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This course gives an overview of industrial engineering methodologies with particular reference to classical industrial engineering and ergonomics. The subject areas covered include: work methods and measurement, engineering economics, plant layout and material handling and industrial ergonomics. Due emphasis will be given to the application of the methodologies in an industrial environment.

CREDIT HOURS: 3 
PREREQUISITES: This course is not intended for graduates of an Industrial Engineering undergraduate programme.
RESTRICTIONS: Restricted to Industrial Engineering students. Students in other programs must contact the instructor for permission to register.

Consideration is given to human's anatomical, physiological and psychological capabilities and limitations for systematic analysis, identification and evaluation of human-machine-environment systems to design consumer products, equipment, tools, and the workstation. Due emphasis will be given to the application of ergonomics principles and data at the human-machine interface in industrial and other occupational settings.

CREDIT HOURS: 3

This class covers advanced topics in facilities planning and design. Models for the planning and design of production and distribution facilities will be presented in the following areas: plant and distribution centre location, layout, and material handling systems design.

CREDIT HOURS: 3 

This class covers advanced topics in Logistics and Supply Chain Management. Models for designing, planning, and operating supply chain logistic networks will be presented. Topics covered include supply chain network design, planning and managing inventories, transportation planning, and the role of information technology.

CREDIT HOURS: 3

This course is a graduate level introduction to the fundamental ideas of operations research. The course focuses on mathematical modelling in deterministic and non-deterministic settings. The course covers topics in the theory and application of mathematical optimization, network analysis, decision theory, inventory theory, and stochastic processes including queuing processes. The course requires background in probability theory and linear algebra as well as some skill in computer programming.

CREDIT HOURS: 3 
PREREQUISITES: This course is not intended for graduates of an Industrial Engineering undergraduate programme.

This course is an introduction to the fundamentals of stochastic processes. Emphasis is placed on the analysis of the probability structure of stochastic models. Topics discussed include renewal processes, counting processes, Markov chains, Markov decision processes, birth and death processes. Stationary processes and their spectral analysis may also be discussed. Applications of stochastic processes in operations research, quality and reliability engineering are presented.

CREDIT HOURS: 3

Computer simulation of industrial systems, the design of discrete simulation models, and the generation of random variables are all covered by this course. Also included is the design of simulation languages. Applications of simulation models in decision making situations arising in production, distribution and economic systems are studied.

CREDIT HOURS: 3

We will study the foundations of decision and risk theory and construct a correct theory and practical methodology - Preference Function Modelling (PFM) - for decision making including group decision making.

CREDIT HOURS: 3

The following topics comprise this course: linear programming, decomposition methods, integer programs, Gomory's algorithms, implicit enumeration, branch and bound, sequencing problem. Graphs and algorithms: Extensions of shortest path problems, their algebra. General flow problems including flows with gain and loss and multicommodity flow. Eulerian paths and Hamiltonian cycles. The Chinese Postman problem. Covering problems.

CREDIT HOURS: 3
PREREQUISITES: IENG 4304.03 or equivalent.

Key issues in engineering design are the optimization of the design parameters and optimization of overall system performance. The objective of this course is to expose the student to modern techniques in finite dimensional optimization. Topics in unconstrained optimization will include steepest descent, conjugate gradient and quasi-Newton methods. In the field of constrained optimization, topics will include Kuhn-Tucker theory and algorithmic methods such as reduced gradients, gradient projection, penalty and barrier methods. The use of constructive dual methods may also be included. Throughout the course, students will be encouraged to apply the theory to engineering decision problems.

CREDIT HOURS: 3

The course will explore the mathematical models in distribution management, and the relationship between theoretical advances and useful applications. The following topics will be covered: location problems, vehicle routing and scheduling with multiple constraints, dynamic routing & scheduling, implementation strategies. Students will be required to undertake a project in solving a distribution management problem.

CREDIT HOURS: 3

This class deals with graduate level topics in design, modelling and optimization of reliability and maintainability, and design of maintenance systems. Topics may include; general repair models with partial repari and imperfect maintenance, CBM methods, and the use of mathematical models int he development of a mintenance information system.

CREDIT HOURS: 3
PREREQUISITES: .03, .03, and one of .03, IENG 4548.03, .032 or equivalent or instructor permission

This course is offered to students enrolled in a Masters program in Industrial Engineering who wish to gain knowledge in a specific area for which no appropriate graduate level courses are offered. Each student taking this course will be assigned a suitable course advisor. The student will be required to present the work of one term (not less than 90 hours in the form of directed research, and individual study) in an organized publication format and may, at the discretion of the advisor, be required to take a formal examination.

CREDIT HOURS: 3

This course is offered to students enrolled in a PhD program in Industrial Engineering who wish to gain knowledge in a specific area for which no appropriate graduate level courses are offered. Each student taking this course will be assigned a suitable course advisor. The student will be required to present the work of one term (not less than 90 hours in the form of directed research, and individual study) in an organized publication format and may, at the discretion of the advisor, be required to take a formal examination.

CREDIT HOURS: 3

The course aims to provide advanced insights in the principles underlying safety and risk, from an engineering perspective, with primary attention to risk and safety in complex socio-technical systems. As a graduate course, the focus is on understanding, analysis, and critical evaluation of state-of-the-art concepts, theories, and methods in safety and risk research. After a broad common foundation is established for all course participants, the remainder of the course is implemented through a co-design approach. The common foundation focuses on i) basic concepts in the discipline (including safety-I vs safety-II, resilience, reliability, risk, etc.), ii) modern accident theories and analysis methods in complex socio-technical systems (including STAMP, FRAM, etc.), and iii) concepts and approaches for validation in different scientific disciplines relevant for engineering safety and risk research. The co-design approach has the objective to enable students to create specific learning objectives aligned with their research for their graduate thesis, in consultation with the course instructor, and maximally in collaboration with other students participating in the course.

CREDIT HOURS: 3

This course will focus on current research of healthcare systems. This course will illustrate how industrial engineering techniques can be applied to healthcare systems. Topics to be discussed include capacity planning, quality, decision analysis, scheduling, optimization models, and waiting line models.

CREDIT HOURS: 3

The research methods course is designed for graduate students in the early stage of their master鈥檚 or doctoral research. It introduces the requirements for graduate studies, engineering science and the research process. To this effect, the course pays attention to library services and the literature search process, making a research plan, academic writing, publishing in peer-reviewed journals, aspects of student well-being, and successfully pursuing an academic career. This course is designed to offer graduate students a roadmap through graduate studies.

CREDIT HOURS: 0

This course gives a basic exposition of the goals and methods of data mining and data warehouses, including concepts, principles, architectures, algorithms, implementations, and applications. The main topics include an overview of databases, data warehouses and data mining technology, data warehousing and on line analytical process (OLAP), concept mining, association mining, classification and predication, and clustering. Software tools for data mining and data warehousing and their design will also be introduced.

CREDIT HOURS: 3

The concept of stochastic search algorithms is introduced by way of answers to the generic machine learning requirements: representation, goal state, and credit assignment. Schema theory is introduced as an underlying model for evolutionary problem solving. The significance of assuming different representations is investigated through various case studies. Different forms of 'goal state' are investigated, including multi-objective models and co-evolution are investigated in some detail and demonstrated to provide the basis for problem decomposition, game behavior design and computational efficiency.

CREDIT HOURS: 3

The course deals with preliminaries of artificial neural systems including fundamental concepts and models. Single layer perception classifiers and multi-layer feed forward networks, single-layer feedback networks, and associative memories are covered.

CREDIT HOURS: 3

This course will emphasize practical rather than theoretical considerations and will make extensive use of computer packages. The topics to be covered include: simple linear regression, analysis of residuals and remedial measures, transformation of data, multiple, polynomial and weighted regression, model selection techniques, joint confidence regions, use of indicator variables, analysis of covariance and an introduction to non-linear regression.

CREDIT HOURS: 3

This course introduces risk assessment and system reliability methodologies, from classical event trees to simulation. Examples of risk-based decision making analyses will be covered, ranging from oil exploration to environmental site remediation. The student will carry out a risk assessment involving design decisions on a project of their own choosing.

CREDIT HOURS: 3

This course is a self-contained introduction to manifold theory. Topics include: elements of surface theory, the tangent space, vector fields, differential forms and more general tensors, the Lie derivative, connections, Riemannian geometry, applications in mechanics and general relativity.

CREDIT HOURS: 3

CROSS-LISTING: 

The course introduces the history and theory of urban design as a distinct area of professional knowledge and skill within the spectrum of planning and design concerns and specialities.

CREDIT HOURS: 3

CROSS-LISTING: .03

RESTRICTIONS: Honours or graduate students in the Faculty of Architecture and Planning, or permission of instructor

This course is intended to make advanced Bayesian methods genuinely accessible to graduate students. The course covers all the fundamental concepts of Bayesian methods, and works from the simplest ideas (characterizations of probability; comparative inference; prior, posterior and predictive distributions) up through hierarchical modes applied to various data. Computational methods include MCMC for posterior simulation.

CREDIT HOURS: 3

LECTURE HOURS PER WEEK: 3
PREREQUISITES:  and 
CROSS-LISTING: 
EXCLUSIONS: 

The theory and application of stochastic processes. Topics to be discussed include the Poisson process, renewal theory, discrete and continuous time Markov processes, and Brownian motion. Applications will be taken from the biological and physical sciences, and queuing theory.

CREDIT HOURS: 3

LECTURE HOURS PER WEEK: 3
PREREQUISITES: .03 or instructor鈥檚 consent
CROSS-LISTING: .03

This course covers statistical methodology, major software and applications in data mining. A variety of supervised learning and unsupervised learning methods will be discussed. Topics include: Linea methods for regression and classification, prototype methods, decision trees, additive models, bagging and boosting, neural networks and support vector machines.

CREDIT HOURS: 3

FORMAT: Lecture 
PREREQUISITES: Permission of instructor