Evaluation of excavator technologies: application of data fusion based MULTIMOORA methods

Abstract

excavators are quite expensive vehicles. therefore, there may be huge losses for decision makers if a wrong decision is made during the purchasing process. A good evaluation of excavator alternatives both reduces costs and increases the benefits the excavator for the purchaser. The aim of this study is to prioritise excavator technologies to help decision makers during the purchasing process and to apply three different "data fusion methods" instead of the "theory of dominance" of the original MULTIMOORA method. the MULTIMOORA method is composed of three methods, namely: the ratio analysis as a part of MOORA, reference Point theory (the reference point approach as a part of MOORA) and the Full Multiplicative Form. it is used to prioritise excavator technologies in this study. the MULTIMOORA method combines three results obtained from these three methods using the theory of dominance. Dominance directed graph, rank position method and Borda count method as data fusion methods are also used to combine these three results instead of the "theory of dominance". the results from this study show that there is no difference between the data fusion methods and the MULTIMOORA method can be applied to technology evaluation of the excavator alternatives successfully.

Full text

JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT
ISSN 1392-3730 / eISSN 1822-3605
2015 Volume 21(8): 977–997
doi:10.3846/13923730.2015.1064468
EVALUATION OF EXCAVATOR TECHNOLOGIES: APPLICATION OF DATA
FUSION BASED MULTIMOORA METHODS
Serkan ALTUNTASa, Turkay DERELIb, c, Mustafa Kemal YILMAZd
aDepartment of Industrial Engineering,Yildiz Technical University, 34349 Istanbul, Turkey
bOfce of the President, Iskenderun Technical University, 31200 Iskenderun, Turkey
cDepartment of Industrial Engineering, Gaziantep University, 27310 Gaziantep, Turkey
dDepartment of Business, Ondokuz Mayıs University, 55270 Samsun, Turkey
Received 03 Dec 2014; accepted 20 May 2015
Abstract. Excavators are quite expensive vehicles. Therefore, there may be huge losses for decision makers if a wrong
decision is made during the purchasing process. A good evaluation of excavator alternatives both reduces costs and in-
creases the benets the excavator for the purchaser. The aim of this study is to prioritise excavator technologies to help
decision makers during the purchasing process and to apply three different “data fusion methods” instead of the “theory
of dominance” of the original MULTIMOORA method. The MULTIMOORA method is composed of three methods,
namely: the ratio analysis as a part of MOORA, Reference Point Theory (the reference point approach as a part of
MOORA) and the Full Multiplicative Form. It is used to prioritise excavator technologies in this study. The MULTI-
MOORA method combines three results obtained from these three methods using the theory of dominance. Dominance
directed graph, Rank position method and Borda count method as data fusion methods are also used to combine these
three results instead of the “theory of dominance”. The results from this study show that there is no difference between
the data fusion methods and the MULTIMOORA method can be applied to technology evaluation of the excavator al-
ternatives successfully.
Keywords: excavator technologies, MULTIMOORA, dominance directed graph, rank position method, Borda count
method, decision making.
Corresponding author: Serkan Altuntas
E-mails: serkan@yildiz.edu.tr; saltuntas2@gmail.com
977 Copyright © 2015 Vilnius Gediminas Technical University (VGTU) Press
www.tandfonline.com/tcem
Introduction
Machinery and equipment selection is essential to rms
in order to be successful in a business environment. The
selection of unsuitable machinery negatively affects
all aspects of business performance. This also leads to
failure in meeting the requirements of the customers in
terms of quality, time and cost. Therefore, the selection of
the most suitable machine among alternatives increases
rms’ competitiveness.
The purchasing function has received considerable
attention because it is vital in determining the survival
and protability of businesses (Bayazıt et al. 2006). The
process of buying a machine is complex and consists of
many stages. In this process, different business manag-
ers try to determine the machine which will be bought
with respect to certain criteria and to inuence the selec-
tion decision based on their own selection priorities. They
also consider different evaluation criteria with respect to
whether they are buying a machine for the rst time.
Selecting the best excavator technologies (in terms
of criteria considered) is not easy since there are many
criteria, and they can be quantitative or qualitative, with
equipment characteristics judged as benecial and non-
benecial. The aim of the excavator selection process is
to choose the option that reduces costs and increases ben-
ets compared with alternative excavators.
In Turkey, ongoing urban renewal is increasing the
demand for excavation equipment, and is making choos-
ing the right equipment all the more important. Both do-
mestic and foreign investors have begun to invest in the
heavy equipment sector in Turkey due to its anticipated
growth (Dunya newspaper 2014). On a project scale, the
selection of the most suitable excavator – an essential
vehicle in urban renewal – is important in order to ob-
tain maximum efciency and effectiveness. According to
the Industry Directorate-General study (2010), in Turkey,
11,500 units of business machinery were sold in 2007, of
which 3,830 were excavators.
Inappropriate excavator selection increases costs
and decreases the benets of the excavator for decision
makers. This paper presents a real-life case study on the
successful application of the MULTIMORA method for
the selection of excavator technologies. The MULTIMO-
RA method provides evaluation of technology alterna-
tives from multiple perspectives. The major contribution
of this paper is to extend different data fusion methods,

978 S. Altuntas et al. Evaluation of excavator technologies: application of data fusion based MULTIMOORA methods
namely dominance directed graph, rank position meth-
od and the Borda count method, in conjunction with the
MULTIMOORA method for excavator selection.
The MULTIMOORA method was introduced by
Brauers and Zavadskas (2010). This method prioritises
alternatives easily in the presence of cost and benet cri-
teria. It is composed of three methods: the ratio analy-
sis as a part of MOORA, Reference Point Theory (the
reference point approach as a part of MOORA) and the
Full Multiplicative Form. The MULTIMOORA method
combines three results obtained from these three methods
using the theory of dominance. Instead of the theory of
dominance in the original MULTIMOORA method, the
dominance directed graph, rank position method and the
Borda count method are also used to combine these three
results in this paper.
1. Literature review
The research considered different criteria in the literature
for the selection of excavator technologies. Among them,
Cebesoy (1999) took into account bucket size, engine
power, weight, breakout force, crowd force, speed, cut
height, digging height, and digging depth for excavator
selection using an improved quality comparison method.
Soykan (2009) selected an excavator using conjoint anal-
ysis and considered walking system, scoop movement,
catalogue language, scoop storage, and working load as
criteria. In addition to these studies, Aykul et al. (2007)
selected hydraulic excavator/truck and surface miner/
truck combinations for highly selective excavation sur-
face coal mining. Kirmanli and Ercelebi (2009) also de-
veloped an expert system for hydraulic excavators and
truck selection in surface mining.
The MULTIMOORA method has been successfully
applied in many elds, such as evaluation of the econo-
my of the Belgian regions (Brauers, Ginevičius 2010),
project management (Brauers, Zavadskas 2010; Brauers
2012), evaluation of Lithuania’s position in the Europe-
an Union (Baležentis et al. 2010), ranking heating losses
in a building (Kracka et al. 2010), the selection of bank
loans (Brauers, Zavadskas 2011a), selection of building
elements for renovations important for energy savings
(Brauers et al. 2012), evaluation of the construction sec-
tor in twenty European countries (Brauers et al. 2013),
evaluation of public debt risk (Stankevičienė, Rosov
2013), ranking climate change mitigation policies in
Lithuania (Streimikiene, Balezentis 2013), evaluation of
the nancial stability of commercial banks (Brauers et al.
2014) and special education and rehabilitation center se-
lection (Özçelik et al. 2014). In addition to these stud-
ies, Brauers and Zavadskas (2012) provided information
about the robustness of the MULTIMOORA method. A
survey of the applications of the MULTIMOORA method
can be found in T. Baležentis and A. Baležentis (2014).
The MULTIMOORA method is composed of the ra-
tio analysis as a part of MOORA, Reference Point The-
ory (the reference point approach as part of MOORA)
and the Full Multiplicative Form. The MOORA method
is applied in many different elds, such as materials se-
lection (Karande, Chakraborty 2012), privatisation in a
transition economy (Brauers, Zavadskas 2006), evalua-
tion of inner climate (Kalibatas, Turskis 2008), assess-
ment of road design alternatives (Brauers et al. 2008a),
contractors’ ranking (Brauers et al. 2008b), assessment
of regional development in Lithuania (Brauers et al.
2010), evaluating contractors’ alternatives in the facili-
ties sector in Lithuania (Brauers, Zavadskas 2009), ro-
bustness in regional development in Lithuania (Brauers,
Ginevičius 2009), decision-making in the manufacturing
environment (Chakraborty 2011) and parametric optimi-
sation of the milling process (Gadakh 2011). Galetakis
et al. (2015) developed an expert system for the predic-
tion of the performance of bucket-wheel excavators. In
some studies, researchers focused on equipment selection
for excavators. For example, Morley et al. (2013) used
discrete event simulation for excavator hauler eet selec-
tion; Qunzhang et al. (2011) proposed analytical hierar-
chy method for monitoring the parameters selection of
the hydraulic system of an excavator. Wang et al. (2009)
proposed a combined simulation and analysis to compare
the performance of excavator types.
The combination of the MULTIMOORA method
and other methods has been extensively documented in
the literature; for example, the combination of the MUL-
TIMOORA method and data envelopment analysis for
multi-criteria assessment and comparison of farming ef-
ciency (T. Baležentis, A. Baležentis 2011a), the com-
bination of the MULTIMOORA method and data en-
velopment analysis for assessing the efciency of the
Lithuanian transport sector (T. Baležentis, A. Baležentis
2011b), the combination of the MULTIMOORA method
and grey set theory for robot selection (Datta et al. 2013)
and the combination of the MULTIMOORA method and
interval value grey number sets for CNC machine tool
evaluation (Sahu et al. 2014). Liu et al. (2014) proposed
interval 2-tuple linguistic MULTIMOORA method for
health-care waste treatment technology selection.
In the literature, the usage of the MULTIMOORA
method is generally preferred if the presence of quanti-
tative criteria is known and there is a possibility to con-
struct the decision matrix easily. However, in this paper,
the excavator selection problem is solved by group deci-
sion makers and according to the quantitative and qualita-
tive criteria of the MULTIMOORA method. In addition,
this is the rst study that extends the original MULTI-
MOORA method using different data fusion methods,
namely, Dominance directed graph, Rank position meth-
od and Borda count method.
2. Methods
2.1. MULTIMOORA
The MULTIMOORA is a relatively new multi-criteria de-
cision making method consisting of three parts: the ratio
analysis as a part of MOORA, Reference Point Theory

Journal of Civil Engineering and Management, 2015, 21(8): 977–997 979
(the reference point approach as a part of MOORA) and
the Full Multiplicative Form (T. Baležentis, A. Baležentis
2014). The MULTIMOORA method makes it a possible
to increase the robustness of the results due to the aggre-
gation of these approaches (Brauers, Zavadskas 2011b).
In the MULTIMOORA method, the theory of dominance
is proposed by Brauers and Zavadskas (2011a) to com-
bine the results of these methods. Details on the theory
of dominance can be found in Brauers and Zavadskas
(2011a) and Brauers et al. (2012). The dominance direct-
ed graph, Rank position method and Borda count method
are also used to obtain a ranking of alternatives. Figure 1
summarises the MULTIMOORA method.
2.1.1. The ratio analysis as a part of MOORA
The steps of the ratio analysis as a part of MOORA are
given below in stepwise fashion.
Step 1: Construct the decision matrix of responses (see
Fig. 1).
Step 2: Normalize the decision matrix by using Eqn (1):
*
2
1
,
ij
ij m
ij
j
x
x
x
=
=
∑
(1)
where: xij – response of alternative j on objective i; j = 1,
2, …, m; m is the number of alternatives; i = 1, 2, …, n;
n is the number of objectives.
Step 3: Calculate the nal preference by using Eqn (2):
** *
11
,
gn
i ij ij
i ig
yx x
= = +
= −
∑∑
(2)
where: i = 1, 2, …, g as the objectives to be maximized;
i = g + 1, g + 2, …, n as the objectives to be minimized;
yj* – the nal preference for jth alternative. Each alterna-
tive is sorted in descending order with respect to yj*.
2.1.2. The Reference Point Approach as a part of MOORA
The Reference Point Approach as a part of MOORA in-
cludes three steps and the rst two steps are the same
steps as the ratio analysis as a part of MOORA. The steps
of the reference point approach as a part of MOORA are
given below in a stepwise manner.
Step 1: Construct the decision matrix which shows the
matrix of responses (see Fig. 1).
Step 2: Normalize the decision matrix by using Eqn (1).
Step 3: Calculate the reference point by using Eqn (3)
and give a preference with respect to the result:
Zi = min(j){max(i)|ri – xij*|}, (3)
where: Zj – the nal preference for the jth alternative;
ri – the ith coordinate of the reference point in the normal-
ized decision matrix.
If the objective should be maxima, we choose the
highest value as ri for related objective. On the other
hand, we choose the lowest value as ri for related objec-
tive, if the objective should be minimal. Herein, the low-
est Zi value shows the best alternative, while the highest
Zi value shows the worst alternative.
2.1.3. The Full Multiplicative Form for Multi-Objectives
The Full Multiplicative Form for Multi-Objectives in-
cludes two steps, the rst of which is that same as the
rst step of MOORA (both Ratio Analysis and Reference
Point Approach). The steps of the full multiplicative form
for multi-objectives are given as follows in a stepwise
manner.
Step 1: Construct the decision matrix of responses (see
Fig. 1).
Step 2: Calculate the overall utilities (Uj) by using Eqn (4):
1
.
n
j ij
i
Ux
=
=∏
(4)
The number between brackets refers to the basic equation used for the related method.
Details on the methods are given in the following.
Fig. 1. Diagram of MULTIMOORA (Brauers et al. 2012)

980 S. Altuntas et al. Evaluation of excavator technologies: application of data fusion based MULTIMOORA methods
If some objectives are to be maximized and others
are to be minimized, we combine these objectives by us-
ing Eqn (4’):
.
j
j
j
A
UB
′=
(4’)
j
U′
’ shows the value of the utility of alternative j with
the objectives to be maximized and objectives to be mini-
mized with:
1
,
i
j gj
g
AX
=
=∏
(5)
j = 1, 2, …, m is the number of alternatives and i is the
number of objectives to be maximized with:
1
,
n
j kj
ki
BX
= +
=∏
(6)
n–i is the number of objectives to be minimized.
2.2. Data fusion methods
The aim of data fusion methods is to merge results ob-
tained from the different resources. Data fusion methods
are extensively used in the literature for the information
retrieval system. However, in addition to utilising the
theory of dominance proposed by Brauers and Zavads-
kas (2011a) in the original MULTIMOORA method, the
dominance directed graph, the rank position method and
the broad count method are also used in this study to
merge the results of the ratio analysis part of MOORA,
the reference point approach part of MOORA and the full
multiplicative form for multi-objectives.
2.2.1. The dominance directed graph
The dominance directed graph is known as a tournaments
because each ranking obtained from the three methods
can be considered to be a tournament. In addition, each
alternative (excavator technology) can also be considered
a team. In the dominance directed graph, Team A can
dominate Team B or vice versa, but not both. The vertex
matrix (M = [mij]) of each tournament should be con-
structed. If Team A dominates Team B, mAB is equal to
1, otherwise 0. Matrix M shows the dominance relation
among alternatives for a tournament. Subsequently, M2
is calculated and then A = M + M2. The row sums of A
show its preference. The highest value of the row sums
is the best alternative, while the lowest value is the worst
alternative. In this study, the sum of each row obtained
by each method for each excavator technology is summed
for the nal ranking.
2.2.2. The rank position method
The rank position method, which is also named the recip-
rocal rank method, considers the current position of each
alternative with respect to each method. The following
formula shows the rank position score (r) for each alter-
native, and is used to obtain nal ranking. The highest
value of the rank position score is the worst alternative
and the lowest value is the best alternative:
r(di) = 1 / (∑j 1/ position dij) for all (j), (7)
k – number of results obtained from the methods, j = 1,
…., k; p – number of alternatives, i = 1, …., p.
In this paper, k = 3 (the results of the ratio analysis
part of MOORA, the reference point approach part of
MOORA and the full multiplicative form for multi-objec-
tives) and p = 13 (the number of excavator technologies).
An example can be given to show how the rank po-
sition method works. There are only two possible ways,
which equal to k, to sort the alternatives with respect to
their priorities. Each possible way is denoted A and B.
There are four alternatives (p), namely x, y, z, t. The rank-
ing lists are given as follows:
A = (x, y, t, z);
B = (t, x, y, z).
The computation of rank position of each alternative
is given as follows:
r(x) = 1 / (1 + 1/2) = 0.67;
r(y) = 1 / (1/2 +1/3) = 1.2;
r(z) = 1/ (1/4 + 1/4) = 2;
r(t) = 1 / ( 1/3 + 1) = 0.75.
Hence, the nal ranked list of the alternatives is:
x > t > y > z.
2.2.3. The broad count method
The broad count method is a simple and effective method
(Erp, Schomaker 2000) and does not require any training
to combine the rankings (Ruta, Gabrys 2000). The num-
ber of alternatives is equal to the number of votes in the
method. The highest ranked alternative (in a p-way vote)
gets p votes and each subsequent alternative gets one vote
less (Nuray, Can 2006). The Broad Count (BC) value is
calculated by summing the votes given to each alterna-
tive in each method. The nal broad score is calculated
by the aggregation of each of the individual scores, which
denoted by BC(i) (Moreira 2011). BC(i) shows the BC
value of ith alternative. The highest BC value is the best
alternative and the lowest value is the worst alternative
in the method. Some examples for the introduction of the
method can be found in Nuray (2003), Nuray and Can
(2006), Bozkur et al. (2007) and Moreira (2011).
The computation of broad count method is presented
in the following by considering the previous small ex-
ample:
BC(x) = 4 + 3 = 7;
BC(y) = 3 + 2 = 5;
BC(z) = 1 + 1 = 2;
BC(t) = 2 + 4 = 6.
Therefore, the nal ranked list of the alternatives is
x > t > y > z. Table 1 summarizes all methods.

Journal of Civil Engineering and Management, 2015, 21(8): 977–997 981
3. Application of the proposed method for evaluat-
ing excavator alternatives
At the beginning of the study, the construction companies
within Bayburt Trade and Industry Cooperation in Turkey
were determined. Interviews were carried out with the
heads of the companies, and one company was expected
to buy an excavator in the near future. Decision makers in
the company stated that they would purchase an excava-
tor weighs about 25–30 tonnes with crawler. Subsequent-
ly, 13 alternatives meeting the requirements of the deci-
sion makers were identied from seven different brands.
Then, the factors inuencing the excavator choice were
analysed through a review of the literature. To nd cri-
teria apart from those in the related literature concerning
the excavator selection process, the opinions of marketing
managers who work in excavator rms were surveyed via
e-mail. Eleven quantitative criteria (ve benecial crite-
ria and six non-benecial criteria) and seven qualitative
criteria (all of them benecial) were determined for the
excavator selection.
Some selection criteria were deemed to be missing
from the catalogues for quantitative criteria. The sales
representatives of the branches of all the brands were
called to determine the missing quantitative criteria. A
questionnaire was administered to the decision makers
group with the aim of measuring their evaluations for
qualitative criteria with regard to the 18 criteria and seven
brands determined through the literature reviews and the
interviews with the marketing managers. All studies up
to that point included the stages of problem identication
and organising the data. The data obtained subsequent-
ly were analysed by using the original MULTIMOORA
method. In the nal part of the study, alternatives were
placed in order using the dominance directed graph, the
rank position method, and the Borda count method adjust-
ed MULTIMOORA methods. All these studies, including
application, analysis and evaluation, are summarised in
Figure 2. Figure 3 presents the hierarchical decision mod-
el for the evaluation of excavator technologies.
3.1. Criteria
In the literature, researchers use objective (quantitative)
criteria only for examining excavator technologies. How-
ever, Chernatony and McDonald (2003) indicate that both
objective and subjective issues have an impact on the
decision makers. In addition, there may be some crite-
ria that should be minimized, while others maximised.
Therefore, we tried to determine the criteria affecting
the excavator selection process based on the above men-
tioned lines. Firstly, some criteria were determined by
reviewing the related literature. To nd criteria apart from
those in the related literature that affect the excavator se-
lection process, an e-mail survey was conducted to gather
the opinions of marketing managers who work in exca-
vator rms. It should be noted that selected excavator
rms are members of the Turkey Construction Machinery
Manufacturers and Distributors Association. Finally, the
literature review and interviews suggested 11 quantitative
criteria (ve benecial criteria and six non-benecial cri-
teria) and seven qualitative criteria (all of them benecial
criteria) for this study. Criteria and objectives of excava-
tor models are given in Table 2. If there are different units
in different multiple objectives, this makes optimisation
difcult (Brauers, Ginevičius 2013). In this study, quali-
tative criteria were measured via a survey study using
a ve point Likert scale (1 = “not at all” and 5 = “to a
great extent”) except for the “brand experience” criteria.
We measured brand experience by conducting a survey,
as well as asking decision makers to use a scale between
1 and 3 with respect to their experiences (1 = “negative”,
2 = “no idea” and 3 = “positive”):
–Motor power (X1): There are different excavator
models with respect to their motor power. Motor
power is an important criterion in terms of the us-
age area of the excavator and the aim of its usage.
Motor power ranges from 93 hp to 464 hp.
–Bucket size (X2): Bucket size is one of the crite-
ria that show excavator capacity. There are different
excavator models with respect to bucket size which
range from 0.19 m3 to 6.6 m3.
Table 1. A summary of all method
No Method Formula
1The Ratio Analysis as a part of MOORA
(as a rst part of
the MULTIMOORA)
** *
11
gn
i ij ij
i ig
yx x
= = +
= −
∑∑
2The Reference Point Approach as a part of MOORA
(as a second part of the MULTIMOORA)Zi = min(j){max(i) |ri – xij*|}
3The Full Multiplicative Form for Multi-Objectives
(as a third part of the MULTIMOORA)
j
j
j
A
UB
′=
4The dominance directed graph A= M + M2
5The rank position method r(di) = 1 / (∑j 1/ position dij) for all (j)
6The broad count method BC(i)

982 S. Altuntas et al. Evaluation of excavator technologies: application of data fusion based MULTIMOORA methods
Fig. 2. Application steps

Journal of Civil Engineering and Management, 2015, 21(8): 977–997 983
–Language of product catalogue (X3): Soykan (2009)
emphasises that the decision makers prefer the lan-
guage of the product catalogue for excavators to be
in both Turkish and English. However, parts of the
product catalogue may be written in only one lan-
guage, i.e. Turkish or English. We use a three-point
scale (1 – English language, 2 – Turkish language,
3 – both English and Turkish language).
–Lead time (X4): Decision makers expect a short
lead time. If the lead time is high for one excavator
model, this negatively affects the company. Delivery
speed and timely deliveries affect the decision as to
which excavator to purchase.
–Cutting Height (X5): Decision makers want to buy
an excavator that has a high cutting height due to the
fact that this provides high work capacity. It enables
the performance of high jobs with less movement,
especially in road works.
–Digging Depth (X6): Digging depth is quite impor-
tant when the excavator works on infrastructure.
Therefore, decision makers prefer an excavator with
greater digging depth.
–References (X7): References are indicative of the
supplier’s relationships with its existing customers
that can be used to evaluate the supplier’s product or
service, management and cooperation performance
(Salminen 2001). Customer references can also be
considered important marketing tools for companies
(Jalkala, Salminen 2009). Ruokolainen and Igel
(2004) indicate that the references can be more im-
portant than price, delivery capability, or new tech-
nological features.
–Country of origin (X8): Negative perceptions of the
product’s country of origin can affect buyers’ per-
ceptions towards that product (Samiee 1994). For
example, Güdüm and Kavas (1996) researched the
Turkish industrial purchasing managers’ perceptions
of foreign and national industrial suppliers. The re-
sults of their study indicate that the managers in Tur-
key prefer German and Japanese suppliers to US and
national suppliers. Therefore, decision makers’ per-
ceptions related to country of origin affect the buy-
ing decision. Details on the studies related to coun-
try of origin criteria can be found in review studies
(Al-Sulaiti, Baker 1998; Dinnie 2004).
–Product reliability (X9): Product reliability attracts
increasing attention from manufacturers as this is
a vital factor in a competitive world (Jiang et al.
2010). Murthy et al. (2008) give the following de-
nition: “product reliability conveys the concept of
dependability, successful operation of performance
and the absence of failures”. Homburg and Rudolph
(2001) emphasise that product reliability is one of
the satisfaction criteria related to product dimension.
–Company reputation (X10): Reputation addresses the
image of the company to all its constituents, includ-
ing investors (Mudambi 2002). The company’s rep-
utation has a strong inuence on buying decisions
in many business markets (Cretu, Brodie 2007). A
buyer’s expectation is also affected by a company’s
Table 2. Criteria and objectives of excavator technologies
Attributes Units of measurement max/min
Motor power (X1)horse power max
Bucket size (X2) m3max
Language of product catalogue (X3)Turkish / English max
Lead time (X4) days min
Cutting Height (X5) mm max
Digging Depth (X6) mm max
References (X7)* – max
Country of origin (X8)* – max
Product reliability (X9)* – max
Company reputation (X10)* – max
Easiness of selling in the second-hand market (X11)* – max
Brand condence (X12)* – max
Brand experience (X13)* – max
Purchasing price (X 14) Dollar min
Hydraulic oil consumption (X15) liter/hour min
Engine oil consumption (X16) liter/hour min
Cab comfort (X17) dba min
Fuel consumption (X18)liter/hour min
Note: *The attribute is measured by scale between 1 and 5 via survey study.

984 S. Altuntas et al. Evaluation of excavator technologies: application of data fusion based MULTIMOORA methods
reputation and its service offering information (Yoon
et al. 1993).
–Easiness of selling in the second-hand market (X11):
Second-hand products have previously been used by
an end user or consumer (Mehrabad et al. 2010).
Decision makers prefer excavators that are easier to
re-sell in the second-hand market. Baykasoğlu et al.
(2012) also addressed the truck selection problem by
considering the “easiness of selling in the second-
hand market” criteria.
–Brand condence (X12): There are many brands of
excavator. During the purchasing process, the buyer
considers the brand condence to decrease the pos-
sibility of defects related to the product in the future.
–Brand experience (X13): Brand experience is a new
consumer psychology concept (Brakus et al. 2012).
Brakus et al. (2009) dened brand experiences as
“subjective, internal consumer responses (sensa-
tions, feelings and cognitions) as well as behavio-
ral responses evoked by brand-related stimuli that
are part of a brand’s design and identity, packaging,
communications and environments”.
–Purchasing price (X14): One of the key factors af-
fecting the purchasing decision is total price. Stock
(2005) dened purchasing price as the actual price
paid by a customer, including all of the costs. Deci-
sion makers want to buy the cheapest excavator that
meets their requirements.
–Hydraulic oil consumption (X15): Many thousands
of litres of hydraulic oil are consumed for the op-
eration of an excavator. The hydraulic oil pan must
be relled when it becomes empty, which is costly.
Therefore, decision makers want to buy an excava-
tor that consumes the least amount of hydraulic oil
per hour.
–Engine oil consumption (X16): Engine oils reduce
wear by reducing friction between moving parts. En-
gine oil consumption for an excavator may be high
depending upon the amount of usage. Therefore, de-
cision makers want to select an excavator that con-
sumes the least amount of engine oil per hour during
its operation.
–Cab comfort (X17): Cab comfort affects operator
fatigue and efciency directly. The excavator cab
should have sound absorption properties, a wide
viewing angle, air conditioning and an ergonomic
design. These factors inuence decision makers and
the decision to buy.
–Fuel consumption (X18): Fuel for vehicles can be
considered one of the most important criteria cur-
rently. Decision makers prefer to buy an excava-
tor which consumes less fuel, especially due to the
higher cost of fuel in Turkey.
As can be seen from Table 1, there are 12 criteria
that should be maximised while the others are minimised.
These 12 criteria are called as benecial criteria. This
means that the outcome of these criteria is desired to be
as high as possible by decision makers to increase the
work capacity and efciency of an excavator. For exam-
ple, the language of the product catalogue (X3) is pre-
ferred to be both in Turkish and English by decision mak-
ers. A three-point scale (1 – English language, 2 – Turkish
language, 3 – both English and Turkish language) is used
to measure this criterion and high outcome for an exca-
vator alternative indicates that this meets the standard of
the decision maker with respect to X3 criterion. In addi-
tion, the value of References (X7) and Country of origin
(X8) are benecial criteria and they are measured by a
scale in between 1 and 5 via survey study. Similar to X3
criterion, the outcome of X7 and X8 criteria are desired
to be as high as possible due to these being benecial
criteria. If a decision-maker marked “5” for an excavator
alternative in the survey study for X7 criterion, this means
that customers have a good perception in the market for
the excavator alternative and it meets the standard of the
decision – maker with respect to X3 criterion. Similarly,
a decision-maker can mark “1” or “2” for an excavator
alternative in the survey study for X8 criterion if he/she
has negative perceptions of the product’s country of ori-
gin. Decision-makers want to buy an excavator that has
high values for benecial criteria and less value for non-
benecial criteria.
3.2. Results
Table 3 presents the matrix of responses of alternatives
on objectives. Based on this matrix as an input, the rank-
ing of the 13 excavator technologies according to the two
parts of MOORA, namely, the ratio analysis and the ref-
erence point approach, and the full multiplicative form
are performed. Details on the calculation of the two parts
of MOORA can be found in Tables 7–10 in Appendix A.
In addition, Table 11, which is presented in Appendix B,
includes the calculation of the full multiplicative form
for multi-objectives. Furthermore, Appendix C, which
is composed of Tables 12–14, gives the details of the
dominance directed graph calculation. Table 4 shows the
ranking by the dominance directed graph based the MUL-
TIMOORA method. Table 5 presents ranking by rank
position based the MULTIMOORA method and Borda
count method based on the MULTIMOORA method, and
Table 6 gives the original MULTIMOORA result for ex-
cavator technologies. As can be seen in Tables 4, 5 and
6, the ranking of excavators according to the original
MULTIMOORA and data fusion methods based MUL-
TIMOORA methods have the same ranking (P – preferred
to): E3 -P- E8 -P- E11 -P- E9 -P- E2 -P- E1 -P- E13
-P- E10 -P- E12 -P- E5 -P- E7 -P- E4 -P- E6. The
results from this study show that there is no difference
between the data fusion methods and the MULTIMORA
method can be applied to an excavator selection problem
successfully. Excavator 3 (E3) can be recommended to
the rm since it ranked rst in all results.

Journal of Civil Engineering and Management, 2015, 21(8): 977–997 985
Table 3. Matrix of responses of alternatives on objectives: (xij)
X1X2X3X4X5X6X7X8X9X10 X11 X12 X13 X14 X15 X16 X17 X18
E1 169 1.61 1 6 11575 6655 5 5 5 4.6 3.6 5 2.6 224941.86 750.00 30 75 18
E2 202 1.70 2 1 14890 14210 3 2.2 3 3.8 3.8 4 2 184043.34 925.00 38 74 18
E3 177 1.50 3 1 10290 7290 4.6 5 4.6 5 4.6 5 2.6 208582.45 700.00 27 70 20
E4 227 1.85 1 15 1140 7790 3 3.2 2.2 4 4.2 4.2 2.6 181440.00 640.00 24 70 21
E5 230 1.85 1 15 11400 8090 3 3.2 2.2 4 4.2 4.2 2.6 147960.00 330.00 24 70 21
E6 197 1.80 1 20 1069 723 4.4 4.4 4 4.2 2.8 4.2 3 170410.50 301.25 22 74 21
E7 216 1.80 1 20 11261 7619 4.4 4.4 4 4.2 2.8 4.2 3 193586.32 181.25 38 74 21
E8 188 1.60 2 10 10130 6940 4.4 5 4.8 4.6 3.6 4.6 2.6 190331.18 330.00 23 74 18
E9 179 1.40 2 10 10000 6920 4.4 5 4.8 4.6 3.6 4.6 2.6 215708.66 337.50 23 74 21
E10 180 1.80 1 5 10730 7600 4.4 3.4 4.8 4.2 3.2 4.2 2.6 159504.22 800.00 32 73 17
E11 170 1.80 1 5 18207 14347 4.4 3.4 4.8 4.2 3.2 4.2 2.6 179953.48 320.00 30 71 22
E12 192 2.10 1 5 11650 7580 4.4 3.4 4.8 4.2 3.2 4.2 2.6 170410.50 1000.00 32 73 17
E13 195 1.40 1 5 10700 7300 4.4 3.4 4.8 4.2 3.2 4.2 2.6 197676.18 400 30 71 22
Table 4. Ranking by Dominance directed graph
Ratio analysis Reference point approach Full multiplicative form Sum Rank
E1 36 36 28 100 6
E2 28 45 45 118 5
E3 80 66 78 224 1
E4 0 3 1 412
E5 6 10 3 19 10
E6 1 1 0 213
E7 3 0 6 911
E8 68 78 55 201 2
E9 56 55 36 147 4
E10 15 15 10 40 8
E11 68 28 66 162 3
E12 10 6 15 31 9
E13 21 21 21 63 7
Table 5. Ranking by Rank position and Borda count methods
Rank position method Borda count method
r(di) Rank BC(i) Rank
E1 1.76 626 6
E2 1.50 528 5
E3 0.40 138 1
E4 3.98 12 612
E5 3.31 10 12 10
E6 4.11 13 513
E7 3.73 11 811
E8 0.60 235 2
E9 1.28 430 4
E10 2.77 817 8
E11 0.86 332 3
E12 2.98 915 9
E13 2.33 721 7

986 S. Altuntas et al. Evaluation of excavator technologies: application of data fusion based MULTIMOORA methods
Fig. 3. Hierarchy for excavator technologies evaluation
Table 6. The original MULTIMOORA results for excavator models
Ratio analysis Reference point approach Full multiplicative form MULTIMOORA
E1 5 5 6 6
E2 6 4 4 5
E3 1 2 1 1
E4 13 11 12 12
E5 10 9 11 10
E6 12 12 13 13
E7 11 13 10 11
E8 3 1 3 2
E9 4 3 5 4
E10 8 8 9 8
E11 2 6 2 3
E12 9 10 8 9
E13 7 7 7 7

Journal of Civil Engineering and Management, 2015, 21(8): 977–997 987
Conclusions
Excavators are very important vehicles for business and
construction machinery. The selection of the right exca-
vator technology with respect to considered criteria for
a rm provides many benets, such as maximum ef-
ciency, effectiveness and long economic life. Thirteen ex-
cavator technologies, which met the requirements of the
decision makers, were ranked in this study according to
18 criteria by using the MULTIMOORA method. These
included qualitative, quantitative, benecial and non-ben-
ecial criteria. There are three reasons for the selection
of the MULTIMOORA method. First, its robustness is
emphasised in the literature. Second, it considers quali-
tative, quantitative, benecial and non-benecial criteria
at the same time. Third, it provides the decision makers
with a means to assess the technologies through multiple
perspectives.
The MULTIMOORA method uses the theory of
dominance to combine the result of the ratio analysis as
part of MOORA, Reference Point Theory (the reference
point approach as part of MOORA) and the Full Multipli-
cative Form. In this study, the dominance directed graph,
the rank position method and the Borda count method as
data fusion methods are also used to combine these three
results instead of the theory of dominance. The results
show that there is no difference between the data fusion
adjusted MULTIMOORA methods and the original MUL-
TIMOORA method. In this study, the MULTIMOORA
with the dominance theory is not beaten by other data
fusion methods, namely dominance directed graph, rank
position method and Borda count method for the evalua-
tion of excavator technologies.
It should be noted that there may be equivalence for
some alternatives based on the results of the broad count
method. For example, there is equivalence for “t” and “y”
if we consider these two ranking list, namely (x, y, z, t)
and (t, x, y, z). There are many different criteria for the
excavator selection. Most commonly used criteria, which
are dened by both reviewing the related literature and
marketing managers who work in excavator rms in this
paper, are taken into account in the technology evaluation
process. More technical criteria such as “breakout force”
and “crowd force” can also be considered for evaluation
of excavator technology.
For future research, sensitivity analyses can be con-
ducted to examine the best alternative with respect to dif-
ferent criteria values for each alternative technology. In
addition, application of the other data fusion methods,
such as concordant method and logistic regression, can
be conducted. Finally, the importance of the criteria can
be taken into account in the evaluation process.
Acknowledgements
The authors would like to thank the two anonymous re-
viewers for their insightful comments and suggestions
that have signicantly improved the paper.
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990 S. Altuntas et al. Evaluation of excavator technologies: application of data fusion based MULTIMOORA methods
Appendix A
Table 7. Sum of squares and their square roots
X1X2X3X4X5X6X7X8X9X10 X11 X12 X13 X14 X15 X16 X17 X18
E1 28561 2.59 1 36 133980625 44289025 25 25 25 21.16 12.96 25 6.76 50598840380.26 562500 900 5625 324
E2 40804 2.89 4 1 221712100 201924100 9 4.84 9 14.44 14.44 16 4 33871950998.36 855625 1444 5476 324
E3 31329 2.25 9 1 105884100 53144100 21.16 25 21.16 25 21.16 25 6.76 43506638448.00 490000 729 4900 400
E4 51529 3.42 1 225 1299600 60684100 9 10.24 4.84 16 17.64 17.64 6.76 32920473600.00 409600 576 4900 441
E5 52900 3.42 1 225 129960000 65448100 9 10.24 4.84 16 17.64 17.64 6.76 21892161600.00 108900 576 4900 441
E6 38809 3.24 1 400 1142761 522729 19.36 19.36 16 17.64 7.84 17.64 9 29039738510.25 90751.5625 462.25 5476 441
E7 46656 3.24 1 400 126810121 58049161 19.36 19.36 16 17.64 7.84 17.64 9 37475663291.14 32851.5625 1444 5476 441
E8 35344 2.56 4 100 102616900 48163600 19.36 25 23.04 21.16 12.96 21.16 6.76 36225956557.54 108900 533.61 5476 324
E9 32041 1.96 4 100 100000000 47886400 19.36 25 23.04 21.16 12.96 21.16 6.76 46530225999.00 113906.25 533.61 5476 441
E10 32400 3.24 1 25 115132900 57760000 19.36 11.56 23.04 17.64 10.24 17.64 6.76 25441596197.81 640000 1024 5329 289
E11 28900 3.24 1 25 331494849 205836409 19.36 11.56 23.04 17.64 10.24 17.64 6.76 32383254964.11 102400 900 5041 484
E12 36864 4.41 1 25 135722500 57456400 19.36 11.56 23.04 17.64 10.24 17.64 6.76 29039738510.25 1000000 1024 5329 289
E13 38025 1.96 1 25 114490000 53290000 19.36 11.56 23.04 17.64 10.24 17.64 6.76 39075872139.39 160000 900 5041 484
Sum of
squares
494162 38.43 30 1588 1620246456 954454124 228.04 210.28 235.08 240.76 166.4 249.44 89.6 458002111196.11 4675434.375 11046.47 68445 5123
Square
roots
702.97 6.20 5.48 39.85 40252.29 30894.24 15.10 14.50 15.33 15.52 12.90 15.79 9.47 676758.53 2162.28 105.10 261.62 71.58

Journal of Civil Engineering and Management, 2015, 21(8): 977–997 991
Table 8. Objectives divided by their square roots and ranking of alternatives for the ratio analysis as a part of MOORA
X1X2X3X4X5X6X7X8X9X10 X11 X12 X13 X14 X15 X16 X17 X18 YiRank
E1 0.240 0.260 0.183 0.151 0.288 0.215 0.331 0.345 0.326 0.296 0.279 0.317 0.275 0.332 0.347 0.285 0.287 0.251 1.701 5
E2 0.287 0.274 0.365 0.025 0.370 0.460 0.199 0.152 0.196 0.245 0.295 0.253 0.211 0.272 0.428 0.362 0.283 0.251 1.686 6
E3 0.252 0.242 0.548 0.025 0.256 0.236 0.305 0.345 0.300 0.322 0.357 0.317 0.275 0.308 0.324 0.257 0.268 0.279 2.292 1
E4 0.323 0.298 0.183 0.376 0.028 0.252 0.199 0.221 0.143 0.258 0.326 0.266 0.275 0.268 0.296 0.228 0.268 0.293 1.041 13
E5 0.327 0.298 0.183 0.376 0.283 0.262 0.199 0.221 0.143 0.258 0.326 0.266 0.275 0.219 0.153 0.228 0.268 0.293 1.503 10
E6 0.280 0.290 0.183 0.502 0.027 0.023 0.291 0.303 0.261 0.271 0.217 0.266 0.317 0.252 0.139 0.205 0.283 0.293 1.056 12
E7 0.307 0.290 0.183 0.502 0.280 0.247 0.291 0.303 0.261 0.271 0.217 0.266 0.317 0.286 0.084 0.362 0.283 0.293 1.423 11
E8 0.267 0.258 0.365 0.251 0.252 0.225 0.291 0.345 0.313 0.296 0.279 0.291 0.275 0.281 0.153 0.220 0.283 0.251 2.019 3
E9 0.255 0.226 0.365 0.251 0.248 0.224 0.291 0.345 0.313 0.296 0.279 0.291 0.275 0.319 0.156 0.220 0.283 0.293 1.887 4
E10 0.256 0.290 0.183 0.125 0.267 0.246 0.291 0.234 0.313 0.271 0.248 0.266 0.275 0.236 0.370 0.304 0.279 0.238 1.588 8
E11 0.242 0.290 0.183 0.125 0.452 0.464 0.291 0.234 0.313 0.271 0.248 0.266 0.275 0.266 0.148 0.285 0.271 0.307 2.126 2
E12 0.273 0.339 0.183 0.125 0.289 0.245 0.291 0.234 0.313 0.271 0.248 0.266 0.275 0.252 0.462 0.304 0.279 0.238 1.567 9
E13 0.277 0.226 0.183 0.125 0.266 0.236 0.291 0.234 0.313 0.271 0.248 0.266 0.275 0.292 0.185 0.285 0.271 0.307 1.619 7
Table 9. The reference point in the normalized the decision matrix
X1X2X3X4X5X6X7X8X9X10 X11 X12 X13 X14 X15 X16 X17 X18
ri0.327 0.339 0.548 0.025 0.452 0.464 0.331 0.345 0.326 0.322 0.357 0.317 0.317 0.219 0.084 0.205 0.268 0.238
Table 10. Deviations from the reference point and ranking of alternatives for the reference point approach as a part of MOORA
X1X2X3X4X5X6X7X8X9X10 X11 X12 X13 X14 X15 X16 X17 X18 max Rank
(Zi) min
E1 0.087 0.079 0.365 0.125 0.165 0.249 0.000 0.000 0.000 0.026 0.078 0.000 0.042 0.114 0.263 0.081 0.019 0.014 0.365 5
E2 0.040 0.065 0.183 0.000 0.082 0.004 0.132 0.193 0.130 0.077 0.062 0.063 0.106 0.053 0.344 0.157 0.015 0.014 0.344 4
E3 0.075 0.097 0.000 0.000 0.197 0.228 0.026 0.000 0.026 0.000 0.000 0.000 0.042 0.090 0.240 0.052 0.000 0.042 0.240 2
E4 0.004 0.040 0.365 0.351 0.424 0.212 0.132 0.124 0.183 0.064 0.031 0.051 0.042 0.049 0.212 0.024 0.000 0.056 0.424 11
E5 0.000 0.040 0.365 0.351 0.169 0.203 0.132 0.124 0.183 0.064 0.031 0.051 0.042 0.000 0.069 0.024 0.000 0.056 0.365 9
E6 0.047 0.048 0.365 0.477 0.426 0.441 0.040 0.041 0.065 0.052 0.140 0.051 0.000 0.033 0.055 0.000 0.015 0.056 0.477 12
E7 0.020 0.048 0.365 0.477 0.173 0.218 0.040 0.041 0.065 0.052 0.140 0.051 0.000 0.067 0.000 0.157 0.015 0.056 0.477 13
E8 0.060 0.081 0.183 0.226 0.201 0.240 0.040 0.000 0.013 0.026 0.078 0.025 0.042 0.063 0.069 0.015 0.015 0.014 0.240 1
E9 0.073 0.113 0.183 0.226 0.204 0.240 0.040 0.000 0.013 0.026 0.078 0.025 0.042 0.100 0.072 0.015 0.015 0.056 0.240 3
E10 0.071 0.048 0.365 0.100 0.186 0.218 0.040 0.110 0.013 0.052 0.109 0.051 0.042 0.017 0.286 0.100 0.011 0.000 0.365 8
E11 0.085 0.048 0.365 0.100 0.000 0.000 0.040 0.110 0.013 0.052 0.109 0.051 0.042 0.047 0.064 0.081 0.004 0.070 0.365 6
E12 0.054 0.000 0.365 0.100 0.163 0.219 0.040 0.110 0.013 0.052 0.109 0.051 0.042 0.033 0.379 0.100 0.011 0.000 0.379 10
E13 0.050 0.113 0.365 0.100 0.186 0.228 0.040 0.110 0.013 0.052 0.109 0.051 0.042 0.073 0.101 0.081 0.004 0.070 0.365 7

992 S. Altuntas et al. Evaluation of excavator technologies: application of data fusion based MULTIMOORA methods
Appendix B
Table 11. The Full Multiplicative Form for Multi-Objectives
1 2 3 4 5 6 7 8
max max 2.1 max min max max max max
X1X22.1=1*2 X33.1=2.1*3 X44.1=3.1/4 X55.1=4.1*5 X66.1=5.1*6 X77.1=6.1*7 X88.1=7.1*8
E1 169 1.61 272.09 1 272.09 6 45.35 11575 524906.96 6655 3493255807.71 5 17466279038.54 5 87331395192.71
E2 202 1.70 343.40 2 686.80 1 686.80 14890 10226452.00 14210 145317882920.00 3 435953648760.00 2.2 959098027272.00
E3 177 1.50 265.50 3 796.50 1 796.50 10290 8195985.00 7290 59748730650.00 4.6 274844160990.00 5 1374220804950.00
E4 227 1.85 419.95 1 419.95 15 28.00 1140 31916.20 7790 248627198.00 3 745881594.00 3.2 2386821100.80
E5 230 1.85 425.50 1 425.50 15 28.37 11400 323380.00 8090 2616144200.00 3 7848432600.00 3.2 25114984320.00
E6 197 1.80 354.60 1 354.60 20 17.73 1069 18953.37 723 13703286.51 4.4 60294460.64 4.4 265295626.83
E7 216 1.80 388.80 1 388.80 20 19.44 11261 218913.84 7619 1667904546.96 4.4 7338780006.62 4.4 32290632029.15
E8 188 1.60 300.80 2 601.60 10 60.16 10130 609420.80 6940 4229380352.00 4.4 18609273548.80 5 93046367744.00
E9 179 1.40 250.60 2 501.20 10 50.12 10000 501200.00 6920 3468304000.00 4.4 15260537600.00 5 76302688000.00
E10 180 1.80 324.00 1 324.00 5 64.80 10730 695304.00 7600 5284310400.00 4.4 23250965760.00 3.4 79053283584.00
E11 170 1.80 306.00 1 306.00 5 61.20 18207 1114268.40 14347 15986408734.80 4.4 70340198433.12 3.4 239156674672.61
E12 192 2.10 403.20 1 403.20 5 80.64 11650 939456.00 7580 7121076480.00 4.4 31332736512.00 3.4 106531304140.80
E13 195 1.40 273.00 1 273.00 5 54.60 10700 584220.00 7300 4264806000.00 4.4 18765146400.00 3.4 63801497760.00

Journal of Civil Engineering and Management, 2015, 21(8): 977–997 993
Table 11. The Full Multiplicative Form for Multi-Objectives (Continued)
9 10 11 12 13 14
max max max max max min
X99.1=8.1*9 X10 10.1=9.1*10 X11 11.1=10.1*11 X12 12.1=11.1*12 X13 13.1=12.1*13 X14 14.1=13.1/14
E1 5 436656975963.54 4.60 2008622089432.29 3.60 7231039521956.25 5.00 36155197609781.30 2.60 94003513785431.30 224941.86 417901380.32
E2 3 2877294081816.00 3.80 10933717510900.80 3.80 41548126541423.10 4.00 166192506165692.00 2.00 332385012331384.00 184043.34 1806014889.38
E3 4,6 6321415702770.00 5.00 31607078513850.00 4.60 145392561163710.00 5.00 726962805818550.00 2.60 1890103295128230.00 208582.45 9061660245.76
E4 2,2 5251006421.76 4.00 21004025687.04 4.20 88216907885.57 4.20 370511013119.39 2.60 963328634110.40 181440.00 5309350.94
E5 2,2 55252965504.00 4.00 221011862016.00 4.20 928249820467.20 4.20 3898649245962.24 2.60 10136488039501.80 147960.00 68508299.81
E6 4 1061182507.33 4.20 4456966530.80 2.80 12479506286.25 4.20 52413926402.26 3.00 157241779206.78 170410.50 922723.54
E7 4 129162528116.58 4.20 542482618089.65 2.80 1518951330651.01 4.20 6379595588734.24 3.00 19138786766202.70 193586.32 98864355.53
E8 4,8 446622565171.20 4.60 2054463799787.52 3.60 7396069679235.07 4.60 34021920524481.30 2.60 88456993363651.50 190331.18 464753043.73
E9 4,8 366252902400.00 4.60 1684763351040.00 3.60 6065148063744.00 4.60 27899681093222.40 2.60 72539170842378.30 215708.66 336283072.00
E10 4,8 379455761203.20 4.20 1593714197053.44 3.20 5099885430571.01 4.20 21419518808398.20 2.60 55690748901835.40 159504.22 349149062.65
E11 4,8 1147952038428.52 4.20 4821398561399.78 3.20 15428475396479.30 4.20 64799596665213.00 2.60 168478951329554.00 179953.48 936236139.08
E12 4,8 511350259875.84 4.20 2147671091478.53 3.20 6872547492731.29 4.20 28864699469471.40 2.60 75048218620625.70 170410.50 440396681.08
E13 4,8 306247189248.00 4.20 1286238194841.60 3.20 4115962223493.12 4.20 17287041338671.10 2.60 44946307480544.90 197676.18 227373411.81
15 16 17 18
Rank
min min min min
X15 15.1 = 14.1/15 X16 16.1 = 15.1/16 X17 17.1 = 16.1/17 X18 18.1 = 17.1/18
E1 750.00 557201.84 30 18573.39 75 1393004.60 18 77389.14 6
E2 925.00 1952448.53 38 51380.22 74 3802136.61 18 211229.81 4
E3 700.00 12945228.92 27 479452.92 70 33561704.61 20 1678085.23 1
E4 640.00 8295.86 24 345.66 70 24196.26 21 1152.20 12
E5 330.00 207600.91 24 8650.04 70 605502.65 21 28833.46 11
E6 301.25 3062.98 22 142.46 74 10542.36 21 502.02 13
E7 181.25 545458.51 38 14354.17 74 1062208.68 21 50581.37 10
E8 330.00 1408342.56 23 60967.21 74 4511573.56 18 250642.98 3
E9 337.50 996394.29 23 43133.95 74 3191912.44 21 151995.83 5
E10 800.00 436436.33 32 13638.64 73 995620.37 17 58565.90 9
E11 320.00 2925737.93 30 97524.60 71 6924246.45 22 314738.47 2
E12 1000.00 440396.68 32 13762.40 73 1004654.93 17 59097.35 8
E13 400.00 568433.53 30 18947.78 71 1345292.69 22 61149.67 7

994 S. Altuntas et al. Evaluation of excavator technologies: application of data fusion based MULTIMOORA methods
A = M + M2
E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E 11 E12 E13 Sum
E1 0 1 0 8 5 7 6 0 0 3 0 4 2 36
E2 0 0 0 7 4 6 5 0 0 2 0 3 1 28
E3 4 5 0 12 9 11 10 3 4 7 1 8 6 80
E4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
E5 0 0 0 3 0 2 1 0 0 0 0 0 0 6
E6 0 0 0 1 0 0 0 0 0 0 0 0 0 1
E7 0 0 0 2 0 1 0 0 0 0 0 0 0 3
E8 3 4 0 11 8 10 9 2 3 6 0 7 5 68
E9 2 3 0 10 7 9 8 0 2 5 0 6 4 56
E10 0 0 0 5 2 4 3 0 0 0 0 1 0 15
E11 3 4 0 11 8 10 9 2 3 6 0 7 5 68
E12 0 0 0 4 1 3 2 0 0 0 0 0 0 10
E13 0 0 0 6 3 5 4 0 0 1 0 2 0 21
Appendix C
Table 12. The result of Dominance Directed Graph for the ratio analysis as a
part of MOORA method
M
E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13
E1 0 1 0 1 1 1 1 0 0 1 0 1 1
E2 00 0 1 1 1 1 0 0 1 0 1 1
E3 1 1 0 1 1 1 1 1 1 1 1 1 1
E4 0 0 0 0 0 0 0 0 0 0 0 0 0
E5 0 0 0 1 0 1 1 0 0 0 0 0 0
E6 00 0 1 0 0 0 0 0 0 0 0 0
E7 0 0 0 1 0 1 0 0 0 0 0 0 0
E8 1 1 0 1 1 1 1 1 1 1 0 1 1
E9 1 1 0 1 1 1 1 0 1 1 0 1 1
E10 00 0 1 1 1 1 0 0 0 0 1 0
E11 1 1 0 1 1 1 1 1 1 1 0 1 1
E12 0 0 0 1 1 1 1 0 0 0 0 0 0
E13 0 0 0 1 1 1 1 0 0 1 0 1 0
M2
E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13
E1 0 0 0 7 4 6 5 0 0 2 0 3 1
E2 0 0 0 6 3 5 4 0 0 1 0 2 0
E3 34 0 11 8 10 9 2 3 6 0 7 5
E4 0 0 0 0 0 0 0 0 0 0 0 0 0
E5 0 0 0 2 0 1 0 0 0 0 0 0 0
E6 0 0 0 0 0 0 0 0 0 0 0 0 0
E7 00 0 1 0 0 0 0 0 0 0 0 0
E8 2 3 0 10 7 9 8 1 2 5 0 6 4
E9 1 2 0 9 6 8 7 0 1 4 0 5 3
E10 0 0 0 4 1 3 2 0 0 0 0 0 0
E11 23 0 10 7 9 8 1 2 5 0 6 4
E12 0 0 0 3 0 2 1 0 0 0 0 0 0
E13 0 0 0 5 2 4 3 0 0 0 0 1 0

Journal of Civil Engineering and Management, 2015, 21(8): 977–997 995
M2
E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13
E1 0 0 0 5 3 6 7 0 0 2 0 4 1
E2 0 0 0 6 4 7 8 0 0 3 1 5 2
E3 2 1 0 8 6 9 10 0 0 5 3 7 4
E4 0 0 0 0 0 0 1 0 0 0 0 0 0
E5 0 0 0 1 0 2 3 0 0 0 0 0 0
E6 0 0 0 0 0 0 0 0 0 0 0 0 0
E7 0 0 0 0 0 0 0 0 0 0 0 0 0
E8 3 2 0 9 7 10 11 0 1 6 4 8 5
E9 1 0 0 7 5 8 9 0 0 4 2 6 3
E10 0 0 0 2 0 3 4 0 0 0 0 1 0
E11 0 0 0 4 2 5 6 0 0 1 0 3 0
E12 0 0 0 0 0 1 2 0 0 0 0 0 0
E13 0 0 0 3 1 4 5 0 0 0 0 2 0
Table 13. The result of Dominance Directed Graph for the reference point approach as a part of
MOORA method
M
E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E 11 E12 E13
E1 0001111001111
E2 1001111001111
E3 1101111011111
E4 0000011000000
E5 0001011000010
E6 0000001000000
E7 0000000000000
E8 1111111011111
E9 1101111001111
E10 0001111000010
E11 0001111001011
E12 0001011000000
E13 0001111001010
A = M + M2
E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E 11 E12 E13 Sum
E1 0 0 0 6 4 7 8 0 0 3 1 5 2 36
E2 1 0 0 7 5 8 9 0 0 4 2 6 3 45
E3 3 2 0 9 7 10 11 0 1 6 4 8 5 66
E4 0 0 0 0 0 1 2 0 0 0 0 0 0 3
E5 0 0 0 2 0 3 4 0 0 0 0 1 0 10
E6 0 0 0 0 0 0 1 0 0 0 0 0 0 1
E7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
E8 4 3 1 10 8 11 12 0 2 7 5 9 6 78
E9 2 1 0 8 6 9 10 0 0 5 3 7 4 55
E10 0 0 0 3 1 4 5 0 0 0 0 2 0 15
E11 0 0 0 5 3 6 7 0 0 2 0 4 1 28
E12 0 0 0 1 0 2 3 0 0 0 0 0 0 6
E13 0 0 0 4 2 5 6 0 0 1 0 3 0 21

996 S. Altuntas et al. Evaluation of excavator technologies: application of data fusion based MULTIMOORA methods
Table 14. The result of Dominance Directed Graph for the full multiplicative form for
multi-objective
M
E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13
E1 0001111001011
E2 1001111011011
E3 1101111111111
E4 0000010000000
E5 0001010000000
E6 0000000000000
E7 0001110000000
E8 1101111011011
E9 1001111001011
E10 0001111000000
E11 1101111111011
E12 0001111001000
E13 0001111001010
M2
E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13
E1 0005463002010
E2 1007685004032
E3 4 2 0 10 9 11 8137065
E4 0000000000000
E5 0000010000000
E6 0000000000000
E7 0001020000000
E8 2008796015043
E9 0006574003021
E10 0002130000000
E11 31098107026054
E12 0003241000000
E13 0004352001000
A = M + M2
E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 Sum
E1 000657400302128
E2 200879601504345
E3 53011 10 12 9 2 4 8 1 7 6 78
E4 0000010000000 1
E5 0001020000000 3
E6 0000000000000 0
E7 0002130000000 6
E8 3 1 0 9 8 10 7 0 2 6 0 5 4 55
E9 100768500403236
E10 000324100000010
E11 4 2 0 10 9 11 8 1 3 7 0 6 5 66
E12 000435200100015
E13 000546300201021

Journal of Civil Engineering and Management, 2015, 21(8): 977–997 997
Serkan ALTUNTAS. He received the B.S. degree in industrial engineering from Eskişehir Osmangazi University in 2006, the M.S.
degree industrial engineering from Dokuz Eylül University in 2010 and the PhD degree in industrial engineering from the University
of Gaziantep in 2014. He is currently an Assistant Professor in the Department of Industrial Engineering at Yildiz Technical Univer-
sity. His research interests include facility layout and technology evaluation. He has published papers in various journals, including
the Total Quality Management & Business Excellence, Technological Forecasting and Social Change, and The International Journal
of Advanced Manufacturing Technology.
Türkay DERELI. He is a professor of IE Department at Gaziantep University (GAUN) and serves as the President of Iskenderun
Technical University, in Turkey. He received his BSc. and MSc. in Mech. Eng. from METU and GAUN, in 1992 and 1994, respec-
tively. He earned his PhD degree from the GAUN, Institute of Natural and Applied Sciences, in 1998. He has published numerous
papers in professional academic journals and has several textbooks on CAD/CAM, IT, brand and quality management. His current
research interests include: technology management, expert systems, economics, CAD/CAM, quality planning and control, TQM,
agile/responsive manufacturing, engineering management, informatics and applications of articial intelligence.
Mustafa Kemal YILMAz. He graduated from Karadeniz Technical University, Faculty of Economics & Administrative Sciences,
and Department of Economics. He started work as a Lecturer at Ataturk University in 2001. He completed his Master degree in
2005 and his PhD in 2010. He is now an Assistant Professor at Ondokuz Mayıs University, Department of Business. His study area
is Industrial Marketing, Service Marketing and Marketing Research.