Applied Operations Research - MB25C07

MB25C07:

Applied Operations Research

Course Objectives:

This course equips students with analytical and quantitative tools essential for modeling and solving complex Business problems. It emphasizes linear programming, decision theory, inventory models, queuing systems, and simulation techniques, enabling data-driven decision-making in logistics, finance, operations, and project management. Learners will gain proficiency in applying optimization and statistical modeling techniques using spreadsheet-based tools and modern software applications. The course prepares students to assess real-time scenarios with practical, strategic, and computational approaches to decision-making.

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)

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 calling source. 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 Business analytics – Use of Excel Solver, R, and Python in operations research – Introduction to prescriptive analytics.

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

Course Outcomes

Upon completion of the course, students will be able to:

● CO1: Demonstrate conceptual knowledge of quantitative decision models, linear programming, transportation and assignment problems, decision theory, game theory, inventory control, replacement models, queuing systems, simulation, and emerging trends and software tools.

● CO2: Interpret and relate the principles of linear programming, transportation and assignment models, decision theory, game theory, inventory and replacement models, queuing theory, and simulation to identify and frame business problems.

● CO3: Apply analytical and quantitative tools, including linear programming, transportation and assignment methods, decision trees, inventory models, and queuing theory, to solve complex business problems.

● CO4: Analyze business problems across different domains, such as logistics, operations, and project management, using techniques like linear programming, game theory, inventory models, and simulation to assess alternative solutions and their impacts.

● CO5: Evaluate the effectiveness of various operations research techniques, including transportation and assignment models, decision theory, and queuing models, to make informed and optimal decisions under real-world constraints and uncertainties.

● CO6: Develop data-driven solutions and strategic recommendations by integrating knowledge of linear programming, inventory management, queuing theory, and simulation, and utilizing modern software tools to solve complex managerial decision problems.