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Course Detail

Course Name Advanced Tools for Decision Support (ATDS)
Course Code 23BA615E
Program MBA
Credits 3
Course category Elective
Area Information Systems and Analytics


Module 1

Module 1: Able to understand the human decision- making process and various approaches fordecision-making (6 hours)

  1. Introduction to the course, structure & evaluation components.
  2. Human decision-making, various approaches for decision making.
  3. Decision support systems – types, classifications, typical components, new trends.
Module 2

Module 2: Able to understand Soft Computing, Artificial Neural Network, Fuzzy logic (6 hours)

  1. Introduction to soft computing.
  2. Introduction to Evolutionary approaches – Evolutionary Programming, Evolution Strategies, Genetic Algorithms, Genetic Programming.
  3. Basics of genetic algorithms (GA), motivation from nature, mechanics of GA.
  4. Artificial Neural Networks (ANN) – comparison with biological neural network, models of artificial neuron,network architectures, learning approaches.
  5. Fuzzy Logic – What is fuzzy logic, brief history, introduction to fuzzy sets, linguistic variables, linguistic modifiers and fuzzy rules.
Module 3

Module 3: Able to do modeling inspread sheets & GA and ANN addins to spreadsheets (6 hours)

  1. Spreadsheet modeling (revising concepts from earlier courses).
  2. Spreadsheet based tools for decision support (especially Genetic Algorithm, and Artificial NeuralNetwork based tools).
  3. Crew scheduling problems.
  4. Tower location problems &Budget allocation problems.
Module 4

Module 4: Able to understand optimization problems, portfolio Management problems in spreadsheets using GA (6 hours)

  1. Portfolio optimization problems (with and without transaction costs), finding the efficient frontier,the scenario approach for portfolio optimization.
  2. Portfolio balancing problems.
  3. Purchasing models – with and without quantity discounts.
  4. Travelling salesman problem& Routing problems with precedence constraints.
Module 5

Module 5: Able to solve advanced problem solving using spreadsheet (6 hours)

  1. Investment analysis with neural networks, classification problems using neural networks, numericprediction using neural networks.
  2. k-mediod clustering, discriminant analysis models.

Course Description  & Course Outcomes

Course Description

Advancements in Information Technology have made data collection, storage and retrieval much easier and faster than before. The challenge before decision makers now is to make best use of the large amounts of available data and take better-informed decisions. This course aims at giving an introduction to latest trends in decision support technologies, with special emphasis on soft computing methods and simulation. Besides giving a basic understanding of the various technologies, there will be emphasis on the application of those. Spreadsheet-based tools will be used for this purpose. Since this elective course is offered after the students have had a good grounding in all the functional areas of management, representative applications will be chosen from all functional areas of management for modeling and solving. This will give the students a fair idea of how they can apply what they have learned, using user-friendly software that are spreadsheet-based.

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 courseseeks 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. Demonstrate an understanding of the various decision-making approaches used by human beings and the biases that affect our decisions. (L2).
  2. Demonstrate an understanding of the theoretical underpinnings and working principles of various soft computing techniques like genetic algorithms, artificial neural networks and fuzzy logic. (L2).
  3. Evaluate a problem situation and apply the relevant soft computing techniques to solve the problem through the use of appropriate spreadsheet add-ins, and draw meaningful conclusions from the results. (L5).

Evaluation Pattern

# Assessment Component Percentage of Marks
1 Continuous Assessment * 60
2 End –Term Examination 40

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

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