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Industrial Engineering Master's Program (with Thesis)



MS Program in Industrial Engineering

The Deficiency Program for students with non-IE background

The MS Curriculum

Approved Electives

Course Descriptions






The Department of Industrial Engineering at the Eastern Mediterranean University provides up-to-date facilities for carrying out highly competitive and demanding industrial engineering programs both at graduate and undergraduate levels.

The Industrial Engineering Department offers a graduate program leading to MS and degree. Earning an MS degree is an exciting undertaking and a wonderful way to invest in your future. Just as every journey begins with a single step, every intellectual journey to university begins with its own sort of step- filling out a form. Please visit our Application Form page (from the link below) to get your journey started. 

All needed information to answer your questions about graduate studies at EMU, applications, acceptance and requirements are available there.


Candidates are required to apply "online" to the Institute of Graduate Studies and Research of the University. Uploading all necessary documents is a pre-requisite for the application to be evaluated. Students documenting a clear "thesis proposal" will have an advantage. 

Click on "Apply Online now" that appears on the top-right of the page. All needed information that answers your questions about graduate studies at EMU, applications, acceptance and requirements are available in the same page.


MS Program in Industrial Engineering with Thesis

The MS program was first offered during the 1998-1999 academic year. This program is designed to provide students a strong analytical basis for advanced theoretical work or for development of new approaches to applications, and to promote fundamental graduate research in Industrial Engineering (IE) and Operations Research (OR) in accordance with scientific and technological developments.

Students are admitted to this program based on several criteria including their previous academic performance, field of undergraduate education, and availability of openings at the department.

Students with non-IE background are given the "Probationary" status and they are required to take pre-requisite preparatory courses or their equivalents in the "Deficiency Program". Depending on the students' background, they may need to take up to 4 deficiency courses. The duration or time to spend by a student in the deficiency program and in the "Graduate English Preparatory Program" is not considered as a part of the normal duration limit of the MS program.

The Deficiency Program for students with non-IE background


MATH322Probability and Statistical Methods(3,1) 3
IENG385 Statistical Applications in Engineering(3,1) 3
IENG313Operations Research - I(4,1) 4
IENG431Production Planning - II(4,1) 4



The MS Curriculum

The MS degree candidates are required to take the following courses:


       CORE COURSES (Total of 6 credits) 




     COURSE TITLE                                     



        IENG513     Probabilistic Models3
        IENG531     Production Planning and Scheduling3
        IENG598     Graduate Research Seminar0
        IENG500     M.S. Thesis0


  ELECTIVE COURSES (Total of 15 credits) 




    COURSE TITLE                                     



        IENGxxx    Approved Elective (Optimization)3
        IENGxxx    Approved Elective3
        IENGxxx    Approved Elective3
        DDDD5xx    Approved Elective3
        DDDD5xx    Approved Elective3



Approved Electives


IENG501Algorithms and Advanced Programming3
IENG502Numerical Methods in IE and OR3
IENG509Occupational Safety and Health Engineering3
IENG511Optimization Theory3
IENG512Advanced Linear Programming3
IENG515Applied Queuing Theory3
IENG516Network Flows3
IENG517Integer and Discrete Programming3
IENG521Multi-Objective Decision Making3
IENG522Decision Analysis3
IENG523Investment Decision Making3
IENG524Financial Engineering3
IENG532Inventory Theory3
IENG533Scheduling in Manufacturing and Service Systems3
IENG537CIM Systems3
IENG538Supply Chain Management3
IENG541Location and Layout Optimization3
IENG542Performance Evaluation of Manufacturing Systems3
IENG556Technology Management 3
IENG561Systems Theory3
IENG562Systems Simulation3
IENG581Design and Analysis of Experiments3
IENG583Advanced Statistics3
IENG584Advanced Quality Engineering3
IENG585Advances in Forecasting3
IENG586Reliability Engineering3
IENG596Special Topics in Industrial Engineering - I3
IENG597Special Topics in Industrial Engineering - II3


In addition to the above list of elective courses, the student may also take the must courses from the PhD Program as an approved elective course.


To qualify for a Master's Degree, candidates must complete the curriculum requirements with a CGPA of at least 3.00 out of 4.00. Furthermore, a thesis work should be conducted under the guidance of a thesis supervisor and successfully defended against a jury. The thesis subject is usually proposed by the thesis supervisor although the research interest of the candidate is also taken into consideration.

Entry Requirements

Applicants are expected to hold a BS degree in IE or related discipline. All candidates must satisfy the requirements of the Institute of Graduate Studies and Research of EMU university. 

Career Prospects

Graduates with an MS degree in Industrial Engineering can work in many areas in the industry including operations research, systems engineering, quality assurance, planning and control of production and inventory systems, ergonomics, computer applications, process control, transportation-logistics, service sector, and administrative duties.


Course Description

IENG500           M.S. Thesis

IENG501           Algorithms and Advanced Programming                        (3,0) 3

Mathematical algorithms: random numbers, polynomials, matrices, integration. Sorting and searching. String processing. Geometric algorithms. Graph algorithms: connectivity weighted and directed graphs, network flow. Dynamic programming. Algorithms will be studied using object-oriented approach as much as possible.

IENG502         Numerical Methods in IE and OR                                     (3,0) 3

A survey of mathematical and numerical methods used in IE and OR related studies presented in a unified structure based upon the theory of computation with special emphasis given to the development of computer codes and design of database.

IENG505     Ergonomics                                                                           (3,0) 3

The objective of this course is to explore the effects of environmental conditions on the human performance. The topics to be covered are effects of control-display design, environmental conditions (illumination, climate, noise and motion), shift-work, human error, accidents and safety.  Moreover, the students will be asked to conduct research projects either in the human factors laboratory or in a real field.  The Statistical Package for Social Sciences (SPSS) will be used in the project work.


IENG509     Occupational Safety and Health Engineering                       (3,0) 3

This course is designed to introduce the student with the principles of safety and health hazards in industrial environment. It provides students with fundamentals of measurement, evaluation, regulation, and control of hazardous conditions, toxic substances, physical agents, and dangerous processes in the industrial environment. Skills development in record keeping, risk assessment and accident cause analysis will also be emphasized. This course will prepare the student for workplace safety and management.

IENG511           Optimization Theory                                                        (3,0) 3

Convex analysis; optimality conditions; generalized linear programming; revised simplex, matrix representation; integer programming; computer applications. Extensions of linear programming: quadratic programming; dynamic programming; geometric programming; methods for unconstrained and constrained non-linear optimization; multi-objective optimization methods.

IENG512         Advanced Linear Programming                                         (3,0) 3

Geometry of LP and Simplex Method. Duality and its implications. Sensitivity. Simplex forms: Revised Simplex, dual Simplex etc. LU factorization. Transportation and transshipment problems, assignment problem. Decomposition Methods. Networks. Problems with upper bounds. Numerical stability and computational efficiency. Karmarkar's method.

IENG513           Probabilistic Models                                                        (3,0) 3

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.


IENG515           Applied Queuing Theory                                                 (3,0) 3

Analysis of birth and death processes. Development of elements of queuing theory. Single and multiple server queues for Markovian and non-Markovian arrival and service time distributions (M/M/1, M/M/c, G/M/c, M/G/1, PH/PH/1). Bulk arrival and service systems. Networks of Markovian queues. Lindley's equation for the G/G/1 queue. Applications of queuing theory in manufacturing and service systems.

IENG516        Network Flows                                                                    (3,0) 3

Network representation and terminology. Network flow problems such as shortest path, minimal spanning tree and maximal flow problems. Graph Theory.

IENG517       Integer and Discrete Programming                                     (3,0) 3

The course covers the basic solution methods: dynamic programming, implicit enumeration, branch and bound, cutting plane and polyhedral approach. It also gives an introduction to heuristic methods as the use of Lagrange multipliers and local search. Traveling Salesperson Problem (TSP) and optimization in graphs are also discussed. The course aims to provide tools for students dealing with integer programming models for developing their own algorithms for their special problems.

IENG521          Multi-Objective Decision Making                                      (3,0) 3

Formulation of the general multi-objective programming problem, classification of multi-objective programming methods; generating techniques, preference oriented methods, multiple-decision-maker methods. Multi-objective analysis of certain problems in public sector.

IENG522           Decision Analysis                                                            (3,0) 3

Bayesian decision models; decision trees; value of information; utility theory, use of judgmental probability, study of strategies; economics of sampling; risk sharing and decisions; implementation of decision models.

IENG523          Investment Decision Making                                            (3,0) 3

The meaning of investment process in general and for creating systems to produce products and services in particular. Classification of investment decision problems with respect to context and the precision of informational support, i.e. certainty, risk and uncertainty. A general mathematical structure for modeling for investment decisions. Deterministic, stochastic, combinatorial, sequential and dynamic investment decision models, and optimization techniques used for their solutions. A mathematical basis for deriving suitable value measures for evaluating investment alternatives and derivation of such measures. Types of risk taking as the fundamental dimension of a class of investment decision making situations.

IENG524          Financial Engineering                                                       (3,1) 3

This course is designed to enable students to understand and conceptualize basics of modern financial markets in the form of mathematical models. Several models of risk free assets and dynamics of risky assets will be discussed. Optimal portfolio management in risky environments will be analyzed. Call and put options of securities in discrete and continuous time settings will be explained. This part includes the famous work of Black-Scholes.

IENG531           Production Planning and Scheduling                              (3,0) 3

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 are studied.


IENG532           Inventory Theory                                                             (3,0) 3

Study of inventory systems. Deterministic and stochastic models. Fixed versus variable reorder intervals. Dynamic and multistage models. Selection of optimal inventory policies for single and multi-item dynamic inventory models, with convex and concave cost functions, known and uncertain requirements. Myopic policies. Multi-echelon models. Heuristic algorithms.


IENG533           Scheduling in Manufacturing and Service Systems        (3,0) 3

Terminology, characteristics and classification of sequencing and scheduling problems. An overview of computational complexity theory. Single Machine Scheduling. Parallel Machine Scheduling. Shop Scheduling: open shop, flow shop, job shop, and mixed shop. Batching.  Scheduling under Resource Constraints. Due-Date Scheduling. Scheduling in Flexible Manufacturing Systems.

IENG537           CIM Systems                                                                   (3,0) 3

Introduction to CIM (Computer Integrated Manufacturing) systems. Computer process interfacing. Computer control. Industrial robots. Flexible manufacturing systems. CIM systems. CIM systems selection criteria.


IENG538           Supply Chain Management                                             (3,0) 3

Supply chain management; New product development; Management and control of purchasing and logistics management systems. Strategic orientation toward the design and development of the supply chain for purchasing, materials, and logistics systems. Total Quality Management to assess and assure customer satisfaction. Global strategies. Expert systems for continuous improvement of the supply chain.


IENG541           Location and Layout Optimization                                   (3,0) 3

Single or multiple facilities location in the plane with minisum or minimax criteria. Discrete or continuous layout optimization. Single facility network location. Applications in public service, production, distribution, warehousing, emergency service, flexible manufacturing.

IENG542           Performance Evaluation of Manufacturing Systems       (3,0) 3

The design and performance issues in production, transfer lines, production/inventory systems, network of production/inventory systems, and flexible manufacturing systems. Phase type processing times, failures and service completion processes. Buffering and blocking issues. Decomposition methods. Control policies in pure inventory and production/inventory systems.

IENG556           Technology Management                                                (3,0) 3

The course covers a discussion of the major aspects of advanced manufacturing and process technologies, selection and implementation of new technologies, and the management of technological and organizational changes.   

IENG561           Systems Theory                                                              (3,0) 3

Analysis of linear continuous systems; controllability, observability, and stability; applications to physical, ecological, and socio-economic systems; control systems; introduction to optimal control.

IENG562           Systems Simulation                                                         (3,0) 3

The design and analysis of simulation models. The use of simulation for estimation, comparison of policies, and optimization. Variance estimation techniques including the regenerative methods, time series methods, and batch means. Variance reduction. Statistical analysis of output of simulations, applications to modeling stochastic systems in Industrial Engineering and Operations Research.

IENG581           Design and Analysis of Experiments                               (3,0) 3

The simple comparative experiments, experiments with a single factor, fixed effect and random effect models, model adequacy checking, choice of sample size, randomized blocks and latin squares design, incomplete block design, factorial designs, rules for sums of squares and expected mean squares, fractional factorial designs and regression analysis. Moreover, the statistical package for social sciences (SPSS) will be introduced.


IENG583           Advanced Statistics                                                         (3,0) 3

Sampling distributions; Point estimation; Measures of goodness estimators; Methods of estimation; Sample size determination; Confidence intervals; Sufficient Statistics; Rao-Blackwell theorem; Hypothesis testing; Errors of type I & II; power of a test; p-values; Neyman-Pearson Theory; Likelihood ratio test; Some commonly used tests for means, variances and for ratio of variance etc.; Method of least squares; Curve fitting; Regression analysis; Some commonly used non-parametric tests for randomness, independence etc.; Goodness-of-fit tests; Sequential tests of hypothesis; Use of order statistics to find statistical intervals etc. Use of computer packages.


IENG584           Advanced Quality  Engineering                                       (3,0) 3

This course is designed to introduce a conceptual and practical notion of advanced quality control in engineering. It also provides students with methods and philosophy of statistical process control. The course contents include introduction to advanced quality control and improvement concepts in production processes, control charts for variable and attributes, cumulative sum control charts, economic design of control charts, fractional factorial experiments for process design, process optimization with designed experiments, advanced acceptance sampling techniques and lot-by-lot acceptance sampling for attributes. 

IENG585           Advances in Forecasting                                                 (3,0) 3

Planning and forecasting; Measures of forecast errors; Correlation, covariances, autocorrelations and autocorrelation function and their use pattern recognition; Smoothing methods; Decomposition methods and seasonal indices; Methods for trend and seasonal patterns including differences, double smoothing, Holt & Winter methods; Fourier series forecast analysis; Box-Jenkins ARIMA methods and their applications; ARIMA intervention analysis and transfer functions; Econometric methods; Multiple regression; Cyclic methods; Technological forecasting; Use of neural networks, expert systems and genetic algorithm in forecasting. Mini-case studies and  software packages.


IENG586         Reliability Engineering                                                       (3,0) 3

Reliability and maintainability. Basic reliability  models. Maintainability. Availability. Reliability testing. Implementation and Case studies.


IENG596/597    Special Topics in Industrial Engineering – I / II               (3,0) 3

Recent advances in selected topics in industrial engineering will be covered. The topics and the contents of the course will depend on the course instructor and will be announced in the beginning of each semester.


IENG598           Graduate Research Seminar                                                  0

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.