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About the Programme Academic Staff Program Qualifications Lessons Matrix of Course & Program Qualifications Turkish Qualifications Framework (TQF) TQF & Program Qualifications
  1. Open Education Faculty
  2. Business Administration (English)
  3. Course Structure Diagram with Credits
  4. Business Decision Models
  5. Description
  • Description
  • Learning Outcomes
  • ECTS Credit Load
  • Course's Contribution to Program
  • Learning Outcomes & Program Qualifications
Last Updated: 06/10/2026

Code - Course Title Course Type Laboratory + Practice ECTS
İŞL353U - Business Decision Models V. SEMESTER 0 +0 5.0
Language of Instruction İngilizce
Course Type Elective Courses
Course Instructor(s) Assoc. Prof. Çağlar Karamaşa
Mode of Delivery Online Education
Place of Delivery
Used Educational Platforms
Prerequisites There are not any prerequisites and co-requisites for this course.
Courses Recomended Recommended optional programme components are YBS401U Operations Research and YBS202U Business Analytics.
Recommended Resources Recommended reading list are as below:Taha. H.A. (2007). Operations Research: An Introduction, 8th ed., Pearson/Prentice Hall, New Jersey, USA.Hillier. F. and Liebermen. G.J. (2004). Introduction to Operations Research, 5th ed., McGraw-HillHigher Education.Winston. W.L. (2003). Operations Research: Applications and Algorithms, 3rd ed., CengageLearning, USA.Balakrishnan, N., Render, B. & Stair, R.M. (2007). Managerial decision modeling with spreadsheets,Second Edition, Prentice Hall, New Jersey.Dewhurst, F. (2006). Quantitative methods for business and management, Second Edition, McGraw-Hill,USA.Monahan, G.E. (2000). Management decision making spreadsheet modeling, analysis and applications, Cambridge University Press, Cambridge.Timor, M. (2010). Yöneylem araştırması, Türkmen Kitabevi, İstanbul.Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., Cochran, J. J., Fry, M. J. & Ohlmann, J. W. (2016). Quantitative Methods for Business (Thirteenth Edition), Change Learning, USA.Evans, J. R. (2007). Statistics, Data Analysis & Decision Modeling (Third Edition), Prentice Hall, NewJersey.Wisniewski, M. (2006). Quantitative Methods for Decision (Fourth Edition), Prentice Hall, England.Ronald. R.L. (2018). Optimization in Operations Research. 2nd ed..Pearson-India.Barron, Emmanuel N. (2013). Game Theory: An Introduction (Second Edition), John Wiley & Sons Inc., New Jersey.Gardner, R. (2003). Games for Business and Economics (Second Edition), John Wiley & Sons Inc., New Jersey.Rasmusen, E. (2007). Games and Information: An Introduction to Game Theory, Blackwell Publishing.Ching, Wai-Ki & Ng, Michael, K. (2006). Markov Chains: Models, Algorithms and Applications,Springer, USA.Durrett, R. (2016). Essentials of Stochastic Processes (Third Edition), Springer, Switzerland.Kayran, Ahmet H. & Yücel, Mehmet N. (2014). Olasılık Teorisi ve Stokastik Süreçler, PapatyaYayıncılık Eğitim, Istanbul.
Work Placement There is not any internship/application for this course.
Aims of the Course

Weeks Learning Outcomes Topics Teaching Methods
Week - 1 The Concept of Decision Making; Common Characteristics of Decision Making; Decision Making Process; Types of Decision Making Environments: Decision making under certainty, Decision making under uncertainty, Decision making under risk.
Week - 2 Decision Making Under Uncertainty;Methods for Decision Making Under Uncertainty: Equally likely (laplace), Criterion of optimism (plunger), Criterion of pessimism (wald), Criterion of realism (hurwicz), Minimax regret (savage).
Week - 3 Decision Making Under Risk: Expected value (or expected monetary value) criterion, Expected opportunity loss criterion, Maximum probability criterion; Expected Value of Perfect Information; Decision Tree: Structure of a decision tree, Constructing and evaluating a decision tree; Bayes’ Theorem.
Week - 4 Linear Programming Basics: Terminology, taxonomy, and assumptions;Modeling With Linear Programming: Agricultural planning, Nutrition problem, Production planning, Inventory control, Manpower planning, Logistics management, Portfolio investments; Graphical Solution of Two Variable-Linear Programs.
Week - 5 Solving problems for midterm exam
Week - 6 Solving problems for midterm exam
Week - 7 Midterm exam
Week - 8 Transition from Graphical to Algebraic Solution; The Algebra of Simplex Method; Simplex Method in Tabular Form.
Week - 9 The Transportation Model: Duality, the concept and the properties, The economic interpretation of the dual model, The dual of the transportation model, Optimality test for the transportation model using the properties of duality; The Solution Method for the Transportation Model: Initialization methods, Test for optimality, Iteration; The Solution Method for the Assignment Model.
Week - 10 Basic Terminology and Classification of Games: Basic concepts of game theory, Classification of games; Two-Person Zero-Sum Games: Mixed Strategies, Optimal mixed strategies and value of a 2×2 zero-sum game, Dominance strategies; Graphical Solution of 2xn and mx2 Games.
Week - 11 Stochastic Processes; Markov Property and Markov Chains: Transition matrix, Transition diagram, N-step transition matrix, Regular transition matrix; Classification of States: Transient and recurrent states, Periodic and aperiodic states, Absorbing states; Steady-State Behavior of Markov Chains.
Week - 12 Solving problems for final exam
Week - 13 Solving problems for final exam
Week - 14 Final exam

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Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 30
Final Exam 1 70
Toplam (%) 100

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