Multi-Criteria Group Decision Making Methods and Integrated Web-Based Decision Support Systems

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Multi-Criteria Group Decision Making Methods and
Integrated Web-Based Decision Support Systems

• Ibrahim Ozer
• University of Ottawa

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Presentation Outline

• Introduction
• Problem
• Methods for Multicriteria Group Decision Making
• Decision-Making Process Diagram
• Illustrative Problem
• Best Site Selection
• Detailed Methods
• Web-based Tool for GDM

Multicriteria Group Decision Making
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Introduction Keywords

• Groups make better judgments than average
  individual members in analysis and evaluation
  tasks. (McGrath, 1984 Nah Benbasat, 1999).
• Never underestimate the power of stupid people
  in large groups. (As read on T-Shirts).

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Introduction Advantages/Disadvantages
Advantages
Disadvantages

• Useful in judgmental tasks
• Produce better decisions than individual
• Reducing effects of individual bias
• Solutions more likely to be accepted

• Takes more time
• One capable person can decide as well as a group
• Satisfaction
• Negative effects of bias decisions
• Not necessarily a consensus

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Problem

• Environmental and natural resource problems
  affecting coastal regions.
• Aspects involve marine use and ecosystem
  multicriteria description
• Resources, Habitat, Effluents, Activities
• Decision Makers
• Local Communities, Federal Scientists,
  Industrial Organizations, Non-Governmental
  Organizations, and Provincial Governments

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Methods for Group Decision Making

1. Analytic Hierarchy Process (AHP)
2. Weighted Sum Method (WSM)
3. Weighted Product Method (WPM)
4. AHP Combined Method
5. Group Evaluation Method
6. Fuzzy AHP
7. Fuzzy AHP Combined
8. Fuzzy AHP Group

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The Decision-Making Process Individual DMers
Select Alternative
Develop Decision Criteria
Allocate Weights to Criteria
Implement Alternative
Develop Alternatives
Analyze Alternatives
Evaluate Results
Multicriteria Group Decision Making
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Illustrative Problem Best Site Selection
The ABC Restaurant Corporation is offering
franchise opportunities. After completing all the
requirements from the applicants, the company
seeks the best site location from a set of
alternative locations. There are three DMs to
make the judgments CEO, CFO, and CIO.
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Methods 1. AHP
The analytic hierarchy process (AHP), which
provides a proven, effective means to deal with
complex decision making, was first introduced by
Thomas Saaty in 1970s
Evaluation phase is divided into four steps given
below 1. Generate pairwise matrices 2. Generate
the weights of the measures 3. Normalize weights
to get the consistency among measures 4.
Calculate the overall ratings
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Methods 1. AHP
Pairwise Comparison Matrix Interactive Feedback
from CEO
Criteria Visibility Accessibility Traffic Convenience Priority
Visibility 1 3 2 2 0.398
Accessibility - 1 1/4 1/4 0.085
Traffic - - 1 1/2 0.218
Convenience - - - 1 0.299
C r i t e r i a C r i t e r i a C r i t e r i a C r i t e r i a
( 0.398 0.085 0.218 0.299 )
Visibility Access Traffic Conv. Priority
Loc.1 0.123 0.608 0.619 0.265 gt 0.315
Loc.2 0.320 0.272 0.284 0.656 gt 0.408
Loc.3 0.557 0.120 0.096 0.080 gt 0.277
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Methods 1. AHP
Overall Location Priorities for DMs
CEO CFO CIO
Loc.1 0.315 0.135 0.483
Loc.2 0.408 0.304 0.323
Loc.3 0.277 0.561 0.194
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Methods 2. WSM
Evaluation of DMs by each DM.

CEO CFO CIO Priority
CEO 1 5 7 0.724
CFO - 1 3 0.193
CIO - - 1 0.083
(0.724 0.193 0.083)
CEO CFO CIO Priority
Loc.1 0.315 0.135 0.483 ? 0.294
Loc.2 0.408 0.304 0.323 ? 0.381
Loc.3 0.277 0.561 0.194 ? 0.325
Overall priority of selecting a best location.
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Methods 3. WPM
Each alternative is compared with the others by
multiplying a number of ratios, one for each
criterion.
C r i t e r i a C r i t e r i a C r i t e r i a C r i t e r i a
( 0.398 0.085 0.218 0.299 )
Visibility Access. Traffic Conv.
Loc.1 0.123 0.608 0.619 0.265 gt R(Loc.1/Loc.2) 0.660
Loc.2 0.320 0.272 0.284 0.656 gt R(Loc.1/Loc.3) 1.349
Loc.3 0.557 0.120 0.096 0.080 gt R(Loc.2/Loc.3) 2.043
Alternatives pairwise comparison matrix and
priority for CEO
Since the CEO has the highest value (0.724) among
the other DMs, his option -Loc.2- will be chosen
to select the best location.
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Methods 4. AHP Combined
Geometric mean approach is used to combine the
inputs of all DMs. After pairwise comparison
matrix is conducted, AHP is used to get overall
ranking.
Criteria Visibility Access. Traffic Conv. Priority
Visibility 1.00 2.62 1.39 1.39 0.357
Access. 0.38 1.00 0.57 0.69 0.149
Traffic 0.72 1.75 1.00 0.91 0.247
Convenience 0.72 1.44 1.10 1.00 0.247
Criteria pairwise comparison matrix and priority
for combined.
Priority
Overall Loc.1 Priority 0.328
Overall Loc.2 Priority 0.356
Overall Loc.3 Priority 0.328
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Methods 5. Group Evaluation
In Group Evaluation, each DM evaluates the other
DMs instead of alternatives. Each DM ranked the
other two DMs with respect to criterion. Pairwise
comparison matrices are created as follow
Visibility CEO CFO CIO Priority
CEO 1 5 8 0.711
CFO - 1 5 0.223
CIO - - 1 0.066
DMs pairwise comparison matrix and priority with
respect to Visibility
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Methods 5. Group Evaluation
Each weight of DMs is multiplied by relevant
criterion to get the following pairwise
comparison.
CEO CFO CIO Priority
Visibility 0.398 0.503 0.145 0.405
Accessibility 0.085 0.273 0.098 0.152
Traffic 0.218 0.145 0.327 0.208
Convenience 0.299 0.079 0.430 0.314
We then weighted each alternative by multiplying
their ranks by corresponding weight.
( 0.405 0.152 0.208 0.314 )
Visibility Access. Traffic Conv.
Loc.1 0.152 0.402 0.462 0.320 0.319
Loc.2 0.304 0.303 0.263 0.572 0.403
Loc.3 0.544 0.295 0.274 0.109 0.356
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Methods 6. Fuzzy AHP
Although the AHP is to capture the experts
knowledge, the traditionaly AHP still can not
really reflect the human thinking
style. Triangular fuzzy numbers are used based on
arithmetic operations to express the decision
makers evaluate on alternatives with respect to
each criterion
Triangular Fuzzy Number and Crisp Number
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Methods 7. Fuzzy AHP Combined
Each decision maker (DM) individually assesses
alternatives and criteria following to the normal
Fuzzy AHP procedures and from their assessments,
the geometric mean is calculated to obtain the
final decision
Criteria Visibility Visibility Visibility Access. Access. Access. Traffic Traffic Traffic Conv. Conv. Conv.
Visibility 1 1 1 1.93 2.93 3.93 1.55 2.44 3.33 1.01 1.71 2.44
Access. 0.26 0.34 0.53 1 1 1 0.78 1.13 1.50 0.56 0.74 0.96
Traffic 0.41 0.61 1.05 2.11 2.72 3.43 1 1 1 0.54 0.81 1.37
Conv. 0.41 0.61 1.05 1.95 2.62 3.30 0.76 1.47 2.20 1 1 1
Normalized Matrix Normalized Matrix Normalized Matrix
Visibility 0.083 0.117 0.150
Access. 0.039 0.049 0.062
Traffic 0.064 0.08 0.110
Conv. 0.059 0.08 0.106
P1 0.022 0.035 0.056
P2 0.023 0.038 0.062
P3 0.020 0.034 0.056
Normalized value of each criterion is multiplied
by corresponding normalized alternative value and
them sum them up. P2 (Loc.2) dominates the other
locations.
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Methods 8. Fuzzy AHP Group
Weights for the DMs were empricially defined
according to the AHP whereby each DM responded to
the overall importance of all other DMs for this
decision. Multiplying DMs judgment of criteria in
fuzzy AHP by weight of each DM is called Fuzzy
AHP Group.
Criteria Visibility Visibility Visibility Access. Access. Access. Traffic Traffic Traffic Conv. Conv. Conv.
Visibility 1 1 1 1.58 2.62 3.63 0.90 1.38 1.95 0.90 1.38 1.95
Access. 0.27 0.38 0.63 1 1 1 0.43 0.57 0.69 0.53 0.69 0.94
Traffic 0.51 0.72 1.10 1.44 1.74 2.32 1 1 1 0.60 0.90 1.58
Conv. 0.51 0.72 1.10 1.06 1.44 1.88 0.62 1.10 1.65 1 1 1
Normalized Matrix Normalized Matrix Normalized Matrix
Visibility 0.083 0.117 0.150
Access. 0.039 0.049 0.062
Traffic 0.064 0.08 0.110
Conv. 0.059 0.08 0.106
P1 0.021 0.033 0.051
P2 0.025 0.041 0.066
P3 0.020 0.034 0.055
P2 (Loc.2) dominates the other locations.
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Methods Strengths and Weaknesses
Methods Strength Weakness
AHP Appropriate for GDM Perfect consistency is very difficult
AHP Handles multiple criteria Time consuming with large numbers
AHP Doesnt involve complex math Doesnt take into account the uncertainty
AHP A certain value of consistency is allowed
AHP Easy to capture and convenient
WSM Strong in a single dimensional problems Difficulty emerges on multi-dimensional problems
WPM Eliminate any unit of measure thus, can be used in single and multi dimensional MCDM No solution with equal weight of DMs
WPM Instead of actual values, it can use relative ones.
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Methods Strengths and Weaknesses contd
Methods Strength Weakness
AHP Combined Simplifying the group pairwise comparisons. Uncertainty
Group Evaluation Evaluating DMs Uncertainty
Group Evaluation Reducing the noise by having DMs weights Additive utility
Fuzzy AHP Deals with uncertainty Time consuming
Fuzzy AHP Similar scale to Saatys can be used Hard to convince DMs
Fuzzy AHP Combined Reducing the of matrices Time consuming
Fuzzy AHP Group Considering the weight of DMs Time consuming
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Web-based Tool for Group Decision Making

• Java, object-oriented programming is used.

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Web-based ToolArchitecture of the Application
Run Time Environment, are run to evaluate the
clients pairwise comparisons and then those
weights are delivered to the Web browser on the
client side.
The weights delivered to the server and stored in
the database. Java Application is used in the
run-time environment to do the required
calculations and results based on the appropriate
methodology.
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Web-based Tool Aquawebsite
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Web-based Tool Aquawebsite
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Web-based Tool Aquawebsite
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