Application of Multi Criteria Decision Analysis
for Selection of Best Alternative Process and Coagulation-Flocculation
Operation
in
Pre-Treatment Water System of Steam Power Plant
Yuni Eko Feriyanto1,a, Udisubakti Ciptomulyono2,b and Endah Angreni3,c
1,2,3Department of Business and Technology Management, Institute of Technology Sepuluh Nopember Surabaya, Indonesia
aYE.Feriyanto@gmail.com, bUdisubakti@gmail.com,
cAngreni_bums@yahoo.com
Keywords: Coagulation-Flocculation, Jar
test, Multicriteria decision analysis, Steam power plant, Water treatment.
Abstract. Water
Treatment Plant (WTP) is one of the system stages in steam power plant which
includes a series of sub-system processes such as screening, pre-treatment
water, desalination and demineralization. At the pre-treatment stage of the
water system, there is a sludge deposition process that is assisted with the
chemical coagulant and coagulant-aid. The most common problem in steam power
plant water treatment is despite the plentiful dose used in the rainy season
and sea tidal condition, the quality of the water produced still does not meet
the standard of steam power plant. The proposed jar-test technique was
performed using process and operation variables that potentially affect the
performance of coagulant-coagulan aid such as the % dosage, residence time and
agitator cycle. The water quality was measured using criteria such as
turbidity, conductivity, pH, TSS and TDS. In this papaer, the use of
multicriteria decision analysis (MCDA) was proposed using two approaches of AHP
and AHP-TOPSIS methods. The sensitivities of both methods were analyzed and the
decision model obtained was subsequently discussed and compared. Based on the
result of this paper, it can be concluded that the best alternative process and
operation coagulation-flocculation in steam power plant pre-treatment water
system based on the acquisition of decision models for two different methods
was the first alternative for D60W30P80 and the second alternative for
D40W20P80.
Introduction
Physical-chemical
treatment in aqueous settling basin is a treatment of reduction in sludge
content carried by feed water and is known as the coagulation-flocculation
system. This research took the subject of feed water treatment at steam power plant
from sea water. According to Table 1, the quality of sea water entering the
steam power plant water treatment varied and depended on seasons and tides.
Table 1. Quality of Feed
Water in Two Different Seasons
|
Parameter
|
Unit
|
Measuring Value
|
|
|
Rain Season
|
Dry Season
|
||
|
Turbidity
|
NTU
|
30-40
|
6.6-8.5
|
|
Conductivity
|
µS/cm
|
± 47,000
|
± 47,000
|
|
pH
|
value
|
± 8
|
± 8
|
|
TSS
|
mg/L
|
± 30
|
± 20
|
The
routine operational cost structure of the steam power plant is divided into
two, namely operating cost which includes water treatment, steam treatment and
operation of human resources management while maintenance cost which includes
replacement of spare parts, predictive maintenance and maintenance of human
resources management. Based on these two costs, there are issues of high
operational costs such as replacement of spare parts for maintenance scope and
water treatment for operation scope.
WTP consists of
several series of systems and each system produces a product that becomes the
next feed water for the system so that the resulting feed water quality standards
are expected to meet steam power plant standards. The high operational cost is
caused by the impact of the chain system. Therefore, it is important to focus
in improving the early stage of WTP system which is pre-treatment water system
to solve the problem. Pre-treatment water system in steam power plant is coagulation-flocculation system. The most
common problem in this system is that the dosage requirement during the rainy
season and high tide is high compared to the dry season, however the water
quality of the system output still does not meet the standard of steam power plant. Thus, a deep study to analyze the
cause of this phenomenon is needed. The result of sampling output of
coagulation-flocculation system in rainy season is described in Table 2.
Table 2. The Comparison
Between Real Water Quality on Rainy Season and Standard
|
Unit
|
Measuring
Value
|
||
|
Real
|
Standard
|
||
|
Turbidity
|
NTU
|
8,43
|
<5
|
|
Conductivity
|
μS/cm
|
48.700
|
<48.900
|
|
pH
|
Nilai
|
7,497
|
7-8
|
|
TSS
|
mg/L
|
10
|
<10
|
|
TDS
|
g/L
|
24,2
|
<24,2
|
Based
on Table 2, it was found that there were 3 of the 5 criteria that not met the
standards of steam power plants at the sampling measurements of
coagulation-flocculation products in the rainy season. These criteria were
turbidity, TSS and TDS. The effectiveness
of coagulation-flocculation process was supported by optimal dosage as well as
appropriate processes and operations such as % dosage, residence time and
agitator cycle [1]. Several parameters of water quality such as turbidity,
conductivity, pH, TSS and TDS were used to know the effectiveness of
coagulation-flocculation [1, 2, 3]. The study used several water quality
parameters to find the right type of coagulant aid using multicriteria decision
analysis system [4].
The jar-test technique is used for
laboratory-scale experiments with the problem taken in such a way similar to
the actual conditions in the field. The principle of jar-test is to conduct
repetitive experiments with various variables so that the information about
variables selection that produce good measurable criteria. Previous studies of
coagulations-flocculation was studies that used standardized process and
operation variables such as %dosage, residence time and agitator cycle [3]. Jar-test
result is data criteria with combination process and operation variables of
coagulation-flocculation as alternatives.
The selection of the best alternative is
difficult to perform because in the decision-making system, it is preferred to
ensure the achievement of decision through a series of activities that analyze
the alternative decision solutions, the parameters and constraints that exist
and then choose the best rather than choose the right choice first and
immediately [5]. The selection of the best combinations of
coagulation-flocculation which involve lot of criteria in measurements often
encounters conflicting situations such as alternatives which likely to be
accepted in turbidity and TSS reduction but still rejected because of the
impact on the increase of conductivity and TDS.
Based on such conditions, it is proposed to
use multicriteria decision analysis (MCDA) method which can be used to
accommodate alternative selection with multicriteria consideration. Selection
of MCDA type is difficult because each type has advantages and disadvantages.
The election is also based on the structure of the problem, the objectives to
be achieved and the existence of constraints if there is any. In general, many
researchers combine MCDA types to complement the deficiencies of each MCDA type
in priority ranking determinations such as AHP-Preference Ranking Organization
Methods for Enrichment Evaluations (PROMETHEEs) [2], AHP-Elimination Et Choix
Tradnisant La Realite (ELECTRE) [6], AHP-Technique for Order Preference by
Similarity to Ideal Solution (AHP-TOPSIS) [7]. In the priority ranking
determination, the AHP method approach is used for weighting criteria by expert
judgment based on the relative importance level so that the decision
alternative can provide satisfaction for the decision maker according to the
desired aspiration level and believe in the process [5].
Method
Tools and Materials. The tool
used for this experiment was a jar-test kit, beaker glass, plastic type sample
bottle, 10-100 μL analytic pipette volume, digital TSS meter, analytic balance,
digital TDS meters, digital pH meter, digital conductivity meter and digital
turbidity meter. The materials used were sea water, demineralized water,
aluminum hydroxychloride type of coagulant and anionic poly-acrylamide type of
coagulant-aid.
Jar Test. The jar-test in
this paper uses 6 paddle motors with 1 liter beaker glass with the following
experimental procedures: (i)
placing sea water in beaker glass then measuring water quality parameters prior
to coagulation-flocculation treatment; (ii)
adjusting the agitator cycle at 150 rpm accompanied by coagulant affixing and
reacting for 0.5 min; (iii)
reducing agitator cycle according to variable that was 40/60/80 rpm accompanied
by coagulant-aid with residence time of suspended solid binding according to
variable that was 10/20/30/40 min; (iv)
the end of experiment was to let the sample for ± 5 minutes and to measure
water quality by taking water sample ± 2 cm from surface; (v) measuring water quality using
criterion variable to obtain water quality data after coagulation-flocculation
treatment.
Initial Data Processing. Based on
the jar-test results data, the alternatives obtained were 48 pieces and the
water quality of each alternative measured before and after the
coagulation-flocculation process. Out of the 48 alternatives, the equalization
of criteria unit was performed with calculation of %increase/decrease of water
quality value. The calculation of %increase/decrease of water quality value was
performed to obtain real data in the field that present 100% dosage and
jar-test result data for variable according to dose. The next stage was the
selection of alternative decision by selecting data with better criteria or
equal to real data in the field then 14 alternative decisions were obtained. The
results of this selection were arranged to Table 3 which was used for data processing
materials.
Table
3.
Data of Decision Alternative Selection Results
|
Alternative
Symbol
|
% Increase/Decrease of Operational
Parameter
|
||||
|
Turbidity
|
Conductivity
|
pH
|
TSS
|
TDS
|
|
|
D40W10P60
|
67,1
|
0,63
|
1,50
|
39,4
|
0,42
|
|
D40W20P80
|
81,4
|
0,21
|
0,45
|
63,9
|
0,42
|
|
D40W30P40
|
72,7
|
0,21
|
1,05
|
60,0
|
0,42
|
|
D40W30P80
|
80,1
|
0,42
|
1,00
|
40,0
|
0,42
|
|
D40W40P40
|
70,7
|
0,00
|
1,57
|
56,1
|
0,00
|
|
D40W40P80
|
76,7
|
0,21
|
1,47
|
30,0
|
0,42
|
|
D60W10P60
|
76,1
|
0,42
|
1,24
|
40,6
|
0,42
|
|
D60W10P80
|
71,5
|
0,21
|
0,38
|
34,4
|
0,42
|
|
D60W20P60
|
67,6
|
0,00
|
0,19
|
29,0
|
0,00
|
|
D60W20P80
|
82,9
|
0,21
|
0,14
|
43,8
|
0,42
|
|
D60W30P80
|
80,4
|
0,84
|
0,97
|
62,9
|
0,00
|
|
D80W10P60
|
76.2
|
0,00
|
0,71
|
53,1
|
0,00
|
|
D80W10P80
|
74.6
|
0,00
|
0,28
|
54,1
|
0,00
|
|
D80W20P80
|
69.9
|
0,21
|
0,25
|
45,0
|
0,42
|
Problems
can be structured mathematically
according to Fig. 1.
Problems can be structured mathematically according to Fig. 1.
Fig. 1. The
Decision Hierarchy
Based on the Fig. 1, the decision hierarchy structure
was obtained by the following information: (i) the research objective was the
selection of the best alternative of coagulation-flocculation process and
operation; (ii) criteria were measurable parameters for water quality such as
turbidity, conductivity, pH, TSS and TDS; (iii) the alternative was a
combination of coagulation-flocculation processes and operations such as %dose
(D) i.e 20/40/60/80%; residence time (W) i.e 10/20/30/40 min and agitator cycle
i.e 40/60/80 rpm.
The
Application of MCDA Method. The MCDA method is an alternative
selection process method for obtaining the optimal solution of some decision
alternatives by taking criteria or objectives that are more than one in
conflicting situations into account [5]. In this paper, the AHP method and
AHP-TOPSIS approach were proposed. The AHP method is measurement theory with
pairwaise comparisons and is based on expert decisions to arrange the priority
scale [8]. In solving multicriteria problems, the AHP method was used to obtain
priority based on the decision maker's preference assessment by pairwise
comparison representing the essential ability of humans to develop their
perceptions gradually, comparing a pair of equivalent solutions to the given
criteria [9]. In the AHP method, a relative scale of interest with a Saaty
scale of 1-9 was used and performed by expert judgment. The form of the
calculation of this method was the decision matrix and system consistency ratio
(CR) involving component consistency index (CI) and random index (RI) was used
in the consistency calculation. Calculations
was performed using the Eq. 1 and the Eq. 2.
The AHP method is
generally used extensively by previous researchers such as the selection of the
optimal technology to rehabilitate the pipes in water distribution system [10],
application for reinforcement of hydropower strategy [11]. The AHP method
according to previous researchers lacks in the ranking system so that the
combination with other MCDA methods is required for the improvement of one of
TOPSIS [12]. Although TOPSIS uses the concept of a popular and simple method,
it often gets input because of its inability in providing space for an
uncertainty and perception for decision makers [13]. To overcome this
deficiency, we used a combined AHP-TOPSIS method with principle of using expert
judgment perception in uncertainty criteria assessment. Previous research for
this method were selection of development projects for oilfields [7], and
selection of sustainable supplier countries for the steel industry [14]. TOPSIS
proposed by Hwang and Yoon (1981) used to determine the positive ideal solution
and the negative ideal solution. The best alternative selection was the data
that had the shortest distance from the positive ideal solution and the
furthest distance from the ideal negative solution [15]. Here are the steps of
the AHP-TOPSIS method [16].
Step 1. Compiling a normalized
decision matrix
Step 2. Arranging the weight of the
normalized decision matrix
Step 3. Determining
the positive and negative ideal solution
Step 4. Calculating the Euclidean
distance between the positive and negative ideal solutions for each variable
Step
5. Calculating the relative closeness to a positive ideal solution for each
alternative
Results and Discussion
Criteria
Weighting. The criteria selected
were based on standard steam power plant manuals book and studies that had been
conducted by previous researchers [1, 2, 3]. The criteria weighting was
proposed using AHP method approach with decision maker by expert judgment
according to qualification which had been determined because this method was
commonly used by previous researcher and its simple calculation [2]. The
criteria weighting scoring system was performed by expert judgment using a
pairwise scale system of Saaty 1-9 and continued by calculating it using expert
choice v11 (EC 11) software assistance to obtain the criteria weight as
shown in Fig. 2
Fig. 2.
Criteria Weight by Expert Judgment using EC 11 Software
Based on the expert's judgment, turbidity
criteria had the highest priority ranking followed by TSS, while other criteria
such as conductivity, TDS and pH were determined to have less effect on water
quality.
Priority Ranking Selection
Approach of AHP Method. The AHP method in
this paper was proposed and used for the determination of criteria weighting
and priority ranking determination to select the best process and operation of
coagulation-flocculation due to the simplicity and easy calculation based on
expert judgment assessment. Initial stages of data to be calculated in this paper was
the scoring process with reference assessment in accordance with the provisions
set by the expert judgment in his knowledge in the water treatment system of
the steam power plant. The results data are presented in Table 4.
Table
4.
Decision Alternative Matrix by AHP Method Approach
|
Alternative
Variables
|
Scoring
Results
|
||||
|
Turbidity
0.433(a)
|
Conductivity
0.097(a)
|
pH
0.034(a)
|
TSS
0.353(a)
|
TDS
0.084(a)
|
|
|
D40W10P60
|
2
|
9
|
3
|
4
|
9
|
|
D40W20P80
|
9
|
6
|
2
|
9
|
9
|
|
D40W30P40
|
5
|
9
|
2
|
9
|
9
|
|
D40W30P80
|
9
|
9
|
4
|
4
|
9
|
|
D40W40P40
|
4
|
9
|
3
|
8
|
2
|
|
D40W40P80
|
7
|
9
|
4
|
2
|
9
|
|
D60W10P60
|
7
|
9
|
3
|
4
|
9
|
|
D60W10P80
|
4
|
5
|
3
|
3
|
9
|
|
D60W20P60
|
2
|
3
|
2
|
2
|
2
|
|
D60W20P80
|
9
|
3
|
3
|
5
|
9
|
|
D60W30P80
|
9
|
9
|
4
|
9
|
2
|
|
D80W10P60
|
7
|
9
|
2
|
8
|
2
|
|
D80W10P80
|
6
|
4
|
2
|
8
|
2
|
|
D80W20P80
|
3
|
4
|
2
|
6
|
9
|
(a) Criteria Weight
The alternative
decision matrix with the scoring system were arrenged and presented, then it
was subsequently calculated to determine the priority ranking using the EC 11
software. The result data could be seen in Fig. 3.
Fig. 3. Priority Ranking by AHP Method Approach using EC 11 Software
Based on the data obtained in
Fig. 3, the priority ranking for 14 alternatives was presented and this result was still based on
the weight performed by the expert judgment so that if there is a change of
decision, the level of consistency rank could not be determined yet.
Sensitivity Analysis of AHP Method. The weight of the criteria has
a significant influence on the priority ranking sequence. Decision makers may
at any time change the provisions that affect the decisions. Therefore,
sensitivity analysis is recommended to use with the principle of altering the
weighting criteria with the assistance of EC 11 software until there is a
significant level of priority ranking changes generated [14]. The
proposed sensitivity analysis used was the weighting of the criteria by +10%,
+20% and -10% for turbidity criteria and TSS as two top priority ranking. As
for the sensitivity of +10%, +20% and -10% turbidity, the result obtained were
the rank 1 to 3 of the standard AHP method did not change the order of
priority. As for the +10% sensitivity of TSS, there was an unchanged
alternatives at rank 1 to 5, for +20% TSS, the unchanged alternative was rank 1
to 2 while in -10% TSS, the unchanged
alternative was rank 1 to 3. Globally, the proposed alternative
chosen by the AHP method approach after sensitivity analysis was the first
alternative for D60W30P80 and the second alternative for D40W20P80.
Approach of AHP-TOPSIS Method and Sensitivity Analysis. AHP-TOPSIS
method combination was chosen in this method because TOPSIS is one type of MCDA
that can accommodate real data obtained for priority ranking considerations [7]
and different from AHP methods that must fully use expert judgment in
determination its decision. In this
paper, the combined AHP-TOPSIS method was performed by following division: (i)
AHP method for weighting criteria; (ii) TOPSIS method for priority ranking
determination. The TOPSIS method used the jar-test data to determine the
priority ranking. The following results are presented in Table 3.
Table
5.
Priority Ranking used AHP-TOPSIS Method Approach
|
Priority Ranking
|
Alternative Variable
|
CCi+
Value
|
|
Rank 1
|
D60W30P80
|
0,764
|
|
Rank 2
|
D40W20P80
|
0,622
|
|
Rank 3
|
D40W30P40
|
0,583
|
|
Rank 4
|
D40W10P60
|
0,514
|
|
Rank 5
|
D40W30P80
|
0,477
|
|
Rank 6
|
D60W10P60
|
0,473
|
|
Rank 7
|
D60W20P80
|
0,442
|
|
Rank 8
|
D40W40P40
|
0,433
|
|
Rank 9
|
D80W20P80
|
0,413
|
|
Rank 10
|
D80W10P80
|
0,410
|
|
Rank 11
|
D80W10P60
|
0,407
|
|
Rank 12
|
D40W40P80
|
0,320
|
|
Rank 13
|
D60W10P80
|
0,313
|
|
Rank 14
|
D60W20P60
|
0,009
|
The sensitivity analysis used in the
AHP-TOPSIS method refers to the weighting of criteria in the AHP method. Based
on the analysis of sensitivity to turbidity, there was +10% of unchanged
alternative order in rank 1 to 8, turbidity of +20% in rank 1 to 3, turbidity
of -10% at rank 1 to 4, TSS of +10% in rank 1 to 4, TSS of +20% in rank 1 to 3
and TSS of -10% in rank 1 to 7. Based
on the result of standard approach of the combined AHP-TOPSIS method which tend
to be stable after the sensitivity analysis, it was concluded that the first
alternative was chosen for D60W30P80, while it was the second alternative for
D40W20P80 and third alternative for D40W10P60.
The Comparison of Priority Ranking Between AHP and AHP-TOPSIS
Method. The comparison for the approach of these two methods
was based on a ranking of priorities that tends to be consistent when given
different levels of sensitivity. Based on both methods, the alternative
suggestions was generated to be chosen. These alternative suggestions was
subsequently compared for two methods and the same ranking alternative were
obtained which were the first alternative for D60W30P80 and the second
alternative for D40W20P80.
Conclusion
The authors in this study concluded
that the use of multicriteria decision analysis (MCDA) method was useful in the
selection of several alternative decision results of jar-test with lots of
criteria. The selection of MCDA types proposed in this study refers to the
subject matter under study, the objectives to be achieved, the existence of constraint and methods that were able to
accommodate the real results of the experiment to be considered in determining
the alternative decision. In the process of criteria weighting proposed by
expert judgment which has several qualifications that have been required
according to their knowledge in steam power plant water treatment. Advanced
calculations from experts were assisted using EC 11 software and were proven to
assist in the determination of criteria weighting, priority ranking of AHP
methods and sensitivity analysis. The software can be used to determine the
priority ranking changes if there is a policy change from decision makers that
affect the assessment of the criteria weight. The problem addressed in this
research was about coagulation-floculation in steam power plant, the approach
of AHP method must go through the initial stages which is quite difficult with
initial stage is scoring system, while approach method of AHP-TOPSIS still use
the data of jar-test result until the determination of rank priority was
performed. The proposed
final conclusions was selected based on the sensitivity analysis and the
different methods used. The conclusion are (i) the first alternatives for
D60W30P80 with definitions of 60% dose, 30 minutes residence time and
80 rpm agitator cycle and (ii) the second alternative for D40W20P80 with the
definition of 40% dose, residence time of 20 minutes and 80 rpm agitator cycle.
The recommendations of this study are to conduct further development of the
results obtained in this paper. It is still necessary for further research such
as pilot experiment test as a calibration process on the simulation system to
improve the validity of the results that have been recommended.
Acknowledgements
The author would like to thank for: Udisubakti
Ciptomulyono as supervisor and Endah Angreni as co-supervisor, Kanapi
Subur Dwiyanto as my leader in steam power plant, Mesiyah as my beloved
mother, Shinta Listyani as my beloved wife, Arqan Neurvagus
Feriyanto as my beloved son and Mahira Auruma Feriyanto as my beloved daughter.
References
[1] Boughou, N. Majdy, I. Cherkaoui, E. Khamar, M. Nounah, A. The Physico-Chemical Treatment by
Coagulation-Flocculation Releases of Slaughterhouse Wastewater in the City of
Rabat (Morocco). CODEN (USA) : PCHHAX Journal.
8(19) (2016) 93-99.
[2] Beltran, P. Roca, J. Pia, A.
Melon, M. Ruiz, E. Application of Multicriteria Decision Analysis to Jar Test
Result for Chemicals Selection in the Physical-Chemical Treatment of Textile
Wastewater. Hazardous Materials J. 164 (2009) 288-295.
[3] Daud, Z.
Awang, H. Latif, A. Nasir, N. Ridzuan, M. Ahmad, Z. Suspended Solid, Color, COD and Oil and Grease Removal
from Biodiesel Wastewater by Coagulation and Flocculation Processes. Proceeding
of The World Conference on Technology, Innovation and Entrepreneurship,
Procedia Social and Behavioral Sciences. 195 (2015) 2407-2411.
[4] Beltran, P. Gonzalez, F. Ferrando, J. Rubio, A. An AHP (Analytic
Hierarchy Process)/ANP (Analytic Network Process)-Based Multi-Criteria Decision
Approach for the Selection of Solar-Thermal Power Plant Investment Projects.
Energy. J. 66 (2014) 222-238.
[5] Ciptomulyono, U. Paradigma Pengambilan Keputusan Multikriteria dalam
Perspektif Pengembangan Projek dan Industri yang Berwawasan Lingkungan. Pidato
Pengukuhan Jabatan Guru Besar Bidang Ilmu Pengambilan Keputusan Multikriteria,
Jurusan Teknik Industri, ITS-Surabaya (2010).
[6] Zak, J. Kruszynski, M. Application of AHP
and ELECTRE III/IV Methods to Multiple Level, Multiple Criteria Evaluation of
Urban Transportation Projects. 18th Euro Working Group on Transportation.
10 (2015) 820-830.
[7] Morteza, P.A. Project Selection for Oil-Fields Development by
Using the AHP and Fuzzy TOPSIS Methods. Expert Systems with Appl. 37 (2010)
6218-6224.
[8] Saaty, T.L. Decision Making with the Analytic
Hierarchy Process. Int. J. Services Sciences. 1 (2008) 1.
[9] Ciptomulyono, Udisubakti. Fuzzy Goal Programming Approach for
Deriving Priority Weights in the Analytical Hierarchy Process (AHP) Method.
Appl. Sciences Research J. 4(2) (2008) 171-177.
[10] Aschilean, I. Badea, G. Giurca, I. Nagiu, G.S. Iloaie, F.G.
Choosing the Optimal Technology to Rehabilitate the Pipes in Water Distribution
Systems Using the AHP Method. Sustainable Solution for Energy and Environment.
112 (2017) 19-26.
[11] Singh, R.P. Nachtnebel H.P. Analytical Hierarchy Process (AHP)
Application for Reinforcement of Hydropower Strategy in Nepal. Renewable and
Sustainable Energy Reviews. 55 (2016) 43-58.
[12] Taylan, O. Bafail, A.O. Abdulaal R.M.S. Kabli, M.R. Construction
Projects Selection and Risk Assesment by Fuzzy AHP and Fuzzy TOPSIS
Methodologies. Applied Soft Computing J. 17 (2014) 105-116.
[13] Krohling,
R.A. Campanharo, V.C. Fuzzy TOPSIS for Group Decision Making : A Case Study for
Accidents with Oil Spill in the Sea. Expert Syst. Appl. J. 38 (4) (2011)
4190-4197.
[14] Azimifard, A. Moosavirad, S.H. Ariafar, S. Selecting Sustainable
Supplier Countries for Iran’s Steel Industry at Three Levels by Using AHP and
TOPSIS Methods. Resources Policy J. 57 (2018)
30-44.
[15] Dagdeviren, M. Yavuz, S. Kilinc, N. Weapon Selection Using the AHP
and TOPSIS Methods Under Fuzzy Environment. Expert Systems with Appl. J. 36 (2009) 8143-8151.
[16] Chang,
K.L. Liao, S.K. Tseng, T.W. Liao, C.Y. An ANP Based TOPSIS Approach for
Taiwanese Service Apartment Location Selection. Asia-Pacific Management Review
J. 20 (2015) 49-55.
Previous
« Prev Post
« Prev Post
Next
Next Post »
Next Post »