14.1.1GRADUATE FACULTY
Lashkari, Reza S.; B.Sc. (Tehran), M.S.I.E., Ph.D. (Kansas State), P. Eng.—1977.
Dutta, Sourin P.; B.E., M.Tech. (Durgapur), Ph.D. (I.I. Sc.), P. Eng.—1984.
El Maraghy, Hoda A.; B.Eng. (Cairo), M. Eng., Ph.D. (McMaster), P.Eng. —1994. (Dean of the Faculty of Engineering)
El Maraghy, Waguih; B.Eng. (Cairo), M. Eng., Ph.D. (McMaster), P.Eng.—1994. (Head of the Department)
Du, Ruxu; B.S. (Wahung Iron and Steel Inst.), M.S. (South China Inst. Tech.), Ph.D. (Michigan)—1991.
Wang, Hunglin (Michael); B.S. (National Tsing-Hua U.), M.S. (State U. of New York, Buffalo), Ph.D. (Iowa)—1991.
Taboun, Salem; B.Sc. (Tripoli), M.Sc. (Miami), Ph.D. (Windsor)—1992.
Salustri, Filippo, B.A.Sc.,M.A.Sc., Ph.D. (Toronto)-1996
The Industrial and Manufacturing Systems Engineering Department offers M.A.Sc. and Ph.D. graduate programs in the area of Manufacturing Systems, encompassing basic as well as applied research.
Courses offered by Industrial and Manufacturing Systems Engineering at the graduate level are listed below. Students may, with the permission of the Department Head and the advisor, take courses from departments other than the one in which the student is registered.
All courses listed will not necessarily be offered in any given year.
Classical theory of optimization. Kuhn-Tucker conditions. Unconstrained optimization; gradient methods, conjugate gradient methods, variable metric methods, search techniques. Constrained optimization. Approximation methods, projection methods, reduced gradient methods; penalty function methods; computational algorithms. Recent advances in optimization. Use of computer software packages. (Prerequisite: 91-312 or equivalent.) (3 lecture hours a week.)
Distributions of functions of variables, estimations and tests of hypotheses, power of tests, non-parametric tests, sampling techniques, analysis of variance, randomized blocks. Latin squares and factorial experiments. (Prerequisite: 91-227 or equivalent.) (3 lecture hours a week.)
Discrete-event system simulation. Random number generation. Stochastic variate generation. Input parameters; identification and estimation. Output analysis. Static and dynamic output analysis; initial and final conditions; measures of performance and their variance estimation; confidence interval. Design of experiments. Various sampling techniques. Single and multifactor designs. Fractional designs. Response surfaces. Regeneration method for simulation analysis; Monte Carlo optimization. (3 lecture hours a week.)
Analysis of production-inventory systems. Inventory systems; deterministic, single-item and multi-item models; quantity discounts; stochastic, single-period models; periodic review and continuous review models. Production planning. Static demand models; product mix and process selection problems; multi-stage planning problems. Dynamic demand models; multi product and multistage models. Operations scheduling; job shop scheduling; line balancing. New directions in production systems research. (Prerequistie: 91-413 or equivalent.) (3 lecture hours a week.)
Theory and computational techniques for solving linear and integer programming problems. Theoretical foundations of the simplex algorithm. Duality, sensitivity analysis and parametric programming. Network flow methods. Integer programming problems. Cut algorithms, branch and bound, and implicit enumeration methods. Dynamic programming. (Prerequisite: 91-312 or equivalent.) (3 lecture hours a week.)
Probabilistic O.R. models. Decision theory and games. Markovian decision process. Queueing theory. Single channel and multichannel queueing systems. Queues with general arrival and service patterns. Bulk queues and priority queues. Applications of queuing models. Probabilistic dynamic programming. (Prerequisite: 91-412 or equivalent.) (3 lecture hours a week.)
Job and skill profiles; workload definition and measurement; workload and performance modelling; information theory applications, models of the process operator; optimal control models of human response; queuing models for monitoring and supervisory behaviour; manual control skills and automation; signal-flow graphs and their uses in operations design and planning. (Prerequisites: 91-315 and 91-415, or equivalent.) (3 lecture hours a week.)
Ergonomics and work design; human workload measurement in industry; visual display terminals at the workplace; signal detection and visual inspection; user-computer interaction; human factors aspects of flexible manufacturing systems; effects of individual and combined environmental stressors on human performance. (Prerequisites: 91-415 or equivalent.) (3 lecture hours a week.)
Design for system effectiveness; reliability program; failure patterns for complex products; reliability measures; static reliability models; mathematical concepts of reliability; interference theory and reliability computation; reliability bounds in probabilistic design; dynamic reliability models; sequential life testing. (Prerequisite: 91-227 or equivalent.) (3 lecture hours a week.)
Development of CIM; the CIM pyramid—key functions. System integration; standards for communications—MAP. Data base as the hub of CIM—types of data base. Role of simulation and support systems—decision support systems and expert systems. Sensor technology, robot vision, and group technology. Impact of CIM. Factory of the future. (Prerequisites: 91-411 or equivalent.) (3 lecture hours a week.)
Principles and methods for engineering analysis of industrial projects and operations. Criteria for economic decisions, project investment analysis, gain and loss estimating and techniques for economic optimization under constraint are included. Emphasis is placed on the construction and use of analytical models in the solution of engineering economy problems. Elements of risk and uncertainty are included through use of probabilistic techniques. (Prerequisite: 85-313 or equivalent.) (3 lecture hours a week.)
Stochastic processes. The Poisson process—relationship to exponential, Erlang and uniform probability distributions. Markov chains—basic limit theorem. Continuous time Markov chains—birth-and-death processes, time-dependent probabilities, limiting probabilities, relationship to the exponential distribution, uniformization. Renewal theory—the renewal function, stopping times, Wald's equation, the key renewal theorem, alternating processes, age, remaining life and total life distributions at an arbitrary time-point. Brownian motion. Random walks. Martingales. (Prerequisite: Statistics 65-542 or equivalent.) (3 lecture hours a week.)
FMS as CIM—implemented at the shop floor. Hierarchial network of computers, programmable controllers and work centres. Data base for parts and factory status. Manufacturing automation protocol. Tool management system—acoustic emission. Signature analysis. Planning, design and implementation of FMS—role of management commitment. Impact of FMS on manufacturing industry—job specification of FMS engineers. (Prerequisite: 91-413 or equivalent.) (3 lecture hours a week.)
Developments in nontraditional methods in EDM and ECM. Trends in automation. Recent developments in manufacturing processes; micromanufacturing—integrated circuits and laser machining. Advances in computer technology, CAD and CAM. Kinematics of manipulation robots, artificial intelligence, monitoring and vision systems. (Prerequisite: 91-321 or equivalent.) (3 lecture hours a week.)
Selected advanced topics in the field of Industrial Engineering. (3 lecture hours a week.)
Current topics include:
Sustainable Manufacturing
Industrial Control & Robotics
Engineering Design, Methodology and Applications
Artificial Intelligence Applications in Manufacturing
Time Series with Applications in Manufacturing
Space Robotics & Design
KAD: Knowledge Aided Design
Development of Knowledge-Based Systems for Manufacturing
Recent Advances in Industrial Ergonomics
Computer-Aided Design (CAD)
Computer-Aided Manufacturing
Management of Technology