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Introduction

PhD Program in Industrial Engineering

PhD Curriculum

Approved Electives

Course Descriptions

 




 

Introduction

The Department of Industrial Engineering at Eastern Mediterranean University provides very good facilities for carrying out highly competitive and demanding industrial engineering programs both at graduate and undergraduate levels.

The mission of the Industrial Engineering Department is to provide a scholarly environment to generate and disseminate new knowledge and technological innovation through research, and to equip future industrial engineers with sound professional background for the benefit of the society.

The Industrial Engineering Department offers a graduate program leading to PhD degree. Earning a PhD 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.

http://grad.emu.edu.tr/index.php/igsr 

All information on the documents required and on how to apply for graduate studies at EMU are available through this link above.


Applications

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.

http://grad.emu.edu.tr/index.php/igsr 

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.

 

 

PhD Program in Industrial Engineering

The PhD program in IE was started in the 2002-2003 Fall Academic semester. This program is designed to involve students in innovative theoretical and technical research and/or to promote 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, fields of undergraduate and previous graduate 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". The deficiency program for students with non-IE background includes four core courses from the MS program in IE plus the courses of MS deficiency program. The total duration of the deficiency program cannot exceed 2 semesters with a maximum of 5 courses each semester. The duration of time spent 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 PhD program.

 

PhD Deficiency Program :

IENG511 Optimization Theory (3,0) 3

IENG513 Probabilistic Models (3,0) 3

IENG531 Production Planning and scheduling (3,0) 3

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

 

The PhD Curriculum

The PhD degree candidates are required to take the following courses

 

 

CORE COURSES

 

 
Course Code

Course Name

 

Credit
IENG518(*)Non-Linear Optimization3
IENG514(*)Stochastic Processes and Applications3
IENG600Ph.D. Thesis0
IENG699Ph.D. Qualifying Exam0

(*) If any of these courses were taken as a partial fulfillment of Master's degree requirements then they should be replaced by a departmental graduate elective course.

 

 

REQUIRED ELECTIVES

 

 
Course Code

Course Name

 

Credit
IENGxxxApproved IE Elective3
IENGxxxApproved IE Elective3
IENGxxxApproved IE Elective3
DDDD5xxApproved Elective3
DDDD5xxApproved Elective3

 

Approved Electives

 

CODE

COURSE TITLE

 

CREDIT
IENG501Algorithms and Advanced Programming3
IENG502Numerical Methods in IE and OR3
IENG505Ergonomics3
IENG509Occupational Safety and Health Engineering3
IENG511Optimization Theory3
IENG512Advanced Linear Programming3
IENG513Probabilistic Models3
IENG515Applied Queuing Theory3
IENG516Network Flows3
IENG517Integer and Discrete Programming3
IENG521Multi-Objective Decision Making3
IENG522Decision Analysis3
IENG523Investment Decision Making3
IENG524Financial Engineering3
IENG531Production Planning and Scheduling3
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

 

Assessment

To qualify for a PhD Degree, candidates must complete the curriculum requirements with a CGPA of at least 3.00/4.00. Furthermore, a dissertation work should be conducted under the guidance of a thesis supervisor and successfully defended against a jury. Before the final jury can be formed, a paper written by the student on related topic must be accepted to be printed by a SCI/SSCI/SCIE journal.  The dissertation subject is usually proposed by the thesis supervisor although the research interest of the candidate is also taken into consideration.

 Publication Requirement

Prior to the appointment of a dissertation defense  jury, the candidate must have published (or have had received acceptance for publication) one paper dealing with issues related to his/her dissertation research in a journal referenced in the "Science Citation Index" or  its expanded version.

Entry Requirements

Applicants are expected to hold a MS with thesis 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 a PhD degree in Industrial Engineering can work in many areas in the R&D departments of industrial and research organizations as experts in 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 in addition to the possibility of teaching in universities.

 

Course Description

 

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.

 

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. 

 

IENG514    Stochastic Processes and Applications  (3,0) 3

Review of conditional probability and conditional expectation. Basic definitions. Homogenous and non-homogenous   Poisson processes, generation of random numbers from Poisson processes, compound Poisson processes, birth-death processes. Markov chains and pure jump processes. Renewal theory and applications. Markov-renewal processes. Applications to queuing, replacement, and inventory problems. Selected topics from stationary processes, rth order Markov Chains, time series as stochastic processes.

 

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 computer 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.

 

IENG518    Non-Linear Optimization   (3,0) 3

Local and global optima. Newton-type, quasi-Newton, and conjugate gradient methods for unconstrained optimization. Kuhn-Tucker theory and Lagrangean duality. Algorithms for linearly constrained optimization, including steepest ascent and reduced gradient methods with applications to linear and quadratic programming. Interior point methods. Non-linearly constrained optimization including penalty and barrier function methods, reduced and projected gradient methods, Lagrangean methods. Computer implementation.

 

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.

 

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.

 

IENG534    Advances in Production & Inventory Systems     (3,0) 3

In this course, students are expected to study critically at least three papers published in the fields of aggregate planning, lot sizing, material requirements planning, continuous and periodic review inventory models, cutting stock, line balancing, single processor scheduling, multi processor scheduling problems. The papers will be selected from the recent issues of periodicals in those fields that are scanned by SCI. Each students is expected to discuss the papers in class and to prepare a literature survey paper on the subject.

 

IENG537    CIM Systems     (3,0) 3

Introduction to cim 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 minimum 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

Major technological aspects of process and manufacturing industries in relation to their management, selection and implementation issues of new technologies, managing technological and the related 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 computer science, engineering and operations research.

 

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.

 

IENG593    Graduate Research Seminar III    (0,3) 1

Students in Ph.D. program are required to give a seminar on a chosen topic of interest (preferably related to their research). A literature survey and a well organized seminar is required.

 

IENG596-597    Special Topics in Industrial Engineering  I - IV      (3,0) 3

Recent advances in selected topics in Industrial Engineering will be covered. The topic and the contents of these courses will depend on the course instructor and will be announced in the beginning of each semester.

 

IENG600      Ph.D. Dissertation