BUSINESS DECISION MODELS

Content

Weeks	Topics
1. Weeks	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.
2. Weeks	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).
3. Weeks	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.
4. Weeks	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.
5. Weeks	Solving problems for midterm exam
6. Weeks	Solving problems for midterm exam
7. Weeks	Midterm exam
8. Weeks	Transition from Graphical to Algebraic Solution; The Algebra of Simplex Method; Simplex Method in Tabular Form.
9. Weeks	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.
10. Weeks	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.
11. Weeks	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.
12. Weeks	Solving problems for final exam
13. Weeks	Solving problems for final exam
14. Weeks	Final exam