Weights Of Evaluation Criteria Research Articles

A Novel Intuitionistic Fuzzy Set-Based Risk Priority Number Method for Solving Chemical Experiment Risk Evaluation

Scientific experiments cover a wide range of fields—from basic to applied scientific research. Chemical experiments are the basis for cultivating chemical knowledge in scientific experiments and are an important way to cultivate scientific thinking and methods. However, due to the toxicity or flammability of the chemical substances in the experiments, hazardous events often lead to personal injuries and environmental damage. Exactly assessing risk factors and reducing the risk of hazards to protect the experimenters and ensure environmental safety are crucial in chemical experiments. However, while the traditional risk evaluation method cannot consider the weight of risk evaluation criteria, it also cannot effectively address problems through hierarchical analysis, as well as imprecise and ambiguous information inherent in human cognition. Therefore, this paper proposed an approach based on failure mode and effects analysis (FMEA) to assess the risk of chemical experiments in a fuzzy information environment. The approach combines the typical analytic hierarchy process (AHP), the risk priority number (RPN) of FMEA, and the intuitionistic fuzzy set (IFS) methods to evaluate risks associated with chemical experiments and consider the damage recovery in chemical experiments. This study applied the case of a university chemistry experiment, “preparation of hydrogels”, to validate the reasonableness and correctness of the proposed approach and compare its numerical verification results with those from the typical RPN, the AHP-RPN, and the AHP-fuzzy risk priority number (AHP-FRPN) methods. The finding demonstrates that the proposed method can more effectively address risk evaluation problems in chemical experiments than the other methods. This result serves as an important reference for reducing chemical experiment risk occurrences.

A Hybrid Triangular Fuzzy SWARA-MAROCS Approach for Selecting Optimal and Smart Logistic Enterprise Based on IoT, Blockchain, and UAVs

With the development of technology and the Internet of Things (IoT), the use of electronic commerce has become necessary. There are many logistical enterprises now use the IoT to support their delivery process via drones, due to the numerous advantages they offer over traditional methods. However they face some restrictions and security concerns, so blockchain technology was used to overcome these restrictions and follow up on the delivery process to meet the requirements of e-commerce. In light of this, choosing the most suitable logistics enterprises to meet the needs of customers in a given situation has become a challenging task for decision-makers. To facilitate this task, we have introduced a systematic approach that uses triangular fuzzy step-wise weight assessment ratio analysis (SWARA) and triangular fuzzy measurement of alternatives and ranking according to compromise solution (MAROCS) methods to evaluate and rank alternatives. Fuzzy SWARA is used to determine the weights of evaluative criteria, while Fuzzy MARCOS is used to rank alternatives and select the best of them. This method eliminates ambiguity and imprecision in the decision-making process.

An integrated design concept evaluation model based on interval valued picture fuzzy set and improved GRP method.

The objective of this research is to enhance the precision and efficiency of design concept assessments during the initial stages of new product creation. Design concept evaluation, which occurs at the end of the conceptual design phase, is a critical step in product development. The outcome of this evaluation significantly impacts the product's eventual success, as flawed design concepts are difficult to remedy in later stages. However, the evaluation of new product concepts is a procedure that encompasses elements of subjectivity and ambiguity. In order to deal with the problem, a novel decision-making method for choosing more logical new product concepts is introduced. Basically, the evaluation process is outlined in three main phases: the construction of evaluation index system for design concept alternatives, the calculation of weights for evaluation criteria and decision-makers, the selection of the best design concept alternatives. These stages are composed of a hybrid method based on kano model, multiplicative analytic hierarchy process (AHP) method, the entropy of IVPFS and improved grey relational projection (GRP) under interval-valued picture fuzzy set (IVPFS). The novel approach integrates the strength of interval-valued picture fuzzy number in handling vagueness, the advantage of multiplicative AHP and the merit of improved GRP method in modelling multi-criteria decision-making. In final, the effectiveness of the proposed model is validated through comparisons with other models. The potential applications of this study include but are not limited to product development, industrial design, and innovation management, providing decision-makers with a more accurate and comprehensive design concept evaluation tool.

Urban Infrastructure Innovation: The Contribution Ratio-Based Distance-to-Target Prioritization Method for Advanced Systems

As urban infrastructure increasingly benefits from adopting advanced systems, several approaches for prioritizing their utilization in urban infrastructure planning have been proposed. However, more research focusing on prioritizing advanced systems in urban infrastructure is required, and many existing prioritization methods rely on subjective inputs to determine the weights (i.e., relative importance) of evaluation criteria. This paper introduces a contribution ratio-based distance-to-target (CR-DTT) method for prioritizing advanced systems to assist urban infrastructure innovation. The novel prioritization method combines DTT normalization and ratio-based weights, and consists of three modules: (a) hierarchy table preparation; (b) input of advanced systems information; and (c) advanced systems prioritization. Each module involves multiple steps that generate outputs, which serve as inputs for the subsequent modules. We applied the prioritization method to a case study involving urban roads to demonstrate its effectiveness in real-world settings. The newly presented CR-DTT prioritization method contributes to the current knowledge in the area of developing prioritization methods by developmentally combining the DTT normalization and the ratio-based weights approaches into a practical data-driven prioritization method, which addresses the temporal aspect of the input parameter and provides objective insights into how best to prioritize advanced systems to assist transparent and credible decision-making in relation to investment in urban infrastructure innovation.

APPLICATION OF PFAHP-GTOPSIS METHODS FOR THIRD-PARTY LOGISTICS PROVIDER SELECTION

Companies are now considering the option of outsourcing as hedges cost and increase productivity by concentrating on their core skills to update their supply chains due to the competition in global markets, the use of higher-quality products, and rising customer demands. They must carefully select and identify which company to collaborate with before outsourcing their numerous logistics-related tasks to Third-Party Logistics Providers (3PLP). However, the existence of uncertainties and human influence in 3PLP selection problems leads to the usage of fuzzy or related set theories. By incorporating Multi-Criteria Decision Making (MCDM) methods with fuzzy numbers and grey numbers, practical tools can be composed to address the imprecision of subjective judgments. From this perspective, an integrated MCDM model is proposed to provide insight into the 3PLP evaluation and selection. The model comprises an integrated framework with Pythagorean fuzzy numbers and grey numbers. The proposed model has applied a 3PLP a company in the food industry to fulfill customer orders. The evaluation criteria weights are calculated using the Pythagorean Fuzzy Analytic Hierarchy Process (PFAHP) method, and the 3PLPs are ranked using the grey Technique for Order Preference by Similarity to Ideal Solution (GTOPSIS) methods to find the best 3PLP. The analyses and findings concluded that cost, service quality, and on-time delivery were the three criteria that had the greatest influence

Evaluation of different types of alternative energy sources in China using hybrid methods

In recent years, alternative energy sources(AES) including hydropower, wind power, nuclear energy, solar power, and biomass are becoming increasingly popular in China. Comprehensive evaluation of each AES is of vital importance for the further development of energy production and utilization. This study combines the Life-Cycle Assessment (LCA) with Multi-Criteria Decision-Making(MCDM) methods and assesses AES in China comprehensively. The results show that hydropower is the best AES in China. However, the study cannot rank the rest of the AES because different evaluation criteria weights and different MCDM methods lead to different results for the AES ranking. By analyzing Chinas policies, the technical aspect should be considered first, following environmental factors, economic feasibility, and social sustainability. There are several limitations of evaluation methods and more studies need to be done.

Sustainable Supplier Selection and Order Allocation Using an Integrated ROG-Based Type-2 Fuzzy Decision-Making Approach

The sustainable Supplier Evaluation and Selection and Order Allocation (SSOA) problem has received significant attention in supply chain management due to its potential to enhance a company’s performance, improve customer satisfaction, and reduce costs. In this study, an integrated methodology is proposed to address the SSOA problem. The methodology combines multiple techniques to handle the uncertainties associated with supplier evaluation, including a new ranking method based on the concept of Radius of Gyration (ROG) for interval type-2 fuzzy sets. The methodology also incorporates both subjective weights obtained using the Simple Multi-Attribute Rating Technique (SMART) and expert preferences, and objective weights calculated using the Method based on the Removal Effects of Criteria (MEREC) method to determine the weights of evaluation criteria. Some criteria for sustainable development are used to evaluate supplier performance, resulting in type-2 fuzzy sets, which are evaluated using the Weighted Aggregated Sum Product Assessment (WASPAS) method. The ROG-based ranking method is employed to calculate the relative scores of suppliers. Finally, a multi-objective decision-making (MODM) mathematical model is presented to identify suitable suppliers and allocate their order quantities. The methodology is demonstrated in a sustainable SSOA problem and is shown to be efficient and effective, as the ROG-based ranking method allows for more accurate supplier performance evaluation, and the use of the criteria highlights the importance of sustainability in supplier selection and order allocation. The methodology’s practicality is further supported by the analysis conducted in this study, which demonstrates the methodology’s ability to handle the uncertainties associated with supplier evaluation and selection. The proposed methodology offers a comprehensive approach to the SSOA problem that can effectively handle the uncertainties in supplier evaluation and selection and promote sustainable practices in supply chain management.

Evaluate teaching quality of physical education using a hybrid multi-criteria decision-making framework.

The teaching quality evaluation of physical education is an important measure to promote the professional development of physical teachers, improve the quality of school teaching and personnel training. It is helpful for students to achieve all-round development and better meet the needs of modern talents in the new era. This study aims to establish a novel MCDM (multi-criteria decision-making) framework for evaluating teaching quality of physical education. First, PFNs (picture fuzzy numbers) are suggested to reflect dissimilar attitudes or preferences of decision makers. Then, the typical SWARA (Step-wise Weight Assessment Ratio Analysis) model is modified with PFNs to calculate the weights of evaluation criteria. Considering that some criteria are non-compensatory during the evaluation process, the idea of ELECTRE (elimination and choice translating reality) is introduced to obtain the ranking results of alternatives. Specially, the MAIRCA (Multi-Attribute Ideal-Real Comparative Analysis) method is extended to construct the difference matrix in a picture fuzzy environment. Last, the hybrid MCDM model is utilized to assess the teaching quality of physical education. Its superiority is justified through comparison analyses. Results show that our approach is practicable and can provide instructions for the teaching quality evaluation of physical education.

Selection of optimal seismic intensity measures using fuzzy-probabilistic seismic demand analysis and fuzzy multi-criteria decision approach

A fuzzy decision-theoretic computational approach for selecting optimal intensity measures (IMs) under the conditions of randomness, fuzziness and uncertainty is proposed in this paper. The main objectives of this study are twofold: (a) to quantify the contribution of multiple evaluation criteria, and (b) to improve the credibility of decision-making results by selecting the appropriate multi-criteria decision making (MCDM) method. To achieve these, three components of the methodology for this approach are utilized: (1) Fuzzy-probabilistic seismic demand analysis (FPSDA) is employed to consider the aleatory and epistemic uncertainties in the seismic demand analysis. (2) Fuzzy analytical hierarchical process (FAHP) is used to determine the importance weights of multiple evaluation criteria. (3) A combination of FAHP and fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) is utilized to determine the optimal IM alternatives. First, the FPSDA method in which the interval PSDA is applied to evaluate the IM performance is developed. The interval results are transformed into triangular fuzzy numbers to establish a fuzzy decision-making matrix. Subsequently, the FAHP is employed to estimate the importance of each evaluation criterion. Then, FAHP–FTOPSIS is utilized to rank the orders of the IM alternatives and select the optimal IM. An illustrative application of the proposed methodology to a five-story reinforced concrete frame structure is presented. The decision results demonstrate that by considering the maximum inter-story displacement angle (θmax), top displacement (Dt), maximum floor velocity (Vmax) and maximum floor acceleration (Amax) as engineering demand parameters (EDPs), the average spectral displacement (Sdam), Sdam, average spectral velocity (Svam), and Vamvatsikos spectral acceleration (VSa2) are the most optimal IMs, respectively. The decision-making results for multiple EDPs indicate that Svam is the optimal IM. Furthermore, the Cordova spectral acceleration (CSa) considering period prolongation has the highest closeness coefficient among the acceleration-related IMs. To verify the effectiveness of the new framework and methodology, the performance of the proposed approach is compared with that of the traditional MCDM method while ignoring fuzziness. The proposed methodology can be applied to other engineering structures and provide a reference for solving similar problems.

Hybrid MADM-based study of key risk factors in house-for-pension reverse mortgage lending in Taiwan's banking industry

The issue of financial problems among the elderly has garnered the attention of multiple generations. In 2018, Taiwan formally attained an ageing society status. In order to address the financial difficulties elderly people face, an annuity system reform was launched by the Taiwanese government. Reverse mortgage lending, which is performed on the basis of house-for-pension, is an alternate method for enhancing their standard of living and sustaining their financial stability. The elderly applicants often mortgage their properties to financial institutions in order to maintain a steady cash flow for the fulfilment of their daily needs and live their remaining years in the mortgaged properties without vacating. Prior studies on house-for-pension in the Taiwanese context primarily concentrated on institutional implementation analysis but limitedly explored the uncertainty risks banks face when implementing the house-for-pension scheme. First, a literature review on the risks associated with house-for-pension reverse mortgage financing and comprehensive interviews with banking industry professionals were conducted. Subsequently, in the Taiwanese banking sector, assessment criteria for important risk factors in house-for-pension reverse mortgage financing were devised. The decision-making trial and evaluation laboratory (DEMATEL) was utilised to assess the interdependence of the assessment criteria. Next, the DEMATEL-based analytic network process (ANP) or DANP were used to calculate the weights of evaluation criteria. By employing modified VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), a gap analysis was undertaken on the assessment criteria and sub-criteria. The study's findings serve as a guideline for the Taiwanese banking industry in formulating, improving, or amending the risk exposure mitigation measures for reverse mortgage lending.

Evaluating the Sustainable Development of the Semiconductor Industry Using BWM and Fuzzy TOPSIS

In recent years, the advancement of electronic technology has been driving a boom in the semiconductor industry worldwide. When implementing business activities, those involved strive to pursue the balanced development of the economy, society, environment, and ecology. Our proposed model provides a systematic analysis process to help conduct a comprehensive evaluation and determine priorities in the semiconductor industry’s pursuit of sustainable development. In the proposed model, we determine the weights of evaluation criteria using the Best Worst Method, which overcomes the shortcomings of AHP. Meanwhile, we incorporate the concept of the aspiration level to optimize the fuzzy TOPSIS technique. The results reveal that the two most important criteria are green resource integration and pollution-discharge treatment for sustainable development in the semiconductor industry. In the traditional TOPSIS method, pollution-discharge treatment is considered the expected value, leading the decision-maker to believe that no improvement is required for pollution-discharge treatment. The model proposed in this study can overcome the abovementioned shortcoming and offer more reliable managerial implications.

An optimization model for rescuer assignments under an uncertain environment by using Dempster–Shafer theory

Emergency decision making and disposal are significant challenges faced by the international community. To minimize the emergency casualties, and reduce probable secondary disasters, it is necessary to immediately dispatch rescuers for emergency rescue in calamity prone areas. The abruptness, destructiveness, and uncertainty of emergencies, the rescue team often faces challenges of pressing time, scattered calamity locations, and diverse tasks. This necessitates the effective organization of rescuers, for their swift dispatch to the areas requiring rescue. A valuable research problem is how to group and dispatch rescuers reasonably and effectively according to the actual needs of the emergency rescue task and the situation to achieve the best rescue effect. This study establishes a dispatch model for rescuers in multiple disaster areas and rescue points. First, this paper combines the Dempster–Shafer theory (DST) and linguistic term set, to propose the concept of an evidential linguistic term set (ELTS), that can flexibly and accurately describe the subjective judgment of emergency decision-makers. It not only lays a theoretical foundation for establishing the rescuers’ dispatch model, but also aids in expressing information in uncertain linguistic environments of decision-making and evaluation. Second, to determine the weight of ability-based rescuer evaluation criteria, this study adopted the evidential best–worst method, combining it with DST to compensate for the limitations of the traditional weightage calculation method in expressing uncertainty. Third, to effectively dispatch rescuers to multiple disaster areas, modeling is carried out based on the above methods to maximize the competence of rescuers and the satisfaction of rescue time, and the best scheme for the allocation of rescuers is determined by solving the model. Finally, the advantages of the constructed model in emergency multitasking group decision-making are demonstrated through an empirical analysis.

Optimal site selection of rural wind–photovoltaic-storage station from a sustainable development perspective

Due to the large amount of greenhouse gas emissions, sustainable power projects like rural wind–photovoltaic-storage stations (WPSS) have been recently proposed. There are a lot of uncertainties decrease investors’ willingness to invest in those projects. To select better location of power station construction and ensure its overall economic optimization base on sustainability perspective is an important issue for investors. This study established practical evaluation criteria for the site selection of WPSS under the fuzzy environment, which consist of 15 criteria from the dimension of resource and technical, environmental, social and policy, and economic. A hybrid method combining Decision Making Trial and Evaluation Laboratory (DEMATEL), Analytic Network Process (ANP) and Interactive Multi-Criteria Decision-Making (TODIM) was proposed to determine the weights of evaluation criteria and screen out the optimal site. The results show that the most important risk factors are economic factors (0.388), among which, utilization level of investment cost is the most important one (0.234). Besides, the priorities of alternative location may change due to different risk preference. In this paper, the ranking is P3>P4>P2>P1. This study helps WPSS investors find out the optimal site in a few candidates. For WPSS projects, decision-makers should focus more on economic risks, especially utilization level of investment cost. While social and policy factors (service radius and public acceptance) remain the secondary and should be carefully dealt with during the site-selection stage.

An integrated MCDM framework based on interval 2-tuple linguistic: A case of offshore wind farm site selection in China

Offshore wind farm (OWF) site selection is critical to the successful development of offshore wind energy and manifests as a complex multi-criteria decision-making (MCDM) process. To promote the further healthy development of offshore wind energy, this study developed a new integrated MCDM framework to better evaluate and rank OWF sites. The main contributions are as follows. First, the interval 2-tuple linguistic (I2TL) provides a simple, interpretable, and precise approach to linguistic information handling and effectively prevents information missing and distortion. Second, different opinions are aggregated using a modified similarity aggregation method (SAM), reducing the errors due to neglecting the effect of individual differences on consistency. Third, a new integrated weighting method combining the simplified best-worst method (SBWM) and method based on the removal effects of criteria (MEREC) is adopted to acquire evaluation criteria weights, which comprehensively reveals the relative importance of criteria. Fourth, the proximity indexed value (PIV) method is expanded with I2TL for alternative ranking to minimize the rank reversal problem. Finally, the applicability and robustness of the proposed framework are validated through a case study in China as well as sensitivity and comparative analyses. The proposed MCDM framework can provide beneficial support for managers to analyze and select the optimal OWF site.

다기준 의사결정 방법을 활용한 사용자 경험의 평가 방법

Objective: This study was conducted for the purpose of proposing a method to evaluate user experience for a system using a multi-criteria decision making model based on the evaluation factors of user experience.<BR>Background: User experience refers to a direct or indirect experience that a user obtains in the process of using a product or service. As various digital products and services become popular and competition intensifies in modern life, user experience is becoming more and more important. Various studies have been attempted to evaluate user experience according to such necessity. Since the user experience that an individual has for the same system can be different, it is important how the user experience evaluation of the system incorporates this subjectivity.<BR>Method: In this study, a method to obtain the user experience score was proposed by using importance weights of the user experience evaluation criteria and averaging the scores for each evaluation criterion of the system. To this end, we first determined the criteria for evaluating user experience based on the honeycomb model. AHP was used as a method to derive the relative importance weight of evaluation criteria. In addition, a Likert scale from 1 to 5 was used to give a rating for the user experience evaluation criteria.<BR>Results: In order to examine the applicability of the method proposed in this study, it was applied to the user experience evaluation problem for car rental websites. In this study,

Electric bus charging station site selection based on the combined DEMATEL and PROMETHEE-PT framework

Electric buses (EBs) are one of the most promising transportation modes to reduce air pollution and greenhouse gas emissions. With the promotion of EBs, the selection of EBs charging station (EBCS) site plays a key role in bus electrification. In order to deal with the information uncertainty and the complicated logical relationship between evaluation criteria, this paper establishes a combined framework of fuzzy decision-making trial and evaluation laboratory (DEMATEL), preference ranking organization method for enrichment evaluations (PROMETHEE) and prospect theory (PT) to evaluate EBCS sites. Firstly, the evaluation criteria system consisting of transportation, economy, technology, and environment for the EBCS site selection problem is established. Secondly, the fuzzy DEMATEL method is adopted to calculate the weights of EBCS evaluation criteria and analyze their causal relationships. Thirdly, the fuzzy PROMETHEE method with PT is combined to obtain the ranking of the alternative EBCS sites. Finally, based on the case study in Nanjing city, the proposed combined framework proves to be effectiveness and scalability. The numerical results show that the long-term influencing criteria (e.g., coordination with power grid planning, total investment cost), the net cause criteria and the criteria with strong mutual influence have great impact on EBCS site selection.

Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method

This research aims to analyze the performance of state-of-the-art machine learning techniques for classifying COVID-19 from cough sounds and to identify the model(s) that consistently perform well across different cough datasets. Different performance evaluation metrics (precision, sensitivity, specificity, AUC, accuracy, etc.) make selecting the best performance model difficult. To address this issue, in this paper, we propose an ensemble-based multi-criteria decision making (MCDM) method for selecting top performance machine learning technique(s) for COVID-19 cough classification. We use four cough datasets, namely Cambridge, Coswara, Virufy, and NoCoCoDa to verify the proposed method. At first, our proposed method uses the audio features of cough samples and then applies machine learning (ML) techniques to classify them as COVID-19 or non-COVID-19. Then, we consider a multi-criteria decision-making (MCDM) method that combines ensemble technologies (i.e., soft and hard) to select the best model. In MCDM, we use the technique for order preference by similarity to ideal solution (TOPSIS) for ranking purposes, while entropy is applied to calculate evaluation criteria weights. In addition, we apply the feature reduction process through recursive feature elimination with cross-validation under different estimators. The results of our empirical evaluations show that the proposed method outperforms the state-of-the-art models. We see that when the proposed method is used for analysis using the Extra-Trees classifier, it has achieved promising results (AUC: 0.95, Precision: 1, Recall: 0.97).

Site selection for hybrid offshore wind and wave power plants using a four-stage framework: A case study in Hainan, China

The site selection for hybrid offshore wind and wave power plants (HOWWPP) is a critical step to a successful HOWWPP project. In this study, a four-stage framework is presented for determining the most suitable marine areas for the siting of HOWWPP. First, wind and wave energy potentials are assessed as a foundation for the implementation of a HOWWPP project. Next, unsuitable areas for the siting of HOWWPP are determined based on exclusion criteria to avoid any potential conflicts of marine spatial planning. Feasible areas (not satisfying the exclusion criteria) are classified and converted into spatial layers separately according to evaluation criteria. Then, the triangular fuzzy analytic hierarchy process is applied to calculate the evaluation criteria weights. Finally, the site suitability of feasible areas is calculated using the weighted overlay approach and then categorized into five classes. To validate the effectiveness of the proposed framework, a case study in Hainan Province of China was conducted. The results indicate that the marine areas with medium to very high suitability are approximately 1312 km2 (4.7% of the study area) for the deployment of HOWWPP. The obtained results of this study can support potential planners in selecting marine areas for the installation of HOWWPP.

An intelligent multi-criteria decision-based approach for sustainable growth of the energy sector: the case study of India and Vietnam

Traditional and sustainable forms of energy are examined from a variety of angles, including economics, technology, society, ecology, politics, and flexibility, to ensure that Vietnam’s energy industry continues to expand sustainably. These sources have been evaluated and assessed using an extended spherical fuzzy MCDM method. Thermal, gas, nuclear, solar, wind, biomass, and hydro energy alternatives are employed in the decision-making model. The weights of evaluation criteria are determined using the spherical fuzzy AHP model, and renewable power choices are prioritized using the WPM model. We used six main criteria, twenty-six sub-criteria, and seven alternatives. In Vietnam, solar power was found to be the most suitable, followed by wind and hydropower, followed by hydro, followed by Biomass. The worst alternative is thermal. After that, fourteen situations were built, taking into consideration the first five renewable technologies (solar, wind, hydro, biomass, and gas power), in assessing the ideal energy mix scenario for the slow gestation of Vietnam's energy sector. Solar, wind, and hydro energy growth in a pass shipping scenario.

Evaluate and select state-owned enterprises with sustainable high-quality development capacity by integrating FAHP-LDA and bidirectional projection methods

In order to win in the market competition, enterprises have considered sustainable high-quality development (SHQD) as an important goal. However, prior studies on SHQD mainly emphasized theoretical discussions, and few scholars have conducted quantitative data analysis, especially in the state-owned enterprises (SOEs) area. Given this research gap, this paper proposes a novel framework to evaluate and select state-owned enterprise with sustainable high-quality development capacity (SHQDC) in China under linguistic distribution assessment (LDA) environment. This study has been done in four stages. In the first stage we present a detailed evaluation index system for SHQDC. In the second stage, to determine the comprehensive weights of evaluation criteria and sub-criteria, we extend the traditional fuzzy analytic hierarchy process (FAHP) method to determine the subjective weights of criteria and sub-criteria, and propose a novel method to compute the objective weights of criteria and sub-criteria under linguistic distribution assessment environment. In the third stage, we extend the traditional bidirectional projection method to the linguistic distribution environment and further put forward a ranking method to select SOEs with SHQDC, in which the ratings of sub-criteria are assessed in linguistic distribution assessments to reflect the qualitative evaluation of experts’ subjective opinions. Finally, we demonstrate the validity of the proposed approach by means of comparing the sustainable high-quality development capacity of three state-owned enterprises in China.