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Optimal power flow (OPF) calculation methods are important for the power system operation and mainly focus on the deterministic power flow calculation, neglecting the impact of demand response on online security calculation of power systems with renewable energy sources. Therefore, this paper proposes an OPF calculation method that considers the uncertainties of wind power, photovoltaic (PV) power generation and demand-side response. Firstly, the research focuses on the renewable energy grid, considering the uncertainties of wind power and PV power generation, and establishes uncertainty models for wind power and PV output. Secondly, based on cloud model theory, an uncertainty model for demand response is established. According to the established models, an efficient OPF model is constructed with a linearized submodels considering multiple uncertainties. By testing on the IEEE 30-bus system as a typical example, we found the effectiveness and superiority of the proposed OPF calculation method can benefit the power system economic operation and demand side resource utilization. [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) calculation methods are important for the power system operation and mainly focus on the deterministic power flow calculation, neglecting the impact of demand response on online security calculation of power systems with renewable energy sources. Therefore, this paper proposes an OPF calculation method that considers the uncertainties of wind power, photovoltaic (PV) power generation and demand-side response. Firstly, the research focuses on the renewable energy grid, considering the uncertainties of wind power and PV power generation, and establishes uncertainty models for wind power and PV output. Secondly, based on cloud model theory, an uncertainty model for demand response is established. According to the established models, an efficient OPF model is constructed with a linearized submodels considering multiple uncertainties. By testing on the IEEE 30-bus system as a typical example, we found the effectiveness and superiority of the proposed OPF calculation method can benefit the power system economic operation and demand side resource utilization.

Promptly development of new products can be achieved through quality function deployment (QFD) process, which is critical to companies' survival. Since the multi-criteria decision-making problem involved in QFD, a novel method integrating cloud model and grey relational analysis is put forward in this paper. Taking into account the subjectivity and ambiguity in linguistic evaluations, some scholars utilise fuzzy theory, rough theory, interval-valued fuzzy-rough sets and MCDM methods to improve traditional QFD. However, much priori information requirements, inability to handle subjectivity and randomness, and lack of mechanism to overcome small sample size problem are some inevitable drawbacks in these methods. To solve these deficiencies, a hybrid methodology is proposed in this paper, integrating the fortes of cloud model in processing ambiguity and randomness, and the merits of grey relational analysis in overcoming small sample size error as well as revealing the inner correlations. The comparative analysis of different approaches as well as the sensitivity analysis of criteria weights is implemented to prove the stability of the novel method. The results obtained in this paper shows that the proposed method can be a practical tool for improving the efficiency and accuracy of traditional QFD in reality management. [ABSTRACT FROM AUTHOR]

Failure Mode and Effect Analysis (FMEA) is one of the most commonly used techniques for identifying and minimizing potential failures in various products and process designs. The traditional FMEA approach has limitations due to generic rating scales and experts' number-based assessments, which might not produce the desired results. The current research uses a hybrid approach of coupling Cloud Model Theory (CMT) with hierarchical Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to deal with uncertainty, including randomness and fuzziness in the identification of risks and failures in a cigarette manufacturing industry. The results show that this hybrid approach is more effective in classifying failures in the production process. The findings reveal that out of the three fundamental units of cigarette manufacturing machinery, most of the failures affecting the production of the cigarette manufacturing process belong to MAX, which supports filtration and inspection of filtered cigarettes. The study identifies salient problem areas that the managers must give special attention to enhance the production process's efficiency. The significance of the study lies in the identification of failure modes with rank order which the managers will find quite valuable for finally achieving the desired level of customer satisfaction and production efficiency. [ABSTRACT FROM AUTHOR]

Microreactors have been applied in chemical processes to prevent thermal runaway. However, the assessment method comparing thermal runaway hazards of chemical processes in microreactors and stirred-tank reactors is hardly reported. Therefore, in order to evaluate and compare the comprehensive thermal runaway hazard of chemical processes using stirred-tank reactors and microreactors under the unified evaluation index system, a modified inherent thermal runaway hazard index (m-ITHI) was proposed. Damage radius (DR), which was a function of process inventory and reaction heat, was introduced to characterize the thermal runaway severity of materials and reactions. Moreover, cloud model theory was applied to deal with the fuzziness and randomness of thermal runaway hazard indicators. The method was illustrated by processes in microreactors and stirred-tank reactors. Then, it was compared with Quantitative Index of Inherently Safer Design (QI2SD) and inherent thermal runaway hazard index (ITHI). Overall, m-ITHI can provide a way to compare thermal runaway hazard of chemical processes in different scale reactors. [ABSTRACT FROM AUTHOR]

Quality function deployment (QFD) has been a popular product or service development method to convert customer needs (CNs) into technical requirements (TRs). Nevertheless, the traditional QFD has vagueness inherent in experts’ opinions in determining CNs’ weights and the relationship between CNs and TRs. In the previous research works, fuzzy set theory is popular to deal with imprecise information in QFD. However, it still suffers from several deficiencies. For example, it needs prior information that leads to relatively fixed intervals expressing vagueness; and it assumes the membership degrees are crisp. To solve the issues, this article develops an integrated QFD method, where rough sets and cloud model are utilized for treating uncertain information. The former expresses impreciseness without any other assumptions and the latter considers expert evaluations’ randomness. In order to obtain more comprehensive importance of CNs, a combination weighting method is utilized. In the end, a compressor rotor's industrial service design is conducted utilizing the proposed method, where its effectiveness is verified. [ABSTRACT FROM AUTHOR]

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(1) In recent years, with China's increasing investment in the transportation industry, the construction of highways and bridges has flourished, bringing great convenience to people's lives. At the same time, there are many uncertain factors in the process of bridge construction, being prone to construction risks. In order to meet the requirements of sustainable development, it is necessary to accurately evaluate the safety risk level of bridge construction. Therefore, it is necessary to establish a new scientific safety risk evaluation system for highway bridge construction. (2) Methods. Based on the relevant standards and specifications, this paper establishes a highway bridge construction safety risk evaluation index system, and then uses the cloud entropy weight method to objectively weight each risk index, using cloud model theory to conduct a risk assessment, and through the cloud model images directly determine the overall risk level of bridge construction, and the level of risk indicators. (3) Results. Applying this method to the construction safety risk assessment of a particular bridge, the overall construction risk level of the bridge is obtained as "level 4", and the risk levels of the four first-level indicators are also all "level 4". (4) Conclusions. The cloud entropy weight method proposed in this paper and the traditional AHP-Extenics method are applied to a bridge construction safety risk evaluation, and the evaluation results obtained are consistent. However, this paper uses the cloud model to improve the entropy weight method in order to calculate the weights, which fully reflects the objectivity of the assignment. The cloud model is used for evaluation, and the risk level of indicators can be determined visually with images. [ABSTRACT FROM AUTHOR]

Seepage analysis has always been the focus of channel safety and stability research. Establishing a diagnosis method based on osmotic pressure monitoring data and combining the detection information to achieve osmotic safety is also an effective way to ensure the safety and stability of osmotic engineering. In this paper, a high-fill channel section of a water diversion project is taken as an example, and the study of osmotic safety is carried out by analyzing the engineering characteristics of linear engineering. High-fill channel sections were selected to study the temporal and spatial characteristics of various monitoring data reflecting the osmotic behavior of linear engineering; that is, these data reflect the time-varying regularity characteristics of the osmotic pressure value and the changing regularity of environmental variables. A single-point multifactor model of the monitoring data was established by establishing an evaluation index system, combining the monitoring index value method and the cloud model theory method according to the distribution law of the measured data and considering the uncertainty of the osmotic pressure data. Additionally, this model was integrated with the set pair analysis method to determine the monitoring data evaluation level; channel detection data information was collected, the abnormal detection of detection information was realized, and the monitoring data results were used to verify the detection results. In this way, an adaptive evaluation method reflecting the working behavior of high-filled channel sections is established, and a diagnostic technology for the safe operation of high-filled channel sections of linear engineering is proposed. The application results show that this method is suitable for engineering an osmotic safety assessment. [ABSTRACT FROM AUTHOR]

With the attention of people to environmental and health issues, health-care waste (HCW) management has become one of the focus of researchers. The selection of appropriate HCW treatment technology is vital to the survival and development of human beings. In the assessment process of HCW disposal alternative, the evaluation information given by decision makers (DMs) often has uncertainty and ambiguity. The expression, transformation and integration of this information need to be further studied. We develop an applicable decision support framework of HCW treatment technology to provide reference for relevant staff. Firstly, the evaluation information of DMs is represented by interval 2-tuple linguistic term sets (ITLTs). To effectively express qualitative information, the cloud model theory is used to process the linguistic information, a novel concept of interval 2-tuple linguistic integrated cloud (ITLIC) is proposed, and the relevant operations, distance measure and possibility degree of ITLICs are defined. Moreover, a weighted Heronian mean (HM) operator based ITLIC is presented to fuse cloud information. Secondly, the HCW treatment technology decision support model based on the BWM and PROMETHEE is established. Finally, the proposed model is demonstrated through an empirical example, and the effectiveness and feasibility of the model is verified by comparison with extant methods. [ABSTRACT FROM AUTHOR]

This study mainly focuses on the risk assessment of public–private partnership (PPP) projects. Evaluating the risks of PPP projects precisely is critical to their successful implementation. However, the traditional approaches often lack mechanisms in manipulating imprecise and vague information, and they need auxiliary information or pre-assumptions (e.g., preset fuzzy membership functions). To solve this problem, an integrated method for evaluating risks in public–private partnership projects is proposed. This method is based on the strength of the group analytic hierarchy process (GAHP), rough set theory, and cloud model theory. The proposed approach integrates the strength of rough set theory in coping with vagueness of the information from experts' assessments without much pre-assumptions, the advantage of cloud model theory in investigating the randomness of experts' judgments, and the merit of the AHP method in evaluation under multiple criteria and complex situation. Finally, an application in a PPP project in Wuhu, China, is provided to show the feasibility and effectiveness of the proposed method. The proposed approach is effective in modeling hierarchy assessment as well as dealing with vagueness and randomness, and it can help managers to make reasonable and effective decisions in risk management. [ABSTRACT FROM AUTHOR]

Information reliability and uncertainty are two important characteristics that need to be taken care of in any kind of decision‐making process. The hesitancy of the human mind is one of the major factors that introduce uncertainty in decision‐making. To include the reliability and to resolve the issue of hesitancy in a single framework, in this study, two novel concepts, namely, dual hesitant Z‐number (DHZN) and correlated distance measure between two DHZNs (CD‐DHZN) are proposed. DHZN = (Ah, Bh ), is a judicious integration of hesitant fuzzy sets and Z‐number. Here, Ah is the expert's opinion and Bh is the reliability of the expert's opinion. Further, the randomness of the information brings uncertainty to the decision‐making process. To circumvent this issue, DHZN is quantified with Cloud model theory and fuzzy envelope. To enhance the information utilization and to reduce the information loss problem while quantifying CD‐DHZN, the correlation and the underlying hidden probabilistic relationship between the two parts of a DHZN are captured using the Pearson correlation coefficient, the maximum entropy principle, and Hellinger distance. Then, the extended failure mode and effect analysis (E‐FMEA) is proposed with the help of DHZN, CD‐DHZN, and VlseKriterijuska Optimizacija IKomoromisno Resenje technique for prioritization of risks. Similarly, extended bow‐tie (E‐BT) is also proposed for the quantification of basic events (BEs), top event, and accident scenarios. Two case studies with systematic experimental investigations are presented and results are compared with other existing techniques. The results show that the proposed models are able to effectively prioritize the failure modes in E‐FMEA and quantify the BEs in E‐BT. The results confirm the feasibility and applicability of the proposed models. Sensitivity analysis is also performed to ensure the plausibility and robustness of the proposed model. [ABSTRACT FROM AUTHOR]

With the implementation of the new international conventions, higher evaluation requirements for a marine engine room simulator have been put forward. Based on the cloud theory, an improved fuzzy comprehensive evaluation method was studied: First, the Delphi method was adopted to get the original cloud drops of the judgments, and the original judgment weight clouds were generated by the backward cloud generator. Second, the judgment cloud matrix was built using the comparison results of the original judgment weight clouds, and the cloud weights of the evaluation factors were further calculated. Third, the appraisal grade clouds were generated, and the cloud appraisal vector taking into account the importance of each factor for each appraisal grade was calculated. Finally, the evaluation cloud result $E$ was aggregated, and the similarity vector between $E$ and the grade clouds was calculated. The calculation process reflects an effective uncertainty conversion between a qualitative concept and quantitative characteristics. The effectiveness was verified using three interrelated examples. The results show the following: the expectation of $E$ is mainly determined by the operating process and, second, by the expectation of the cloud weights; the uncertainty index and the randomness index of $E$ are determined by the parameter values of the cloud weights and the impact of the closest grade cloud; and the similarity vector is directly affected by $E$ and the distribution of the grade clouds. The introduction of cloud model theory into fuzzy comprehensive evaluation is an effective evaluation method.

Existing models for assessing ship collision risk involve complex calculations that complicate the simultaneous qualitative and quantitative analysis of the factors affecting ship navigation safety. Therefore, these models often exhibit slow generation of the risk index and evaluation results with reduced accuracy. To resolve these issues, we model the ship collision risk based on the cloud model theory. Specifically, we select “distance of closest point of approach (DCPA)” and “time to closest point of approach (TCPA)” as the main factors affecting the ship collision risk and analyze the data of DCPA, TCPA, and collision risk index (CRI) based on their cloud models. By combining these analyses with a double-condition-single-rule generator, we construct a cloud model for ship collision risk and finally develop a cloud model-based inference engine system to assess ship collision risk. This engine allows us to establish different ship collision risk analysis models according to the scenario encountered by the ship, which can be used to verify the feasibility of the proposed algorithm for ship collision risk modeling. Through comparisons with traditional ship collision risk models, the proposed ship collision risk model is found to be superior owing to its simple implementation, accurate results, and shorter time required to generate the risk model. The model established in this study enables the crew to determine the key objects to be avoided in case of potential collision with multiple ships. At last,analysis and research of cloud model ship collision risk based on global sensitivity and uncertainty are done to reduce the dimension of the risk parameters and show the main factors of unstable collision risk,therefore,the uncertain results in the calculation of the degree of danger are avoided, some reasonable suggestions are proposed for real navigation safety. the maritime pilot can make correct decisions promptly to reduce or avoid the occurrence of collision accidents.

(1) In recent years, with China’s increasing investment in the transportation industry, the construction of highways and bridges has flourished, bringing great convenience to people’s lives. At the same time, there are many uncertain factors in the process of bridge construction, being prone to construction risks. In order to meet the requirements of sustainable development, it is necessary to accurately evaluate the safety risk level of bridge construction. Therefore, it is necessary to establish a new scientific safety risk evaluation system for highway bridge construction. (2) Methods. Based on the relevant standards and specifications, this paper establishes a highway bridge construction safety risk evaluation index system, and then uses the cloud entropy weight method to objectively weight each risk index, using cloud model theory to conduct a risk assessment, and through the cloud model images directly determine the overall risk level of bridge construction, and the level of risk indicators. (3) Results. Applying this method to the construction safety risk assessment of a particular bridge, the overall construction risk level of the bridge is obtained as “level 4”, and the risk levels of the four first-level indicators are also all “level 4”. (4) Conclusions. The cloud entropy weight method proposed in this paper and the traditional AHP-Extenics method are applied to a bridge construction safety risk evaluation, and the evaluation results obtained are consistent. However, this paper uses the cloud model to improve the entropy weight method in order to calculate the weights, which fully reflects the objectivity of the assignment. The cloud model is used for evaluation, and the risk level of indicators can be determined visually with images.

The river health evaluation is typically complex non-linear system with characteristics of fuzziness and randomness. However, conventional gray clustering method has difficult to effectively describe fuzzy and random information simultaneously. For this purpose, the cloud model and fuzzy entropy theory are introduced to establish 2D gray cloud clustering-fuzzy entropy comprehensive evaluation model. Different with health level models, it reflects river health situation from aspects of health level and corresponding water body complexity simultaneously. The health level is obtained by gray cloud whitened weight function (first sub-system) and fuzzy entropy represents complexity and fuzziness of river health situation (second sub-system). Moreover, multi-level river health evaluation indicator system is constructed with dividing indicators into common and distinct sections according to differences on river characteristics. Meanwhile, indicator weights are determined by renewed combined weighting method based on minimum deviation principle. Finally, we conduct health evaluation work for rivers in the Taihu basin. The evaluation health levels and fuzzy entropy for river A–G are H3 (0.4888, relatively significant); H2 (0.5476, relatively fuzzy); H2 (0.7526, fuzzy); H2 (0.4731, relatively significant); H2 (05138, relatively fuzzy); H3 (0.5822, relatively fuzzy), and H2 (0.4064, relatively significant), respectively. Results are consistent with current river health situation and more intuitive than compared models. Furthermore, evaluation results with four different weighting methods are compared to further demonstrate rationality of the weighting method and evaluation model. Hence, the model proposed is demonstrated to provide new insight for solving river health assessment problem effectively. [ABSTRACT FROM AUTHOR]

With the increasing speed of railway vehicles, deciding how to reasonably distribute impact energy to each vehicle has been a widespread concern in safety protection systems. This article formulates a three-dimensional train–track coupling dynamics model using MAthematical DYnamic MOdels (MADYMO) multibody dynamics software. A train-to-train collision is then simulated using this model. A hybrid solution methodology that combines the non-dominated sorting genetic algorithm II (NSGA-II), modified best and worst method with cloud model theory and grey relational analysis is proposed. The optimization parameters and objectives are determined based on the EN15227 crashworthiness requirements for railway vehicles. An empirical case of an existing train with eight vehicles that have been in operation in China is applied to verify this dynamics model derived from a high-speed train and solution methodology. Analysis and discussion are conducted to monitor the robustness of the results and the practical implications for rail transportation are summarized. The results prove that the obtained optimal solution by this research has better crashworthiness than an existing solution. [ABSTRACT FROM AUTHOR]

• Investigation of intertwined faults that tarnish the quality and quantity of production and lead to the failures of production process. • Identification of production line faults that make the industry incur severe financial as well as reputational losses. • Not only unveils the faults plaguing the production process but also determines their interdependence and significance. • Bridges the gap of the existing researches that overlook the interplay of intermingling faults in manufacturing industry. • Identification, ranking and investigation of interdependencies of the faults that impede the efficiency and make failures inevitable. Although Failure Mode and Effect Analysis is a common method for identifying and mitigating potential problems in various manufacturing processes, its usefulness is questionable unless it is extended to cope with complex problems. Moreover, it is also widely accepted that the faults plaguing manufacturing processes are intertwined in such a way that they cannot be considered as independent of each other. Instead, they have intermingling impacts and by not paying due heed to such interrelationships, research may tarnish the authenticity of its outcomes. Therefore, it is pertinent to identify not only the prominence but also the nature of these faults. In order to cater to such needs of industry and to overcome the limitations of traditional methods, the proposed approach integrates the applications of cloud model theory and Decision-Making Trial and Evaluation Laboratory method. Three contributions of this approach are: First, the cloud model theory is applied to handle the problem of processing random and uncertain judgements. Decision-Making Trial and Evaluation Laboratory method is expanded to take into account the cloud model setting in order to allow for unveiling the crucial faults. Third, a case study is offered which demonstrates the benefits and usefulness of the approach. The combination of Cloud model theory and Decision-Making Trial and Evaluation Laboratory method to expand traditional Failure Mode and Effect Analysis and to realize its applicability in production processes underscore the novelty of this research. This is how this approach makes managers cognizant of the most vulnerable areas, allowing them to come up with pre-emptive measure. Consequently, the manufacturing process witnesses efficiency and effectiveness owing to significant reduction in the losses it incurs due to interrelated faults. [ABSTRACT FROM AUTHOR]

The scientific and reasonable evaluation of the carrying capacity of water resources is of guiding significance for solving the issues of water resource shortages and pollution control. It is also an important method for realizing the sustainable development of water resources. Aiming at an evaluation of the carrying capacity of water resources, an evaluation model based on the cloud model theory and evidential reasoning approach is studied. First, based on the existing indicators, a water resources evaluation index system based on the pressure-state-response (PSR) model is constructed, and a classification method of carrying capacity grade is designed. The cloud model theory is used to realize the transformation between the measured value of indicators and the degree of correlation. Second, to obtain the weight of the evaluation index, the weight method of the index weights model based on the entropy weight method and evidential reasoning approach is proposed. Then, the reliability distribution function of the evaluation index and the graded probability distribution of the carrying capacity of water resources are obtained by an evidential reasoning approach. Finally, the evaluation method of the carrying capacity of water resources is constructed, and specific steps are provided. The proposed method is applied to the evaluation of water resources carrying capacity for Hunan Province, which verifies the feasibility and effectiveness of the method proposed in the present study. This paper applies this method of the evaluation of the water resources carrying capacity of Hunan Province from 2010 to 2019. It is concluded that the water resources carrying capacity of Hunan Province belongs to III~V, which is between the critical state and the strong carrying capacity state. The carrying capacity of the province’s water resources is basically on the rise. This shows that the carrying capacity of water resources in Hunan Province is in good condition, and corresponding protective measures should be taken to continue the current state.

It is a basic task to measure the similarity between two uncertain concepts in many real-life artificial intelligence applications, such as image retrieval, collaborative filtering, public opinion guidance, and so on. As an important cognitive computing model, cloud model has been used in many fields of artificial intelligence. It can realise the bidirectional cognitive transformation between qualitative concept and quantitative data based on the theory of probability and fuzzy set. The similarity measure of two uncertain concepts is a fundamental issue in cloud model theory. Popular similarity measure methods of cloud model are surveyed in this study. Their limitations are analysed in detail. Some related future research topics are proposed.

Abstract Failure mode and effect analysis (FMEA) has been commonly utilized for recognition of failures, causes and influences in a system/process. However, the traditional FMEA method has been subjected to a lot of criticisms, e.g., equal importance of risk factor, lack of mechanism in manipulating imprecise information; Although the traditional FMEA is improved by various approaches based on fuzzy set theory, it still suffers from several limitations, such as requirement of prior information, construction of many fuzzy rules, and ignoring randomness in risk evaluation. Therefore, this study integrates the advantage of rough set theory in flexibly and objectively expressing vagueness without extra information and the merit of cloud model theory in taking randomness of experts' risk assessments into account. Meanwhile, an integrated weighting method considering both subjective and objective aspects is employed to estimate risk factor weights. Then, the failure modes are ranked by an extended TOPSIS (technique for order performance by similarity to ideal solution) method. To finish, a case study about risk evaluation of failure modes in a steam valve system is analyzed to show the effectiveness of the proposed method. Highlights • A novel risk priority approach is developed in this paper for the improvement of FMEA. • Vagueness and subjectivity are dealt with by rough set theory. • Randomness is handled by cloud model theory. • An integrated weighting method is used to compute risk factor weights. • The approach's effectiveness is validated with a case of steam valve system. [ABSTRACT FROM AUTHOR]

Image fusion can provide more extensive information since it combines two or more different images. Cloud model is a recently proposed theory in artificial intelligence and has the advantage of taking the randomness and fuzziness into account. In this paper, we introduce a novel multimodal medical image fusion method by cloud model theory. The proposed method fits the histograms of input images using the high-order spline function firstly and then divides intervals in line with the valley point of the fitted curve. On this basis, cloud models are generated adaptively through the reverse cloud generator. Finally, cloud reasoning rules are designed to achieve the fused image. Experimental results demonstrate that the fused images by proposed method show more image details and lesion regions than existing methods. The objective image quality assessment metrics on the fused images also show the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The scientific and reasonable evaluation of the carrying capacity of water resources is of guiding significance for solving the issues of water resource shortages and pollution control. It is also an important method for realizing the sustainable development of water resources. Aiming at an evaluation of the carrying capacity of water resources, an evaluation model based on the cloud model theory and evidential reasoning approach is studied. First, based on the existing indicators, a water resources evaluation index system based on the pressure-state-response (PSR) model is constructed, and a classification method of carrying capacity grade is designed. The cloud model theory is used to realize the transformation between the measured value of indicators and the degree of correlation. Second, to obtain the weight of the evaluation index, the weight method of the index weights model based on the entropy weight method and evidential reasoning approach is proposed. Then, the reliability distribution function of the evaluation index and the graded probability distribution of the carrying capacity of water resources are obtained by an evidential reasoning approach. Finally, the evaluation method of the carrying capacity of water resources is constructed, and specific steps are provided. The proposed method is applied to the evaluation of water resources carrying capacity for Hunan Province, which verifies the feasibility and effectiveness of the method proposed in the present study. This paper applies this method of the evaluation of the water resources carrying capacity of Hunan Province from 2010 to 2019. It is concluded that the water resources carrying capacity of Hunan Province belongs to III~V, which is between the critical state and the strong carrying capacity state. The carrying capacity of the province's water resources is basically on the rise. This shows that the carrying capacity of water resources in Hunan Province is in good condition, and corresponding protective measures should be taken to continue the current state. [ABSTRACT FROM AUTHOR]

With the increasing awareness of environmental protection, green supplier selection as an indispensable part of green supply chain management has received extensive attention. Effective and reliable green supplier evaluation criteria are crucial to the success of green supplier selection. Therefore, identifying the critical criteria and determining the causality of these criteria is an important requirement of stakeholders. However, the existing approaches on identifying critical causal criteria suffer at least two weaknesses: firstly it assumes the same cognitive levels between different decision makers by using a pre-determined and uniformed formation to characterize evaluation judgements, which may cause potential decision bias; secondly there is a lack of effective methods to analyse and identify critical causal criteria in the face of heterogeneous judgements, potentially causing the duplication consideration of the impacts of some criteria in green supplier selection. To address these issues, this study proposes a new approach integrating cloud model and DEMATEL (decision making trial and evaluation laboratory) to determine critical causal criteria for green supplier evaluation with qualitative heterogeneous judgements. The contribution of this study is threefold. First, to address the difficulty in processing uncertain and heterogeneous judgements, the cloud model theory is utilized and further developed to convert heterogeneous qualitative judgements into homogeneous quantitative data with the form of interval integrated clouds, which realizes the flow of uncertainty from qualitative judgements to quantitative data. Second, to enable the identification of critical causal criteria, the DEMATEL method is extended to accommodate the cloud model environment to solve the identification problem. Third, a case study, followed by a comparison analysis is provided to illustrate the applicability and advantages of the proposed approach. The results indicate that the proposed approach can handle heterogeneous judgements effectively as well as that staff environmental training, green production innovation, green marketing and green corporate culture are the critical causal criteria for the given application. • Heterogeneous judgements are used to express influential interrelationships among criteria. • Some methods for converting heterogeneous judgements into integrated clouds are proposed. • A cloud-based DEMATEL approach is proposed to identify critical causal criteria of green supplier evaluation. • A case study and a comparison analysis are provided to illustrate the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Design scheme decision is a vital activity at the early stage of product development, which is usually conducted by expert assessment. Since various uncertainties inherently exist in experts' linguistic assessments, such as vagueness, randomness, and diversity, single uncertainty manipulation methods may not be sufficient to select suitable design alternatives. However, existing methods usually only employ single models to handle experts' assessment uncertainties. Besides, the relative weights of experts and assessment criteria are essential information in decision system which can reasonably capture the relationship of different factors. However, most current references only consider the criteria's weight, which also influences the rationality of assessment results. Hence, this study proposes a new decision model to address the above-mentioned limitations. First, a novel linguistic manipulation model, namely interval rough integrated clouds (IRICs), is developed to handle various uncertainties by combining the interval rough number theory and cloud model theory. Second, two hybrid-weighting methods are proposed to respectively identify the overall weights of experts and assessment criteria by considering both subjective and objective aspects. Finally, a real-world case of alternative assessment is conducted to demonstrate its feasibility and reliability. Some existing known methods evaluate the proposed method's performance, and the results illustrate that the proposed method is outperforming many existing assessment methods. [ABSTRACT FROM AUTHOR]

• A cloud theory-based multi-objective portfolio selection model is proposed. • The crisp numerical characteristics of the cloud model theory are defined. • The acceptance and rejection functions with variable risk appetite are constructed. • A compromise programming approach is utilized to aggregate the objectives. • Several schemes as per investors' preferences regarding all objectives are devised. This study proposes a cloud theory-based multi-objective portfolio selection model with variable risk appetite, which incorporates four objectives of mean, variance, skewness, and liquidity constrained by several realistic constraints. Cloud model theory is employed to characterize the return rates and liquidity of assets due to the superiority of simultaneously capturing the ambiguity and randomness of information. The crisp numerical characteristics of the cloud model are defined to obtain the crisp form of the proposed model. To highlight and portray the investors' risk (averse-neutral-seeking) appetites, the generalized acceptance and rejection functions are modeled by using the extreme values of each objective and introducing a variable risk appetite parameter. Thus the corresponding model is transformed with the objective functions of maximizing acceptance and minimizing rejection, which is solved through the compromise programming approach. The extended model provides investors with an opportunity to adjust risk parameters according to current market status. Moreover, the preference ratio vector is introduced when optimizing, which provides investors with overall control over the preferences regarding all objectives, so that investors can derive optimal portfolios well compatible with their expectations through customized weighting schemes. A real-world empirical application is presented to demonstrate the effectiveness of the proposed model [ABSTRACT FROM AUTHOR]

Failure mode and effect analysis (FMEA) is a proactive quality management instrument to improve the reliability of systems. Nevertheless, the classical FMEA technique has suffered from many weaknesses, e.g., inability to handle inaccurate information, strong sensitiveness to variations in assessments. Although fuzzy theories are utilized to enhance the classical FMEA, they still have some deficiencies, e.g., requiring extra assumptions, lacking mechanism to describe the hesitation and randomness of assessment information simultaneously, ignoring the psychological effects of experts, and considering only three risk aspects among most of them. Hence, this work presents a novel concept of interval-valued intuitionistic fuzzy clouds (IVIFCs), which combines the merit of interval-valued intuitionistic fuzzy set in reflecting vagueness and hesitation of decision information and the strength of cloud model in manipulating randomness of quantitative information, and a new FMEA based on IVIFCs. Then, the individual bounded rationalities are determined by a developed weighting method considering both subjective and objective importance. Moreover, a hierarchical structure containing eight risk elements is established to identify risk orders of failures. Additionally, a well-defined Excel computational program is presented to reduce the calculation burden effectively. Finally, a real application of a machine tool is conducted by the proposed FMEA to illustrate its effectiveness and superiority. • A new risk evaluation method is presented to conduct the FMEA improvement. • Fuzziness and hesitation are handled by interval-valued intuitionistic fuzzy set. • Cloud model is utilized to manipulate randomness in risk assessment. • Two overall weighting methods are developed to determine two synthetic weights. • An integrated EXCEL procedure is presented to reduce its computational burden. [ABSTRACT FROM AUTHOR]