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TECHNIQUES FOR DATAAND
KNOWLEDGE DRIVEN MULTI-
CRITERIA DECISION MODELS
PRESENTED BY
M.CHAIRMAN AP/ECE
KARPAGAM INSTITUTE OF TECHNOLOGY
WHAT IS MULTI-CRITERIA DECISION ANALYSIS
• Multi-criteria decision analysis (MCDA) is a formal, structured and transparent decision making
methodology. Its ism is to assist groups or individual decision makers to explore their decisions in the case
of complex situations with multiple criteria.
• MCDA does not provide the ‘right’ answer.
• MCDA does not provide an objective analysis.
• MCDA does not relieve decision makers of the responsibility of making difficult judgments.
MCDM
MCDA assists the decision maker in confidently reaching a decision by:
• enabling decision makers to gain a better understanding of the problem faced;
• organising and synthesizing the entire range of information;
• integrating objective measurements with value judgements;
• making explicit and managing the decision maker’s subjectivity; and
• ensuring that all criteria and decision factors have been taken properly into account.
STEPS OF DECISION MAKING PROCESS
Step 1. Define the problem
“This process must, as a minimum, identify root causes, limiting assumptions, system
and organizational boundaries and interfaces, and any stakeholder issues. The goal is to
express the issue in a clear, one-sentence problem statement that describes both the initial
conditions and the desired conditions.”
Step 2. Determine requirements
“Requirements are conditions that any acceptable solution to the problem must meet.
Requirements spell out what the solution to the problem must do.”
STEPS OF DECISION MAKING PROCESS
Step 3. Establish goals
Goals are broad statements of intent and desirable programmatic values.... Goals go beyond the
minimum essential must have’s (i.e. requirements) to wants and desires.” In mathematical form, the
goals are objectives contrary to the requirements that are constraints. The goals may be conflicting
but this is a natural concomitant of practical decision situations.
Step 4. Identify alternatives
“Alternatives offer different approaches for changing the initial condition into the desired
condition.” Be it an existing one or only constructed in mind, any alternative must meet the
requirements. If the number of the possible alternatives is finite, we can check one by one if it
meets the requirements.
STEPS OF DECISION MAKING PROCESS
Step 5. Define criteria
“Decision criteria, which will discriminate among alternatives, must be based on the goals. It is
necessary to define discriminating criteria as objective measures of the goals to measure how well each
alternative achieves the goals.” Since the goals will be represented in the form of criteria, every goal
must generate at least one criterion but complex goals may be represented only by several criteria.
Step 6. Select a decision making tool
There are several tools for solving a decision problem. Some of them will be briefly described here, and
references of further readings will also be proposed. The selection of an appropriate tool is not an easy
task and depends on the concrete decision problem, as well as on the objectives of the decision makers.
STEPS OF DECISION MAKING PROCESS
Step 7. Evaluate alternatives against criteria
Every correct method for decision making needs, as input data, the evaluation of the
alternatives against the criteria.
Step 8. Validate solutions against problem statement
The alternatives selected by the applied decision making tools have always to be validated
against the requirements and goals of the decision problem.
CLASSIFICATION OF DECISION
MAKING METHODS
OPTIMALITY UNDER CONSTRAINTS
• The choices / decisions of the decision maker differs with his preferences,
• The constraints limit the available choices,
• The decisions under multiple preferences / goals / objectives are more complex
and
• It may not be possible to attain / achieve many goals at all circumstances.
PURCHASE OF CAR
• Consider the cost, performance, comfort and warranty data of different models of car as
given in Table I.
• Table II provides the choice of various buyers under different preferences and no
restrictions on other attributes.
• Under certain restrictions given as conditions, the choices of the buyers differ and are
shown in Table III. Table IV provides the solutions for multiple preferences.
TABLE I DATA OF DIFFERENT MODELS OF
CAR
Car Model Criteria / Attributes
Price in Rs Fuel Efficiency
in km per liter
Comfort Index
Max: 100
Warranty period
inyears
A 5,00,000 16 70 6
B 6,00,000 20 75 4
C 4,00,000 13 60 6
D 8,00,000 18 90 5
TABLE II CHOICE OF THE BUYER FOR
DIFFERENT PREFERENCES UNDER NO
CONDITIONS
Preference Best choice (Optimal)
Minimum Price C
Maximum Fuel Efficiency B
Maximum comfort level D
Maximum Warranty A, C
TABLE III CHOICE OF THE BUYER FOR
DIFFERENT PREFERENCES UNDER
CONDITIONS
Preference Condition(s) Best choice (Optimal)
Minimum price Fuel Efficiency 15 A
Minimum price Fuel Efficiency 15
Comfort level 75
B
Minimum price Fuel Efficiency 15
Comfort level 75
Warranty5
D
TABLE IV CHOICE OF THE BUYER FOR MORE THAN ONE
PREFERENCE
Preferences Condition(s) Best choice
(Optimal)
Minimum Price & Maximum Fuel
Efficiency
NIL NIL
Minimum Price & Maximum Fuel
Efficiency
Comfort level 5 NIL
Minimum Price & Maximum Warranty NIL C
Minimum Price & Maximum Warranty Comfort level 5 NIL
SINGLE OBJECTIVE VS MULTI OBJECTIVE
• Depending on the form and functional description of the optimization problem, different optimization
techniques can be used for the solution, linear programming, nonlinear programming, discrete optimization, etc.
• The case when we have a finite number of criteria but the number of the feasible alternatives (the ones
meeting the requirements) is infinite belongs to the field of multiple criteria optimization. Also, techniques of
multiple attribute optimization can be used when the number of feasible alternatives is finite but they are given
only in implicit form.
GENERAL OR MODEL
Any OR Problem is defined with:
• Objective Criterion: Performance Evaluation Parameter
• Constraints: Restrictions, Limitations, Availability
• Decision Variables: Influencing Parameters
ANALYTICAL HIERARCHY PROCESS (AHP)
• AHP was developed in the late 1970s. Today it is the most widely used MCDA method.
• AHP generates all criteria weighting and alternative preference within each criteria by eliciting
these values from the decision maker through a series of pairwise comparisons, as opposed to
utilising numerical values directly.
• Thus, a complex decision is reduced to a series of simpler ones, between pairs of alternative
values within criteria or between pairs of criteria. The decision maker’s preference is always
explicit. However, the decision maker may be asked to make very many small decisions. Hence,
it becomes important to generate an optimised hierarchy of criteria and alternatives, to reduce the
number of pairwise decisions.
STEPS IN AHP
Step 1: Construct the problem hierarchy
Model, usually visually, the problem decision identifying relationships between criteria and alternatives.
Step 2: Pairwise comparison of criteria
Undertake pairwise comparison between criteria, identifying decision maker preference for criteria on
which alternatives are evaluated.
Step 3: Pairwise comparison of alternatives within each criterion
Undertake pairwise comparison between alternatives based on their performance within each criterion.
Step 4: Compute the vector of criteria weights
From a matrix of pairwise comparison results AHP utilises a variety of matrix transformations to
calculate criteria weight vectors representing normalised criteria weightings.
STEPS IN AHP
Step 5: Compute the matrix of alternative scores
From the results of the pairwise comparisons on alternatives within each criterion a nxm (where n is the number of criteria and m is the
number of alternatives) matrix is constructed representing the normalised performance (score) of each alternative for each criteria.
Step 6: Ranking the alternatives
Utilising the vectors of criteria weights and the matrix of alternative scores a global score and hence ranking for each alternative is calculated
using:
where: a is the alternative, c is the criteria, g is the global score of the alternative, w is the criteria weight and s is the alternative score. A
function of the ranking equation, aggregating across each criteria means that trade-offs between criteria in fundamental to the final ranking.
AHP
DECISION MAKING SOFTWARE (DMS)
Software
Supported
MCDA
method(s)
Pair-wise
comparison
Time
analysis
Sensitivity
analysis
Group
evaluation
Risk
management
Web-
based
version
AIRM Online AIRM No No Yes No Yes Yes
1000Minds PAPRIKA Yes No Yes Yes No Yes
Analytica No Yes Yes No Yes Yes
Criterium
DecisionPlus
AHP No No Yes No No No
DecideIT MAUT Yes No Yes Yes Yes Yes
Decision Lab Yes No Yes No Yes No
Decision Lens AHP, ANP Yes Yes Yes Yes
D-Sight MAUT,
PROMETHEE
Yes No Yes Yes Yes Yes
Expert Choice AHP Yes No Yes Yes Yes Yes
Hiview3 No No Yes Yes No No
Logical Decisions AHP, MAUT Yes No Yes Yes Yes No
MakeItRational AHP Yes No Yes Yes No Yes
MindDecider AHP Yes Yes Yes Yes Yes No
Rational Focal Point
(RFP), RFP Smarter
Government AHP, ANP Yes Yes Yes Yes Yes Yes
TreeAge Pro No No Yes No Yes No
Very Good Choice ELECTRE Yes No Yes Yes Yes No
CONCLUSION
• These methods can provide solutions to increasing complex management prob-lems. They
provide better understanding of inherent features of decision problem, promote the role of
participants in decision-making processes, facilitate compromise and collective decisions and
provide a good platform to understand the perception of models and analysts in a realistic
scenario.
• The methods help to improve quality of decisions by making them more explicit, rational
and efficient. Negotiating, quantifying and communicating the priorities are also facilitated with
the use of these methods.
• It should be noted that methods and results are not necessarily comparable. Every method has its
restrictions, mostly due to model assumptions, which should be considered when the method is
used.
THANK YOU!!!

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SLIDESHARE PPT-DECISION MAKING METHODS.pptx

  • 1. TECHNIQUES FOR DATAAND KNOWLEDGE DRIVEN MULTI- CRITERIA DECISION MODELS PRESENTED BY M.CHAIRMAN AP/ECE KARPAGAM INSTITUTE OF TECHNOLOGY
  • 2. WHAT IS MULTI-CRITERIA DECISION ANALYSIS • Multi-criteria decision analysis (MCDA) is a formal, structured and transparent decision making methodology. Its ism is to assist groups or individual decision makers to explore their decisions in the case of complex situations with multiple criteria. • MCDA does not provide the ‘right’ answer. • MCDA does not provide an objective analysis. • MCDA does not relieve decision makers of the responsibility of making difficult judgments.
  • 3. MCDM MCDA assists the decision maker in confidently reaching a decision by: • enabling decision makers to gain a better understanding of the problem faced; • organising and synthesizing the entire range of information; • integrating objective measurements with value judgements; • making explicit and managing the decision maker’s subjectivity; and • ensuring that all criteria and decision factors have been taken properly into account.
  • 4. STEPS OF DECISION MAKING PROCESS Step 1. Define the problem “This process must, as a minimum, identify root causes, limiting assumptions, system and organizational boundaries and interfaces, and any stakeholder issues. The goal is to express the issue in a clear, one-sentence problem statement that describes both the initial conditions and the desired conditions.” Step 2. Determine requirements “Requirements are conditions that any acceptable solution to the problem must meet. Requirements spell out what the solution to the problem must do.”
  • 5. STEPS OF DECISION MAKING PROCESS Step 3. Establish goals Goals are broad statements of intent and desirable programmatic values.... Goals go beyond the minimum essential must have’s (i.e. requirements) to wants and desires.” In mathematical form, the goals are objectives contrary to the requirements that are constraints. The goals may be conflicting but this is a natural concomitant of practical decision situations. Step 4. Identify alternatives “Alternatives offer different approaches for changing the initial condition into the desired condition.” Be it an existing one or only constructed in mind, any alternative must meet the requirements. If the number of the possible alternatives is finite, we can check one by one if it meets the requirements.
  • 6. STEPS OF DECISION MAKING PROCESS Step 5. Define criteria “Decision criteria, which will discriminate among alternatives, must be based on the goals. It is necessary to define discriminating criteria as objective measures of the goals to measure how well each alternative achieves the goals.” Since the goals will be represented in the form of criteria, every goal must generate at least one criterion but complex goals may be represented only by several criteria. Step 6. Select a decision making tool There are several tools for solving a decision problem. Some of them will be briefly described here, and references of further readings will also be proposed. The selection of an appropriate tool is not an easy task and depends on the concrete decision problem, as well as on the objectives of the decision makers.
  • 7. STEPS OF DECISION MAKING PROCESS Step 7. Evaluate alternatives against criteria Every correct method for decision making needs, as input data, the evaluation of the alternatives against the criteria. Step 8. Validate solutions against problem statement The alternatives selected by the applied decision making tools have always to be validated against the requirements and goals of the decision problem.
  • 9. OPTIMALITY UNDER CONSTRAINTS • The choices / decisions of the decision maker differs with his preferences, • The constraints limit the available choices, • The decisions under multiple preferences / goals / objectives are more complex and • It may not be possible to attain / achieve many goals at all circumstances.
  • 10. PURCHASE OF CAR • Consider the cost, performance, comfort and warranty data of different models of car as given in Table I. • Table II provides the choice of various buyers under different preferences and no restrictions on other attributes. • Under certain restrictions given as conditions, the choices of the buyers differ and are shown in Table III. Table IV provides the solutions for multiple preferences.
  • 11. TABLE I DATA OF DIFFERENT MODELS OF CAR Car Model Criteria / Attributes Price in Rs Fuel Efficiency in km per liter Comfort Index Max: 100 Warranty period inyears A 5,00,000 16 70 6 B 6,00,000 20 75 4 C 4,00,000 13 60 6 D 8,00,000 18 90 5
  • 12. TABLE II CHOICE OF THE BUYER FOR DIFFERENT PREFERENCES UNDER NO CONDITIONS Preference Best choice (Optimal) Minimum Price C Maximum Fuel Efficiency B Maximum comfort level D Maximum Warranty A, C
  • 13. TABLE III CHOICE OF THE BUYER FOR DIFFERENT PREFERENCES UNDER CONDITIONS Preference Condition(s) Best choice (Optimal) Minimum price Fuel Efficiency 15 A Minimum price Fuel Efficiency 15 Comfort level 75 B Minimum price Fuel Efficiency 15 Comfort level 75 Warranty5 D
  • 14. TABLE IV CHOICE OF THE BUYER FOR MORE THAN ONE PREFERENCE Preferences Condition(s) Best choice (Optimal) Minimum Price & Maximum Fuel Efficiency NIL NIL Minimum Price & Maximum Fuel Efficiency Comfort level 5 NIL Minimum Price & Maximum Warranty NIL C Minimum Price & Maximum Warranty Comfort level 5 NIL
  • 15. SINGLE OBJECTIVE VS MULTI OBJECTIVE • Depending on the form and functional description of the optimization problem, different optimization techniques can be used for the solution, linear programming, nonlinear programming, discrete optimization, etc. • The case when we have a finite number of criteria but the number of the feasible alternatives (the ones meeting the requirements) is infinite belongs to the field of multiple criteria optimization. Also, techniques of multiple attribute optimization can be used when the number of feasible alternatives is finite but they are given only in implicit form.
  • 16. GENERAL OR MODEL Any OR Problem is defined with: • Objective Criterion: Performance Evaluation Parameter • Constraints: Restrictions, Limitations, Availability • Decision Variables: Influencing Parameters
  • 17. ANALYTICAL HIERARCHY PROCESS (AHP) • AHP was developed in the late 1970s. Today it is the most widely used MCDA method. • AHP generates all criteria weighting and alternative preference within each criteria by eliciting these values from the decision maker through a series of pairwise comparisons, as opposed to utilising numerical values directly. • Thus, a complex decision is reduced to a series of simpler ones, between pairs of alternative values within criteria or between pairs of criteria. The decision maker’s preference is always explicit. However, the decision maker may be asked to make very many small decisions. Hence, it becomes important to generate an optimised hierarchy of criteria and alternatives, to reduce the number of pairwise decisions.
  • 18. STEPS IN AHP Step 1: Construct the problem hierarchy Model, usually visually, the problem decision identifying relationships between criteria and alternatives. Step 2: Pairwise comparison of criteria Undertake pairwise comparison between criteria, identifying decision maker preference for criteria on which alternatives are evaluated. Step 3: Pairwise comparison of alternatives within each criterion Undertake pairwise comparison between alternatives based on their performance within each criterion. Step 4: Compute the vector of criteria weights From a matrix of pairwise comparison results AHP utilises a variety of matrix transformations to calculate criteria weight vectors representing normalised criteria weightings.
  • 19. STEPS IN AHP Step 5: Compute the matrix of alternative scores From the results of the pairwise comparisons on alternatives within each criterion a nxm (where n is the number of criteria and m is the number of alternatives) matrix is constructed representing the normalised performance (score) of each alternative for each criteria. Step 6: Ranking the alternatives Utilising the vectors of criteria weights and the matrix of alternative scores a global score and hence ranking for each alternative is calculated using: where: a is the alternative, c is the criteria, g is the global score of the alternative, w is the criteria weight and s is the alternative score. A function of the ranking equation, aggregating across each criteria means that trade-offs between criteria in fundamental to the final ranking.
  • 20. AHP
  • 21. DECISION MAKING SOFTWARE (DMS) Software Supported MCDA method(s) Pair-wise comparison Time analysis Sensitivity analysis Group evaluation Risk management Web- based version AIRM Online AIRM No No Yes No Yes Yes 1000Minds PAPRIKA Yes No Yes Yes No Yes Analytica No Yes Yes No Yes Yes Criterium DecisionPlus AHP No No Yes No No No DecideIT MAUT Yes No Yes Yes Yes Yes Decision Lab Yes No Yes No Yes No Decision Lens AHP, ANP Yes Yes Yes Yes D-Sight MAUT, PROMETHEE Yes No Yes Yes Yes Yes Expert Choice AHP Yes No Yes Yes Yes Yes Hiview3 No No Yes Yes No No Logical Decisions AHP, MAUT Yes No Yes Yes Yes No MakeItRational AHP Yes No Yes Yes No Yes MindDecider AHP Yes Yes Yes Yes Yes No Rational Focal Point (RFP), RFP Smarter Government AHP, ANP Yes Yes Yes Yes Yes Yes TreeAge Pro No No Yes No Yes No Very Good Choice ELECTRE Yes No Yes Yes Yes No
  • 22. CONCLUSION • These methods can provide solutions to increasing complex management prob-lems. They provide better understanding of inherent features of decision problem, promote the role of participants in decision-making processes, facilitate compromise and collective decisions and provide a good platform to understand the perception of models and analysts in a realistic scenario. • The methods help to improve quality of decisions by making them more explicit, rational and efficient. Negotiating, quantifying and communicating the priorities are also facilitated with the use of these methods. • It should be noted that methods and results are not necessarily comparable. Every method has its restrictions, mostly due to model assumptions, which should be considered when the method is used.

Editor's Notes

  1. At the first level criteria A1 is compared against A2, A3 and A4; criteria A2 against A3 and A4; and criteria A3 against A4, making 6 in total. The same is true for B1 to B4 and C1 to C4, making a total of 18 pairwise comparisons at this first level. In addition, pairwise comparisons are carried out at the higher level between criteria A, B and C which adds a further 3 pairwise comparisons. In total 21 pairwise comparisons have been undertaken, a significant reduction from the 66 required earlier. Perhaps more significantly, consistency only needs to be maintained within each group of pairwise comparisons so in this example across at most 6. It must be noted that grouping of criteria into multi-tier hierarchies is only possible where it makes sense to do so. Within the area of sustainability assessment criteria groups are usually already well understood as being either economic, environmental or social criteria, thereby making multi-tier hierarchies an extremely powerful tool.