INTERNATIONAL JOURNAL ResearchJanuary - April (2013), © IAEME 0976 –International Journal of Industrial Engineering OF IND...
International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –6979(Print), ISSN 0976 – 698...
International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –6979(Print), ISSN 0976 – 698...
International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –6979(Print), ISSN 0976 – 698...
International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –6979(Print), ISSN 0976 – 698...
International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –6979(Print), ISSN 0976 – 698...
International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –6979(Print), ISSN 0976 – 698...
International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –6979(Print), ISSN 0976 – 698...
International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –6979(Print), ISSN 0976 – 698...
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Determination of importance of criteria analytic hierarchy process ahp

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Determination of importance of criteria analytic hierarchy process ahp

  1. 1. INTERNATIONAL JOURNAL ResearchJanuary - April (2013), © IAEME 0976 –International Journal of Industrial Engineering OF INDUSTRIAL(IJIERD), ISSN6979(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 IJIERD© IAEME: www.iaeme.com/ijierd.aspJournal Impact Factor (2013): 5.1283 (Calculated by GISI) ©IAEMEwww.jifactor.com 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 makingmethod. It provides ratio-scale measurements of the priorities of elements in various levels ofa hierarchy. These priorities are obtained through pairwise comparisons of elements in onelevel with reference to each element in the immediate higher level. In this research paper anattempt has been made to present/discuss the principles and techniques of the AnalyticHierarchy Process (AHP) in the prioritization/selection of criteria of automobile steeringtechnology. 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 themost possible adequate selection based on tangible and intangible criteria which are promptlychosen 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 areidentified.Keywords: Analytic Hierarchy Process, Automobile Steering, Multi-Criteria Decision-Making, Pair wise Comparisons.1. INTRODUCTION Dynamic business environment, rapid technological change(s) and increasingcustomers’ awareness are posing major challenges in today’s business. Technology-based 10
  2. 2. International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –6979(Print), ISSN 0976 – 6987(Online) Volume 4, Issue 1, January - April (2013), © IAEMEcompanies look for R&D investment in emerging technologies as a key solution. Successfulimplementation of technologies can strongly boost a company’s competitiveness. However, dueto funding constraints/compulsion, companies must cautiously evaluate/ estimate technologiesbefore they invest. The integration of the activities derived from disciplines of science and engineering, theessential and related, functional administrative disciplines and managing them in order to meetthe operational objectives of an enterprise could be a possible definition for technologymanagement. These concepts of technology management need special attention in the field ofautomobile 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. Tomention a few among them are Fuel system, Steering system, Brake system, Emission controlsystem. (Figure 1 to Figure 3)These technological changes necessitate the automobile industries to manage and adaptthemselves when these changes take place. The scientific and technological research baseavailable in the country is substantial. This needs to be fully exploited to take advantage ofemerging opportunities. The country, as a whole, needs to be poised for development and fullyprimed towards attainment of technological excellence. Technology management provides animportant tool to achieve this objective. The companies have a strong tendency to provide newproducts to their respective markets that give a feeling of satisfaction and confidence to theircustomer. Fig. 1: Technological Evolution of brakes Fig. 2: Technological Evolution of fuel injection 11
  3. 3. 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 Fig. 3: Technological Evolution of steering2. LITERATURE REVIEW Thomas L. Saaty, given The Analytic Hierarchy Process (AHP) is a theory ofmeasurement through pairwise comparisons and relies on the judgments of experts to derivepriority scales. It is these scales that measure intangibles in relative terms. The comparisons aremade using a scale of absolute judgments that represents how much more; one element dominatesanother with respect to a given attribute. [1] The Analytic Hierarchy Process – A multicriteriadecision making approach in which factors is arranged in a hierarchic structure [2]. Belton andGear (1983) observed that the AHP may reverse the ranking of the alternatives when analternative identical to one of the already existing alternatives is introduced. In order to overcomethis deficiency, Belton and Gear proposed that each column of the AHP decision matrix to bedivided by the maximum entry of that column. Thus, they introduced a variant of the originalAHP, called the revised-AHP. Later, Saaty (1994) accepted the previous variant of the AHP andnow it is called the Ideal Mode AHP. Besides the revised-AHP, other authors also introducedother variants of the original AHP. The fact that rank reversal also occurs in the AHP when nearcopies are considered, has also been studied by Dyer and Wendell (1985). Saaty (1983a and1987) provided some axioms and guidelines on how close a near copy can be to an originalalternative without causing a rank reversal. He suggested that the decision maker has to eliminatealternatives from consideration that score within 10 percent of another alternative.[3] AHP helpscapture both subjective and objective evaluation measures, providing a useful mechanism forchecking their consistency relative to considered alternatives, thus reducing bias in decisionmaking. 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 anumber of alternatives in terms of a number of criteria. This problem may become a very difficultone when the criteria are expressed in different units or the pertinent data are difficult to bequantified. The Analytic Hierarchy Process (AHP) is an effective approach in dealing with thiskind of decision problems. [3] The Analytic Hierarchy Process (AHP) has been proposed inrecent literature as an emerging solution approach to large, dynamic, and complex real worldmulti-criteria decision making problems, such as the strategic planning of organizationalresources and the justification of new manufacturing technology. [5] Nathasit Gerdsria, & DundarF. Kocaoglu emphasizes how the Analytical Hierarchy Process (AHP) is applied as a part of theTDE framework. The TDE is developed to transform the technology roadmapping approach to alevel in which it is dynamic, flexible, and operationalizable. This new approach provides aneffective 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 isused. [6] Technology Development Envelope (TDE) tool supports in the technologyroadmapping, Daim Tugrul, Gerdsri Northeast, Kockan Irmak, and Kocaoglu Dundar used for theautomobile sector. TDE is a combination of Technology forecasting, Technologicalcharacterization, Technology assessment, Hierarchical modeling, and Mathematical modeling. [7] 12
  4. 4. International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –6979(Print), ISSN 0976 – 6987(Online) Volume 4, Issue 1, January - April (2013), © IAEME3. THE ANALYTIC HIERARCHY PROCESS OVERVIEW The Analytic Hierarchy Process (AHP) is a theory of measurement through pairwisecomparisons and relies on the judgments of experts/ professional to derive preference scales. It isthese scales that measure intangibles in relative terms. The comparisons are made using a scale ofabsolute judgments that represents, how much more, one element relates to another with respectto a given attribute. The judgments may be inconsistent, and how to measure inconsistency andimprove 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. Whenconfronted with a complex problem, humans tend to group elements of the problem by certainproperties that we believe we can compare. Since factors of a decision are usually interrelated, itis necessary to establish a measuring scheme that allows each factor to influence the goal inproportion to its importance relative to all other factors. This poses the question for eachcomparison factor: How strongly do the factors at the lowest level of the hierarchy influence thetop 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 influentialfactors, but also yields relative measures of influence differentials. AHP uses simple pairwisecomparison 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 fourlevels: 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. 13
  5. 5. International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –6979(Print), ISSN 0976 – 6987(Online) Volume 4, Issue 1, January - April (2013), © IAEME3.3 Fundamental Scale The decision-maker expresses his/her opinion regarding the relative importance of thecriteria 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 choiceoption is extremely preferred over the other) (Table 1). The AHP scoring system is a ratioscale where the ratios between values indicate the degree of preference. The nine-point scalehas been the standard rating system used for the AHP (Saaty, 2000). Its use is based uponresearch by psychologist George Miller, (1956) which indicated that decision makers wereunable to consistently repeat their expressed gradations of preference finer than ‘seven plusor 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 physicalfrom ratio scales applications. Alternatively, often one estimates the ratios of such magnitudes by using judgment 14
  6. 6. International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –6979(Print), ISSN 0976 – 6987(Online) Volume 4, Issue 1, January - April (2013), © IAEME4. 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 beenmade to apply decision-making tool in the context of automobile steering technologies. Thedecision-maker needs to be very cautious that when some alternatives appear to be very closewith each other. The following section describes the application of AHP model of automobile steeringtechnologies:4.1. AHP Modeling4.1.1. STEP1: Technology Characterization Table 2 describes the criteria and corresponding factors affecting each criteria inachieving the objective technological competitiveness for automobile steering. Six criteriaand 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 driver4.1.2. 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 relativeimportance of factors associated with each criterion this are in Table 3 to Table 7. 15
  7. 7. 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 Table 3: Cost Effectiveness comparative judgments 9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9C1:Cost √ C2:Effectiveness PerformanceC1:Cost √ C3:Effectiveness ControllingC1:Cost √ C4: MarketEffectivenessC1:Cost √ C5:Serviceability,Effectiveness Maintenance and ReliabilityC1:Cost √ C6:FlexibilityEffectiveness Table 4: Performance comparative judgments 9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9C2: √ C3:Performance ControllingC2: √ C4: MarketPerformanceC2: √ C5:Serviceability,Performance Maintenance and ReliabilityC2: √ C6:FlexibilityPerformance 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 C3: √ C6:Flexibility 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 C5: √ C6:Flexibility Serviceability, Maintenance and Reliability 16
  8. 8. International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –6979(Print), ISSN 0976 – 6987(Online) Volume 4, Issue 1, January - April (2013), © IAEME5. PAIR WISE COMPARISONS MATRIX OF STEERING FACTORS Criteria in the left vertical column are compared with the criteria in the top row andthe comparisons scored with the 1–9 system (in Table 3). A comparison is assigned areciprocal score if the item in the left vertical column is preferred less than that in the toprow. Each criterion compared with itself results in a diagonal of 1s (i.e. equal preference). Asimilar scoring table would also be developed for each alternative criterion. [10] There aretwo pairwise comparison matrices, these criteria are: Cost Effectiveness, Performance,Controlling, Market, Serviceability Maintenance and Reliability and last Flexibility. Expertsprovided their comparative judgments to determine the Weightage of the criteria with respectto the goal, which is shown here in Table 8Table 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 1C1: 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 usingpairwise 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
  9. 9. 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 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 Criterion6. CONCLUSIONS AND DISCUSSION Weightages of criteria with respect to each other have been determined by using pair wisecomparison. It has been found that Cost Effectiveness has been adjusted as the most important criteriaamounts all. These weightage will be used for further analysis of factors, which will finally lead tofacilities in selection of appropriate technology.REFERENCES[1] Thomas L. Saaty, Decision making with the analytic hierarchy process, International JournalServices Sciences, 1(1), 2008, 83-98[2] Thomas L. Saaty, How to make a decision: The Analytic Hierarchy Process, EuropeanJournal of Operational Research 48, 1990, 9-26[3] Evangelos Triantaphyllou and Stuart H. Mann, Using the analytic hierarchy process fordecision making in engineering applications: some challenges, International Journal of IndustrialEngineering: 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 Firms OverallPerformance 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) tobuild a strategic framework for technology roadmapping, Mathematical and Computer Modelling, 46,2007, 1071–1080[7] DAIM Tugrul, GERDSRI Nathasit, KOCKAN Irmak and KOCAOGLU Dundar, TechnologyDevelopment Envelope Approach for the Adoption of Future Powertrain Technologies: A Case Studyon Ford Otosan Roadmapping Model, Journal of transportationSystems engineering and information technology, 11(2), 2011, 58-69[8] N. gerdsri, An analytical approach to building a technology development envelope (TDE) forroadmapping of emerging technologies, International Journal of Innovation and TechnologyManagement, 4(2), 2007, 121–135[9] Thomas L. Saaty, Relative Measurement and Its Generalization in Decision Making WhyPairwise Comparisons are Central in Mathematics for the Measurement of Intangible Factors TheAnalytic 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 improvingpublic 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 Depotsusing 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. 18

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