Course Objectives:

To make capable of managing data, analyzing data such as sorting, pivoting tables, and applying statistical analysis in a spreadsheet environment. To familiarize with forecasting methods, linear programming, inventory models. To make familiar with simulation in decision-making under risk and uncertainty with the use of risk analysis software such as CRYSTAL BALL. To make capable in applying the knowledge gained during the course for solving real problems in decision-making.

  1. Introduction to Modeling for Decisions & Data Management and Analysis
    (7 hours)
    1. application and benefits of Operations Research
    2. developing Models
    3. analyzing and solving models; interpretation and Use of Model Results
    4. applications of Data Management and Analysis
    5. data Storage and Retrieval & data Visualization
  2. Regression Analysis & Time series analysis
    (10 hours)
    1. Regression Analysis
      1. Simple linear regression
      2. Multiple linear regression
    2. Forecasting models with
      1. trend components
      2. seasonal components
      3. trend and seasonal components
      4. Selecting the best forecasting methods
      5. Forecasting with CB predictor
  3. Introduction to optimization
    (10 hours)
    1. Modeling optimization problem in EXCEL
    2. Building Linear Programming Models
    3. Solving Linear Programming Models
    4. Network modeling
    5. Interpreting Solver Results and Sensitivity Analysis
    6. solving Multi-objective Models
    7. using Premium Solver for Linear Programming
    8. Goal programming & multi-objective programming
    9. genetic algorithms
  4. Decision Analysis
    (4 hours)
    1. Application of Decision analysis
    2. Structuring Decision Problems
    3. Demand limiter
    4. Expected Value decision-making
    5. Optimal Expected Value Decision Strategies
  5. Risk Analysis
    (10 hours)
    1. Monte Carlo Simulation
    2. Applications of Monte Carlo Simulation
    3. Building Monte Carlo Simulation Models
    4. Different Probability Distributions
    5. Building Simulation Models with CRYSTAL BALL & analysis
  6. Optimization and Simulation
    (4 hours)
    1. Optimization under uncertainty
    2. Optimization and Monte carlo simulation


Course project on real and practical problems such as forecasting, queuing, inventory and optimization problems has to be done. The report has to be submitted on the acceptable format at the end of the course. Group presentation should be carried out at the end of the course period.


  1. Ragsdale, Cliff T., “Spreadsheet Modeling and Decision Analysis, A Practical Introduction to Management Science”, South Western, Cengage Learning.
  2. Wayne Winston, and S. Christian Albright, “Practical Management Science: Spreadsheet modeling and applications”, Thompson Learning.
  3. Camm, Jeffrey D. and James R. Evans, “Management Science & Decision Technology”, South – Western College Publishing, A Division of Thompson Learning, USA.
  4. Hillier, Frederick S., Mark S. Hillier, and Gerald J. Lieberman, “Introduction to Management Science: A Modeling and Case Studies Approach with Spreadsheets”, McGraw-Hill International Editions.
  5. Evans, James R. and David L. Olson, “Introduction to Simulation and Risk Analysis”, Prentice Hall, Upper Saddle River, New Jersey.
  6. Winston, Wayne L., “Operations Research: Applications and Algorithms”, International Thompson Publishing.

Evaluation Scheme:

The questions will cover all the chapters in the syllabus. The evaluation scheme will be as indicated in the table below:

Unit Chapter Topics Marks*
1 1 all 16
2 2 all 16
3 3 all 16
4 4 & 6 all 16
5 5 all 16
Total 80

*There could be minor deviation in mark distribution.

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