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Course Description Course Name : Optimization Techniques - I Course Code : İSB221 Course Type : Compulsory Level of Course : First Cycle Year of Study : 2 Course Semester : Fall (16 Weeks) ECTS : 5 Name of Lecturer(s) : Prof.Dr. SELAHATTİN KAÇIRANLAR Learning Outcomes of the Course : Describes the properties of the DP problem Builds model, uses a graphical and analytical methods of solution Use the Simplex Solution Method Distinguish the difference Simplex method between the two-phase method Uses the Two-Phase Method Uses Big M Method Write Dual of the linear model , distinguishes the relationship between the original and the Dual models Solutions Apply Dual Simplex Method Write Balanced and unbalanced transportation model, apply the methods of solution Use Package in solving models Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : To establish a model for Linear Programming Problems and established methods to solve a variety of models, to solve Dual model , to learn Transportation models Course Contents : Hyperplanes, convex sets, introduction to Linear Programming Problem (LPP), geometric solutions, the simplex method, duality, relations between the primal and dual problems, the dual simplex method, sensitivity analysis, transportation problem, assignment problem. Language of Instruction : Turkish Work Place : Faculty of Arts and Sciences Annex Classrooms   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Definitions with DP , Examples, and Model Building on DP Source reading Lecture discussion and problem-solving 2 Hyper Planes, Convex Sets, Convex Linear Functions on Sets Source reading Lecture discussion and problem-solving 3 Graphical Solution Methods Source reading Lecture discussion and problem-solving 4 Gauss Jordan Reduction, the canonical form for DPP Source reading Lecture discussion and problem-solving 5 Analytical Solution Source reading Lecture discussion and problem-solving 6 Simplex Solution Method Source reading Lecture discussion and problem-solving 7 Two-Phase Method (First phase) Source reading Lecture discussion, problem-solving and using package programs 8 Mid-term Exam Rewview the topics discussed in the lecture notes and sources Written exam 9 Two-Phase Method (Two phase) Source reading Lecture discussion, problem-solving and using package programs 10 Big M method Source reading Lecture discussion, problem-solving and using package programs 11 The dual of the linear model, Relationships the original models and Dual Between Solutions Source reading Lecture discussion, problem-solving and using package programs 12 Dual Simplex Method Source reading Lecture discussion, problem-solving and using package programs 13 Transportation Model, Solution Methods Source reading Lecture discussion, problem-solving and using package programs 14 to take advantage of the package programs that the solution of Models Source reading , package programs Lecture discussion, problem-solving and using package programs 15 Problem solving Problem solving Problem solving 16/17 Final exam Rewview the topics discussed in the lecture notes and sources Written exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Elementary Linear Programing With Applications, Bernard Kolman and Robert E. Beck, Academic Press,1980 Required Course Material(s)  Yöneylem Araştırması, Ahmet Öztürk, Ekin Yayınevi,2009  Optimizasyon, Ayşen Apaydın,A.Ü.F.F. Dön. Ser. Yayınları, 1996  Yöneylem Araştırması, Hamdy A. Taha(Çevirenler : Ş. Alp Baray- Şakir Esnaf), Literatür Yayıncılık, 2000  Optimizasyon Teknikleri, Hasan Bal,Gazi Üniversitesi Yayınları, 1995   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 80     Homeworks/Projects/Others 10 20 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 Utilize computer systems and softwares 2 2 Apply the statistical analyze methods 1 3 Make statistical inference(estimation, hypothesis tests etc.) 1 4 Generate solutions for the problems in other disciplines by using statistical techniques 5 5 Discover the visual, database and web programming techniques and posses the ability of writing programme 0 6 Construct a model and analyze it by using statistical packages 5 7 Distinguish the difference between the statistical methods 4 8 Be aware of the interaction between the disciplines related to statistics 5 9 Make oral and visual presentation for the results of statistical methods 4 10 Have capability on effective and productive work in a group and individually 3 11 Develop scientific and ethical values in the fields of statistics-and scientific data collection 1 12 Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics 4 13 Emphasize the importance of Statistics in life 4 14 Define basic principles and concepts in the field of Law and Economics 0 15 Produce numeric and statistical solutions in order to overcome the problems 4 16 Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events 5 17 Use proper methods and techniques to gather and/or to arrange the data 3 18 Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs 0 * 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 10 3 30     Mid-term Exams (Written, Oral, etc.) 1 10 10     Final Exam 1 10 10 Total Workload: 134 Total Workload / 25 (h): 5.36 ECTS Credit: 5