Weights Of Experts Research Articles

Analysis of interrelationships of human errors using linguistic decision-making trial and evaluation laboratory with consensus reaching process

Analyzing human errors' interrelationships is one of the most important assignments for human reliability improvement in sociotechnical systems. Human factor analysis and classification system (HFACS) is effective in human error analysis due to its taxonomy and systematical perspective. It reveals interrelationships among human errors emerging in a multi-hierarchy of systems. However, the conventional HFACS method is incapable of quantifying their interrelationship. Especially, due to the nature of human errors, their objective data is limited. Experts' opinions are important resources to facilitate human error analysis. However, limited improved HFACS considers experts' consensus on interrelationships analysis results, especially in linguistic environments. Accordingly, this paper aims to address HFACS-based interrelationships analysis problems utilizing linguistic decision-making trial and evaluation laboratory (DEMATEL) with consensus reaching process (CRP). First, probabilistic linguistic terms are utilized to represent experts' opinions on human errors' interrelationships. Second, CRP is introduced to derive consensual opinions on human errors' interrelationships, shifting the focus to identifying human errors with low consensus levels rather than experts. Then, a hybrid weighting method is introduced to determine the weight of experts' opinions in the information fusion phase, which reflects inherent uncertainty and inter-recognition of experts’ opinions. Furthermore, DEMATEL is introduced to model direct and indirect interrelationships among human errors. Finally, a case study of a drug administration process is conducted to validate the efficiency of the proposed method. The case study indicates that neglect of safety culture development and limited financial and human resources are the top two human errors, with importance degree 0.148 and 0.107.

Grey preferences selection index with trimmed group preference for evaluating university dormitory renovation design

Renovating old buildings is a strategic approach to resource optimization and sustainability. However, dormitory design decisions are often made by a limited panel of experts, which risks excluding student preferences-key to the design process. Expert assumptions about student needs can lead to biased outcomes, and attempts to gather student input may suffer from respondent bias and inconsistent engagement, introducing data noise. This paper presents the Grey Trimmed Preferences Selection Index (GTPSI) Multi-Criteria Decision Making (MCDM) method, an improved preference selection index MCDM method that integrates expert weights and refines student feedback by filtering outliers, using interval grey numbers to address uncertainty. Applied to dormitory renovation proposals, GTPSI provides a stable ranking framework, as confirmed by whitening sensitivity analysis, ensuring consistency across grey bounds. To further validate the selected alternative, results are cross-verified with TOPSIS and ELECTRE-III, reinforcing GTPSI’s robustness in aligning renovation designs with actual student needs. The GTPSI method thus offers a comprehensive solution for integrating user-centered preferences into expert evaluations, promoting designs that better meet stakeholder expectations.

Decision-Making Using the Correlation Coefficient Measures of Intuitionistic Fuzzy Rough Graph

The hybrid approach produced by combining mathematical representations of intuitionistic fuzzy sets and rough sets is called an intuitionistic fuzzy rough framework. This novel approach addresses vagueness and soft computation by using the lower and upper approximation spaces. The degree of connection between intuitionistic fuzzy rough preference relations is assessed in this study using the correlation coefficient method. An improved comprehension of the link between fuzzy elements is made possible by the superior features of the suggested correlation coefficient measure over the current one. An intuitionistic fuzzy rough environment in which attribute decision-making is based on integrated with the correlation coefficient measure. Additionally, a novel method for determining expert weights based on intuitionistic fuzzy rough preference relations uncertainty and the degree of each IFRPR's correlation coefficient is proposed in the paper. The correlation coefficient measurements between each option and the optimal choice are used in the study to calculate the ranking order of the alternatives. Finally, we introduce a cooperative decision-making method in a cotton seed; this concept may be developed in several advantageous cotton seedlings.

Jointly stochastic fully symmetric interpolatory rules and local approximation for scalable Gaussian process regression

When exploring the broad application prospects of large-scale Gaussian process regression (GPR), three core challenges significantly constrain its full effectiveness: firstly, the O(n3) time complexity of computing the inverse covariance matrix of n training points becomes an insurmountable performance bottleneck when processing large-scale datasets; Secondly, although traditional local approximation methods are widely used, they are often limited by the inconsistency of prediction results; The third issue is that many aggregation strategies lack discrimination when evaluating the importance of experts (i.e. local models), resulting in a loss of overall prediction accuracy. In response to the above challenges, this article innovatively proposes a comprehensive method that integrates third-degree stochastic fully symmetric interpolatory rules (TDSFSI), local approximation, and Tsallis mutual information (TDSFSIRLA), aiming to fundamentally break through existing limitations. Specifically, TDSFSIRLA first introduces an efficient third-degree stochastic fully symmetric interpolatory rules, which achieves accurate approximation of Gaussian kernel functions by generating adaptive dimensional feature maps. This innovation not only significantly reduces the number of required orthogonal nodes and effectively lowers computational costs, but also maintains extremely high approximation accuracy, providing a solid theoretical foundation for processing large-scale datasets. Furthermore, in order to overcome the inconsistency of local approximation methods, this paper adopts the Generalized Robust Bayesian Committee Machine (GRBCM) as the aggregation framework for local experts. GRBCM ensures the harmonious unity of the prediction results of each local model through its inherent consistency and robustness, significantly improving the stability and reliability of the overall prediction. More importantly, in response to the issue of uneven distribution of expert weights, this article creatively introduces Tsallis mutual information as a metric for weight allocation. Tsallis mutual information, with its sensitive ability to capture information complexity, assigns weights to different local experts that match their contribution, effectively solving the problem of prediction bias caused by uneven weight distribution and further improving prediction accuracy. In the experimental verification phase, this article conducted comprehensive testing on multiple synthetic datasets and seven representative real datasets. The results show that the TDSFSIRLA method not only achieves significant reduction in time complexity, but also demonstrates excellent performance in prediction accuracy, fully verifying its significant advantages and broad application prospects in the field of large-scale Gaussian process regression.

Preventive Maintenance Strategy Prediction of the Firewater Systems Based on the Pythagorean Fuzzy Cost–Benefit–Safety Analysis and Fuzzy Dematel

The firewater system is a complex system associated with the safety process of Hydrogen storage tanks. Predicting preventive maintenance strategies is essential to ensure the long-term reliability of this system. Therefore, it is necessary to evaluate the multistate reliability of the firewater system in order to predict preventive maintenance strategies and provide safety measures. A polymorphic fuzzy fault tree analysis (PFFTA) for the risk analysis of complex systems has attracted much attention because of its powerful evaluation capability and its ability to analyze relationships among basic events. However, obtaining multistate failure probability (MFP) data for basic events in PFFTA has always been a major challenge. It is also difficult to quantify the minimum cut set (MCS) in PFFTA and determine the critical components for selecting a preventive maintenance strategy. In this study, we propose the Pythagorean fuzzy cost–benefit–safety analysis by using the PFFTA, an improved consistency aggregation method (I-CAM), and fuzzy Dematel for a predictive preventive maintenance strategy. In the proposed approach, the I-CAM method was used to collect and aggregate weights of experts’ opinions to evaluate the MFP of basic events in PFFTA. As a result, a triptych cost–benefit–safety analysis based on Pythagorean fuzzy sets (PFSs) and the sum-product method (SPM) was estimated to reduce expert subjectivity, support an improved cost-effectiveness index to rank critical components, and fuzzy Dematel to evaluate influence of proposed preventive maintenance actions. To clarify the effectiveness and feasibility of the proposed methodology, a case study of the firewater system related to the plant is located in SONELGAZ electricity power plant (OUMACHE Unit) was demonstrated. Both evaluations of the cost–benefit–safety analysis of the critical component were performed, and selected the influence of preventive maintenance strategy of the firewater system was predicted.

Uncovering hazards and adaptive capacity: A comprehensive risk assessment study in three conservation areas in Spain

The increase in the frequency, duration and severity of disturbances can disrupt forest composition and structure, posing threats to ecosystem services. Adaptive forest management has the potential to adjust forests to future disturbance regimes, by reducing their susceptibility to these threats and, therefore, increasing their adaptation capacity. However, few comprehensive studies have integrated the factors involved in adaptive forest management by comparing the biomass of national parks of the same region, or examined how global change might affect them in the near future. In this study, we integrate data of forest inventories, wildfires, high-resolution climate and pests information in climate model projections to assess climate-sensitive risks associated with key abiotic disturbances (such as fires, droughts and pests) in coniferous Mediterranean mountain forests, considering various forest management approaches and climate scenarios in three Spanish national parks. We employed the conceptual framework introduced by Lecina-Diaz et al. (2021a) to evaluate risks to forests and considered Exposed values (E), Hazard Magnitude (HM), Susceptibility (S) and Lack of Adaptive Capacity (LAC). We applied statistical weights using expert weights and the analytic hierarchy process (AHP) as the basis for determining the global ranking score of each indicator of the risk components. We extracted information about the climatic scenarios from the Coupled Model Intercomparison Project Phase 6. Three forest management scenarios were generated, named “low intervention», «traditional silviculture”, and "reduction of climate vulnerability", and mapped the risks of carbon sequestration for the different scenarios. The importance of each component was estimated using the AHP. HM was most significantly affected by the Aridity Index and the Fire Risk Index, with weights of 27.7 % and 24.2 %, respectively; S was affected by fuel load (29.0 %) and the Suppression Difficulty index (21.0 %), and LAC was influenced by forest management (32.0 %) and resprouting capacity (22.0 %). The three forest management scenarios exhibited different risk values that decreased with increasing intensity of the intervention in the three protected areas. Likewise, when two climate scenarios were considered, risk increased in the protected areas (Teide National Park, 10.69 %; Sierra Nevada National Park, 10.9 %; Sierra de las Nieves National Park, 6.17 %). Patterns of spatial arrangement of risk, which included HM, S and LAC, were consistent with the risk level, with the highest risk values being concentrated in the buffer areas (natural parks), with areas with a greater presence of coniferous plantations. The risk maps allowed us to identify areas that are critical under extreme conditions and where the greatest disturbance events may occur. Our study underscores that risks of disturbances in mountain ecosystems dominated by conifers within protected areas can be mitigated by forest management. The application of forest management adaptation strategies in national parks and buffer areas has proven effective in reducing risks of disturbances and preserving certain ecosystem services, such as carbon sequestration.

Novel decision making approach for sustainable renewable energy resources with cloud fuzzy numbers

Decision making approaches depending on the assessments of individual decision makers produce inaccurate results due to the existence of multiple uncertainties. To model intrapersonal uncertainty, interpersonal uncertainty and randomness in decision making assessments, this research study proposes a novel approach by integrating linear and non-linear type of fuzzy numbers with cloud model theory using novel technique of computing entropy of these fuzzy numbers. A novel mathematical model known as cloud fuzzy numbers is introduced using the concepts of fuzzy numbers and cloud theory. The self-evaluated relative weights of experts are computed using a non-linear optimization method which is based on maximum deviation method and Lagrange multipliers of cloud fuzzy numbers. The new cloud fuzzy numbers are then combined with CODAS (combinative distance based assessment) approach that is based on the largest Euclidean and Taxicab distances for the selection of suitable criteria. Firstly, the linguistics evaluations are converted into the fuzzy numbers and then cloud fuzzy numbers using formulae of expectation and entropy ensuring that the obtained interval cloud values follows a normal distribution. Secondly, the cloud fuzzy weighted arithmetic averaging operator is used to aggregate cloud fuzzy numbers using the self-evaluated fuzzy weights Thirdly, the assessment score is determined to rank the alternatives by computing the distance between normalized weighted matrix and the negative ideal solution. Finally, a case study is discussed for the selection of best renewable energy resource in Turkey to elaborate the significance of the proposed research. The convergence and accuracy of the proposed model is proved with certain mathematical and theoretical results.

A Relative Projection-Based Multiattribute Group Decision-Making Model With Noncooperative Behavior Management and Application to NEV Supplier Selection.

This article reports a novel consensus model where a group of internal and external experts evaluate alternatives under multiple attributes and provide mutual evaluations. First, different from previous studies, the cognitive and interest conflicts of internal and external experts are considered simultaneously. But interest conflict is emphasized for internal experts, and cognitive conflict is mainly considered for external experts. Second, we explore the categorization and management methods of noncooperative behaviors (NCBs) of experts. The relative projection-based indexes are proposed for the first time to measure the degrees of cognitive and interest conflicts by using multiattribute preference matrices (MAPMs) and the weight vectors of attributes. A group of experts are divided into three categories and the corresponding management strategies are developed. Third, we investigate the consensus mechanism among experts with cognitive and interest conflicts. For reaching an acceptable consensus level, an adjustment process is proposed to revise some local entries in MAPMs and mutual evaluation matrix (MEM). A penalty mechanism is further established to dynamically update the weights of experts. An algorithm is designed to capture the consensus reaching process in multiattribute group decision making, where internal and external experts are distinguished by proposing a parameter. Finally, the high-performance battery supplier selection of new energy vehicle is studied to illustrate the proposed model. The results reveal that the efficiency of reaching consensus can be enhanced by using the developed model with effective management of NCBs.

Susceptibility assessment of multi-hazards using random forest—back propagation neural network coupling model: a Hangzhou city case study

As the demand for regional geological disaster risk assessments in large cities continues to rise, our study selected Hangzhou, one of China’s megacities, as a model to evaluate the susceptibility to two major geological hazards in the region: ground collapse and ground subsidence. Given that susceptibility assessments for such disasters mainly rely on knowledge-driven models, and data-driven models have significant potential for application, we proposed a high-accuracy Random Forest—Back Propagation Neural Network Coupling Model. By using nine evaluation factors selected based on field surveys and expert recommendations, along with disaster data, the model's predictive results indicate a 3–40% improvement in model performance metrics such as AUC, accuracy, precision, recall, and F1-score, compared to single models and traditional SVM and logistic regression models. Ultimately, using the predictive results of this model, we created susceptibility maps for individual disasters and developed a muti-hazards susceptibility map by employing the expert weight discrimination method and the overlay evaluation method. Furthermore, we discussed the feature importance in the prediction process. Our study validated the feasibility of using advanced machine learning models for urban geological disaster assessment, providing a replicable template for other cities.

Hydrogen energy storage technology selection through a cutting-edge probabilistic linguistic decision framework

As a viable alternative to traditional energy forms, hydrogen energy proves effective owing to its feasible cost and reduced pollution. The storage of such a renewable energy source is essential for ensuring energy security and promoting sustainability. However, earlier studies on energy storage technology (EST) selection cannot handle complex linguistic expressions and hesitation during the decision process. Motivated by the issue, a probabilistic linguistic decision approach is developed in the present study to model linguistic expressions in daily conversations effectively. Besides, a new integrated approach is introduced by considering the regret factor for expert weight determination, weighted Cronbach’s approach for criteria weight determination, and a novel ranking procedure with a weighted approximation for prioritizing ESTs. A case example is exemplified by considering nine criteria and six ESTs to clarify the usefulness and practicality of the proposed framework. The main novelty/benefits of the study are: (i) handle uncertainty better; (ii) capture interrelationship among criteria; (iii) reduce bias by methodically determining the importance of experts; and (iv) provide a personalized ranking of ESTs. Findings depict that total cost is the foremost driver for hydrogen EST selection, while pressure cylinder storage is the most viable technology. Finally, the benefits and usefulness of the study are realized through sensitivity and comparison analyses. This study can guide and assist energy officials, academicians, governments, and other stakeholders facing hydrogen EST selection issues.

Optimization model for fair judging of large-scale innovation contests based on experts’ neutral weights

The group decision-making method and standard score method are commonly used for evaluating large-scale innovative contest entries. However, the standard score method assumes that the quality distribution of entries evaluated by each expert remains consistent, which is difficult to achieve in practice due to different experts reviewing different entries. To enhance fairness in judging results for such contests, we propose an improved standard score method based on weighting. First, we establish a mathematical model for cross-assignment of entries and design an algorithm to solve it while discussing one property on its optimal solution. This model enables any two experts to be assigned as many identical entries as possible, facilitating reliable pairwise comparisons matrix while avoiding assigning the best or worst entries to multiple experts in one judgment. Second, inspired by cross entropy concepts, we introduce a definition of “cross entropy-like” and establish a mathematical model using this concept along with minimum sum of squared errors method to determine neutral weights for experts. Finally, experiments demonstrate that our proposed improved standard score method is more scientific and reasonable than traditional standard score method while enhancing objectivity and fairness of evaluation results.

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.

Sustainable flood control strategies under extreme rainfall: Allocation of flood drainage rights in the middle and lower reaches of the yellow river based on a new decision-making framework

Climate change has exacerbated the frequency and magnitude of extreme rainfall, which has led to the perpetuation of flooding as a hazard to humans and society. China has begun to consider introducing Flood drainage rights (FDR), a sustainable flood control measure, into non-engineering measures as a complement to engineering measures for flood control. FDR represent the right of regions to discharge regional floodwaters caused by extreme rainfall into the river, and are the primary means of controlling the amount of floodwaters from regions when regional flood capacity is exceeded. However, existing studies on quantitative FDR allocation still have limitations, and some previous methods have resulted in allocation schemes that are not entirely reasonable and fair because they do not comprehensively consider the influencing factors of FDR or the allocation method is unreasonable. This paper explores the impact of flooding on rural and agricultural areas. We incorporate the factors of agricultural economy and security and construct a system of the allocation indicators of FDR composed of five principles: Natural Environmental Endowment, General Economic and Social Development, Agricultural Economy and Security, Macro policy regulation, and Respect for Historical Background. Second, considering the influence of expert judgment and data of different time nodes on the allocation of FDR, we introduce the concepts of expert weight and time weight into the allocation model of FDR, and construct a new set of framework for the allocation of FDR, i.e., "[(expert weight + subjective weight)+(time weight + objective weight)]+decision making model ". To reduce the loss of information during the transformation of subjective judgments, we also introduced triangular fuzzy numbers for the transformation between expert judgments and numbers. Finally, we take the five provinces in the middle and lower reaches of the Yellow River as an example. Using the data from 2010 to 2021, we obtain the final allocation scheme (proportion) of FDR as Henan (33.26%) > Shaanxi (23.08%) > Inner Mongolia (21.31%) > Shanxi (14.44%) > Shandong (7.91%). On this basis, this paper utilizes sensitivity analysis and comparative validation to demonstrate the rationality and effectiveness of the method, and identifies several indicators that have a greater impact on the results of the allocation of FDR. FDR can form part of a set of integrated flood management system together with flood control projects, which greatly alleviates the drainage conflicts arising from flooding caused by extreme precipitation. Under extreme rainfall conditions, FDR improves drainage efficiency and minimizes the overall damage caused by flooding in the watershed. This study can contribute to the sustainable development of the watershed and provide a reference for the promotion and utilization of sustainable flood control measures.

Multi-attribute perceptual fuzzy information decision-making technology in investment risk assessment of green finance Projects

Abstract In the investment risk assessment of green finance (GF) projects, the application of multi-attribute perceptual fuzzy information decision technology is taken as the main research object. With the promotion of the concept of environmental protection and the development of green economy, the investment risk assessment of GF projects has become more and more important. However, this requires dealing with a large amount of fuzzy information and multi-attribute decision problems, which is a big challenge for traditional decision techniques. Based on this background, a new decision model, intuitionistic fuzzy preference theory-based tomada de decisão interativa multicritério (IF-PT-TODIM), is adopted, which can better deal with fuzzy information and multi-attribute decision problems by taking two different choices as reference. By knowing the weight distribution of experts, the model can better assess the influence of various factors on the decision. In the research results, the calculated results of expert weights are 0.2796, 0.2221, 0.1914, 0.1328, and 0.1745, respectively, showing that each expert has different degrees of influence on decision-making. In addition, the application of IF-PT-TODIM model can effectively reduce the investment risk. Compared with national bank of Kuwait, systematic review, evolutionary algorithm, the improved method can reduce the risk of venture capital by 28.14, 15.47, and 11.05%, respectively. This result further confirms the advantage of the IF-PT-TODIM model in dealing with fuzzy information and multi-attribute decision problems. This study has practical implications for understanding and improving the investment risk assessment of GF projects. It not only provides a new decision model for risk assessment, but also provides an effective method to deal with fuzzy information and multi-attribute decision problems. This provides new ideas and methods for the risk management of GF projects and also provides a new perspective and reference for research in related fields.

An investment decision framework for offshore CCUS project under interval-valued fermatean fuzzy environment

ABSTRACT Carbon Capture, Utilization and Storage (CCUS) is an indispensable technology for achieving a net-zero emission society. The offshore CCUS project is still in its infancy. To promote its sustainable development, developing a comprehensive framework for investment decision-making is very crucial. First, a comprehensive evaluation criteria system is established. Second, in order to characterize the ambiguity and uncertainty of information in the process of making decisions, the interval-valued fermatean fuzzy set (IVFFS) is introduced, and the extended variance method of IVFFS is proposed to systematically calculate the weights of experts. Then, the power weighted average (PWA) operator based similarity measure of IVFFSs is developed to aggregate different expert information. Meanwhile, the fuzzy-weighted zero-inconsistency (FWZIC) method and the method based on the removal effects of criteria (MEREC) are used to determine the criteria weights. In addition, considering the interactions between the criteria, we introduce the Hamacher operator into the measurement of alternatives and ranking according to the compromise solution (MARCOS) method to select the optimal alternative in the interval-valued fermatean fuzzy (IVFF) environment. The suggested framework is then used to analyse a case study. After that, sensitivity and comparative analyses are conducted to confirm its robustness and viability. This study creates a practical investment framework for offshore CCUS projects, identifies a number of investment-sensitive criteria and provides management insights. The proposed framework expands the methods and applications in the field of decision-making and provides a scientific approach for investment decision-making in offshore CCUS projects, which can be a useful reference for managers.

A multi-period intuitionistic fuzzy consensus reaching model for group decision making problem in social network

A new intuitionistic fuzzy consensus reaching model is developed with multi-period public opinions and expert evaluation values in social network environment. First, the public opinions are obtained by using the crawler software and sentiment analysis technology is used to transform public opinions into intuitionistic fuzzy decision matrix in each period. Attribute weights are calculated by using the time attenuation factor and changes in public opinion. Second, the social trust relationship is modeled and incomplete social trust relationships are completed by using Archimedean t-norm. The expert weights are calculated by using the dynamic trust degree and similarity degree. Third, a consensus framework is proposed for multiple-period decision making problem, which coordinates conflicts between experts through dual feedback paths. The collective opinion scores are calculated by using weights of periods and attribute weights obtained from the word frequency of public opinions. The tourism attraction recommendation method is used to illustrate the proposed method.

Bounded rationality consensus reaching process with prospect theory and preventing individual weight manipulation for multi-attribute group decision making

Owing to the development and popularization of social media, multi-attribute group decision-making (MAGDM) adopting social network analysis (SNA) has appealed to widespread focus for a few years. However, most existing research on SNA has not simultaneously considered the expert's weight manipulation behavior and bounded rationality during the consensus-reaching process (CRP). To overcome this limitation, an original consensus model predicated on prospect theory (PT) and preventing individual weight manipulation is proposed in this paper. First, the experts’ behavior under certain risks is characterized by using PT, where a dynamic reference point is proposed to provide a more objective description of the experts’ psychological behavior. Then, the PT is integrated into the CRP, and the three-level prospect consensus measurement and a three-stage feedback identification mechanism are investigated. Subsequently, to safeguard against individual weight manipulation and obtain the appropriate expert weights, a feedback model incorporating minimum adjustment and maximum entropy rooted in PT is developed. This model is established from the perspectives of “efficiency” and “equity.” At last, the proposed method is applied to evaluate low-carbon investment projects in a power generation enterprise to verify its effectiveness and applicability. The superiority and robustness of the proposed method are also demonstrated through comparative analysis and sensitivity analysis.

A generalized TODIM evaluation approach based on the novel score function and trust network under interval-valued hesitant fuzzy environment

In multi-attribute group decision-making (MAGDM), the social relationships among group members, professional backgrounds and psychological behaviors of decision makers are important factors for achieving high-quality group decisions. However, realizing the consideration of these factors in fuzzy and complex group decision-making scenarios is an important and urgent issue. In MAGDM problems, interval-valued hesitant fuzzy sets (IVHFSs) can better show the hesitant and fuzzy uncertainty information because they are an extension of fuzzy sets. This paper focuses on the score function of IVHFSs and develops a generalized TODIM evaluation method utilizing the novel score functions of IVHFEs and trust networks. The developed method effectively realizes the application of these factors in fuzzy and complex group decision making scenarios. In this contribution, a new deviation function for IVHFSs is created in this paper. It takes into account two types of deviation that are common in IVHFSs: the ambiguity of interval width and the hesitation and uncertainty of multiple intervals. In the sequel, a novel score function for IVHFSs is developed with help of the novel deviation function and the traditional score function. Concerning the perspective of group consistency, a model for determining expert weights is constructed using quality parameters, which combine the novel score function with the trust network. A subjective and objective comprehensive weighting model is made to get attribute weights based on how certain decision information is and the experts’ subjective experience. This is done because the more uncertain something is, the higher the risk. Eventually, we develop a generalized TODIM technique for MAGDM based on the novel score function and trust network in IVHFS environment. The developed approach is demonstrated with a preventive engineering decision-making problem. Sensitivity and comparative analyses are implemented to demonstrate the superiority, stability, and validity of the proposed method in this paper. According to the findings of this study, we conclude that the proposed methodology offers a comprehensive and systematic approach to evaluate MAGDM in the context of IVHFS with trust networks.

Evaluation and selection of sustainable hydrogen production technologies with unknown expert weights based on extended MARCOS under hybrid information

The selection of sustainable hydrogen production technologies (HPT) is a paradigm transformation for industrial enterprises to pursue commercial benefits and environmental performance, which has increasingly attracted the attention in academic circles. Therefore, the chief orientation of this research is to construct a novel sustainability-oriented decision-support framework for hydrogen production system. Firstly, an approach is developed to determine expert weights under each criteria in a linguistic environment. Secondly, the Measurement of Alternatives and Ranking according to Composite Solution (MARCOS) method is extended to prioritize sustainable HPT under hybrid information (HMARCOS). Finally, a case study of 5 typical HPT regarding 15 sustainability indicators is conducted. The results display the biomass gasification has the highest sustainability priority for hydrogen production compared with wind turbine electrolysis, photovoltaic electrolysis, steam methane reforming and coal gasification. Furthermore, the reliability and availability of the integrated framework is demonstrated by the comparisons with other approaches.

Incorporating ergonomic and psychosocial stressors: A comprehensive model for assessing miners’ work-related musculoskeletal disorders

Although the influence of ergonomic and psychosocial stressors on work-related musculoskeletal disorders (WMSDs) has been widely recognized, a risk assessment method that comprehensively considers these stressors is still lacking. As such, this study developed a fuzzy Bayesian network (FBN) model that incorporated a comprehensive set of ergonomic and psychosocial stressors to investigate how they interacted to affect frontline miners’ WMSDs risk. A methodology combining fuzzy set theory, Leaky Noisy-OR gate, and an expert weight dynamic adjustment algorithm was employed to deal with fuzziness, uncertainty, limited data, and conflicting judgments and to provide a probabilistic assessment. Meanwhile, the FBN was validated using 10-fold cross-validation through data collected from 542 frontline miners. Results indicated that the probability of frontline miners suffering from WMSDs is 79.7%. Vibration, awkward posture, inadequate organizational support, inadequate organizational justice, and bad organizational culture were the most influential stressors to frontline miners’ WMSDs. The proposed FBN model can be a powerful decision-support tool to improve frontline miners’ musculoskeletal health by managing a more targeted subset of ergonomic and psychosocial stressors.