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

INTRODUCTION

Reasons for choosing this topic

The fourth industrial revolution is on the horizon globally, marked by remarkable advancements in science and technology However, addressing environmental challenges must take precedence Fast fashion, a business model often criticized for its detrimental effects on both the environment and society, exemplifies the urgent need for sustainable practices According to the United Nations, this industry poses significant risks that require immediate attention.

Fast fashion is the second most polluting industry globally, contributing 10% to worldwide emissions and generating 1.2 billion tons of carbon dioxide annually, surpassing the combined emissions of the shipping and aviation sectors If current trends continue, the industry's greenhouse gas emissions are projected to increase by over 50% by 2030 These emissions arise throughout the entire fast fashion supply chain, from raw material sourcing and manufacturing to shipping and transit.

As environmental concerns and fashion waste rise, consumers are increasingly drawn to the green living movement, prompting them to consider the origins of their products Consequently, garment enterprises, especially fast fashion brands, must prioritize sustainability in their supply chains, moving beyond mere cost-cutting and profit maximization A key strategy in this shift is the assessment and selection of green suppliers, which can significantly reduce environmental impact and enhance sustainable performance.

Green purchasing is vital for the sustainable growth of businesses, significantly influencing their operational practices and environmental strategies (Lo et al., 2018) In the high-tech sector, up to 80% of firms engage in the procurement of raw materials and components, highlighting the importance of effective purchasing strategies in managing a green supply chain (Kokangul & Susuz, 2009; Lee & Drake, 2010) Selecting the right supplier involves navigating a complex array of factors, making it a multi-criteria decision-making challenge (Kumar, Rahman, & Chan, 2017).

Effective supplier selection significantly influences an organization's profitability and competitive edge Consequently, numerous researchers have developed models to assess green suppliers, particularly in industries like textiles and garments, to enhance evaluation processes.

Utama (2021) highlights a case study on a garment company in Indonesia, revealing that quality is the most significant criterion in green supplier selection, facilitated by the integration of AHP and VIKOR methodologies Wang et al (2019) explore a hybrid approach combining FAHP and TOPSIS to identify the best sustainable suppliers in the garment sector Similarly, Roy et al (2020) propose a framework utilizing PROMETHEE and FAHP for evaluating sustainable supplier selection techniques Karami et al (2021) employ an integrated DEA-PCA-VIKOR approach to assess suppliers in the garment industry Jia (2015) utilizes the Fuzzy TOPSIS method to determine the optimal supplier for sustainable materials in fashion clothing production, focusing on twelve sustainability criteria Kaushik et al (2022) prioritize supplier selection in the online fashion retail industry based on seven key factors, applying an integrated Best Worst Method (BWM) and VIKOR approach to effectively choose the most suitable suppliers.

In the rapidly evolving garment industry, prioritizing environmental sustainability is crucial, making the selection of appropriate green suppliers essential for businesses Despite this need, there is a significant gap in research models focused on green supplier assessment within the fast-fashion sector Notably, there is a lack of studies utilizing Interval-valued neutrosophic AHP-TOPSIS methods for evaluating and selecting green suppliers in the fast fashion industry.

The author has conducted a research paper titled "Evaluating and Selecting Green Suppliers by Integration of Neutrosophic AHP - TOPSIS Approach: A Case of the Fast-Fashion Industry," focusing on the criteria for assessing green suppliers within the fast-fashion sector This study not only addresses the general criteria for supplier evaluation but also emphasizes the importance of sustainable practices in supplier selection.

3 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 study seeks to enhance the theoretical framework of green supplier selection and establish a systematic, effective approach for identifying green suppliers within the fast fashion sector.

Research objectives

General objective: Evaluating and selecting green suppliers in the fast-fashion industry Detail objective:

- Identifying importance criteria and sub-criteria in the fast-fashion industry when choosing green suppliers

- 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

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

Research methodology

This study employed the expert consultation method to collect evaluation data on the relationships between various criteria The participants in the interviews were seasoned professionals with significant expertise in supplier assessment and selection, as well as specialists in procuring raw materials for garment manufacturing.

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

The structure of the subject

The thesis consists of 5 chapters as follows:

Presentation of reasons for choosing topic, research objective, object and scope of research, methodology and structure of topic

Outlining concepts and theories about green supplier, fast-fashion industry, basic theory of AHP-TOPSIS method and interval-valued neutrosophic number

This article reviews previous studies to establish criteria and develop a research model It provides a detailed description of the research methods utilized, specifically focusing on the integration of the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

Analyzing and interpreting data based on interviews and using the methods outlined, including interval-valued neutrosophic AHP and TOPSIS results

From the results of chapter 4 on the ranking of options and make a green supplier recommendation for the fast-fashion industry

This study addresses the challenges of selecting green suppliers in the fast fashion industry to reduce environmental impact Utilizing the AHP-TOPSIS method along with interval-valued neutrosophic numbers, the research evaluates and identifies suitable green suppliers through expert interviews The comprehensive five-chapter analysis focuses on synthesized criteria for effective supplier selection.

LITERATURE REVIEW

Green supply chain management

Green supply chain management integrates supply chain and environmental management, aiming to help businesses balance their economic and environmental performance Its primary objectives are to reduce the environmental impact of products and services while fostering a sustainable image for the company.

Green supply chain management aims to integrate logistical and financial data to enhance the competitiveness of supply chain products and services, ultimately fostering sustainable development and improved environmental protection (Sarkar et al., 2017) The industrial sector has seen a shift towards more environmentally conscious production methods, with many companies adopting environmental guidelines for waste management and considering the recyclability of raw materials sourced from suppliers (Chen et al., 2012).

A green supply chain is an efficient system that prioritizes environmental sustainability by utilizing eco-friendly inputs and transforming products and their by-products into recyclable materials This approach enables the reuse of outputs and by-products at the end of their life cycle, ultimately fostering a sustainable supply chain.

Green supplier selection

Sustainability encompasses various strategies for environmental governance within enterprises, with green supplier selection being a crucial aspect This approach highlights the importance of choosing eco-friendly suppliers to enhance overall sustainability efforts in business operations.

The assessment and selection of green suppliers varies across industries, with the high-tech sector employing diverse methodologies Lee et al (2009) utilized the Analytic Hierarchy Process (AHP) alongside fuzzy numbers to establish criteria for supplier evaluation.

The selection of green suppliers has been enhanced through various methodologies across different industries Freeman & Chen (2015) introduced the AHP-Entropy-TOPSIS framework for a Chinese electronic machinery manufacturer, while Çalık (2021) utilized AHP and TOPSIS within a Pythagorean fuzzy environment for an agricultural machinery firm Ecer (2022) advanced the supplier selection process for a home appliance manufacturer by employing an interval type-2 fuzzy AHP model to address ambiguity In the automobile sector, Yu & Hou (2016) implemented a modified multiplicative analytic hierarchy process to tackle green supplier selection challenges Similarly, Sharma & Rawani (2016) applied AHP in the Indian cement industry Other methods include the integrated AHP and TOPSIS approach (Sukmawati & Setiawan, 2022) and Fuzzy Axiomatic Design (Kannan et al., 2015) In the agri-food sector, Banaein et al (2018) integrated fuzzy sets into TOPSIS, VIKOR, and GRA methods, while Tirkolaee et al (2021) used AHP and Fuzzy TOPSIS Additionally, Puška & Stojanović (2022) evaluated criteria with fuzzy SWARA and ranked alternatives using fuzzy MABAC, MARCOS, and CRADIS techniques.

Fast-fashion industry

Fast fashion refers to the rapid transformation of trendy designs into affordable clothing accessible to the public This industry aims to increase consumer purchasing frequency by enticing customers to visit stores more often By offering inexpensive apparel that is produced and sold faster than traditional clothing, fast fashion shortens market cycles and introduces more seasonal trends Consequently, companies must develop flexible production and design capabilities to quickly source materials and collaborate with suppliers to meet changing consumer demands.

Numerous studies have critically examined the environmental and social impacts of the textile and fast fashion industries (Choi, Chiu, & Govindan, 2014) To enhance environmental performance, these industries face pressure to adopt sustainable practices (Diabat et al., 2014) Fast fashion, characterized by its responsiveness to consumer needs, significantly influences both the fashion industry and its customers (Fulton & Lee, 2010) Research by Turker & Altuntas (2014) indicates that fast fashion companies are establishing sustainability standards for their suppliers Additionally, Shen (2014) explored H&M's sustainability initiatives, revealing a strong connection between fast fashion and sustainability, which ultimately affects supplier selection within the industry.

Selecting and evaluating green suppliers in the fast-fashion industry involves a complex multi-criteria decision-making process that utilizes various approaches and methodologies Numerous research articles have explored this topic, focusing specifically on the textile and garment sectors to enhance sustainable practices.

Recent studies have highlighted various methodologies for evaluating green suppliers in the textile industry Utama et al (2021) applied the AHP method to assess eight primary criteria and fifteen sub-criteria for green suppliers, integrating the MOORA method to rank top suppliers in Indonesia's textile sector Similarly, Celik et al (2021) utilized a combination of the Best Worst Method and TODIM approaches, enhanced by an interval type-2 fuzzy set framework, to evaluate green suppliers in Turkey Zafar et al (2019) employed fuzzy analysis hierarchical processes alongside level analysis methods to select suppliers based on three main criteria and six sub-criteria Additionally, ZBEK & Yildiz (2020) adopted the interval type-2 fuzzy TOPSIS approach to identify the best supplier among digitally advanced garment industry providers utilizing Industry 4.0 technologies.

Interval-Valued Neutrosophic Sets

The green supplier selection process is increasingly vital due to inherent subjectivity and uncertainty (Sun & Cai, 2021) Recent research has focused on optimizing supplier selection judgments, highlighting the use of fuzzy uncertainty numbers as a prominent method for addressing multi-criteria decision-making challenges (Mallik, Mohanty, & Mishra, 2022).

In 1965, Zadeh introduced the concept of fuzzy sets, highlighting their utility in measuring human subjectivity Unlike classical logic, which defines truth values strictly as 0 or 1, fuzzy logic allows for varying degrees of truth While fuzzy sets offer valuable context, they fall short in addressing the biases and uncertainties inherent in information, as noted by Atanassov.

In 1986, the intuitionistic fuzzy set (IFS) was introduced to address gaps in traditional fuzzy logic by incorporating both a degree of membership and a degree of non-membership, enhancing intuitive problem-solving An intuitionistic fuzzy set is characterized by its truth-membership and false-membership functions In 1989, Atanassov expanded on this concept by introducing interval-valued intuitionistic fuzzy sets However, both intuitionistic fuzzy sets and their interval-valued counterparts are limited to handling incomplete data, falling short in addressing the ambiguous and inconsistent data often found in belief systems To more effectively manage uncertainty, Smarandache developed the neutrosophic set in 1998, offering a more advanced approach to uncertainty management.

Neutrosophic logic introduces a novel parameter known as "uncertainty," which effectively represents ambiguity by conveying more information compared to fuzzy logic (Kahraman, Oztaysi, & Cevik Onar, 2020) While fuzzy logic limits uncertainty to membership values ranging from 0 to 1, Wang et al (2011) utilized the technical framework of neutrosophic sets to define single-valued neutrosophic sets Additionally, Wang et al (2005) proposed the concept of interval-valued neutrosophic sets (IVNS), which focus on the subunitary interval [0, 1], classifying it as a subclass of neutrosophic sets.

Figure 2 1 From crisp sets to neutrosophic sets

Numerous studies have explored green supplier selection using various applications of interval-valued neutrosophic methods For instance, Abdel et al (2019) utilized an interval neutrosophic set combined with the Analytic Network Process (ANP) to weight three main criteria and twelve sub-criteria, employing the TOPSIS method for ranking green suppliers in an Egyptian dairy and foodstuff corporation Similarly, Van et al (2018) implemented an Interval Neutrosophic Set Quality Function Deployment (QFD) approach to assess and select green suppliers for a transportation parts company in northern Vietnam Yazdani et al (2021) proposed a supplier assessment framework based on multiple criteria and an interval-valued fuzzy neutrosophic model for the Iranian dairy industry Additionally, Yalcin et al (2020) combined two decision-making techniques, ANP and TODIM, within an interval-neutrosophic context for evaluating sustainable suppliers in a filter manufacturing case study Despite these advancements in green supplier evaluation, there remains a gap in research applying interval-neutrosophic methods specifically to the fast-fashion industry.

Interval valued Intuitionstic fuzzzy set

Intuitionstic fuzzy set Fuzzy setClassical set

The Analytic Hierarchy Process (AHP)

The Analytic Hierarchy Process (AHP) is a measurement theory based on pairwise comparisons and expert opinions, developed by Myers and Alpert in 1968 and later expanded by Thomas L Saaty in the 1970s Today, AHP is widely used in multi-criteria decision-making across various fields Saaty designed this technique to facilitate complex decision-making and systematically determine priorities.

Analytical Hierarchy Process (AHP) is performed according to the following steps:

Step 1: Define the issue and decide what knowledge is needed

Step 2: Build the decision hierarchy from the top down, starting with the objective, then the intermediate levels (criteria on which the succeeding parts depend), and finally the lowest level (which is often a collection of alternatives)

Figure 2 2 Structure diagram of AHP

Step 3: Create a set of matrices for pairwise comparisons by scale of absolute numbers of

Saaty (1980) Every element at a higher level is compared to every element in the level directly beneath it

Table 2 1 Saaty’s pairwise comparison scale

2,4,6,8 Intermediate valued between two adjacent judgments

Reciprocals When activities i compared to j is assigned one of the above numers, then activities j compared to i assigned its reciprocal

Step 4: After completing the aforementioned matrix, determine the weights for the criterion by adding up each column's values, then dividing each result by the sum

The weighted numbers shown here, however, should not be taken as the last word; rather, it is important check the consistency of the experts' opinions

Step 5: The value of the correlation matrix coefficient completely depends on the subjectivity of the researcher in quantifying the weights for the goals, hence it is impossible to prevent certain discrepancies in the final judgment matrix

The Analytic Hierarchy Process (AHP) calculates a consistency ratio (CR) to evaluate the consistency index of a judgment matrix against that of a random matrix (RI), ensuring reliable consistency testing (Saaty T., 1980)

Check consistency using the equation:

𝐶𝑅 = 𝐶𝐼𝑅𝐼Where CI is consistency index, RI is Random Consistency Index, CR is consistency rate

The equation of consistency index (CI) is given as:

Table 2 2 The RI value corresponds to the number of factors n n 1 2 3 4 5 6 7 8 9 10

The judgment matrix is acceptable if 𝐶𝑅 ≤ 0.1; otherwise, the pairwise comparison matrix must be re-evaluated if 𝐶𝑅 > 0.1

Below is a flowchart of the steps of the AHP method

Figure 2 3 Flowchart of AHP method

Final weight of each evaluation index

The Technique for Order of Preference by Similarity to Ideal Solution

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), developed by Hwang and Yoon in 1981, is a multi-criteria decision-making method that identifies the best alternative by evaluating its proximity to defined ideal solutions In this approach, the positive ideal solution represents the best possible outcome, while the negative ideal solution signifies the least desirable option The optimal choice is determined by being closest to the positive ideal solution and furthest from the negative ideal solution.

The TOPSIS method consist of these steps:

Figure 2 4 Flowchart of TOPSIS method

Summary of related work

After a brief overview of previous research papers related to the topic "Evaluating and selecting green suppliers by integration of neutrosophic AHP - TOPSIS approach: a

Decision makers construct the evaluation matrix

Determined the Weight normalized matrix

Determined the Distance of Negative ideal solution to Weight normalized matrix

Calculate the relative closeness to the ideal solution

Ranking the alternatives and chosen the best

14 case of the fast-fashion industry" Below is a summary of related studies regarding green supplier selection

Table 2 3 Summary of related work in green supplier selection

Lee et al., (2009) Fuzzy AHP High-tech industry

Freeman & Chen (2015) AHP-Entropy-TOPSIS Chinese electronic machinery manufacturer Çalık (2021) Pythagorean fuzzy AHP and TOPSIS

Agricultural machinery and tool company

Ecer (2022) IT2F-AHP Home appliance manufacturer

Yu & Hou (2016) MM-AHP Automobile manufacturing industry Sharma & Rawani (2016) AHP Indian cement industry

AHP and TOPSIS Manufacturing industry

Kannan et al (2015) FAD Manufacturing industry

Banaein et al (2018) Fuzzy TOPSIS, VIKOR, and GRA

Tirkolaee at al (2021) AHP and Fuzzy TOPSIS Agri-food industry

Fuzzy SWARA and fuzzy MABAC, MARCOS, and CRADIS

Utama et al (2021) AHP and MOORA Indonesia textile industry

Celik et al (2021) Type-2 fuzzy BWM and

Zafar (2019) Fuzzy AHP Textile industry ệZBEK & Yildiz (2020) Interval type-2 fuzzy

Abdel et al (2019) Interval neurtrosophic

An Egyptian dairy and foodstuff company

Van et al (2018) Interval Neutrosophic Set

Transportation Parts Company Limited in northern Vietnam

Yazdani et al (2021) Interval-valued fuzzy neutrosophic

Yalcin et al (2020) IVN-ANP and TODIM Business and manufactures filters

In today's dynamic business landscape, effectively addressing environmental challenges can create new opportunities for organizations to achieve sustainable development goals and enhance their competitive edge Despite this potential, there is a notable gap in research regarding the evaluation of green suppliers within the fast-fashion sector, particularly through the lens of interval-valued neutrosophic AHP-TOPSIS To bridge this gap, the author is developing a decision-making model that utilizes interval-valued numbers to tackle the green supplier selection issue.

This section provides an overview of existing research on green suppliers across various contexts, specifically focusing on the fast fashion industry Given the limited studies addressing environmental concerns within fast fashion, the authors emphasize the importance of understanding and implementing sustainable practices in this sector.

METHODOLOGY

Research process

Research method Selecting the suppliers of available alternative

Selected criteria approved by expert

Calculate the weight decision matrix

Determine positive and negative ideal solution

Determine distances from ideal solution

The proposed method

Evaluating green suppliers in the fast fashion industry poses significant challenges, necessitating a thorough analysis of previous research on sustainable practices within the garment sector Collaborating with industry experts, the author has distilled key criteria for assessing suitable expenditures The evaluation framework comprises three primary criteria and eleven sub-criteria, as detailed in Table 3.1, aimed at guiding the selection of environmentally responsible suppliers in the fast fashion market.

When selecting a green supplier, environmental factors are crucial, as highlighted by Yu et al (2019) Key issues include managing the depletion of renewable and non-renewable resources and addressing pollution This study identifies three sub-criteria within this framework: pollution control (C12), the implementation of an environmental management system (C11), and the provision of green products (C13).

Economic factors play a crucial role in decision-making, focusing on maximizing income while minimizing capital investment (Gửren, 2018) This criterion encompasses five key sub-criteria: Quality (C21), Delivery (C22), Service (C23), Cost (C24), and Strategic Alliance (C25).

Social factors significantly influence social problems, and according to Sun & Cai (2021), the social perspective is often neglected in discussions surrounding conventional and green alternatives This perspective encompasses three key sub-criteria: ethical issues and legal compliance (C31), vocational health and safety systems (C32), and information disclosure (C33) Recognizing these elements is crucial for a comprehensive analysis of social issues.

Table 3 1 Criteria for evaluating and selecting green suppliers

A set of systematic processes and practices that enable a supplier to reduce its environmental impacts, which includes the organizational structure, planning and implementing policy for environmental protection

Acar et al (2016), Heari et al (2019), Azadi et al (2015),

Controlling waste disposal properly, using harmful materials

Acar et al (2016) Heari et al (2019), Azadi et al (2015), Sun & Cai (2021)

Green packaging and product recyclability, remanufacturing which represents a large quantity of products restored to a new state, is reusing the capabilities of suppliers

Acar et al (2016), Heari et al (2019), Azadi et al (2015), Sun & Cai (2021)

Raw materials that meet company standards are not damaged

Efficient in delivering the goods at the right time

Acar et al (2016), Heari et al (2019),

Business must be more responsive to client requests and develop an effective stock management system to fulfill orders from customers under volatile market circumstances

Yu et al (2019), Hendiani et al

Lowest possible product price without sacrificing quality

The establishment of a mutually advantageous and vital connection between businesses and suppliers to enhance their competitiveness

Acar et al (2016), Hendiani et al

Ethical issue and legal compliance

Suppliers must adhere to ethical principles and law, reputation

Abdel et al (2019), Azadi et al (2015),

Vocational health and safety systems

Monitor the workers’ health and safety, control the underage employment in business operation

Giving its clients and stakeholders information on the materials utilized, carbon emissions, toxins generated during production, etc

The alternatives correspond to companies:

The four options represent notable material suppliers within the fast-fashion industry These well-recognized companies require experts with relevant experience to effectively assess their offerings and capabilities.

• Inditex：Inditex is the parent company of fast fashion brands such as Zara, Pull &

Bear, Massimo Dutti, Bershka, and Stradivarius

• Fast Retailing：Subsidiaries under Fast Retailing include Uniqlo, GU, Theory, PLST,

• H&M：H&M is committed to employing recycled textiles and 100% organic cotton, as well as investing in cutting-edge production techniques, in order to reduce its environmental impact

• Patagonia：Patagonia is an American-based outdoor fashion brand that has committed to using various recycled and synthetic materials to manufacture their clothing and accessories

The theoretical basis and previous studies related to green supplier and fast-fashion industry are the basis for the author to apply in his research model The author proposes a

21 hierarchical model with the goal of evaluating and selecting green suppliers with 3 main criteria and 11 sub-criteria:

Environment factor (C1): This main-criteria consist of 3 sub-criteria: Environmental management system (C11), Pollution control (C12), and green product (C13)

Economics factor (C2): This main-criteria consist of 5 sub-criteria: Quality (C21), Delivery (C22), Service (C23), Cost (C24), and Strategic alliance (C25)

Social factors (C3): Consist of 3 sub-criteria: ethical issue and legal compliance (C31), vocational health and safety systems (C32), and information revelation (C33)

Figure 3 2 Hierarchical structure of application

Evaluating and selecting green supplier

C11 Environmental management system C12 Pollution control C13 Green product

C21 Quality C22 Delivery C23 Service C24 Cost C25 Strategic alliance

C31 Ethical issue and legal compliance C32 Vocational health and safety systems C33 Information revelation

3.3.4.1 Preliminaries of interval-valued neutrosophic

This section offers a comprehensive introduction to neutrosophic sets and interval-valued neutrosophic numbers A neutrosophic set, denoted as 𝑄̃, is characterized by three key components: the truth-membership function (T), the indeterminacy-membership function (I), and the falsity-membership function (F), which collectively define its structure.

Definition 1: Smarandache (1998) let X be a universe A neutrosophic set 𝑄̃ in X is illustrated a truth-member function 𝑇 𝑞 (𝑥) , indeterminacy-membership function 𝐼 𝑞 (𝑥) , falsity membership function 𝐹 𝑞 (𝑥)

𝑇 𝑞 (𝑥) , 𝐼 𝑞 (𝑥) , 𝐹 𝑞 (𝑥) are the real standard elements of [0,1], a neutrosophic set 𝑄̃ can be provided in Eq 1:

𝑄̃ = {< 𝑥, ( 𝑇 𝑞 (𝑥), 𝐼 𝑞 (𝑥), 𝐹 𝑞 (𝑥)) > ∶ 𝑥 𝜖 𝑋, (𝑇 𝑞 (𝑥), 𝐼 𝑞 (𝑥), 𝐹 𝑞 (𝑥) 𝜖 ]0, 1 + [ )} (1) The sum of 𝑇 𝑞 (𝑥), 𝐼 𝑞 (𝑥), and 𝐹 𝑞 (𝑥) is unrestricted, so 0 − ≤ 𝑇 𝑞 (𝑥) + 𝐼 𝑞 (𝑥) + 𝐹 𝑞 (𝑥) ≤ 3 +

Definition 2: Liu et al (2016) let E be a space of point An interval-valued neutrosophic set 𝑀̃ in E is illustrated a truth-member function𝑇 𝑚 (𝑥) , indeterminacy-membership function 𝐼 𝑚 (𝑥), falsity membership function 𝐹 𝑚 (𝑥)

An interval-valued neutrosophic set 𝑀̃ can defined as:

Definition 3: Bolturk & Kahraman (2018) proposes the deneutrosophic of interval-valued neutrosophic, the equation is given as below:

Definition 4: Karasan & Kahraman (2018) and Zhang et al (2014) let 𝑀̃ = 1 [𝑇 𝑚 𝐿 1 , 𝑇 𝑚 𝑈 1 ], [𝐼 𝑚 𝐿 1 , 𝐼 𝑚 𝑈 1 ], [𝐹 𝑚 𝐿 1 , 𝐹 𝑚 𝑈 1 ] and 𝑀̃ = [𝑇 2 𝑚 𝐿 2 , 𝑇 𝑚 𝑈 2 ], [𝐼 𝑚 𝐿 2 , 𝐼 𝑚 𝑈 2 ], [𝐹 𝑚 𝐿 2 , 𝐹 𝑚 𝑈 2 ] be two interval-valued neutrosophic numbers Their mathematical operations are provided as Equation (7)-(14)

𝑀̃ = 𝑀 1 ̃ 2 if and only if 𝑀̃ ⊆ 𝑀 1 ̃ 2 and 𝑀̃ ⊆ 𝑀 2 ̃ 1 (11)

Definition 5: Ye & Du (2019) defined between two interval-valued neutrosophic numbers

𝑀̃ and 𝑀 1 ̃ 2 , there is a Euclidian distance

Definition 6: Ye & Du (2019) defined between two interval-valued neutrosophic numbers

𝑀̃ and 𝑀 1 ̃ 2 , there is a Hamming distance

3.3.4.2 Steps in interval-valued neutrosophic AHP

Step 1: Create a hierarchy of main criteria and sub-criteria for the problem The importance of the criteria and the level of evaluation of the suppliers is expressed by the linguistic variables Linguistic variables are represented as interval-valued neutrosophic

Table 3 3 IVN number scales of linguistic variables

Strongly More Importance 0.7 0.8 0.15 0.25 0.2 0.3 Very Strong Importance 0.75 0.85 0.1 0.2 0.15 0.25 Very Strongly More Importance 0.8 0.9 0.05 0.1 0.1 0.2

Step 2: Using the interval-valued neutrosophic number to contruct the pair-wise comparison matrix According to Bolturk & Kahraman (2018), to check the consistency of interval-valued neutrosophic set, using the deneutrosophic equation (6) to scrip interval- neutrosophic number It can be claimed that the neutrosophic pairwise matrix is also

25 consistent if the deneutrosophicated pairwise comparison matrix is Equation (17) show the interval-valued neutrosophic pair-wise comparison matrix

Step 3: To aggregated pair-wise comparison matrix, the author uses the equation proposed by

Zhang et al (2014) is the interval-valued neutrosophic weighted arithmetic averaging (IVNWAA), assume that 𝒷 𝑘 (𝑘 = 1, 2, , 𝑝) is the collection of interval-valued neutrosophic numbers and 𝒲 𝑘 (𝑘 = 1, 2, , 𝑝) is weight of decision maker, ∑ 𝑝 𝑘=1 𝒲 𝑘 = 1

Step 4: According to Bolturk & Kahraman (2018), to normalized the pairwise comparison matrix equation is given as follow:

Step 5: According to Bolturk & Kahraman (2018), to calculate the importance weight of interval-valued neutrosophic number by using the arithmetic mean of each row:

Step 6: For each sub-criteria, the aforementioned processes are repeated, and the sub- criteria's neutrosophic weights are obtained

Step 7: To calculate the global-weight, multiply the main criteria weights by the local- weight

3.3.4 Integration of Interval-valued neutrosophic AHP-TOPSIS

Step 1: The evaluation matrix is constructed by decision makers including m alternative and p criterion Each alternative's intersection with a criterion is represented by x

Table 3 4 Scale for IVN values for alternative evaluation

Step 2: To obtain the normalized evaluation matrix, use the equation is given as

Step 3: Structure the weighted matrix as follows by multiplying the weights of each criterion determined by interval-valued neutrosophic AHP (𝔚̃ 𝑠𝑘 ) by the normalized decision matrix (ℜ⃛ 𝑠𝑘 )

Step 4: The interval valued neutrosophic positive ideal solution (IVNPIS) 𝒮 + ̇ and the interval valued neutrosophic negative ideal solution (IVNNIS) 𝒮 − ̇

Step 5: Calculate distance from Positive ideal solution (PIS) and Negative ideal solution

(NIS) to weight normalized matrix by using the Euclidean distance equation

Step 6: Compute revised closeness for each alternative (ℰ 𝑠 ) by using Equation

Step 7: Ranking the alternatives based on the revised closeness

The author introduces a hierarchical model comprising three primary criteria—environmental factors (C1), economic factors (C2), and social factors (C3)—along with 11 sub-criteria to assess four green suppliers, as discussed by experts Additionally, this chapter outlines the foundational formulas and procedures for the Interval-valued neutrosophic AHP-TOPSIS method.

RESULT

Data description

The study involved interviews with six experts in supply chain management and the fast-fashion industry, focusing on the perspective of a company's raw material purchaser Utilizing a structured questionnaire, direct interviews were conducted to gather data for the research article The face-to-face interview method was chosen to facilitate clear communication and minimize errors during the interview process Following the interviews, the collected data will be analyzed using the interval-valued neutrosophic AHP-TOPSIS method.

Table 4 1 Group of experts participating in the interview

Expert Gender Work experience Job position

1 Female More than 6 years supply chain and production planning at garment company

Merchandizer in Phong Phu Corp

2 Female 5 years of experience in the position of raw material purchasing at garment company

Material planner in Phong Phu Corp

3 Female 3 years of experience in support buyers, work together with merchandiser to handle quality goods to suit the needs of buyers

Quality control in Phong Phu Corp

4 Male 4 years of experience in manages the sourcing, procurement of materials and resources for the production

Material planner in Asmara Ltd

5 Female 6 years of experience in ensure the quality product, audit the shipment before delivery to the customer in garment company

Quality control in Asmara Ltd

6 Male 8 years of experience in position material purchasing and sources for production

Data analytic result

Step 1: A hierarchical framework of criteria and sub-criteria is used to design the problem as given in Figure 3 3

Step 2: By utilizing the linguistic variables listed in Table 3 5, and expert evaluations, pairwise comparison matrices are created Equation (7) is used to determine the symmetrical equivalents of linguistic evaluations in paired comparison matrices

Step 3: Then use equation (18) to aggregated the opinions of the experts The results of the table aggregating the opinions of the experts are presented in the Table 4.2

Table 4 2 Aggregated pairwise comparison matrix for main criteria

Step 4: Perform a pairwise comparison matrix normalization using the Equation (19) The normalized pairwise comparison matrix for main criteria is shown in table 4.3

Table 4 3 Pairwise comparison matrix normalization for main criteria

Step 5: To calculate the interval-valued neutrosophic importance weights using Equation

(20) The result is given in table 4.4

Table 4 4 Importance weights for main criteria

Step 6: Repeat the above steps for sub-criteria Pair-wise comparison matrix, normalization anf importance weights of sub-criteria are shown in tables

Table 4 5 Aggregated pairwise comparison matrix for sub-criteria (C11-C13)

Source: Author Table 4 6 Pairwise comparison matrix normalization for sub-criteria (C11-C13)

Table 4 8 Pairwise comparison matrix normalization for sub-criteria (C21-C25)

Table 4 10 Pairwise comparison matrix normalization for sub-criteria (C31-C33)

Table 4 11 Importance weight for sub-criteria

Step 7: Calculate the global weight of the criteria by multiplying the weight of each main criterion by the corresponding local weights Using Equation (6) to deneutrosophic the global weight

Based on Table 4.12 and the weighted of 3 main criteria is Environment factor (C1),

Economics factor (C2), and social factor (C3) with 11 criteria: it can be seen that pollution control (C12) is the highest criterion with 13.16%, followed by green product (C13),

Vocational health and safety systems (C32) with 12.98%, 12.87%, respectively In addition, low-weight criteria such as service (C23) with 4.65%, strategic alliance (C25) with only

5.3% It can be said that the criterion with the highest volume has a higher importance

Table 4 12 Global weight for each criteria

Local weight Global weight Deneutrosophic

This study employs the TOPSIS method to rank green suppliers within the Fast-fashion industry, focusing on the evaluation criteria for green suppliers derived from existing literature and expert insights.

Step 1: Evaluators evaluate suppliers according to criteria using the linguistics term in Table 3 6 Using equation (18) to aggregate results from 6 experts and the results are presented in table (4.13 - 4.14)

Table 4 13 Aggregate the decision matrix for the criteria (C11 - C23)

Source: Author Table 4 14 Aggregate the decision matrix for the criteria (C24 - C33)

Step 2: Using equation (21) to interval-valued neutrosophic normalize the evaluation matrix of step 1 The normalized decision matrix shown as table (4.15 – 4.16)

Table 4 15 IVN normalized decision matrix for the criteria (C11 - C23)

Source: Author Table 4 16 IVN normalized decision matrix for the criteria (C24 - C33)

Step 3: To weighting the normalize decision matrix using global weight calculated in Interval-valued neutrosophic AHP in table (4.12) and equation (22) The weight normalize table is given in table (4.17 – 4.18)

Table 4 17 IVN weight normalized decision matrix for the criteria (C11 - C23)

Source: Author Table 4 18 IVN weight normalized decision matrix for the criteria (C24 - C33)

Step 4: Determining the IVN-NIS and IVN- PIS using equation (23-24)

Table 4 19 IVN Positive ideal solution and IVN Negative ideal solution

Step 5: Euclidean distance is obtained to find distance IVN-NIS to weight normalized matrix and IVN-PIS to weight normalized matrix show as table 4.20

Table 4 20 Euclidean distance from IVN-PIS and IVN-NIS

Step 6 - 7: Computing revised closeness for each green suppliers (ℰ 𝑠 ) by using Equation

(25) Four green suppliers in the fast-fashion business are ranked according to their revised proximity (scores) for each alternative in Table 4.21

Figure 4 1 Separation measure and the revised closeness for each supplier

Table 4.21 highlights that the top-rated green supplier is S2 (Fast Retailing) with a score of 0.540, followed by S1 (Inditex) at 0.498, S4 (Patagonia) at 0.467, and the lowest, S3 (H&M) at 0.390 H&M, a Swedish fashion brand, faces criticism for its contribution to global clothing waste, particularly due to its use of polyester fabric, which decomposes slowly and poses significant environmental risks Although H&M has launched an eco-friendly fashion collection, eliminating environmentally harmful clothing entirely remains a challenge.

Separation measure and the revised closeness for each supplier

Utilizing the AHP-TOPSIS technique for evaluating green suppliers enables decision-makers to conduct rapid assessments while clarifying the selection criteria This method provides a structured framework for identifying sustainable suppliers within the fast fashion industry, recognized as the second most polluting sector globally By applying specific criteria, it systematically evaluates green suppliers across various sectors, including apparel and textiles, promoting environmentally responsible practices.

Sensitivity analysis provides insights into the stability of rankings, highlighting the importance of scrutinizing weights when rankings show significant sensitivity to minor criterion adjustments (Chang, Wu, Lin, & Chen, 2007).

A sensitivity analysis was conducted to evaluate the efficiency of the proposed technique by altering the distance measurement method in the Interval-valued Neutrosophic TOPSIS framework Specifically, the author replaced the Euclidean distance with Hamming distance in step 5, calculated using Equation (16) The resulting distances from the Interval-valued Neutrosophic Negative Ideal Solution (IVN-NIS) and Interval-valued Neutrosophic Positive Ideal Solution (IVN-PIS) to each green supplier were used to rank the suppliers based on their final scores, as detailed in Table 4.22.

Table 4 22 Hamming distance from IVN-PIS and IVN-NIS

After calculating each green supplier's final score using Hamming distances, the green suppliers are ordered based on the results Table 4.23 show the final score by using Hamming distance equation

Table 4 23 Risk ranking of the supplier according to sensitivity analysis

Table 4.23 reveals the evaluation of green suppliers using the Hamming distance equation, highlighting that S2 (Fast Retailing) leads with a score of 0.574, followed by S1 (Inditex), S4 (Patagonia), and S3 (H&M) at lower ranks with scores of 0.527, 0.472, and 0.343, respectively Additionally, Figure 4.2 indicates that the application of various distance measures resulted in minimal differences in the overall rankings.

By changing the method from Euclidean distance to using Hamming distance to check the sensitivity of information, there was no change in raking green suppliers

This chapter presents the findings from the selection process of green suppliers within the fast-fashion industry The analysis identifies S2 (Fast Retailing) as the top green supplier, followed by S1 (Inditex), S4 (Patagonia), with S3 (H&M) receiving the lowest ranking Additionally, a sensitivity analysis is conducted to evaluate the robustness of these results.

CONCLUSION

Conclusion

The fashion industry, often criticized for its environmental impact, is increasingly prioritizing sustainability A recent study highlights the importance of green suppliers in achieving these sustainability goals Environmental, economic, and social factors have become critical considerations in supply chain management, influencing decision-making and strategic implementation.

This study highlights the importance of choosing environmentally friendly suppliers to reduce the industry's environmental impact By selecting suppliers that utilize sustainable products and practices, businesses can minimize their ecological footprint The AHP-TOPSIS technique provides a structured approach for assessing suppliers based on objective data, enabling companies to make informed decisions that align with their sustainability goals.

This article employs the TOPSIS method to analyze and rank providers based on specified criteria, while also evaluating the relationships between these criteria to establish their weights It introduces novel extensions of the AHP and TOPSIS methods within an interval-valued neutrosophic set framework, addressing parameter uncertainty and reducing ambiguity in real-world data to enhance result accuracy Additionally, the proposed approach focuses on selecting a green supplier within the fast-fashion industry.

Using the AHP-TOPSIS method within an interval-valued neutrosophic framework, expert opinions and interview data were analyzed to evaluate green suppliers The findings revealed Fast Retailing as the top green supplier, followed by Inditex in second place, Patagonia in third, and H&M ranked last Additionally, a sensitivity analysis was conducted to assess the robustness of the data.

Implication

Sustainability is becoming increasingly vital in the fast fashion industry as brands recognize the significant environmental impact of their practices This shift towards more sustainable approaches is essential for addressing the ecological challenges posed by the fashion sector.

Innovative technologies are being leveraged by businesses to tackle pressing sustainability challenges, exemplified by the AHP-TOPSIS neutral method This approach incorporates fuzzy logic and advanced decision-making processes, setting it apart from traditional supplier assessment methodologies.

The study emphasizes the versatile application of the impartial AHP-TOPSIS approach, originally designed for evaluating green suppliers in the fast fashion industry This methodology can be customized to address the specific sustainability challenges faced by various sectors, showcasing its adaptability as a valuable assessment tool across diverse industries.

The article "Evaluating and Selecting Green Suppliers by Integrating Neutrosophic AHP Approach - TOPSIS: A Case of the Fast Fashion Industry" highlights the importance of sustainability and impartial evaluation methods in supplier selection It showcases the innovative application of the unbiased AHP-TOPSIS methodology, which holds potential for broader use beyond the fast fashion sector This research serves as a valuable resource for businesses across various industries aiming to enhance their sustainability practices.

This article explores the benefits of selecting environmentally friendly suppliers and highlights the importance of sustainability in the fast fashion industry It employs the AHP-TOPSIS method to evaluate and rank suppliers based on specific criteria The findings underscore the applicability of this approach across various sectors facing sustainability challenges Additionally, the article provides recommendations for businesses on how to effectively utilize this model to identify the most suitable green suppliers for their operations.

Abdel-Baset, M., Chang, V., Gamal, A., & Smarandache, F (2019) An integrated neutrosophic ANP and VIKOR method for achieving sustainable supplier selection:

A case study in importing field Computers in Industry(106), 94-110.

Acar, A Z., ệnden, İ., & Gỹrel, ệ (2016) Evaluation of the parameters of the green supplier selection decision in textile industry Fibres & Textiles in Eastern Europe,

Atanassov, K T (1986) Intuitionistic fuzzy sets Fuzzy Sets and Systems, 20(1), 87-96

Atanassov, K T (1989) More on intuitionistic fuzzy sets Fuzzy sets and systems, 33(1),

Awasthi, A., & Chauhan, S S (2011) Using AHP and Dempster–Shafer theory for evaluating sustainable transport solutions Environmental Modelling & Software, 26(6), 787-796

Azadi, M., Jafarian, M., Saen, R F., & Mirhedayatian, S M (2015) A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context Computers & Operations Research,(54), 274-

Banaeian, N., Mobli, H., Fahimnia, B., Nielsen, I E., & Omid, M (2018) Green supplier selection using fuzzy group decision making methods: A case study from the agri- food industry Computers & Operations Research(89), 337-347

Barnes, L., & Lea‐Greenwood, G (2006) Fast fashioning the supply chain: shaping the research agenda Journal of Fashion Marketing and Management: An International

Bhardwaj, V., & Fairhurst, A (2010) Fast fashion: response to changes in the fashion industry The international review of retail, distribution and consumer research, 20(1), 165-173

Bolturk, E., & Kahraman, C (2018) A novel interval-valued neutrosophic AHP with cosine similarity measure Soft Computing(22), 4941-4958

46 Çalık, A (2021) A novel Pythagorean fuzzy AHP and fuzzy TOPSIS methodology for green supplier selection in the Industry 4.0 era Soft Computing, 25(3), 2253-2265

Celik, E., Yucesan, M., & Gul, M (2021) Green supplier selection for textile industry: a case study using BWM-TODIM integration under interval type-2 fuzzy sets

Environmental Science and Pollution Research, 28, 64793-64817

Chang, C W., Wu, C R., Lin, C T., & Chen, H C (2007) An application of AHP and sensitivity analysis for selecting the best slicing machine Computers & Industrial

Chen, C C., Shyur, H J., Shih, H S., & Wu, K S (2012) A business strategy selection of green supply chain management via an analytic network process Computers & Mathematics with Applications, 64(8), 2544-2557

Choi, T M., Chiu, C H., & Govindan, K & (2014) Sustainable fashion supply chain management: The European scenario European Management Journal, 32(5), 821-

822 de FSM Russo, R., & Camanho, R (2015) Criteria in AHP: a systematic review of literature Procedia Computer Science(55), 1123-1132

Diabat, A K (2014) Analysis of enablers for implementation of sustainable supply chain management–A textile case Journal of cleaner production(83), 391-403

Ecer, F (2022) Multi-criteria decision making for green supplier selection using interval type-2 fuzzy AHP: a case study of a home appliance manufacturer Operational Research, 22(1), 199-233

Fleischmann, M (2019) World Bank Retrieved from How Much Do Our Wardrobes Cost to the Environment?: https://www.worldbank.org/

Freeman, J., & Chen, T (2015) Green supplier selection using an AHP-Entropy-TOPSIS framework Supply Chain Management: An International Journal

Fulton, K., & Lee, S E (2010) An overview of sustainability in the fashion industry The

International Journal of Environmental, Cultural, Economic and Social Sustainability, 6(1), 1-14

Gửren, H (2018) A decision framework for sustainable supplier selection and order

Gulum, P., Ayyildiz, E., & Gumus, A T (2021) A two level interval valued neutrosophic

AHP integrated TOPSIS methodology for post-earthquake fire risk assessment: An application for Istanbul International Journal of Disaster Risk Reduction,(61),

Haeri, S A S., & Rezaei, J (2019) A grey-based green supplier selection model for uncertain environments Journal of cleaner production(221), 768-784

Hendiani, S., Liao, H., Ren, R., & Lev, B (2020) A likelihood-based multi-criteria sustainable supplier selection approach with complex preference information

Hwang, C L., Yoon, K (1981) Methods for multiple attribute decision making Multiple attribute decision making: methods and applications a state-of-the-art survey, 58-

Jia, P., Govindan, K., Choi, T M., & Rajendran, S (2015) Supplier selection problems in fashion business operations with sustainability considerations Sustainability, 7(2), 1603-1619

Kahraman, C., Oztaysi, B., & Cevik Onar, S (2020) Single & interval-valued neutrosophic

AHP methods: Performance analysis of outsourcing law firms Journal of Intelligent

Kannan, D., Govindan, K., & Rajendran, S (2015) Fuzzy axiomatic design approach based green supplier selection: a case study from Singapore Journal of Cleaner Production(96), 194-208

Karami, S., Ghasemy Yaghin, R., & Mousazadegan, F (2021) Supplier selection and evaluation in the garment supply chain: An integrated DEA–PCA–VIKOR approach The Journal of the Textile Institute, 112(4), 578-595

Karasan, A., & Kahraman, C (2018) Interval-valued neutrosophic extension of EDAS method Springer International Publishing, 2, 343-357

Kaushik, V., Kumar, A., Gupta, H., & Dixit, G (2022) A hybrid decision model for supplier selection in Online Fashion Retail (OFR) International Journal of Logistics Research and Applications, 25(1), 27-51

Kokangul, A., & Susuz, Z (2009) Integrated analytical hierarch process and mathematical programming to supplier selection problem with quantity discount Applied mathematical modelling(33.3), 1417-1429

Kumar, D., Rahman, Z., & Chan, F T (2017) A fuzzy AHP and fuzzy multi-objective linear programming model for order allocation in a sustainable supply chain: A case study International Journal of Computer Integrated Manufacturing, 30(6), 535-551

Lee, A H., Kang, H Y., Hsu, C F., & Hung, H C (2009) A green supplier selection model for high-tech industry Expert systems with applications, 36(4), 7917-7927

Lee, D M., & Drake, P R (2010) A portfolio model for component purchasing strategy and the case study of two South Korean elevator manufacturers International Journal of Production Research, 48(22), 6651-6682

Liu, P., Zhang, L., Liu, X., & Wang, P (2016) Multi-valued neutrosophic number

Bonferroni mean operators with their applications in multiple attribute group decision making International Journal of Information Technology & Decision Making, 15(05), 1181-1210

Lo, H., Liou, J., Wang, H., & Tsai, Y (2018) An integrated model for solving problems in green supplier selection and order allocation Journal of cleaner production(190), 339-352

Mallik, S., Mohanty, S., & Mishra, B (2022) Neutrosophic Logic and Its Scientific

Applications In Biologically Inspired Techniques in Many Criteria Decision Making: Proceedings of BITMDM 2021 Singapore: Springer Nature Singapore., 415-432

Myers, J H., & Alpert, M I (1968) Determinant buying attitudes: meaning and measurement Journal of Marketing, 13-20

Nation, U (2019) UN News Retrieved from UN launches drive to highlight environmental cost of staying fashionable: https://news.un.org/en/story/2019/03/1035161

49 ệZBEK, A., & Yildiz, A (2020) Digital supplier selection for a garment business using interval type-2 fuzzy topsis Textile and Apparel, 30(1), 61-72

Puška, A., & Stojanović, I (2022) Fuzzy multi-criteria analyses on green supplier selection in an agri-food company J Intell Manag Decis, 1(1), 2-16

Roy, S A (2020) A framework for sustainable supplier selection with transportation criteria International Journal of Sustainable Engineering, 13(2), 77-92

Saaty, T (1980) The analytic hierarchy process, new york: Mcgrew hill 9, 19-22

International, Translated to Russian, Portuguesses and Chinese, Revised edition, Paperback (1996, 2000),Pittsburgh: RWS Publications(9), 19-22

Saaty, T L (2008) Decision making with the analytic hierarchy process International journal of services sciences(1.1), 83-98

Sarkar, S., Lakha, V., Ansari, I., Maiti, J (2017) Supplier selection in uncertain environment: a fuzzy MCDM approach In Proceedings of the First International

Conference on Intelligent Computing and Communication, 257-266

Sarkis, J (2001) Manufacturing’s role in corporate environmental sustainability–Concerns for the new millennium International Journal of Operations & Production Management, 21(5), 666-686

Sharma, D G., & Rawani, A M (2016) Green supplier selection for Indian cement industry: AHP based approach Research Journal of Engineering and Technology

Shen, B (2014) Sustainable fashion supply chain: Lessons from H&M Sustainability,

Smarandache, F (1998) Neutrosophy: neutrosophic probability, set, and logic: analytic synthesis & synthetic analysis 12–20

Srivastava, S (2007) Green supply-chain management: a state-of-the-art literature review

International Journal of Management Reviews, 9(1), 53-80

Sukmawati, M., & Setiawan, A (2022) A conceptual model of green supplier selection in the manufacturing industry using AHP and TOPSIS methods IEEE, 659-664

Sull, D., & Turconi, S (2008) Fast fashion lessons Business Strategy Review, 19(2), 4-11

Sun, Y., & Cai, Y (2021) A flexible decision-making method for green supplier selection integrating TOPSIS and GRA under the single-valued neutrosophic environment

Tirkolaee, E B., Dashtian, Z., Weber, G W., Tomaskova, H., Soltani, M., & Mousavi, N

S (2021) An integrated decision-making approach for green supplier selection in an agri-food supply chain: Threshold of robustness worthiness Mathematics, 9(11),

Turker, D., & Altuntas, C (2014) Sustainable supply chain management in the fast fashion industry: An analysis of corporate reports European Management Journal, 32(5), 837-849

Utama, D (2021) Penyelesaian Green Supplier Selection Menggunakan Integrasi AHP dan VIKOR n Prosiding SENTRA (Seminar Teknologi dan Rekayasa), 6, 31-37

Utama, D M., Asrofi, M S., & Amallynda, I (2021) Integration of AHP-MOORA algorithm in green supplier selection in the Indonesian textile industry In Journal of Physics: Conference Series, 1933, 012058

Van, L H., Yu, V F., Dat, L Q., Dung, C C., Chou, S Y., & Loc, N V (2018) New integrated quality function deployment approach based on interval neutrosophic set for green supplier evaluation and selection Sustainability, 10(3), 838

Wang, C N (2019) A fuzzy multicriteria decision-making (MCDM) model for sustainable supplier evaluation and selection based on triple bottom line approaches in the garment industry Processes, 7(7), 400

Wang, H., Smarandache, F., Sunderraman, R., & Zhang, Y Q (2005) interval neutrosophic sets and logic: theory and applications in computing: Theory and applications in computing (Vol 5) Infinite Study

Wang, H., Zhang, Y., Sunderraman, R., & Smarandache, F (2011) Single valued neutrosophic sets, Fuzzy Sets Rough Sets and Multivalued Operations and Applications, 3(1), 33-39

Yalcin, A S (2020) An integrated model with interval valued neutrosophic sets for the selection of lean and sustainable suppliers In Intelligent and Fuzzy Techniques in

Big Data Analytics and Decision Making: Proceedings of the INFUS 2019 Conference, Istanbul, Turkey, July 23-25, 693-701

Yazdani, M T (2021) An interval valued neutrosophic decision-making structure for sustainable supplier selection Expert Systems with Applications, 183

Yazdani, M., Chatterjee, P., Zavadskas, E K., & Zolfani, S H (2017) Integrated QFD-

MCDM framework for green supplier selection Journal of Cleaner Production(142), 3728-3740

Ye, J., & Du, S (2019) Some distances, similarity and entropy measures for interval- valued neutrosophic sets and their relationship International Journal of Machine

Yu, C., Shao, Y., Wang, K., & Zhang, L (2019) A group decision making sustainable supplier selection approach using extended TOPSIS under interval-valued Pythagorean fuzzy environment Expert Systems with Applications(121), 1-17

Yu, Q & (2016) An approach for green supplier selection in the automobile manufacturing industry Kybernetes

Zadeh, L A (1965) Zadeh, fuzzy sets Inform Control, 8, 338-353

Zafar, A., Zafar, M., Sarwar, A., Raza, H., & Khan, M T (2019) A fuzzy AHP method for green supplier selection and evaluation n Proceedings of the Twelfth International Conference on Management Science and Engineering Management,

Zhang, H Y., Wang, J Q., & Chen, X H (2014) Interval neutrosophic sets and their application in multicriteria decision making problems The Scientific World Journal,

The importance of various factors can be categorized into distinct levels: "Absolutely More Importance," "Extremely High Importance," "Extreme Importance," and "Very Strongly More Importance." Other classifications include "Very Strong Importance," "Strongly More Importance," "Strong Importance," "Moderately More Importance," and "Moderate Importance." Additionally, there are "Weakly More Importance" and "Equal Importance" categories, reflecting a spectrum of significance Understanding these levels helps prioritize actions and decisions effectively.

C1 AMI EHI EXI VSMI VSI SMI SI MMI MI WMI EI WMI MI MMI SI SMI VSI VSMI EXI EHI AMI C2

Alternative evaluation of experts for sub-criteria

Tiêu đề	Evaluating And Selecting Green Suppliers By Integration Of Neutrosophic AHP-TOPSIS Approach: A Case Of The Fast-Fashion Industry
Tác giả	Le Thi Quynh Giang
Người hướng dẫn	Dr. Nguyen Phan Anh Huy
Trường học	Ho Chi Minh City University of Technology and Education
Chuyên ngành	Industrial Management
Thể loại	Thesis
Năm xuất bản	2023
Thành phố	Ho Chi Minh City

Định dạng
Số trang	68
Dung lượng	4,37 MB