Multi-attributive Border Approximation Area Comparison Research Articles

Determining of gully erosion susceptibility based on UAV and machine learning in Loess Plateau

In order to determine the susceptibility of gully erosion at a small watershed scale on the Loess Plateau of China, three hybrid models were developed. These models were based on the Multi-Attributive Border Approximation Area Comparison (MABAC), frequency ratio (FR), CatBoost (CB), LightGBM (LG), and extremely-randomized tree (ET). Based on the Unmanned Aerial Vehicles (UAV) photos, a total of 83 gullies with 12,150 gully pixels and 8 conditioning variables were extracted and used to create the gully inventory database. The correlations between the conditioning parameters and the pixels of gullies were then determined using FR, and the relative importance of these conditioning factors was quantified using machine learning. Then, for gully erosion susceptibility mapping (GESM), three hybrid gully erosion susceptibility models called MABAC-FR-CB, MABAC-FR-LG, and MABAC-FR-ET were developed. The performance of three hybrid models was assessed using the receiver operating characteristic curve (ROC) and the Kappa coefficient. The results claimed that slope steepness greatly influenced the erosion of the gully. The MABAC-FR-ET performed the most precisely, with area under curvature (AUC) of 0.998 and a Kappa of 0.952. As a result, it was determined that MABAC-FR-ET is the most exact and accurate method for predicting the susceptibility to gully erosion in the study watershed.

Time-series bidirectional adjustable N-soft expert MABAC method and its application for multi-attribute group decision-making

In hybrid models of soft expert sets, experts express only agreed or disagreed opinions about existing grades. This paper proposes a time-series bidirectional adjustable N-soft expert set model to address the shortcomings of existing models that cannot adjust existing grades to a more reasonable state or describe decision problems involving different times. Firstly, this model can explain the experts’ uncertain opinions and make positive or negative adjustments about existing grades. Secondly, the model contains information about time, describes dynamic multi-attribute group decision-making problems and explores objects’ changes and developments over time. And some related operations and propositions are derived. In addition, a new method called the bidirectional adjustable N-soft expert MABAC (multi-attributive border approximation area comparison) is proposed. On the one hand, the proposed method uses deviation maximizing and exponential decay methods to determine the time weights, ensuring the reliability of the time weights. On the other hand, it ranks objects based on their distances from an approximate boundary region, limiting the unconditional compensation among attribute values. Finally, this paper presents an example to verify its effectiveness and reliability by results analysis, sensitivity analysis, and comparison analysis.

Selection of sustainable construction material from recycled waste plastics by q-rung orthopair fuzzy SWARA-MABAC approach

Recycling of waste plastics and agro-industrial waste for the development of sustainable polymeric composites is recognized as a viable approach to overcome the detrimental environmental effects of plastics waste. Despite of immense potential of sustainable composites in the Circular Economy (CE), its implementation is still insignificant due to the lack of an effective material selection approach. The existence of several influencing aspects in the process of material selection considers it a multi-criteria decision making (MCDM) problem. In the present work, an Aggregation Operator (AO) based integrated Stepwise Weight Assessment Ratio Analysis (SWARA) and Multi-attributive Border Approximation Area Comparison (MABAC) has been proposed to deal with the issues of material selection for polymer based sustainable composites. Moreover, q-rung orthopair fuzzy numbers (q-ROPFNs) have been implemented to tackle the uncertainty in the information. The effectiveness of the proposed approach has been confirmed by different comparative and sensitivity investigations. The developed composites have shown excellent properties whereas the responses of the materials vary invariably with compositions. The proposed method has identified the amalgamation of 10 wt percentage of rice husk ash and 10 wt percentage of sand with 80 wt percentage of high-density polyethylene (HDPE) as an appropriate material for the development of sustainable floor tiles as the composites resulted to optimum mechanical performances and minimum abrasive wear. The proposed model gives reliable and robust results and is sensitive to the criteria weights and mathematical parameters. The outcome of the research has exposed that the suggested mathematical approach can be effectively applied for material selection of sustainable polymeric composites for different applications.

Implementasi Metode SAW dan MABAC Pada Sistem Pendukung Keputusan Penerima Beasiswa Anak Tempatan

The Bengkalis Regency Education Office (Disdik) provides Local Children's Scholarships to students in an effort to help students to alleviate the dependents of the Single Tuition Fee (UKT) in pursuing education. In determining the recipient of the Tempatan Children's Scholarship, the Education Office government carries out several stages manually such as collecting all the files of students who register, after which the Education Office checks the completeness of the student files one by one, gives a value for each predetermined criterion and manually calculates the value of the criteria. A decision support system for recipients of web-based Local Children's Scholarships is needed to help determine recipients of Local Children's Scholarships to be faster and more accurate. This system is made using the Simple Additive Weighting (SAW) method and Multi Attributive Border Approximation Area Comparison (MABAC) to facilitate the calculation and comparison of the value of each alternative in the system, by determining the ranking based on the largest value as the best alternative. The result of the application of the SAW and MABAC methods in the system is that the system displays alternative data sequentially based on rankings that can help in determining the recipient of the Local Children Scholarship. From 60 field data, the application of SAW and MABAC methods on this system has an accuracy of 83.3%. The development method used is the Extreme Programming method.

An integrated multi-attributive border approximation area comparison (MABAC) method for evaluating resilience and knowledge sharing of suppliers in pythagorean fuzzy environment

This paper aims to determine the resilience scores of suppliers by considering the importance of knowledge sharing and incorporating a set of criteria that affect the supply chain’s resilience, along with knowledge sharing. In order to achieve this goal, we propose a new multi-criteria decision-making (MCDM) method named MULTIMABAC to evaluate suppliers’ resilience. This method is based on the multi-attributive border approximation area comparison (MABAC) method. The new method consists of criteria weighting, expert weighting, and supplier evaluation. In the criteria weighting section, we extend the BWM (Best–Worst method) using Pythagorean fuzzy sets (PFSs). The weight of experts is determined by incorporating the average concept into the MABAC method. The resilience score of suppliers is calculated using an extended MABAC method with ideal positive and negative solutions. All indices are aggregated using the ordinal priority approach (OPA) to obtain a unique ranking. Additionally, we extend the MULTIMABAC method using PFSs to address uncertainty in supply chain decision-making. Results and sensitivity analysis demonstrate that the proposed method effectively facilitates knowledge sharing in selecting resilient suppliers. Therefore, organizations can use the proposed method to evaluate and choose resilient suppliers. Moreover, the MULTIMABAC method demonstrates robustness to variations in the weights of its constituent parameters.

A Fuzzy Decision Support System for Risk Prioritization in Fine Kinney-based Occupational Risk Analysis

Analyzing Occupational Health and Safety (OHS) risks requires prioritizing risk, a vital step in hazard and effect analysis (HEA). Currently, there is no existing approach to determine the priority of risk in HEA-based occupational risk analysis that incorporates interactive risk factors and spherical fuzzy risk information. This paper presents a novel approach to address the constraints by introducing a fresh framework for evaluating job-related hazards using HEA by combining the spherical fuzzy set (SFS), the multi-attributive border approximation area comparison (MABAC) technique, and the Ordered Weighted Averaging (OWA) operator, a more sophisticated approach is achieved within this framework. The SFS is utilized in this framework to depict the more ambiguous and unsure data given by specialists, offering a more efficient approach to handling the fuzzy risk information, encompassing the non-membership degree and hesitation. In addition, an advanced MABAC method is used to prioritize occupational risk. Afterward, we provide a practical instance of utilizing the MABAC technical-oriented risk prioritization approach for evaluating potential occupational dangers in risk analysis.

Group decision making method for third-party logistics management: An interval rough cloud optimization model

Group decision making in third-party logistics service selection plays an essential role for improving service quality, increasing efficiency and reducing the net cost. Fuzzy and uncertain linguistic variables are commonly used to represent experts‘rankings in optimization problems. To recognize the limits of human cognition and subjectivity of human evaluations, several optimization approaches have been studied to select remanufacturing alternatives in decision making processes, however these methods have certain deficiencies such as lacking manipulation tools of diverse information, randomness, use of predefined parameters increasing uncertainty, interpersonal relations among evaluation criteria. The integration of interval numbers, rough approximations, and cloud model theory plays a significant role to model incomplete and inadequate information occurring in decision making problems. This research paper focuses on the integration of dual interval rough integrated cloud model with best-worst optimization technique, Multi-Attributive Border Approximation area Comparison (MABAC) and Weighted Aggregated Sum Product Assessment (WASPAS) approaches. A novel min–max optimization model based dual interval rough integrated cloud values is designed to compute the weight coefficients and consistency ratio for each criteria. The consistency of proposed optimization model is checked using a consistency ratio test. Secondly, the alternatives are ranked using the proposed DIRI cloud based MABAC and WASPAS approaches using interval clouds based weighted sum and weighted product coefficients, approximation area values and distance formulae. The significance of the proposed model is highlighted with a case study of third-party logistics service management of an electronic firm. The rationality and out-performance of the proposed methodology is studied by a comparative analysis with existing approaches and detailed sensitivity analysis on different variations of criteria weights and parameter.

MEREC-MABAC method based on cumulative prospect theory for picture fuzzy sets: Applications to wearable health technology devices

Since there is a causal connection between the performance of the alternative with criteria removed and the assessment of the criteria weights, Method based on the Removal Effects of Criteria (MEREC) can reflect the objectivity of statistical data from this new perspective. Multi-Attributive Border Approximation area Comparison (MABAC) approach considers the subjective psychological characteristics of decision makers (DMs), particularly personal preferences and cognitive limitations, through expert scoring. Cumulative prospect theory (CPT) takes into account both potential gains and losses. We propose applying the MEREC method to picture fuzzy sets (PFS) for the first time to determine criteria weights. We suggest an alternate ranking approach for the CPT-MABAC algorithm based on the Hamming distance of the picture rejection membership degree that corresponds to four behavioral states: acceptance, opposition, abstention, and rejection. We develop the PFS-MEREC-CPT-MABAC evaluation framework to tackle the multiple attribute decision-making (MADM) problems while evaluating the performance of wearable health technology devices (WHTDs). The model achieves a compromise between objectivity and subjectivity as well as capturing the psychological behavior of the experts. Finally, in order to determine the soundness and effectiveness of this integrated research approach, sensitivity and comparison analysis are done to compare the results of its operation with those of existing models.

Strategies to promote patient-centeredness within the healthcare industry: A grey-based multicriteria decision making methods.

The international policy agenda has recently advocated for the development of patient-centeredness in healthcare service delivery. Consequently, various stakeholders in the healthcare systems have expressed a vital need for identifying strategies and tools that can enhance patient-centeredness. The objective of this paper was to prioritise and benchmark the strategies that can improve patient-centeredness in healthcare service delivery. We employed a multi-stage research scenario that consisted of two phases: a phase including of a scoping review to identify the current strategies to improve patient-centeredness (PC); And, a phase including of a multicriteria best-worst method to assign weights to PC principles, and a questionnaire administered to a sample of experts for benchmarking the strategies derived from the literature using the Grey Multi-Attributive Border Approximation Area Comparison (MABAC-G) method. The most important principle of patient-centeredness was deemed to be access to care, while telehealth tools and Electronic Health Information Systems were respectively suggested as the most efficacious platforms for promoting patient-centeredness. We recommend that administrators and policy makers in the healthcare industry prioritise the implementation and research of strategies such as telehealth tools and electronic health information systems to enhance access and patient-centeredness in the healthcare systems.

Enhancing urban livability: Analyzing Tehran through equitable land use distribution

Urban livability, a concept integral to human settlements, lacks a universally agreed-upon definition. It encompasses various aspects influencing the attractiveness and quality of life in a city. The research adopts an objective evaluation method, focusing on equitable access to urban services. The study aims to address these challenges by examining the livability of Tehran's 22 districts, emphasizing access to essential services. Employing a comprehensive methodology, the research utilizes analytical tools such as Standard Deviational Ellipse analysis (SDE), Average Nearest Neighbor (ANN), and the Multi-Attributive Border Approximation area Comparison (MABAC) technique. The spatial distribution analysis of urban land uses in Tehran reveals distinct patterns through Standard Deviational Ellipse analysis. Urban land uses exhibit directional orientations and concentrations, providing valuable insights for urban planning. Average Nearest Neighbor analysis indicates clustered distribution across various land uses, emphasizing the need for targeted interventions. The study ranks Tehran's districts based on livability using MABAC technique. District 21 emerges as the most livable, while District 10 faces challenges in service distribution. The findings highlight disparities and call for focused urban planning to enhance overall livability in Tehran, recognizing the importance of equitable access to urban services for a sustainable and cohesive urban environment.

"Thin" Structure of Relations in MCDM Models. Equivalence of the MABAC, TOPSIS(L1) and RS Methods to the Weighted Sum Method

This paper introduces the conceptual framework of the multi-criteria decision-making (MCDM) rank model, which embodies the integration and harmonization of the aggregation method, the weighing method, the decision matrix normalization technique, and the selection of distance metrics. This definition serves to broaden the spectrum of acceptable MCDM methodologies for problem-solving and specifiing the associated tools. A Multi-Method Model (3M) approach is employed for multi-criteria selection to enhance the reliability of the results. The methodology is outlined for adjusting the rankings of alternatives to account for the distinguishability of ratings in a particular MCDM model using the Relative Performance Indicator (RPI) of alternatives. Through RPI, four methods are established for aggregating individual characteristics of alternatives that yield identical results: Weighted Sum Model (WSM), Multi-Attributive Border Approximation area Comparison (MABAC), Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS (L1)), and Ratio System approach (RS), eliminating the need to duplicate these methods in the 3M approach. A comprehensive comparison of numerous multi-criteria methods is conducted based on two lists: ranking and rating. Additionally, a method for step-by-step linear transformation of alternative ratings obtained from various MCDM models is defined, facilitating comparison and aggregation of ratings.

Prioritizing complex health levels beyond autism triage using fuzzy multi-criteria decision-making

This study delves into the complex prioritization process for Autism Spectrum Disorder (ASD), focusing on triaged patients at three urgency levels. Establishing a dynamic prioritization solution is challenging for resolving conflicts or trade-offs among ASD criteria. This research employs fuzzy multi-criteria decision making (MCDM) theory across four methodological phases. In the first phase, the study identifies a triaged ASD dataset, considering 19 critical medical and sociodemographic criteria for the three ASD levels. The second phase introduces a new Decision Matrix (DM) designed to manage the prioritization process effectively. The third phase focuses on the new extension of Fuzzy-Weighted Zero-Inconsistency (FWZIC) to construct the criteria weights using Single-Valued Neutrosophic 2-tuple Linguistic (SVN2TL). The fourth phase formulates the Multi-Attributive Border Approximation Area Comparison (MABAC) method to rank patients within each urgency level. Results from the SVN2TL-FWZIC weights offer significant insights, including the higher criteria values "C12 = Laughing for no reason" and "C16 = Notice the sound of the bell" with 0.097358 and 0.083832, indicating their significance in identifying potential ASD symptoms. The SVN2TL-FWZIC weights offer the base for prioritizing the three triage levels using MABAC, encompassing medical and behavioral dimensions. The methodology undergoes rigorous evaluation through sensitivity analysis scenarios, confirming the consistency of the prioritization results with critical analysis points. The methodology compares with three benchmark studies, using four distinct points, and achieves a remarkable 100% congruence with these prior investigations. The implications of this study are far-reaching, offering a valuable guide for clinical psychologists in prioritizing complex cases of ASD patients.

Self-confidence Fermatean fuzzy trust network-driven consensus fusion framework: A new energy vehicle power battery recycling service outlet selection

The selection of new energy vehicle power battery (NEVPB) recycling service outlet will promote the rapid development of new energy vehicle industry. In order to cope the green and low-carbon development path of NEVPB recycling under information uncertainty and interaction. This paper intends to construct a multidimensional consensus fusion framework of self-confidence Fermatean fuzzy trust network. Firstly, individuals express their opinions based on Fermatean fuzzy sets with self-confidence (FFS-SC). Subsequently, a novel trust propagation and aggregation is built to obtain complete trust relations, and weights of individuals are determined by trust network, self-confidence, and relative deviation. Meanwhile, the trust network- driven consensus feedback mechanism is developed to collect evaluation information. Furthermore, the correlation coefficient to eliminate the homogeneity influence of indicators. In addition, the decision results are derived by Multi-Attributive Border Approximation area Comparison (MABAC) method. Finally, an applied case with five NEVPB recycling service outlets selection is presented to verify the feasibility of the established framework, and the effectiveness is further demonstrated through sensitivity analysis and comparative analysis. The case results show that the indicator with demonstration of recycling service outlet and market supervision departments strengthen supervision indicators have great impact on NEVPB recycling service outlets selection. Furthermore, this paper has reference significance for standardizing the management policy and technology of power battery recycling and realizing the green and low-carbon environmental protection of the whole life cycle of new energy vehicles.

Blending Uncertainty Theory Innovative into Decision Support Framework for Selecting Agricultural Machinery Suppliers

The rivalry among suppliers and stakeholders created pressure, making the selection of the best provider a crucial subject based on a set of criteria for the responsiveness of clients. Consequently, several criteria need to be considered to select the most suitable supplier. As a result, certain criteria may overlap and contradict one another. Multi-criteria decision-making (MCDM) techniques are widely used in many fields to address selection problems when there are numerous competing criteria and multiple alternatives. Hence, we are leveraging MCDM techniques in constructing a decision support framework (DSF) as MEthod based on the Removal Effects of Criteria (MEREC) and Multi-Attributive Border Approximation Area Comparison (MABAC). In our DSF, we harnessed the uncertainty theory of triangular neutrosophic sets (TrNSs) which is considered one of the advantages that DSF provides.TrNSs support experts in their judgments when facing problems such as imprecise judgments and insufficient data. As well, we applied the constructed DSF in a real case study for five suppliers for machinery agriculture and evaluated the suppliers based on ten criteria. The DSF’s findings indicated that supplier 5 is the optimal one for machinery agriculture otherwise supplier 2 is the worst one. Also, we applied another ranker technique of MCDM technique entailed in the weighted sum model (WSM) and compared WSM results with MABAC under the authority of TrNSs.

Beyond throughput: Evaluating maritime port competitiveness using MABAC and Bayesian methods

The performance ranking framework of maritime ports conducted by the World Shipping Council relies solely on a singular criterion, such as container throughput. This ranking method lacks a comprehensive assessment of port competitiveness and may have biases. Therefore, this work proposes a new approach to measure the competitiveness of maritime ports by considering multiple performance criteria, such as port processing time, bunker fuel quality, bunker fuel suppliers, geographical advantages, and port security. To ensure fairness in weight estimation, both objective method (using the Entropy method) and subjective method (using the Best Worst method) are employed and integrated using Bayes’ theorem. Furthermore, the Multi-Attributive Border Approximation Area Comparison (MABAC) method is utilized to evaluate the ranking of ports based on the combined effect of these criteria, referred to as the Port Competitiveness Index (PCI). To test the effectiveness of the proposed methodology, the top 20 major maritime ports on the Asia-Europe service route are determined. This finding highlights the practical insights gained from the study, which can be applied to improve the performance of ports across different service routes worldwide.

Multi-objective capacity configuration optimization of the combined wind - Storage system considering ELCC and LCOE

The optimal capacity configuration of combined wind-storage systems (CWSSs) serves as a foundation and premise for building new electricity system. This paper proposes a novel capacity configuration model of CWSS. The model simultaneously considers economy, stability and low carbon by respectively minimizing Levelized cost of electricity (LCOE) and maximizing effective load carrying capacity (ELCC). The multi-objective optimization model is solved by Non-dominated Sorting Genetic Algorithm II (NSGA-II). And Multi-Attributive Border Approximation Area Comparison (MABAC) method is used to score the alternative solutions on the Pareto front and make decisions under different scenarios. The results of the case study show that: (1) The evaluation of the ELCC for CWSS can quantify its ability to replace the installed capacity of thermal power, thus greatly reducing the uncertainty of wind power output. (2) Under low-carbon scenario, economic scenario and standard scenario, the results of optimal capacity configuration for the CWSS show that LCOE and ELCC are negatively correlated. When the weights of ELCC and LCOE are equal, the three goals of economy, low carbon and safety and stability are achieved. (3) The results of a single sensitivity analysis on LCOE show that the sensitivity coefficient of electricity generation is the highest, exerting the greatest impact on the capacity configuration decision of the CWSS. A 20 % increase in power generation can reduce LCOE by 0.1203RMB/kWh. This paper provides energy planning recommendations for decision-makers in CWSSs, contributing to the development of a reliable new electricity system.

Evaluation of agile attributes for low-cost carriers to achieve sustainable development using an integrated MCDM approach

PurposeSince conducting agile strategies provides sustainable passenger satisfaction and revenue by replacing applied policies with more profitable ones rapidly, the focus of this study is to evaluate agile attributes for managing low-cost carriers (LCCs) operations by means of resources and competences based on dynamic capabilities built on resource-based view (RBV) theory and to achieve sustainable competitive advantage in a volatile and dynamic air transport environment. LCCs in Turkey are also evaluated in this study since the competition among LCCs is high to gain market share and they can adapt quickly to all kinds of circumstances.Design/methodology/approachTwo well-known Multi-Criteria Decision-Making Methods (MCDM) named as the Stepwise Weight Assessment Ratio Analysis (SWARA) and multi-attributive border approximation area comparison (MABAC) methods by employing Picture fuzzy sets (PiFS) are employed to determine weight of agile attributes and superiority of LCCs based on agile attributes in the market, respectively. To check the consistency and robustness of the results for the proposed approach, comparative and sensitivity analysis are performed at the end of the study.FindingsWhile the ranking orders of agile attributes are Strategic Responsiveness (AG1), Financial Management (AG4), Quality (AG2), Digital integration (AG3) and Reliability (AG5), respectively, LCC2 is selected as the best agile airline company in Turkey with respect to agile attributes. SWARA and MABAC method based on PiFS is appropriate and effective method to evaluate agile attributes that has important reference value for the airline companies in aviation industry.Practical implicationsThe findings of this study will support managers in the airline industry to conduct airline operations more flexibly and effectively to take sustainable competitive advantage in unexpected and dynamic environment.Originality/valueTo the author' best knowledge, this study is the first developed to identify the attributes necessary to increase agility in LCCs. Thus, as a systematic tool, a framework is developed for the implementation of agile attributes to achieve sustainable competitive advantage in the airline industry and presented a roadmap for airline managers to deal with crises and challenging situations by satisfying customer and increasing competitiveness.

Comparison Of G-7 Countries' Macroeconomic Performance with SD and MABAC Methods

Countries determine their economic policies according to macroeconomic performance criteria. Macroeconomic performance criteria provide basic information on the level of development of countries. A reduction in unemployment, price stability and a balanced budget are important for achieving economic growth and competitiveness. The aim of this study is to analyse the macroeconomic performance of the G7 (Germany, the United States, the United Kingdom, Italy, France, Japan and Canada), a union of the seven countries with the highest level of development in the world, between 2018 and 2022. . In the study, macroeconomic criteria (unemployment, inflation, external balance, and growth), which are called "magic diamonds" by OECD (Organization for Economic Cooperation and Development), were applied using SD (Standard Deviation) and MABAC (Multi-Attributive Border Approximation Area Comparison) methods. According to the results of the SD method, the inflation criterion has the greatest impact on macroeconomic performance in 2018 and 2019, the unemployment criterion in 2020 and 2022, and the growth criterion in 2021. According to the results of the MABAC method, Germany had the highest macroeconomic performance in 2020, and Japan had the highest macroeconomic performance in the other analyzed years.

A Parameter Reduction-Based Decision-Making Method with Interval-Valued Neutrosophic Soft Sets for the Selection of Bionic Thin-Wall Structures.

Bio-inspired thin-wall structures with excellent mechanical properties, high-energy absorption capabilities, and a desirable lightweight level have been extensively applied to the passive safety protection of transportation and aerospace. Collaboration matching and the selection of optional structures with different bionic principles considering the multiple attribute evaluation index and engineering preference information have become an urgent problem. This paper proposes a parameter reduction-based indifference threshold-based attribute ratio analysis method under an interval-valued neutrosophic soft set (IVNS-SOFT) to obtain the weight vector of an evaluation indicator system for the selection of bionic thin-wall structures, which can avoid the problem of an inadequate subjective evaluation and reduce redundant parameters. An IVNS-SOFT-based multi-attributive border approximation area comparison (MABAC) method is proposed to obtain an optimal alternative, which can quantify uncertainty explicitly and handle the uncertain and inconsistent information prevalent in the expert system. Subsequently, an application of five bio-inspired thin-wall structures is applied to demonstrate that this proposed method is valid and practical. Comparative analysis, sensitivity analysis, and discussion are conducted in this research. The results show that this study provides an effective tool for the selection of bionic thin-wall structures.

Analysis of Trade Competitiveness of the World s Leading Textiles Exporters by Hybrid MCDM Methods

Purpose –This paper demonstrates the application of the IDOCRIW-based MABAC and MAIRCA methods in the evaluation of the trade competitiveness of the top fifteen textiles exporters in the world. Design/methodology/approach –The trade competitiveness index was used for assessing the trade competitiveness of exporters in the textile industry. The IDOCRIW method was used to obtain the weight of criteria. The analysis of the trade competitiveness of exporters was carried out using the new multi-criteria methods, namely the MABAC (Multi-Attributive Border Approximation area Comparison) and the MAIRCA (Multi-Atributive Ideal-Real Comparative Analysis). Findings –According to the results obtained by the IDOCRIW method, share in the world market (SWM) and net exports (NEX) were determined as the most and least important criteria with weight values of 0,3297 and 0,0816, respectively. After determining the criteria weight, the alternatives were ranked using the MABAC and MAIRCA methods. The results for both methods indicate that China, Italy, Luxembourg, Belgium, and Spain have the highest trade competitiveness, while Croatia, Ireland, the Republic of Cyprus, Malta, and Mexico have the lowest trade competitiveness in the textile industry. The findings demonstrated that MABAC and MAIRCA provided consistent solutions for ranking the alternatives. Additionally, the consistency and robustness of the results were tested using two different scenarios. The overall results revealed that China and Italy have the best trade competitiveness in the textile industry. Discussion –As expected, China and the European Unioun (EU) countries have the highest trade competitiveness in the world’s textile industry. It can also be stated the textile industry is strategic sector for both the EU countries and China.