Weights Of Experts Research Articles

A large-scale group decision making method based on spatial information aggregation and empathetic relationships of experts

Large-scale group decision making (LSGDM) involving a large number of experts has attracted more and more scholars’ attention. Many LSGDM methods assumed that experts were independent to make evaluations, but the development of social media promotes the communication among experts, which makes experts no longer independent. In addition, existing LSGDM methods mainly adopted aggregation strategies such as the weighted average operator and arithmetic average operator to integrate the opinion of experts in a cluster, which makes the aggregation results cannot reflect the real opinion of the expert group. To address these issues, considering the empathetic network of experts, this study proposes an LSGDM method based on a new aggregation method for expert space information. Firstly, we determine objective weights of experts according to the objective empathetic relationships among experts. Then, the Steiner-Weber point problem is used as a prototype to establish an aggregation method called the spatial optimal aggregation (SOA) method to fuse the spatial information of experts. The model is solved by the genetic algorithm. Finally, an illustrative example about the selection of the most urgent risk in the transportation of COVID-19 vaccines is presented to show the validity and practicability of the proposed model.

An asymmetric cost consensus based failure mode and effect analysis method with personalized risk attitude information

Failure mode and effect analysis (FMEA) method is widely utilized as an important reliability management tool to effectively evaluate and prevent risk problems that occur in all aspects of production. Considering the restriction of enterprise resources and diversity of FMEA expert interests, building a consensus based FMEA method based on expert cost and risk attitude can improve the quality of reliability management. Therefore, this paper develops an improved FMEA method based on risk attitude and asymmetric cost consensus. Firstly, the FMEA consensus reaching process based on the risk attitudes and asymmetric costs is proposed. Then, the FMEA expert evaluation aggregation process of dynamic expert weight calculation model is constructed. Further, the failure mode final ranking process is developed based on the HARA (hyberbolic absolute risk aversion) utility function and Disappointment theory. The reliability and effectiveness of the proposed FMEA method can be demonstrated by the practical case and simulation experiments. The improved FMEA method can enhance the efficiency and reliability of risk assessment and further promote the acceptance and execution of assessment results. Findings proved that the adaptive consensus based FMEA method can not only save the enterprise costs but also improve the flexibility and quality of risk management.

Prioritizing populations based on recovery potential

AbstractFor wide‐ranging species, it is often too expensive or politically challenging to effectively implement conservation action across their range. In these cases, conservation actions may be vigorously applied where the situation appears most dire, but inadvertently at the expense of where success is more probable. Consequently, it is prudent to use a prioritization approach that highlights areas of probable success. Using Southern Mountain Caribou as a target species, we develop a simple algorithm that integrates scaled habitat quality measures and population characteristics known to affect the demographics of caribou and weights them according to their relative importance as defined by expert opinion. The algorithm ranks subpopulations by their relative conservation status and, as a result, how likely they are to respond to additional conservation efforts and contribute to long‐term species persistence. Sensitivity analyses are then used to measure the implications of variance among key criteria and the potential variance in expert weighting. The transparent method quickly allows for real, or potential changes in criteria values, scaling, or their relative weighting, thus providing a baseline metric for conservation discussion, subpopulation comparisons, and adaptive management action. A web‐based application of the algorithm can be used directly or adapted for other species. This transparent framework can be used by conservation scientists and managers for prioritizing populations for receiving recovery actions to maximize long‐term conservation impact.

Smartphone Addiction Assessment Using Pythagorean Fuzzy CRITIC-TOPSIS

Addiction to smartphones, particularly among adolescents, has reached alarming proportions, rivaling or perhaps exceeding internet addiction as the most widespread kind of dependence in modern culture. Evaluating the degree of problematic smartphone use habits by experts and identifying the vulnerable ones to steer to the right treatment program has become a critical issue. Since such a task may involve an abundance of criteria and candidates, as well as the inherent subjectivity of multiple decision experts participating in the process, the assessment of smartphone addiction can be framed as an uncertain multi-criteria decision-making (MCDM) problem. As an extension of intuitionistic fuzzy sets, Pythagorean fuzzy sets can be used to efficiently manage ambiguity and uncertainty during decision-making. This study provides an integrated fuzzy MCDM methodology based on Pythagorean fuzzy sets for evaluating the smartphone addiction level of adolescents. The Criteria Importance Through Inter-criteria Correlation (CRITIC) method is used to determine the importance levels of criteria in an objective manner, and smartphone addiction levels of the selected candidates are ranked using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. A sensitivity analysis is conducted to examine the variations in candidate rankings caused by changes to the criteria and weights of the decision experts. Moreover, in the context of comparative analysis, the Evaluation based on Distance from Average Solution (EDAS) approach is used to validate the acquired findings.

A Trapezoidal Fuzzy Number-Based VIKOR Method with Completely Unknown Weight Information

Multi-attribute group decision-making plays an important role in modern politics, economy, culture, and life. The multi-attribute decision-making process is limited by policymakers’ experience, and knowledge of uncertainty factors, such as weight information, is difficult to directly provide. Moreover, many types of complex rescue information are difficult to accurately describe in numerical terms, which reduces the accuracy and objectivity of the decision results, although the fuzzy theory to solve these problems provides a suitable tool. In order to solve the above problems, a new VIKOR decision method based on trapezoidal fuzzy numbers (TFNs) is proposed. Firstly, the expert weight is calculated by the distance measurement method, the criterion weight is calculated by the deviation maximization method, and then the VIKOR method is used to solve the problem. In order to verify the effectiveness and feasibility of this method, it is applied to the emergency alternative selection problem. Finally, the symmetry analysis of the method is carried out by contrast experiment and sensitivity test.

Evaluation of Schools of University and Analysis of Obstacle Factors under the Background of High-Quality Development

Currently, promoting the high-quality development (HQD) of higher education is an urgent need of the country, which is the requirement for achieving stable and sustainable development. As the main bodies of the university, the development levels of the schools directly affect universities’ development. To improve universities’ comprehensive strength, this paper proposes an integrated framework for HQD evaluation and obstacle-factor analysis for schools. To address this problem, combining the management by objectives (MBO) constructs an HQD evaluation model of schools of university covering different aspects. Meanwhile, the obstacle diagnosis model is utilized to analyze the main obstacle factors that restrict the improvement of HQD level of the schools. Moreover, taking into account the vagueness and imprecision in real life, index weights and expert weights are determined through the intuitionistic fuzzy set (IFS). Finally, a practical problem was chosen to illustrate the efficiency and applicability of the proposed framework and some suggestions from different perspectives are given according to the analysis of results.

A Novel Integrated q-Rung Fuzzy Framework for Biomass Location Selection with No Apriori Weight Choices

Biomass is a promising form of clean energy that could be utilized worldwide for huge household demand. As the world is constantly fighting climate change and carbon emissions, the adoption of biofuels for households minimizes the ill effects on the ecosystem from households. A recent report from IndiaSpend shows that Indian households bring approximately 3.78 tonnes/capita of carbon, which includes electricity, consumables, and food sources. To bring a balance between utilization demand and ecofriendliness within the household, biomass is an attractive option. Location for producing biomass is a crucial decision problem as it involves multiple criteria that are competing and conflicting with one another. Previous studies on location selection for biomass cannot promptly model uncertainty and consider hesitation and interactions of experts and criteria. To handle these issues, a novel integrated decision approach is put forward. Initially, a generalized orthopedic structure is adapted to model uncertainty from three dimensions. Further, the weights of experts and criteria are determined via variance measure and the CRITIC method. A ranking procedure is put forward with combined compromise solution formulation for rational selection of biomass production location. The usefulness of the developed framework is testified by using a case example and comparison with extant approaches, revealing the superiorities and limitations of the framework.

Fermatean Fuzzy CODAS Approach with Topology and Its Application to Sustainable Supplier Selection

A Fermatean fuzzy set (FFS) is a reliable method for representing uncertainty in “multi-criteria decision-making” (MCDM). This research seeks to examine the topological properties of FFSs and to establish the notion of “Fermatean fuzzy topology” (FFT). An FFT is the generalisation of existing fuzzy topologies. Several aspects of FFT are examined and various novel concepts are proposed, which include Fermatean fuzzy α-continuity between FFTSs and Fermatean fuzzy connectedness. To deal multiple challenges in sustainable supply chain management, a Fermatean fuzzy “combinative distance-based assessment” (CODAS) method was developed. The proposed FF CODAS technique involves various key features for MCDM. Firstly, a known reputation vector or equal expert weights is determined based on the reputation, experience and qualifications of the experts. Secondly, the Fermatean fuzzy direct rating approach is used to establish the relative relevance of criteria based on the expert group’s evaluation preferences. Thirdly, the Fermatean fuzzy CODAS approach is used to construct alternative orderings based on their assessment scores. Finally, an application is developed to show the benefit of the suggested supplier selection approach. Additionally, the symmetry of an optimal decision in application is carried out by a comparison analysis of the suggested models with some existing models.

An integrated group decision-making method for the evaluation of hypertension follow-up systems using interval-valued q-rung orthopair fuzzy sets.

It is imperative to comprehensively evaluate the function, cost, performance and other indices when purchasing a hypertension follow-up (HFU) system for community hospitals. To select the best software product from multiple alternatives, in this paper, we develop a novel integrated group decision-making (GDM) method for the quality evaluation of the system under the interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs). The design of our evaluation indices is based on the characteristics of the HFU system, which in turn represents the evaluation requirements of typical software applications and reflects the particularity of the system. A similarity is extended to measure the IVq-ROFNs, and a new score function is devised for distinguishing IVq-ROFNs to figure out the best IVq-ROFN. The weighted fairly aggregation (WFA) operator is then extended to the interval-valued q-rung orthopair WFA weighted average operator (IVq-ROFWFAWA) for aggregating information. The attribute weights are derived using the LINMAP model based on the similarity of IVq-ROFNs. We design a new expert weight deriving strategy, which makes each alternative have its own expert weight, and use the ARAS method to select the best alternative based on these weights. With these actions, a GDM algorithm that integrates the similarity, score function, IVq-ROFWFAWA operator, attribute weights, expert weights and ARAS is proposed. The applicability of the proposed method is demonstrated through a case study. Its effectiveness and feasibility are verified by comparing it to other state-of-the-art methods and operators.

Assessing agriculture's vulnerability to drought in European pre-Alpine regions

Abstract. Droughts are natural hazards that lead to severe impacts in the agricultural sector. Mountain regions are thought to have abundant water, but have experienced unprecedented drought conditions as climate change is affecting their environments more rapidly than other places. The effect radiates by reducing water availability well beyond the mountains' geographical locations. This study aims to improve the understanding of agriculture's vulnerability to drought in Europe's pre-Alpine region, considering two case studies that have been severely impacted in the past. We applied a mixed-method approach combining the knowledge of regional experts with quantitative data analyses in order to define the region-specific vulnerability based on experts' identified factors. We implemented two aggregation methods by combining the vulnerability factors that could be supported with subregional data. Whereas the equal weighting method combines all factors with the same weight, the expert weighting method combines the factors with varying weight based on the expert's opinion. These two methods resulted in vulnerability maps with the expert weighting showing a higher vulnerability, and partly relocating the medium and lower vulnerabilities to other subregions within the case study regions. In general, the experts confirmed the resulting subregions with higher vulnerability. They also acknowledged the value of mapping vulnerability by adopting different aggregation methods confirming that this can serve as a sensitivity analysis. The identified factors contributing most to the regions' vulnerability point to the potential of adaptation strategies decreasing the agriculture's vulnerability to drought that could enable better preparedness. Apart from region-specific differences, in both study regions the presence of irrigation infrastructure and soil texture are among the most important conditions that could be managed to some extent in order to decrease the regions' vulnerability. Throughout the analyses, the study benefited from the exchange with the experts by getting an in-depth understanding of the regional context with feedback relations between the factors contributing to vulnerability. Qualitative narratives provided during the semi-structured interviews supported a better characterization of local vulnerability conditions and helped to better identify quantitative indicators as proxies to describe the selected vulnerability factors. Thus, we recommend applying this mixed-method approach to close the gap between science and practitioners.

A Large Group Emergency Decision Making Method Considering Scenarios and Unknown Attribute Weights

Once an emergency event (EE) happens, emergency decision-making (EDM) plays a key role in mitigating the loss. EDM is a complex problem. Compared with conventional decision-making problems, more experts participate in decision-making. It usually has the feature of large group emergency decision-making (LGEDM). This paper proposes a large group emergency decision-making method based on Bayesian theory, relative entropy, and Euclidean distance, which is used for large group emergency decision-making with uncertain probabilities of occurrence, unknown attribute weights, and expert weights. In order to improve the accuracy of decision-making, Bayesian method is introduced into the calculation of scenario probability in the process of LGEDM. In the decision-making process, the experts’ risk preference is considered. The experts’ decision preference information is a symmetric and uniformly distributed interval value. The perceived utility values of the experts are obtained by introducing prospect theory. Euclidean distance is used to measure the contributions of experts to aggregation similarity, and different weights are given to experts according to their contributions. A relative entropy model with completely unknown weight information constraints is established to obtain attribute weights, which takes into account the differences of different alternatives under the same attribute and the differences between alternatives and the ideal solution. An example of nuclear power emergency decision-making illustrates the effectiveness of this method.

From Proper Scoring Rules to Max-Min Optimal Forecast Aggregation

There are many ways to elicit honest probabilistic forecasts from experts. Once those forecasts are elicited, there are many ways to aggregate them into a single forecast. Should the choice of elicitation method inform the choice of aggregation method? In “From Proper Scoring Rules to Max-Min Optimal Forecast Aggregation,” Neyman and Roughgarden establish a connection between these two problems. To every elicitation method they associate the aggregation method that improves as much as possible upon the forecast of a randomly chosen expert, in the worst case. This association maps the two most widely used elicitation methods (Brier and logarithmic scoring) to the two most well-known aggregation methods (linear and logarithmic pooling). The authors show a number of interesting properties of this connection, including a natural axiomatization of aggregation methods obtained through the connection, as well as an algorithm for efficient no-regret learning of expert weights.

A multi-criteria group decision-making framework for investment assessment of offshore floating wind-solar-aquaculture project under probabilistic linguistic environment.

The offshore floating wind-solar-aquaculture (WSA) system with its advantages such as strong seakeeping ability, considerable power generation, and full utilization of ocean space and water resources will have a bright prospect in the future. In order to accelerate the sustainable development of the energy industry, it is very important to build a reasonable investment decision-making framework. Therefore, this paper aims to build a multi-criteria group decision-making (MCGDM) framework for investment decision-making of this project. Firstly, a comprehensive criteria system has been established. Secondly, probabilistic language term sets (PLTSs) are introduced to describe the uncertainty and fuzziness of decision information. Thirdly, the expert weight determination model is established based on the correlation measure and correlation coefficient of PLTSs, and the PL-fuzzy decision-making trial and evaluation laboratory (DEMATEL) method and the information entropy method are introduced to determine the subjective and objective weights of the criteria. In addition, considering the decision maker's psychological behavior, we choose probabilistic language the interactive and multiple attribute decision-making (TODIM) method to determine the optimal investment alternative. Finally, we apply the proposed framework to a case study. The results illustrate that the alternative A3 possesses the optimal comprehensive performance with the overall value is 1. Then, we conduct sensitivity analysis and comparative analysis to verify its robustness and feasibility. Scenario analysis in TODIM method showed that it is reasonable to express decision preference by setting different recession coefficients in the actual decision-making environment. This study can provide some reference for decision-makers, and also extend the method of decision-making field.

A new risk quantification method in project-driven supply chain by MABACODAS method under interval type-2 fuzzy environment with a case study

Simultaneous decision-making in the supply chain and project management has attracted much attention. The integration of these two areas is called the project-driven supply chain. Timely supply of materials and services from the supply chain is significant for project activities. For this purpose, this paper presents a new method for the first time in the literature to quantify the supply risks in project activities and objectives. At first, the project’s critical path is specified according to the time, cost, risk, and quality criteria through a new decision model, namely MABACODAS, under parametric fuzzy values. The MABACODAS consists of two parts; One is about the degree of criticality of the paths, and the other is about determining the weight of experts in group decision-making. Then, a dynamic view is investigated under type-2 fuzzy numbers in the project, and it is observed that the critical path changes for different alpha values. The project suppliers’ resilience and each activity’s resilience are determined through the proposed MABACODAS model to obtain the appropriate alpha value. In other words, by using the resilience of each activity, the alpha value is selected, and the critical path is specified by considering the risk of the supply chain. Notably, the MABACODAS model is extended under interval type-2 fuzzy sets for uncertainty considerations. The main contribution of this study is introducing a risk quantification method. This method uses the MABACODAS method for specifying the criticality index of paths and resilience scores of suppliers. Also, this method is expanded under the IT2FSs. Finally, a real case study of the construction project is presented to show the applicability of the introduced model.

Failure Mode and Effects Analysis Based on Z-Numbers and the Graded Mean Integration Representation

Failure mode and effects analysis (FMEA) is a widely used safety assessment method in many fields. Z-number was previously applied in FMEA since it can take both possibility and reliability of information into consideration. However, the use of fuzzy weighted mean to integrate Z-valuations may have some drawbacks and is not suitable for some situations. In this paper, an improved method is proposed based on Z-numbers and the graded mean integration representation (GMIR) to deal with the uncertain information in FMEA. First, Z-numbers are constructed based on the evaluations of risk factors O, S, D for each failure mode by different experts. Second, weights of the three risk factors and experts are determined. Third, the integration representations of Z-numbers are obtained by a new method based on the GMIR method. Finally, risk priorities of the failure modes are derived considering the weights of experts and risk factors. Two examples and a case study are given to show the use of the proposed method and comparison with other methods. The results show that the proposed method is more reasonable, universal and simple in calculation.

Multi criteria group decision (MCGDM) for selecting third-party logistics provider (3PL) under Pythagorean fuzzy rough Einstein aggregators and entropy measures

<abstract> <p>In real life, with the trend of outsourcing logistics activities, choosing a third-party logistics (3PL) provider has become an inevitable choice for shippers. One of the most difficult decisions logistics consumers are facing the selecting the 3PL provider that best meets their needs. Decision making (DM) is an important in dealing with such situations because it allows them to make reliable decisions in a short period of time, as incorrect decisions can result in huge financial losses. In this regard, this article provides a new multi criteria group decision making method (MCGDM) under Pythagorean fuzzy rough (PyFR) set. A series of new PyFR Einstein weighted averaging aggregation operators and their basic aspects are described in depth. To evaluate the weights of decision experts and criteria weights we established the PyFR entropy measure. Further, using multiple aggregation methods based on PyFR information, a novel algorithm is offered to solve issues with ambiguous or insufficient data to obtain reliable and preferable results. First, decision-experts use PyFR sets to represent their evaluation information on alternatives based on the criteria. Then, apply all these proposed PyFR Einstein aggregation lists to rank all alternatives and find the best optimal result. Finally, to demonstrate the feasibility of the proposed PyFR decision system, a real example of choosing a 3PL is given.</p> </abstract>

Non-Cooperative Behavior Management in Large-Scale Group Decision-Making Considering the Altruistic Behaviors of Experts and Its Application in Emergency Alternative Selection

Emergency decision-making problems usually involve many experts with different professional backgrounds and concerns, leading to non-cooperative behaviors during the consensus-reaching process. Many studies on non-cooperative behavior management assumed that the maximum degree of cooperation of experts is to totally accept the revisions suggested by the moderator, which restricted individuals with altruistic behaviors to make more contributions in the agreement-reaching process. In addition, when grouping a large group into subgroups by clustering methods, existing studies were based on the similarity of evaluation values or trust relationships among experts separately but did not consider them simultaneously. In this study, we introduce a clustering method considering the similarity of evaluation values and the trust relations of experts and then develop a consensus model taking into account the altruistic behaviors of experts. First, we cluster experts into subgroups by a constrained K-means clustering algorithm according to the opinion similarity and trust relationship of experts. Then, we calculate the weights of experts and clusters based on the centrality degrees of experts. Next, to enhance the quality of consensus reaching, we identify three kinds of non-cooperative behaviors and propose corresponding feedback mechanisms relying on the altruistic behaviors of experts. A numerical example is given to show the effectiveness and practicality of the proposed method in emergency decision-making. The study finds that integrating altruistic behavior analysis in group decision-making can safeguard the interests of experts and ensure the integrity of decision-making information.

The Correlation Coefficient of Hesitancy Fuzzy Graphs in Decision Making

The hesitancy fuzzy graphs (HFGs), an extension of fuzzy graphs, are useful tools for dealing with ambiguity and uncertainty in issues involving decision-making (DM). This research implements a correlation coefficient measure (CCM) to assess the strength of the association between HFGs in this article since CCMs have a high capacity to process and interpret data. The CCM that is proposed between the HFGs has better qualities than the existing ones. It lowers restrictions on the hesitant fuzzy elements’ length and may be used to establish whether the HFGs are connected negatively or favorably. Additionally, a CCM-based attribute DM approach is built into a hesitant fuzzy environment. This article suggests the use of weighted correlation coefficient measures (WCCMs) using the CCM concept to quantify the correlation between two HFGs. The decision-making problems of hesitancy fuzzy preference relations (HFPRs) are considered. This research proposes a new technique for assessing the relative weights of experts based on the uncertainty of HFPRs and the correlation coefficient degree of each HFPR. This paper determines the ranking order of all alternatives and the best one by using the CCMs between each option and the ideal choice. In the meantime, the appropriate example is given to demonstrate the viability of the new strategies.

Transportation Mode Selection for Organ Transplant Networks by a New Multi-Criteria Group Decision Model Under Interval-Valued Intuitionistic Fuzzy Uncertainty

This study introduces a new multi-criteria group decision-making model in organ transplant transportation networks under uncertain situations. A new combined weighting approach is presented to obtain expert weights with various kinds of opinions by integrating similarity measure and subjective judgments of experts. Also, the CRITIC approach is given to obtain transportation criteria weights. Finally, a novel integrated ranking approach is proposed to calculate the rank of each alternative based on ideal point solution and relative preference relation (RPR) methods. This study regards an interval-valued intuitionistic fuzzy set to cope with the vagueness of uncertain conditions in a real case study.

An Extended Fuzzy-DEMATEL System for Factor Analyses on Social Capital Selection in the Renovation of Old Residential Communities

China has been promoting the renovation of old residential communities vigorously. Due to the financial pressure of the government and the sustainability of the renovation of old residential communities, public-private partnerships (PPP) have already gained attention. The selection of social capital is key to improving the efficiency of the PPP model in renovating old residential communities. In order to determine the influencing factors of social capital selection in the renovation of old residential communities, this paper aims to find an effective approach and analyze these factors. In this paper, a fuzzy decision-making and trial evaluation laboratory (fuzzy-DEMATEL) technique is extended and a more suitable system is developed for the selection of social capital using the existing group decision-making theory. In the first stage, grounded theory is used to extract the unabridged key influencing factors for social capital selection in the renovation of old residential communities. Secondly, by considering the impact of expert weights, the key influencing factors are identified. The interactions within these influencing factors are discussed and the credibility of the results is verified by sensitivity analysis. Finally, these key influencing factors are sorted by importance. Based on the results, the government should focus on a technical level, organizational management abilities, corporate reputation, credit status, etc. This study provides the government with a theoretical basis and a methodology for evaluating social capital selection.