Pemilihan Tranportasi an ANP

8/3/2019 Pemilihan Tranportasi an ANP

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Selection of Transportation Company: An analytic network

process (ANP) approach

M.N. Qureshi#, Pradeep Kumar and Dinesh Kumar

MIED, Indian Institute of Technology, Roorkee#[email protected], [email protected], [email protected],

Abstract

Supply chain is playing a vital role in a business success. There is no true competition

among the business, but pervades in their supply chains at large. Business managers are

really facing dilemma in revising their strategy to combat competition in their supply

chains. Inbound and outbound logistics plays a vital role to have robust supply chain, by

delivering the right material at right cost at right place in right time. The Transport

Company (TC) if not selected judiciously for a given supply chain, can abruptly put the

robust supply chain in a danger.

TC plays a vital role hence a care must be taken in their judicious selection. Many

criteria have been reported in the literature for the selection of TC. Many methodologies

adopted in actual practice dont take care of the influence of each criterion on its

selection. Analytic net work process (ANP) is capable of taking the influence of each

criterion on overall decision making. Relevant criteria, for the selection of a potential TC,

have been identified and used to construct an ANP model. Selection methodology has

also been illustrated using a case problem.

Key words: Analytic network process (ANP), Selection criteria, Transport Company

(TC), Overall Weightage Index (OWI)

1. IntroductionThe globalization has created many opportunities for business with enhanced challenges.

People exploit the newer means to exploit the maximum benefits out of the deployedsystem. Thus, in this digital era, the business is greatly influenced by the Information and

communication Technology (ICT). The supply chain has a greater role to play in the

business process. The supply chain is mainly responsible for the success of the business

by supplying the right material, at right place at right cost in right time. Supply chain

management (SCM), is mainly responsible for satisfying the customer thereby enhancing


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the market share of the business. A simple supply chain involves inbound logistics and

outbound logistics mainly responsible for smooth materials management and distribution

management. The Supply chain process so evolved emphasized on the role to be played

by each player of the supply chain to make collaborative effort for the business success.

As per Towill (1997) all players must think and act as one so that the supply chain is

seamless with both information and material flows fully integrated. The various flows for

instance information flow, financial flow and physical flow are vital to be taken care as

depicted in the Figure1.

SUPPLIERS MANUFACTURERS DISTRIBUTORS RETAILERS CUSTOMERS

Inbound Logistics Outbound Logistics

Material Management Physical Distribution

Figure 1 The Supply chain Process

Third Party Logistics Providers

Flow of Information

Flow of Goods

Flow ofFinance along with Information

ULTIMATESUPPLIERS

The physical flow includes the movement of goods from a supplier to customers and

returns. Every transaction, meant for physical exchange of goods further involves flows

of man power and capital equipment. SCM applications have the potential to improve the

time-to-market, reduce costs, and enable all parties in the supply chain to better manage

current resources with systematic plan for future needs.

Companies today invest huge capital and human resources attempting to eliminate

every bit of inefficiency out of their physical supply chains. The supply chain efficacy is

at stake if the logistics is not managed properly; hence a TC is being loaded with diligent

responsibility to manage inbound and outbound logistics. A care must be taken in the

selection of potential TC to meet the expectation.

From the above premises the objective of this research is two-fold

1. To develop a framework for the comprehensive criteria in the potential selectionof TC


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2. To develop the quantitative methodology for the selection of TC using Analyticnetwork process (ANP)

2. Literature surveyA comprehensive literature review reveals many methodologies proposed for

various transportation problems. Verma et al. (1997), proposed a method using a special

type of non-linear (hyperbolic and exponential) membership functions to solve the multi-

objective transportation problem. Teng and Tzeng (1998) presented the fuzzy

multiobjective programming using the fuzzy spatial algorithm for the problem of

transportation investment project selection. Chanas and Kuchta (1998) proposed an

algorithm to solve the integer transportation problem with fuzzy supply and demand

values having integrality condition imposed on the solution. Shih (1999), applied three

fuzzy linear programming models as well as crisp linear programming models to solve

the cement transportation planning problem in Taiwan. Li and Lia (2000) proposed a new

fuzzy compromise programming approach to multi-objective transportation problems.

Avineri et al. (2000) presented a methodology for the selection and ranking of

transportation projects using fuzzy sets theory. From the above it is evident that the

Analytic network process (ANP) model has not been attempted to solve, the selection

problem of TC.

3. Framework for the criteria selection for the selection ofTCResearchers have identified many criteria for the selection of TC. According to Bardi

(1973), TC can be selected on the basis of the selection determinants like reliability;

security; user satisfaction; availability; capability; transit time; business practice; and

transport costs. A study by DEste and Meyrick (1989) has categorized the factors that

influence the choice of selection of TC to the following:

Route (frequency, capacity, convenience, directness, flexibility).

Cost (freight rate and other costs).

Service factors (delays, reliability and urgency, damage avoidance, loss and theft, fast

response to any problems, co-operation with the carrier, tracing ability). McGinnis (1989)

presents a comprehensive summary of 11 previous empirical studies and categorizes the

studies under seven categories: freight rates; reliability; transit time; over-supply, short-


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supply and damaged; shipper market considerations; carrier considerations; and product

characteristics. Many criteria of relevance are Billing services, Loss/damage history,

Domestic distribution/consolidation services, International distribution/consolidation

services, Quality of sales personnel, Quality of drivers, Quality of dispatchers, Quality of

customer services, Completeness of services and Loading and unloading facilities.

4. MethodologySelection of a potential TC requires systematic methodology. The steps involved in the

selection are outlined below which self are explained.

4.1. Selection of Decision Makers for expert opinion4.2. Define specifications for potential TC4.3. Identify TC4.4. Development and evaluation of request for information (RFI)4.5. Develop request for proposal (RFP)4.6. Evaluate RFP responses4.7. Final selection using ANP5. Application of ANP methodologyThe case company is interested to renew their yearly transport contracts. In order to

ascertain selection of potential TC Request for proposal (RFP) was asked from the

current and the new TCs. A team of expert scrutinized the proposal and earmarked three

main TCs. Analytic Network Process (ANP) followed to identify the potential TC is

explained below.

6. The Analytic Network Process methodologyThe Analytic Network Process (ANP) methodology has been used to find the potential

TC. The various steps involved are illustrated in the following nine steps.

6.1. Step 1. Model development and problem formulationBased on the literature review many criteria have been identified for selecting a potential

TC. ANP model has been developed based on the criteria identified. The criteria have

been classified in to various levels for instance determinants, dimensions, and enablers.

Generally the higher level criteria or the determinant play a crucial role in strategic

decision making thus the criteria of compatibility (CPT), cost of service (CST), quality

(QLT), and reputation (RPT) of TC are grouped in highest level. In the middle-level


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criteria are named as dimensions, these are long-term relationships (LTR), operational

performance (OP), financial performance (FP), and risk management (RM). The third-

level criteria in the ANP model are termed as enablers. The enablers support the

respective dimensional criteria as well as other enabler as well. Hence interdependencies

persist in among the enabler which is also shown in the figure. The various TC

alternatives are placed at the bottom for the required decision making. The ANP model is

depicted graphically in Figure 2. The developed model in accordance to other models

found in the literature (Meade and Sarkis, 1999; Jharkharia and Shankar, 2007)

Selection of Transport

Company

Overall Weightage Index

(OWI)

Compatibility (C) Cost (C) Quality (Q) Reputation (R)Determinants

Dimensions

Enablers

Long-term relationship

(LTR)

Operational

Performance (OP)Financial Performance

(FP)

Risk Management (RM)

Transport Company

ATransport Company

B

Transport Company

C

Handling

Capabili ties (HC)Pricing Flexibility (PC)

Carrier Reputation (CR)

Claim Services & Bil ling

Service (CBS)Quality of Personnel (QP)

Speed of

Transportation (ST)

Familiarity with

Carrier (FC)

Loading & Unloading Facility(LUF)Delivery Performance (DP)

IT Capability (ITC)

Market Share(MS)Carrier Coverage

(CC)

Range of services and

Geographical Spread(RSGS)

Reliable Pi ckup

Service(RPS)Reliable Transit Time(RTT)

Flexibility in Operationand Delivery (FOD)

Figure 2 ANP-based model for the selection of TC.

6.2. Step 2. Pairwise comparison of determinantsA pairwise comparison will give the relative importance of the criteria. Hence using the

saaty scale (Saaty, 1980) various comparisons may be made with consistency check as

described below:

(i) Construct a pairwise comparison matrix using a scale of relative importance. The

judgments are entered using the fundamental scale of the AHP proposed by Saaty (1980).


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Using pairwise comparison intensity of relative importance between two criteria can be

established using Table 1.

Table 1 Sattys scale (1980)

Intensity of

relative importance

Definition

1 Equally preferred

3 Moderately preferred

5 Essentially preferred

7 Very strongly preferred

9 Extremely preferred

2, 4, 6, 8 Intermediate importance between two adjacent

judgments

Assuming M criteria, the pairwise comparison of criterion i with criterion j gives a

square matrix 1MXMA where ija denotes the relative importance of criterion i with respect

to criterion j . In the matrix, 1ija = when i j= and 1 .ijjia a=

(ii) Find the relative normalized weight ( iW ) of each criterion by calculating the

geometric mean of thi row and normalizing the geometric mean of rows in the

comparison matrix.

1

1

GM

MM

i ij

j=

a

= (1)

and

1

GM GMM

i i j

j=

W = (2)

(iii) Calculate matrix 3A and 4A such that 3 1* 2A A A= and 4 3 2A A A= , where

[ ]T

1 2 N2 , , , , .iA W W W W = K

(iv) Find out the maximum eigen value max which is the average of matrix 4A .

(v) Calculate the consistency index ( ) ( )maxC.I. 1M M= . The smaller the value of

C.I., the smaller is the deviation from the consistency.


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(vi) Obtain the random index R.I., Saaty (1980), for the number of criteria used in

decision making.

(vii) Calculate the consistency ratio C.R.= C.I. R.I. usually, a C.R. of 0.1 or less is

considered as acceptable as it reflects an informed judgment which could be attributed to

the knowledge of the analyst about the problem under study.

Table 2 Pairwise comparisons of determinants (C.R.= 0.015 )

Compatibility Quality Cost Reputation e-vector

(CO) (Q) (C) (R)

Compatibility (CO) 1.00 5.00 2.00 3.00 0.488

Quality (Q) 0.20 1.00 0.50 0.50 0.100

Cost (C) 0.50 2.00 1.00 2.00 0.251

Reputation (R) 0.33 2.00 0.50 1.00 0.161

Table 2 shows the relative importance among the Compatibility, Quality, Cost and

Reputation. The e-vectors calculated would be used in the calculation of overall weighted

index (OWI).

6.3. Step 3. Pairwise comparison of dimensionsPairwise comparisons of various dimensions for a determinant can be derived using the

AHP methodology described earlier. In selection of potential TC, there are four matrices

for the dimensions. One such matrix for the compatibility is shown in Table 3. Similar

table for Cost, Quality and Reputation can be derived. The e-vector obtained will be used

in the further calculation.

Table 3 Pairwise comparisons of dimensions (C.R.= 0.071 )

(LTR) (OP) (FP) (RM) e-vector

Long-Term relationship (LTR) 1.00 0.5 5.00 3.00 0.332

Operational Performance (OP) 2.00 1.00 3.00 5.00 0.457

Financial Performance (FP) 0.20 0.33 1.00 2.00 0.127

Risk Management (RM) 0.33 0.20 0.50 1.00 0.084


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6.4. Step 4. Pairwise comparison of enablersThe pairwise comparison of enabler can be carried out at various levels with respect to

the upper level dimension and determinants. One such pairwise comparison matrix for

LTR dimension under CPT determinant is shown in Table 4.

Table 4 Pairwise comparison matrix for long-term relationships under the compatibility

C.R =0.082

Enabler HC PC CR CBS QP e-vector

Handling Capabilities (HC) 1.00 3.00 6.00 2.00 5.00 0.417

Pricing Flexibility (PC) 0.33 1.00 3.00 0.50 7.00 0.202

Carrier Reputation (CR) 0.17 0.33 1.00 0.33 0.50 0.061

Claim Services & Billing

Service (CBS)0.50 2.00 3.00 1.00 5.00 0.251

Quality of Personnel (QP) 0.20 0.14 2.00 0.20 1.00 0.070

For the pairwise comparison, the question asked to the decision-maker is: what is the

relative impact on long-term relationship by enabler a when compared to enabler b in

improving compatibility between the user and the transporter? . From the Table 4 it is

observed that handling capabilities (HC) has the highest e-vector of 0.417.Similarly other

compression matrix may be prepared.

6.5. Pairwise comparison matrices for interdependenciesPairwise comparison matrices for interdependencies may be prepared for each enabler

with reference to the determinant and dimension. One such paiwise matrix is shown in

Table 5.

Table 5 Pairwise comparison matrix for long-term relationships under the compatibility

for Quality of personnel (C.R =0.0235)

Enabler HC PC CR CBS e-vector

Handling Capabilities (HC) 1.00 4.00 3.00 0.33 0.24

Pricing Flexibility (PC) 0.25 1.00 0.50 0.14 0.06

Carrier Reputation (CR) 0.33 2.00 1.00 0.14 0.10

Claim Services & BillingService (CBS)

3.00 7.00 7.00 1.00 0.60

6.6. Step 6. Evaluation of TCThe final earmarked transport companies are (A, B, and C) from which a potential TC

needs to identify. The performance of each TC is judged on the enabler. The total


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enablers are 16 hence 16 matrices for each determinant will be prepared. One such

pairwise comparison matrix is shown in Table 6.

Table 6 Matrix for alternatives impact on enabler WIL in influencing the

compatibility determinant (C.R =0.028)

Transport Company A B C e-vector

A 1.00 0.14 0.33 0.085

B 7.00 1.00 4.00 0.701

C 3.00 0.25 1.00 0.213

In this table, the impacts of three alternatives are evaluated on the enabler pricing

flexibility in influencing the determinant CPT.

6.7. Step 7. Super-matrix formationThe super-matrix allows for a resolution of interdependencies that exist among the

elements of a system. It is a partitioned matrix where each sub-matrix is composed of a

set of relationships between and within the levels as represented by the decision-makers

model. The super-matrix M, may be prepared for the relative importance measures for

each of the enablers for the compatibility determinant. The elements of the super-matrix

have been imported from the comparison matrices of interdependencies (Table 5).

In the next stage, the super-matrix is made to converge to obtain a long-term stable

set of weights. For convergence to occur, the super-matrix needs to be column stochastic.

In other words, the sum of each column of the supermatrix needs to be one. Raising the

super-matrix to the power 2k+1, where k is an arbitrarily large number, allows

convergence. In this example, convergence is reached at 64. The converged super-matrix

is shown in Table 7.

6.8. Step 8. Selection of the potential TCThe selection of the potential TC depends on the values of various desirability indices.

These desirability indices indicate the relative importance of the alternatives in

supporting a determinant. In the present case, for each determinant, there are threedesirability indices, one each for the three alternative providers A, B, and C. The

desirability index, iaD , for the alternative i and the determinant a is defined as

D I.

1 1

jaKJ

ia ja kja kja ikjaj k

D P A A S= =

= (3)


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In this equation, jaP is the relative importance of dimension j in influencing the

determinant a. Dkja

A is the relative importance of an enabler k in influencing the

determinant a through dimension j for the dependency (D) relationships. I

kja

A is the

stabilized importance weight of the enabler kin the dimensionj and determinant a cluster

for interdependency (I) relationships. These values are taken from the converged super-

matrix..ikja

S is the relative impact of alternative i on enabler k of dimension j for

determinant a. Kja is the index set of enablers for dimension j of determinant a, andJis

the index set for dimension j. Table 8 shows the desirability indices ( iaD ) and their

normalized values (Nia

D ) for the compatibility determinant. These are based on the

compatibility hierarchy using the relative weights obtained from the pairwise comparison

of alternatives, dimensions, and weights of enablers from the converged super-matrix

Table 7 Super-matrixMfor compatibility after convergence

HC PC CR CBS QP ST FC LUF DP ITC MS CC RSGS RPS RTT FOD

HC 0.18 0.18 0.18 0.18 0.18

PC 0.13 0.13 0.13 0.13 0.13

CR 0.25 0.25 0.25 0.25 0.25

CBS 0.35 0.35 0.35 0.35 0.35

QP 0.11 0.11 0.11 0.11 0.11ST 0.07 0.07 0.07 0.07 0.07

FC 0.12 0.12 0.12 0.12 0.12

LUF 0.31 0.31 0.31 0.31 0.31

DP 0.32 0.32 0.32 0.32 0.32

ITC 0.22 0.22 0.22 0.22 0.22

MS 0.23 0.23 0.23

CC 0.34 0.34 0.34

RSGS 0.43 0.43 0.43

RPS 0.40 0.40 0.40

RTT 0.20 0.20 0.20

FOD 0.40 0.40 0.40


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Table 8 Compatibility desirability indices

Dimensions CriteriaLTR

Table 3

Conv Mat rix

Table 4

Transport Company alternatives Final Values of Alternatives

A B C A B C

LTR 0.332 PM 0.42 0.18 0.64 0.28 0.07 0.0160 0.0070 0.0018

LTR 0.332 WIL 0.20 0.13 0.09 0.70 0.21 0.0007 0.0061 0.0019LTR 0.332 FBP 0.06 0.25 0.11 0.63 0.26 0.0005 0.0032 0.0013

LTR 0.332 QM 0.25 0.35 0.60 0.32 0.08 0.0176 0.0092 0.0024

LTR 0.332 INF 0.07 0.11 0.74 0.19 0.08 0.0019 0.0005 0.0002

OP 0.457 IT 0.46 0.07 0.67 0.24 0.09 0.0097 0.0035 0.0013

OP 0.457 FA 0.19 0.12 0.68 0.25 0.07 0.0072 0.0026 0.0007

OP 0.457 ESP 0.06 0.31 0.75 0.13 0.12 0.0063 0.0011 0.0010

OP 0.457 DP 0.21 0.32 0.70 0.21 0.09 0.0220 0.0067 0.0027

OP 0.457 ESL 0.08 0.22 0.66 0.26 0.08 0.0052 0.0021 0.0006

FP 0.127 MS 0.67 0.23 0.63 0.26 0.11 0.0124 0.0051 0.0021

FP 0.127 RS 0.27 0.34 0.67 0.24 0.09 0.0077 0.0028 0.0010

FP 0.127 GS 0.06 0.43 0.62 0.32 0.07 0.0021 0.0011 0.0002

RM 0.084 SC 0.74 0.4 0.69 0.16 0.15 0.0171 0.0040 0.0037

RM 0.084 CAR 0.17 0.2 0.65 0.23 0.12 0.0018 0.0006 0.0003

RM 0.084 FOD 0.09 0.4 0.58 0.31 0.11 0.0018 0.0010 0.0003

Desirability indices 0.1302 0.0567 0.0217

Normalized Desirability indices 0.6353 0.2561 0.1086

jaP

iaNDiaD

6.9. Step 9. Calculation of OWIThe OWI for an alternative i (OWIi ) is the summation of the products of the normalized

desirability indices ( iaND ) and the relative importance weights of the determinants (Ca).

In the calculation of OWI the use of normalized values of iaD ensures that the OWI

values of the alternatives do not change with a large range of absolute values of Dia for

different determinants. In other words, it may be said that the values of OWI using

normalized values of iaD are unit invariant. The normalized values of desirability indices

also ensure that the sum of OWI values is equal to 1.00 (Refer Table 9). OWI is

mathematically represented as

OWIi iaN D Ca= (4)


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Table 9 Overall weighted index (OWI) for alternatives

Alternative Compatibility Quality Cost ReputationOWI

e-vector 0.488 0.251 0.100 0.161

A 0.1302 0.1382 0.1311 0.1223 0.6353

B 0.0567 0.0527 0.0458 0.0456 0.2561C 0.0217 0.0202 0.0201 0.0298 0.1086

6.10. Conclusion and future researchThe paper presents ANP model for the selection of potential TC for a shipper. The ANP

approach is well suited over AHP when the enabler influences other enabler as well as

the dimension and determinants in a hierarchy. In such influence AHP can not be used

hence ANP takes care of this influence. The future may employ fuzzy ANP to take care

of the vagueness and impreciseness of the data and the judgmental decision taken by the

decision makers. Thus the accuracy of the research may be enhanced by considering the

fuzzy approach with ANP.

References

Avineri, E., Prashker, J. and Ceder, A. (2000), Transportation projects selection process

using fuzzy sets theory, Fuzzy Sets and Systems, Vol.116, pp.3547.

Bardi, E.J. (1973), Carrier selection from one mode, Transportation Journal, Vol. 13

No. 1, pp. 239.Chanas, S. and Kuchta, D. (1998), Fuzzy integer transportation problem, Fuzzy Sets

and Systems, Vol. 98, pp.29198.

DEste, G. and Meyrick, S. (1989), More than the bottom line: how users select a

shipping service, 14th Australian Transport Research Forum, Vol. 1, Western

Australian Department of Transport, Perth, pp.65-82.

Jharkharia, S. and Shankar, R. (2007) Selection of logistics service provider: An analytic

network process (ANP) approach, Omega, Vol. 35 No.3.pp.274-289.

Li, L. and Lai, K.K. (2000), A fuzzy approach to multiobjective transportation problem,

Computers & Operations Research, Vol.27, pp. 4357.

McGinnis, M.A. (1989), A comparative evaluation of freight transportation choice

models, Transportation Journal, Winter, pp. 3646.


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Meade, L.M and Sarkis, J.(1999) Analyzing organizational project alternatives for agile

manufacturing processes: an analytic network approach, International Journal of

Production Research, Vol.37 No.2,pp. 24161.

Saaty, T.L. (1980) The Analytic Hierarchy Process, McGraw-Hill, New York.

Shih, L.-H. (1999), Cement transportation planning via fuzzy linear programming,

International Journal of Production Economics, Vol. 58, pp.277287.

Teng, J.-Y.and Tzeng, G.-H. (1998), Transportation investment project selection using

fuzzy multiobjective programming, Fuzzy Sets and Systems, Vol.96,pp. 25980.

Towill, D.R. (1997), The seamless supply chain, International Journal Technology

Management, Vol. 13 No.1, pp. 3756.

Verma, R., Biswal, M. P. and Biswas, A. (1997), Fuzzy programming technique to solve

multi-objective transportation problems with some non-linear membership

functions, Fuzzy Sets and Systems,Vol.91, pp. 3743.

(M. N Qureshi obtained his bachelors degree (Hons) in Mechanical

Engineering with Production Management as a specialization fromM.S. University of Baroda, Gujarat, India in 1986. Subsequently, he

obtained his Masters in Mechanical Engineering from the same

university. He has eight years of Industrial experience, currently

working as senior faculty member at Faculty of Technology andEngineering, M .S.University of Baroda, Gujarat, India at present he is

pursuing his Ph.D. in Mechanical Engineering at Indian Institute ofTechnology (IIT),Roorkee, India. His areas of interests are Supply

chain management, Industrial management and Quality management.

Pradeep Kumar is Professor in Mechanical and IndustrialEngineering Department, IIT Roorkee, India. He has obtained his

bachelors degree in industrial engineering in 1982 from Roorkee

University and masters in production engineering from University ofRoorkee in 1989. He received Ph.D. degree in 1994 in mechanical

engineering with specialization in Industrial engineering from

University of Roorkee, India. He has about 20 years research/teaching

experience. He has guided number of students for their undergraduateprojects, master dissertations and Ph.D. degrees. He had contributed

150 publications in international/national journals/conferences of repute. His fields of

interests are advanced manufacturing processes, quality engineering, metal casting andindustrial engineering.)


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Dinesh Kumar is Professor in Mechanical and Industrial

Engineering Department, IIT Roorkee, India. He obtained his B.Sc.

Engineering with Hons. (Mechanical engineering) in 1980 from

Punjab University and Masters in Mechanical Engineering from

University of Roorkee in 1984. He received Ph.D. degree in 1991 inMechanical Engineering from University of Roorkee, India. He has

about 25 years research/teaching and industrial experience and 80publications in international/national journals/conferences. He has

guided number of students for their undergraduate projects, masters

dissertations and Ph.D. degrees. His fields of interests are System behavior in industry,Maintenance Engineering and Reliability Analysis.

Pemilihan Tranportasi an ANP

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