OPERATIONS RESEARCH TECHNIQUES
- Course
- EMNT517 - OPERATIONS RESEARCH TECHNIQUES
- Department
- Engineering Management - English - Master
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 3
- ECTS
- 0
- T+P+L
- 3 + 0 + 0
- Course Coordinator(s)
- -
- Prerequisite
- -
Course Description
Introduce students to developed techniques, methodologies and models used in Operations Research (OR). Operations Research (or Management Science) is a field of Applied Mathematics that uses mathematical methods and computers to make rational decisions in solving a variety of optimization problems. Most OR techniques require the use of computer software to solve large, complex problems in industry, business, science and technology, management, decision support and other areas and disciplines. In this course Deterministic Problems are considered – the data and future outcomes are known with certainty. Optimization of the solution is the primary goal. Matlab and Excel are used for representing and solving the problems.
OPERATIONS RESEARCH TECHNIQUES
Evaluation Tools (Active Term)
No evaluation items have been defined.
Course outcomes
- 01 Formulate a real-world problem as a mathematical programming model. Implement and solve the model in EXCEL and LINDO.
- 02 Understand the theoretical workings of the simplex method for linear programming and perform iterations of it by hand
- 03 Perform sensitivity analysis to determine the direction and magnitude of change of a model’s optimal solution as the data change.
- 04 Solve specialized linear programming problems like the transportation and assignment problems.Solve network models like the shortest path, minimum spanning tree, and maximum flow problems.
- 05 Understand the applications of, basic methods for, and challenges in integer programming and other programming
Course Syllabus
| Week | Topic |
|---|---|
| Week 1 | Conversion of qualitative attributes to quantitative |
| Week 2 | Normalization methods |
| Week 3 | Evaluation of weights of attributes |
| Week 4 | SAW (Simple Additive Weighted) |
| Week 5 | TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) |
| Week 6 | ELECTRE (Elimination et Choice in Translating to Reality) |
| Week 7 | AHP (Analytic Hierarchy Process) |
| Week 8 | Midterm Exam |
| Week 9 | Grouping AHP & Revised AHP |
| Week 10 | ANP (Analytic Network Process) |
| Week 11 | Prioritization strategies |
| Week 12 | Article Presentations |
| Week 13 | Article Presentations |
| Week 14 | Article Presentations |
| Week 15 | Final Exam |
Reference Books & Course Materials
- 01 A Handbook on Multi-Attribute Decision-Making Methods, Wiley Series in Operations Research and Management Science, 2021
- 02 Multiple Attribute Decision Making Methods and applications, Gwo-Hshiung Tzeng Jih-Jeng Huang, CRC Press Taylor & Francis Group, 2011
- 03 New Methods and Applications in Multi-Attribute Decision-Making, Alireza Alinezhad, Springer
Learning Outcomes
No learning outcomes have been defined.
Program Outcomes
- Demonstrate mastery of advanced research methodologies (quantitative, qualitative, and mixed methods) by critically analyzing literature, identifying research gaps, and designing original studies that contribute to MIS theory and practice.
- Conduct and defend an original doctoral dissertation that reflects independent scholarly inquiry, academic rigor, and a significant contribution to the advancement of knowledge in MIS.
- Exhibit readiness for thesis monitoring and defense by articulating the philosophical foundations of research paradigms, positioning one's research within these frameworks, and responding to scholarly critique.
- Apply ethical principles, academic integrity, and responsible conduct of research in all phases of the research process, including data collection, analysis, reporting, and publication.
- Identify and address the social, legal, and ethical implications of information systems research and its applications within organizational and societal contexts.
- Employ advanced data science techniques, including statistical modeling, machine learning, and AI-based analytics, to examine complex datasets and extract meaningful insights in MIS research.
- Recognize and evaluate emerging technologies such as artificial intelligence, big data, blockchain, and the Internet of Things, assessing their transformative impact on organizational processes and digital ecosystems.
- Collaborate and lead in interdisciplinary research environments, establishing productive scientific partnerships and managing research projects that integrate diverse academic perspectives.
- Publish high-quality research in peer-reviewed journals, present findings at international conferences, and actively engage in academic service such as journal reviewing, conference organizing, and committee participation.
- Identify challenges and propose innovative, research-based solutions at the intersection of information systems, technology, and organizational strategy.
- Deliver advanced-level MIS courses, supervise graduate research, and nurture academic development through effective mentorship and scholarly teaching.
- Develop advanced information systems and decision support systems that align IT capabilities with organizational strategies using systems thinking and innovative design methodologies.
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