Evaluating and selecting green suppliers by intergration of neutrosophic ahp topsis approach a case of the fast fashion industry

MINISTRY OF EDUCATION AND TRAINING
HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION
FACULTY FOR HIGH QUALITY TRAINING

CAPSTONE PROJECT
INDUSTRIAL MANAGEMENT

EVALUATING AND SELECTING GREEN SUPPLIERS
BY INTEGRATION OF NEUTROSOPHIC AHP-TOPSIS
APPROACH: A CASE OF THE FAST- FASHION
INDUSTRY.

LECTURER: Dr. NGUYEN PHAN ANH HUY
STUDENT : LE THI QUYNH GIANG

SKL010195

Ho Chi Minh City, May 2023


HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION
FACULTY FOR HIGH QUALITY TRAINING

THESIS

EVALUATING AND SELECTING GREEN SUPPLIERS
BY INTEGRATION OF NEUTROSOPHIC AHPTOPSIS APPROACH: A CASE OF THE FASTFASHION INDUSTRY.

LE THI QUYNH GIANG
Student ID: 19124057
Major: INDUSTRIAL MANAGEMENT

Advisor: Dr. NGUYEN PHAN ANH HUY

Ho Chi Minh City, May 2023


COMMENTS OF ADVISOR
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------HCMC, DD/MM/YYYY
Advisor

i


COMMENTS OF REVIEWER
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------HCMC, DD/MM/YYYY
Advisor

ii


ACKNOWLEDGEMENTS
During the period of completing the thesis, I received a lot of support from the
lecturers. Through this, I would like to express my sincere thanks to the lecturers of Ho
Chi Minh University of Technology and Education for creating conditions as well as
teaching me useful knowledge and things. These pieces of knowledge are a solid
foundation for me to complete this thesis, as well as the knowledge to step into my future
career.
I would like to thank the group of experts who supported the author, facilitated, and
wholeheartedly helped the author to carry out the thesis smoothly.
Sincere thanks to Dr. Nguyen Phan Anh Huy for helping and guiding me in the
process of making and completing this thesis.

Finally, I would like to wish all of you who have supported me in the process of
doing the thesis to have more health and better development. We wish you much success
in your noble career and in life.
Best regards!
HCMC, 04/05/2023
Student name

Le Thi Quynh Giang

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LIST OF ABBREVIATION
Abbreviation

Explanation

AHP

The Analytic Hierarchy Process

IVN

Interval-valued Neutrosophic

NIS

Negative Ideal Solution

PIS

Positive Ideal Solution

TOPSIS

The Technique for Order of Preference by
Similarity to Ideal Solution

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LIST OF TABLES
Table 2. 1. Saaty’s pairwise comparison scale .................................................................. 11
Table 2. 2 The RI value corresponds to the number of factors n....................................... 12
Table 2. 3. Summary of related work in green supplier selection ..................................... 14
Table 3. 1 Criteria for evaluating and selecting green suppliers ....................................... 18
Table 3. 2 Alternatives determination ................................................................................ 20
Table 3. 3. IVN number scales of linguistic variables. ...................................................... 24
Table 3. 4. Scale for IVN values for alternative evaluation .............................................. 26
Table 4. 1. Group of experts participating in the interview ............................................... 28
Table 4. 2. Aggregated pairwise comparison matrix for main criteria .............................. 29
Table 4. 3. Pairwise comparison matrix normalization for main criteria .......................... 29
Table 4. 4. Importance weights for main criteria............................................................... 29
Table 4. 5. Aggregated pairwise comparison matrix for sub-criteria (C11-C13) .............. 30
Table 4. 6. Pairwise comparison matrix normalization for sub-criteria (C11-C13) .......... 30
Table 4. 7. Aggregated pairwise comparison matrix for sub-criteria (C21-C25) .............. 31
Table 4. 8. Pairwise comparison matrix normalization for sub-criteria (C21-C25) .......... 31
Table 4. 9. Aggregated pairwise comparison matrix for sub-criteria (C31-C33) .............. 32
Table 4. 10. Pairwise comparison matrix normalization for sub-criteria (C31-C33) ........ 32
Table 4. 11. Importance weight for sub-criteria ................................................................ 33

Table 4. 12. Global weight for each criteria. ..................................................................... 34
Table 4. 13. Aggregate the decision matrix for the criteria (C11 - C23) ........................... 35
Table 4. 14. Aggregate the decision matrix for the criteria (C24 - C33) ........................... 35
Table 4. 15. IVN normalized decision matrix for the criteria (C11 - C23) ....................... 36
Table 4. 16. IVN normalized decision matrix for the criteria (C24 - C33) ....................... 36

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Table 4. 17. IVN weight normalized decision matrix for the criteria (C11 - C23) ........... 37
Table 4. 18. IVN weight normalized decision matrix for the criteria (C24 - C33) ........... 37
Table 4. 19. IVN Positive ideal solution and IVN Negative ideal solution ...................... 38
Table 4. 20. Euclidean distance from IVN-PIS and IVN-NIS .......................................... 38
Table 4. 21. Ranking green supplier .................................................................................. 39
Table 4. 22. Hamming distance from IVN-PIS and IVN-NIS .......................................... 40
Table 4. 23. Risk ranking of the supplier according to sensitivity analysis. ..................... 41

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LIST OF FIGURES
Figure 2. 1. From crisp sets to neutrosophic sets ................................................................ 9
Figure 2. 2. Structure diagram of AHP .............................................................................. 10
Figure 2. 3. Flowchart of AHP method ............................................................................. 12
Figure 2. 4. Flowchart of TOPSIS method ........................................................................ 13
Figure 3. 1 Research process ............................................................................................. 16
Figure 3. 2. Hierarchical structure of application .............................................................. 21
Figure 4. 1. Separation measure and the revised closeness for each supplier. .................. 39
Figure 4. 2. Sensitivity analysis ......................................................................................... 41

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TABLE OF CONTENT
COMMENTS OF ADVISOR ............................................................................................... i
COMMENTS OF REVIEWER ........................................................................................... ii
ACKNOWLEDGEMENTS ............................................................................................... iii
LIST OF ABBREVIATION ............................................................................................... iv
LIST OF TABLES ............................................................................................................... v
LIST OF FIGURES ........................................................................................................... vii
TABLE OF CONTENT ....................................................................................................viii
CHAPTER 1: INTRODUCTION........................................................................................ 1
1.1 Reasons for choosing this topic. .................................................................................... 1
1.2 Research objectives ....................................................................................................... 3
1.3 Research object and scope ............................................................................................. 3
1.4 Research methodology .................................................................................................. 3
1.5 The structure of the subject ........................................................................................... 4
CHAPTER 2: LITERATURE REVIEW ............................................................................. 5
2.1 Green supply chain management................................................................................... 5
2.2 Green supplier selection ................................................................................................ 5
2.3 Fast-fashion industry ..................................................................................................... 6
2.4 Interval-Valued Neutrosophic Sets ................................................................................ 8
2.5 The Analytic Hierarchy Process (AHP) ...................................................................... 10
2.6 The Technique for Order of Preference by Similarity to Ideal Solution ..................... 13
2.7 Summary of related work ............................................................................................ 13
CHAPTER 3: METHODOLOGY ..................................................................................... 16
3.1 Research process ......................................................................................................... 16
3.2 The proposed method .................................................................................................. 17
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3.2.1 Criteria determination ........................................................................................... 17
3.3.2 Alternatives determination.................................................................................... 20
3.3.3 Hierarchical structure ........................................................................................... 20
3.3.4 Interval-valued neutrosophic AHP ....................................................................... 22
3.3.4.1 Preliminaries of interval-valued neutrosophic .............................................. 22
3.3.4.2 Steps in interval-valued neutrosophic AHP................................................... 24
3.3.4 Integration of Interval-valued neutrosophic AHP-TOPSIS.................................. 26
CHAPTER 4: RESULT ..................................................................................................... 28
4.1 Data description ........................................................................................................... 28
4.2 Data analytic result ...................................................................................................... 29
4.2.1 Interval-valued neutrosophic AHP ....................................................................... 29
4.2.2 Interval-valued neutrosophic TOPSIS .................................................................. 34
4.2.3 Sensitivity analysis ............................................................................................... 40
CHAPTER 5: CONCLUSION .......................................................................................... 43
5.1 Conclusion ................................................................................................................... 43
5.2 Implication ................................................................................................................... 44
REFERENCES .................................................................................................................. 45
APPENDIX 1 .................................................................................................................... 52
APPENDIX 2 .................................................................................................................... 54

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CHAPTER 1: INTRODUCTION
1.1 Reasons for choosing this topic.
The fourth industrial revolution is about to begin throughout the world. Besides the
outstanding development of science and technology, environmental problems are the issues
that need to be given top priority. One of the business models criticized for its negative
impact on the environment and society is fast fashion. According to the United Nation

(2019), Fast fashion is the second most polluting business behind the oil industry. It
contributes 10% to world emissions, generating more carbon dioxide annually - 1.2 billion
tons - than the shipping and aviation sectors put together. The industry's greenhouse gas
emissions are anticipated to rise by more than 50% by 2030 if this rate is maintained. These
emissions are produced along the whole supply chain of the fast fashion sector, from the
origin to the origin of the raw materials, manufacture, and processing, to shipping and in
transit (Fleischmann, 2019).
Faced with concerns about the environmental situation and the increase in the amount
of fashion waste. Consumers are more and more interested in the green living movement
and begin to worry about the origin of products. Therefore, the supply chain of garment
enterprises in general as well as fast fashion businesses in particular must pay attention to
the "green" issue in the supply chain in their operations besides stopping at maximum cost
saving, high profit. An important aspect of this is the assessment and selection of green
suppliers, which can help companies reduce their environmental impact and improve their
sustainable performance.
Lo et al. (2018) said about green purchasing is a crucial component of the growth of
sustainable businesses, and it frequently has an impact on the operations and environmental
protection strategies of an organization. According to Kokangul and Susuz (2009) and Lee
and Drake (2010), the percentage of high-tech firms that purchase raw materials and
components can reach 80%, making buying techniques essential to the management of a
green supply chain. Choosing the suitable company as a product or case supplier requires
consideration of many complex factors and is therefore considered a multi-criteria
decision-making problem (Kumar, Rahman, & Chan, 2017).

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Supplier selection has a direct impact on an organization's profitability and competitive
position. Therefore, this problem has been implemented by many researchers to build
models to evaluate green suppliers in related industries such as textile industry, garment

industry in the most effective way.
Utama (2021) conducts a case study of a garment company in Indonesia. Research
results shows that the quality criterion gives the largest weight. The results of this study
show that the integration of AHP and VIKOR can be used to solve green supplier selection
problems. Wang et al. (2019) demonstrates how to choose the best sustainable suppliers in
the garment industry using a hybrid approach combining FAHP and TOPSIS. While, Roy
et al. (2020) suggests a framework to evaluate sustainable supplier selection techniques
utilizing the preference ranking organization method for enrichment evaluation
(PROMETHEE) and the fuzzy analytical hierarchy process (FAHP). Karami et al. (2021)
applying three approaches to evaluate the supplier in garment industry by integrated DEA–
PCA–VIKOR. In another research paper, Jia (2015) selected the optimal supplier to supply
sustainable materials in fashion clothing production by using Fuzzy TOPSIS method to
evaluate 12 criteria of sustainable suppliers. Based on seven key factors—operational
competence, product attribute, logistic warehousing, ethics, status, business competencies,
and versatility – Kaushik et al. (2022) selects suppliers for the online fashion retail industry.
Prioritizing the criteria and choosing the most practical suppliers from a pool of potential
suppliers required the application of an integrated Best Worst Method (BWM) VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approach.
In the context of the rapid development of the garment industry, which must ensure
environmental issues, choosing a suitable green supplier for businesses is extremely
important. However, there are currently few research models on green supplier assessment
in the fast-fashion industry. As the best as my knowledge, there is lack of research on
evaluating and selecting green suppliers by using Interval-valued neutrosophic AHPTOPSIS in fast fashion industry.
For the above reasons, the author has conducted a research paper: "Evaluating and
selecting green suppliers by integration of neutrosophic AHP - TOPSIS approach: a case
of the fast-fashion industry." to conduct research on the criteria of the green suppliers in
fast-fashion industry. In addition to the general criteria for supplier evaluation, this study
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assesses the priorities of “green” issues, which can play an important role in sourcing and

attempts to propose important environment variables that can be used in supplier selection.
This paper aims to contribute to the theoretical development of green supplier selection
and to develop a systematic and effective framework for the selection of green suppliers in
the fast fashion industry.
1.2 Research objectives
General objective: Evaluating and selecting green suppliers in the fast-fashion industry
Detail objective:
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Identifying importance criteria and sub-criteria in the fast-fashion industry when
choosing green suppliers.

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Evaluating criteria and sub criteria of green suppliers in the fast-fashion industry
base on AHP – TOPSIS method and using the interval-valued neutrosophic number

-

Raking green suppliers through the evaluation of criteria

1.3 Research object and scope
Research object: The object of the study is a green supplier in the fast-fashion industry
Research scope: Fast-fashion industry
1.4 Research methodology
Data collection methods
In order to gather evaluation data on the relationships between the criteria, this study
used the expert consultation method of data collecting. The experts taking part in the
interview process are individuals with extensive knowledge and experience in assessing
and choosing suppliers, as well as those with expertise in the area of procuring raw

materials for garment factories.
Interval-valued neutrosophic integration model AHP-TOPSIS
In this study, the Analytic Hierarchy Process method combines TOPSIS and
interval-valued neutrosophic set to evaluate and select green suppliers. Input data is
through data collection method
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1.5 The structure of the subject
The thesis consists of 5 chapters as follows:
Chapter 1: Introduction
Presentation of reasons for choosing topic, research objective, object and scope of
research, methodology and structure of topic.
Chapter 2: Literature Review
Outlining concepts and theories about green supplier, fast-fashion industry, basic
theory of AHP-TOPSIS method and interval-valued neutrosophic number.
Chapter 3: Methodology
As well as listing previous studies from which to point out the criteria and build a
research model for the article. Describing in detail the research methods. In particular, the
method is implemented based on the combination of AHP and TOPSIS.
Chapter 4: Result
Analyzing and interpreting data based on interviews and using the methods outlined,
including interval-valued neutrosophic AHP and TOPSIS results.
Chapter 5: Conclusion
From the results of chapter 4 on the ranking of options and make a green supplier
recommendation for the fast-fashion industry.
Summary of Chapter 1:
Conducting research to find out how to solve the challenges of choosing green
suppliers to minimize the impact on the environment. In this study, the AHP-TOPSIS
method combined with the interval-valued neutrosophic number was used to evaluate and

select green suppliers for the fast fashion industry through interviews with experts with
specialized knowledge. The 5-chapter study will evaluate and select green suppliers based
on the criteria that have been synthesized.

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CHAPTER 2: LITERATURE REVIEW
2.1 Green supply chain management
According to Srivastava (2007), green supply chain management is a combination of
supply chain management and environmental management. The goal of a green supply
chain is to assist businesses in maintaining a balance between their environmental and
economic performance, decrease the environmental effect of their goods and services, and
promote a green image (Sarkis, 2001)
According to Sarkar et al (2017), the aim of green supply chain management is to
integrate logistical and financial data thoroughly, in order to promote the competitiveness
of supply chain units' products or services, which leads to sustainable firm development
and enhanced environmental protection. Many people in the industrial sector have changed
the way they produce things and encouraged their companies to become more
environmentally conscious. Some companies have also created environmental guidelines
for the handling of waste goods and take into account the recyclable nature of the raw
materials they buy from suppliers (Chen, Shyur, Shih, & Wu, 2012).
Taking everything into account, a green supply chain can be defined as an efficient
supply chain that still ensures environmental friendliness and efficient use of
environmentally friendly inputs and turns products by-products of use into something that
can be improved or recycled in the current environment. This process makes it possible for
the outputs and by-products to be reused at the end of their life cycle, thus creating a
sustainable supply chain.
2.2 Green supplier selection
Sustainability is an expanded overview of strategies for governing the environment

from multiple perspectives but at the level of enterprises. The essential component of
sustainability in the overall scheme can be considered to be green supplier selection.
(Yazdani, M., Chatterjee, P., Zavadskas, E. K., & Zolfani, S. H., 2017).
The assessment and selection of green suppliers is applied in many different industries
and approaches. In high-tech industry, Lee et al., (2009) applied AHP method in
conjunction with a fuzzy set of numbers to distinguish between the standards for evaluating
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ordinary suppliers and green suppliers. Freeman & Chen (2015) developed the AHPEntropy-TOPSIS framework for green supplier selection at a Chinese-based electronic
machinery manufacturer. Çalık (2021) practical instance using the AHP and TOPSIS
methodologies in the Pythagorean fuzzy environment to choose a green supplier for an
agricultural machinery and tool firm. To better deal with ambiguity and vagueness while
addressing the supplier selection problem while taking into account green concepts in a
home appliance manufacturer, Ecer (2022) uses an extension to the analytical hierarchy
process (AHP) under interval type-2 fuzzy environment (IT2FAHP) model. In the
automobile manufacturing industry, Yu & Hou (2016) applied modified multiplicative
analytic hierarchy process (MMAHP) method to solved green supplier selection issues.
Sharma & Rawani (2016) applied AHP approach to discuss the green supplier selection in
Indian cement industry. The manufacturing industry, it is possible to use a method such as
the integrated AHP and TOPSIS method (Sukmawati & Setiawan, 2022), Fuzzy Axiomatic
Design (FAD) approach (Kannan, D., Govindan, K., & Rajendran, S., 2015). In the agrifood industry to solving the evaluating green supplier, Banaein et al. (2018) using fuzzy
set integration into TOPSIS, VIKOR, and GRA method, Tirkolaee at al. (2021) applying
AHP and Fuzzy TOPSIS method, Puška & Stojanović (2022) evaluated criteria by using
fuzzy SWARA and ranked alternative by using fuzzy MABAC, MARCOS, and CRADIS
techniques.
2.3 Fast-fashion industry
Fast fashion is defined as the conversion of trendy design into items that are
available to the general public (Sull & Turconi, 2008). In order to improve the frequency
with which customers buy trendy fashions, this business seeks to get customers into stores

as frequently as possible (Barnes & Lea‐Greenwood, 2006). This is accomplished by using
inexpensive clothing that sells for less money and is available more quickly than
conventional attire (Bhardwaj & Fairhurst, 2010). As a result, market cycles are shortened,
there are more seasons, and organizations need to be able to acquire certain things with
short lead times, including extremely flexible production and design skills that can
combine trendy clothing raw materials and suppliers (Barnes & Lea‐Greenwood, 2006).

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Numerous earlier studies have critically examined the effects of the textile and fastfashion industries on the environment and social welfare in the literature (Choi, Chiu, &
Govindan, 2014). According to Diabat et al (2014), In order to improve environmental
performance, industries, particularly those in the textile industries, are under pressure to
adopt sustainable practices in their operations. Observe that fast fashion is a trend in the
market and that an increasing number of fast fashion businesses are more responsive to
customer needs. Fulton & Lee (2010) emphasize in the document that the industry "Fast
fashion" has an impact on both the fashion business and customers. By studying their
company reports and analyzing the present sustainability condition of fast fashion firms,
Turker & Altuntas (2014) found that these companies also established sustainability
standards for their suppliers. Shen (2014) studied the H&M instance to get knowledge
about sustainability initiatives in modern business practices. According to the findings
mentioned above, fast fashion and sustainability are highly associated, and this fact affects
the industry's selection of suppliers.
The selection and evaluation of green suppliers in the industry Fast-fashion is a
multi-criteria decision-making problem with many different approaches and methods. Here
are some research articles on the selection and evaluation of green suppliers in textile and
garment industries:
According to Utama et al. (2021), the authors used the AHP method to evaluate the
weight of 8 main criteria which are 15 sub-criteria of green suppliers along with which
integrates the MOORA method to rank the best green suppliers in the Indonesia textile

industry. Celik et al. (2021) also used the combined Best worst method and TODIM
approaches, which were combined under an improved fuzzy notion of interval type-2 fuzzy
sets, to evaluate green suppliers in the Turkish textile industry. Fuzzy analysis hierarchical
process and level analysis method are used to evaluate and select green suppliers based on
three main criteria and six sub criteria in textile industry (Zafar, A., Zafar, M., Sarwar, A.,
Raza, H., & Khan, M. T. , 2019). ÖZBEK & Yildiz (2020) employed the interval type-2
fuzzy TOPSIS approach with the goal of choosing the best provider among the suppliers
of a parent company working in the garment industry who are digitalized utilizing industry
4.0 technology.

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2.4 Interval-Valued Neutrosophic Sets
A green supplier selection process is becoming more and more important due to the
subjectivity, ambiguity, and uncertainty (Sun, Y., & Cai, Y., 2021) In recent years,
researchers' interest in how to proceed with the best supplier selection judgements has
grown. Fuzzy uncertainty numbers are one of the well-known and often employed
approaches to solving the issue in multi-criteria decision-making problem (Mallik,
Mohanty, & Mishra, 2022).
Zadeh (1965) proposed the fuzzy set and suggested fuzzy numbers were useful for
gauging subjectivity in people. The truth values of variables in classical logic are either 0
or 1. The result of a statement is either true (1) or false (0). Contrarily, fuzzy logic captures
the degree to which something is true. Fuzzy Set has been used to provide some context,
but it is unable to account for the information's level of bias and indeterminacy. Atanassov
(1986) created the intuitionistic fuzzy set (IFS) to close this gap, where in addition to the
degree of membership, a degree non-member is also used to solve issues intuitively. The
truth-membership function and the false-membership function represent an intuitionistic
fuzzy set. Later, interval-valued intuitionistic fuzzy sets were added to intuitionistic fuzzy
sets, according to Atanassov (1989). However, intuitionistic fuzzy sets and interval-valued

intuitionistic fuzzy sets are only able to handle incomplete data, not the ambiguous and
inconsistent data that are frequently present in belief systems. Smarandache (1998)
developed neutrosophic set which is a more sophisticated variation of this strategy to
manage uncertainty more effectively.
Neutrosophic logic provides a new parameter called "uncertainty" that depicts
ambiguity better by carrying more information than fuzzy logic (Kahraman, Oztaysi, &
Cevik Onar, 2020). Fuzzy logic assigns uncertainty to membership variables between 0
and 1. Wang et al. (2011) used the technical definition of neutrosophic sets to describe the
idea of a single-valued neutrosophic set. Interval valued neutrosophic set (IVNS) were
proposed by Wang et al. (2005). We refer to it “interval" since we only take into account
the subunitary interval [0, 1] and it is a subclass of the neutrosophic set.

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Neutrosophic set
Interval
neutrosophic set
Interval valued
Intuitionstic fuzzzy set

Intuitionstic
fuzzy set
Fuzzy set
Classical
set

Figure 2. 1. From crisp sets to neutrosophic sets
Source: (Mallik, Mohanty, & Mishra, 2022)
There are many green supplier selection studies using different variations of the

interval-valued neutrosophic in the literature. Abdel et al (2019) using interval
neutrosophic set combine with ANP to weighting the 3 main criteria, 12 sub-criteria and
TOPSIS method to ranking green supplier in An Egyptian dairy and foodstuff corporation.
Van et al (2018) applied Interval Neutrosophic Set QFD approach was used to the case of
Transportation Parts Company Limited in northern Vietnam for the assessment and
selection of green suppliers. An SS framework based on several criteria and the intervalvalued fuzzy neutrosophic model was provided by Yazdani et al. (2021) and evaluated in
the supplier assessment of an Iranian dairy industry. Two effective decision-making
techniques, ANP and TODIM, have been combined by Yalcin et al (2020) in an intervalneutosophic environment. In a case study of a business that manufactures filters, the IVNANP was used to balance the factors for assessing sustainable suppliers. There are many
studies in the implementation of green supplier evaluation and selection, however, there is
no study using interval-neutrosophic to perform green supplier assessment in the fastfashion industry.

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