MB25C07 Applied Operations Research Notes, Important Questions, Question Bank, MCQ, Question Paper, Syllabus study materials
Introduction to Quantitative Decision Models
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. (Theory and Problem)
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) MB25C07 Applied Operations Research MCQ
Decision making under risk – Decision trees – Decision making under uncertainty. Game TheoryTwo-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) MB25C07 Applied Operations Research Notes
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) Important Questions
Integration of quantitative techniques in Business analytics – Use of Excel Solver, R, and Python in
operations research – Introduction to prescriptive analytics.
| Syllabus | Click Here |
| Notes | Click Here |
| Important Questions | Click Here |
| Question Bank | Click Here |
| MCQ | Click Here |
References:
1. Hamdy A. Taha, Operations Research: An Introduction, 11th Ed., Pearson Education, 2022
2. G. Srinivasan, Operations Research: Principles and Applications, PHI Learning, 2nd Ed., 2011
3. N. D. Vohra, Quantitative Techniques in Management, Tata McGraw Hill,2010
4. R. Paneerselvam, Operations Research, PHI Learning, 4th Ed., 2018
5. Frederick Hillier & Mark Hillier, Introduction to Management Science, McGraw Hill India,
6th Ed., 2023
6. Bernard W. Taylor III, Introduction to Management Science, Pearson, 9th Ed., 2020
7. S. Kalavathy, Operations Research, Vikas Publishing House, 4th Ed., 2022
8. Nagraj B., Barry R. & Ralph M. S. Jr., Managerial Decision Modelling with Spreadsheets,
Pearson Education, 2nd Ed., 2007
Anna University Exam Time Table