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As a system reliability analysis technology, failure mode and effect analysis (FMEA) has been widely used in various industries to reduce the failure risk of products, systems, and services. In recent years, many researchers have found that this technology has limitations in failure mode assessment and failure mode prioritization in complex uncertain environment. In order to solve this problem, this article proposes a new FMEA model, which combines hesitant Pythagorean fuzzy sets (HPFSs) and ORESTE method. Specifically, HPFSs are used to describe the risk assessment information of experts. The ORESTE method is extended to determine the risk priority of the failure modes in FMEA. Moreover, the PIR structure of ORESTE is improved to reduce the subjective influence of experts. Finally, an example of risk assessment of intelligent aquaculture system is given to verify the applicability and effectiveness of the proposed FMEA model. The results show that the proposed FMEA model can not only accurately describe the hesitation and uncertainty of experts’ risk assessment information, but also obtain more suitable risk ranking of failure modes, based on which managers can further take corresponding preventive measures.

With the rise of emerging technology in the fourth industrial revolution, modern agricultural technologies are gradually adopted to increase the yield and efficiency. Meanwhile, they also may bring the negative effect to social and environment sustainable development. Ignoring the requirement of sustainability will have an impact on the development of the country and society. Besides, the uncertainty and multistakeholder on sustainable modern agricultural technology assessment (SMATA) have made this appraisal much more complex, including their entanglement with rapidly changing cultural norms and social relations. Thus, in order to address these concerns, this article develops a holistic multistakeholder transdisciplinary approach with supporting SMATA. More precisely, due to the uncertainty of environment and vague knowledge possessed by stakeholders, we first investigate the evaluation measure of SMATA by introducing $q$ -rung orthopair fuzzy set ( $q$ -ROFS). Second, group opinion of stakeholders is fused by developing a new transdisciplinary evaluation aggregation operator. Third, to achieve more robust weights of key factors, three extended best worst method models from three different comparative perspectives of $q$ -ROFS are established. The final weight of key factors is further obtained by utilizing the variable entropy method. Fourth, we design a new decision approach of SMATA by improving evaluation based on distance from average solution method. Finally, a modern agricultural project base of Xichang city in China is used to demonstrate the effectiveness and rationality of our proposed SMATA method, including the simulation experiments on sensitivity analysis and comparative analysis. These results provide some exploratory insight and guidelines of SMATA for government and organization.

Recently, frequent emergencies seriously impact the people's life and production. The timely emergency material can save more people. Hence, emergency material demand prediction is a core of emergency rescue. However, the emergency is a continuous uncertain evolution process. The emergency of different periods has various situations. The time-varying and uncertain characteristics of emergency have made this representation and prediction work much more complex. In order to address this concern, this paper proposes a time-varying intuitionistic fuzzy set (TVIFS) to precisely depict the time-varying scenario and the uncertainty of emergencies. For utilizing the TVIFS of different time frames, we deeply explore the integral aggregation operator of TVIFS, which can take full account of decision hesitation and possible information. Due to the different time importance of different time frames, we investigate the integral of TVIFS with constant period weight in advance. Considering the time-varying period weight, the TVIFS integral with time-varying period weight is investigated. For the sake of calculation feasibility, inspired by the relationship between continuity and dispersion, we translate the TVIFS integral with variable period weight into the TVIFS integral with constant period weight. Furthermore, we develop time-varying intuitionistic fuzzy case-based reasoning model to forecast the emergency materials demand by designing hybrid similarity between cases. Finally, we elaborate the application of time-varying intuitionistic fuzzy case-based reasoning model by using an example of forest fire and verify the validity of our proposed method.

Three-way decision is consistent with human cognitive processes and applied to different information systems. However, since the real world is more imprecision, hesitation and ambiguity, experts can select interval fuzzy information to represent their evaluations. In the evaluation process, experts sometimes may not guarantee the interval fuzzy information satisfy the constraint between membership and non-membership and further impact make decisions. Interval-valued q-rung Orthopair fuzzy set (IVq-ROFS) are proposed to improve the fault-tolerant ability and ensure the experts' evaluation make sense. In this case, this paper generalizes IVq-ROFS to three-way decision, which consists of the construction of three-way decision and the aggregation of interval continuity of IVq-ROFS. For considering the aggregation of interval continuity of IVq-ROFS, we firstly investigate the IVq-ROFS integral to aggregate continuous IVq-ROFSs by developing simplified interval-valued q-rung Orthopair fuzzy set (SIVq-ROFS). Particularly, in order to obtain integrable region, we design $\varepsilon$ -amendment to amend integral region of IVq-ROFS for removing unmeaning point in advance. To simplify the calculation, we further utilize Taylor series of the integral function to replace the complicated integral function. Secondly, a new equivalence class method is constructed to support three-way decision. Thirdly, with the aid of the integral of IVq-ROFS and equivalence class method, we availably fuse the loss functions of the equivalence class and obtain the classifying rules of three-way decision by comparing the expected losses. Finally, we elaborate three-way decision by using a case of medical diagnosis about nephropathy and prove the validity of the investigated model.

The appropriate rating of software ecosystem health is to the benefit of its sustainable development. The rating method based on an expert panel with specialised knowledge is reliable for rating software ecosystem health. Complexity often causes obstacles for expert panels to give appropriate ratings. Facing complexity, hierarchical criteria system provides evaluation guides with multiple levels and multiple perspectives. However, at present, effective group decision-making under hierarchical criteria system remains a challenge. Thus, this article systematically investigates consensus reaching process and rating method under hierarchical criteria system of software ecosystem health, which are two important components of group decision-making. To master the consensus situation of all criteria and consensus relationships of pair-wise experts under hierarchical criteria system, we construct hierarchical consensus evolution network. Then, we further propose a novel consensus degree measurement and design a consensus reaching process with pair-wise adjustment by utilising hierarchical characteristics. For rating software ecosystem health efficiently, there are usually three ratings including health, sub-health and ill-health. Three-way decisions exactly provide semantic interpretation for these three ratings. Meanwhile, three-way decisions can reduce the risk of incorrect ratings. Considering these advantages, we develop three-way group decisions with hierarchical consensus evolution network to comprehensively rate software ecosystem health under hierarchical criteria system. Finally, take the health rating of the software ecosystem on GitHub as an example, we develop a series of experiment analysis to verify the effectiveness of the proposed method.

As an effective tool to deal with uncertain decision-making, three-way decisions (TWD) have gained wide attention in many applications. Decision-theoretic rough sets (DTRSs) as a classic model of TWD contain two key elements, i.e., conditional probability and loss functions. In this paper, we study the determination of these two elements in depth via the information interaction and modification strategy, and further propose a novel model of TWD. First, fuzzy c-means (FCM) is used to cluster the condition attribute information and loss function information, respectively. Considering the interaction of two types of information, we design the corresponding fusion tactic of these clustering results to get equivalent classes and develop a new calculation method of conditional probability. Then, we use probabilistic hesitant fuzzy sets (P-HFSs) to aggregate the loss functions of different members of the same equivalence class and get the probabilistic hesitant fuzzy elements (P-HFEs) loss functions. In this case, P-HFSs not only reflect the hesitant situation of decision-makers, but also depict the proportion of different opinions. Regarding P-HFEs loss functions, we also investigate the modification methods of its outliers in detail. Moreover, based on the P-HFEs loss functions, we propose TWD with probabilistic hesitant fuzzy decision-theoretic rough sets (P-HFDTRSs). Finally, in order to verify the effectiveness of our proposed method, we develop a series of comparative experiments and discuss the decision results on six UCI datasets.

In order to adequately utilize and integrate both ratings and comments from multiple websites, this paper proposes a new hotel evaluation model with probabilistic linguistic information processing. Taking consumers' possible psychological activities when leaving their reviews into consideration, this paper adapts the Weber-Fechner Law with the linguistic scale function and develop a novel unbalanced linguistic scale function. This paper also attempts to develop a method that enables adjusting linguistic-term formations among different websites to make full use of information. Then, we learn the decision criteria and the corresponding weight of hotel evaluation based on analyzing rating rules and consumer comments. Moreover, considering the interrelationships among criteria, this paper extends the Choquet integral to the probabilistic linguistic term set (PLTS) environment and designs some novel fusion operators. Furthermore, considering the fact that different websites mostly focus on heterogeneous hotel criteria, this paper puts forward a weighted averaging linear assignment based ranking method with the aid of PLTS Choquet integral. Finally, a case study of hotel evaluation is given to illustrate the validity and applicability of our proposed approach.

The emergence of sports tourism has compelled sports managers to rethink the management and improvement of sports facilities. Through service quality analysis, sports managers can identify the strengths and weaknesses of their activities for possible advancement. Hence, this study aims to develop a decision support model based on integrating online reviews and data envelopment analysis to measure the degree of tourist satisfaction and provide benchmarking goals for service improvement. The proposed model employs text mining techniques to discover service quality attributes from text reviews. According to the discovered service quality attributes, we conduct sentiment analysis to reveal the sentiment polarities of the text reviews. Then, we refine the polarities and ratings of online reviews into linguistic distribution assessments. Furthermore, we develop a linguistic distribution output-oriented non-discretionary bestpoint slack-based measure (BP-SBM) to compute the degree of tourist satisfaction and benchmarking goals. The linguistic distribution output-oriented non-discretionary BP-SBM can handle both positive and negative data values, thus overcoming the flaws of the traditional model. Meanwhile, the proposed decision support model investigates how the service-quality attributes interact to provide improvement pathways for an underperforming stadium based on association rule mining. We test the applicability of the proposed decision support model on some Elite stadia in Europe.

In the crowdsourcing task allocation scheme, there is an emerging and realistic co-opetition phenomenon. To availably solve the crowdsourcing task allocation problem with co-opetition, this paper designs a two-stage co-opetition model by constructing novel three-way decision (TWD), including a competition-optimization model and a negotiation-cooperation model. Unlike the other studies, the two-stage co-opetition model with TWD can not only protect the profits of the task candidates, but also optimize the overall benefits. Specifically speaking, in the competition-optimization model, we construct an optimization model based on the data envelopment analysis (DEA) method in advance, which maximizes the personal benefit. By integrating information system and the loss function matrix, we consider the linkage of evaluation information and risk information and then improve the original TWD to make an initial allocation. In the negotiation-cooperation model, considering that the relationship among the candidates may influence the task performance, the fuzzy measure is introduced to describe a broader partnership. Meanwhile, we also design two different schemes to coordinate and optimize the best task allocations based on the initial allocation. In order to choose the best scheme, the selection strategy between schemes is further investigated under the guidance of the utility and the loss. Finally, we give an example of a medical supply chain crowdsourcing problem to illustrate and verify our proposed approach.

The onset of the COVID-19 pandemic has changed consumer usage behavior towards mobile payment (m-payment) services. Consumer usage behavior towards m-payment services continues to increase due to access to usage experiences shared through online consumer reviews (OCRs). The proliferation of massive OCRs, coupled with quick and effective decisions concerning the evaluation and selection of m-payment services, is a practical issue for research. This paper develops a novel decision evaluation model that integrates OCRs and multi-attribute decision-making (MADM) with probabilistic linguistic information to identify m-payment usage attributes and utilize these attributes to evaluate and rank m-payment services. First and foremost, the attributes of m-payment usage discussed by consumers in OCRs are extracted using the Latent Dirichlet Allocation (LDA) topic modeling approach. These key attributes are used as the evaluation scales in the MADM. Based on an unsupervised sentiment algorithm, the sentiment scores of the text reviews regarding the attributes are calculated. We convert the sentiment scores into probabilistic linguistic elements based on the probabilistic linguistic term set (PLTS) theory and statistical analysis. Furthermore, we construct a novel technique known as probabilistic linguistic indifference threshold-based attribute ratio analysis (PL-ITARA) to discover the weight importance of the usage attributes. Subsequently, the positive and negative ideal-based PL-ELECTRE I methodology is proposed to evaluate and rank m-payment services. Finally, a case study on selecting appropriate m-payment services in Ghana is examined to authenticate the validity and applicability of our proposed decision evaluation methodology.

In the study of two-sided matching decision problems, preference ordinal information is a key factor. However, in real life, it is often difficult to ascertain complete preference ordinal informati...

Three-way group decisions provide an efficient method to settle complex and high risk decision-making problems. To obtain reasonable decision results that satisfy different backgrounds and knowledge of decision makers, it is necessary to design a proper consensus reaching process (CRP) for loss functions of decision-theoretic rough sets (DTRSs). Unlike existing researches, this paper not only extends the group relationship among decision makers to the social network, but also considers the externality of social trust network in group decision making. In light of this idea, we design a new CRP with the externality of social network for three-way group decisions. In the CRP, the adjustment of a decision maker who is persuaded by the moderator can influence other decision makers to accordingly adjust evaluations. Thus, by using the linkage externality influence among decision makers, we establish a two-stage mixed 0–1 linear optimization consensus model for the determination of loss functions of DTRSs. Then, based on Bayesian decision procedure, we construct a complete decision procedure for three-way group decisions with social network. Finally, we apply our proposed method to assess desert locust invasion areas and verify its validity.

In a social network, people may express opinions and interact with others for a hot issue, in which the trust relationship plays an important role. For the determination of the trust relationship, ...

With the introduction of the interval-valued intuitionistic fuzzy sets, the interval-valued intuitionistic fuzzy numbers are used instead of precise numbers to provide fuzzy characterization of feature attribute values and misclassification loss function values, which is more in line with the realistic fuzzy decision-making environment. Also, the constructive covering algorithm is introduced into the three-way decisions model, which effectively solves the shortcomings of the traditional decision-theoretic rough sets model in dealing with multi-classification problems, such as too many artificial parameters, complicated computation, redundant decisions, decisional conflicts and excessively large boundary domains. At the same time, in order to avoid the one-sidedness of individual decisions, the group decision-making method is introduced into the preliminarily constructed multi-classification model in this paper to build a multi-classification group decision-making model for interval-valued intuitionistic fuzzy three-way decisions based on the constructive covering algorithm. This model determines the initial weights of feature attributes by the precise weighting method, and determines the expert weights by the grey relational precise weighting method, which effectively achieves the consistency of group decision-making. The decision-making process and rules are also deduced, which expand the model of three-way decisions as well as its practical application value and scope.

In this paper, we propose a probabilistic linguistic belief thermodynamic multiple attribute decision making method for evaluating mobile health Apps. To extract the evaluation information for mobile health Apps, the quantification transformation of linguistic evaluation of decision makers is critical. However, decision makers may have different perceived values for linguistic terms in their minds. Thus, we deeply investigate the quantification process of linguistic terms based on the psychological perception by introducing prospect theory which can describe the psychological influences of decision makers. Meanwhile, we extend the operational laws of probabilistic linguistic term set (PLTS). In order to efficiently use probabilistic linguistic evaluation information, we further introduce the thermodynamic method to measure the quantity and quality of evaluation information. To process the qualitative rankings for attributes, we propose a non-linear programming model for the determination of attribute weights by improving Borda score which considers the different preferences of decision makers for the rankings. Then, to address the uncertainty situation of probabilistic linguistic evaluation information during the aggregation process, we use the Dempsters��� combination rule of the evidence theory. Considering inconsistent of the ranking results of alternatives based on the exergy indicator and the entropy indicator, we construct a comprehensive score by combining the two indicators. Finally, we apply our proposed method to evaluate existing mobile health Apps and validate it by comparison analysis and sensitivity analysis.

In real life, there are many complex multiple attribute group decision making (MAGDM) problems with high decision risk and uncertainty. The decision-making process of the complex MAGDM can encounter the following three problems: (1) Since different experts have different knowledge structures and interests, they master different individual attribute information of alternatives. (2) Experts may have different consensus degrees for alternatives under different attributes. (3) For some alternatives, the experts can not make an immediate decision in the actual decision-making process. The experts need much more information to decide on these alternatives in the subsequent decision step. To solve the problems as mentioned above, we propose sequential three-way multiple attribute group decision making (STWMAGDM) with individual attributes by introducing sequential three-way decisions. Meantime, we construct a multilevel granular structure based on the consensus degree of attributes. Further, at each granularity level , the experts need to reach consensus before deducing decision results. For improving the consensus reaching process, we take into account the social influence among experts with the aid of opinion dynamics. In this case, we construct social networks based on the similarity of experts and the amount of attribute information mastered by experts to describe the social influence. Meanwhile, we modify the model of opinion dynamics by introducing the interaction willingness of experts and establish the corresponding adjustment rules of interaction willingness. Finally, we use two diagnosis examples of breast cancer and heart disease to explain our model in detail. In order to verify the effectiveness of our method, we also perform the corresponding comparative experiments and sensitivity analyses.

In our real life, the decision maker is always of bounded rationality under risk and uncertainty. The decision result also can be impacted by the risk appetite of the decision maker. However, the existing research works of three-way decisions rarely involve the risk appetite of the decision maker. To address this drawback and enrich the application, we introduce the risk appetite into three-way decisions. More specifically, under the dual hesitant fuzzy environment, we utilize TODIM (an acronym in Portuguese for Interactive Multi-Criteria Decision Making) as a valuable tool to handle the risk appetite character to construct risk appetite dual hesitant fuzzy three-way decisions. This study takes into account the risk appetite of decision maker and designs a series of decision analysis methodologies for three-way decisions. It can preferably satisfy the practical situation and vastly extend the range of applications. Firstly, based on the dual hesitant fuzzy loss functions, we mainly study dual hesitant fuzzy entropy and cross-entropy measures for determining the conditional probability. In this scenario, the conditional probability information can be objectively learned from the loss function matrix with the aid of the power average (PA) aggregated operator. Then, considering the different comparison methods of dual hesitant fuzzy elements (DHFEs), we further design two types of strategies to deduce risk appetite dual hesitant fuzzy three-way decisions in the framework of TODIM, i.e., Strategies 1 and 2, which mainly rely on the pairwise comparison of loss functions with DHFEs. Strategy 1 is designed based on the score function of DHFEs. Strategy 2 is the likelihood of DHFEs. Meanwhile, some properties are carefully investigated. Finally, the project investment evaluation of online peer-to-peer (P2P) is applied to illustrate and validate these proposed strategies.

The constructive covering algorithm (CCA) has unique advantages and functions in dealing with multiple-classification problems. To better address the defects and deficiencies of the existing multi-class decision-theoretic rough sets (DTRSs) model, this paper introduces the CCA into DTRSs and constructs a multi-class DTRSs model based on the CCA. First, through the process of machine learning, this method achieves automatic multi-class clustering and effectively addresses the shortcomings of DTRSs in solving multi-class problems, namely, the large amount of computation and the impact of subjective evaluation factors on the inference of threshold values and decision rules as a result of excessive parameters. Second, by optimizing the covering centre and selecting the covering radius, the proposed method improves the efficiency of machine learning. Third, by setting up a distance parameter between covering classes, the method effectively addresses decision redundancy and conflicts when a DTRSs model is used to solve multi-class problems. Then, by organically integrating the cost sensitivity, inter-class distance parameter and class quality function and systematically re-classifying the objects in the boundary domain, it effectively solves the problem of an excessively large boundary domain. Finally, we summarize the decision-making algorithm of the multi-class DTRSs model based on the CCA and demonstrate the feasibility and effectiveness of the method through experiments.

Supply chain finance (SCF) is a financial service that provides convenient loan transactions for small- and medium-sized enterprises (SMEs) upstream and downstream of the supply chain. SCF can help to smooth the capital flow of many SMEs. However, it is difficult for agricultural SMEs to participate in SCF because this kind of SME usually has various risks. Objectively, evaluating the credit rating of agricultural SMEs is difficult for commercial banks. Furthermore, unlike general manufacturing enterprises, agricultural enterprises produce directly based on nature. Sustainable development based on the natural environment is very important for agricultural enterprises. In this paper, considering sustainability, we construct a criteria system to evaluate the credit ratings of agricultural SMEs for SCF. Moreover, we study a method for processing the hybrid heterogeneous evaluation information of SMEs. A co-decision method is proposed to classify the credit ratings of agricultural SMEs with the help of three-way decisions. Agricultural SMEs are evaluated by both criteria evaluation information and misclassification loss. Finally, a credit rating evaluation example is presented to demonstrate the application of our method. The results show that our proposed method can be used to fully evaluate agricultural SMEs with fine classification effects. It can also provide a reference for commercial banks to determine the credit of agricultural SMEs with a low decision risk.