Quantitative Techniques for Decision Making

Syllabus

BA4201

Quantitative Techniques for Decision Making

UNIT I: Introduction to Linear Programming (LP)                                                                                9

Relevance of Quantitative Techniques in Management Decision Making. Linear Programming-Formulation, Solution by Graphical and Simplex Methods (Primal - Penalty, Two Phase), Special Cases.
Sensitivity Analysis.

UNIT II: Linear Programming Extensions                                                                                              9

Transportation Models (Minimising and Maximising Problems) – Balanced and Unbalanced Problems – Initial Basic Feasible Solution by N-W Corner Rule, Least Cost and Vogel’s Approximation Methods. Check for Optimality. Solution by MODI / Stepping Stone Method. Case of Degeneracy. Transhipment Models.

Assignment Models (Minimising and Maximising Problems) – Balanced and Unbalanced Problems. Solution by Hungarian and Branch and Bound Algorithms. Travelling Salesman Problem. Crew Assignment Models.

UNIT III: Decision and Game Theories                                                                                                   9

Decision Making under Risk – Decision Trees – Decision Making under Uncertainty.

Game Theory-Two-Person Zero Sum Games-Saddle Point, Dominance Rule, Convex Linear Combination (Averages), Methods of Matrices, Graphical and LP Solutions.

UNIT IV: Inventory and Replacement Models                                                                                        9

Inventory Models – EOQ and EBQ Models (With and without Shortages), Quantity Discount Models.

Replacement Models-Individual Replacement Models (With and without Time Value of Money) – Group Replacement Models.

UNIT V: Queuing Theory and Simulation                                                                                                9

Queuing Theory - Single and Multi-Channel Models – Infinite Number of Customers and Infinite Calling Source.

Monte Carlo Simulation – use of Random Numbers, Application of Simulation Techniques