NNFL 13- Guru Nanak Dev Engineering College

“Principles of Soft Computing, 2nd
Edition”
by S.N. Sivanandam & SN Deepa
Copyright © 2011 Wiley India Pvt. Ltd. All rights reserved.
CHAPTER 13
FUZZY DECISION
MAKING

FUZZY DECISION MAKING
Decision making is a very important social, economical and scientific
endeavor.
The decision-making process involves three steps:
1. Determining the set of alternatives,
2. Evaluating alternatives,
3. Comparison between alternatives.
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Certain fuzzy decision-making process are as follows:
Individual decision making,
Multiperson decision making,
Multiobjective decision making,
Multiattribute decision making,
Fuzzy Bayesian decision making.
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INDIVIDUAL DECISION MAKING
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MULTIPERSON DECISION MAKING
In multiperson decision making, the decision makers have access to
different information upon which to base their decision.
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MULTIOBJECTIVE DECISION MAKING
In making a decision when there are several objectives to be realized,
then the decision making is called multiobjective decision making.
Many decision processes may be based on single objectives such as
cost minimization, time consumption, profit maximization
and so on.
The main issues in multiobjective decision making are:
To acquire proper information related to the satisfaction of the
objectives by various alternatives.
 To weigh the relative importance of each objective.
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MULTIATTRIBUTE DECISION MAKING
 The evaluation of alternatives can be carried out based on several
attributes of the object called multiattribute decision making.
 The attributes may be classified into numerical data, linguistic data
and qualitative data.
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The problem of decision-making structure in multiattributes deals with
determination of an evaluation structure for the multiattribute decision
making from the available multiattribute data xi (i = 1 to n) and
alternative evaluations y shown in the table.
MULTIATTRIBUTE DECISION MAKING
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FUZZY BAYESIAN DECISION MAKING
 Probability theory.
 Bayesian inference:
• Use probability theory and information about independence.
• Reason diagnostically [from evidence (effects) to conclusions
(causes)] or casually (from causes to effects).
 Bayesian networks:
• Compact representation of probability distribution over a set of
propositional random variables.
• Take advantage of independence relationships.
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FUZZY BAYESIAN DECISION MAKING
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Expected Utility
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SUMMARY
The chapter has discussed the various decision-making process.
In many decision-making situations, the goals, constraints and
consequences of the defined alternatives are known imprecisely, which
is due to ambiguity and vagueness.
Methods for addressing this form of imprecision are important for
dealing with many of the uncertainties we deal within human systems.

NNFL 13- Guru Nanak Dev Engineering College

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