Multi Criteria Decision Making (MCDM)

Summary

In this lecture, Dr. Hozairi introduces Multi-Criteria Decision Making (MCDM), an approach to decision-making that considers multiple criteria for selecting the best alternative. The lesson covers key concepts like criteria, alternatives, preferences, and MCDM methods (e.g., AHP, TOPSIS). Students will learn to apply MCDM techniques, including how to weight criteria, evaluate alternatives, and calculate scores. Benefits such as reduced bias and better decision quality are highlighted, along with challenges like data limitations. Practical steps in MCDM and data collection methods (e.g., surveys) are also discussed, with students engaging in practical exercises to solidify understanding.

Takeaways

• 😀 Multi-Criteria Decision Making (MCDM) helps decision-makers evaluate multiple factors when choosing the best alternative.
• 😀 MCDM considers more than one criterion, with the goal of finding the optimal solution based on various preferences and factors.
• 😀 Key components of MCDM include criteria, alternatives, preferences, and analysis methods.
• 😀 Common methods of MCDM analysis include SAW, AHP, TOPSIS, PROMETHEE, and others.
• 😀 MCDM requires assigning weights to criteria to reflect their importance in the decision-making process.
• 😀 There are two main categories of MCDM: Multi-Attribute Decision Making (MADM) and Multi-Objective Decision Making (MODM).
• 😀 The process of MCDM involves identifying criteria, assigning weights, evaluating alternatives, and analyzing data to make a decision.
• 😀 MCDM methods help reduce bias in decision-making by considering multiple criteria instead of focusing on a single factor.
• 😀 MCDM can result in more accurate and holistic decision-making by evaluating alternatives in a well-rounded way.
• 😀 Data collection for MCDM can involve interviews, focus groups, questionnaires, and surveys, with Google Forms being a popular tool for data gathering.

Q & A

What is Multi-Criteria Decision Making (MCDM)?

MCDM is an approach used in decision-making that takes into account more than one criterion or factor when choosing the best alternative. The decision-making process involves considering various objectives and criteria, typically more than three, to select the optimal solution.

What are the key learning objectives for students in this lecture on MCDM?

The learning objectives are: 1) To understand the basic concepts of MCDM, including criteria, alternatives, and preference methods. 2) To become familiar with different MCDM analysis methods like AHP, TOPSIS, and ELECTRE. 3) To learn how to perform MCDM analysis, including determining criteria, alternatives, and weighting them. 4) To understand the working mechanisms of MCDM.

How does MCDM help in the decision-making process?

MCDM simplifies the decision-making process by allowing decision-makers to evaluate alternatives holistically based on multiple criteria. It helps reduce bias, provides more optimal decisions, and facilitates better communication and coordination between decision-makers and decision-support systems.

What are the principles of MCDM?

The principles of MCDM include: 1) Assigning weights to each criterion, 2) Identifying alternative solutions, 3) Using a comparison scale between criteria, and 4) Ensuring that decisions are based on a comparison of alternatives with the established criteria.

What is the difference between multi-attribute and multi-objective decision-making?

Multi-attribute decision-making focuses on evaluating and ranking alternatives based on multiple attributes or criteria, whereas multi-objective decision-making involves solving optimization problems where multiple conflicting objectives need to be considered simultaneously. Techniques like goal programming and genetic algorithms are often used in multi-objective decision-making.

What methods are commonly used in MCDM analysis?

Common methods used in MCDM analysis include the Simple Additive Weighting (SAW), Analytic Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and PROMETHEE.

What are the key characteristics of problems that use MCDM?

The key characteristics of MCDM problems include: 1) Involvement of multiple criteria or factors, 2) A subjective nature, 3) Non-linearity, 4) Multiple alternatives, 5) Usage of analytical methods for evaluation, and 6) Decision outcomes depending on the preferences of the decision-maker.

What are the advantages and disadvantages of using MCDM?

Advantages of MCDM include: 1) Considering multiple factors, 2) Reducing bias, 3) Simplifying decision-making, and 4) Providing more accurate results. The disadvantages include: 1) The impact of limited data, which can influence the final outcomes, and 2) The complexity of methods requiring careful implementation.

How are data collected for MCDM analysis?

Data for MCDM analysis can be collected through various methods, including interviews, surveys, focus group discussions, observations, literature studies, and notably, questionnaires. For this course, students are encouraged to use tools like Google Forms for data collection.

What are the general steps for implementing MCDM?

The general steps for implementing MCDM include: 1) Determining relevant criteria for evaluating alternatives, 2) Assigning weights or importance to each criterion or sub-criterion, 3) Evaluating alternatives based on the selected criteria, 4) Processing the data by calculating the final scores for each alternative, and 5) Selecting the best alternative based on the final scores.