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| OREN3045 | Operations Research | 2+1+0 | ECTS:4 | | Year / Semester | Fall Semester | | Level of Course | First Cycle | | Status | Compulsory | | Department | DEPARTMENT of FOREST INDUSTRY ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face, Practical | | Contact Hours | 14 weeks - 2 hours of lectures and 1 hour of practicals per week | | Lecturer | Dr. Öğr. Üyesi İbrahim YILDIRIM | | Co-Lecturer | ASSIST. PROF. DR. İBRAHİM YILDIRIM, | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The course aims to expose the students to the operational research methods such as linear programming, integer programming, goal programming, network models and simulation for solving resource-constrained planning problems. Students learn to create and solve models that represent real forestry industry and forest engineering problems, and how to present results appropriately. They learn to critically analyze assumptions that are inherent in modeling technology or in formulation, and to accurately describe and interpret the essential elements of models. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Define and explain the importance of operations research (OR) techniques as applied to forestry and wood processing plants | 2,4,6 | 1,3 | | LO - 2 : | Formulate a real forestry problem in mathematical symbols using OR techniques such as linear programming, goal programming and network techniques. | 2,4,6 | 1,3 | | LO - 3 : | Solve the models using appropriate solutions techniques such as simplex method, stepping stone method and modified distribution method, | 2,4,6 | 1,3 | | LO - 4 : | Solve a model using a computer program and present the outcome to the managers | 2,4,6 | 1,3 | | LO - 5 : | Analyze and interpret model results with sensitivity analysis techniques | 2,4,6 | 1,3 | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | | |
| Introduction to operations research: system, model and model types, OR principles, model structure, Decision making techniques: linear programming, integer programming, goal programming, transportation problems and meta-heuristic programming techniques (simulated annealing) Model semantics: structure of a model, decision variables, model objectives, model constraints. Model construction: mathematical formulation of a number of sample forestry problems in linear programming (LP) techniquesModel solution: graphical solution of LP model, matrix generation, basic steps of Simplex Method, special cases in solving an LP model with simplex method (degeneracy, multiple optimal solution, unbounded problems and infeasible solution) Duality and sensitivity analysis: duality concept, sensitivity of constraints, sensitivity of coefficients in the objective function, and introduction of a new product. Goal programming: model construction, multiple goals, priorities, achievement function, target deviations, solving a goal programming model with a modified simplex methodNetwork techniques: Network concept, network constructionTransportation problems: transportation model, solving the model with a stepping stone method, north-west corner method, MODİ method, interpretation of the model outcome. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction to operations research | | | Week 2 | Basic principles of operations research techniques and introduction to system, model, model types and problem solving methods | | | Week 3 | Introduction to Linear Programming (LP) | | | Week 4 | Building LP models of some sample problems | | | Week 5 | Building LP models of some sample forest engineering problems | | | Week 6 | Solution of LP models with graphical method and exercise | | | Week 7 | Solution of LP models with Simplex method and exercise | | | Week 8 | Sensitivity analysis (Economic analysis) and exercise | | | Week 9 | Mid-term exam | | | Week 10 | Sensitivity analysis (Economic analysis continued) and exercise | | | Week 11 | Introduction to LINGO programs and exercise | | | Week 12 | LINGO programs and exercises with sample models | | | Week 13 | Introduction to goal programming (GP): model construction, solution and exercise | | | Week 14 | Presentation of Homework | | | Week 15 | Solution of sample problems with GP and exercise | | | Week 16 | End-of-term exam | | | |
| 1 | Başkent, E.Z. 2004. Yöneylem Araştırması, Modelleme ve doğal kaynak uygulamaları, KTÜ Yayınları, Trabzon, 478 p. | | | |
| 1 | Rao, K.V. 1986. Management Science. McGraw Hill Book Company, 630p. | | | 2 | Davis K.R. and McKeown, P. G. 1981. Quantitative Models for Management. Kent Publishing Company, Boston, 735 P. | | | 3 | Osman Halaç, Kantitatif Karar Verme Teknikleri (Yöneylem Araştırması), 3. Baskı, Evrim Dağıtım, İstanbul-1991 | | | 4 | W.L. Winston, Operations research Applications and Algorithms, Second Edition, Duxbury Press, California, 1991 | | | 5 | Dykstra, D.P. 1984. Mathematical Programming for Natural Resource Management, McGraw-Hill Book Company, 318 p. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 16/11/2022 | 2 | 25 | | Homework/Assignment/Term-paper | 14 | 27/12/2022 | 2 | 25 | | End-of-term exam | 16 | 11/01/2023 | 2 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 1 | 14 | 14 | | Laboratuar çalışması | 0 | 0 | 0 | | Arasınav için hazırlık | 8 | 2 | 16 | | Arasınav | 2 | 1 | 2 | | Uygulama | 1 | 14 | 14 | | Klinik Uygulama | 0 | 0 | 0 | | Ödev | 0 | 0 | 0 | | Proje | 0 | 0 | 0 | | Kısa sınav | 0 | 0 | 0 | | Dönem sonu sınavı için hazırlık | 5 | 2 | 10 | | Dönem sonu sınavı | 2 | 1 | 2 | | Diğer 1 | 0 | 0 | 0 | | Diğer 2 | 0 | 0 | 0 | | Total work load | | | 100 |
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