Applied Operations Research

Syllabus

Course Code: MB25C07

Applied Operations Research

Introduction to Quantitative Decision Models (Theory and Problem)

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.

Extensions of Linear Programming –Transportation & Assignment (Problems)

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.

Decision Theory and Game Theory Applications (Theory and Problem)

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.

Inventory Control and Replacement Models (Theory and Problem)

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.

Queuing Models and Monte Carlo Simulation (Problem)

Queuing Theory -single and multi-channel models –infinite number of customers and infinite callingsource. Monte Carlo simulation –use of random numbers, application of simulation techniques.

Emerging Trends and Tools for Quantitative Decision-Making (Theory and Problem)

Integration of quantitative techniques in Businessanalytics –Use of Excel Solver, R, and Python in operations research –Introduction to prescriptive analytics.