Undergraduate Courses 2025-26
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 1010Academic and Professional Development I0 Credit(s)DescriptionA 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Describe the key concepts and principles of industrial engineering, decision analytics and engineering management.
- 2.Formulate and solve simple industrial engineering, decision analytics and engineering management problems.
- 3.Communicate effectively, both orally and in writing.
- 4.Apply basic quantitative methods and appropriate software tools to an examination of issues in industrial engineering, decision analytics and engineering management.
- IEDA 1020Academic and Professional Development II0 Credit(s)DescriptionA compulsory one-year course for IEDA students, which is a continuation of IEDA 1010. Graded P or F.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Describe the key concepts and principles of industrial engineering, decision analytics and engineering management.
- 2.Formulate and solve simple industrial engineering, decision analytics and engineering management problems.
- 3.Communicate effectively, both orally and in writing.
- 4.Apply basic quantitative methods and appropriate software tools to an examination of issues in industrial engineering, decision analytics and engineering management.
- IEDA 1250Optimizing Decisions for Personal and Business Development3 Credit(s)Previous Course Code(s)CORE 1251Exclusion(s)CIVL 2170, IEDA 3010, ISOM 3710DescriptionThis 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?Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Recognize real-life personal and business problems and formulate them into analytical problems
- 2.Explain different optimization tools and their applications
- 3.Explain the tradeoffs in different optimization problems and use the qualitative insights for decisions
- 4.Describe the human behavioral impact in decision making and develop strategies to manage such impact
- IEDA 1901Industrial Training and Experience0 Credit(s)DescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Apply theories in hands-on exercises and appreciate the practice in industrial engineering
- 2.Develop technical knowledge in the use of equipment, materials and various work methods in industrial engineering practices
- 3.Develop practical skill, such as IT skills, to analyze and manage information efficiently for a business operation
- 4.Understand occupational health and safety related to working in industry
- IEDA 1990Industrial Training0 Credit(s)Exclusion(s)IEDA 1991DescriptionA practical training course in an industrial simulated environment. For students of the Industrial Engineering and Decision Analytics Department only. Graded P, PP or F.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Apply theories in hands-on exercises and appreciate the practice in industrial engineering.
- 2.Develop technical knowledge in the use of equipment, materials and various work methods in industrial engineering practices.
- 3.Develop practical skill, such as IT skills, to analyze and manage information efficiently for a business operation.
- 4.Understand occupational health and safety related to working in industry.
- IEDA 1991Industrial Experience0 Credit(s)Exclusion(s)IEDA 1990DescriptionFull-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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Apply theories in hands-on exercises and appreciate the practice in industrial engineering and logistics management
- 2.Develop technical knowledge in the use of equipment, materials and various work methods in industrial engineering and logistics management practices
- 3.Develop practical skill, such as IT skills, to analyze and manage information efficiently for a business operation
- 4.Understand occupational health and safety related to working in industry
- IEDA 2010Introduction of Industrial Engineering and Decision Analytics3 Credit(s)Exclusion(s)IEDA 2200DescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Identify the main components of modern logistics systems and why/how they developed to their current state
- 2.Model and solve operations decisions problems
- 3.Model and solve decentralized decision problems including basic game theory
- IEDA 2100Computing in Industrial Applications3 Credit(s)DescriptionIntroduction to microprocessor technologies and computer hardware with industrial applications. Computer systems for industrial control. Digital communication, mobile computing and RFID technology.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Acquire and practice the ability to design, construct, analyze and critique a simple control system with sensor and actuators.
- 2.Acquire and practice the ability to identify, compare and contrast the basic architecture of different computers.
- 3.Acquire and practice the ability to program a Programmable Logic Controller to perform some automated tasks.
- 4.Practice the ability to solve automation technology problems through self-learning.
- IEDA 2200Engineering Management3 Credit(s)Exclusion(s)IEDA 2010DescriptionTechniques 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Model and analyze project management as a scientific decision problem; apply network analysis techniques for designing project management schedules
- 2.Understand the flow of materials and information, as well as the role of various resources used for production activities of forms
- 3.Model and analyze operations of production systems, including the use of statistical tools for forecasting, and use analytical tools for planning the schedule of activities
- 4.Understand the basic principles and importance of quality control and methods for quality improvements using basic statistical tools
- 5.Understand the basics of accounting and finance in the operations of firms, focusing on depreciation of resources and inventory accounting, measurement of productivity etc
- 6.Understand and apply human behavioral models to management problems, including motivation, teamwork and leadership
- IEDA 2410Introduction to Modern Logistics3 Credit(s)DescriptionIntroduction to the operations of common transportation modes, including road, water, and air transportation, as well as their roles in logistics. Learning the organization of modern logistics such as third-party and fourth-party logistics, international logistics, logistics coordination, and supply chain management. Discussion of characteristics, issues, and practices of the above topics.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Name the key players and organizations in the freight logistics industry.
- 2.Explain the roles of these players and their interrelationships.
- 3.Describe the major operating structures and the associated costs of different freight transportation modes.
- 4.Identify the key challenges and opportunities in the logistics industry.
- 5.Communicate your ideas effectively through discussions, presentations and written documents.
- IEDA 2520Probability for Engineers3 Credit(s)Prerequisite(s)MATH 1014 OR MATH 1020 OR MATH 1024Exclusion(s)ELEC 2600, ELEC 2600H (prior to 2022-23), MATH 2421, MATH 2431DescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Understand basic concepts of probability.
- 2.Analyze randomness by using probability theory.
- 3.Be familiar with a range of widely used special random variables.
- 4.Calculate the probability of random events and expectation in practice.
- IEDA 2540Statistics for Engineers3 Credit(s)Prerequisite(s)IEDA 2520Exclusion(s)MATH 2411, ISOM 2500, LIFS 3150DescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Explain basic ideas and elements of modern statistics, including estimation theory, hypothesis testing, regression models.
- 2.Implement statistical models in Python.
- 3.Conduct empirical analysis of real-world data.
- 4.Plan for more advanced statistical courses, such as machine learning, data mining and Bayesian statistics.
- 5.Build statistical solutions based on quantitative data analysis.
- IEDA 3010Prescriptive Analytics3 Credit(s)Corequisite(s)MATH 2111Exclusion(s)CIVL 2170, ISOM 3710Cross-Campus Equivalent CourseSMMG 3030DescriptionIntroduction 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Identify real-world objectives and constraints based on the descriptions of actual decision-making problems.
- 2.Create mathematical optimization models.
- 3.Work through solution techniques.
- 4.Derive solutions using software.
- 5.Make recommendations based on solutions, analysis, and limitations of models.
- IEDA 3130Ergonomics and Safety Management3 Credit(s)Prerequisite(s)IEDA 2520 AND IEDA 2540DescriptionIntroduction 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Identify workplace and work process in which further optimization can be performed using knowledge about the users and the task(s).
- 2.Know what ergonomic data are available; where to find them; and how to use them to improve safety.
- 3.Use knowledge and research technique in ergonomics to help industry to response to the Noise at Work regulation, Manual Handling Operation regulation, and Display Screen regulation in HKSAR.
- IEDA 3180Data-Driven Portfolio Optimization3 Credit(s)Alternate code(s)ELEC 3180Prerequisite(s)(IEDA 2520 OR IEDA 2540 OR ELEC 2600) AND (MATH 2111 OR MATH 2121 OR MATH 2131 OR MATH 2350)DescriptionModern 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Acquire basic knowledge on optimization problems.
- 2.Solve optimization problems in practice with Python.
- 3.Learn about financial data and modeling techniques.
- 4.Learn about portfolio design in financial systems.
- 5.Employ the theoretical and practical knowledge on optimization for portfolio design.
- 6.Learn more sophisticated portfolio optimization formulations and solve them.
- IEDA 3230Engineering Economics and Accounting3 Credit(s)Exclusion(s)FINA 2303DescriptionApplication 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Understand basic cost concepts, terminology, and methods to estimate and optimize cost of design projects.
- 2.Understand time value of money and apply this knowledge through various methods to evaluate alternative projects.
- 3.Apply the techniques to account for depreciation, tax, and inflation in the evaluation of engineering projects.
- 4.Apply the techniques to deal with uncertainty in decision making.
- IEDA 3250Stochastic Models3 Credit(s)Prerequisite(s)(IEDA 2520 AND IEDA 2540) OR (MATH 2411 AND MATH 2421)DescriptionPoisson process, Markov process, and Markov decision processes; inventory theory, reliability, queuing theory. Application softwares.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Reinforce knowledge of basic probability and mathematics.
- 2.Explain basic Markov processes and their applications (reliability, inventory management, and queueing).
- 3.Demonstrate the abilities in analytical thinking.
- IEDA 3270Data-Driven Quality Technology3 Credit(s)Prerequisite(s)(IEDA 2520 AND IEDA 2540) OR MATH 2411Exclusion(s)ISOM 3730Mode of Delivery[BLD] Blended learningDescriptionControl charts and statistical on-line quality control methods, off-line quality control and parameter design, modern quality philosophy and Taguchi method.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Explain fundamental concepts of statistical, machine-learning, and AI-driven quality analytics.
- 2.Apply DMAIC, SPC, and ML/AI techniques to monitor and diagnose process performance.
- 3.Develop and validate predictive or prescriptive models—including DOE and AI-based optimization—to enhance product and service quality.
- 4.Communicate ethically grounded, data-driven recommendations that support continuous improvement initiatives.
- IEDA 3300Industrial Data Systems3 Credit(s)Prerequisite(s)COMP 1021 OR COMP 1022PExclusion(s)COMP 3311, ISOM 3260DescriptionFundamental 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).Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Learn the basic Python programming grammar.
- 2.Use Pandas and NumPy to do data analysis and visualization.
- 3.Learn the principle of relational database, e.g., Entity-Relationship models, functional dependencies and normalization, relational algebra, etc.
- 4.Write and use SQL to manipulate database.
- IEDA 3302E-Commerce Technology and Applications3 Credit(s)Prerequisite(s)COMP 1021 OR COMP 1022PExclusion(s)IEDA 3560DescriptionA 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Understand the different e-commerce business models.
- 2.Understand the functioning and implementation of internet security.
- 3.Understand the basics of web crawling for data collection.
- 4.Be able to use simple techniques for visualization.
- 5.Understand the basics of regression and its use in Ecommerce and analysis.
- 6.Understand fundamental analytics tools for Ecommerce firms, including clustering, classification.
- IEDA 3330Introduction to Financial Engineering3 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 2431Exclusion(s)FINA 3203, FINA 4303DescriptionFor 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Understand the basic knowledge of financial system (structure of financial markets, financial institutions, and financial instruments).
- 2.Understand the basic asset pricing theory, including fix income pricing, mean-variance analysis, capital asset pricing model, factor models, etc.
- 3.Understand the basic option theory, including the definitions, markets, and its pricing.
- 4.Understand the basic FinTech theory, including robot-advisor, machine learning pricing, cryptocurrencies, green finance, etc.
- IEDA 3410Routing and Fleet Management3 Credit(s)Prerequisite(s)IEDA 3010DescriptionApplications and algorithms for network optimization, vehicle routing, shortest path problems, maximum flow problems, matching models and dynamic vehicle allocation.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Formulate and solve shortest path problems.
- 2.Formulate and solve TSP/VRP problems.
- 3.Formulate and solve max flow problems.
- 4.Formulate and solve min-cost network flow problems.
- 5.Formulate and solve spanning tree problems.
- 6.Apply the above skills to solve real-world complex logistics problems.
- IEDA 3460Demand and Supply Analytics3 Credit(s)Prerequisite(s)IEDA 2520DescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Understand how to make supply chain design and policy decisions to develop the supply chain capabilities required to support the business strategy and improve the performance of a firm and of an entire supply chain.
- 2.Learn how to examine and improve the flow of materials and information through a network of suppliers, manufacturers, distributors, and retailers in order to help firms get the right product to the right customer in the right amount and at the right time.
- 3.Learn how to make decisions on the following fundamental supply chain performance drivers: facilities, inventories, transportation, information, sourcing and pricing.
- 4.Special emphasis is given to gaining an understanding of how supply chain decisions have to account for coordination requirements within and across firms, the impact of uncertainty, and the specific product and customer characteristics that derive from the overall business strategy.
- IEDA 3560Predictive Analytics3 Credit(s)Prerequisite(s)IEDA 2540Exclusion(s)COMP 4211, IEDA 3302DescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Master a range of tools to analyze data.
- 2.Think critically about data.
- 3.Identify opportunities for creating value using predictive analytics.
- 4.Apply these tools on real data in real applications.
- 5.Implement your methods using Python.
- IEDA 3901Transportation Systems3 Credit(s)Corequisite(s)IEDA 2410Exclusion(s)CIVL 3610DescriptionIntroduction 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Understand basic concepts in the classification, structure and evaluation of transportation systems
- 2.Demonstrate fundamental knowledge in traffic flow theory
- 3.Outline the four-step model for transportation planning: trip generation, trip distribution, mode choice and route assignment
- 4.Understand planning process for transportation infrastructure, with special emphasis on logistics infrastructure
- 5.Apply the principles of transportation economic theory
- 6.Associate logistics operations with transportation systems concepts
- 7.Apply transportation economics and demand modeling techniques to analyze freight transportation and logistics systems
- IEDA 4000Special Topics1-3 Credit(s)DescriptionSelected topics in Industrial Engineering, Logistics Management or Decision Analytics. May be repeated for credit, if different topics are covered.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Equip with broad and useful knowledge to various topics which are not covered by existing courses
- 2.(Each offering under the umbrella will have specific learning outcomes)
- IEDA 4100Integrated Production Systems3 Credit(s)Prerequisite(s)IEDA 2520 AND IEDA 3010Exclusion(s)ISOM 2700DescriptionBasic 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Understand the basic concepts and principles in production systems and operations management.
- 2.Develop quantitative models to generate demand forecast for production firms.
- 3.Formulate and analyze inventory problems for manufacturing firms.
- 4.Use aggregate planning techniques to provide guidelines for production firms.
- 5.Recognize the driving force of MRP and JIT manufacturing systems; understand the pros and cons of each system.
- IEDA 4130System Simulation3 Credit(s)Prerequisite(s)IEDA 2520Exclusion(s)ISOM 4720DescriptionBasic 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Determine an appropriate simulation techniques to address the relevant scientific questions.
- 2.Implement the chosen simulation techniques using good programming practice.
- 3.Evaluate the efficiency of the implementation and apply certain instruments to achieve improvement.
- IEDA 4180Service Engineering and Management3 Credit(s)Prerequisite(s)IEDA 2520Cross-Campus Equivalent CourseSMMG 4620DescriptionService 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Identify the fundamental models in many service industries.
- 2.Analyze the models with various tools, contrast and critique solution that have been used in practice.
- 3.Provide (feasible) suggestions and solutions to improve the existing services.
- 4.Work effectively in a team and lead a team.
- 5.Communicate with the industry and know how things are done in real word and how to connect practice with knowledge learned in class.
- 6.Read non-text literature critically and present the key findings and approach used by the author.
- IEDA 4200Design of Logistics and Manufacturing Systems3 Credit(s)Prerequisite(s)IEDA 3010Cross-Campus Equivalent CourseSMMG 4630DescriptionFacility location, process and material flow analysis, space allocation and plant layout, computerized layout planning, material handling equipment, material handling system design.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Explain the basic principles of facility planning from a supply chain view point.
- 2.Construct facility location models.
- 3.Design product, process, and production schedules.
- 4.Conduct flow and activity relationship analysis.
- 5.Determine space requirement and plant layout.
- 6.Identify and apply different algorithms used in layout design.
- IEDA 4331Quantitative Methods in Financial Engineering3 Credit(s)Prerequisite(s)(FINA 3203 OR IEDA 3330) AND (IEDA 3250 OR ISOM 2500)DescriptionThe 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Understand the discrete-time quantitative models in the financial markets.
- 2.Understand the continuous-time quantitative derivatives pricing models.
- 3.Acquire basic computation skills for solving quantitative models.
- 4.Acquire basic skills that are required for most entry-level quantitative financial positions.
- IEDA 4410Data Driven Supply Chain Management3 Credit(s)Prerequisite(s)IEDA 4100Exclusion(s)EEMT 5300, ISOM 3770Cross-Campus Equivalent CourseSMMG 4640DescriptionAn 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Understand the fundamental concepts and principles of supply chain management.
- 2.Master the basic theories and models in supply chain management.
- 3.Evaluate and analyze the practical problems in various industries.
- 4.Leverage data analytics to extract insights and propose better solutions for supply chains.
- IEDA 4420Dynamic Pricing and Revenue Optimization3 Credit(s)Prerequisite(s)IEDA 3010 AND IEDA 3250DescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Understand the basic concepts and principles in dynamic pricing and revenue management.
- 2.Understand different pricing strategies in revenue management practices.
- 3.Identity opportunities for revenue optimization of firms from various industries.
- 4.Apply qualitative frameworks and quantitative models to a firm’s revenue optimization problem.
- 5.Apply qualitative frameworks and quantitative models to a firm’s revenue optimization problem.
- IEDA 4500Engineering Foundations of FinTech3 Credit(s)Prerequisite(s)IEDA 3330 OR FINA 3203DescriptionFinTech, 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Understand the principles of P2P lending, crowdfunding, and microloans
- 2.Understand the mechanism design for P2P lending platforms
- 3.Understand the principles of wealth management technologies such as robo-advisors
- 4.Understand the theories of blockchain technologies
- 5.Apply finance knowledge to online wealth management
- 6.Analyze and manage risks in P2P lending, crowdfunding, and microloans
- IEDA 4510Systems Risk Management3 Credit(s)Prerequisite(s)IEDA 3010 AND IEDA 3250DescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Understand the basic concepts and principles in risk management.
- 2.Understand the functions of financial institutions such as banks, mutual funds, ETFs, and hedge funds.
- 3.Develop quantitative models for trading in financial markets.
- 4.Understand the role of interest rates in risk management.
- 5.Use valuation and scenario analysis to quantify risks, and apply value at risk and expected shortfall to measure risks.
- IEDA 4520Numerical Methods for Financial Engineering3 Credit(s)Prerequisite(s)(IEDA 3250 OR ISOM 2500) AND (IEDA 3330 OR FINA 3203)DescriptionThe 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Understand the theory of the lattice methods used in financial engineering
- 2.Understand the principles of the Monte Carlo simulation method and various variance reduction techniques
- 3.Know how to apply the Monte Carlo simulation method to derivatives pricing and sensitivity estimation
- 4.Know how the finite difference methods are applied in derivatives pricing
- 5.Analyze financial engineering problems from the computational perspective
- 6.Develop practical skills of solving financial engineering problems through various numerical methods
- IEDA 4900Independent Study in Industrial Engineering and Decision Analytics3 Credit(s)DescriptionUndertaken 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Perform a literature search on a specific topic, including reading and understanding research papers
- 2.Define a research topic and to identify aspects of the problem that are new
- 3.Write a project proposal
- 4.Conduct independent research including formulation of hypothesis and its experimental or theoretical verification
- IEDA 4901Final Year Thesis6 Credit(s)DescriptionStudents 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Define an engineering problem and articulate its significance to a business/society.
- 2.Prepare a clear project proposal identifying the problem statement, the key issue, potential methods or approaches that can solve the issues, and mechanisms to integrate the solutions of these components in an optimal way.
- 3.Model the problem to facilitate the analysis.
- 4.Run experiments or collect data to estimate accurate parameters for the problem models.
- 5.Solve models using IE/OR tools, and to analyse the results.
- 6.Product physical or abstract prototype model solutions to problems.
- 7.Work in a cross functional team.
- 8.Communicate effectively with various parties including with team members, supervisors and possibly industry partners.
- 9.Report the findings of the project.
- IEDA 4920Decision Analytics Final Year Project6 Credit(s)Exclusion(s)IEDA 4901, IEDA 4960DescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Define an engineering problem and articulate its significance to a business/society.
- 2.Prepare a clear project proposal identifying the problem statement, the key issue, potential methods or approaches that can solve the issues, and mechanisms to integrate the solutions of these components in an optimal way.
- 3.Model the problem to facilitate the analysis.
- 4.Run experiments or collect data to estimate accurate parameters for the problem models.
- 5.Solve models using IE/OR tools, and to analyse the results.
- 6.Product physical or abstract prototype model solutions to problems.
- 7.Work in a cross functional team.
- 8.Communicate effectively with various parties including with team members, supervisors and possibly industry partners.
- 9.Report the findings of the project.
- IEDA 4950Industrial Engineering Special Project1-4 Credit(s)DescriptionA 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Identify the goal of the project and plan the workflow.
- 2.Practice the ability to solve problems through self-learning.
- IEDA 4960Industrial Engineering and Engineering Management Final Year Project6 Credit(s)Exclusion(s)IEDA 4901, IEDA 4920, IEDA 4930 (prior to 2021-22), IEDA 4990DescriptionA 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Define an engineering problem and articulate its significance to a business/society.
- 2.Prepare a clear project proposal identifying the problem statement, the key issue, potential methods or approaches that can solve the issues, and mechanisms to integrate the solutions of these components in an optimal way.
- 3.Model the problem to facilitate the analysis.
- 4.Run experiments or collect data to estimate accurate parameters for the problem models.
- 5.Solve models using IE/OR tools, and to analyse the results.
- 6.Product physical or abstract prototype model solutions to problems.
- 7.Work in a cross functional team.
- 8.Communicate effectively with various parties including with team members, supervisors and possibly industry partners.
- 9.Report the findings of the project.
- IEDA 4990Industrial Engineering Design Project6 Credit(s)DescriptionA 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Identify the goal of the project and plan the workflow.
- 2.Practice the ability to solve problems through self-learning.
- 3.Execute a complete project from problem formulation, time management, design and implementation.