Offered to MAN ECN IRL THM IBT Elective. Course Level Course Code Year Semester ECTS Weekly Course Hours

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1 Offered by Department of Business Administration Course Status Offered to MAN ECN IRL THM IBT Compulsory Offered to MAN ECN IRL THM IBT Elective Course Title Management Science Course Level Course Code Year Semester ECTS Weekly Course Hours Undergraduate / First Cycle QMT Fall 5 Instruction Language English Mode of Delivery In Class Prerequisite Prerequisite to QMT 4211 T A L QMT 4224 COURSE DESCRIPTION COURSE OBJECTIVE The course aims to provide students the basics of linear programming and management science tools for solving business-oriented optimization problems. LEARNING OUTCOMES At the end of this course the students are expected to, 1. Have a knowledge understanding the basic concepts of mathematical models, linear programming methods and management science techniques used to optimize business problems 2. Be able to use essential tools of linear programming for making business decisions. 3. Employ critical thinking and independent problem-solving skills to optimize real world business problems. 4. Be able to use Spreadsheet Software and a solver to perform analysis and support presentations. 5. Communicate clearly the results of an basic optimization methods and explain the managerial implication. LEARNING AND TEACHING STRATEGIES 1. Lectures Class lecture is highly interactive and format is direct. The instructor prompts students for response to questions posed and solicits their thoughts on issues discussed. Lectures will focus on the transfer of basic optimization concepts and techniques where comprehension is substantially enhanced by additional elaboration and illustration. The emphasis is on business applications rather than rigorous mathematics. 2. Review Sessions and Class Discussions Review sessions will be handled by the instructor each week in the last session of a lecture. In-class assignments and homework assignments are the basis of problems to be solved in these sessions. Individual participation by students in classroom discussion is strongly encouraged. 3. Computer Applications In the laboratory component, Spreadsheet Software with solver add-in and a particular solver package will be introduced to construct optimization models. Instruction on the use of this software as it relates to optimization problems will be provided in class and in the book. 1

2 ASSESSMENT METHODS GRADING % 1. Exams %60 There will be two exams during the semester. Midterm Exam (%30) Final Exam (%30) Exams will measure the ability to identify and apply the appropriate technique and/or method to real world business problems. Each exam will cover course materials and include problems like those assigned for homework, questions on lecture materials, and additional items covered in class meetings. 2. Home works and Participation Homework problems will be assigned frequently. It is imperative that you work and understand these problems to successfully complete the course. It is strongly recommended the students to work all homework problems as a study tool for the exams. %20 By completing homework assignments, each student will enhance analytical skills, as well as, improve competency utilizing Spreadsheet Software with solver add-in and a solver package for optimization and analysis. By actively participating in class discussions and in-class assignments, each student will improve communication and analytical skills through learning optimization concepts and business applications. 3. Case Analysis Case studies will offer an excellent opportunity for students to perform analysis, model formulation and develop solutions to realistic situations. For the case analysis, a group including three students should be formed. Any deviation from this target number requires approval of the instructor. The cases will be assigned to each group by the instructor in the beginning of the semester. Topics consist of the case analysis of a optimization problems found in managing a business, government, or non-profit organization, whether product or service oriented. %20 Case reports will be submitted to the instructor prior to the start of the last week of class as both a handout and a digital file named as course, departmentname and groupname (for example, ManagemenScience_Business_GroupOne). Each case report should be typed by using Microsoft Word and/or Excel and comprises the following: (i) a title page with the case title and full names of the authors, (ii) the main body of the report starting on the second page, and (iii) the report appendix. The main body of the report is where the group provides answers to each of the case questions, and can be up to three pages long. Numerical results, tables, exhibits, figures, etc., should be professionally presented in the report appendix. Case presentations will also be performed by each group in the last week of class. The case report will be presented using a PowerPoint, keeping it within minutes for the presentation. Group members will be prepared to answer questions regarding describing problem domain and data sources, tools, methods or algorithm applied, and interpretation of findings. By studying with a group, students will improve teamwork, analytical, and communication skills through identifying and applying optimization techniques to the real world business problems. Case reports and presentations will enable students improve their competency using the language of optimization to communicate the results. 2

3 ASSESSMENT CRITERIA 1. In exams, there will be one major part for each chapter. In each part, one or more questions are asked. Students are supposed to get at least 10 points from final exam so that he/she can be included in the bellcurve calculations. If any exam question is left unanswered, the value of that question will be subtracted from the exam score. If only the answer is given (i.e., no work showing how that answer was determined), the question will be graded at 25% of its value. 2. Grade for Student Participation will depend on (i) your class attendance, (ii) the quality of the answers you provide to questions posed by the instructor during class, and (iii) the general contribution you make to the creation of a positive learning environment. 3. A good attendance record may bring the grade up one level, for grades on the boundary between two grade levels. 4. The case analysis requires a cooperative effort. It is the responsibility of the team to assure that each team member has contributed approximately equally to the group work. Cases will be graded by the instructor and by the team members. Each member of the group will be asked at the end of the semester to evaluate his or her own contribution, and those of other team members. A peer evaluation form will be supplied during the last week of class. 5. Case reports will be evaluated for such factors as apparent understanding of the topic, originality of treatment and discussion, accuracy of results, comprehensiveness of the report s content and depth of the analysis, clarity and mechanics of presentation such as organization, format, punctuation, grammar, and quality of exhibits and charts. TEXT BOOK(S)/ REFERENCES / MATERIALS 1. Text Books: Introduction to Management Science, Hillier and Hillier, 2010, Pearson Education. İşletme Problemleri için Optimizasyon -Adım Adım Uygulama Örnekleriyle, Sabri ERDEM, Makina Mühendisleri Odası Yayınları, 2010 Introduction to Operations Research, by Hillier & Hillier, Lieberman, McGraw-Hill 2000 and later Introduction to Management Science 7th Ed. By Bernard W. Taylor III, Pearson Education Operations Research an Introduction, 9th Edition by Hamdy A. TAHA Sayısal Yöntemler: Yönetsel Yaklaşım by Ş. GÜMÜŞOĞLU, H. TÜTEK, , Beta Yayıncılık 2. Lecture Slides: Complementary of the text book. 3. Software: Spreadsheet Software with Solver add-in. Lindo/Lingo (optional) 4. Calculator: Students will need a scientific calculator for various calculation problems in and out of class, and during exams. 3

4 COURSE POLICIES AND RULES 1. Attending at least 70 percent of lectures is mandatory. 2. Plagiarism of any type will result in disciplinary action. 3. Absence will not be considered an excuse for submitting homework assignments late. 4. Delayed case reports will suffer grade decay equivalent to one letter grade per day late. 5. Students are required to have their own calculator for this course. It will not be allowed to share a calculator during exams. Cellular phones cannot be used as a calculator during an exam. COURSE OUTLINE WEEK TOPICS NOTES 1 Introduction to course Basic Concepts and Model Building Start group selection for case analysis Lecture Contents: (Basic Concepts of Optimization Types of Models Types of Mathematical Models Building Mathematical Models) 2 Introduction to Linear Programming Lecture Content: (LP Models, Assumptions of LP, Demonstrating LP Models Graphically, Optimize LP Graphically, Applying Sensitivity Analysis Analytically Special Case Analysis of LP) Assigning case studies to the groups 3 LP Applications in Business Lecture Content: (Investment and Portfolio Problems, Timetable Scheduling, Diet Problems, Assignment and Product Mix Problems) 4 Simplex Method Lecture Content: (Canonical Form of LP, Basic and Non-Basic Variables, Slack and Surplus Variables, Simplex Table, Deciding Pivot Row, Column and Values, Applying Matrix Row Elimination Method, Entering-Leaving Variables, Deciding Optimum Table, Differing Alternative Solution, Degeneracy, Unfeasible and Unbounded Solution, Working with Minimization Problems) 5 Duality and Sensitivity Analysis Lecture Content: (DUALITY: Duality, Primal-Dual Relationships, Economical Interpretations, Converting Minimization and Maximization Problems into Each Other, SENSITIVITY: Interpretation of Optimum Simplex Table, Shadow Price, Reduced Cost, Changing RHS and Objective Function Coefficients, 100% Rule) Computer Lab Applications: Introducing Excel Solver 6 Solving Minimization Problems Lecture Content: (Big M Method, Dual Simplex, Two Phase Method) Review Session 7 Solving Integer Problems Lecture Content: (Types of Integer Problems, Graphical Demonstration, Gomory Cutting Plane Method, Branch 4

5 and Bound Method, Enumeration Method) 8 Transportation and Assignment Algorithms Lecture Content: (TRANPORTATION ALGORTIMHS: Describing and Modeling Transportation Problems, Balanced and Unbalanced Cases, LP Formulation, NWC, Least Cost, VAM, Stepping Stone and MODI Methods ASSIGNMENT ALGORITHMS: Describing and Modeling Assignment Problems, Balanced and Unbalanced Cases, LP Formulation, Hungarian Algorithm, Solving Maximization Problems) 9 Project Planning Lecture Content: (Project Scheduling, Earliest and Latest Start and Finish Times, Critical Path, CPM, Three Time Estimation, Beta Distribution and PERT ) 10 Project Crashing Lecture Content: (Project Crashing, Crashing Methods, Resource Balancing) Review Session 11 Queue Theory Lecture Content: (Basic Queue Terms, Performance Indicators, Basic Queue Types, M/M/1, M/M/K queues, Optimizing cost of M/M/k queue) Computer Lab Applications 12 Case Study Presentations Lecture Content: (Presentation of Case Reports Peer Evaluations) 5