Operations Research III (IE 313)

2019 Summer
Faculty of Engineering and Natural Sciences
Industrial Engineering(IE)
3
6.00 / 6.00 ECTS (for students admitted in the 2013-14 Academic Year or following years)
Yunus Sar─▒kaya ysarikaya@sabanciuniv.edu,
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English
Undergraduate
IE305
Formal lecture,Recitation
Other
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CONTENT

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.

OBJECTIVE

The mission of this course is to continue the study of modeling and solution of decision problems using operations research techniques with a particular emphasis on stochastic aspects.

LEARNING OUTCOME

Have a basic knowledge of discrete time Markov chains (DTMCs), formulate suitable applications as DTMCs and analyze their transient and steady-state behaviors.
Have a basic knowledge of continuous time Markov chains (CTMCs), formulate suitable applications as CTMCs and analyze their transient and steady-state behaviors.
Develop a deeper understanding of certain CTMC classes including Poisson processes, birth-and-death processes, and queueing models.
Perform computation analysis of the stochastic processes of interest using a programming language.

PROGRAMME OUTCOMES


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


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


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. 4

2. Design and develop appropriate analytical solution strategies for problems in integrated production and service systems involving human capital, materials, information, equipment, and energy. 1

3. Implement solution strategies on a computer platform for decision-support purposes by employing effective computational and experimental tools. 3


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


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


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. 1

ASSESSMENT METHODS and CRITERIA

  Percentage (%)
Exam 40
Assignment 60
Other 3

RECOMENDED or REQUIRED READINGS

Textbook

Introduction to Stochastic Processes with R., Robert P. Dobrow, 1st Ed., Wiley.