Determination of importance of criteria analytic hierarchy process ahp

INTERNATIONAL JOURNAL ResearchJanuary - April (2013), © IAEME 0976 –
International Journal of Industrial Engineering OF INDUSTRIAL(IJIERD), ISSN
6979(Print), ISSN 0976 – 6987(Online) Volume 4, Issue 1,
and Development ENGINEERING

RESEARCH AND DEVELOPMENT (IJIERD)
ISSN 0976 – 6979 (Print)
ISSN 0976 – 6987 (Online)
Volume 4, Issue 1, January - April (2013), pp. 10-18
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DETERMINATION OF IMPORTANCE OF CRITERIA: ANALYTIC
HIERARCHY PROCESS (AHP) IN TECHNOLOGICAL EVOLUTION
OF AUTOMOBILE STEERING

1 2
Rajnish Katarne , Dr. Jayant Negi
1
(Mechanical Engineering Department, Swami Vivekanand College of Engineering/ Rajiv
Gandhi Proudhyogiki Vishwavidyalaya, Bhopal, M.P., India)
2
(Mechanical Engineering Department, Swami Vivekanand College of Engineering/ Rajiv
Gandhi Proudhyogiki Vishwavidyalaya, Bhopal, M.P., India)

ABSTRACT

The Analytic Hierarchy Process (AHP) is a popular multi-criteria decision making
method. It provides ratio-scale measurements of the priorities of elements in various levels of
a hierarchy. These priorities are obtained through pairwise comparisons of elements in one
level with reference to each element in the immediate higher level. In this research paper an
attempt has been made to present/discuss the principles and techniques of the Analytic
Hierarchy Process (AHP) in the prioritization/selection of criteria of automobile steering
technology.
AHP is one of the mathematical models available to guide the decision theory.
However, judgment making is, in its totality, a cognitive and mental process derived from the
most possible adequate selection based on tangible and intangible criteria which are promptly
chosen by those who make the decisions. It also demonstrates AHP in a step-by-step manner,
where the resulting/emerging precedence are shown and the possible inconsistencies are
identified.

Keywords: Analytic Hierarchy Process, Automobile Steering, Multi-Criteria Decision-
Making, Pair wise Comparisons.

1. INTRODUCTION

Dynamic business environment, rapid technological change(s) and increasing
customers’ awareness are posing major challenges in today’s business. Technology-based

10

International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –
6979(Print), ISSN 0976 – 6987(Online) Volume 4, Issue 1, January - April (2013), © IAEME

companies look for R&D investment in emerging technologies as a key solution. Successful
implementation of technologies can strongly boost a company’s competitiveness. However, due
to funding constraints/compulsion, companies must cautiously evaluate/ estimate technologies
before they invest.
The integration of the activities derived from disciplines of science and engineering, the
essential and related, functional administrative disciplines and managing them in order to meet
the operational objectives of an enterprise could be a possible definition for technology
management. These concepts of technology management need special attention in the field of
automobile sector. Automobile sector industry has gone through several changes in the past.
These changes have become more frequent in resent past, and are likely to increase in future. To
mention a few among them are Fuel system, Steering system, Brake system, Emission control
system. (Figure 1 to Figure 3)
These technological changes necessitate the automobile industries to manage and adapt
themselves when these changes take place. The scientific and technological research base
available in the country is substantial. This needs to be fully exploited to take advantage of
emerging opportunities. The country, as a whole, needs to be poised for development and fully
primed towards attainment of technological excellence. Technology management provides an
important tool to achieve this objective. The companies have a strong tendency to provide new
products to their respective markets that give a feeling of satisfaction and confidence to their
customer.

Fig. 1: Technological Evolution of brakes

Fig. 2: Technological Evolution of fuel injection
11


Fig. 3: Technological Evolution of steering
2. LITERATURE REVIEW
Thomas L. Saaty, given The Analytic Hierarchy Process (AHP) is a theory of
measurement through pairwise comparisons and relies on the judgments of experts to derive
priority scales. It is these scales that measure intangibles in relative terms. The comparisons are
made using a scale of absolute judgments that represents how much more; one element dominates
another with respect to a given attribute. [1] The Analytic Hierarchy Process – A multicriteria
decision making approach in which factors is arranged in a hierarchic structure [2]. Belton and
Gear (1983) observed that the AHP may reverse the ranking of the alternatives when an
alternative identical to one of the already existing alternatives is introduced. In order to overcome
this deficiency, Belton and Gear proposed that each column of the AHP decision matrix to be
divided by the maximum entry of that column. Thus, they introduced a variant of the original
AHP, called the revised-AHP. Later, Saaty (1994) accepted the previous variant of the AHP and
now it is called the Ideal Mode AHP. Besides the revised-AHP, other authors also introduced
other variants of the original AHP. The fact that rank reversal also occurs in the AHP when near
copies are considered, has also been studied by Dyer and Wendell (1985). Saaty (1983a and
1987) provided some axioms and guidelines on how close a near copy can be to an original
alternative without causing a rank reversal. He suggested that the decision maker has to eliminate
alternatives from consideration that score within 10 percent of another alternative.[3] AHP helps
capture both subjective and objective evaluation measures, providing a useful mechanism for
checking their consistency relative to considered alternatives, thus reducing bias in decision
making. AHP concepts can be applied to problems of size estimating in support of cost modeling.
[4] In many industrial engineering applications the final decision is based on the evaluation of a
number of alternatives in terms of a number of criteria. This problem may become a very difficult
one when the criteria are expressed in different units or the pertinent data are difficult to be
quantified. The Analytic Hierarchy Process (AHP) is an effective approach in dealing with this
kind of decision problems. [3] The Analytic Hierarchy Process (AHP) has been proposed in
recent literature as an emerging solution approach to large, dynamic, and complex real world
multi-criteria decision making problems, such as the strategic planning of organizational
resources and the justification of new manufacturing technology. [5] Nathasit Gerdsria, & Dundar
F. Kocaoglu emphasizes how the Analytical Hierarchy Process (AHP) is applied as a part of the
TDE framework. The TDE is developed to transform the technology roadmapping approach to a
level in which it is dynamic, flexible, and operationalizable. This new approach provides an
effective way to help organizations to overcome the challenge of keeping a roadmap alive.
Authors Integrate AHP into the TDE framework. Time-based format with multilayer linking is
used. [6] Technology Development Envelope (TDE) tool supports in the technology
roadmapping, Daim Tugrul, Gerdsri Northeast, Kockan Irmak, and Kocaoglu Dundar used for the
automobile sector. TDE is a combination of Technology forecasting, Technological
characterization, Technology assessment, Hierarchical modeling, and Mathematical modeling. [7]
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3. THE ANALYTIC HIERARCHY PROCESS OVERVIEW

The Analytic Hierarchy Process (AHP) is a theory of measurement through pairwise
comparisons and relies on the judgments of experts/ professional to derive preference scales. It is
these scales that measure intangibles in relative terms. The comparisons are made using a scale of
absolute judgments that represents, how much more, one element relates to another with respect
to a given attribute. The judgments may be inconsistent, and how to measure inconsistency and
improve the judgments, when possible to obtain better consistency is a concern of the AHP.

3.1 Analytical Hierarchy Process Background

The precepts of AHP are reflected in observations of workings of the human mind. When
confronted with a complex problem, humans tend to group elements of the problem by certain
properties that we believe we can compare. Since factors of a decision are usually interrelated, it
is necessary to establish a measuring scheme that allows each factor to influence the goal in
proportion to its importance relative to all other factors. This poses the question for each
comparison factor: How strongly do the factors at the lowest level of the hierarchy influence the
top factor (goal)? In most cases, the answer to this is that each has a non-uniform influence,
which necessitates use of an intensity measure – one that not only defines the most influential
factors, but also yields relative measures of influence differentials. AHP uses simple pairwise
comparison of components of a decision to produce intensity measures.[4]

3.2 Conceptual Hierarchical Model

The evaluation model (N. Gerdsri 2007) is used/adopted in a hierarchical format with four
levels: objective, criteria, factors, and characteristic metrics as shown in Fig 4.

Fig. 4: Hierarchical model for evaluating emerging technologies.[6]

It uses a multi-level hierarchical structure of objectives, criteria, sub criteria, and alternatives.
The pertinent data are derived by using a set of pairwise comparisons.

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3.3 Fundamental Scale

The decision-maker expresses his/her opinion regarding the relative importance of the
criteria and preferences among the alternatives by making pairwise comparisons using a nine-
point system ranging from 1 (the two choice options are equally preferred) to 9 (one choice
option is extremely preferred over the other) (Table 1). The AHP scoring system is a ratio
scale where the ratios between values indicate the degree of preference. The nine-point scale
has been the standard rating system used for the AHP (Saaty, 2000). Its use is based upon
research by psychologist George Miller, (1956) which indicated that decision makers were
unable to consistently repeat their expressed gradations of preference finer than ‘seven plus
or minus two.’[8]

Table 1: Fundamental Scale of Absolute Numbers [9]
Intensity of Definition Explanation
Importance
1 Equal Importance Two activities contribute equally to the objective
2 Weak or slight
3 Moderate importance Experience and judgment slightly favor one activity
over another
4 Moderate plus
5 Strong importance Experience and judgment strongly favor one activity
over another
6 Strong plus
7 Very strong or An activity is favored very strongly over another; its
demonstrated dominance demonstrated in practice
8 Very, very strong
9 Extreme importance The evidence favoring one activity over another is of
the highest possible order of affirmation
1.1 to1.9 When activities are very A better alternative way to assigning the small
close a decimal decimals is to compare two close activities with other
is added to 1 to show widely contrasting ones, favoring the larger one a
their difference as little over the smaller one when using the 1–9 values.
appropriate

Reciprocals of If activity i has one of A logical assumption
above the above nonzero
numbers assigned to it
when compared
with activity j, then j has
the reciprocal
value when compared
with i
Measurements When it is desired to use such numbers in physical
from ratio scales applications. Alternatively, often one estimates the
ratios of such magnitudes by using judgment

14


4. AHP MODELING OF AUTOMOBILE STEERING

The AHP provides a convenient approach for solving complex Multi-criteria decision-
making (MCDM) problems in engineering. However, as in this paper an attempt has been
made to apply decision-making tool in the context of automobile steering technologies. The
decision-maker needs to be very cautious that when some alternatives appear to be very close
with each other.

The following section describes the application of AHP model of automobile steering
technologies:

4.1. AHP Modeling

4.1.1. STEP1: Technology Characterization

Table 2 describes the criteria and corresponding factors affecting each criteria in
achieving the objective technological competitiveness for automobile steering. Six criteria
and factors associated with each criterion on the measure of effectiveness were finalized.

Table 2 : Hierarchical structure
Objective Criteria Factors
C1:Cost F11: Steering cost
Effectiveness F21: Maintenance cost
C2: F21:System Efficiency
Performance F22:System control technology
F23:Vehical Width
F24:Dureability
C3: F31:Turning Radius
Controlling F32:Technology
To achieve technological
competitiveness for F33: Effort required
Steering mechanism C4: Market F41: Availability of vehicle
F42: Sales Volume
C5: F51: Time period of service
Serviceability, F52: Maintenance Time
Maintenance F53: Performance condition
and Reliability
F54: % of failure of steering
C6: Flexibility F61: Upgrade ability
F62: Adjustment as per driver

4.1.2. STEP 2: Hierarchical Modeling
Experts provided their comparative judgments for each pair of criteria and factors.
The inputs were analyzed to determine the relative priority of criteria as well as the relative
importance of factors associated with each criterion this are in Table 3 to Table 7.

15


Table 3: Cost Effectiveness comparative judgments
9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9
C1:Cost √ C2:
Effectiveness Performance
C1:Cost √ C3:
Effectiveness Controlling
C1:Cost √ C4: Market
Effectiveness
C1:Cost √ C5:Serviceability,
Effectiveness Maintenance and
Reliability
C1:Cost √ C6:Flexibility
Effectiveness

Table 4: Performance comparative judgments
9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9
C2: √ C3:
Performance Controlling
C2: √ C4: Market
Performance
C2: √ C5:Serviceability,
Performance Maintenance and
Reliability
C2: √ C6:Flexibility
Performance

Table 5: Controlling comparative judgments
9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9
C3: √ C4: Market
Controlling
C3: √ C5:Serviceability,
Controlling Maintenance and
Reliability
Controlling

Table 6: Market comparative judgments
9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9
C4: Market √ C5:Serviceability,
Maintenance and
Reliability
C4: Market √ C6:Flexibility

Table 7: Serviceability, Maintenance and Reliability comparative judgments
9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9
Serviceability,
Maintenance
and Reliability

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5. PAIR WISE COMPARISONS MATRIX OF STEERING FACTORS

Criteria in the left vertical column are compared with the criteria in the top row and
the comparisons scored with the 1–9 system (in Table 3). A comparison is assigned a
reciprocal score if the item in the left vertical column is preferred less than that in the top
row. Each criterion compared with itself results in a diagonal of 1s (i.e. equal preference). A
similar scoring table would also be developed for each alternative criterion. [10] There are
two pairwise comparison matrices, these criteria are: Cost Effectiveness, Performance,
Controlling, Market, Serviceability Maintenance and Reliability and last Flexibility. Experts
provided their comparative judgments to determine the Weightage of the criteria with respect
to the goal, which is shown here in Table 8

Table 8 : Pairwise comparison of steering main criteria matrix with respect to the Goal
C1 C2 C3 C4 C5 C6

C1 1 5/3 7/2 3 7/2 3

C2 3/5 1 3/5 2 3/7 2

C3 2/7 5/3 1 3 2/9 3/2

C4 1/3 1/2 1/3 1 2/7 2

C5 2/7 7/3 9/2 7/2 1 7

C6 1/3 1/2 2/3 1/2 1/7 1

C1: Cost Effectiveness, C2: Performance, C3: Controlling, C4: Market, C5: Serviceability,
Maintenance and Reliability, C6: Flexibility
The relative importance of one criterion over another can be expressed by using
pairwise comparisons, and calculated Eigenvector values for each criteria shown in Table 9.

Table 9 : Eigenvector Value for criteria 1 to criteria 7
C1 C2 C3 C4 C5 C6 Eigenvector
C1 1 1.666 3.5 3 3.5 3 0.34
C2 0.6 1 0.6 2 0.4285 2 0.11
C3 0.2857 1.6666 1 3 0.2222 1.5 0.12
C4 0.3333 0.5 0.3333 1 0.2857 2 0.08
C5 0.2857 2.3333 4.5 3.5 1 7 0.29
C6 0.3333 0.5 0.6666 0.5 0.1428 1 0.07

17


Table 10: Weightage of steering technology criteria
Criteria Eigen Value/ Rank/Importance
Weightage
C1 Cost Effectiveness, 0.34 The Most Important Criterion
C2 Performance 0.11 Fourth Most Important Criterion
C3 Controlling 0.12 Third Most Important Criterion
C4 Market 0.08 Fifth Most Important Criterion
C5 Serviceability, Maintenance 0.29 Second Most Important Criterion
and Reliability
C6 Flexibility 0.07 Sixth Most Important Criterion

6. CONCLUSIONS AND DISCUSSION

Weightages of criteria with respect to each other have been determined by using pair wise
comparison. It has been found that Cost Effectiveness has been adjusted as the most important criteria
amounts all. These weightage will be used for further analysis of factors, which will finally lead to
facilities in selection of appropriate technology.

REFERENCES

[1] Thomas L. Saaty, Decision making with the analytic hierarchy process, International Journal
Services Sciences, 1(1), 2008, 83-98
[2] Thomas L. Saaty, How to make a decision: The Analytic Hierarchy Process, European
Journal of Operational Research 48, 1990, 9-26
[3] Evangelos Triantaphyllou and Stuart H. Mann, Using the analytic hierarchy process for
decision making in engineering applications: some challenges, International Journal of Industrial
Engineering: Applications and Practice, 2(1), 1995, 35-44
[4] Bruce E. Fad, Analytical Hierarchy Process (AHP) Approach to Size Estimation,
[5] Jiaqin Yang and Ping Shi,Applying Analytic Hierarchy Process in Firm's Overall
Performance Evaluation: A Case Study in China, International Journal of Business, 7(1), 2002, 29-45
[6] Nathasit Gerdsri and Dundar F. Kocaoglu, Applying the Analytic Hierarchy Process (AHP) to
build a strategic framework for technology roadmapping, Mathematical and Computer Modelling, 46,
2007, 1071–1080
[7] DAIM Tugrul, GERDSRI Nathasit, KOCKAN Irmak and KOCAOGLU Dundar, Technology
Development Envelope Approach for the Adoption of Future Powertrain Technologies: A Case Study
on Ford Otosan Roadmapping Model, Journal of transportation
Systems engineering and information technology, 11(2), 2011, 58-69
[8] N. gerdsri, An analytical approach to building a technology development envelope (TDE) for
roadmapping of emerging technologies, International Journal of Innovation and Technology
Management, 4(2), 2007, 121–135
[9] Thomas L. Saaty, Relative Measurement and Its Generalization in Decision Making Why
Pairwise Comparisons are Central in Mathematics for the Measurement of Intangible Factors The
Analytic Hierarchy/Network Process, Rev. R. Acad. Cien. Serie A. Mat., 102 (2), 2008, 251–318
[10] Theresa Mau-Crimmins, J.E. de Steiguer and Donald Dennis, AHP as a means for improving
public participation: a pre–post experiment with university students, Forest Policy and Economics 7,
2005, 501– 514
[11] Dr. R. Dilli Babu, R.Baskaran and Dr.K.Krishnaiah, “Performance Evaluation of Bus Depots
using AHP” International Journal of Industrial Engineering Research and Development (IJIERD),
Volume 1, Issue 1, 2010, pp. 49 - 63, ISSN Print: 0976 – 6979, ISSN Online: 0976 – 6987.

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Determination of importance of criteria analytic hierarchy process ahp

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