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Course Description Course Name : Network Models in Operations Research Course Code : EM-545 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Fall (16 Weeks) ECTS : 6 Name of Lecturer(s) : Prof.Dr. RIZVAN EROL Learning Outcomes of the Course : Develops a network representation of an operations research problem. Selects and/or develops a suitable network solution algorithm for a problem. Evaluates the computational complexity of the network flow problems. Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : This course aims to study basic network optimization problems and algorithms along with applications in transportation, logistics, manufacturing, computer science, project management, and finance. Course Contents : Network flow problems, Transportation and assignment problems, Shortest path problem, Maximum flow problem, Minimum cost flows, Network simplex method, Multicommodity flow problems, Generalized networks, Special purpose algorithms, Advanced computational techniques. Language of Instruction : English Work Place : seminar room   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Introduction. Paths, Trees and Cycles. reading the related textbook chapter lecturing, discussion 2 Algorithm Design and Analysis. reading the related textbook chapter lecturing, discussion 3 Shortest Paths: Label Setting Algorithms, Label Correcting Algorithms. reading the related textbook chapter lecturing, discussion 4 Maximum Flows: Basic Ideasi Polynomial Algorithms. reading the related textbook chapter lecturing, discussion 5 Minimum Cost Flows: Basic Algorithms. reading the related textbook chapter lecturing, discussion 6 Minimum Cost Flows: Polynomial Algorithms. Minimum Cost Flows: Network Simplex Algorithms. reading the related textbook chapter lecturing, discussion 7 Assignments and Matchings reading the related textbook chapter lecturing, discussion 8 Minimum Spanning Trees. reading the related textbook chapter lecturing, discussion 9 Midterm Exam prepare for the exam written exam 10 Convex Cost Flows. reading the related textbook chapter lecturing, discussion 11 Generalized Flows. reading the related textbook chapter lecturing, discussion 12 Lagrangian Relaxation and Network Optimization. reading the related textbook chapter lecturing, discussion 13 Multicommodity Flows. reading the related textbook chapter lecturing, discussion 14 Computational Testing of Algorithms. reading the related textbook chapter lecturing, discussion 15 Project Presentations prepare for the presentation written exam 16/17 Final Exam prepare for the exam written exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Ravindra K. AHUJA, Thomas L. MAGNANTI, James B. ORLIN, 1993, Network Flows: Theory, Algorithms, and Applications, Prentice-Hall.  Shimon EVEN, 1979, Graph Algorithms, Computer Science Press. Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 75     Homeworks/Projects/Others 5 25 Total 100 Rate of Semester/Year Assessments to Success 40   Final Assessments 100 Rate of Final Assessments to Success 60 Total 100   Contribution of the Course to Key Learning Outcomes # Key Learning Outcome Contribution* 1 Understand, interpret and apply knowledge in his/her field domain both in-depth and in-breadth by doing scientific research in industrial engineering. 3 2 Acquire comprehensive knowledge about methods and tools of industrial engineering and their limitations. 5 3 Work in multi-disciplinary teams and take a leading role and responsibility. 4 4 Identify, gather and use necessary information and data. 3 5 Complete and apply the knowledge by using scarce and limited resources in a scientific way and integrate the knowledge into various disciplines. 3 6 Keep up with the recent changes and applications in the field of Industrial Engineering and analyze these innovations when necessary. 4 7 Work in multi-disciplinary teams, take a leading role and responsibility and develop solutions for complex problems. 4 8 Analyze Industrial Engineering problems, develop innovative methods to solve the problems. 5 9 Have the ability to propose new and/or original ideas and methods in developing innovative solutions for designing systems, components or processes. 3 10 Design and perform analytical modeling and experimental research and analyze/solve complex matters emerged in this process. 5 11 Follow, study and learn new and developing applications of industrial engineering. 4 12 Use a foreign language in verbal and written communication at least B2 level of European Language Portfolio. 3 13 Present his/her research findings systematically and clearly in oral and written forms in national and international platforms. 2 14 Understand social and environmental implications of engineering practice. 2 15 Consider social, scientific and ethical values in the process of data collection, interpretation and announcement of the findings. 3 * Contribution levels are between 0 (not) and 5 (maximum).   Student Workload - ECTS Works Number Time (Hour) Total Workload (Hour) Course Related Works     Class Time (Exam weeks are excluded) 14 3 42     Out of Class Study (Preliminary Work, Practice) 14 3 42 Assesment Related Works     Homeworks, Projects, Others 5 7 35     Mid-term Exams (Written, Oral, etc.) 1 10 10     Final Exam 1 10 10 Total Workload: 139 Total Workload / 25 (h): 5.56 ECTS Credit: 6