group decision support system based on enhanced ahp for tender evaluation
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Group Decision Support System Based on Enhanced AHP for Tender Evaluation
Fadhilah Ahmad1 , M Yazid M Saman
2, Fatma Susilawati Mohamad3, Zarina Mohamad4,
Wan Suryani Wan Awang5
1,3,4,5Faculty of Informatics and Computing
University Sultan Zainal Abidin
Tembila Campus, Besut, 22200,Terengganu, Malaysia
E-mail: {fad,fatma,zarina,suryani}@unisza.edu.my,
2 Faculty of Science and Technology
Universiti Malaysia Terengganu(UMT), 21030 Mengabang Telipot, Kuala Terengganu, Malaysia
E-mail: yazid@umt.edu.my
ABSTRACT
Application of model base in group decision making
that makes up a Group Decision Support System
(GDSS) is of paramount importance. Analytic
Hierarchy Process (AHP) is the multi-criteria decision
making (MCDM) that has been applied in GDSS. In
order to be effectively used in GDSS, AHP needs to be
customized so that it is more user friendly with ease of
used features. In this paper, we propose an enhanced
AHP model for GDSS tendering. The enhanced AHP
method used is the Guided Ranked AHP (GRAHP). It
is a technique where decision matrix tables are
automatically filled in based on ranked data. However,
the generated values in the decision matrix tables can
still be altered by following the guidelines which in
turn serve the purpose of improving the consistency of
the decision matrix table. This process is transparent to
Decision Makers because the degree of data
inconsistency is visible. A prototype system based on
tendering process has been developed to test the
GRAHP model in terms of its applicability and
robustness.
KEYWORDS
Group Decision Support System (GDSS), Multi-
Criteria Decision Making (MCDM), Analytic
Hierarchy Process (AHP), Tender Evaluation.
1 INTRODUCTION
Decision support system (DSS) is seen as building
blocks that offers the best combination of
computational power, value for money and
significantly offers efficiency in certain decision
making problem solving [1,25]. Based on these
building blocks, modern DSS applications
comprise of integrated resources working together
which are model base, database or knowledge
base, algorithms, user interface and control
mechanisms used to support certain decision
problem [2].
There are many application areas suitable for DSS
which include academic advising, water resource
planning, direct mailing decisions, e-sourcing,
tendering decisions and many more. DSS has a
vast field of research scopes which are categorized
as model management, design, multi-criteria
decision making (MCDM), implementation,
organization science, cognitive science, and group
DSS (GDSS). DSS also has direct relation with
Human Computer Interaction (HCI) and Database
Management System (DBMS).
MCDM constitutes an advanced field of research
[21-24] that is dedicated to the development and
implementation of DSS tools and methodologies
to handle complex decision problems involving
multiple criteria, goals or objectives of conflicting
nature. MCDM is broadly classified into two
categories which are Multiple Attribute Decision
Making (MADM) and Multiple Objective
Decision Making (MODM) [5]. MADM methods
are used for selecting single most preferred
alternative or short listing a limited number of
alternatives, while MODM methods are used for
designing a problem involving an infinite number
of alternatives implicitly defined by mathematical
mailto:fad@unisza.edu.mymailto:yazid@umt.edu.my -
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constraints. Evaluation of a problem in DSS can
either be done by a single decision Maker (DM) or
a group of decision makers (DMs). If it involves a
single DM, the DSS is called Single DSS (SDSS)
and if a group of DMs are involved, the term
group DSS (GDSS) is used. GDSS comprises a
large body of research and it remains an active
area of investigation. A GDSS in web-based
environment is a computerized system that makes
use of model base and database/knowledge base
which delivers decision support information or
decision support tools to a group of DMs/users
using a web browser such as Netscape Navigator
or Internet Explorer [3].
There is a need to consider a group of DMs in
improving the productivity of decision making and
also the quality of decision results. [4] states that
groups have an advantage in combining talents
and providing innovative solutions to possibly
unfamiliar problems. This is because groups
possess a range of skills and knowledge compared
to individual DM. A well-known MCDM model
that has been used in GDSS is Analytic Hierarchy
Process (AHP) [26-28]. [18] used Group Analytic
Network Process (GANP) to support hazard
planning and emergency management under
incomplete information. They showed that both
AHP and GANP have great potential to be
deployed in specified case involving a group of
DMs. Group fuzzy prioritization processes for
AHP/ANP was also suggested to be used if the
nature of the problems is tentative, imprecise,
approximate and uncertain [11]. A group decision
approach for evaluating educational web sites
using several soft computing technologies e.g.
fuzzy theory, grey system and group decision
method has been proposed by [19]. A GDSS for
evaluation of tenders of ICT equipment based on
multi attribute group decision models and the
software WINGDSS was developed by 12]. The
winner of a tender would be the one who makes
the best offer after the prequalification process and
the ranking processes.
Even though many GDSS have been developed
using various model bases, none of them provides
flexibility to DMs in terms of the followings:
1) Giving guidelines on how to enter data into
AHP decision matrices, 2) freedom to choose
other enhanced AHP versions, and 3) transparency
in data consistency checking in just one generic
DSS.
Consequently, we have addressed all these issues
in a research as presented in this paper. A
tendering case study was employed to demonstrate
the issues of conflicting evaluation criteria in
decision making and a model were proposed to
solve the problem. Tendering problem has a finite
number of evaluation criteria that are experienced,
technical skills, previous work performance, and a
few others. In terms of alternatives, only limited
numbers of choices are taken into consideration
since some of the alternatives had already been
discarded in the pre-requisite analysis.
One simple and flexible MADM model used by
many scholars [6], [8], [9], [10] in appraisal
evaluation is the Analytic Hierarchy Process
(AHP). AHP [15] has many advantages such as
easy to use, well accepted by decision makers, can
be used in SDSS and GDSS, and has matured
through multiple revisions.
This paper is organized as follows. Section 2
outlines the Guided Ranked AHP (GRAHP)
model, which is an enhanced version of AHP.
Next, the implementation of the model in GDSS
tendering and the results of the implementation is
presented in Section 3. Finally, a summary of the
paper is accomplished in Section 4.
2 GDSS RELATED WORKS
There are various models that can be used in
GDSS such as AHP, Fuzzy AHP, Group Analytic
Network Process (GANP), Delphi, Maximized
Agreement Heuristic (MAH), TOPSIS, nominal
group technique (NGT), and a few others. Table
2.4 describes some of the research carried out on
GDSS using specific models. [18] used GANP to
support hazard planning and emergency
management under incomplete information. They
showed that GANP have great potential for use in
specified case involving a group of DMs. If the
problem involves tentative, imprecise,
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approximate and uncertainty, then the model base
suggested is group fuzzy prioritization processes
based on AHP or GANP.
Another GDSS approach has been studied by [11]
on journal evaluation. The method that has been
used is an integration of subjective (eg. experts
judgments on journals) and objective approach
(eg. Impact factors of journals). Fuzzy set
approach is used when dealing with imprecise or
missing information.
Gwo-Jen et al. (2004) have proposed a group
decision approach for evaluating educational web
sites. Several soft computing technologies have
been employed in the approach, such as fuzzy
theory, grey system and group decision method. A
computer-assisted website evaluation system,
EWSE (Educational Web Site Evaluator), has
been developed based on an experimental
approach. The system is capable of selecting the
proper criteria for an individual web site and
achieves greater accuracy when evaluating the
results.
There is a work on tender evaluation focusing on
the selection of supplier for ICT equipment
(Rapcsak et al., 2000). Two Multi attribute group
decision models known as criterion tree and
weight system have been used. The tender is
awarded to the one who makes the best offer. The
ranking of the offer are based on the price and
multitude of criteria. The tendering process
consists of two stages. The first round is the
prequalification process and followed by the final
ranking of alternatives, accomplished by the price
adjustment method. Arithmetic means technique
is used to aggregate individual results to form the
group result.
Table 1. Summarization of studies on GDSS using particular model base
Year Authors Model Fields Issues Addressed
2014 Kar Fuzzy AHP and
Fuzzy Goal Programming
Selection of
supplier
Use of Geometric Mean in Fuzzy AHP
2014 Taylan et al. Fuzzy AHP and
Fuzzy TOPSIS
Selection of
construction
projects
Creating weight using Fuzzy AHP for
linguistic variable
2013 Srdjevic and
Srdjevic
AHP Selection of
Wetland area
AHP synthesis of the best local priority
vectors based on the most consistent
decision makers
2007 Levy and Taji GANP Hazard planning
and emergency
management
GANP DSS that used quadratic
programming and interval information
to cope with incomplete information.
2007 Saaty and Shang AHP Voting Preference intensity using cardinal
approach several-issues-at-time
decision-making
2006 Ratnasabaphthy
and Rameezdeen,
Statistical and Delphi Procurement Four rounds of Delphi surveys, several
statistical methods, and interviews
2005 Shih, Huang and
Shyur
AHP,
TOPSIS, Nominal Group
Technique (NGT),
Bordas function
Recruitment and
selection
Enhancing consensus among DMs,
GDSS framework
2005 Kengpol and
Tuominen
ANP, Delphi, MAH Evaluation of
information
technology
Achieving consensus in quantitative
and qualitative judgments
2005 Limayem,
Banerjee and Ma
Adaptive Structured Theory
(AST), Faithfulness of
Appropriation (FOA)
GDSS process
enhancement
Requirement of embedded decisional
guidelines, tailored training and
decisional guidelines
2005 Turban, Zhou and
Ma
Fuzzy set theory Evaluation of
journals
Integration of objective and subjective
judgements using fuzzy set approach to
deal with imprecise and missing
information.
2004 Gwo-Jen, Tony, Fuzzy theory, grey system, Evaluation of Open evaluation criteria, uncertainty and
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and Judy AHP educational web incomplete information
2002 Mikhailov Fuzzy AHP Model base
enhancement
(AHP process
enhancement)
Group prioritisation process using fuzzy
programming optimization to deal with
missing judgements.
2000 Rapcsak, Sagi,
Toth and Ketszeri
Criterion tree, Weight
system, Voting power
vector, Software WINGS
Evaluation of
tenders in
information
technology
Model building, GDSS system
development methodology, and methods
of aggregation the score by each DM.
1996 Tavana, Kennedy
and Joglekar
AHP, Delphi, Maximized
Agreement Heuristic
(MAH)
Recruitment and
selection
Improving consistency among DMs.
3 ANALYTIC HIERARCHY PROCESS
There are some well-known numeric discrete
techniques of MADM models. They are Analytic
Hierarchy Process (AHP), Weighted Sum Model
(WSM) or sometimes it is called Additive Value
Function (AVF), Weighted Product Model
(WPM), Technique for Order Preference by
Similarity of the Ideal Solution (TOPSIS),
ELimination Et Choix Traduisant la REalit to
mean ELimination and Choice Expressing REality
(ELECTRE), and Preference Ranking
Organization Method for Enrichment Evaluations
(PROMETHEE).
Comparing the models in order to choose the best
method for a particular problem is not an easy task
(Triantaphyllou, 2000; Zanakis et al., 1998). Each
of them has its own strengths and weaknesses. A
study made by Zankis et al. (1998) has concluded
that AHP appears to perform closest to WSM and
TOPSIS. PROMETHEE and ELECTRE behave
differently because these methods present different
ranking philosophy and do not assume that unique
ranking always exists in practice. The result of the
study made by Triantaphyllou (2000) has
recommended that for most of the cases, for
certain evaluation criteria, AHP appears to be the
best decision making method. However, based on
the literature, we found that the selection of the
model depends on the type of problem to be
solved and the nature of criteria used for the
evaluation of alternatives
AHP was introduced by Thomas L. Saaty in 1980
(Saaty, 1980). It is a multi-attribute decision
making methodology for choosing the best among
a set of alternatives via pair comparison process.
It uses numeric technique to help DMs choose
among discrete set of alternative decisions. The
AHP method is based on the following principles:
i. Build a hierarchy of criteria, by decomposing the problem into a hierarchy tree. The left end
side of the tree represents the goal to be
achieved and the right end side represents the
alternatives among which to decide the
preferred one (Figure 1);
ii. Perform a sequence of pair-wise comparisons for the criteria on the same level of hierarchy
for each node;
iii. Perform a sequence of pair-wise comparison on the alternatives for each criteria;
iv. Establish weighting among the elements in the hierarchy;
v. Synthesize the results in order to obtain the overall ranking of alternatives with respect to
goal;
vi. Evaluate the consistency of judgment to make sure that the original preference ratings are
consistent.
Table 2 shows the pair wise comparisons scheme
as proposed by Saaty. The scheme can be used to
translate linguistic judgment comparisons into
numbers, which are then inserted into the decision
matrix, A.
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.
.
.
.
Criteria 1
Criteria 2
Goal Criteria 3
Criteria N
Sub-criteria ..
Sub-criteria ..
Sub-criteria
3.2
Sub-criteria
3.1
Alternative N
Alternative C
Alternative B
Alternative A
Figure 1. Hierarchical Diagram for AHP Approach
Table 2. Pairwise comparison scale
Intensity Definition
1 Equal importance of both elements
3 Slight importance of one element over another
5 Moderate importance of one element over another
7 Essential or strong importance of one element over another
9 Extremely importance of one element over another
2, 4, 6, 8 Intermediate values between two adjacent judgments
The decision matrix, A for pair wise comparison in
AHP method is as follows:
A1 A2 A3 An
A1 1 a12 a13 a1n
A2 1/a21 1 a23 a2n
A 3 1/a31 1/a32 1 a3n
A m 1/am1 1/am2 1/am3 1
The decision matrix, A is an (m x n) matrix in
which element aij is a pair wise comparison
between alternative, i (row) and alternative, j
(column) when it is evaluated in terms of each
decision criterion. The diagonal is always 1 since
aij = 1 (since the criteria or alternatives are being
compared to themselves) and the lower
triangular matrix is filled using Equation 2.
AHP has gone through multiple revisions over the
years. Some works have been done on improving
the AHP itself in terms of decision matrix
consistency. Saaty (2003) has investigated the
quintessence of eigenvector principal in decision-
making and its influence in the judgments of the
AHP decision matrix. Previous work by Harker
(1987) in this area is referred and evaluated
together with his work in terms of how to improve
the consistency of judgments, and transform an
inconsistent matrix to a near consistent one.
However, their works do not provide a step-by-
step guideline to DMs on how to enter consistent
data into the matrix. Hence, a guideline is needed
to assist DMs to enter consistent or near consistent
data into the decision matrix.
4 GUIDED RANKED AHP
Guided Ranked Analytic Hierarchy Process
(GRAHP) model is a set of guidelines [20]
synthesized with Ranked AHP (RAHP). RAHP is
A = (1)
(2)
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a decision analysis method introduced by Othman
[13] in 2004. This method can be used to fill in
AHP decision matrices based on the priority
ranking of each element value in each criteria or
sub criteria for each pairwise element comparison.
The elements are all ranked according to their
priority values in hierarchical form. In terms of
assigning the value to each comparison elements
in AHP decision matrices, the pairwise
comparison value can be obtained using the
following rules:
This method adheres to the input range value
concept proposed by Saaty with the highest
priority element is marked as 1 while the lowest is
9. The maximum comparison value assigned to
the elements which are greater than 9 (10 and
above) is still 9.
RAHP was claimed to be better than the primitive
Saatys pairwise comparison method because it is
always consistent and easy to use just by following
the above guidelines. This reduces the time to get
the results because RAHP provides a formula on
how to enter data into the decision matrices.
On the other hand, the RAHP model proposed by
Othman lacks the process of converting the item
values in each criteria or sub-criteria for each
alternative to ranked values. As an enhancement,
we propose the conversion process as follows:
Applying GRAHP causes AHP decision matrices
to be automatically filled in. However, DMs can
still alter these values based on their own
discretion or they can follow the guidelines given
to reduce data inconsistency. This process is
transparent to DMs where the degree of data
inconsistency is acknowledged.
In AHP, the group prioritization process was used.
Possible approaches to estimate the weight of
elements in AHP are; agreement of each group
member to enter the decision matrix table, voting
process, aggregation of individual evaluation via
geometric mean or arithmetic mean. These
approaches have their own challenges. In our case,
the arithmetic mean approach was chosen for
GRAHP owing to its simplicity.
5 IMPLEMENTATION OF GRAHP IN GDSS
TENDERING
The process flow of GDSS tender evaluation in
Malaysia is depicted in Fig. 2. At the beginning of
the process, a DM (the company
management/group leader) defines the number of
contractors for a certain project and the group
features. Then, the DMs (either company
management/group leader or group members) rank
the criteria (Fig. 4, step 1) and assigns the scales
for the decision matrices (Fig. 4, step 2) through
the form interfaces. These data are then stored in a
hybrid database, weight and GRAHP model bases.
These are the initial input accepted by the GDSS
tendering after the client tendering evaluation
request. Guidelines regarding the decision
matrices input scales are displayed on top of the
matrices. This information can be used by the
DMs to define the degree of importance of the
strategic level evaluation criteria. These are the
distinguishing part of our model, since many AHP
applications did not provide such guidelines and
alert messages to ensure consistency input scales.
These outstanding features together with the
automatic fill in of table matrices enable the DMs
to focus on the evaluation of alternatives instead of
decision making problems themselves. GRAHP
operations are carried out automatically by the
GDSS tendering system. These operations include
the calculation of priority vector and the weight
Assume that Pi (i = 1, 2, , 9) is a rank for i-th element,
i. If Pi = Pj then aij = 1
ii. If Pi < Pj then aij = (Pj - Pi + 1) and
1/aij
Sort the criteria value from the most to the least importance.
Assume that the most importance has bigger original value:
Initialise rank_value;
For ( there is record ){ if ( element [ i-th] > element [i-th + 1] ){
assign rank_value to the i-th element;
assign rank_value + 1 to the next element; increment rank_value
}elseif (element [ i-th] = = element [i-th + 1]){
assign rank_value to the i-th element; assign rank_value to the next element;
}Increment i}
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[15] for each criteria and contractors. Arithmetic
means are used to combine judgment by individual
DM [16] into group preferences in order to
produce the final ranking. The ranking is displayed
in tabular and graphical forms (Fig. 4). All the
operations of the tendering process starting from
the initial input to the final ranking involve
accessing the database and various model bases
that include statistic, weight and GRAHP. This is
another unique feature of our model where data
about contractors are stored in the tendering
database, and the model base operation results
are stored in the specific model base repositories.
Hence, the properly structured data in a few types
of categories (database and model bases) will ease
the maintenance and programming aspects of it
compared to keeping data in an unstructured way
6 SUMMARY
This paper has discussed the GRAHP model as an
enhancement of AHP in handling data
consistency. Our findings suggest that GRAHP
enable DMs to be more intuitive in their decision
making processes. Furthermore, GRAHP guides
the DMs in terms of selecting suitable input scales
for decision matrix tables. In terms of system
design and development, we have produced
flexible and user friendly interfaces. At the top of
each GAHP model form, there is a brief
description of AHP scales. DMs can easily select
the AHP scales using drop down menus provided
in the forms. There is also set of guidelines on
how to choose the scale values to reduce
inconsistency of data entry if DMs are not satisfied
with the calculated input values performed by the
system. The use of GRAHP approach simplifies
the evaluation process because most of the time
the DMs will not be bothered with inconsistent
data in the matrices. The DMs will be alerted with
warning messages if the problem still persists. The
degree of inconsistency of data is also displayed to
the DMs to enable the values in the decision
matrix tables to be re-adjusted in order to assist the
DMs with re-evaluation.
7 ACKNOWLEDGEMENT
The authors are very grateful to the Ministry of
Higher Education Malaysia and University Sultan
Zainal Abidin, Malaysia for the grants, and
support
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User request (Admin)
Choose project
No
Yes
Group features
Integrated Model
Operations
Who rank criteria ?
Group Leader Each DM in the group
Assign rank to criteria
Assign scales to GRAHP
model
Weighted
Model
GRAHP
Model Bases
Determine no. of
contractors for
evaluation
Statistical Model
Use Arithmetic
Means to integrate
weight & produce
ranking
Display ranking of contractors
Calc. priority vector
& weight for each
criterion & tenderer
User request
Choose project
Group Leader?
Display criteria rank
No
Yes
Each DM
ranks criteria?
?
Database
Determine no.
of DMs
Determine DM &
group leader
Figure 2. The Process Flow of GDSS Tendering using GRAHP Model
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Display results in tabular & graphical forms.
Step 1: Assign weight to evaluation criteria
The original values of the criteria
experience for
each tenderer.
The rank values after automatic conversion
process from the original
values of experience
criteria.
All the values in this decision matrix are
automatically entered
using RAHP technique. The DM can re-judge
these values if the
automatic final ranking produced are
unsatisfactory.
Step 2: Evaluate alternatives
Figure 3. Group Characteristics of GDSS Tendering
Figure 4. A two-step process for GRAHP
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