AM 76502

Course objective
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, and 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 modelling for decisions & data management and analysis(6 hours)
    1. Historical background
    2. Application and benefits of operations research
    3. Model: types, characteristics, benefits
    4. Analyzing and solving models; interpretation and use of model results
    5. Applications of data management and analysis
    6. Data storage and retrieval & data visualization
    7. Use of spreadsheet in data management

  2. Regression analysis & time series analysis(10 hours)
    1. Regression analysis
      1. Simple linear regression
      2. Multiple linear regression
    2. Time series analysis
      1. Stationary, non-stationary and seasonal data
      2. Stationary models
      3. Seasonality models
      4. Trend models
      5. Trend and seasonal components
      6. Selecting the best forecasting methods
      7. Forecasting with CB predictor

  3. Introduction to optimization(16 hours)
    1. Mathematical programming and its applications
    2. Characteristics of optimization problems
    3. Formulating and solving LP problems : intuitive and graphical approach
    4. Modeling and solving LP problems in a spreadsheet
    5. Interpreting solver results and sensitivity analysis
    6. Network analysis
    7. Integer linear programming
    8. Goal programming & multi-objective programming
    9. Nonlinear programming and genetic programming

  4. Decision analysis(5 hours)
    1. Application of decision analysis
    2. Characteristics of decision problems
    3. Payoff matrix
    4. Decision rules: probabilistic and non-probabilistic methods
    5. Decision trees

  5. Risk analysis(8 hours)
    1. Random variables and risk
    2. Methods of risk analysis
    3. Monte Carlo simulation and its application
    4. Random number generators (RNGs)
    5. Different probability distributions
    6. Building Monte Carlo simulation models
    7. Building simulation models with CRYSTAL BALL & analysis
    8. Optimization and simulation using OPTQUEST and CRYSTAL BALL

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 of the syllabus. The evaluation scheme will be as indicated in the table below:




1 & 3

1 all & 3.1 to 3.5






3.6 to 3.9










*There might be minor deviation in marks distribution.

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