequisite(s)

  IEDA 2540

  Exclusion(s)

  COMP 4211, IEDA 3302

  Description

  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.
• IEDA 3901

  Transportation Systems

  3 Credit(s)
  Corequisite(s)

  IEDA 2410

  Exclusion(s)

  CIVL 3610

  Description

  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. For IEDA students only.
• IEDA 4000

  Special Topics

  1-3 Credit(s)
  Description

  Selected topics in Industrial Engineering, Logistics Management or Decision Analytics. May be repeated for credit, if different topics are covered.
• IEDA 4100

  Integrated Production Systems

  3 Credit(s)
  Prerequisite(s)

  IEDA 2520 AND IEDA 3010

  Exclusion(s)

  ISOM 2700

  Description

  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.
• IEDA 4130

  System Simulation

  3 Credit(s)
  Prerequisite(s)

  IEDA 2520

  Exclusion(s)

  ISOM 4720

  Description

  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.
• IEDA 4180

  Service Engineering and Management

  3 Credit(s)
  Prerequisite(s)

  IEDA 2520

  Description

  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.
• IEDA 4200

  Design of Logistics and Manufacturing Systems

  3 Credit(s)
  Prerequisite(s)

  IEDA 3010

  Description

  Facility location, process and material flow analysis, space allocation and plant layout, computerized layout planning, material handling equipment, material handling system design.
• IEDA 4331

  Quantitative Methods in Financial Engineering

  3 Credit(s)
  Prerequisite(s)

  (FINA 3203 OR IEDA 3330) AND (IEDA 3250 OR ISOM 2500)

  Description

  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.
• IEDA 4410

  Data Driven Supply Chain Management

  3 Credit(s)
  Prerequisite(s)

  IEDA 4100

  Exclusion(s)

  EEMT 5300, ISOM 3770

  Description

  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.
• IEDA 4420

  Dynamic Pricing and Revenue Optimization

  3 Credit(s)
  Prerequisite(s)

  IEDA 3010 AND IEDA 3250

  Description

  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.
• IEDA 4500

  Engineering Foundations of FinTech

  3 Credit(s)
  Prerequisite(s)

  IEDA 3330 OR FINA 3203

  Description

  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.
• IEDA 4510

  Systems Risk Management

  3 Credit(s)
  Prerequisite(s)

  IEDA 3010 AND IEDA 3250

  Description

  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.
• IEDA 4520

  Numerical Methods for Financial Engineering

  3 Credit(s)
  Prerequisite(s)

  (IEDA 3250 OR ISOM 2500) AND (IEDA 3330 OR FINA 3203)

  Description

  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.
• IEDA 4900

  Independent Study in Industrial Engineering and Decision Analytics

  3 Credit(s)
  Description

  Undertaken by students under the supervision of a faculty member. Course requirements include readings on the relevant topic and a research or survey project specifically defined for the research option. For students of the Department of Industrial Engineering and Decision Analytics only. Instructor's approval is required for enrollment in the course.
• IEDA 4901

  Final Year Thesis

  6 Credit(s)
  Description

  Students who opt for the research option must register for this course instead of the final year project. The course requires a research project under the supervision of an instructor, and the results must be reported in the form of a thesis or a research paper. For students of the Department of Industrial Engineering and Decision Analytics only. Instructor's approval is required for enrollment in the course.
• IEDA 4920

  Decision Analytics Final Year Project

  6 Credit(s)
  Exclusion(s)

  IEDA 4901, IEDA 4960

  Description

  This is a one-year course undertaken by students in their final year. The course will require students to engage in a company sponsored project that provides practical experience to the students on topics they have learnt in their major courses. The project requires regular involvement by a specific professional in the sponsoring company, and will be supervised by a faculty in the department. Depending on the complexity of the project and the capacity of the sponsoring company, the project may be undertaken by either a single student, or a group with a maximum of four students. For projects with multiple members in a group, the role and deliverables of each student must be clearly laid out at the outset. For DA students in their fourth year of study only. May be graded PP.
• IEDA 4930

  Logistics Management and Engineering Project

  6 Credit(s)
  Description

  A one year long final year project related to logistics engineering and management. Supervised by a faculty member. A project proposal and a final report are required. Credit load will be spread over the year.
• IEDA 4950

  Industrial Engineering Special Project

  1-4 Credit(s)
  Description

  A special project supervised by a faculty member. A project proposal and a final report are required. May be repeated for credit if the projects cover different topics.
• IEDA 4960

  Industrial Engineering and Engineering Management Final Year Project

  6 Credit(s)
  Exclusion(s)

  IEDA 4901, IEDA 4920, IEDA 4930, IEDA 4990

  Description

  A one year long final year project related to industrial engineering and engineering management. Supervised by a faculty member. A project proposal and a final report are required. Credit load will be spread over the year. For IEEM students in their fourth year of study only. May be graded PP.
• IEDA 4990

  Industrial Engineering Design Project

  6 Credit(s)
  Description

  A one year long final year project related to industrial engineering and engineering management. Supervised by a faculty member. A project proposal and a final report are required. Credit load will be spread over the year.