MCDM Techniques Using R Course | Prof. Gaurav Dixit | IIT Roorkee

Course Details

Master Multi-Criteria Decision Making with the Power of R Programming

In today's complex business and engineering landscapes, decisions are rarely based on a single factor. Choosing the right supplier, selecting an optimal project portfolio, or prioritizing sustainability initiatives requires balancing multiple, often conflicting, criteria. This is where Multi-Criteria Decision Making (MCDM) becomes an indispensable skill. And when combined with the statistical prowess of R programming, it transforms into a powerful framework for robust, data-driven analysis.

This detailed guide explores a structured 4-week course designed by Prof. Gaurav Dixit of IIT Roorkee that equips students and professionals with the expertise to implement sophisticated MCDM techniques using R.

Course Overview: Bridging Theory and Practical Application

This intensive course is meticulously crafted to move from foundational concepts to advanced applications. Over four weeks, participants will gain hands-on experience with the most prominent MCDM methodologies, learning to code and execute them in the R environment. The goal is not just to understand the theory but to build the capability to solve real-world decision problems.

Intended Audience:

• Undergraduate & Postgraduate Engineering Students (all branches)
• MBA Students specializing in Operations, Analytics, or IT
• PhD Scholars conducting research requiring decision models
• Professionals in R&D, Government Agencies, and Consultancy Firms

Prerequisite: A foundational knowledge of R programming is required to fully engage with the course's practical components.

Meet Your Instructor: Prof. Gaurav Dixit

The course is led by Dr. Gaurav Dixit, an Assistant Professor in the Department of Management Studies at the Indian Institute of Technology (IIT) Roorkee. Prof. Dixit brings a unique blend of academic excellence and industry experience to the classroom.

• Educational Pedigree: He holds a doctoral degree from IIM Indore and an engineering degree from IIT (BHU) Varanasi.
• Industry Expertise: His prior roles as a Software Engineer at Hewlett-Packard (HP) and a Project Manager ensure the course content is grounded in practical reality.
• Research Focus: His research in IT strategy, e-commerce, data mining, and big data analytics, published in top-tier journals, informs the course with cutting-edge insights.

Detailed 4-Week Course Curriculum

The course is logically segmented into weekly modules, each building upon the last to provide a comprehensive learning journey.

Week 1: Foundations of Decision Making

The first week establishes the core principles of MCDM and introduces fundamental value-based approaches.

• Basics and Principles of MCDM: Understanding the problem structure, criteria weighting, and decision matrices.
• MAVT & MAUT (Multi-Attribute Value/Utility Theory): Learning to model decision-maker preferences and aggregate scores across multiple attributes to rank alternatives.

Week 2: Prioritization and Distance-Based Methods

Week two delves into widely-used methods for deriving weights and evaluating alternatives based on an ideal benchmark.

• AHP Method (Analytic Hierarchy Process): A deep dive into using pairwise comparisons to break down complex decisions, establish priority weights for criteria, and check for consistency. Practical implementation in R is a key focus.
• Distance-Based Methods: Introduction to techniques like TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), which identifies the alternative closest to the ideal solution and farthest from the negative-ideal solution.

Week 3: Outranking, Group Decisions, and Efficiency Analysis

The third week covers advanced topics, including methods that handle imperfect data, group consensus, and efficiency measurement.

• Outranking Methods (e.g., PROMETHEE, ELECTRE): These methods model the real-world scenario where one alternative may outperform another on some criteria but underperform on others, without forcing full comparability.
• Group Decision Making: Techniques to aggregate preferences and judgments from multiple stakeholders or experts to reach a collective decision.
• DEA Method (Data Envelopment Analysis): A non-parametric linear programming method used to measure the relative efficiency of multiple decision-making units (like branches, suppliers, or projects) with multiple inputs and outputs.

Week 4: Structural Models and Multi-Objective Optimization

The final week explores models for understanding complex systems and solving problems with multiple objective functions.

• Structural Models: Introduction to methods like Interpretive Structural Modeling (ISM) and DEMATEL, which help map and understand the complex interrelationships between factors in a decision problem.
• MODM Solving Methods (Multi-Objective Decision Making): Transitioning to problems where objectives are expressed as mathematical functions. Introduction to solution concepts and techniques for generating Pareto-optimal fronts.

Essential Reference Materials

The course draws from authoritative texts to provide a strong theoretical backbone:

• Tzeng, G-H. & Huang, J-J. Multiple Attribute Decision Making: Methods and Applications. Chapman and Hall/CRC, 2011. A comprehensive guide to MADM techniques.
• Cohon, J.L. Multiobjective Programming and Planning. Dover Publications, 2004. A classic text on multi-objective optimization methods.

Why This Course is a Career Catalyst

Mastering MCDM techniques with R is not just an academic exercise; it's a significant career investment. This skillset is in high demand across:

• R&D Organizations: For project selection, technology assessment, and resource allocation.
• Government & Policy Agencies: For public policy analysis, sustainable development planning, and infrastructure prioritization.
• Consultancy Firms: As a core methodology for providing strategic advice to clients across industries.
• Corporate Strategy & Operations: For supplier selection, logistics optimization, portfolio management, and performance evaluation.

By completing this course, you will move from facing complex decisions with uncertainty to tackling them with a structured, analytical, and defensible methodology powered by one of the world's leading data analysis tools. Enroll to transform your decision-making capabilities and add a powerful, sought-after competency to your professional toolkit.

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