Industrial Engineering

Industrial Engineering (AEINDBASC)

Undergraduate Program Administrator and Academic Advisor, Year 2
Gayle Lesmond
Room MC109, Mechanical Engineering Building
416-978-4731
undergrad@mie.utoronto.ca

Undergraduate Student Advisor, Years 3-4
Yanna Sventzouris
Room MC109, Mechanical Engineering Building
416-978-2454
undergrad@mie.utoronto.ca

Industrial Engineering (IE) is a discipline that applies engineering principles to the design and operation of organizations. Industrial Engineering students learn to analyze, design, implement, control, evaluate and improve the performance of complex organizations, taking into consideration people, technology and information systems. Industrial engineers use operations research, information engineering, artificial intelligence and human factors tools and methods to improve and optimize systems operations and performance.

Industrial engineers share the common goal of increasing an organization’s efficiency, profitability and safety in a variety of industries including health care, finance, retail, entertainment, government, information technology, transportation, energy, manufacturing and consulting. Unlike traditional disciplines in engineering and the mathematical sciences, IE addresses the role of the human decision-maker as a key contributor to the inherent complexity of systems and the primary benefactor of the analyses.

Industrial Engineering bears a close resemblance to management science, management engineering, operations research, operations management and systems engineering.

The objective of the Industrial Engineering program curriculum is to educate engineers who:

Employ effective analysis and design tools.
Integrate perspectives into a systems view of the organization.
Understand both the theory and the practice of Industrial Engineering.

In the first two years of the curriculum, the emphasis is placed on fundamental principles of engineering and core industrial engineering concepts. Tools taught in the second year include probability, psychology for engineers, fundamentals of object-oriented programming, operations research, differential equations, statistics, human-centered systems design and data modeling.

In third-year, students learn various perspectives on the operation of organizations, including productivity, information, ergonomics and economics. They also select technical electives allowing them to specialize in information engineering, artificial intelligence, operations research and human factors and investigate other IE areas such as business process engineering, design of information systems and data analytics. These same courses may be taken as fourth-year technical electives (schedule permitting). Therefore, students may use their fourth-year electives to pursue their specializations further in-depth or to investigate other IE areas.

In fourth-year, the central theme is the design and management of an organization as an integrated system. All students participate in a Capstone Design course that requires students to draw on knowledge from all years of the IE program to tackle a real-world project with an industry partner. There is also a research thesis option.

Job opportunities for IE graduates are diverse and offer challenging careers in a wide variety of industries, including consulting. Three prototypical jobs for new graduates include:

Manage an organizational supply chain to ensure new products can be successfully introduced into global sales channels.
Test the interaction features of a new software application.
Identify the increased capacity requirements necessary to accommodate the expected surgical volume of hospitals.

Minors

The Cross-Disciplinary Programs Office (CDP) offers a variety of minors and certificate programs that complement the Industrial Engineering curriculum. Students interested in pursuing an Engineering minor and/or certificate are encouraged to consult with the CDP.

Graduate Studies in Industrial Engineering

The Department offers graduate studies and research opportunities in a wide range of fields within Industrial Engineering. These include human factors engineering, information engineering, management science, manufacturing, operations research, systems design and optimization, reliability and maintainability engineering. Subject areas include queuing theory, cognitive engineering, human-computer interaction and human factors in medicine. The programs available lead to MEng, MASc and PhD degrees. Evening courses are offered to accommodate participants who work full-time and are interested in pursuing M.Eng degrees. Additional information can be obtained from the Mechanical & Industrial Engineering Graduate Studies Office and mie.utoronto.ca/graduate.

INDUSTRIAL ENGINEERING (AEINDBASC)

FIRST YEAR INDUSTRIAL ENGINEERING

Fall Session – Year 1		Lect.	Lab.	Tut.	Wgt.
Core Required Courses
APS100H1: Orientation to Engineering	F	1	-	1	0.25
APS110H1: Engineering Chemistry and Materials Science	F	3	1	1	0.50
APS111H1: Engineering Strategies & Practice I	F	3	1	1	0.50
CIV100H1: Mechanics	F	3	-	2	0.50
MAT186H1: Calculus I	F	3	-	1	0.50
MAT188H1: Linear Algebra	F	3	1	1	0.50

Winter Session – Year 1		Lect.	Lab.	Tut.	Wgt.
Core Required Courses
APS106H1: Fundamentals of Computer Programming	S	3	2	1	0.50
APS112H1: Engineering Strategies & Practice II	S	2	2	-	0.50
ECE110H1: Electrical Fundamentals	S	3	1	2	0.50
MAT187H1: Calculus II	S	3	-	1	0.50
MIE100H1: Dynamics	S	3	-	2	0.50
MIE191H1: Seminar Course: Introduction to Mechanical and Industrial Engineering	S	1	-	-	0.15

Approved Course Substitutions

Students are able to substitute MAT186H1 with the online calculus course APS162H1.
Students are able to substitute MAT187H1 with the online calculus course APS163H1.
Students are able to substitute APS110H1 with the online course APS164H1.
Students are able to substitute CIV100H1 with the online course APS160H1.

SECOND YEAR INDUSTRIAL ENGINEERING

Fall Session – Year 2		Lect.	Lab.	Tut.	Wgt.
MAT238H1: Differential Equations and Discrete Math	F	3	-	2	0.50
MIE236H1: Probability	F	3	-	2	0.50
MIE242H1: Foundations of Cognitive Psychology	F	3	3	-	0.50
MIE250H1: Fundamentals of Object Oriented Programming	F	2	3	-	0.50
MIE262H1: Deterministic Operations Research	F	3	2	1	0.50

Winter Session – Year 2		Lect.	Lab.	Tut.	Wgt.
MIE223H1: Data Science	S	3	2	-	0.50
MIE237H1: Statistics	S	3	1	2	0.50
MIE240H1: Human Factors Engineering	S	3	-	2	0.50
MIE245H1: Data Structures and Algorithms	S	3	1	1	0.50
MIE263H1: Stochastic Operations Research	S	3	-	2	0.50

THIRD YEAR INDUSTRIAL ENGINEERING

Fall Session – Year 3		Lect.	Lab.	Tut.	Wgt.
Core Required Courses
MIE353H1: Data Modelling	F	3	2	1	0.50
MIE358H1 (Engineering Economics, formerly MIE258H1)	F	3	-	1	0.50
MIE360H1: Systems Modelling and Simulation	F	3	2	1	0.50
MIE370H1: Introduction to Machine Learning	F	3	2	-	0.50
Technical Elective (Choose One):
APS360H1: Applied Fundamentals of Deep Learning	F	3	1	-	0.50
CSC384H1: Introduction to Artificial Intelligence	F	3	-	-	0.50
MIE344H1: Ergonomic Design of Information Systems	F	3	3	-	0.50
MIE354H1: Business Process Engineering	F	3	2	-	0.50
MIE365H1: Advanced Operations Research	F	3	2	1	0.50
MIE368H1	F	2	3	1	0.50
MIE434H1 (formerly MIE343H1)	F	3	3	-	0.50

Winter Session - Year 3		Lect.	Lab.	Tut.	Wgt.
Core Required Courses
MIE350H1: Design and Analysis of Information Systems	S	3	1	1	0.50
MIE359H1: Organization Design	S	4	-	-	0.50
MIE363H1: Operations and Supply Chain Management	S	3	-	2	0.50
Technical Elective (Choose One):
APS360H1: Applied Fundamentals of Deep Learning	S	3	1	-	0.50
MIE304H1: Introduction to Quality Control	S	3	1	2	0.50
MIE345H1: Case Studies in Human Factors and Ergonomics	S	3	-	2	0.50
MIE367H1: Cases in Operations Research	S	3	-	2	0.50
MIE369H1: Introduction to Artificial Intelligence	S	3	2	-	0.50
Complementary Studies Elective
CS Elective	S				0.50

Practical Experience Requirement - As described in the beginning pages of this chapter, students are required to have completed a total of 600 hours of acceptable practical experience before graduation (normally during their summer periods).
1. For students following the “old” curriculum: At least two of the four (0.5 credit) Complementary Studies Electives to be taken between third and fourth year must be Humanities/Social Sciences courses (see the Complementary Studies section at the beginning of this chapter). Students are responsible for ensuring that each elective taken is approved. Please consult the electives list available on the Engineering Office of the Registrar's website.
2. For students following the “new” curriculum: At least two of the three (0.5 credit) Complementary Studies Electives to be taken between third and fourth year must be Humanities/Social Sciences courses (see the Complementary Studies section at the beginning of this chapter). Students are responsible for ensuring that each elective taken is approved. Please consult the electives list available on the Engineering Office of the Registrar's website.

PROFESSIONAL EXPERIENCE YEAR

Students registered within this program, and all other undergraduate programs within the Faculty of Applied Science and Engineering, may elect to enroll and participate in the Professional Experience Year Co-Op Program (PEY Co-Op). The PEY Co-op program requires that qualified students undertake a paid, full-time 12-16 month continuous work period with a cooperating industry. Details are described in the beginning of this chapter. More information can be found in the PEY Co-op section of the calendar.

FOURTH YEAR INDUSTRIAL ENGINEERING

Note: The Industrial Engineering program is undergoing a major curriculum change that will take effect over multiple stages. The fourth year of the program as outlined below corresponds to the requirements of the old program. If you are following the new curriculum, please review the fourth year requirements here.

Fall Session – Year 4		Lect.	Lab.	Tut.	Wgt.
Core Required Courses:
MIE463H1: Integrated System Design	F	3	-	2	0.50
MIE490Y1: Capstone Design	Y	-	-	4	1.00
Technical Electives (Choose Two):
APS360H1: Applied Fundamentals of Deep Learning	F	3	1	-	0.50
APS502H1: Financial Engineering	F	-	-	3	0.50
CSC384H1: Introduction to Artificial Intelligence	F	3	-	-	0.50
MIE434H1 (formerly MIE343H1)	F	3	3	-	0.50
MIE344H1: Ergonomic Design of Information Systems	F	3	3	-	0.50
MIE354H1: Business Process Engineering	F	3	2	-	0.50
MIE365H1: Advanced Operations Research	F	3	2	1	0.50
MIE368H1: Analytics in Action	F	2	3	1	0.50
MIE435H1: *Early-stage design methods	F	2	2	1	0.50
MIE440H1: * Design of Effective Products	F	2	2	1	0.50
MIE451H1: Decision Support Systems	F	3	1	1	0.50
MIE498H1: Research Thesis	F	-	-	4	0.50
MIE498Y1: Research Thesis	Y	-	-	4	1.00
MIE523H1: Engineering Psychology and Human Performance	F	3	3	-	0.50
MIE524H1: Data Mining	F	3	2	-	0.50
MIE562H1: Scheduling	F	3	-	2	0.50
MIE566H1: Decision Making Under Uncertainty	F	3	-	2	0.50
Complementary Studies Elective
CS Elective	F/Y				0.50

Winter Session - Year 4		Lect.	Lab.	Tut.	Wgt.
Core Required Courses:
MIE359H1 (Organization Design, formerly MIE459H1)	S	4	-	-	0.50
MIE490Y1: Capstone Design	Y	-	-	4	1.00
Technical Electives (Choose Two):
APS360H1: Applied Fundamentals of Deep Learning	S	3	1	-	0.50
APS502H1: Financial Engineering	S	-	-	3	0.50
BME466H1: Drug Delivery at Biological Barriers and Interfaces	S	3	-	1	0.50
BME488H1: Introduction to Immunoengineering	S	2	-	1	0.50
MIE345H1: Case Studies in Human Factors and Ergonomics	S	3	-	2	0.50
MIE367H1: Cases in Operations Research	S	3	-	2	0.50
MIE369H1: Introduction to Artificial Intelligence	S	3	2	-	0.50
MIE424H1: Optimization in Machine Learning	S	3	1	-	0.50
MIE457H1: Knowledge Modelling and Management	S	3	1	1	0.50
MIE469H1: Reliability and Maintainability Engineering	S	3	-	2	0.50
MIE498H1: Research Thesis	S	-	-	4	0.50
MIE498Y1: Research Thesis	Y	-	-	4	1.00
MIE519H1: * Advanced Manufacturing Technologies	S	3	-	-	0.50
MIE535H1: Electrification Via Electricity Markets	S	3	1	1	0.50
MIE542H1: Human Factors Integration	S	3	-	2	0.50
MIE561H1: Case Studies in Healthcare	S	3	-	2	0.50
MIE567H1: Multi-agent Reinforcement Learning	S	3	-	2	0.50
Complementary Studies Elective
CS Elective	S/Y				0.50

The Department is not able to schedule all fourth-year courses without conflict. However, students are required to select courses that allow for a conflict-free timetable.
Technical electives in each of the 3F, 3W and 4F, 4W sessions must be chosen from the provided listings. Students who want to take a technical elective substitute are required to obtain formal Departmental approval from the Undergraduate Office.
Industrial Engineering students are required to complete a two-term Capstone Design project, MIE490Y1, supervised by a licensed member of the University of Toronto teaching staff.
1. For students following the “old” curriculum: At least two of the four (0.5 credit) Complementary Studies Electives to be taken between third and fourth year must be Humanities/Social Sciences courses (see the Complementary Studies section at the beginning of this chapter). Students are responsible for ensuring that each elective taken is approved. Please consult the electives list available on the Engineering Office of the Registrar's website.
2. For students following the “new” curriculum: At least two of the three (0.5 credit) Complementary Studies Electives to be taken between third and fourth year must be Humanities/Social Sciences courses (see the Complementary Studies section at the beginning of this chapter). Students are responsible for ensuring that each elective taken is approved. Please consult the electives list available on the Engineering Office of the Registrar's website.
Approval to register for the fourth-year thesis course (MIE498H1 or MIE498Y1) must be obtained from the Associate Chair - Undergraduate and is normally restricted to fourth year students with a cumulative grade point average of at least 2.7. A summer thesis course is also available.

MINORS

The Cross Disciplinary Programs Office (CDP) offers a variety of minors and certificate programs that complement the Industrial Engineering curriculum. Students interested in pursuing an Engineering minor and/or certificate are encouraged to consult with the CDP.

Courses not offered in 2025-2026

MIE509H1
MIE520H1

Industrial Engineering Courses

Applied Science and Engineering (Interdepartmental)

APS100H1 - Orientation to Engineering

Credit Value: 0.25
Hours: 12.8L/12.8T

Designed to help students transition into first-year engineering studies, and to develop and apply a greater understanding of the post-secondary academic learning environment, the field of engineering, application of mathematics and sciences in an engineering context, and properly frame engineering (education) as a socio-technical, people-centred endeavor. Topics include techniques for effective learning, time management, problem solving, successful teamwork, effective communications, test and exam preparation, stress management and wellness, engineering ethics and professionalism, academic integrity and the Student Code of Conduct, applications of math and science in engineering undergraduate research, extra- and co-curricular involvement, and engineering disciplines and career opportunities.

Total AUs: 18.3 (Fall), 18.3 (Winter), 36.6 (Full Year)

APS106H1 - Fundamentals of Computer Programming

Credit Value: 0.50
Hours: 38.4L/12.8T/25.6P

An introduction to computer systems and software. Topics include the representation of information, algorithms, programming languages, operating systems and software engineering. Emphasis is on the design of algorithms and their implementation in software. Students will develop a competency in the Python programming language. Laboratory exercises will explore the concepts of both Structure-based and Object-Oriented programming using examples drawn from mathematics and engineering applications.

Total AUs: 54.9 (Fall), 54.9 (Winter), 109.8 (Full Year)

APS110H1 - Engineering Chemistry and Materials Science

Credit Value: 0.50
Hours: 38.4L/12.8T/12.8P

The principle of the structure-property relationship refers to an understanding of the microstructure of a solid, that is, the nature of the bonds between atoms and the spatial arrangement of atoms, which permits the explanation of observed behaviour. Observed materials behaviour includes mechanical, electrical, magnetic, optical, and corrosive behaviour. Topics covered in this course include: structure of the atom, models of the atom, electronic configuration, the electromagnetic spectrum, band theory, atomic bonding, optical transparency of solids, magnetic properties, molecular bonding, hybridized orbitals, crystal systems, lattices and structures, crystallographic notation, imperfections in solids, reaction rates, activation energy, solid-state diffusion, materials thermodynamics, free energy, and phase equilibrium.

Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

APS111H1 - Engineering Strategies & Practice I

Credit Value: 0.50
Hours: 38.4L/12.8T/12.8P

An introduction to, and implementation of, a framework for the design process, which is used to teach in context, problem solving, professional communication, and team skills. Students are introduced to design, communication and teamwork as integral and inter-related components of engineering practice. This first course in the two Engineering Strategies and Practice course sequence introduces students to the process of engineering design, including broader considerations, written professional communication, and to strategies for successful team work. Students will write a series of team and individual engineering reports.

Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

APS112H1 - Engineering Strategies & Practice II

Credit Value: 0.50
Hours: 25.6L/25.6P

An introduction to, and implementation of, a framework for the design process, which is used to teach in context, problem solving, professional communication, and team skills.
Students are introduced to design, communication, and teamwork as integral and inter-related components of engineering practice. Building on the first course, this second course
in the two Engineering Strategies and Practice course sequence introduces students to project management, oral professional communication, and to the design process in greater
depth. Students work in teams on a term length design project. Students will write a series of team based and individual engineering reports and give a team based design project oral
presentation

Total AUs: 36.6 (Fall), 36.6 (Winter), 73.2 (Full Year)

APS360H1 - Applied Fundamentals of Deep Learning

Credit Value: 0.50
Hours: 38.4L/12.8P

A basic introduction to the history, technology, programming and applications of the fast evolving field of deep learning. Topics to be covered may include neural networks, autoencoders/decoders, recurrent neural networks, natural language processing, and generative adversarial networks. Special attention will be paid to fairness and ethics issues surrounding machine learning. An applied approach will be taken, where students get hands-on exposure to the covered techniques through the use of state-of-the-art machine learning software frameworks.

Prerequisite: APS105H1/APS106H1/ESC180H1/CSC180H1; APS163/MAT187H1/ESC195H1; MAT185H1/MAT188H1
Recommended Preparation: CHE223H1/CME263H1/ECE302H1/MIE231H1/MIE236H1/MSE238H1/STA286H1/ECE286H1/MIE286H1
Total AUs: 42.7 (Fall), 42.7 (Winter), 85.4 (Full Year)

APS490Y1 - Multi-Disciplinary Capstone Design

Credit Value: 1.00
Hours: 38.4T

An experience in multi-disciplinary engineering practice through a significant, open-ended, client-driven design project in which student teams address stakeholder needs through the use of a creative and iterative design process.

Prerequisite: Permission of student's home department
Exclusion: CHE430Y1/CIV498H1/MIE490Y1/MIE491Y1/ECE496Y1/ ESC470H1/ESC471H1/ESC472H1/MSE498Y1
Total AUs: 98.1 (Fall), 98.1 (Winter), 196.2 (Full Year)

APS502H1 - Financial Engineering

Credit Value: 0.50
Hours: 38.4L

This course will focus on capital budgeting, financial optimization, and project evaluation models and their solution techniques. In particular, linear, non-linear, and integer programming models and their solutions techniques will be studied. The course will give engineering students a background in modern capital budgeting and financial techniques that are relevant in practival engineering and commercial settings.

Prerequisite: MAT186H1, MAT187H1, MAT188H1, MIE236H1, MIE237H1, or equivalent.
Exclusion: MIE375H1
Total AUs: 18.3 (Fall), 18.3 (Winter), 36.6 (Full Year)

Civil Engineering

CIV100H1 - Mechanics

Credit Value: 0.50
Hours: 38.4L/25.6T

The principles of statics are applied to composition and resolution of forces, moments and couples. The equilibrium states of structures are examined. Throughout, the free body diagram concept is emphasized. Vector algebra is used where it is most useful, and stress blocks are introduced. Shear force diagrams, bending moment diagrams and stress-strain relationships for materials are discussed. Stress and deformation in axially loaded members and flexural members (beams) are also covered.

Exclusion: APS160H1
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

Electrical and Computer Engineering

ECE110H1 - Electrical Fundamentals

Credit Value: 0.50
Hours: 38.4L/25.6T/12.8P

An overview of the physics of electricity and magnetism: Coulomb's law, Gauss' law, Ampere's law, Faraday's law. Physics of capacitors, resistors and inductors. An introduction to circuit analysis: resistive circuits, nodal and mesh analysis, 1st order RC and RL transient response and sinusoidal steady-state analysis.

Total AUs: 54.9 (Fall), 54.9 (Winter), 109.8 (Full Year)

Mathematics

MAT186H1 - Calculus I

Credit Value: 0.50
Hours: 38.4L/12.8T

Topics include: limits and continuity; differentiation; applications of the derivative - related rates problems, curve sketching, optimization problems, L'Hopital's rule; definite and indefinite integrals; the Fundamental Theorem of Calculus; applications of integration in geometry, mechanics and other engineering problems.

Exclusion: APS162H1
Total AUs: 42.7 (Fall), 42.7 (Winter), 85.4 (Full Year)

MAT187H1 - Calculus II

Credit Value: 0.50
Hours: 38.4L/12.8T

Topics include: techniques of integration, an introduction to mathematical modeling with differential equations, infinite sequences and series, Taylor series, parametric and polar curves, and application to mechanics and other engineering problems.

Prerequisite: APS162H1/MAT186H1
Exclusion: APS163H1/MAT197H1
Total AUs: 42.7 (Fall), 42.7 (Winter), 85.4 (Full Year)

MAT188H1 - Linear Algebra

Credit Value: 0.50
Hours: 38.4L/12.8T/12.8P

This course covers systems of linear equations and Gaussian elimination, applications; vectors in Rn, independent sets and spanning sets; linear transformations, matrices, inverses; subspaces in Rn, basis and dimension; determinants; eigenvalues and diagonalization; systems of differential equations; dot products and orthogonal sets in Rn; projections and the Gram-Schmidt process; diagonalizing symmetric matrices; least squares approximation. Includes an introduction to numeric computation in a weekly laboratory.

Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MAT238H1 - Differential Equations and Discrete Math

Credit Value: 0.50
Hours: 38.4L/25.6T

Ordinary differential equations. Equations of first order and first degree. Linear equations of order n. Systems of simultaneous equations. Difference equations. Forecasting. Business dynamics. Basic Set Theory. Counting, Cartesian Product, Combinations, Permutations. Basic Propositional Logic and Proofs. Throughout the course: formulating and analysing differential equation, difference equation, and discrete mathematical models for real-world problems.

Total AUs: 42.7 (Fall), 42.7 (Winter), 85.4 (Full Year)

Mechanical and Industrial Engineering

MIE100H1 - Dynamics

Credit Value: 0.50
Hours: 38.4L/25.6T

This course on Newtonian mechanics considers the interactions which influence 2-D, curvilinear motion. These interactions are described in terms of the concepts of force, work, momentum and energy. Initially the focus is on the kinematics and kinetics of particles. Then, the kinematics and kinetics of systems of particles and solid bodies are examined. Finally, simple harmonic motion is discussed. The occurrence of dynamic motion in natural systems, such as planetary motion, is emphasized. Applications to engineered systems are also introduced.

Exclusion: APS161H1
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE191H1 - Seminar Course: Introduction to Mechanical and Industrial Engineering

Credit Value: 0.15
Hours: 12.8L

This is a seminar series that will preview the core fields in Mechanical and Industrial Engineering. Each seminar will be given by a professional in one of the major areas in MIE. The format will vary and may include application examples, challenges, case studies, career opportunities, etc. The purpose of the seminar series is to provide first year students with some understanding of the various options within the Department to enable them to make educated choices for second year. This course will be offered on a credit/no credit basis. Students who receive no credit for this course must re-take it in their 2S session. Students who have not received credit for this course at the end of their 2S session will not be permitted to register in session 3F.

Total AUs: 12.2 (Fall), 12.2 (Winter), 24.4 (Full Year)

MIE223H1 - Data Science

Credit Value: 0.50
Hours: 38.4L/25.6P

Introduction to the methods of Data Science. Exploratory data analysis and visualization; tools for reproducible analysis. Principles and tools for data collection; awareness of bias in collection methods. Data cleaning. Descriptive statistics and feature analysis. Assessment of data with respect to scientific theories. Data interpretation fallacies. Geographical data representation and manipulation. Text processing, the natural language processing pipeline, and sentiment analysis. Fundamentals of social network analysis and centrality measures. Cloud-based data processing.

Prerequisite: APS105H1/APS106H1 or equivalent; MIE236H1/ECE286H1/ECE302H1 or equivalent
Total AUs: 42.7 (Fall), 42.7 (Winter), 85.4 (Full Year)

MIE236H1 - Probability

Credit Value: 0.50
Hours: 38.4L/25.6T

Introduction to probability (the role of probability and data in engineering; concepts of population vs. sample). Sample space and events. Definitions of probability. Conditional probability and Bayes' rule. Concept of random variables. Discrete, continuous, and joint distributions. Statistical independence. Expectation, variance, covariance, and correlation. Important discrete and continuous distributions that explain engineering-related phenomena. Brief introduction to the homogeneous Poisson process and related distributions. How to derive distributions. Transformation of random variables. Fundamental sampling distributions, Chi-square, t, and F distributions. Central limit theorem, laws of large numbers. One sample estimation (methods of maximum likelihood, bootstrapping, and jackknife) and hypothesis testing.

Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE237H1 - Statistics

Credit Value: 0.50
Hours: 38.4L/25.6T/12.8P

Data gathering motivation and methods (observational vs. experimental). Modeling for inference vs. prediction. Data visualizations. Two sample estimation and hypothesis testing. Choice of sample size. Fitting distributions to data. Goodness of fit tests. Simple linear regression and correlation. Multiple linear regression. Model building and model assessment. Design and analysis of single and multi-factor experiments. Analysis of variance. Fixed and random effects models. Multiple comparisons.

Prerequisite: MIE231H1/MIE236H1 or equivalent
Total AUs: 54.9 (Fall), 54.9 (Winter), 109.8 (Full Year)

MIE240H1 - Human Factors Engineering

Credit Value: 0.50
Hours: 38.4L/25.6T

Introduction to principles, methods, and tools for the analysis, design, and evaluation of human-centred systems. Consideration of impacts of human physical, physiological, perceptual, and cognitive factors on the design and use of engineered systems. Basic concepts of anthropometrics, work-related hazards, shiftwork, workload, human error and reliability, system complexity, and human factors standards. The human-centred systems design process, including task analysis, user requirements generation, prototyping, and usability evaluation. Design of work/rest schedules, procedures, displays and controls, and information and training systems; design for error prevention and human-computer interaction; design for accessibility and aging populations.

Prerequisite: MIE242H1 recommended
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE242H1 - Foundations of Cognitive Psychology

Credit Value: 0.50
Hours: 38.4L/38.4P

Introduction to neuroanatomy and processes that are core to perception, memory, executive functions, language, decision making, and action. Introduction to stress and emotions, regulation of thought and behaviour, and reward processing. Case studies in Addiction, Depression, Dementia, ADHD, and Dyslexia. Role of neuroimaging and brain lesions in demonstrating the functioning of different pathways and regions of interest within the brain. Use of experiments to test hypotheses concerning brain activities and computations. Conducting a literature review and reporting experimental research, use of elementary statistics, and satisfaction of research ethics requirements.

Total AUs: 54.9 (Fall), 54.9 (Winter), 109.8 (Full Year)

MIE245H1 - Data Structures and Algorithms

Credit Value: 0.50
Hours: 38.4L/12.8T/12.8P

Introduction to algorithms (principles involved in designing, analyzing, and implementing algorithms). Basic data structures (lists, sets, maps, stacks, queues). Graphs and graph search. Decision algorithms (greedy methods and approximation algorithms). Sorting, divide-and-conquer, and recursive algorithms. Trees, heaps, and priority queues. Hashing and hash tables. Algorithmic analysis: big-O complexity. Numerical methods as examples of algorithms and big-O analysis (matrix inversion, matrix decomposition, solving linear system of equations).

Prerequisite: MIE262H1
Exclusion: CSC263H1, CSC373H1
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE250H1 - Fundamentals of Object Oriented Programming

Credit Value: 0.50
Hours: 25.6L/12.8T/38.4P

Introduction to object-oriented programming using the Java programming language with heavy emphasis on practical application; variable types; console and file input/output; arithmetic; logical expressions; control structures; arrays; modularity; functions; classes and objects; access modifiers; inheritance; polymorphism; common data structures; regular expressions; GitHub; Java Swing; unit testing; introduction to complexity analysis; introduction to parallel computing; design and implementation of programs relevant to industrial engineering needs according to strict specifications.

Prerequisite: APS105H1/APS106H1 or equivalent
Total AUs: 42.7 (Fall), 42.7 (Winter), 85.4 (Full Year)

MIE262H1 - Deterministic Operations Research

Credit Value: 0.50
Hours: 38.4L/12.8T/25.6P

Introduction to deterministic operations research. Formulations of mathematical models to improve decision making; linear and integer programming; the simplex method; the revised simplex method; branch-and-bound methods; sensitivity analysis; duality; network models; network simplex method; Dijkstra's algorithm; Prim’s and Kruskal’s algorithms; deterministic dynamic programming; applications of deterministic OR in machine learning; common metaheuristics.

Prerequisite: MAT186H1, MAT188H1
Total AUs: 54.9 (Fall), 54.9 (Winter), 109.8 (Full Year)

MIE263H1 - Stochastic Operations Research

Credit Value: 0.50
Hours: 38.4L/25.6T

Modeling and analysis of systems subject to uncertainty using probabilistic methods. Derivation and application of Bernoulli and Poisson processes, Markov chains, Markov decision processes, Monte Carlo simulation, and queuing models. Applications to engineering, health care, finance, and management.

Prerequisite: MIE231H1 or MIE236H1
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE344H1 - Ergonomic Design of Information Systems

Credit Value: 0.50
Hours: 38.4L/38.4P

Application of information and interaction design principles in interactive systems. Focus on design and methods for understanding user needs, making sense of user research, prototyping, evaluation methods and iterative design. The course will include in depth coverage of rapid prototyping, scenario-based design, usability inspection methods, summative and formative usability evaluation, and comparison tests. Eye tracking, remote testing and experience/ journey mapping will be introduced.

Prerequisite: MIE240H1 or permission of the instructor
Total AUs: 54.9 (Fall), 54.9 (Winter), 109.8 (Full Year)

MIE345H1 - Case Studies in Human Factors and Ergonomics

Credit Value: 0.50
Hours: 38.4L/25.6T

A detailed analysis will be made of several cases in which human factors methods have been applied to improve the efficiency with which human-machine systems operate. Examples will be chosen both from the area of basic ergonomics and from high technology. Emphasis will be placed on the practical use of material learned in earlier human factors courses.

Prerequisite: MIE240H1
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE350H1 - Design and Analysis of Information Systems

Credit Value: 0.50
Hours: 38.4L/12.8T/12.8P

The course covers the software lifecycle of user-centered, computer-based information systems. Topics include software development methodologies, requirement engineering, use case analysis, process modelling, data flow diagrams, UML, design, model-driven architecture, and implementation. The course will emphasize user-centered perspectives and effective communication across the software lifecycle of information systems.

Prerequisite: MIE353H1
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE353H1 - Data Modelling

Credit Value: 0.50
Hours: 36.6L/12.2T/24.4P

This course provides an understanding of the principles and techniques of information modelling and data management, covering both relational theory and SQL database systems (DBMS), as well as entity-relation conceptual modelling. The course also provides an introduction to graph databases (RDF, SPARQL, and knowledge graphs), as well as UML class diagrams. The laboratory focuses on database application development using SQL DBMS, OLAP queries and data modelling.

Prerequisite: MIE250H1
Total AUs: 54.9 (Fall), 54.9 (Winter), 109.8 (Full Year)

MIE354H1 - Business Process Engineering

Credit Value: 0.50
Hours: 38.4L/25.6P

This course focuses on understanding and applying multiple perspectives for organizing, assessing, designing, and implementing integrated distributed information systems to support an organization's objectives. The emphasis is on; 1) understanding how Business Process Management techniques and tools can contribute to align an organization's business and information technology perspectives; 2) designing, developing, and deploying Business Processes as information systems. The course introduces Blockchain technologies, an emerging class of distributed information system providing the foundation for decentralized applications. Students will work in the laboratory to develop business processes that integrate blockchain smart contracts. The business processes will be specified using process modeling languages such as BPMN (an industry standard diagrammatic notation). Students will implement and test executable business processes that combine code generated from process models with additional programming.

Prerequisite: MIE245H1, MIE250H1
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE359H1 - Organization Design

Credit Value: 0.50
Hours: 51.2L

Study of work systems design in new and existing organizations. Consideration will be given to fundamental organizational theory topics such as structure, lifecycle, culture, and ethics. These concepts will be the foundation for an understanding of concepts such as bureaucracy, incentives, innovation, international business, trends in technology, and hiring. An emphasis will be placed on applying these concepts to real-world organizational examples and case studies.

Prerequisite: APS111H1/APS112H1/ESC102H1, MIE358H1 or an equivalent engineering economics course
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE360H1 - Systems Modelling and Simulation

Credit Value: 0.50
Hours: 38.4L/12.8T/25.6P

Principles for developing, testing and using discrete event simulation models for system performance improvement. Simulation languages, generating random variables, verifying and validating simulation models. Statistical methods for analyzing simulation model outputs, and comparing alternative system designs. Fitting input distributions, including goodness of fit tests. Role of optimization in simulation studies.

Prerequisite: MIE231H1/MIE236H1 or equivalent
Total AUs: 54.9 (Fall), 54.9 (Winter), 109.8 (Full Year)

MIE363H1 - Operations and Supply Chain Management

Credit Value: 0.50
Hours: 38.4L/25.6T

This course focuses on features of production/service systems and methods of modelling their operation; the material flow, information flow and control systems. Topics include demand forecasting, inventory management, supply chain management, capacity planning, and lot size planning. Emphasis will be placed on the modelling aspects of operations management, as well as the application of analytical methods in the design of production/service systems. Students will be asked to address open-ended design problems in various activities of the course.

Prerequisite: MIE231H1/MIE236H1, MIE262H1 or equivalent
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE365H1 - Advanced Operations Research

Credit Value: 0.50
Hours: 38.4L/12.8T/25.6P

Linear programming extensions: goal programming. Cooperative game theory (shapley values and nucleolus), interior point methods, large scale decomposition methods (Lagrangian relaxation, Dantzig-Wolfe decomposition, benders decomposition and column generation), stochastic programming. Karush-Kuhn-Tucker (KKT) conditions with application to quadratic programming and bi-level programming. Mathematical Programming formulation choices. Markov Decision Process (MDP) problems.

Prerequisite: MIE262H1, MIE263H1
Total AUs: 54.9 (Fall), 54.9 (Winter), 109.8 (Full Year)

MIE367H1 - Cases in Operations Research

Credit Value: 0.50
Hours: 38.4L/25.6T

This course focuses on the integration of the results from earlier operations research courses and an assessment of the different methods with regard to typical applications. The course is taught using the case method. Students are expected to analyze cases based on real applications on their own, in small groups and during lecture sessions, and solve them using commercial software packages.

Prerequisite: MIE263H1
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE368H1 - Analytics in Action

Credit Value: 0.50
Hours: 25.6L/12.8T/38.4P

This course showcases the impact of analytics focusing on real world examples and case studies. Particular focus on decision analytics, where data and models are combined to ultimately improve decision-making. Methods include: linear and logistic regression, classification and regression trees, clustering, linear and integer optimization. Application areas include: healthcare, business, sports, manufacturing, finance, transportation, public sector.

Prerequisite: MIE237H1/ECE286H1, MIE262H1/MIE376H1, MIE263H1/STA347H1, or permission of the instructor
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE369H1 - Introduction to Artificial Intelligence

Credit Value: 0.50
Hours: 38.4L/25.6P

Introduction to Artificial Intelligence. Search. Constraint Satisfaction. Propositional and First-order Logic Knowledge Representation. Representing Uncertainty (Bayesian networks). Rationality and (Sequential) Decision Making under Uncertainty. Reinforcement Learning. Weak and Strong AI, AI as Engineering, Ethics and Safety in AI.

Prerequisite: MIE250H1/ECE244H1/ECE345H1/CSC263H1/CSC265H1/ESC190, MIE236H1/ECE286H1/ECE302H1
Exclusion: ROB311H1, CSC384H1
Total AUs: 49.7 (Fall), 49.7 (Winter), 99.4 (Full Year)

MIE370H1 - Introduction to Machine Learning

Credit Value: 0.50
Hours: 35.4L/23.6P

Intro to Machine Learning, Hypothesis Spaces, Inductive Bias. Supervised Learning: Linear and Logistic Regression. Cross Validation (CV). Support Vector Machines (SVMs) and Regression. Empirical Risk Minimization and Regularization. Unsupervised Learning: Clustering and PCA. Decision Trees, Ensembles and Random Forest. Neural Net Fundamentals. Engineering Design considerations for Deployment: Explainability, Interpretability, Bias and Fairness, Accountability, Ethics, Feedback Loops, and Technical Debt.

Prerequisite: ECE286H1, or (MIE236H1/ECE302H1 and MIE237H1)
Exclusion: CSC311H1, ECE421H1, ECE521H1, ROB313H1
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE424H1 - Optimization in Machine Learning

Credit Value: 0.50
Hours: 38.4L/12.8T/12.8P

1. To enable deeper understanding and more flexible use of standard machine learning methods, through development of machine learning from an Optimization perspective.

2. To enable students to apply these machine learning methods to problems in finance and marketing, such as stock return forecasting, credit risk scoring, portfolio management, fraud detection and customer segmentation.

Prerequisite: MIE365H1/MIE376H1/ECE367H1/ROB310H1, or equivalent
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE434H1 - Industrial Ergonomics and the Workplace

Credit Value: 0.50
Hours: 38.4L/38.4P

The Biology of Work: anatomical and physiological factors underlying the design of equipment and workplaces. Biomechanical factors governing physical workload and motor performance. Circadian rhythms and shift work. Measurement and specification of heat, light, and sound with respect to design of the work environment. The influence of practical and psychosocial factors on workplace ergonomic decision-making.

Prerequisite: MIE231H1/MIE236H1 or equivalent
Total AUs: 54.9 (Fall), 54.9 (Winter), 109.8 (Full Year)

MIE435H1 - *Early-stage design methods

Hours: 24.4L/12.2T/24.4P

This course will present design methods that focus on the conceptual and configuration stages of product design. Methods include: identification of unmet/underserved user needs through lead users; roles of function and affordance in products; fixation and cognitive biases as obstacles to creativity; concept generation methods (e.g., Theory of Inventive Problem Solving (TRIZ/TIPS), use of stimuli and analogy); configuration design methods (e.g., design for transformation, manufacture, assembly, reuse, repair, and recycling).

Exclusion: MIE440H1
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE440H1 - * Design of Effective Products

Credit Value: 0.50
Hours: 24.4L/12.2T/24.4P

Products should be used as intended to be effective. Thus, product design must better incorporate possible user behavior. For example, sustainability-minded products should be both technically efficient, and support people to use such products more sustainably. In addition, many products and systems nudge people to behave in ways contrary to the user's best interests. To address the above, the course focuses on design that increases intended product use, and pro-social / pro-environmental behaviors. For projects, students will develop, prototype and test concepts that aim to increase desired behaviors.

Prerequisite: MIE221H1 or instructor permission
Exclusion: MIE435H1
Recommended Preparation: MIE240H1, MIE242H1, MIE243H1, MIE315H1, MIE345H1
Total AUs: 42.7 (Fall), 42.7 (Winter), 85.4 (Full Year)

MIE451H1 - Decision Support Systems

Credit Value: 0.50
Hours: 38.4L/12.8T/12.8P

Provides students with an understanding of the role of a decision support system in an organization, its components, and the theories and techniques used to construct them. Focuses on information analysis to support organizational decision-making needs and covers topics including information retrieval, descriptive and predictive modeling using machine learning and data mining, recommendation systems, and effective visualization and communication of analytical results.

Prerequisite: MIE350H1, MIE353H1
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE457H1 - Knowledge Modelling and Management

Credit Value: 0.50
Hours: 38.4L/12.8T/12.8P

This course explores both the modelling of knowledge and its management within and among organizations. Knowledge modelling will focus on knowledge types and their semantic representation. It will review emerging representations for knowledge on the World Wide Web (e.g., schemas, RDF). Knowledge management will explore the acquisition, indexing, distribution and evolution of knowledge within and among organizations. Emerging Knowledge Management System software will be used in the laboratory.

Prerequisite: MIE350H1, MIE353H1
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE463H1 - Integrated System Design

Credit Value: 0.50
Hours: 38.4L/25.6T

Integrated System Design is a capstone course that integrates the various perspectives of an integrated system taught in third year, including: Optimization, Quality, Management, Information, and Economics. The course approaches systems design from a Business Process perspective. Beginning with the Business Processes, it explores the concept of Business Process Re-engineering. It extends the concept of business processes to incorporate perspectives such as cost, quality, time, behaviour, etc. The second part of the course focuses on business process design tools. Namely, software tools to both design, simulate and analyse business processes. The third part of the course explores the application of process design to various domains. Guest speakers are used to provide domain background.

Prerequisite: Fourth-year, Industrial Engineering standing
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE469H1 - Reliability and Maintainability Engineering

Credit Value: 0.50
Hours: 38.4L/25.6T

An introduction to the life cycle costing concept for equipment acquisition, operation, and replacement decision-making. Designing for reliability and determination of optimal maintenance and replacement policies for both capital equipment and components. Introduction to quality engineering, statistical process control and process capability analysis. Topics include: identification of an item's failure distribution and reliability function, reliability of series, parallel, and redundant systems design configurations, age and block replacement policies for components, the economic life model for capital equipment, provisioning of spare parts.

Prerequisite: MIE231H1/MIE236H1/MIE286H1, MIE358H1/CHE374H1
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE490Y1 - Capstone Design

Credit Value: 1.00
Hours: 51.2T

An experience in engineering practice through a significant design project whereby student teams meet specific client needs through a creative, iterative, and open-ended design process. The project must include:
• The application of disciplinary knowledge and skills to conduct engineering analysis and design,
• The demonstration of engineering judgment in integrating economic, health, safety, environmental, social or other pertinent interdisciplinary factors,
• Elements of teamwork, project management and client interaction, and
• A demonstration of proof of the design concept.

Exclusion: APS490Y1
Total AUs: 98.1 (Fall), 98.1 (Winter), 196.2 (Full Year)

MIE498H1 - Research Thesis

Credit Value: 0.50
Hours: 51.2T

An opportunity to conduct independent research under the supervision of a faculty member in MIE. Admission to the course requires the approval of a project proposal by the Undergraduate office. The proposal must: 1) Explain how the research project builds upon one or more aspects of engineering science introduced in the student's academic program, 2) provide an estimate of a level of effort not less than 130 productive hours of work per term, 3) specify a deliverable in each term to be submitted by the last day of lectures, 4) be signed by the supervisor, and 5) be received by the Undergraduate Office one week prior to the last add day.

Note: Approval to register for the fourth-year thesis course (MIE498H1 or MIE498Y1) must be obtained from the Associate Chair - Undergraduate and is normally restricted to fourth year students with a cumulative grade point average of at least 2.7.

Prerequisite: Approval to register for the fourth-year thesis course (MIE498H1 or MIE498Y1) must be obtained from the Associate Chair - Undergraduate and is normally restricted to fourth year students with a cumulative grade point average of at least 2.7.
Exclusion: MIE498Y1
Total AUs: 49 (Fall), 49 (Winter), 98 (Full Year)

MIE498Y1 - Research Thesis

Credit Value: 1.00
Hours: 51.2T

Prerequisite: Approval to register for the fourth-year thesis course (MIE498H1 or MIE498Y1) must be obtained from the Associate Chair - Undergraduate and is normally restricted to fourth year students with a cumulative grade point average of at least 2.7.
Exclusion: MIE498H1
Total AUs: 98.1 (Fall), 98.1 (Winter), 196.2 (Full Year)

MIE509H1 - AI for Social Good

Credit Value: 0.50
Hours: 35.4L/23.6P

The issue of design and development of AI systems that have beneficial social impact will be discussed and analyzed. The focus will not be on the mechanics of AI algorithms, but rather on the implementation of AI methods to address societal problems. Topics to be covered will include: Safeguarding of human interests (e.g., fairness, privacy) when AI methods are used; partnering of humans and AI systems to implement AI effectively; evaluation of AI assisted interventions; practical considerations in the selection of AI methods to be used in addressing societal problems. The issues that arise in implementing AI for beneficial social impact will be illustrated in a set of case studies aimed at creating beneficial social impact. Class activities will include lectures, seminars, labs, and take-home assignments.

Prerequisite: MIE223, MIE237, or an Introductory Machine Learning, or equivalent
Exclusion: CSC300H1 (Computers and Society)
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE519H1 - * Advanced Manufacturing Technologies

Credit Value: 0.50
Hours: 38.4L

This course is designed to provide an integrated multidisciplinary approach to Advanced Manufacturing Engineering, and provide a strong foundation including fundamentals and applications of advanced manufacturing (AM). Topics include: additive manufacturing, 3D printing, micro- and nano-manufacturing, continuous & precision manufacturing, green and biological manufacturing. New applications of AM in sectors such as automotive, aerospace, biomedical, and electronics.

Recommended Preparation: MIE270H1
Total AUs: 36.6 (Fall), 36.6 (Winter), 73.2 (Full Year)

MIE523H1 - Engineering Psychology and Human Performance

Credit Value: 0.50
Hours: 38.4L/38.4P

An examination of the relation between behavioural science and the design of human-machine systems, with special attention to advanced control room design. Human limitations on perception, attention, memory and decision making, and the design of displays and intelligent machines to supplement them. The human operator in process control and the supervisory control of automated and robotic systems. Laboratory exercises to introduce techniques of evaluating human performance.

Prerequisite: MIE231H1/MIE236H1/ECE286H1 or equivalent required; MIE237H1 or equivalent recommended
Total AUs: 54.9 (Fall), 54.9 (Winter), 109.8 (Full Year)

MIE524H1 - Data Mining

Credit Value: 0.50
Hours: 3L/2P

Introduction to data mining and machine learning algorithms for very large datasets; Emphasis on creating scalable algorithms using MapReduce and Spark, as well as modern machine learning frameworks. Algorithms for high-dimensional data. Data mining and machine learning with large-scale graph data. Handling infinite data streams. Modern applications of scalable data mining and machine learning algorithms.

Prerequisite: MIE350H1 or equivalent; MIE236H1/ECE286H1/ECE302H1 or equivalent; MIE245H1 or equivalent
Total AUs: 42.7 (Fall), 42.7 (Winter), 85.4 (Full Year)

MIE535H1 - Electrification Via Electricity Markets

Credit Value: 0.50
Hours: 36.6L/12.2T/12.2P

Challenges of meeting net-zero, fundamentals of markets, structures and participants, spot markets, economic dispatch, day-ahead markets, optimal unit commitment, forward markets, settlement process, storage and demand management, renewable and distributed energy resources, trading over transmission networks, nodal pricing, reliability resources, generation and transmission capacity investment models, capacity markets.

Prerequisite: CHE249H1 or CME368H1 or ECE472H1 or CHE374H1 or MIE358H1 or equivalent
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE542H1 - Human Factors Integration

Credit Value: 0.50
Hours: 38.4L/25.6T

The integration of human factors into engineering projects. Human factors integration (HFI) process and systems organizational/process constraints, HFI tools, and HFI best practices. Examples of HFI are drawn from energy, healthcare, military, and software systems. Application of HFI theory and methods to a capstone design project, including HFI problem specification, requirements generation, concept development, communication of design issues, and consideration of risk, through an iterative and open-ended design process.

Prerequisite: MIE240H1/MIE1401H1 or equivalent or permission from the instructor.
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE561H1 - Case Studies in Healthcare

Credit Value: 0.50
Hours: 38.4L/25.6T

MIE 561 is a "cap-stone" course. Its purpose is to give students an opportunity to integrate the Industrial Engineering tools learned in previous courses by applying them to real world problems. While the specific focus of the case studies used to illustrate the application of Industrial Engineering will be the Canadian health care system, the approach to problem solving adopted in this course will be applicable to any setting. This course will provide a framework for identifying and resolving problems in a complex, unstructured decision-making environment. It will give students the opportunity to apply a problem identification framework through real world case studies. The case studies will involve people from the health care industry bringing current practical problems to the class. Students work in small groups preparing a feasibility study discussing potential approaches. Although the course is directed at Industrial Engineering fourth year and graduate students, it does not assume specific previous knowledge, and the course is open to students in other disciplines.

Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE562H1 - Scheduling

Credit Value: 0.50
Hours: 38.4L/25.6T

This course takes a practical approach to scheduling problems and solution techniques, motivating the different mathematical definitions of scheduling with real world scheduling systems and problems. Topics covered include: job shop scheduling, timetabling, project scheduling, and the variety of solution approaches including constraint programming, local search, heuristics, and dispatch rules. Also covered will be information engineering aspects of building scheduling systems for real world problems.

Prerequisite: MIE262H1
Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

MIE566H1 - Decision Making Under Uncertainty

Credit Value: 0.50
Hours: 38.4L/25.6T/25.6P

Methods of analysis for decision making in the face of uncertainty and opponents. Topics include subjective discrete and continuous probability, utility functions, decision trees, influence diagrams, bayesian networks, multi-attribute utility functions, static and dynamic games with complete and incomplete information, bayesian games. Supporting software.

Prerequisite: MIE231H1/MIE236H1 or equivalent
Total AUs: 61 (Fall), 61 (Winter), 122 (Full Year)

MIE567H1 - Multi-agent Reinforcement Learning

Credit Value: 0.50
Hours: 38.4L/25.6T

This course is to provide fundamental concepts and mathematical frameworks for sequential decision making of a team of decision makers in the presence of uncertainty. Topics include Markov decision processes, reinforcement learning, theory of games and stochastic games, multi-agent reinforcement learning and decentralized Markov decision processes. The course places an emphasize on conceptual understanding of core concepts and expects students to be able to implement the concepts to demonstrate their understanding.

Total AUs: 48.8 (Fall), 48.8 (Winter), 97.6 (Full Year)

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Course Description by Course Code

Industrial Engineering

Industrial Engineering (AEINDBASC)

Minors

Graduate Studies in Industrial Engineering