Weights Of Experts Research Articles

Study on the Method of Selecting Sustainable Food Suppliers Considering Interactive Factors

The existing sustainable supplier selection methods are not sufficient to deal with the problem of sustainable food supplier selection with the interaction of criteria under uncertainty. Therefore, this paper proposes a method of sustainable food supplier selection based on an extended decision model. Firstly, a processing method for supplier evaluation information is constructed using the Pythagorean fuzzy set has the function of processing complex uncertain information. Secondly, to obtain the objective weights of decision experts, a Pythagorean fuzzy weighted distance measure model is constructed, and an expert information fusion method based on a weighted power mean operator is proposed to construct the group decision matrix. Then, the decision experiment and evaluation experiment methods are integrated with the traditional MARCOS method, to construct a sustainable food supplier selection method considering the interaction of factors. This method can effectively deal with the complicated and uncertain problem of sustainable food supplier selection with interactive factors. Finally, the feasibility of the proposed method is verified by an example of sustainable food supplier selection. In addition, parameter sensitivity analysis and multi-method comparative analysis verified the rationality of the proposed selection method for sustainable food supplier selection.

Distortion risk measures for tail-risk multicriteria decision-making based on a probabilistic linguistic environment

Probabilistic linguistic term sets (PLTSs) provide a flexible tool to express linguistic preferences, and several multi-criteria decision models based on PLTSs have been recently developed. In this framework, distortion risk measures are extensively used in finance and insurance applications, but are rarely applied in fuzzy systems. In this paper, distortion risk measures are applied to fuzzy tail decisions. In particular, three tail risk measurement methods are put forward, referred to as probabilistic linguistic VaR (PLVaR), expected probability linguistic VaR (EPLVaR), and Wang tail risk measure and extensively study their properties. Our novel methods help to clarify the connections between distortion risk measure and fuzzy tail decision-making. In particular, the Wang tail risk measure is characterized by consistency and stability of decision results. The criteria and expert weights are unknown or only partially known during the decision making process, and the maximising PLTSs deviations are showed how to determine them. The theoretical results are showcased on an optimal stock fund selection problem, where the three tail risk measures are compared and analyzed.

Research on probabilistic language multi-attribute group decision-making method based on correlation coefficient and improved entropy

To address the issue of unknown expert and attribute weights in the comprehensive assessment of hospitals, as well as the potential challenges posed by distance measures, this paper presents a probabilistic language multi-attribute group decision-making (MAGDM) approach that utilizes correlation coefficients and improved entropy. First, the correlation function, called the probabilistic linguistic correlation coefficient, is introduced into the probabilistic linguistic term set(PLTS) to measure the consistency among experts, so as to obtain the weights of experts. Next, based on Shannon entropy, an improved probabilistic linguistic entropy is proposed to measure the uncertainty of PLTS considering the number of alternatives and information quantity. Then, based on the correlation coefficient and improved entropy, the attribute weights are obtained. In addition, in order to overcome the counter-intuitive problem of existing distance measurement, this paper proposes a probabilistic language distance measurement method based on the Bray-Curtis distance to measure the differences between PLTSs. On this basis, by applying the technique for order preference by similarity to ideal solution (TOPSIS) method and using PLTSs to construct the MAGDM method, the ranking of alternative schemes is generated. Finally, the improved MAGDM method is applied to an example of the comprehensive evaluation of the smart medical hospitals. The results show that compared with the existing methods, this method can determine the weight information more reasonably, and the decision-making results are not counter-intuitive, so it can evaluate the hospital more objectively.

A maximum satisfaction consensus-based large-scale group decision-making in social network considering limited compromise behavior

With the enrichment of social platforms, large-scale group decision-making (LSGDM) in social networks has gradually taken shape. In practice, experts may show limited compromise behavior during consensus reaching process (CRP), which may lead to bias or even failure in decision-making results. To tackle this issue, considering limited compromise behavior, this paper proposes a maximum satisfaction consensus (MSC) model, and focusing on LSGDM with fuzzy preference relation in social networks. Firstly, an expert weight determination algorithm integrating the interactive weights and individual weights is constructed. The interactive weights are determined by social influence strength, while the individual weights are measured by the compromise degree of limited compromise behavior and the consistency level of preferences. Subsequently, a grey clustering method is developed to classify experts into subgroups, considering both opinions similarity and limited compromise behavior similarity. In CRP, considering the limited compromise behavior, an MSC model under the limited budget determined by the minimum cost consensus (MCC) model is proposed. Based on MSC and MCC models, satisfaction-cost ratio is defined to measure the efficiency of CRP. Finally, a case study of selecting of shared bicycle operators and further discussion are conducted to verify the feasibility and superiority of the study.

Investment risk assessment of anaerobic fermentation based hydrogen production project based on regret linguistic Z-number averaging Choquet integral operator

Anaerobic fermentation based hydrogen production technology has a good market investment prospect for its advantages of abundant raw material sources, low cost and low energy consumption. The industry is in the early stage of development and will face many risks. The corresponding risk assessment has not yet attracted attention. Therefore, this paper constructs a risk assessment framework. Firstly, 17 critical risk factors affecting the anaerobic fermentation based hydrogen production project is identified and grouped into five categories. The linguistic Z-number is then used to describe the uncertainty. Later, a regret linguistic Z-number averaging Choquet integral operator is put forward to deal with interrelationships between criteria and simulate the decision-makers’ regret avoidance attitudes. In addition, a novel relative entropy measure is determined, wherein the expert weight model is established by combining the reliability measure. Subsequently, a case study is conducted and the result demonstrates the risk level of the project located in Hubei, China is between “Medium” and “Slightly high”. Finally, some measures are proposed to deal with these risks. This work can provide a theoretical basis for the risk prevention and investment decision of anaerobic fermentation based hydrogen production projects.

Prioritization of control measures in leakage scenario using Hendershot theory and FBWM-TOPSIS.

Currently, there is increasing concern about the safety and leakage of process industries. Therefore, the present study aims to prioritize control measures before and after the leakage scenario by using the Hendershot theory and MCDM techniques. In this study, two proactive and reactive layers were selected before and after leakage of tanks, respectively. Then, criteria and alternatives were selected to perform fuzzy TOPSIS (FTOPSIS) and find the best alternative based on the literature review and Hendershot approach. The linear model of the fuzzy Best-Worst method (FBWM) was constructed and resolved using Lingo 17 software. Subsequently, criteria were assigned weights based on thorough calculations of the inconsistency rate. The weight of study experts was equal to 0.25. The results of FBWM showed that the reliability index with a weight of 0.3727 was ranked first and the inconsistency rate ([Formula: see text]) was calculated to be equal to 0.040. Inherent Safety Design (ISD) (0.899) and passive safety (0.767) also ranked first before and after tank leaks, respectively. Using the FBWM method leads to fewer pairwise comparisons and at the same time more stability. Although ISD and passive strategies are more valid and strict, elements of all strategies are necessary for a comprehensive process safety management program.

Multi-attribute decision-making research on investment suitability assessment of hydropower-wind-photovoltaic-storage complementary system based on dynamic social network

The hydropower-wind-photovoltaic-storage (HWPS) complementary system is one of the essential ways for China to build a new type of power system and to realize the dual-carbon goal. However, China's HWPS complementary system is still in the exploratory stage, and it is important to propose a rational multi-attribute decision-making (MADM) framework for investment suitability assessment based on completed hydropower plants. Therefore, this study first established a limitation indicator system for preliminary screening hydropower plants and an HWPS investment suitability assessment indicator system containing six dimensions: nature, market, infrastructure, land, society, and environment. Second, the weights among experts were determined based on the dynamic social network, and a mechanism for updating expert weights was proposed. Again, triangular intuitionistic fuzzy number (TIFN) was used to quantify the qualitative indicators and combined the weights according to the best-worst method (BWM) and criteria importance through intercriteria correlation (CRITIC) methods. Finally, the TODIM (an acronym in Portuguese for interactive and MADM) method was used for scheme suitability assessment with sensitivity analysis and method comparison. The results show that 62 eligible hydropower plants were preliminarily screened out of 647 completed medium- and large-scale hydropower plants in China by combining GIS technology. Natural factors are the most important of the six dimensions, followed by social factors. According to the suitability assessment results of TODIM, #23 hydropower station has the highest suitability for HWPSCS investment, which provides a reference for the layout of China's HWPS complementary system. With different decision makers' risk preferences and different MADM methods, #23 hydropower plant is still the best investment option, which proves the effectiveness of the MADM framework proposed in this study.

Risk assessment of deep-sea floating offshore wind power projects based on hesitant fuzzy linguistic term set considering trust relationship among experts.

The development of deep-sea floating offshore wind power (FOWP) is the key to fully utilizing water resources to enhance wind resources in the years ahead, and then the project is still in its initial stage, and identifying risks is a crucial step before promoting a significant undertaking. This paper proposes a framework for identifying risks in deep-sea FOWP projects. First, this paper identifies 16 risk criteria and divides them into 5 groups to establish a criteria system. Second, hesitant fuzzy linguistic term set (HFLTS) and triangular fuzzy number (TFN) are utilized to gather and describe the criterion data to ensure the robustness and completeness of the criterion data. Third, extending the method for removal effects of criteria (MEREC) to the HFLTS environment through the conversion of TFNs, under the influence of subjective preference and objective fairness, a weighting method combining analytic network process (ANP) and MEREC is utilized to calculate criteria weights, and the trust relationship and consistency between experts are used to calculate the expert weights to avoid the subjective weighting given by experts arbitrariness. Fourth, the study's findings indicated that the overall risk level of the deep-sea FOWP projects is "medium." Fifth, sensitivity and comparative analyses were conducted to test the reliability of the assessment outcomes. lastly, this research proposes risk management measures for the deep-sea FOWP project's establishment from economic, policy, technology, environment, and management aspects.

Geographic information system-based multi-criteria decision-making analysis for investment assessment of wind-photovoltaic-shared energy storage power stations: a case study of Shanxi Province.

As the center of the development of power industry, wind-photovoltaic (PV)-shared energy storage project is the key tool for achieving energy transformation. This research seeks to construct a feasible model for investment appraisal of wind-PV-shared energy storage power stations by combining geographic information system (GIS) and multi-criteria decision-making (MCDM) method. Firstly, a comprehensive criteria system is established from the perspectives of orography, economy, resources, climate, and society, and the evaluation data is described using probabilistic linguistic term sets (PLTSs). Then, to avoid the weight deviation produced by the single weighting approach, a comprehensive weighting model including the best-worst method (BWM) and entropy weight method is provided to calculate the weights of criteria. Next, expert weights are calculated based on trust analysis. Finally, alternatives are ranked by the improved gained and lost dominance score (GLDS) method. To verify the validity of the model, an empirical investigation is carried out in Shanxi Province. The results show that the economy is the primary factor influencing the investment decision. Among all the projects approved by the government, alternative F4 located in Yanzhuang Town, Yuanping City is the best investment object. Furthermore, to illustrate the stability of the result, triple sensitivity analysis and comparative analysis are conducted in Shanxi Province. This study expands the application scope of GIS and MCDM method by first providing support for government and investors to identify optimal investment targets.

Reliability analysis of subway sliding plug doors based on improved FMECA and Weibull distribution

Using traditional FMECA to analyze the hazard of subway sliding plug door system, there are problems such as easy-to-take repetitive values, irrational allocation of expert's weights, and failure to consider the weights of evaluation factors. Hence, this paper proposes to improve FMECA by using linear interpolation to increase the differentiation of the same fault probability occurrence among various fault modes. Apply the DUOWA to dynamically assign weights to different experts. The AHP is used to endow weights to diverse evaluation factors to make them more suitable for engineering needs. The operation records of metro trains were collected from the Shanghai subway manufacturing plant for 1836 days. The hazards of the 17 common faults of the door were first analyzed using an improved FMECA,the EDCU is the component most prone to failure. Next, use a RCM strategy to determine maintenance periods for different faults. Finally, through the Weibull distribution fitting test, the fault rate function of the door is obtained, and the RUL of the door is predicted.

An improved multi-attribute group decision-making method for selecting the green supplier of community elderly healthcare service

With rapid social and economic development, the process of population aging has increased the demand for community elderly healthcare service (CEHS) in China. However, the traditional government-oriented service supply cannot meet the various needs of CEHS, and it is critical to select a suitable supplier of CEHS to provide high-quality green services in the community. Therefore, this study focuses on the issue of green supplier selection of CEHS, explores an improved transformation method for processing multi-type data, and proposes an integrated method of multi-attribute group decision-making (MAGDM) which innovatively applies the degree of overall deviation measure (ODM) to determine expert weight. Finally, the effectiveness and accuracy of the new method are verified by experimental analysis. The results show that H2 is the top choice in the green supplier selection of CEHS, followed by H1, H4, H8, H5, H6, H3, H7, H12, H11, H9, and H10. In addition, the authors apply the traditional ED method to calculate expert weights and compare the results of ODM and ED. It is a fact that the improved ODM method should be more efficient and accurate than the traditional ED method.

Strategic outsourcing in reverse logistics: Neutrosophic integrated approach with a hierarchical and interactive quality function deployment

The efficiency of strategic outsourcing in reverse logistics (RL) depends on stakeholder requirements (SRs) directing organization development and experts’ knowledge decomposing and reconstructing strategic decision, which urges the adoption of hierarchical and interactive quality function deployment (QFD). Considering the uncertainty within a QFD, single valued neutrosophic number (SVNN) is introduced to capture both indeterminacy and inconsistency hidden in quantitive votes and qualitative judgments of stakeholders and experts. With unknown weight assignments, two maximum deviation models are constructed to obtain the priority of SRs and Shapley weights of experts. An SVNN-enabled grey relational analysis is extended to identify the interdependence priority of engineer characterizers (ECs). Due to the interactions among components such as experts in a group or ECs in QFD, two information aggregation tools under neutrosophic environments are presented based on fuzzy integral delineating the positive or negative effect. Built on these, we propose an integrated neutrosophic approach composed of a transformation module, weight module, integration module and QFD analysis module to support the outsourcing decision. Finally, an illustration example is used to confirm the practicality of the proposed approach. Sensitivity analysis and comparative analysis with multiple views have been conducted to show the flexibility and superiority of the given decision.

A multi-attribute group decision making method based on novel distance measures and regret theory under probabilistic dual hesitant fuzzy sets

As a decision information preference which includes membership degree (MD), non-membership degree (NMD), and probability, the probabilistic dual hesitant fuzzy set (PDHFS) is a crucial tool for effectively expressing uncertain information. In the domains of multi-attribute decision making (MADM) and multi-attribute group decision making (MAGDM), distance measures are extremely helpful tools. In this study, a novel PDHFS distance measure is put out, on which a MAGDM method that takes decision-makers’ (DMs’) psychological behavior into account is proposed. First, a novel distance measure is put forward to effectively assess the difference between different PDHFSs by adding consideration of the distances between MDs and between NMDs. Second, a similarity-trust analysis method based on the new distance measure is employed to calculate expert weights for integrating group decisions, and the group satisfaction index and regret theory are extended to a probabilistic dual hesitant fuzzy information environment. A MAGDM method based on the novel distance measure and regret theory is proposed. Finally, the proposed method is applied to the selection of radiation protection strategies in nuclear power plants, and it is also determined through parametric analysis that DMs’ tendency to avoid regret has an impact on the outcomes of decisions. When the method proposed in this study is compared to existing approaches, the findings demonstrate that the method’s performance in resolving MAGDM issues in a PDHFS environment is superior.

A Z-number and MABAC method based on reliability analysis and evaluation of product design concept

Modular design is a significant method for complicated product development. In the context of modular design, involving users in concept assessment boosts a product's appeal but also introduces decision uncertainty and unreliability. As a solution, this paper proposed a hybrid method by integrating expert consensus modeling, attribute weighting, Z-number, and the Multi-Attribute Border Approximation Area Comparison (MABAC) method. Initially, a consensus model is established using consistency theory to determine expert weights, and attribute priorities are determined through the entropy weighting method. Subsequently, the Z-number-based MABAC method ranks the alternatives, determining the optimal solution among them. Using an automated outdoor cleaning vehicle as an example, the proposed method is compared to other techniques. The sensitivity analysis and the comparisons show that the proposed method improves the reliability and objective of the decision-making process.

A multi-stage multi-criterion group decision-making method for emergency management based on alternative chain and trust radius of experts

Multi-criterion group decision-making (MCGDM) is common when a group of experts collectively evaluate feasible alternatives regarding a set of criteria. With the increasing complexity of decision-making activities, there may be intricate interrelationships between criteria. In addition, many MCGDM problems have a dynamic characteristic when alternatives are presented to experts sequentially. In such a multi-stage setting, previous experts have influence on later experts. To overcome these challenges in MCGDM, this study first introduces two concepts, namely, trust radius, to model the social influence of experts, and alternative chain, to represent multiple alternatives that can be coordinated at adjacent stages, and then proposes a multi-stage MCGDM method based on the alternative chain and trust radius of experts. In this method, the weights of experts are determined according to the degree that an expert is influenced by others and the degree that an expert has influence on others. The weights of criteria are determined by the Decision Making Trial and Evaluation Laboratory (DEMATEL) method. This study obtains the optimal alternative chain from a 0–1 optimization model based on comprehensive scores of alternatives at each stage and compatible relationships between adjacent stages. The applicability of the proposed method is demonstrated by an illustrative example regarding emergency management of dammed lakes after natural disasters. Comparative analyses show advantages of this method.

A 2-additive Choquet integral-based multi-criterion decision-making method with complex linguistic information in drug value assessment

With the escalating incidence and mortality rates of lung cancer, the targeted drugs assessment has emerged a key problem, serving as the foundation for incorporating targeted drugs into the national basic medical insurance. This paper executes drug value assessment within a multi-criterion decision-making framework. Recognizing that experts naturally provide assessments in linguistic representations during drug evaluations, we employ the generalised probabilistic linguistic term set to uniformly model diverse types of linguistic information. Considering the bounded rationality of experts, we propose a method to obtain expert weights through minimizing regret degree of experts. Based on the determined weights of experts, a method to simplify the generalised probabilistic linguistic term set is developed. Afterwards, this study establishes a 2-additional Choquet integral-based aggregation method to fuse the assessments of drugs considering interactive criteria. To examine the effectiveness of the proposed method, we apply the proposed method in drug value assessment for lung cancer. The proposed method provides a feasible attempt to address decision-making problems involving complex linguistic information.

The Quality-of-Life Measurement with a Stochastic Choice of Parameters of the Weighted Principal Component

The quality of life of the population is a latent category, and due to the impossibility of direct measurement it has to be assessed as an integral indicator of many variables. According to the established methodology, one of the main tools in this case is the first principal component, that is, a linear convolution of variables, which has the property of minimizing the variation of the original characteristics. The fact that parameters’ variation is taken into account with equal weight may cause criticism from economists. The use of a weighted principal component which is free of this drawback can be considered as a development of the initial method. In this case to minimize the total variation, the weighting coefficients of features are set expertly. However, in this case, a logical question arises: won’t expert subjectivity have a significant impact on the final integral indicator, as it happens in the case of simple linear convolution with expert weights? Thus, the purpose of this work is to test the applicability of the weighted first principal component as the main tool in constructing an integral indicator of the population quality of life. In particular, it is necessary to test the hypothesis that the influence of heterogeneity in the weights of expert assessments on the final integral indicator is insignificant. In this case, it would be useful not only to illustrate the presence or absence of this influence, but also to estimate its extent. To do this, the simulation modeling is carried out to assess the latent variable “quality of life of the population”, based on empirical expert weights and macro statistics data. Moreover, in contrast to most works related to the topic, the values of the integral indicator (and, accordingly, the ranking of observations) are presented as an interval estimate. In other words, the result is presented as a random variable where the element of randomness is the subjectivity of the expert choice of weights for the weighted principal component. It turns out that even in this case it is possible to obtain robust and meaningful results that are in good agreement with the conclusions of well-known research in this area.

A Decision-Making Model With Intuitionistic Fuzzy Information for Selection of Enterprise Resource Planning Systems

Involvement of small and medium-scale enterprises is important for developing countries like India. Henceforth, enhancing their competency with the adoption of a suitable enterprise resource planning system (ERPS) is a vital way to enhance their competitiveness. The choice of an appropriate ERPS can be considered as a complex multicriteria decision-making (MCDM) problem where several alternatives (ERPS packages) are assessed based on certain types of characteristics, namely domain knowledge of the supplier, system reliability, service and support, functionality, compatibility, information security, cross-module integration, etc. One critical issue associated with the evaluation of ERPSs is the handling of uncertain information. In this article, we first develop a new methodology to resolve the issue of MCDM problems. In this methodology, the weights of decision experts are systematically calculated with the extended variance approach on intuitionistic fuzzy sets. For calculating the criteria weights, an optimization model based on cross-entropy is proposed, and for the aggregation of the criteria values, a new model called intuitionistic fuzzy improved measurement alternatives and ranking based on the compromise solution is developed, which allows both the vector and linear normalizations and having the advantages of comprising two kinds of aggregation models. Next, an ERPS cloud vendor assessment problem is deliberated with three alternatives (A1: Systems, Applications, and Products (SAP), A2: Oracle Corporation, A3: Microsoft Corporation) and 16 criteria to interpret the reasonableness of the introduced framework. According to the outcomes, SAP is the most suitable alternative. Next, a sensitivity analysis is exhibited with diverse parameter values to inspect the permanence of the presented approach. The advantage of the presented approach is discussed with the comparative investigation.

A Multi-Attribute Group Decision-Making Method Based on Trust Relationship and DEA Regret Cross-Efficiency

In multiattribute group decision-making (MAGDM) problems, the data envelopment analysis (DEA) cross-efficiency method is an effective tool to evaluate each decision-making unit. However, this method usually assumes that the decision-maker (DM) is completely rational, which ignores the risk psychology of the DM in the decision-making process. To compensate for this deficiency, this article proposes an MAGDM method based on trust relationships and DEA regret cross-efficiency. First, we construct a modified model based on the CCR model. Then, we propose the DEA regret cross-efficiency method. This method involves the risk psychology of the DM in the decision-making process. Next, a weighting model is developed to aggregate the cross-efficiency comprehensive utility matrix. Subsequently, we construct trust propagation and trust aggregation models to obtain the weights of experts. Finally, the MAGDM method based on trust relationships and DEA regret cross-efficiency is designed to rank all alternatives. A numerical example is provided to illustrate the superiority and rationality of our proposed method.

A novel CODAS approach based on Heronian Minkowski distance operator for T-spherical fuzzy multiple attribute group decision-making

T-spherical fuzzy sets (TSFSs) are more flexible and efficient tools to deal with ambiguous, uncertain and vague information in complex real-world decision-making problems than various extended intuitionistic fuzzy sets. This paper aims to develop a novel T-spherical fuzzy (TSF) Combinative Distance-Based ASsessment (CODAS) based on the Heronian Minkowski distance aggregation operator, this new method can capture interrelationship between input arguments. Some TSF weighted Heronian Minkowski distance (TSFWHMD) aggregation operators with generalization are developed based on Heronian mean and Minkowski-type distance, their properties are discussed as well as their families are analyzed. Furthermore, the TSF MAGDM methodology based on the improved CODAS is designed, where the Minkowski-type distance is used to define the TSF similarity for computing the expert weights and to construct the maximizing deviation method (MDM) for determining the attribute weights, respectively. The TSF ordered weighted Heronian Hamming distance (TSFOWHHD) and TSF ordered weighted Heronian Euclidean distance (TSFOWHED) operators derived from the TSFOWHMD operator are integrated into the CODAS method, which is an improved method for both measuring the deviation of the negative ideal solution from each alternative and capturing the correlation between attributes. Finally, the feasibility and practicality of developed methodology are illustrated with an example of CAE (Computer Aided Engineering) software selection for lithium-ion power battery (LiPB) design, sensitivity analysis and method comparisons are performed to elucidate the reliability and validity of the developed methodology.