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Code IE 302
Term 201602
Title Stochastic Models in Operations Research
Faculty Faculty of Engineering and Natural Sciences
Subject Industrial Engineering(IE)
SU Credit 4
ECTS Credit 8.00 / 7.00 ECTS (for students admitted in the 2013-14 Academic Year or following years)
Instructor(s) Hans Frenk,
Language of Instruction English
Level of Course Undergraduate
Type of Course Click here to view.
(only for SU students)
IE301 MATH203 MS301
Mode of Delivery Formal lecture,Recitation
Planned Learning Activities Other

Introduction to stochastic processes with examples based on the appropriate manufacturing and service systems; decision making under uncertainty; Markov chains; production/inventory models; queuing systems; forecasting models; reliability engineering problems.


The mission of this course is to learn students to think probabilistically and study the modeling of stochastic systems and their solution techniques.

Learning Outcome

Upon the completion of this course, the students are expected to be able to

Have a basic knowledge of probability theory and being able to calculate cumulative distribution functions and (conditional) expectations. Learning outcome 1 is also present in the other learn?ng outcomes and is fundamental in understanding the other learning outcomes.
Have a basic knowledge of stochastic arrival processes (Poisson process, renewal process) and being able to model those processes and compute the main characteristics of those processes.
Have a basic knowledge of regenerative processes (renewal reward theorem, main theorem of regenerative processes) and being able to model those processes and compute in examples steady state probabilities and the long run expected average cost per time unit associated with a regenerative process .
Have a basic knowledge of Markov chains and being able to model those processes and compute in examples the steady state probabilities and the long-run expected average cost per unit time associated with a Markov chain
Have a basic knowledge of Markov processes,, birth-death processess and Markovian queueing models. Being able to model those processes and compute in examples the steady state probabilities and the long-run expected average cost per unit time associated with a Markov process

Programme Outcomes
Common Outcomes For All Programs
1 Understand the world, their country, their society, as well as themselves and have awareness of ethical problems, social rights, values and responsibility to the self and to others. 1
2 Understand different disciplines from natural and social sciences to mathematics and art, and develop interdisciplinary approaches in thinking and practice. 1
3 Think critically, follow innovations and developments in science and technology, demonstrate personal and organizational entrepreneurship and engage in life-long learning in various subjects. 2
4 Communicate effectively in Turkish and English by oral, written, graphical and technological means. 2
5 Take individual and team responsibility, function effectively and respectively as an individual and a member or a leader of a team; and have the skills to work effectively in multi-disciplinary teams. 1
Common Outcomes ForFaculty of Eng. & Natural Sci.
1 Possess sufficient knowledge of mathematics, science and program-specific engineering topics; use theoretical and applied knowledge of these areas in complex engineering problems. 3
2 Identify, define, formulate and solve complex engineering problems; choose and apply suitable analysis and modeling methods for this purpose. 3
3 Develop, choose and use modern techniques and tools that are needed for analysis and solution of complex problems faced in engineering applications; possess knowledge of standards used in engineering applications; use information technologies effectively. 3
4 Ability to design a complex system, process, instrument or a product under realistic constraints and conditions, with the goal of fulfilling specified needs; apply modern design techniques for this purpose. 2
5 Design and conduct experiments, collect data, analyze and interpret the results to investigate complex engineering problems or program-specific research areas. 1
6 Knowledge of business practices such as project management, risk management and change management; awareness on innovation; knowledge of sustainable development. 1
7 Knowledge of impact of engineering solutions in a global, economic, environmental, health and societal context; knowledge of contemporary issues; awareness on legal outcomes of engineering solutions; understanding of professional and ethical responsibility. 1
Industrial Engineering Program Outcomes Required Courses
1 Formulate and analyze problems in complex manufacturing and service systems by comprehending and applying the basic tools of industrial engineering such as modeling and optimization, stochastics, statistics. 5
2 Design and develop appropriate analytical solution strategies for problems in integrated production and service systems involving human capital, materials, information, equipment, and energy. 3
3 Implement solution strategies on a computer platform for decision-support purposes by employing effective computational and experimental tools. 1
Molecular Biology, Genetics and Bioengineering Program Outcomes Area Electives
1 Comprehend key concepts in biology and physiology, with emphasis on molecular genetics, biochemistry and molecular and cell biology as well as advanced mathematics and statistics. 1
2 Develop conceptual background for interfacing of biology with engineering for a professional awareness of contemporary biological research questions and the experimental and theoretical methods used to address them. 1
Materials Science and Nano Engineering Program Outcomes Area Electives
1 Applying fundamental and advanced knowledge of natural sciences as well as engineering principles to develop and design new materials and establish the relation between internal structure and physical properties using experimental, computational and theoretical tools. 1
2 Merging the existing knowledge on physical properties, design limits and fabrication methods in materials selection for a particular application or to resolve material performance related problems. 1
3 Predicting and understanding the behavior of a material under use in a specific environment knowing the internal structure or vice versa. 1
Computer Science and Engineering Program Outcomes Area Electives
1 Design, implement, test, and evaluate a computer system, component, or algorithm to meet desired needs and to solve a computational problem. 1
2 Demonstrate knowledge of discrete mathematics and data structures. 1
3 Demonstrate knowledge of probability and statistics, including applications appropriate to computer science and engineering. 2
Assessment Methods and Criteria
  Percentage (%)
Midterm 70
Exam 30
Recommended or Required Reading

Ross, S.M., Introduction to Probability Models (edition 10), Academic Press, 2003


Additional material: course notes IE302 written by instructor