Elective for Mechanical Engineering
ME72504
Lecture |
: |
3 |
Year |
: IV |
Tutorial |
: |
1 |
Part |
: I |
Practical |
: |
3/2 |
3.7.using Premium Solver for Linear Programming
3.8.Goal programming & multi-objective programming
3.9.genetic algorithms
4. Decision Analysis |
(4 hours) |
4.1.Application of Decision analysis
4.2.Structuring Decision Problems
4.3.Demand limiter
4.4.Expected Value decision-making
4.5.Optimal Expected Value Decision Strategies
Course Objectives: |
5. |
Risk Analysis |
(10 hours) |
|||||
To make capable of managing data, analyzing data such as sorting, pivoting tables, |
5.1. Monte Carlo Simulation |
|||||||
5.2. Applications of Monte Carlo Simulation |
||||||||
and applying statistical analysis in a spreadsheet environment. To familiarize with |
||||||||
5.3. Building Monte Carlo Simulation Models |
||||||||
forecasting methods, linear programming, inventory models. To make familiar with |
||||||||
5.4. Different Probability Distributions |
||||||||
simulation in |
decision-making under risk and uncertainty with |
the use |
of risk |
|||||
5.5. Building Simulation Models with CRYSTAL BALL & analysis |
||||||||
analysis software such as CRYSTAL BALL. To make capable in applying the |
||||||||
knowledge gained during the course for solving real problems in decision-making. |
6. |
Optimization and Simulation |
(4 hours) |
|||||
6.1. Optimization under uncertainty |
||||||||
1. |
Introduction to Modeling for Decisions & Data Management and |
Analysis |
(7 |
6.2. Optimization and Monte carlo simulation |
||||
6.3. Use of OPTQUEST and CRYSTAL BALL |
||||||||
hours) |
||||||||
1.1. application and benefits of Operations Research |
||||||||
1.2. developing Models |
||||||||
1.3. analyzing and solving models; interpretation and Use of Model Results |
||||||||
1.4. applications of Data Management and Analysis |
Practical: |
|||||||
1.5. data Storage and Retrieval & data Visualization |
||||||||
Course project on real and practical problems such as forecasting, queuing, inventory and |
||||||||
2. Regression Analysis & Time series analysis |
(10 hours) |
optimization problems has to be done. The report has to be submitted on the acceptable |
||||||
2.1. Regression Analysis |
format at the end of the course. Group presentation should be carried out at the end of the |
|||||||
2.1.1. Simple linear regression |
course period. |
|||||||
2.1.2. Multiple linear regression |
||||||||
2.2. Forecasting models with |
||||||||
2.2.1. trend components |
References: |
|||||||
2.2.2. seasonal components |
||||||||
2.2.3. trend and seasonal components |
1. |
Ragsdale, Cliff T., “Spreadsheet Modeling and Decision Analysis, A Practical |
||||||
2.2.4. Selecting the best forecasting methods |
Introduction to Management Science”, South Western, Cengage Learning. |
|||||||
2.2.5. |
Forecasting with CB predictor |
2. |
Wayne Winston, and S. Christian Albright, “Practical Management Science: |
|||||
3. |
Introduction to optimization |
(10 hours) |
Spreadsheet modeling and applications”, Thompson Learning. |
|||||
3. |
Camm, Jeffrey D. and James R. Evans, “Management Science & Decision Technology”, |
|||||||
3.1. Modeling optimization problem in EXCEL |
||||||||
South – Western College Publishing, A Division of Thompson Learning, USA. |
||||||||
3.2. Building Linear Programming Models |
||||||||
4. |
Hillier, Frederick S., Mark S. Hillier, and Gerald |
J. Lieberman, “Introduction to |
3.3. Solving Linear Programming Models |
Management Science: A Modeling and Case Studies Approach with Spreadsheets”, |
|
3.4. Network modeling |
||
McGraw-Hill International Editions. |
3.5.Interpreting Solver Results and Sensitivity Analysis
3.6.solving Multi-objective Models
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:
Units |
Chapters |
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 |