evaluating the operating efficiency of international ports in asia the deatopsis approach 1

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Proceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007

EVALUATING THE OPERATING EFFICIENCY OF INTERNATIONAL

PORTS IN

ASIA: THE DEA/TOPSIS APPROACH

Chen-Huei YEH

Ph.D. StudentDepartment of Shipping and TransportationManagementNational Taiwan Ocean University2 Peining Rd., Keelung 20224Taiwan, R.O.C.Fax: +886-2-2463-1903E-mail: [email protected]

Kuang LINProfessorDepartment of Shipping and TransportationManagementNational Taiwan Ocean University2 Peining Rd., Keelung 20224Taiwan, R.O.C.Fax: +886-2-2463-1903E-mail: [email protected]

Kee-Kuo CHENAssociate ProfessorDepartment of Shipping and Transportation

ManagementNational Taiwan Ocean University2 Peining Rd., Keelung 20224Taiwan, R.O.C.Fax: +886-2-2463-1903E-mail: [email protected]

Ta-Shun CHOResearch assistantDepartment of Communications and

Guidance EngineeringNational Taiwan Ocean University2 Peining Rd., Keelung 20224Taiwan, R.O.C.Fax: +886-2-2463-3492E-mail: [email protected]

Hsuan-Shih LEEProfessor, Corresponding authorDepartment of Shipping and TransportationManagementNational Taiwan Ocean University

2 Peining Rd., Keelung 20224Taiwan, R.O.C.Fax: +886-2-2463-1903E-mail: [email protected]

Ming-Tao CHOU

Assistant Professor

Department of Aviation and Maritime

Management

Chang Jung Christian UniversityTaiwan 711, Taiwan.

Fax: +886-6-278-5056

E-mail: [email protected]

Abstract: This paper aims to recommend a promising alternative approach for evaluating the

operation efficiency of the top 20 international container ports in Asia for the year 2004.

Evaluation of efficiency for the target DMU (Decision Making Unit) with conventional DEA

(Data Envelopment Analysis) is to determine the most beneficial multipliers of input and

output and derive the best efficiency that the target DMU can achieve with these multipliers.However, the available studies have not yet provided a satisfactory answer to the problem of

making international comparisons of port efficiency. For performance enhancement, TOPSIS

(Technique for Order Preference by Similarity to Ideal Solution) can be employed to

aggregate efficiencies in different aspects, which leads to an innovative two-stage relative

efficiency estimation technique, called DEA/TOPSIS. Superior to the traditional DEA

approach, determination of the overall ranks of the container ports with better precision is

accessible based on the DEA/TOPSIS approach.

Key Words: DEA/TOPSIS, decision making unit (DMU), operation efficiency, Asian container

ports




1. INTRODUCTION

In view of the contemporary global economic development, both international and regional

trades have become increasingly important, and the related issues on ocean shipping have

been getting attractive. In particular, container transportation plays a key role in the process

for traders in coastal countries where access to seaports is considered to be easy. This isexplained by the significance of ports in the international environment; the increasing levels

of operation and competition the ports have been facing for many coastal countries or areas

such as Taiwan, where development of the shipping industry and increase of the port

performance are important issues for increasing the international competitive ability.

Due to the relatively low production costs and high trade ratio, the overall ocean shipping

market for the Asian developing countries has reached up to 60% of the world. Taking the

advantages of superior geography location, infrastructure and superstructure requirements,

Taiwan has very good trade competitive capability. However, as compared to the other major

competitive ports in Asia, Taiwan’s growth rate in container transportation is relatively slow

and the level of competitive capability is relatively low. Summary of the top 20 Asian

international container ports and their ranking in the world (indicated in the parenthesis) in

terms of volume for the year 2004 is shown in Table 1. According to the data adopted from

the Containerisation International Yearbook , as shown in Table 1, China is the country with

the largest growth rate of number of containers. Among the top 30 international container

ports in the world, there are 8 container ports located in China, which are ports of Hong Kong

(1), Shanghai (3), Shanghai (4), Qingdao (14), Ningbo (17), Tianjin (18), Guangzhou (22),

and Xiamen (26), respectively. (The numbers in the parenthesis indicate the ranks of the

ports.) Most of the ports (except Hong Kong) have the growth rate over 20% in recent years.

This information shows the rapid economic growth in China has increasingly brought

significant impact to the global economy.

Ports form a vital link in the overall trading chain monitoring and comparing one’s port with

other ports in terms of overall efficiency has become an essential part of many countries’

microeconomic reform programs. Port efficiency is an important contributor to a nation's

international competitiveness. Efficiency is usually defined as the ratio of benefits achieved

(outputs) to resources used (inputs). The factors in port management are multiple and

complex. This complexity of factors affecting port performance leads to considerable

difficulty in determining efficiency or the extent to which a port's resources are fully

employed in attainment of goals. In order to support trade oriented economic development,

port authorities have increasingly been under pressure to improve port efficiency by ensuring

that port services are provided on an internationally competitive basis.

Most literatures aim to provide the piecemeal estimation on port performance, while a few

literatures aim to provide the whole estimation on port performance. The performance

evaluation for ports can be more comprehensive if efficiency ratios are considered. There

might be several weight restrictions related to criteria that lead to various versions of the

interviewer by questionnaire. Data envelopment analysis (DEA) can be used to measure the

port efficiency of Decision Making Unit (DMU) with multiple outputs and inputs. DEA could

be used to handle problems with a number of inputs and outputs, and would not be influenced

by different quantified units, and deals with highly flexible in ordinal data, and applies

mathematical programming techniques to measure each DMU's efficiency. DEA is a

nonparametric approach without requiring any assumptions about the functional forms of the

production function. Three of the DEA models that are most often associated with the DEA




methodology are the CCR, BCC (Banker et al., 1984) and Additive models. This paper

incorporates the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)

method into DEA, which leads to the DEA/TOPSIS approach. TOPSIS is a rational method

developed by Hwang and Yoon (1981). The advantages of TOPSIS method are that it is

relatively simple and yields a highly reliable preference order. The underlying concept is that

most preferred alternative should not only have shortest distance from ‘ideal’ solution, butalso longest distance from ‘negative-ideal’ solution. The objective is to find a solution closest

to the ‘positive ideal solution’ and furthest from the ‘negative ideal solution’. ‘Positive ideal

solution’ refers to the most effective or least costly value among a set of feasible solutions.

Conversely, negative ideal solution refers to the least effectiveness and highest costly value

among a set of feasible solutions.

Available studies have not provided a satisfactory answer to the problem of making

international comparisons of port efficiency. In certain case, the operation efficiency of

container ports may be concluded to be effective while the relative efficiency is not available

based on the conventional DEA approach. This problem can be resolved by the DEA/TOPSIS

approach. This study aims to contribute to this important task by applying an innovative

approach to port efficiency ratings covering a selected sample of ports. Relying on

mathematical programming techniques, the approach, called DEA/TOPSIS, is applied to a

wide number of different situations where efficiency comparisons are required due to its

inherent advantages compared with conventional approaches. This study examines efficiency

with respect to containerized cargoes across ports recognized for their high level performance

(in terms of throughput) in Asia. In this study, the ports’ efficiency estimation is conducted

based on the top 20 international container ports in Asia. The data sources for this study cover

all the related documents of container port performance indicator for the year 2004. The

innovative two-stage relative efficiency estimation technique, DEA/TOPSIS, is employed to

evaluate the performance.

The rest of the paper is organized as follows. Section 2 gives a brief review of related studies.

Section 3 introduces the proposed DEA/TOPSIS methodology. Section 4 provides the

empirical results and analysis for the proposed DEA/TOPSIS approach applied to 20

container ports. Finally, Section 5 summarizes the main results in the paper.

2. LITERATURE REVIEW

Originally proposed by Charnes, Cooper and Rhodes (CCR) (1978a, 1979) to measure the

relative efficiency for organizations or firms, DEA (Data Envelopment Analysis) is a

nonparametric approach without requiring any assumptions about the functional forms of theproduction function. DEA is a special method that does not use decision matrix directly. It

evaluates the efficiency of a group of alternatives, but does not indicate a clear winner. The

advantage of DEA is that no explicit functional forms need to be imposed on the data. Thus,

the use of DEA has become increasingly widespread since then. Charnes, Cooper and Rhodes

introduced a ratio of efficiency, also called the CCR ratio, which generalizes the single-output

to single-input classical engineering-science ratio to multiple outputs and inputs without

requiring preassigned weights. CCR introduced a mathematical programming technique to

measure each DMU's efficiency or productivity by the multiple inputs and outputs. The CCR

model is a fractional programming, which can be transformed to linear programming (LP).

Solve the LP for each of the N firms; one obtains the efficiency score for each firm.




Table 1 Summary of the top 20 Asian international container ports and their ranking in the

world in terms of volume for the year 2004 (Unit: TEU)

Ranking Port Country/Area Year 2004 Year 2003Growth rate

(%)

1(1) Hong Kong China 21932000 20449000 7.3

2(2) Singapore Singapore 20600000 18100000 13.83(3) Shanghai China 14557200 11283000 29.0

4(4) Shenzhen China 13650000 10614900 28.6

5(5) Busan Korea 11430000 10407809 10.4

6(6) Kaohsiung Taiwan 9710000 8840000 9.8

7(13) Port Klang Malaysia 5243593 4840000 8.3

8(14) Qingdao China 5139700 4239000 21.2

9(16) Tanjung Pelepas Malaysia 4020421 3487320 15.3

10(17) Ningbo China 4005500 2772000 44.5

11(18) Tianjin China 3814000 3015000 26.5

12(19) Laem Chabang Thailand 3624000 3181050 13.9

13(20) Tokyo Japan 3580000 3313647 8.0

14(22) Guangzhou China 3308200 2761700 19.8

15(23) Tanjung Priok Indonesia 3248149 2757513 17.8

16(26) Xiamen China 2871700 2331000 23.2

17(28) Manila Philippines 2629342 2552187 3.0

18(29) Yokohama Japan 2576522 2504628 2.9

19( -- ) Keelung Taiwan 2070192 2000001 3.5

20( -- ) Taichung Taiwan 1245000 1244826 0.01

Source: Containerisation International Yearbook (March 2005)

Data envelopment analysis (DEA) has recently been successfully applied to a number of

different economic efficiency measurement situations. DEA has recently attracted much

attention for evaluating competing alternatives performing essentially the same task. Roll and

Hayuth (1993) have advocated the use of this approach to the measurement of port efficiency,

and demonstrated, based on hypothetical port data, how the relative efficiency ratings of ports

could be obtained. They show how DEA can be useful in assessing the relative effectiveness

using seven variables from annual reports in twenty ports, in which there are three input

variables: manpower, capital, cargo uniformity; and four output variables: cargo throughput,

level of service, users’ satisfaction, ship calls. Tsai (1994) suggested that DEA model can be

useful in evaluating and comparing the efficiency of Taiwan international ports from the data

during three years for ports of Keelung, Taichung, Kaohsiung, Hualien, and Suao,respectively, using four input variables (number of cranes, number of berths, ton-day of

capacity and man-hour), and two output variables (number of containers, ton-day of storage).

Tongzon (2001) applied the DEA-CCR and DEA-Additive model to evaluate 4 Australian

and 12 other international container ports for the year 1996. Six inputs were used: number of

cranes, number of container berths, number of tugs, terminal area, delay time, labor; and two

outputs were used: cargo throughput, ship working rate. Wang et al. (2003) employed the

DEA-CCR, DEA-BCC, and Free Disposal Hull (FDH) approaches for analyzing the port

efficiency. The sample for analysis comprised a total of 57 observations, of either container

ports or individual terminals within container ports. In his study, five inputs variables (total

quay length, terminal area, number of gantry cranes, number of yard gantry cranes, number of

straddle carriers), and one output variable (container throughput) were selected. Tseng (2004)




investigated the efficiency for 27 international container ports during the years 1999～2002.

He employed four approaches: Cobb-Douglas, translog methods, CCR, and BCC. In his work,

three inputs (number of cranes, length of container berth, number of cargo handling cranes),

and one output (cargo throughput) were used. Using the same data for the same objective as

in Tongzon (2001), Lee et al. proposed the improved version of DEA, called recursive DEA

(RDEA) in 2005. Applications of DEA for evaluation of port efficiency are summarized inTable 2.

Tongzon (2001) has demonstrated that DEA provides a viable method of evaluating relative

port efficiency. The technique offers a significant alternative to classical econometric

approaches to extracting efficiency information from sample observations, such as the use of

stochastic frontier production functions. Important features of DEA are that the technique is

non-parametric and that more than one output measure can be specified. In the case of port

efficiency, the ability to handle more than one output is particularly appealing because a

number of different measures of port output are available, depending on which features of

port operation are being evaluated. In addition to providing relative efficiency rankings, DEA

also provides results on the sources of input and output inefficiency, as well as the port, which

were used for the efficiency comparison. The ability to identify the sources of inefficiency

could be useful to port authority managers in inefficient ports, acting as a guide to focusing

efforts at improving port performance.

DEA has a multi-criteria flavor: minimize all inputs, and maximize all outputs. There are

several weight restrictions related to criteria that lead to various versions of the method. Since

the early work of Charnes, Cooper and Rhodes, there have been a number of extensions to the

DEA model. In addition to the CCR model, two other DEA models are also often associated

with the DEA methodology (e.g., Ali et al., 1995): the BCC model and the Additive model.

The models differ mainly in their envelopment surface orientation and projection path to theefficient frontier for an inefficient DMU. The CCR model results in a constant returns to scale,

piece-wise linear envelopment surface with both input and output orientations for projection

paths. The BCC model provides a variable returns to scale, piece-wise linear envelopment

surface, which is similar to the Additive model. The primary aim of DEA is in general to

identify the alternatives that are not ‘efficient’ in some sense, and to assess where the

inefficiencies arise, rather than to rank or select one or more competing alternatives.

Furthermore, the selection of inputs and outputs to be included in the evaluation process is

often subject to some difficulty (Deng H., Yeh C.-H., and Willis, R. J., 2000).

TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is rational and

relatively simple method developed by Hwang and Yoon (1981). TOPSIS is developed torank competing companies in terms of their overall performance on multiple financial ratios.

The underlying concept is that most preferred alternative should not only have the shortest

distance from ‘ideal’ (or ‘positive ideal’) solution, but also the longest distance from

‘negative ideal’ solution. As indicated by Hwang and Yoon, the chosen alternative should

have the shortest distance from the positive-ideal solution and the longest distance from the

negative-ideal solution. The ideal solution is the collection of the ideal scores (or ratings) in

all attributes considered. TOPSIS defines a ‘similarity index’ (relative closeness) by

combining the proximity to the positive ideal solution and the remoteness of the negative-

ideal solution. The basic concept of TOPSIS is that the most preferred object should not only

have the shortest distance from the positive ideal solution, but also have the longest distancefrom the negative ideal solution. Euclidean norm is used as the distant measure, to calculate




the distance. If the evaluated object is closer to the ideal and farther to the negative-ideal

solution, then the value of the object is larger.

Table 2 Applications of DEA for evaluation of port efficiency

ReferencesData

descriptionModel (s) Inputs Outputs

Roll and

Hayuth

(1993)

Hypothetical

numerical

example of 20

ports

CCR

Manpower

Capital

Cargo uniformity

Cargo throughput

Level of service

User’ satisfaction

Ship Calls

Tsai (1994)

5 ports in

Taiwan

(Keelung

Taichung

Kaohsiung

Hualien

Suao)

CCR

BCC

Number of cranes

Number of berths

Ton-day of capacity

Man-hour

Cargo throughput

Ton-day of storage

Tongzon (2001)

4 Australian

and 12 other

international

container ports

for the year

1996

Additive

CCR

Number of cranes

Number of container berths

Number of tugs

Terminal area

Delay time

Labor

Cargo throughput

Ship working rate

Wang et al.

(2003)

57 ports

(container ports

or individual

terminals

withincontainer ports)

CCR

BCC

FDH

Total quay length

Terminal area

Number of gantry cranes

Number of yard gantry cranes

Number of straddle carriers

Container throughput

Tseng (2004)

27 international

container ports

during the

years 1999～

2002

Cobb-

Douglas,

Translog

CCR

BCC

Number of gantry cranes

Container berth length

Number of cargo handling

cranesCargo throughput

Lee et al.

(2005)

Same data as in

Tongzon (2001)

RDEA Same data as in Tongzon

(2001)

Same data as in

Tongzon (2001)

Source: Authors

3. DEA/TOPSIS METHODOLOGY

DEA is a clear method that does justice to the multiple input multiple output character of

organizations. An insufficient number of DMUs for the variables being used would tend to

rate all DMUs 100% efficient simply because of inadequate number degrees of freedom.

Incorporation of the TOPSIS into the DEA is an ideal way to overcome the deficiency of

DEA, which yield an innovative approach, called DEA/TOPSIS. The DEA/TOPSIS approach

is a ‘two-stage’ TOPSIS-coupled DEA approach, which gains the merits from each individual

ones. Using the hybrid DEA/TOPSIS, the problem of inadequate number degrees of freedom

can be resolved and the reasonable ranking of efficiency can be obtained. Figure 1 provides

the framework of hybrid DEA/TOPSIS model for efficiency evaluation and ranking.




Figure 1 Framework of hybrid DEA/TOPSIS model for efficiency evaluation and ranking

Detailed DEA/TOPSIS methodology is introduced as follows. The decision matrix for the

problem can be obtained as

mntj

nmnn

m

m

l

lll

lll

lll

L ×=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

= ][

21

22221

11211

L

LLLL

L

L

(1)

and the efficiency

∑

∑

=

==m

iik

k i

rk

s

r

k r

k

xv

yu

h

1

1 (2)

where k r u and k

iv are defined using the CCR model. Symbolically, we have

max rk

s

r

k r k yuh ∑

=

=1

(3)

subject to 11

=∑=

m

iik

k i xv (4)

011

≤−∑∑==

m

iij

k irj

s

r

k r xv yu n j ,,1K= (5)

sr u k r ,,1 0 K=>≥ ε

mivk i ,,1 0 K=>≥ ε

Let the optimal solution for the above equation be

Specification of

input/output measure

Identify efficiency ratios

for DMUs via DEA-CCR

Decision matrix

Efficiency evaluation and

ranking

TOPSIS




∗k iv ,

∗k r u ,

∑

∑

=

∗

=

∗

=m

iit

k i

rt

s

r

k r

tk

xv

yu

l

1

1 (6)

TOPSIS evaluates a decision matrix in several steps starting by normalizing columns and thenmultiplying values in columns is by corresponding weights of criteria. Then, best and worst

value in each column is identified followed by creation of two sets of these values across all

columns named positive ideal solution and negative ideal solution, respectively. Using the

matrix L, the positive ideal solution and negative ideal solution are determined, where + Z

represents the positive ideal solution:

),...,,( 21++++ = m Z Z Z Z

while − Z represents the negative ideal solution:

),...,,(21

−−−− =m

Z Z Z Z

In the above expressions, tj

n

t j l Z

1max=

+ = , and tj

n

t j l Z

1min=

− = .

The so-called separation measures for all alternatives are computed based on their Euclidean

distances from ideal and negative-ideal solutions (across all criteria). Calculation of the

Euclidean (or Frobenius) norm between the decision matrix L and the alternatives + Z and− Z for obtaining the ideal solution ( +

i D ):

∑=

++ −=m

j

jiji Z l D

1

2)( (7)

and the negative-ideal solution ( −i D ):

∑=

−− −=m

j jiji Z l D

1

2)( (8)

The overall performance index for each alternative is computed by

−+

−

+=

ii

ii

D D

DC (9)

The larger the index value iC , the better the performance of the alternative.

The algorithm for the DEA/TOPSIS approach for port efficiency rating is summarized as

follows.

Step 1. Selection of the decision making units. Efficiency is usually defined as the ratio of

benefits achieved (outputs) to resources used (inputs). DEA generalizes this single

output/input technical efficiency measure to multiple outputs/inputs by constructing

a relative efficiency measure based on a single ‘virtual’ output and a single virtual

input. The efficient frontier is then determined by selecting DMUs which are most

efficient in producing the virtual output from the virtual input. Since DMUs on the

efficient frontier have an efficiency score equal to 1, inefficient DMUs are measured

relative to the efficient DMUs. A rule of thumb (Bowlin, 1987) for maintaining anadequate number of degrees of freedom when using DEA is to obtain at least two




decision-making units (DMU) for each input or output measure. An insufficient

number of DMUs for the variables being used would tend to rate all DMUs 100%

efficient simply because of an inadequate number degrees of freedom. In this article,

20 international container ports in Asia for the year 2004 are selected as the decision-

making units.

Step 2. Specification of input/output measure. One of the disadvantages of DEA is that in

most variants, the method only compares organizations with comparable production

levels. The method therefore stands or falls on the presence or lack of comparable

organizations. Extremely large organizations are quickly seen as efficient due to the

fact that there is no other organization present with a similar production level. In

addition, the more products that are distinguished, the fewer organizations with

comparable production levels there will be, and this will increase the number of

efficient organizations.

Step 3. Selection of the DEA model. There have been a number of extensions to the DEA

model. In this article, the CCR model is employed on empirical analysis. The DEA-

CCR model is popular since it is very convenient in terms of Scale Efficiency andTechnical Efficiency.

Step 4. Utilization of the DEA software tool. DEA is an easily comprehensible method for

which a large number of standard packages are available. This makes the

calculations relatively easy to carry out and interpret. A number of different

commercial software packages are now available, for which some examples of the

well known packages are LINGO, EXCEL, IDEAS, WARWICK, Frontier Analyst,

Onfront, Banxia, EMS, DEA-solver, WDEA and DEAP. In addition, a DEA model

can basically be transformed to a LP problem, and the model can be solved using the

LP software.

Step 5. Implementation of the DEA/TOPSIS efficiency evaluation. Through solving the

constructed DEA model, the DMU efficiency evaluation value can be obtained and

substituted into the normalized TOPSIS performance matrix (decision matrix).

Step 6. Efficiency evaluation and ranking. The relative closeness to ideal solution is

calculated for each alternative, and alternatives are appropriately ranked. The

resulting jC is used as the ranking criterion. According to the idea from TOPSIS,

top-ranked alternative is with the shortest distance from ideal solution and TOPSIS

guarantees that it also has the longest distance from negative-ideal solution. This

means that a larger jC implies better efficiency whereas a smaller

jC implies worse

efficiency.

4. EMPIRICAL RESULTS AND ANALYSIS

In this section, efficiency ranking for the 20 Asian international container ports in the world

for the year 2004 is adopted for empirical study. The study is conducted based on the

DEA/TOPSIS two-stage efficiency evaluation technique and the result will be compared to

the conventional DEA-CCR approach. For the DEA/TOPSIS, the first stage deals with the

conventional DEA-CCR processing, while the second stage deals with the TOPSIS

processing, as presented in Section 3.

4.1 Sources of the Data




Data availability is particularly important and difficult due to confidentiality reasons. Apart

from the data obtained from the survey, the study has to depend on secondary sources. The

required secondary data are mainly taken from various issues of the Containerisation

International Yearbook (2005), Lloyd’s Ports of the World (2004), and Cargo System

Supplement (2005). The data available on port/terminal throughput (from annual report andinternet website information) is quite reliable and unbiased and was chosen as the basis for

the analysis.

4.2 Selected Sample of Ports

Given the multiplicity of ports and cargoes handled, it is necessary to restrict the scope of the

analysis to a limited number of ports and a specific type of cargo. The sample is selected

based on size, geographical location, and data availability. When using DEA, Bowlin (1987)

suggested that at least two DMUs for each input or output measure is necessary for

maintaining an adequate number of degrees of freedom. If the selected sample size is too

small, the result of statistical testing tends to be inadequate. This paper examines the

efficiency rating on 20 container ports for their high level performance (in terms of

throughput) in Asia, i.e., the Decision Making Units (DMUs) are the international container

ports in Asia for the year 2004, which include the ports of Hong Kong, Singapore, Shanghai,

Shenzhen, Busan, Kaohsiung, Port Klang, Qingdao, Tanjung Pelepas, Ningbo, Tianjin, Laem

Chabang, Tokyo, Guangzhou, Tanjung Priok, Xiamen, Manila, Yokohama, Keelung, and

Taichung.

4.3 Inputs and Outputs Variables

Based on the production framework in container port industry, port inputs can be generalizedas land, labor and capital (including equipment). The major capital inputs in port operations

are the number of cranes, berth length, and terminal area. The most fundamental labor input is

the number of cranes due to a lack of information on this particular variable. The most

important output in port performance and the advantage of economies of scale is the number

of containers due to the highly relative of equipment and service efficiency in port; moreover,

the suitable and easy evaluation of port performance is measured in terms of the number of

containers moved through a port (throughput) on the assumption that ports are throughput

maximizes. In this paper, six inputs which are the number of cranes, berths, tugs, berth length,

terminal area, terminal storage; and one output which is the number of containers is adopted

to evaluate the efficiency of the international container ports in Asia.

4.4 Analysis of Results

In general, port performance cannot be assessed on the basis of a single value or measure.

Evaluations are made by comparing indicator values for a given port over time as well as

across ports for a given time period. The efficiency evaluation with respect to containerized

cargoes across ports recognized for their high level performance (in terms of throughput) for

20 Asian international container ports for the year 2004 is examined in this section. The

innovative two-stage relative efficiency estimation technique, called DEA/TOPSIS, is

performed and the result is compared to the conventional DEA-CCR approach. Six inputs

(number of cranes, berth length, number of berths, number of tugs, terminal area, terminal

storage), and one output (number of containers) are adopted to evaluate the performance.




Table 3 summarizes the rankings for port efficiency based on DEA/TOPSIS as compared to

DEA-CCR. In addition, Figure 2 provides plots for the port efficiency versus their rankings

given by DEA-CCR and DEA/TOPSIS approaches. The input-output efficiency of a decision-

making unit (DMU) is a value between 0 and 1 in the ratio form. According to the DEA-CCR

model dentition, a DMU is recognized the most efficient when its efficiency ratio equal to 1;

while a DMU is recognized the least efficient when its efficiency ratio equal to 0. Accordingto the result based on the DEA-CCR, there are 10 ports that obtain efficiency ratio 1 among

the Asia 20 international container ports, including Hong Kong, Singapore, Shanghai,

Shenzhen, Kaohsiung, Tanjung Pelepas, Ningbo, Guangzhou, Xiamen, Manila. Furthermore,

the efficiency ratios for ports of Yokoham, Taichung are only 0.174813 and 0.226307,

respectively.

Table 3 Rankings for port efficiency based on DEA-CCR and DEA/TOPSIS

DEA-CCR DEA/TOPSISPorts

Efficiency Ranking Efficiency Ranking

Singapore 1 1 0.989702 1

Guangzhou 1 1 0.987611 2

Shenzhen 1 1 0.979228 3

Xiamen 1 1 0.969907 4

Hong Kong 1 1 0.959519 5

Ningbo 1 1 0.914535 6

Shanghai 1 1 0.860108 7

Kaohsiung 1 1 0.848356 8

Tianjin 0.815386 11 0.794208 9Busan 0.596436 12 0.696997 10

Tanjung

Pelepas1 1 0.670822 11

Laem Chabang 0.483351 14 0.517387 12

Port Klang 0.442265 16 0.462603 13

Tokyo 0.42314 17 0.392056 14

Qingdao 0.482735 15 0.381043 15

Tanjung Priok 0.402461 18 0.361715 16

Keelung 0.555347 13 0.272760 17Manila 1 1 0.234333 18

Taichung 0.226307 19 0.149018 19

Yokohama 0.174813 20 0.086605 20

The two-stage hybrid DEA/TOPSIS approach is employed for evaluating port efficiency. As

discussed in Section 2, the primary aim of DEA is to identify those companies that are not

‘efficient’ in some sense, and to assess where the inefficiencies arise, rather than to rank or

select one or more competing alternatives. If there were several DMUs whose efficiency

ratios equal to 1’s, there will be often subject to some difficulty in interpreting some of the

empirical results. Fortunately, this problem has been be resolved by DEA/TOPSIS model.The precise ranking regarding the efficiency ratios for some of the ports may not be




accessible while this problem can be easily resolved by DEA/TOPSIS model, for which an

overall order is feasible. The result from DEA/OPSIS shows that, in terms of efficiency, the

top 3 ports are Singapore (1), Guangzhou (2), Shenzhen (3), respectively; the worst 3 ports

are Yokoham (20), Taichung (19) and Manila (18), respectively. It should be noted that

Manila’s ranking for port efficiency has the largest difference between DEA and

DEA/TOPSIS. It can also be seen that efficiency monotonically decreases when the rankingincreases in the DEA/TOPSIS derived result. The study has demonstrated that DEA/POSIS

provides a convincing methodology for evaluating precise relative port efficiency.

(a) DEA-CCR

(b) DEA/TOPSIS

Figure 2 Rankings and their port efficiency resulting from two approaches: (a) DEA-CCR; (b)

DEA/TOPSIS.

Difference on rankings for port efficiency (DMU) between DEA-CCR and DEA/TOPSIS isgiven in Table 4. The last column shows the difference in ranking for each DMU based on the




results of the DEA-CCR as compared to that of DEA/TOPSIS. It can be seen that those

DMUs which are determined “efficient” all have the same efficiency (e.g.,1) and ranking

(e.g.,1) in the DEA-CCR approach. Using the results obtained from the DEA/TOPSIS method,

they now become distinguishable (or comparable). Furthermore, the inefficient DMUs can

now be ranked more accurately using the results from the DEA/TOPSIS method.

Table 4 Difference on rankings for port efficiency between DEA-CCR and DEA/TOPSIS

Ports DEA-CCR(1) DEA/TOPSIS(2) (1)-(2)

Singapore 1 1 0

Guangzhou 1 2 -1

Shenzhen 1 3 -2

Xiamen 1 4 -3

Hong Kong 1 5 -4

Ningbo 1 6 -5

Shanghai 1 7 -6

Kaohsiung 1 8 -7

Tianjin 11 9 2

Busan 12 10 2

Tanjung Pelepas 1 11 -10

Laem Chabang 14 12 2

Port Klang 16 13 3

Tokyo 17 14 3

Qingdao 15 15 0

Tanjung Priok 18 16 2

Keelung 13 17 -4

Manila 1 18 -17

Taichung 19 19 0

Yokohama 20 20 0

Among the rankings between the two approaches, Tanjung Pelepas and Manila have largest

differences, which are descended by 10 and 17 respectively. With CCR, both Tanjung Pelapas

and Manila are measured as efficient and ranked at the first place. However, withDEA/TOPSIS, both Tanjung Pelaps and Manila are ranked at the 11

thand 18

thplaces. The

reason why there exists such large discrepancy is that DEA-CCR determines the efficiency by

only taking the weights that are favorable to the measured DMU while DEA/TOPSIS

determines the efficiency by considering all aspects. When measuring Manila with DEA-CCR,

there exist weights of inputs and outputs such that Manila is measured as efficient. When

measuring Singapore with DEA-CCR, we can identify another set of weights that are

favorable to Singapore. With the set of weights that are favorable to Singapore, the efficiency

of Manila becomes 0.070862 as shown in Table 5. If we regard different set of weights as

different scenario, there will be 20 scenarios for each port. As shown in Table 5, Manila is

identified as efficient only under the “Manila” scenario and inefficient under other scenarioswith efficiency no higher than 0.190553. As a whole, Manila is not as good as we perceived




with DEA-CCR. Therefore, the DEA/TOPSIS model has a number of advantages over the

DEA-CCR method. Those merits in DEA method has been retained while more complete

ranking with more accurate ratio is accessible.

Table 5 Efficiency of Tanjung Pelepas and Manila with CCR weights favorable to different

portsPorts Tanjung Pelepas Manila

Singapore 0.461554 0.070862

Guangzhou 0.461554 0.070862

Shenzhen 0.461554 0.070862

Xiamen 0.484094 0.059741

Hong Kong 0.461554 0.070862

Ningbo 0.615764 0.038092

Shanghai 0.654315 0.04394

Kaohsiung 0.34055 0.188378

Tianjin 0.340548 0.18837

Busan 0.484094 0.059741

Tanjung Pelepas 1 0.091839

Laem Chabang 0.461554 0.070862

Port Klang 0.40748 0.086225

Tokyo 0.334854 0.168638

Qingdao 1 0.092537

Tanjung Priok 0.408125 0.085825

Keelung 0.09435 0.121509

Manila 1 1

Taichung 0.409836 0.085318

Yokohama 0.341102 0.190553

Manila Port and Tanjung Pelepas Port are much less ranked in DEA/TOPSIS in spite of their

higher rank in DEA-CCR which implies some improvements must be done by their managers.

For example, the efficiency of Manila Port under “Ningbo” scenario is 0.038092. The peer

ports of “Ningbo” scenario are Shenzhen, Xiamen, Hong Kong, and Ningbo. Therefore,Manila Port may regard these ports as his benchmark of the improvement. The smallest

efficiency of Tanjung Pelepas Port is 0.09435 under “Keelung” scenario whose peer port is

Guangzhou Port. Hence Tanjung Pelepas Port may treat Guangzhou Port as the benchmark of

the improvement.

5. SUMMARY AND CONCLUSIONS

A new procedure based on the modified DEA, called DEA/TOPSIS, is proposed. The

DEA/TOPSIS, which is composed of DEA and TOPSIS, is a two-stage approach proposed to

rank DMUs based on the synergy of DEA and TOPSIS. The DEA/TOPSIS approach has beensuccessfully applied to the evaluation of operating efficiency for the top 20 Asian




international container ports. The technique offers a significant alternative to the conventional

evaluation approach for extracting efficiency information from the samples’ observations.

This study has demonstrated that DEA/POSIS provides a promising method for evaluating

relative port efficiency.

The DEA technique is useful in resolving the measurement of port efficiency because thecalculations are non-parametric, and can handle more than one output without requiring an

explicit a priori determination of relationships between output and inputs, as is required for

conventional estimation of efficiency using assumed functions. Although DEA is based on the

comparison of the efficiency of international container ports, whereby the weights that

aggregate the various resources and products used are determined by the data and are not

established arbitrarily in advance.

There is a major problem in applying DEA. DEA/TOPSIS can be a promising and easily

adaptable approach for obtaining the efficiency ratios. The precise ranking regarding the

efficiency ratios for some of the ports may not be accessible while this problem can be easily

resolved by DEA/TOPSIS model, for which an overall order is feasible. In the present study,

several efficiency ratios of the DMUs have been estimated to be ones (which are determined

“efficient”), and therefore, not precisely distinguishable (all with ranking “1”). This problem

can be resolved by incorporating the TOPSIS. Using the results obtained from the

DEA/TOPSIS method, they can be distinguishable (or comparable). Furthermore, the

inefficient DMUs can now be ranked more accurately using the results from the

DEA/TOPSIS method.

The DEA//TOPSIS efficiency ratings can be a useful tool for port managers or regulators,

highlighting the status of the operating efficiency and providing a deeper insight into port

performance. DEA/TOPSIS model analysis can be of great significance and value to themanagerial decisions of ports and to the strategic decisions of port authorities. Performance

evaluation for shipping industry can be made more comprehensive if efficient, financial and

other ratios are considered. At the same time, the ports’ weaknesses can be detected, leading

the way to improve potential improvements.

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evaluating the operating efficiency of international ports in asia the deatopsis approach 1

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