Undergraduate Courses 2017-18

A compulsory one-year course for IELM 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.

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

A practical training course in an industrial simulated environment. For students of the Industrial Engineering and Logistics Management Department only. Graded P or F.

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 logistics management profession. Students must also complete the USTSIEB Safety Training module. For IEEM and IELM 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.

This course provides an introduction to industrial engineering (IE). It comprises of two parts. The first part introduces basic IE analytical tools, such as optimization, game theory, probability and statistics, stochastic processes and simulation, at a conceptual level. In the second part, many of the IE practical concepts, including production and operations management, logistics and supply chain management, financial engineering, are introduced.

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

This course will introduce students to the practical skills needed to translate engineering product ideas into three dimensional functional prototype models. Topics covered include understanding the framework for developing prototype models, selecting appropriate scale, working to time frames, and use of various architectural model-building materials. The course will be hands-on with lectures to teach the basic principles of model making. Instructor's approval is required for enrollment in the course.

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.

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.

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.

This is a systematic introduction to basic probability theory and statistics for engineering, including data collection and analysis, sample space and sampling methods, calculus of probability, conditional probability, expectation, moments, discrete and continuous probability distributions, point and interval estimation, hypothesis testing and linear regression analysis.

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.

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.

Introduction to deterministic optimization modeling and algorithms. Topics include linear programming, dynamic programming, network flows, and some nonlinear models. Application softwares.

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.

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.

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.

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

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

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).

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.

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

Supply management, purchasing, store management, distribution of goods and services, pricing; productivity in services, training and services logistics.

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.

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 IELM and LMGBM students under the four-year degree only.

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

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.

For Engineering students only. This is a series of seminars presented by professionals from social and engineering sectors and faculty members on topics including the introduction to local industry, responsibility and accountability of a profession in engineering, professional ethics, the impact of information technology revolution on society and engineering, legal aspects of engineering, business fundamentals, project management, product engineering and quality assurance, and environmental and occupational risk management. Graded P or F.

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.

This course covers different aspects of product development management. Topics include innovation management, techniques for idea generation, CAD, product lifecycle management, rapid prototyping and organizing and managing the development teams. Also projects and business plans will be carried out in the course.

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.

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

This course introduces important aspects of modern day "product" design integrating technology, art, business and innovation. The course will focus on how to elicit needs of customers, structure problems, create ideas, evaluate alternatives and then construct aesthetically pleasing and functional prototypes. Emphasis will be on creating concepts and taking them through uncertainties to achieve realistic and working prototypes. Instructor's approval is required for enrollment in the course.

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.

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.

Introduction to cognitive engineering and human performance. Perception, psychophysics, attention, time-sharing, workload and their implications on human performance.

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 Logistics Management only. Instructor's approval is required for enrollment in the course.

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 Logistics Management only. Instructor's approval is required for enrollment in the course.

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.

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.

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.