Fuzzy Additive Ratio Assessment Research Articles

Effective waste management in service industry: Fuzzy-based modelling approach for strategic decision-making.

Hospitals need to identify issues of greater importance on waste management because the implementation of many different strategies may lead to an unconscious increase in costs. Accordingly, the purpose of this study is to define the most effective waste management strategies in the service industry. For this purpose, a novel fuzzy decision-making model is proposed that has two different stages. In this context, six JCI-based indicators are weighted by using sine trigonometric fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) methodology. Additionally, a comparative evaluation has also been conducted with sine trigonometric fuzzy Criteria Importance Through Intercriteria Correlation (CRITIC) technique to check the reliability of the findings. On the other hand, five different strategy alternatives are selected by considering the principles of the integrated waste management hierarchy approach. These items are evaluated by considering sine trigonometric fuzzy Technique for Order Preference by Similarity (TOPSIS). On the other side, these factors are also ranked with the help of sine trigonometric fuzzy Additive Ratio Assessment (ARAS) to test the consistency of the results. The main contribution is that prior strategies can be presented to the hospitals to have appropriate waste management process by defining the most important factors. Criteria weighting and alternative ranking results are the same in all combinations. Therefore, it is seen that the proposed model creates coherent and consistent results. It is defined that efficient storage of waste is the key issue to have effective waste management process. Moreover, 'reduce' is found as the most critical stage of this process.

Evaluation and Selection of a Green and Sustainable Supplier by Using a Fuzzy ARAS Mathematical Modeling

In the present scenario, the main aim of each and every organization is to be competitive, eco-friendly, and sustainable in order to run effectively and efficiently. Therefore, to meet and fulfill the above objectives, each organization must identify and choose the appropriate and best green and sustainable supplier, as a green supplier considers green technology, green products, green packaging, etc., while a sustainable supplier considers all three characteristics of sustainability, namely social, economic, and environmental. The problem of the evaluation and selection of green and sustainable suppliers is an example of a multi-criteria decision-making (MCDM) problem. In this research paper, to handle the uncertainty hidden in the green supply chain, the extended version of a MCDM technique named fuzzy additive ratio assessment (F-ARAS) is implemented for the evaluation of the relative weights of the green and sustainable supplier selection criteria, namely social, economic, and environmental, and also to determine the best green and sustainable supplier through fuzzy theory by using linguistic terms, which includes the application of interval-valued triangular fuzzy numbers (IVTFNs) in an organization. Finally, the technique, F-ARAS, has performed well and enables the organization’s managers to attain their required solution and final decision.

A Framework for a Valuation of Digital Start-Ups Using Artificial Intelligence and Fuzzy Sets

Abstract The purpose of this article is to present an innovative framework for assessing digital start-ups and smaller companies using a fuzzy set approach, considering the founder’s expertise, product marketability, financial health, and social media presence. Four Czech digital startups were analyzed by both human experts and an artificial intelligence model. The methodology is based on using a fuzzy additive ratio assessment. Each start-up was evaluated on a five-point scale, with the results compared to Deloitte’s FAST 50 rank. The AI and human evaluations differed, with humans placing more emphasis on the founder’s experience and product appeal. As a practical contribution, the article suggests a valuation framework involving both human and AI expertise for interactive comparison and update. The article highlights the value of AI in start-up assessment, stressing the necessity of merging human and artificial intelligence in decision-making. To date of the publication, no study combining human expertise and artificial intelligence using fuzzy sets was found. Therefore, both methodology and results can be considered innovative and original.

Evaluation of Green Marketing Strategies by Considering Sustainability Criteria

The purpose of this study is to identify and prioritize green marketing strategies in the food industry by taking sustainability goals into consideration since it is very difficult to prioritize and select green marketing strategies when considering sustainability criteria in their implementation. Therefore, it is necessary to use an appropriate solution to prioritize these strategies. For this purpose, sustainability criteria and green marketing strategies are recognized. Then, sustainability criteria are screened using the fuzzy Delphi method. Subsequently, using the fuzzy stepwise weight assessment ratio analysis method, the criteria are weighted according to expert recommendations. Next, green marketing strategies in the Iranian food industry are ranked using several fuzzy multiple criteria decision-making methods: fuzzy additive ratio assessment, fuzzy complex proportional assessment, fuzzy technique for order preference by similarity to the ideal solution, and fuzzy Viekriterijumsko Kompromisno Rangiranje methods. Finally, the results are integrated with the help of the Copeland method to choose the best strategies. The results indicated that the criteria of ‘minimizing waste’, ‘minimizing environmental impacts’, and ‘consumer participation’ had the highest weight. In addition, the strategies for the ‘development of waste reduction technology, ‘development of market research to detect green needs’, and ‘use of recycled or reusable materials in products’ received a higher rank.

Picture fuzzy Additive Ratio Assessment Method (ARAS) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method for multi-attribute decision problem and their application

The purpose of this paper is to study the multi-attribute decision-making problem under the fuzzy picture environment. First, a method to compare the pros and cons of picture fuzzy numbers (PFNs) is introduced in this paper. Second, the correlation coefficient and standard deviation (CCSD) method is used to determine the attribute weight information under the picture fuzzy environment regardless of whether the attribute weight information is partially unknown or completely unknown. Third, the ARAS and VIKOR methods are extended to the picture fuzzy environment, and the proposed PFNs comparison rules are also applied in the PFS-ARAS and PFS-VIKOR methods. Fourth, the problem of green supplier selection in a picture-ambiguous environment is solved by the method proposed in this paper. Finally, the method proposed in this paper is compared with some methods and the results are analyzed.

The Selection of a Possible Organizational Structure of Railway Companies by Application Fuzzy-ARAS Method

The European railway sector doesn't represent a single, generic type of organizational structure. The Directives from the 1990s offer the possibility for national interpretation giving a wide range of organizational structures for European railways. Different organizational structures of railways are present in the European railway market, and all of them are aligned with the national belief of the country - how to manage the railway. This paper focuses on Multi-Criteria Decision Making (MCDM) methods for ranking and selecting organizational structures of railways. In decision-making, we deal with different types of uncertainty and inaccuracy. For this purpose, we need to use some specific tools. In this paper, we use triangular fuzzy numbers to quantify linguistic data. To overcome the complexity of the decision-making problem, we propose the fuzzy Additive Ratio ASsessment (ARAS) method, which also includes linguistic variables and a group approach to decision-making. With the proposed methodology an evaluation of the alternatives to the organization structure of the railway company in Bosnia and Herzegovina is considered. Separation of railway transport from infrastructure management is ranked as the best organizational structure for this company.

Classifying environmental significance levels of production-oriented operational activities with F-ARAS and F-MOORA methods.

In most decision analysis approaches, application results are obtained in the form of a ranking or selection set. However, classificationis needed for analysis of results. In this study, "environmental significance levels and range values" were created for the ranking results obtained using fuzzy Multi Criteria Decision Making approaches. Environmental significance levels have been established for 26 operational activities for which environmental impacts have been determined. Operational activities were analyzed using the Fuzzy-Additive Ratio Assessment (F-ARAS), Fuzzy-Multi Objective Optimization by Ratio Analysis (F-MOORA) Ratio,and F-MOORA Reference Point methods with sixevaluation criteria, including the total impact score criteria. The results obtained were analyzed with "environmental significance levels and range values," which can also be named as environmental risk classes created for the first time in this study depending on the changes in standard deviation. According to the comparison results, when F-ARAS and F-MOORA Ratio methods were evaluated together, 19 activities were in the same class (73%). Integr Environ Assess Manag 2023;19:763-774. © 2022 SETAC.

Hybrid Extended Fuzzy AHP and Fuzzy ARAS Methodology for Evaluating Train Organization Systems in Bosnia and Herzegovina

The operational transport strategies of a state are developed as a combination of train organization systems and network usage systems. Due to the central importance of these operational transport strategies, special attention is paid to analyzing their functioning and the conditions under which different combinations of train organization systems and network usage become feasible and commercially viable. This study proposes a hybrid fuzzy approach to multi-criteria decision-making methods for evaluating and assessing train organization systems and network usage in Bosnia and Herzegovina. For this purpose, an extended Fuzzy Analytic Hierarchy Process (E-FAHP) was applied to determine the relative significance of each criterion, followed by ranking train systems using the Fuzzy Additive Ratio Assessment Method (Fuzzy ARAS). To validate the efficacy of the proposed framework, a case study was conducted using a real-world example. The findings demonstrate tangible potential for practical implementation.

A Novel MDCM Approach for Sustainable Supplier Selection in Healthcare System in the Era of Logistics 4.0

The COVID-19 pandemic has led to major disruptions in workflows across all industries. All sectors are trying to sustain operations during this extremely difficult time and the healthcare sector is the most important of them. It is unthinkable to stop the operations of the health system because it serves human life. Health institutions must supply the products such as masks, gloves, and ventilators subject to service on time for certain activities to continue indefinitely under all conditions. By adopting modern logistics activities and technologies, healthcare organizations can provide sustainable diagnosis and treatments to patients by automating their various operations. With the COVID-19 pandemic, how to select an appropriate sustainable supplier has become an important task in the era of Logistics 4.0. From this viewpoint, a sustainable supplier selection framework is implemented for a health institution under the effect of the pandemic. To determine the direct effects of the pandemic in the health sector, fuzzy Multi-Criteria Decision-Making (MCDM) methods are utilized in the application. After a thorough review of the literature and interviews with experts, the criteria are organized in a comprehensive hierarchical structure. The fuzzy Best-Worst Method (F-BWM) technique is employed to find the weights for the determined criteria. Consequently, the fuzzy Additive Ratio Assessment Method (F-ARAS) method was applied to rank the alternative suppliers. In addition, with a comprehensive sensitivity analysis, alternative situations are examined against possible breaks in the supply chain. Thus, from the perspective of Logistics 4.0 and sustainability, this study contributes to the literature with an analysis of the health system’s survival in difficult and fragile periods, such as COVID-19. Investigating the importance of SSS can be a road map for the policymakers and the decision-makers is beneficial since the impact of COVID-19 on SSS is studied from the perspective of Logistics 4.0.

A similarity measure-based Pythagorean fuzzy additive ratio assessment approach and its application to multi-criteria sustainable biomass crop selection

In the modern world, the selection of an optimal sustainable biomass crop type for biofuel production can be assumed as a multi-criteria decision-making problem due to numerous conflicting criteria. Therefore, uncertainty usually arises in sustainable biomass crop selection problems, and the Pythagorean fuzzy set, an extension of the intuitionistic fuzzy set, has been shown as a prolific tool to tackle uncertain and ambiguous information. Thus, the aim of the paper is to introduce an extended additive ratio assessment methodology to assess the sustainable biomass crop selection problem on Pythagorean fuzzy sets. In this method, a new Pythagorean fuzzy similarity measure is proposed. Afterward, a weighting procedure is developed based on the introduced similarity measure and score function to compute the criteria significance degrees. The computational process of the developed framework is illustrated using a case study of sustainable biomass crop selection with uncertain information, which verifies the feasibility and efficacy of the developed approach. Further, this work makes comparative and sensitivity analyses to confirm the steadiness and robustness of the presented method. The results illustrate that the developed method is a useful, practical, and valuable way to rank the sustainable biomass crop alternatives with uncertainty.

A Divergence-Based Medical Decision-Making Process of COVID-19 Diagnosis

This study is concerned with introducing a class of parametric and symmetric divergence measures under hesitant fuzzy environment. The proposed divergence measures have several interesting properties which make their use attractive. In order for exploring the features of proposed divergence measures for hesitant fuzzy sets (HFSs), we compare them with other existing ones in terms of divergence-initiated weighs and counter-intuitive cases. In the process of comparison, we first modify the conventional framework of hesitant fuzzy additive ratio assessment (HFARAS) using the proposed divergence measures, and then, the superiority of proposed measures is further demonstrated in a COVID-19 case study. There, we notify that the other existing divergence measures may not provide satisfactory results.

An Integrated Multi-criteria Structural Equation Model for Green Supplier Selection

The aims of the current study are to determine the criteria to be considered in a green supplier selection (GSS) process, develop an integrated decision-making approach using structural equation modelling (SEM) and fuzzy additive ratio assessment (ARAS) techniques to rank suitable suppliers, and investigate the effectiveness of this approach in collective decision-making. The study’s ultimate objective is to propose a method created from the perspective of fuzzy logic in which criterion weights are determined using SEM to resolve decision-making challenges such as GSS, in which a compromise solution is desired. In the first stage of the study, relevant literature as examined in detail. Eight main criteria and 27 sub-criteria were detected, taking into account classical, green, and social supplier selection (SS) criteria. A questionnaire was administered to 254 participants who were actively involved in SS processes for their companies, and the data were analyzed using SEM. In the second phase, six suppliers were evaluated considering the criteria found to have a positive impact on GSS, and fuzzy ARAS was applied. The results showed that the factors affecting GSS are quality (0.291), social responsibility (0.222), service (0.21), cost (0.149) and green product (0.128). The study presents an effective, objective, and fast method that can be used in decision-making problems such as GSS, that prevents potential loss of information and lack of transparency through an integrated approach, and that can be tested with a generalizable framework. The proposed hybrid method is an efficient and practical approach that can be tested on many decision-making problems, such as SS.

Low-carbon tourism strategy evaluation and selection using interval-valued intuitionistic fuzzy additive ratio assessment approach based on similarity measures.

Recently, the assessment and selection of most suitable low-carbon tourism strategy has gained an extensive consideration from sustainable perspectives. Owing to participation of multiple qualitative and quantitative attributes, the low-carbon tourism strategy (LCTS) selection process can be considered as multi-criteria decision-making (MCDM) problem. As uncertainty is usually occurred in LCTSs evaluation, the theory of interval-valued intuitionistic fuzzy sets (IVIFSs) has been established as more flexible and efficient tool to model the uncertain decision-making problems. The idea of the present study is to develop an extended method using additive ratio assessment (ARAS) framework and similarity measures in a way to find an effective solution to the decision-making problems using IVIFSs. The bases of the proposed method are the IVIFSs operators, some modifications in the traditional ARAS framework and a calculation procedure of the weights of the criteria. To calculate criterion weight, new similarity measures for IVIFSs are developed aiming at the achievement of more realistic weights. Also, a comparison is demonstrated to the currently used similarity measures in order to show the efficiency of the developed approach. To confirm that the developed IVIF-ARAS approach can be successfully employed to practical decision-making problems, a case study of LCTS selection problem is considered. The final results from the developed approach and the extant models are compared for the validation of the proposed approach in this study.

Path selection of lean digitalization for traditional manufacturing industry under heterogeneous competitive position

Lean digitization is a management method that integrates lean thinking into the process of enterprise production management by virtue of digital technology, and the competitive position has become an important situational factor affecting the implementation of lean digitalization. Most manufacturing enterprises are not clear about the path of lean digitalization, nor can they develop a set of tangible lean digitalization schemes combined with their own competitive position. In order to help enterprises to form proper implementation scheme of lean digitalization with limited resources, we conducted a survey on some traditional manufacturing enterprises carrying out lean digitalization. This study analyzes the influencing factors of lean digitalization and the multiple concurrent causal relationships among factors from four stages of “point, line, plane and cube” by using the fuzzy additive ratio assessment (ARAS-F) and fuzzy sets qualitative comparative analysis (fsQCA) methods. The results showing: (1) In the stages of “point, line, plane and cube”, all the internal and external influencing factors cannot be solely regarded as the necessary conditions for the efficient implementation of lean digitization, in other words, the explanatory power of a single influencing factor is not strong. Each factor must be combined to form the necessary conditions for lean digitization; (2) In the stages of “point, line, plane and cube”, the multi-combination paths of lean digitalization are got under different competitive position. This study points out the implementation path of lean digitalization for traditional manufacturing enterprises under different competitive positions, and provides the possibility of theoretical support for the practice of lean digitalization.

Selection of In-Flight Duty-Free Product Suppliers Using a Combination Fuzzy AHP, Fuzzy ARAS, and MSGP Methods

Over a billion people travel by air all over the world every year, and there are many in-flight retailing opportunities for the airline industry. This paper proposes a novel integration fuzzy analytical hierarchy process (FAHP), fuzzy additive ratio assessment (FARAS), and multisegment goal programming (MSGP) methods to select the best supplier for in-flight duty-free product in airline industry. The advantage of this proposed method is that it allows decision makers (DMs) to set multisegment aspiration vague levels considering both the qualitative and quantitative criteria for supplier selection simultaneously. To the best of our knowledge, a simultaneous consideration of qualitative and quantitative criteria for supplier selection of in-flight duty-free product has never been applied under the airline retail industry context. This research will fill into the gaps of supplier selection in in-fight duty-free product for airline industry. The integrated model is illustrated by an example in an airline company in Taiwan.

A FUZZY DECISION-MAKING APPROACH FOR EVALUATION AND SELECTION OF THIRD PARTY REVERSE LOGISTICS PROVIDER USING FUZZY ARAS

Business environment is full of ups and down and this makes companies to develop different ways of using resources. By expanding life cycle of products, these ways can be cost effective and not harmful for environment. As Reverse Logistics (RL) uses a product after end of its life, it reduces pollution, therefore it has been considered as a part of sustainable development. The core goal of current research is developing a framework by which it evaluates Third Party RL Provider (3rdPRLP) using Multi-Criteria Decision-Making (MCDM) based on Fuzzy Additive Ratio ASsessment (FARAS). Thirty-seven criteria were identified, which are classified into seven main criteria. The main criteria were ranked as follows: product lifecycle position C1, RL process function C2, organizational performance C3, organizational role of RL C4, IT system and communication C5, general company consideration C6, geographical location C7. Market coverage, destination, financial considerations, integrated system, reclaim, efficiency and quality, and growth are each group’s dominant sub-criteria. In addition, the current research helps the logistics managers to better understand the key attributes’ complex relationships in the environment of decision-making.

Multi-criteria IT personnel selection on intuitionistic fuzzy information measures and ARAS methodology

Global challenge and the speedy growth of information technologies compel organizations to constantly change their ways. At the present time, associations need IT personnel who create a difference by creative thoughts and who preserve with the rapid amendments. Since the evaluation of IT personnel selection (ITPS) consists of different alternatives and criteria, therefore, IT personnel selection could be regarded as a multi-criteria decision making (MCDM) problem. The doctrine of intuitionistic fuzzy sets (IFSs) is an effective tool to elucidate the uncertain information in an MCDM problem. The main objective of the paper is to choose the best IT personnel candidate by integrating intuitionistic fuzzy Additive Ratio Assessment (IF-ARAS) method with divergence measure, improved score function and IF-aggregation operators. In the developed methodology, the weights of criteria and decision experts (DEs) are computed based on proposed IF-divergence measure method intuitionistic fuzzy preference evaluation method, respectively. Next, the decision experts’ judgments are aggregated of the proposed method to evade the loss of data. Finally, the proposed IF-ARAS method is implemented to solve the IT-personnel selection (ITPS) problem to indicate the applicability of the presented approach. In addition, a comparative analysis is provided to discuss the obtained results for validating the developed methodology. The analysis illustrates that the IF-ARAS method is effective and well consistent with the existing ones.

Picture Fuzzy ARAS Method for Freight Distribution Concept Selection

Companies can perform their freight distribution in three different ways. The first concept, the in-house concept, represents the use of a company’s own resources and knowledge to organize transportation from the production to retailers or from the warehouse to customers. The opposite concept is to outsource distribution activities by hiring third-party logistics providers. The third concept represents a combination of the previous two. Although the arguments in favor of outsourcing can be found in the literature, an appropriate selection of a freight distribution concept is specific for each company and depends on many evaluation criteria and their symmetrical roles. This paper presents a methodology that can be used by companies that need to choose their freight distribution concept. An advanced extension of the Additive Ratio ASsessment (ARAS) method is developed to solve the freight distribution concept selection problem. To illustrate the implementation of the proposed methodology, a tire manufacturing company from the Czech Republic is taken as a case study. However, the proposed picture fuzzy ARAS method is general and can be used by any other company. To validate the novel picture fuzzy ARAS method, a comparative analysis with the nine existing state-of-the-art picture fuzzy multi-criteria decision-making methods is provided.

A Novel Integrated PIPRECIA–Interval-Valued Triangular Fuzzy ARAS Model: E-Learning Course Selection

The development of information and communication technologies has revolutionized and changed the way we do business in various areas. The field of education did not remain immune to the mentioned changes; there was a gradual integration of the educational process and the mentioned technologies. As a result, platforms for distance learning, as well as the organization of e-learning courses of various types, have been developed. The rapid development of e-learning courses has led to the problem of e-learning course selection and evaluation. The problem of the e-learning course selection can be successfully solved by using multiple-criteria decision-making (MCDM) methods. Therefore, the aim of the paper is to propose an integrated approach based on the MCDM methods and symmetry principles for e-learning course selection. The pivot pairwise relative criteria importance assessment (PIPRECIA) method is used for determining the weights of criteria, and the interval-valued triangular fuzzy additive ratio assessment (ARAS) method is used for the ranking of alternatives i.e., e-learning courses. The suitability of the proposed integrated model is demonstrated through a numerical case study.

An integrated framework for prioritizing the outsourcing performance outcomes

Purpose Outsourcing is a crucial tool for an organization to focus on its core business operations. The purpose of this study is to find critical success factors (CSFs) of outsourcing and to prioritize the performance outcomes of outsourcing because of the adoption of its CSFs. Design/methodology/approach This research proposes the hybrid framework of fuzzy Delphi, fuzzy analytic hierarchy process (F-AHP) and fuzzy additive ratio assessment (F-ARAS) to fulfill the research objectives. Fuzzy Delphi is used to finalize the CSFs of outsourcing and F-AHP provides the relative weights to it. F-ARAS is used to prioritize the outsourcing performance indicators with respect to CSFs. The empirical case investigation of three Indian large-scale public sector process industries is conducted to demonstrate the applicability of the proposed framework. Findings The result shows that increased dominance in core activity, ability to increase or decrease capacity, improved financial performance, optimized resource utilization and increased market share are the top five performance outcomes because of the adoption of outsourcing CSFs. Research limitations/implications Factors such as cultural, political, environmental and size of the organization may have a significant implication on the research results. Originality/value This is a novel approach towards the prioritization of performance outcomes of outsourcing. Moreover, the present research is a helpful tool to manage outsourcing and choosing which performance outcome of outsourcing ought to be used to measure implementation. This will help to recognize potential opportunities and preparing strategic planning for both short-term and long-term goals.