Industrial Engineering Master's Program (with Thesis) (M.S., 1 - 2 Years)

This is a master thesis course which is based on research in the field of Industrial Engineering. It offers students with the opportunity to conduct their own research on a specific topic. The topic of the thesis should be determined by consultation with a supervisor and approved by the department chair. The student can register this course as early as the second academic term. The thesis work has to be presented and defended in front of at least three-member jury

Axiomatic approach to probability; conditional probability; random variable. Commonly used discrete and continuous distributions. Expectation of a random variable; jointly distributed random variables; marginal and conditional distributions; independent random variables. Multinomial and multivariate normal distributions. Functions of random variables, moments, conditional expectation, m.g.f. and p.g.f., Markov inequality, law of large numbers, central limit theorem. Discrete-time Markov chains, Kolmogorov-Chapman equations, classification of states, steady-state probabilities. Applications from different areas.

Analysis of some specific problem areas within the context of planning and scheduling of production activities. Definition, formulation and available solution procedures for aggregate planning, lot sizing, material requirements planning, cutting stock, line balancing, single processor scheduling, multi processor scheduling problems.

This course is designed to orient the students for research by emphasizing reading, comprehension, discussion and performing exercises on IE/OR problem areas. For this purpose, each student is required to choose an IE/OR topic that is suitable to his/her academic background and interests, study this topic under the guidance of faculty members, make a literature survey, and point out the relevant further research areas. Throughout this course each student is also required to read and study some technical papers and give a series of seminars.