1

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

2

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,

3

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

4

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.

5

.

.

.

.

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)

6

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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9

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

10

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