Decision-making method based on new entropy and refined single-valued neutrosophic sets and its application in typhoon disaster assessment

Novel Decision Modeling for Manufacturing Sustainability under Single-Valued Neutrosophic Hesitant Fuzzy Rough Aggregation Information

We developed a multicriteria decision-making method based on the list of novel single-valued neutrosophic hesitant fuzzy rough (SV-NHFR) weighted averaging and geometric aggregation operators to address the uncertainty and achieve the sustainability of the manufacturing business. In addition, a case study on choosing the optimum elements for a sustainable manufacturing sector was carried out. The proposed decision support method is then compared to other relevant methodologies, and a validity test is performed to show the reliability and validity of the new methodology. Sustainability is one of the most important issues the world economy is facing today. Several industrial businesses have incurred large financial losses as a result of their ignorance of sustainability issues. Manufacturers in industrialized countries have done a decent job of making sure that their businesses are sustainable over the long run. Modern companies use a lot of modern technologies. These include blockchain, artificial intelligence (AI), the Internet of Things (IoT), big data analytics (BDA), and fuzzy logic (fuzziness). These modern technologies support the continuation of life, either directly or indirectly. Therefore, it is of utmost importance to concentrate on those elements that encourage the adoption of sustainability. The goal of this study is to provide a framework for using cutting-edge technology to increase the adoption of sustainability in manufacturing firms. Under the guidance of single-valued neutrosophic hesitant fuzzy rough (SV-NHFR) aggregate information, it was advised to place a strong emphasis on addressing sustainability, waste management, environmental protection, manufacturing cost savings, and chemicals and resources. The results suggest that the proposed technique can solve the inadequacy of the existing decision method by the SV-NHFR aggregation operators in terms of decision adaptability.

Picture Fuzzy Einstein Hybrid-Weighted Aggregation Operator and Its Application to Multicriteria Group Decision Making

As an extension of intuitionistic fuzzy sets (IFSs), picture fuzzy sets (PFSs) can better model and represent the hesitancy and uncertainty of decision makers’ preference information. In this study, we propose a multicriteria group decision making (MCGMD) method based on picture fuzzy sets. We first define some basic picture Einstein operations with closed properties among PFSs on the basis of the Einstein t-norms and t-conorms. Then, utilizing the hybrid-weighted operator and the developed picture Einstein operations laws, we put forward a picture fuzzy Einstein hybrid-weighted aggregation operator for aggregating PFSs and discuss its several important properties. Furthermore, we present a new MCGMD method based on the proposed picture fuzzy Einstein hybrid-weighted aggregation operator. Finally, an example is conducted to validate the effectiveness of the proposed MCGMD method.

Single-valued neutrosophic Schweizer-Sklar Hamy mean aggregation operators and their application in multi-attribute decision making

Single-valued neutrosophic sets can efficiently depict a great deal of imprecise, uncertain and discordant information. Hamy mean operator can consider the interrelationships among multiple integrated arguments and Schweizer-Sklar operations express great flexibility in the process of information aggregation. To give full consideration to these advantages, we merge the Hamy mean operator with the Schweizer-Sklar operations in single-valued neutrosophic environment, proposing a single-valued neutrosophic Schweizer-Sklar Hamy mean operator and a single-valued neutrosophic Schweizer-Sklar weighted Hamy mean operator. Besides, we illustrate some specific cases and attributes of the two operators. Moreover, based on the entropy weight method and the single-valued neutrosophic Schweizer-Sklar weighted Hamy mean operator, this paper presents a single-valued neutrosophic Schweizer-Sklar entropic weighted Hamy mean method to tackle multi-attribute decision making problems. At last, the method and other three existing methods are applied to solve a practical multi-attribute decision making problem, which validates the credibility and validity of the single-valued neutrosophic Schweizer-Sklar entropic weighted Hamy mean method by comparing the differences among them.

A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement

BACKGROUND

As the world's largest coal producer, China was accounted for about 46% of global coal production. Among present coal mining risks, methane gas (called gas in this paper) explosion or ignition in an underground mine remains ever-present. Although many techniques have been used, gas accidents associated with the complex elements of underground gassy mines need more robust monitoring or warning systems to identify risks. This paper aimed to determine which single method between the PCA and Entropy methods better establishes a responsive weighted indexing measurement to improve coal mining safety.

METHODS

Qualitative and quantitative mixed research methodologies were adopted for this research, including analysis of two case studies, correlation analysis, and comparative analysis. The literature reviewed the most-used multi-criteria decision making (MCDM) methods, including subjective methods and objective methods. The advantages and disadvantages of each MCDM method were briefly discussed. One more round literature review was conducted to search publications between 2017 and 2019 in CNKI. Followed two case studies, correlation analysis and comparative analysis were then conducted. Research ethics was approved by the Shanxi Coking Coal Group Research Committee.

RESULTS

The literature searched a total of 25,831publications and found that the PCA method was the predominant method adopted, and the Entropy method was the second most widely adopted method. Two weighting methods were compared using two case studies. For the comparative analysis of Case Study 1, the PCA method appeared to be more responsive than the Entropy. For Case Study 2, the Entropy method is more responsive than the PCA. As a result, both methods were adopted for different cases in the case study mine and finally deployed for user acceptance testing on 5 November 2020.

CONCLUSIONS

The findings and suggestions were provided as further scopes for further research. This research indicated that no single method could be adopted as the better option for establishing indexing measurement in all cases. The practical implication suggests that comparative analysis should always be conducted on each case and determine the appropriate weighting method to the relevant case. This research recommended that the PCA method was a dimension reduction technique that could be handy for identifying the critical variables or factors and effectively used in hazard, risk, and emergency assessment. The PCA method might also be well-applied for developing predicting and forecasting systems as it was sensitive to outliers. The Entropy method might be suitable for all the cases requiring the MCDM. There is also a need to conduct further research to probe the causal reasons why the PCA and Entropy methods were applied to each case and not the other way round. This research found that the Entropy method provides higher accuracy than the PCA method. This research also found that the Entropy method demonstrated to assess the weights of the higher dimension dataset was higher sensitivity than the lower dimensions. Finally, the comprehensive analysis indicates a need to explore a more responsive method for establishing a weighted indexing measurement for warning applications in hazard, risk, and emergency assessments.

New concepts of pentapartitioned neutrosophic graphs and applications for determining safest paths and towns in response to COVID-19

Pentapartitioned neutrosophic sets are a generalization of the single-valued and quadri-partitioned single-valued neutrosophic sets, and utilizes five symbol-valued neutrosophic logic. In this paper, we introduce some novel concepts regarding pentapartitioned neutrosophic graphs (PPNGs), and emphasize the effectiveness at interpreting extremely heterogeneous data that are prevalent in our daily life, particularly data gathered from various different sources which are becoming increasingly common place in the current times. The applicability of the proposed PPNG is demonstrated by applying the PPNGs on a potential real-life scenario on responding to the spread of COVID-19, where PPNGs are used to determine the safest path of travel and the safest place to stay to minimize the chances of getting infected. Both of this information have proven to be vital aspects in the efforts to combat the spread of the COVID-19 pandemic while providing the necessary support to the domestic economies, most of which are currently in recession due to the adverse effects brought upon by the pandemic. Hence, the PPNGs are applicable to all countries around the world and can be used under any circumstances such as pandemics or even in regular situations to optimize the travelling time and distance.

Prioritising teaching modalities by extending TOPSIS to single-valued neutrosophic environment

An effective teaching modality not only improves the efficiency of the teaching process but also promotes active learning. Teaching modalities are continuously evolving due to advancement in technology. There is a range of contradictory reports available in the extant literature regarding the effectiveness of different teaching modalities. This work assesses and prioritises four teaching modalities namely face-to-face teaching, pure online teaching, blended teaching and flipped classroom teaching. Assessment is made based on 8 criteria identified from the literature in consultation with the experts. Analysis revealed that the flipped classroom and blended teaching approaches are the most effective teaching modalities. Examination of the literature indicates that to date no study evaluated all the four aforementioned teaching modalities in a single study. The study is the first attempt to assesses 4 teaching modalities based on 8 eight common criteria by extending TOPSIS to a single-valued neutrosophic environment. Current work provides valuable insights to various stakeholders including academicians, policymakers and practitioners, who are interested in giving a rich learning experience to learners.

Resilience against the COVID-19 pandemic: is the hotel and tourism supply chain on the right path?

Purpose

The hotel and tourism industry forms a crucial economic sector for all the economies around the world. However, it suffered the worst hit during the COVID-19 outbreak. Considering the hotel and tourism sector's critical situation, this manuscript aims to emphasise the importance of resilience in the hotel and tourism supply chain (HTSC) and explores the crucial barriers that tend to disturb the inculcation of stability in the hotel and tourism sector. The present research analyses the factors influencing the hotel and tourism sector's resilience and also takes into consideration the various critical success factors (CSFs) needed to build a resilient HTSC.

Design/methodology/approach

A two-phase research approach has been proposed and used in this study. In the first phase, eight CSFs and sixteen factors influencing the hotel and tourism sector's resilience during the COVID-19 pandemic were identified. The basis of the identification of the CSFs and factors was literature and inputs received from experts. In the second phase, the grey-Entropy-EDAS, a qualitative and quantitative analysis, was used to analyse the identified CSFs and factors to determine the priority of concern.

Findings

In this research, the most imperative facet influencing the hotel and tourism sector's resilience has been identified, and the findings will assist hotel and tourism sector in managing and mitigating the repercussions of the COVID-19 pandemic. The analysis of the results indicates that out of all the critical success factors, supply chain visibility is the most crucial aspect in building HTSC's resilience, whereas economic catastrophe is the most influential factor. Sensitivity analysis is also conducted to examine the priority ranking stability.

Practical implications

The results of this study can be used by the hotel supply chain managers and policymakers to plan for various challenges faced by them as they try to implement resilience-based strategies in their supply chain.

Originality/value

This research is unique as it analyses the general factors hindering the pathway of resilience in the hotel and tourism supply chain. This is also the first kind of study that has used grey-Entropy to analyse the critical success factors and grey-EDAS for analysing the impact of various factors influencing the hotel and tourism sector's resilience.

A Single-Valued Neutrosophic Extension of the EDAS Method

This manuscript aims to propose a new extension of the EDAS method, adapted for usage with single-valued neutrosophic numbers. By using single-valued neutrosophic numbers, the EDAS method can be more efficient for solving complex problems whose solution requires assessment and prediction, because truth- and falsity-membership functions can be used for expressing the level of satisfaction and dissatisfaction about an attitude. In addition, the indeterminacy-membership function can be used to point out the reliability of the information given with truth- and falsity-membership functions. Thus, the proposed extension of the EDAS method allows the use of a smaller number of complex evaluation criteria. The suitability and applicability of the proposed approach are presented through three illustrative examples.

Multi-attribute Cognitive Decision Making via Convex Combination of Weighted Vector Similarity Measures for Single-Valued Neutrosophic Sets

Similarity measure (SM) proves to be a necessary tool in cognitive decision making processes. A single-valued neutrosophic set (SVNS) is just a particular instance of neutrosophic sets (NSs), which is capable of handling uncertainty and impreciseness/vagueness with a better degree of accuracy. The present article proposes two new weighted vector SMs for SVNSs, by taking the convex combination of vector SMs of Jaccard and Dice and Jaccard and cosine vector SMs. The applications of the proposed measures are validated by solving few multi-attribute decision-making (MADM) problems under neutrosophic environment. Moreover, to prevent the spread of COVID-19 outbreak, we also demonstrate the problem of selecting proper antivirus face mask with the help of our newly constructed measures. The best deserving alternative is calculated based on the highest SM values between the set of alternatives with an ideal alternative. Meticulous comparative analysis is presented to show the effectiveness of the proposed measures with the already established ones in the literature. Finally, illustrative examples are demonstrated to show the reliability, feasibility, and applicability of the proposed decision-making method. The comparison of the results manifests a fair agreement of the outcomes for the best alternative, proving that our proposed measures are effective. Moreover, the presented SMs are assured to have multifarious applications in the field of pattern recognition, image clustering, medical diagnosis, complex decision-making problems, etc. In addition, the newly constructed measures have the potential of being applied to problems of group decision making where the human cognition-based thought processes play a major role.

Occupational Health and Safety Risk Assessment based on Combination Weighting and Uncertain Linguistic Information: Method Development and Application to a Construction Project

OCCUPATIONAL APPLICATIONSOccupational hazards and work-related accidents are a substantial problem in countries around the world. Therefore, it is of great importance to develop appropriate techniques to assess and reduce the risk of occupational hazards. In many situations, however, exact data are inadequate to model real-life scenarios, because of the complexity of occupational health and safety (OHS) risk assessment problems. We present a new OHS risk assessment model to assess and rank the risk of occupational hazards based on combination weighting and uncertain linguistic information. Moreover, a practical example of a shopping mall construction project is given to illustrate the effectiveness of the proposed model. The new model was found to provide a useful, practical, and flexible way for risk evaluation in OHS. In particular, it offered a new method for capturing domain expert opinions and prioritizing potential occupational hazards to improve the health and safety of workers.