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