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Consulting experts is an effective way to utilize tacit resource. The purpose of the paper is to optimize the matching between panels of experts and groups of demanders to improve the efficiency of tacit knowledge sharing.

Experts and demanders express preferences using linguistic terms. The estimate method based on trust is developed to get missing ratings. Weights of demanders are determined and knowledge needs are identified. Three kinds of satisfaction are measured based on grey relational analysis. To maximize satisfaction of experts and demanders and safeguard meetings of knowledge needs as well as the workload of experts, the optimization model is constructed and the solution is optimal matching results.

The presented approach not only optimizes the matching between demanders and experts but also sets up a panel of experts in case that knowledge needs exceed a single expert’s capacity.

The approach expands research works of methods for tacit knowledge sharing. The continuous updating of matching results and the processing of the data with mixing formats need to be studied further.

The presented approach acts as a valuable reference for the development of knowledge management systems. It can be used in any scene that needs the match between experts and demanders.

The approach provides a new way of helping demanders to find appropriate experts. Both experts’ and demanders’ preferences are considered. A panel of experts is set up when needed. Expert resources are utilized more efficiently and knowledge needs are met more comprehensively.

This paper aims to develop a dynamic two-sided stable matching method based on preference information of the matching objects in uncertain environments, so as to solve the matching problem of cloud manufacturing tasks and services with load balancing.

For dynamic two-sided matching, due to the complexity of social environment and the limitation of human cognition, hesitation and fuzziness always exist in the process of multi-criteria assessment. First, in order to obtain the accurate preference information of each matching object, uncertain linguistic variables, uncertain preference ordinal and incomplete complementary matrices are used to evaluate multi-criteria preference information. This process is undertaken by considering the probability of each possible matching pair. Second, the preference information at different times is integrated by using the time-series weight to obtain the comprehensive satisfaction degree matrices of the matching objects. Further, the load adjustment parameter is used to increase the satisfaction degree of the matching objects. Afterward, a dynamic two-sided stable matching optimization model is constructed by considering stable matching conditions. The model aims to maximize the satisfaction degree and minimizes the difference in the satisfaction degree of matching objects. The optimal stable matching results can be obtained by solving the optimization model. Finally, a numerical example and comparative analysis are presented to demonstrate the characteristics of the proposed method.

Uncertain linguistic variables, uncertain preference orders and incomplete complementary matrices are used to describe multi-criteria preference information of the matching objects in uncertain environments. A dynamic two-sided stable matching method is proposed, based on which a DTSMDM (dynamic two-sided matching decision-making) model of cloud manufacturing with load balancing can be constructed. The study proved that the authors can use the proposed method to obtain stable matching pairs and higher matching objective value through comparative analysis and the sensitivity analysis.

A new method for the two-sided matching decision-making problem of cloud manufacturing with load balancing is proposed in this paper, which allows the matching objects to elicit language evaluation under uncertain environment more flexibly to implement dynamic two-sided matching based on preference information at different times. This method is suitable for dealing with a variety of TSMDM (two-sided matching decision-making) problems.

The need for the thermal insulation of masonry buildings in Algeria is no longer debated. This paper aims to propose an integrated fuzzy multi-criteria decision aid method for the thermal insulation of masonry buildings to rank the thermal insulation solutions.

The proposed method combines the fuzzy analytical hierarchy process with the fuzzy preference ranking organization method for enrichment evaluation.

A case study using the proposed method is detailed in this paper. The building users’ preferences obtained by the fuzzy analytical hierarchy process had a higher level of consistency and accuracy. The case study demonstrates how in a highly uncertain field such as thermal insulation of masonry buildings, the fuzzy preference ranking organization method for enrichment evaluation can prevent the loss of valuable evaluation data and overcome difficulty in integrating linguistic assessments of the thermal insulation alternatives.

The proposed method extends current knowledge by using the fuzzy analytical hierarchy process to consider uncertainties regarding the building users’ preferences, and the fuzzy preference ranking organization method for enrichment evaluation to get a complete ranking of the thermal insulation solutions taking into account the uncertainties related to the alternatives’ evaluations.

This study aims to propose a consensus model that considers dynamic trust and the hesitation degree of the expert's evaluation, and the model can provide personalized adjustment advice to inconsistent experts.

The trust degree between experts will be affected by the decision-making environment or the behavior of other experts. Therefore, based on the psychological “similarity-attraction paradigm”, an adjustment method for the trust degree between experts is proposed. In addition, we proposed a method to measure the hesitation degree of the expert's evaluation under the multi-granular probabilistic linguistic environment. Based on the hesitation degree of evaluation and trust degree, a method for determining the importance degree of experts is proposed. In the feedback mechanism, we presented a personalized adjustment mechanism that can provide the personalized adjustment advice for inconsistent experts. The personalized adjustment advice is accepted readily by inconsistent experts and ensures that the collective consensus degree will increase after the adjustment.

The results show that the consensus model in this paper can solve the social network group decision-making problem, in which the trust degree among experts is dynamic changing. An illustrative example demonstrates the feasibility of the proposed model in this paper. Simulation experiments have confirmed the effectiveness of the model in promoting consensus.

The authors presented a novel dynamic trust consensus model based on the expert's hesitation degree and a personalized adjustment mechanism under the multi-granular probabilistic linguistic environment. The model can solve a variety of social network group decision-making problems.

This study proposes an objective assessment model to evaluate the performance of internal and external capabilities of firms. It facilitates firms to invest appropriate resources to cultivate the organizational capability necessary to meet the requirements of the performance indicators.

This study integrates the concepts of resource-based theory, the organizational capability concept, and conduct a performance analysis to the four perspectives of the BSC by implementing the fuzzy set theory and data employment analysis.

The findings show that the appropriate strategies help allocate available resources and capabilities during the different product life cycle, which provides practical guidelines for firms to achieve sustaining competitive advantage.

The selected factors were focused on four resources and capabilities rather than all possible factors.

An objective assessment model was created based on internal and external competitive performance efficiency in this research field. This model facilitates the ability of the top management to make decisions for resource allocation that will enhance firm's performance.

The purpose of the paper is to develop a model for the selection of knowledge management system (KMS), in which the assessment criteria are defined and the TOPSIS method with multiple distances in fuzzy environment is proposed.

First, the paper establishes the evaluation criteria from functional, performance and economic aspects. Second, a new TOPSIS method is proposed to deal with the linguistic evaluation information. In the proposed method, in order to eliminate the bias of TOPSIS with single distance, six kinds of distances that are commonly used in TOPSIS including Hamming distance, Euclidean distance, D_p,q distance, Hausdorff distance, L₂ distance and vertex distance are extended in fuzzy environment and employed in the TOPSIS to generate six independent pre-rankings. Afterwards these pre-rankings are combined by Condorcet method to generate the final joint ranking.

Since the final ranking is the collective result, the bias in each single pre-ranking is eliminated and the selection is more objective and accurate. The example shows the proposed model is practical.

The linguistic preferences are given in the single granularity linguistic information.

The proposed model can be applied as a tool for decision makers in the evaluation and selection of KMS.

The paper gives an overall evaluation of KMS and proposes the new TOPSIS method with multiple distances in fuzzy environment.

The purpose of this paper is to develop a novel recommendation method using online reviews with emotional preferences for facilitating online purchase decisions. This leads to better use of information-rich online reviews for providing users with personalized recommendations.

A novel method is developed for producing personalized recommendations in online purchase decision-making. Such a method fuses the belief structure and the Shapley function together to effectively deal with the emotional preferences in online reviews and adequately tackle the interaction existent between product criteria with the use of a modified combination rule for making better online recommendations for making online purchase decisions.

An example is presented for demonstrating the applicability of the method for facilitating online purchase. The results show that the recommendation using the proposed method can effectively improve customer satisfaction with better purchase decisions.

The proposed method can better utilize online reviews for satisfying personalized needs of consumers. The use of such a method can optimize interface design, refine customer needs, reduce recommendation errors and provide personalized recommendations.

The proposed method adequately considers the characteristics of online reviews and the personalized needs of customers for providing customers with appropriate recommendations. It can help businesses better manage online reviews for improving customer satisfaction and create greater value for both businesses and customers.

The consensus opinion helps with the achievement of fairness of the decision process, especially when the new decision makers (DMs) are added into the group decision making (GDM). This paper aims to show by constructing consensus models that when some conditions are met, the consensus level does not decrease even when new DMs are invited to join the GDM (new opinions are added to the GDM).

This paper first constructs a deviation which is the difference between each two individual opinions. The smaller these differences are, the higher consensus degree (level) of the group. Then, a consensus optimization model based on individual differences (IDCO) is constructed by minimizing the aggregated deviations. Lastly, based on the optimization model, the condition of reaching a high level of consensus when new DMs are added into the GDM is discussed.

The discussion on the properties of the IDCO indicates that once the consensus with all DMs reaching an acceptable level, the consensus deviation degree decreases as the number of DMs increases when certain conditions are met, and the consensus level does not decrease even when new DMs join the GDM.

Practically, high-level consensus needs to be reached no matter how difficult it is when the new opinions are added into the GDM. To see how this end could be achieved, this paper takes the fuzzy preference relations as particular instances to investigate the conditions of consensus under which new DMs (opinions) are added into the GDM. The rather holistic analysis leads us to such a conclusion that the authors can replace this particular kind of preference, as used in the discussion, in many other cases by, for instance, multiplicative preference relation, linguistic preference relation, etc.

Establishing a performance‐oriented evaluation in public sectors is the key to successful administrations. However, because of lacking relative comparable measuring standards, it is difficult to measure the relative performance of one unit while comparing to other units with regard to the multiple criteria decision making (MCDM) of performance evaluation. This paper aims to focus on the performance ranking of research and development (R&D) projects in Taiwan's public sectors.

The algorithm in this paper is based on the concept of fuzzy set theory and the hierarchical structure analysis. The analyzing method adopts the methods of standard normal distribution, linear transformation, and fuzzy MCDM, carrying on the analysis of multiple criteria of the performance evaluation.

This paper constructs linguistic values to the subjective judgments and analyzes the ranking results of the performance evaluation with respect to 45 R&D projects of one of Taiwan's electric power companies. Thus, the paper demonstrates a successful way of evaluating R&D projects in the public sector.

In this paper, a decision algorithm based on the fuzzy set theory is proposed to solve the performance evaluation of R&D projects in public sectors. In order to solve the difficulties of measuring one unit of the relative performance of quantitative criteria comparing to the other units, the method of standard normal distribution is adopted while measuring the quantitative criteria. The concept of linguistic values and fuzzy numbers are used in this paper since they could easily be used to describe the subjective measurement of the appropriateness of alternatives and the importance weightings of criteria.

The purpose of this study is to identify the risks that exist in halal meat supply chain activities and to carry out a risk assessment using the fuzzy best-worst method (FBWM) along with mitigating risks using the risk mitigation number (RMN).

The method used is to collect several literature reviews related to the halal meat supply chain, which has information relevant to the risks of the meat industry in Indonesia. Then, a focus group discussion was held with several experts who play a role in the meat industry in Indonesia, and 33 identified risks were identified in halal meat supply chain activities. The proposed methodology uses FBWM and RMN in conducting risk assessment and mitigation in the meat industry in Indonesia.

The analysis reveals that priority risk is obtained by using the global weight value on the FBWM, and then risk mitigation is carried out with RMN. Priority mitigation strategies can mitigate some of the risks to the meat industry in Indonesia. The proposed mitigation strategy is designed to be more effective and efficient in preventing risks that can interfere with product halalness in halal meat supply chain activities in the Indonesian meat industry.

The implications of this study highlight the need for collaboration among stakeholders, improved risk assessment methodologies and the expansion of research into other halal supply chains. By addressing these implications, the halal industry can enhance its integrity, consumer confidence and overall contribution to the global market.

This research provides an integrated approach to identifying, analyzing, assessing and mitigating risks to the meat industry in Indonesia.