International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 04, April 2019, pp. 39-48. Article ID: IJMET_10_04_006
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=4
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication
Scopus Indexed
CASSAVA FOLIAGE HARVESTING MACHINE
SELECTION DECISION MAKING FACTORS:
THE CASE STUDY IN THAILAND
Supattra Buasaengchan
Technopreneurship and Innovation Management, Graduate School, Chulalongkorn
University, Bangkok, Thailand.
Somchai Pengprecha
Faculty of Science, Chulalongkorn University, Bangkok, Thailand.
Pakpachong Vadhanasindhu
Faculty of Commerce and Accountancy, Chulalongkorn University, Bangkok,Thailand.
Kriengkri Kaewtrakulpong
Faculty of Agriculture, Kasetsart University, Bangkok, Thailand.
ABSTRACT
Machine and tooling selection are very important for agriculture economy which
base on labor intensive that increase time usage and cost. Cassava foliage harvesting
selection is very challenging in choosing the machine since it will be the key importance
to change the cassava supply chain that cannot bring cassava foliage to use in the
commercial way. The framework of this study start from the cassava farmers’ aspect,
link with factors concerned from literature review and then grouping the suitable
criteria and sub-criteria. The specific questionnaire was conducted with the
representative of the cassava farmer, agriculture machine maker and the expert user in
cassava foliage. The Analytical Hierarchy Process (AHP) is used to set the hierarchy
structure of the criteria, rating and prioritization. The results of the study illustrate the
machine factors and cost for cassava foliage harvesting machine selection decision
making. The prioritized factors are durability, low cost of harvesting, safety, technology
and quality of output respectively. It can be used not only cassava foliage harvesting
machine selection case but also the other agriculture machine or equipment.
Keywords: cassava foliage harvesting machine, AHP, agriculture machine selection,
multi criteria decision making
http://www.iaeme.com/IJMET/index.asp
39
editor@iaeme.com
Cassava Foliage Harvesting Machine Selection Decision Making Factors: the Case Study in
Thailand
Cite this Article Supattra Buasaengchan, Somchai Pengprecha, Pakpachong
Vadhanasindhu and Kriengkri Kaewtrakulpong, Cassava Foliage Harvesting Machine
Selection Decision Making Factors: The Case Study in Thailand, International Journal
of Mechanical Engineering and Technology, 10(4), 2019, pp. 39-48.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=4
1. INTRODUCTION
Cassava foliage, cassava leaf or cassava hay in Thailand is accepted in the high crude protein
nutrition for animal feeds comparing to the other sources such as fish meal or soy bean. From
the prior empirical study of the author “The reason why we can’t use cassava leaf for
commercial purpose in Thailand” [1] shows the importance of machine as unmet need. 75% of
the samples are interested in harvesting tools due to the lack of labor, wastes in process, time
which bring to the high cost of harvesting and unprofitability.
The objective of the study is to identify the suitable factors for cassava foliage harvesting
machine selection decision making that can generate revenue and profit from the cassava
foliage with productivity, fit to Thai farming characteristics, easy to use, and reduce labor cost.
The suitable model for machine selection factors and process are essential in order to maximize
the harvesting outcome.
This article is divided into five sections. The introduction shows the importance for this
study, literature review with the theoretical base and relevant researches, and the methodology
of the study. The result of the study from both the survey and the Analytical Hierarchy Process
(AHP). The last section is conclusion, discussion of the result, and the recommendation for
further study.
2. LITERATURE REVIEW
Analytic Hierarchy Process (AHP) method is one of the well-known decision-making
consideration with multiple criteria developed by Thomas Saaty [2]. AHP can be used in both
qualitative and quantitative criteria for the judgment in decision-making. The steps in AHP
comprise of structuring the framework, questionnaire design, sampling & questionnaire survey,
weight the priorities, and then summarize the results and conclusions.
In the process of comparison, the numbers are identified accordingly to the importance scale
of each comparison in line with the definition [3]. The absolute numbers are assigned for each
pair of factors to represent the importance of factor to be selected by the respondent and then
calculated to be used for the systematic decision making.
From the literature review, the criteria, machine and cost, and sub-criteria are defined as in
Table1 in order to group the various criteria and definition from the twelve literatures together
with the result from the empirical study. The factors are 2 major criteria: the Machine factor
and the Cost factor. The machine factors consist of 7 sub-criteria: easy to use, productivity,
quality, suitability to scale of production, safety, durable and technology.
For the Cost Factor, the 5 sub-criteria are economical investment, reduce labor, energy
saving, maintenance cost and low cost of harvesting.
http://www.iaeme.com/IJMET/index.asp
40
editor@iaeme.com
Supattra Buasaengchan, Somchai Pengprecha, Pakpachong Vadhanasindhu and Kriengkri
Kaewtrakulpong
Table 1 Expected Cassava Foliage Harvesting Factors and Definition
Expected Cassava Foliage Harvesting Factors and Definition
Main Criteria
1 Machine Factors
2 Cost Factors
Sub-Critetia
Definition
1-1 Easy to Use
Easy to Use/Control/Ergonomics
1-2 Productivity
Effectiveness/Reduce Harvesting Time/Productivity
1-3 Quality of Output
Quality/Low Foreign material
1-4 Suitability to Scale of production
Suitability to Scale of production/Shape of Tree
1-5 Safety
1-6 Durable
Safety
Durable
1-7 Techonology
Techonology/ Automation
2-1 Economical Investment
Economical Cost of M/C
2-2 Reduce Labor Cost
Reduce Labor Cost
2-3 Energy Saving
Energy Saving
2-4 Maintenance Cost
Maintenance Cost
2-5 Low Cost of Harvesting
Low Cost of Harvesting
Agriculture machine selection is one of the importance topics for agriculture development
purpose in many countries. Twelve papers published during the year of 2008 to 2019 was
reviewed as shown in Table 1. The tools reference in each paper are various, 50% were in
machine design to meet customers’ expectation [4-9]. Thirty three percent use AHP Model [1013], the others uses descriptive statistics [14] and purposive interview [15].
The twelve literature review of the criteria and sub-criteria are scored as shown in Table2.
Low cost of harvesting has the highest score at 10 among all criteria. The second one is
productivity with 9 scores, the third one is easy to use with 8 scores. These criteria will be used
to map with the factors in the questionnaire as shown in the framework of the study (Figure.1).
Table 2 Literature Review on Agriculture Machine Design and Selection
1
Reference Literatures
Supplier Slection
in Automobile
industry
Surakrit
AHP
Criteria-Subcriteria and Scores
Main Criterias
Sub Criterias
1. Machine Factors 1-1 Easy to Use
1-2 Productivity
1-3 Quality of Output
1-4 Suitability to Scale of production
1-5 Safety
1-6 Durable
1-7 Techonology
2. Cost Factors
2008
3
4
5
6
7
8
9
10
A. F. Abed
Rabou,et al
Machine Design
Atthasat et al
Rattarut
Kritsada
Descriptive
statistics
Prashant
Inkane,et al
Machine Design
Machine Design
Ravindra Lahane
et al
Machine Design
Carl J. Bern
AHP
Rubayet Karim, et
al
AHP and TOPSIS
Ashkan
Machine Design
AHP
Machine Design
2009
2011
2013
2014
2014
2016
2017
2018
2018
2018
2019
Scores
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
8
9
4
6
3
4
2
✓
✓
✓
✓
✓
✓
✓
✓
7
4
5
4
✓
✓
✓
10
✓
✓
✓
✓
✓
Design And
DESIGN AND
Multi-Crop
Olive harvesting
Calculation Of FABRICATION OF Harvesting Machine
Machine
Solar Power
HARVESTING
Operated
MACHINE-Reaper
Sugarcane
M/C Soybean
Harvesting Machine
12
Redmond Ramin
Shamshiri
Purposive
Interview+LR
✓
✓
✓
✓
Amar et al
✓
Harvesting and
Postharvest
Management
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
11
Machine Selection
by AHP and
TOPSIS Methods
✓
2-1 Economical Investment
2-2 Reduce Labor Cost
2-3 Energy Saving
2-4 Maintenance Cost
2-5 Low Cost of Harvesting
2
Development of a Manufacturing
Factors
Jewel Factory
Rice Farmers’
Mechanical
local machine suit
Influencing
Machine Selection
Decision to
Harvesting
for harvesting Decision Making to
Purchase
Machine for Highsugar beet
Middle Size Tractor
Agricultural
density Citrus
Machine for Land
Groves
Preparation
✓
✓
✓
3. METHODOLOGY
The framework of cassava foliage harvesting machine selection decision making factors (Figure
1.) for this study was set in 3 steps. The first step is the result summary of the cassava foliage
harvesting perception from the author’s prior empirical study [15]. The second step is the
questionnaire design covering the factors concluded from literature review and the survey
http://www.iaeme.com/IJMET/index.asp
41
editor@iaeme.com
Cassava Foliage Harvesting Machine Selection Decision Making Factors: the Case Study in
Thailand
mentioned in step 1. The last importance step is the AHP analysis of the data gathered from the
result of the study in step 2.
Framework of Cassava Foliage Harvesting Machine Decision Making Selection Factors
Literature Review
Source
Prior Empirical
Study
Process
1. In-depth Interview
for factors concerned
1. Review Literature
empharsized on
Agriculture Machine
Selection Factors
2. Machine Selection
Factors in
Qualitative
Interview
1 Structuring the Framework
2. Questionnaire Design
3. Sampling & Questionnaire Survey
2. Factors Concerned
Grouping
4. Weigh the priorities
5. Results and Conclusion
3. Review Factors on
Interview and
Literature
3. Conclusion
Samples
Description
AHP
Survey
260 Samples
Cassava Farmers
7 Samples
2-Cassava Farmers : Head of the Association/ Cooperation
2-Machine Designer & Maker
3-Agriculture Expert (Cassava Foliage)
12 Papers on
Agriculture
Machine Design
Figure 1. Framework of Cassava Foliage
Harvesting Machine Decision Making Selection Factors
3.1. Prior Empirical Survey
The author’s empirical survey for the cassava foliage harvesting machine selection to know the
factors concerned revealed 2 factors comprised of machine factors and cost factors.
3.2. Factors Review from Literature
The further step is to review factors from the interview and literature that can be grouped into
2 main criteria which are machine factors and cost factors. The sub-criteria of each factor are
summarized as shown in Table 3.
Table 3 Factors concerned from Literature Review
Factors concerned from Literature Review
Main Criteria
Sub-Criteria
Scores
1 Machine Factors 1-1 Easy to Use
8
1-2 Productivity
9
1-3 Quality of Output
4
1-4 Suitability to Scale of production
6
1-5 Safety
3
1-6 Durable
4
1-7 Techonology
2
2-1 Economical Investment
7
2-2 Reduce Labor Cost
4
2-3 Energy Saving
5
2-4 Maintenance Cost
4
2 Cost Factors
2-5 Low Cost of Harvesting
http://www.iaeme.com/IJMET/index.asp
42
10
editor@iaeme.com
Supattra Buasaengchan, Somchai Pengprecha, Pakpachong Vadhanasindhu and Kriengkri
Kaewtrakulpong
3.3. AHP Survey
The step of AHP survey are as followed. There are five phases which are structuring the
framework, questionnaire design, sampling and questionnaire survey, weigh the priorities and
results and conclusion.
3.3.1. Structuring the framework
From the factors identified, the hierarchical structure of the criteria is conducted as in Figure 2.
Starting from the top of the hierarchical structure, Level 1, the objective of the model is to
evaluate the cassava foliage harvesting machine selection decision making factors. In Level 2,
the main criteria in both machine function and cost function are directly related to Level 1.
Level 3, the sub-criteria directly linked to criteria in Level 2 are set to evaluate the multiple
alternative in decision-making process.
Figure 2. Hierarchical Structure of the Criteria
3.3.2. Questionnaire Design
We design questionnaire to interview the samples using the pairwise comparison for each
factor. The sample of the questionnaire are shown as below: (Table 4)
http://www.iaeme.com/IJMET/index.asp
43
editor@iaeme.com
Cassava Foliage Harvesting Machine Selection Decision Making Factors: the Case Study in
Thailand
Table 4 AHP Questionnaire Sample
Comparison Score
Sub- Criteria : Machine
More than
Equal
Sub- Criteria : Machine
Less than
1-1 Easy to Use
9
7
5
3
1
3
5
7
9
1-1 Easy to Use
1-2 Productivity
9
7
5
3
1
3
5
7
9
1-1 Easy to Use
1-3 Quality of Output
9
7
5
3
1
3
5
7
9
1-1 Easy to Use
1-4 Suitability to Scale of production 9
7
5
3
1
3
5
7
9
1-1 Easy to Use
1-5 Safety
9
7
5
3
1
3
5
7
9
1-1 Easy to Use
1-6 Durable
9
7
5
3
1
3
5
7
9
1-1 Easy to Use
1-7 Techonology
9
7
5
3
1
3
5
7
9
1-1 Easy to Use
Pairwise comparison is set from a scale of numbers that can evaluate the level of each
criteria on another one [2] (Table 5).
Table 5 Scale of Evaluation
Scale of Evaluation (Satty,1980,2008)
Intensity of
Importance
1
3
5
7
9
Explanation
Definition
Equal Importance
Two criterias contribute equally to the
objective.
Moderate Importance Experience and judgement slightly favor one
criteria over another
Strong Importacne
Experience and judgement strongly favor
one criteria over another
Very Strong Importance An criteria is favored very strongly over
another.
Extreme Importance
The criteria favoring one activity over
another os of the highest possible order of
affirmation.
3.3.3. Sampling and Questionnaire Survey
The samples using for AHP analysis are 7 people selected from three groups which are cassava
farmers, agriculture machine expert and agriculture expert. The description and details are
shown in Table 6.
Table 6 Respondent Description
No.
Description
Gender
1
2
3
4
5
6
7
Cassava Farmer-National Outstanding Farmer
Agriculture Machine Expert
Agriculture Farming Expert
Cassava Farmer-manager of the Cooperative
Agriculture Farming Expert
Agriculture Farming Expert
Agriculture Machine Expert
Male
Male
Male
Female
Female
Male
Male
http://www.iaeme.com/IJMET/index.asp
44
Age
51-60
41-50
51-60
51-60
41-50
31-40
41-50
Years
Years
Years
Years
Years
Years
Years
Work Experience
> 30 Years
21-30 Years
21-30 Years
21-30 Years
11-20 Years
< 5 Years
11-20 Years
Agriculture/Machi
ne Experience
21-30 Years
21-30 Years
11-20 Years
< 5 Years
< 5 Years
< 5 Years
11-20 Years
editor@iaeme.com
Supattra Buasaengchan, Somchai Pengprecha, Pakpachong Vadhanasindhu and Kriengkri
Kaewtrakulpong
3.3.4. Weigh the Priorities
The criteria and sub-criteria listed are one by one compared to evaluate which one is more
importance. Pairwise Comparison Matrix are decided to match with the answers from the
respondent. The eligible factors selection under the balance of the different opinion together
with the ranking under the weighted score.The matrix are developed into each level: criteria
and sub-criteria. The answers from the samples will be fulfilled to the degree of importance for
the cassava foliage harvesting machine selection decision making factors. (Table 7)
Table 7 Pairwise Matrix Comparison Sample: Machine Factors
1-1
Easy to Use
1-1 Easy to Use
1-2 Productivity
1-2
Productivity
1-3
Quality
1-4
Suitability to
Scale of
production
1-5
Safety
1-6
Durable
1-7
Techonology
1.00
1.00
1-3 Quality of Output
1.00
1-4 Suitability to Scale
of production
1.00
1-5 Safety
1.00
1-6 Durable
1.00
1-7 Techonology
1.00
We use Super Decisions software to analyze the answers weighed from the respondent. The
analysis is shown in the next section.
4. RESULT AND DISCUSSION
4.1. Result
For the main criteria, the score for cost factor is 0.3333 and the score for machine is 0.6667
with no inconsistency. (Table.8)
Table 8 Main Criteria Result from AHP
Main Criteria
Factors
Cost
Machine
Normalized
0.3333
0.6667
Inconsistency
-
The inconsistency for sub-criteria less than 0.10 which shows the accepted consistence of the
answers [2]. The inconsistency value of machine factors is 0.0629 and the value of cost factors
is 0.0562. (Table 9)
http://www.iaeme.com/IJMET/index.asp
45
editor@iaeme.com
Cassava Foliage Harvesting Machine Selection Decision Making Factors: the Case Study in
Thailand
Table 9 Sub-Criteria Result from AHP
Sub-criteria : Machine Factor
1-1
1-2
1-3
1-4
1-5
1-6
1-7
Sub-criteria : Cost Factor
Factors
Easy to Use
Productivity
Quality of Output
Suitability to Scale of production
Safety
Durable
Techonology
Normalized
0.0640
0.1099
0.1734
0.0921
0.1809
0.1988
0.1809
Inconsistency
0.0629
2-1
2-2
2-3
2-4
2-5
Factors
Normalized
Economical Investment
0.0776
Reduce Labor Cost
0.0798
Energy Saving
0.2292
Maintenance Cost
0.2314
Low Cost of Harvesting
0.3821
Inconsistency
0.0562
From the priorities for both set of sub-criteria, we can set the rank of priorities from pairwise comparison. In sub-criteria: machine factor, durable is the first rank at 0.1988. The second
rank is safety and technology at the same 0.1809 score. The third rank is quality of product at
0.1734.
For cost factor, low cost of harvesting is the first rank with 0.3821.The second important
cost sub-criteria is maintenance cost with 0.2314 score and the third rank is energy saving with
0.2292 score.
All the criteria are re-prioritized by weigh with the main criteria score so we have the new
priority started from durable as the first priority at 0.1326 score (Table 10). Low cost of
harvesting is the second priority with 0.1274 score. The third rank is technology and safety at
0.1206 score.
From the model, we can set the priority of Cassava Foliage Harvesting Machine Selection
Decision Making Factors Selection by using AHP which can reduce the confusion of the
mathematics score by paring the factors.
Table 10 Factors Conclusion, Score and Ranking
Factors Conclusion, Score and Ranking
Main Criteria
A
1 Machine Factors0.6667
2 Cost Factors 0.3333
Sub-Criteria
B
Scores (A*B)
RANK
1-1 Easy to Use
0.0640
0.0427
9
1-2 Productivity
0.1099
0.0733
7
1-3 Quality of Output
0.1734
0.1156
4
1-4 Suitability to Scale of production
0.0921
0.0614
8
1-5 Safety
0.1809
0.1206
3
1-6 Durable
0.1988
0.1326
1
1-7 Techonology
0.1809
0.1206
3
2-1 Economical Investment
0.0776
0.0259
11
2-2 Reduce Labor Cost
0.0798
0.0266
10
2-3 Energy Saving
0.2292
0.0764
6
2-4 Maintenance Cost
0.2314
0.0771
5
2-5 Low Cost of Harvesting
0.3821
0.1274
2
1.0000
http://www.iaeme.com/IJMET/index.asp
46
editor@iaeme.com
Supattra Buasaengchan, Somchai Pengprecha, Pakpachong Vadhanasindhu and Kriengkri
Kaewtrakulpong
5. CONCLUSION
This research aimed to evaluate cassava harvesting machine selection decision making factors
which has never be created before. The case study was developed under the circumstance of
Thai cassava plantation. The data collection began from the author’s prior empirical survey,
machine design and decision factors literature review. The most important criteria and subcriteria for the objective were identified to prepare the hierarchical structure. It can be used not
only cassava foliage harvesting machine selection case but also the other agriculture field
requirement.
Then, the paired comparison of the criteria and sub-criteria from the samples was filled in
and calculated by SuperDecisions software together with the consistency of the criteria and subcriteria verified. Since we have two levels in criteria and sub-criteria, priorities analysis was
used to synchronize the relative priorities by calculating the score weight of criteria to the score
weight of the sub-criteria (Table 11)
Table 11 Factors Prioritized by Weighted Score Criteria and Sub-criteria
Factors Prioritized by Weighted Score Criteria and Sub-criteria
1 Machine Factors 1-6 Durable
2 Cost Factors
2-5 Low Cost of Harvesting
1 Machine Factors 1-7 Techonology
1 Machine Factors 1-5 Safety
1 Machine Factors 1-3 Quality of Output
2 Cost Factors
2-4 Maintenance Cost
2 Cost Factors
2-3 Energy Saving
1 Machine Factors 1-2 Productivity
1 Machine Factors 1-4 Suitability to Scale of production
0.1326
1
0.1274
0.1206
2
0.1206
0.1156
3
0.0771
0.0764
5
0.0733
0.0614
7
9
10
11
2 Cost Factors
2-2 Reduce Labor Cost
0.0427
0.0266
2 Cost Factors
2-1 Economical Investment
0.0259
1 Machine Factors 1-1 Easy to Use
3
4
6
8
The further study is to design the model in cassava foliage harvesting selection factors
basing on the result factors which can meet the stakeholder’ requirement, not only the cassava
farmers but also the engineer and the end-user of the cassava foliage.
The limitation of this study is that it doesn’t link the actual cost with the machine
specification required and the limited number of sample in this study. The further contribution
is the AHP analysis by using the weighted criteria and sub-criteria to evaluate the suitable
cassava foliage harvesting together with cost concerned. Feasibility study of the machine
selected is one of the important tools to study both output and outcome of the machine.
REFERENCES
[1]
[2]
Supattra Buasaengchan, Somchai Pengprecha, Pakpachong Vadhanasin, Kriengkri
Kaewtrakulpong. The reason why we can’t use cassava leaf for commercial purpose in
Thailand, International Conference on Sustainable Agriculture (Icsa-19), Bangkok, 2019,
pp. 49-56
Andre Andrade Longaray, Joao de Deus Rodrigues Gois, Paulo Roberto da Silva Munhoz.
Proposal for using AHP method to evaluate the quality of services provided by outsourced
http://www.iaeme.com/IJMET/index.asp
47
editor@iaeme.com
Cassava Foliage Harvesting Machine Selection Decision Making Factors: the Case Study in
Thailand
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
companies, Information Technology and Quantitative Management (ITQM2015), Procedia
Computer Science 55, 2015,pp. 715-724
Saaty, Thomas L. Relative Measurement and its Generalization in Decision Making: Why
Pairwise Comparisons are Central in Mathematics for the Measurement of Intangible
Factors – The Analytic Hierarchy/Network Process, Review of the Royal Academy of
Exact, Physical and Natural Sciences, Series A: Mathematics (RACSAM). 102(2), pp. 251–
318.
A. F. Abed Rabou, ELyamani A. E, and Z. Emara. Manufacturing local machine suit for
harvesting sugar beet, Ag. Eng. Res. Inst. (AEnRI), Giza, Egypt
Atthasat Wiseansart, Suchint Simaraks. Factors Influencing Decision Making to Middle
Size Tractor, Utilization of Farmers in Nam-Oam Sub-district, Kanuan District, Khon Kaen
Province, KKU Res J (GS), October - December 2013 13 (4)
Prashant Inkane, Yogesh Burati, Himanshu Bhendarkar, Ramkrishna Gondane4. Design
And Calculation Of Solar Power Operated Sugarcane Harvesting Machine, International
Research Journal of Engineering and Technology (IRJET), Mar -2017, 04 (03)
Amar B. Mule, Pravin T. Sawarkar, Akshay A. Chichghare , Akash N. Bhiwapurkar ,
Dhananjay D. Sirsikar ,Kapil R. Gaurkar. Design And Fabrication Of Harvesting Machine,
International Research Journal of Engineering and Technology (IRJET), Jan-2018, 05 (01)
Ravindra Lahane, Ankush Fuse, Shantanu Dahake, Saurabh Wakchaware, Parth Ditanwala.
Multi-Crop Harvesting Machine, International Research Journal of Engineering and
Technology (IRJET), May-2018, 05 (05)
Carl J. Bern, Graeme Quick and Floyd L. Herum, Harvesting and Postharvest Management,
Elsevier Inc. in cooperation with AACC International, 2019, pp.109-145
Surakrit Narttaradol, Application of fuzzy analytic hierarchy process for supplier selection
of automobile and electronic industries M.Eng. Dissertation, Chiang Mai University,2008.
Rattarut Titichattawong, Prioitizing Key Importance Factor for Macine Selection by
Analytic Hierarchy Process: A Case Study of Jewelry Factory. B.Eng. Dissertation.
Rajamangala University of Technology Thanyaburi), 2014
Rubayet Karim, C. L Karmaker, Machine Selection by AHP and TOPSIS Methods,
American Journal of Industrial Engineering, 4 (1) , 2016, pp. 7-13
Ashkan Hafezalkotob, Aida Hami-Dindar, Naggmah Rabir, Arian Hafezalkotob. A decision
support system for agriculture machines and equipment selection: A case study on olive
harvesting machines. Computers and Electronics in Agriculture, 2018,pp.207-216
Kritsada Yokubon. Factors Influential to Rice Farmers’ Decision to Purchase Agricultural
Machine for Land Preparation: Amphoe Sapphaya, Province Chainat, Thailand,
Independent Study, Master of Business Administration, Nation University, 2014.
Redmond Ramin Shamshiri. Development of a Mechanical Harvesting Machine for Highdensity Citrus Groves Research Proposal, University of Florida, 2009
http://www.iaeme.com/IJMET/index.asp
48
editor@iaeme.com