Module 1

Module 1: Outline of course and formulate and solve linear programming problems on spreadsheets (7.5 hours)

1. Introduction to the course, structure & evaluation components.
2. Linear programming – introduction
3. Spreadsheet formulation and solution.
4. Indicative list of models – product mix models, blending models, aggregate planning models.
5. Financial models.

Module 2

Module 2: Sensitivity Analysis and interpreting results (7.5 hours)

1. Linear programming – sensitivity analysis.
2. Interpretation of sensitivity reports.
3. Linear programming – formulating the dual, solution and economic interpretation.
4. Transportation and transshipment problems –formulation, solution.
5. Sensitivity analysis.
6. Assignment models with different types of constraints – formulation and solution.

Module 3

Module 3: Network Optimization problems in spreadsheets (7.5 hours)

1. Network optimization models – shortest route problem, minimal spanning tree problem, maximal flow problem.
2. Integer programming models.
3. Specialized integer programming models – fixed charge problems, set covering problems, knapsack problems, facility location problems, travelling salesman problems, cutting stock problems, capital budgeting problems.

Module 4

Module 4: Nonlinear Programming Models (7.5 hours)

1. Nonlinear programming models – formulation and solution. Managerial economics models, portfolio optimization models.
2. Multi criteria decision making: goal programming models – formulation and solution.
3. Multi criteria decision making: Analytic Hierarchy Process (AHP) – formulation, solution andconsistency check.

Course Description

Spreadsheets play an important role in the business operations of organizations. Managers can use them effectively for various types of data storage and analyses. The challenge before decision makers now is to make best use of the large amounts of available data and take better-informed decisions.

In this course, the focus is on using spreadsheets for prescriptive analytics. The students will learn to tackle various problems from the different functional areas of management by modeling and solving them using spreadsheets. Since this elective course is offered after the students have hada good grounding in all the functional areas of management, representative applications will be chosen from all functional areas of management for modeling and solving.

Course Outcomes& Learning levels

This course builds on the first-year core courses that introduced students to the different functional areas of management and also various types of analytics that help managers take decisions. This course seeks to build on that foundation and develop knowledge and skills in the usage of soft computing methods for decision-making. At the end of this course, the student will be able to:

1. Formulate and solve linear optimization models in spreadsheets, and interpret the results(L5)
2. Formulate and solve integer programming models in spreadsheets, and interpret the results(L5).
3. Formulate and solve nonlinear optimization models in spreadsheets, and interpret the results(L5).
4. Formulate and solve multi-criteria decision-making models in spreadsheets, and interpret theresults (L5).

Evaluation Pattern

* Based on assignments / Tests / Quizzes / Case Studies / Projects / Term paper / Field visit report.

1. Winston, W. L., & Albright, S. C. (2018). Practical management science (6th ed.). India: Cengage Learning.
2. Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., Cochran, J. J., Fry, M.J., & Ohlmann, J. W. (2019). An introduction to management science: quantitative approaches todecision making (14th ed.). India: Cengage learning.
3. Albright, S. C., & Winston, W. L. (2019). Business analytics: Data analysis & decision making. (6thed.). India: Cengage Learning.
4. Hillier, F. S, & Hillier, M. S. (2019). Introduction to management science: a modeling and case studies approach with spreadsheets (5th ed.). India: McGraw-Hill.
5. Stevenson, W. J., & Ozgur, C. (2007). Introduction to management science. India: McGraw-Hill, Inc.