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The purpose of this paper is to construct a dynamic information aggregation decision-making model based on variable precision rough set.

To deal with the dynamic decision-making problems, the grey relational analysis method, grey fixed weight clustering based on the centre triangle whitening weight function and maximum entropy principle is used to establish the dynamic information aggregation decision-making model based on variable precision rough set. The method, to begin with, the grey relational analysis method is used to determine the attributes weights of each stage; taking the proximity of the attribute measurement value and positive and negative desired effect value and the uncertainty of time weight into account, a multi-objective optimisation model based on maximum entropy principle is established to solve the model with Lagrange multiplier method, so that time weights expression are acquired; what is more, the decision-making attribute is obtained by grey fixed weight clustering based on the centre triangle whitening weight function, so that multi-decision-making table with dynamic characteristics is established, and then probabilistic decision rules from multi-criteria decision table are derived by applying variable precision rough set. Finally, a decision-making model validates the feasibility and effectiveness of the model.

The results show that it the proposed model can well aggregate the multi-stage dynamic decision-making information, realise the extraction of decision-making rules.

The method exposed in the paper can be used to deal with the decision-making problems with the multi-stage dynamic characteristics, and decision-making attributes contain noise data and the attribute values are interval grey numbers.

The paper succeeds in realising both the aggregation of dynamic decision-making information and the extraction of decision-making rules.

The purpose of this paper is to explore how differently aggregated order data may affect inventories and service levels in a serial supply chain and compares the results against various levels of information sharing. By performing sensitivity analysis, critical parameters are identified and conjectures for explaining the divergent results on the value of information sharing in prior literature are given.

By using discrete event simulation, the paper analyses various approaches of differently aggregated order data compared to shared demand information.

The experiments show that suppliers cannot accurately estimate demand means and variances because of time‐depending order quantities and biasing effects of order inter‐arrival times. This may lead to inappropriate computations of reorder points and safety stocks. The aggregation of order data can improve the calculations resulting in lower inventories with almost identical service levels. The mean inventory can also be reduced by sharing information but may lead to considerably lower service levels.

As discovered in this paper, simplifications in the supply chain structure may have large effects on the experimental results. Therefore, the value of information sharing and order aggregation strategies should be analyzed in a more complex supply chain network.

Some ordering mechanisms have the effect of increasing the demand variance for upstream companies. This amplification may lead to inefficiencies throughout the entire supply chain. The paper proposes solutions to managers on how they can benefit from order data aggregation and information sharing. The per period variances may be reduced leading to smaller safety stocks and lower costs for the entire supply chain.

The paper shows that the performance of a supply chain may be improved by aggregating order data and compares the results with improvements derived from information sharing strategies.

This is mainly because the restrictive condition of intuitionistic hesitant fuzzy number (IHFN) is relaxed by the membership functions of Pythagorean probabilistic hesitant fuzzy number (PyPHFN), so the range of domain value of PyPHFN is greatly expanded. The paper aims to develop a novel decision-making technique based on aggregation operators under PyPHFNs. For this, the authors propose Algebraic operational laws using algebraic norm for PyPHFNs. Furthermore, a list of aggregation operators, namely Pythagorean probabilistic hesitant fuzzy weighted average (PyPHFWA) operator, Pythagorean probabilistic hesitant fuzzy weighted geometric (PyPHFWG) operator, Pythagorean probabilistic hesitant fuzzy ordered weighted average (PyPHFOWA) operator, Pythagorean probabilistic hesitant fuzzy ordered weighted geometric (PyPHFOWG) operator, Pythagorean probabilistic hesitant fuzzy hybrid weighted average (PyPHFHWA) operator and Pythagorean probabilistic hesitant fuzzy hybrid weighted geometric (PyPHFHWG) operator, are proposed based on the defined algebraic operational laws. Also, interesting properties of these aggregation operators are discussed in detail.

PyPHFN is not only a generalization of the traditional IHFN, but also a more effective tool to deal with uncertain multi-attribute decision-making problems.

In addition, the authors design the algorithm to handle the uncertainty in emergency decision-making issues. At last, a numerical case study of coronavirus disease 2019 (COVID-19) as an emergency decision-making is introduced to show the implementation and validity of the established technique. Besides, the comparison of the existing and the proposed technique is established to show the effectiveness and validity of the established technique.

Paper is original and not submitted elsewhere.

This paper aims to examine the antecedent factor, top management's commitment to environmental sustainability, for the adoption of a sophisticated internal environmental information system; measured by the broad‐scope, timeliness, aggregation and integration of such information. The paper also seeks to examine whether the availability of such a system would lead to improved environmental performance.

The paper investigates responses from a survey of Chief Financial Officers or chief management accountants in the top 200 listed companies in Australia. It uses linear regression analysis based on a multiple‐mediator model with percentile‐based bootstrap, bias‐corrected (BC) and bias‐corrected and accelerated (BCa) bootstrap confidence intervals to identify significant mediators.

It was found in this study that top management commitment to environmental sustainability was associated with the adoption of a sophisticated internal environmental information system. Further, the availability of aggregated environmental information was found to mediate the relationship between top management commitment to environmental sustainability and environmental performance. However, there was no significant relationship to other mediating variables.

Limitations relate to the collinearity of mediators which make it difficult to identify the impact of specific mediators in a multi‐mediator model. The implications are that other methods may provide further value, but these may need to be based on either different data or larger samples.

The findings point to the importance of aggregated environmental accounting information to organisations aiming to improve their environmental performance.

The study contributes to the corporate environmental accounting literature by empirically linking the top management commitment to environmental sustainability and to environmental performance through the adoption of accounting information provisions. The results of this study also provide guidance to practitioners about how to ensure their commitment to environmental sustainability will be translated to environmental performance and to some extent provide some answer to whether countries such as Australia should implement Emission Trading Scheme (ETS) to account for carbon costs.

Single-valued neutrosophic sets (SVNSs) are efficient models to address the complexity issues potentially with three components, namely indeterminacy, truthness and falsity. Taking advantage of SVNSs, this paper introduces some new aggregation operators (AOs) for information fusion of single-valued neutrosophic numbers (SVNNs) to meet multi-criteria group decision-making (MCGDM) challenges.

Einstein operators are well-known AOs for smooth approximation, and prioritized operators are suitable to take advantage of prioritized relationships among multiple criteria. Motivated by the features of these operators, new hybrid aggregation operators are proposed named as “single-valued neutrosophic Einstein prioritized weighted average (SVNEPWA) operator” and “single-valued neutrosophic Einstein prioritized weighted geometric (SVNEPWG) operators.” These hybrid aggregation operators are more efficient and reliable for information aggregation.

A robust approach for MCGDM problems is developed to take advantage of newly developed hybrid operators. The effectiveness of the proposed MCGDM method is demonstrated by numerical examples. Moreover, a comparative analysis and authenticity analysis of the suggested MCGDM approach with existing approaches are offered to examine the practicality, validity and superiority of the proposed operators.

The study reveals that by choosing a suitable AO as per the choice of the expert, it will provide a wide range of compromise solutions for the decision-maker.

The purpose of this paper is to present an intelligent mobile based emergency response system (MERS) framework, a text information extraction and aggregation algorithm to integrate information from multiple sources in the MERS system, and an ontology‐supported case‐based reasoning system for the MERS system.

The paper explains the components of information extraction and aggregation process, and a CBR‐Ontology approach for the MERS system.

The result of this study will offer a new opportunity to the interaction between government, citizens, responders, and other non‐government agencies in emergency situations, and therefore improve the services of the government in an emergency situation.

The paper indicates the need for usage of mobile technologies to assist the government to get information and make decisions in responding to disasters anytime and anywhere.

The attributes that influence the selection of applicants and the relevant crediting decisions are naturally distinguished by interactions and interdependencies. A new method of possibilistic discrimination analysis (MPDA) was developed for the second stage to address this phenomenon. The method generates positive and negative discrimination measures for each alternative applicant in relation to a particular attribute. The obtained discrimination pair reflects the interaction of attributes and represents intuitionistic fuzzy numbers (IFNs). For the aggregation of applicant's discrimination intuitionistic fuzzy assessments (with respect to attributes), new intuitionistic aggregation operators, such as AsP-IFOWA and AsP-IFOWG, are defined and studied. The new operators are certain extensions of the well-known Choquet integral and Yager OWA operators. The extensions, in contrast to the Choquet aggregation, take into account all possible interactions of the attributes by introducing associated probabilities of a fuzzy measure.

For optimal planning of investments distribution and decreasing of credit risks, it is crucial to have selected projects ranked within deeply detailed investment model. To achieve this, a new approach developed in this article involves three stages. The first stage is to reduce a possibly large number of applicants for credit, and here, the method of expertons is used. At the second stage, a model of improved decisions is built, which reduces the risks of decision making. In this model, as it is in multi-attribute decision-making (MADM) + multi-objective decision-making (MODM), expert evaluations are presented in terms of utility, gain, and more. At the third stage, the authors construct the bi-criteria discrete intuitionistic fuzzy optimization problem for making the most profitable investment portfolio with new criterion: 1) Maximization of total ranking index of selected applicants' group and classical criterion and 2) Maximization of total profit of selected applicants' group.

The example gives the Pareto fronts obtained by both new operators, the Choquet integral and Yager OWA operators also well-known TOPSIS approach, for selecting applicants and awarding credits. For a fuzzy measure, the possibility measure defined on the expert evaluations of attributes is taken.

The comparative analysis identifies the applicants who will receive the funding sequentially based on crediting resources and their requirements. It has become apparent that the use of the new criterion has given more credibility to applicants in making optimal credit decisions in the environment of extended new operators, where the phenomenon of interaction of all attributes was also taken into account.

Energy constraint is always a serious issue in wireless sensor networks, as the energy possessed by the sensors is limited and non‐renewable. Data aggregation at intermediate base stations increases the lifespan of the sensors, whereby the sensors' data are aggregated before being communicated to the central server. This paper proposes a query‐based aggregation within Monte Carlo simulator to explore the best and worst possible query orders to aggregate the sensors' data at the base stations. The proposed query‐based aggregation model can help the network administrator to envisage the best query orders in improving the performance of the base stations under uncertain query ordering. Furthermore, it aims to examine the feasibility of the proposed model to engage simultaneous transmissions at the base station and also to derive a best‐fit mathematical model to study the behavior of data aggregation with uncertain querying order.

The paper considers small and medium‐sized wireless sensor networks comprised of randomly deployed sensors in a square arena. It formulates the query‐based data aggregation problem as an uncertain ordering problem within Monte Carlo simulator, generating several thousands of uncertain orders to schedule the responses of M sensors at the base station within the specified time interval. For each selected time interval, the model finds the best possible querying order to aggregate the data with reduced idle time and with improved throughput. Furthermore, it extends the model to include multiple sensing parameters and multiple aggregating channels, thereby enabling the administrator to plan the capacity of its WSN according to specific time intervals known in advance.

The experimental results within Monte Carlo simulator demonstrate that the query‐based aggregation scheme show a better trade‐off in maximizing the aggregating efficiency and also reducing the average idle‐time experienced by the individual sensor. The query‐based aggregation model was tested for a WSN containing 25 sensors with single sensing parameter, transmitting data to a base station; moreover, the simulation results show continuous improvement in best‐case performances from 56 percent to 96 percent in the time interval of 80 to 200 time units. Moreover, the query aggregation is extended to analyze the behavior of WSN with 50 sensors, sensing two environmental parameters and base station equipped with multiple channels, whereby it demonstrates a shorter aggregation time interval against single channel. The analysis of average waiting time of individual sensors in the generated uncertain querying order shows that the best‐case scenario within a specified time interval showed a gain of 10 percent to 20 percent over the worst‐case scenario, which reduces the total transmission time by around 50 percent.

The proposed query‐based data aggregation model can be utilized to predict the non‐deterministic real‐time behavior of the wireless sensor network in response to the flooded queries by the base station.

This paper employs a novel framework to analyze all possible ordering of sensor responses to be aggregated at the base station within the stipulated aggregating time interval.

The purpose of this article is to conduct a main path analysis of 627 articles on the theme of Pythagorean fuzzy sets (PFSs) in the Web of Science (WoS) from 2013 to 2020, to provide a conclusive and comprehensive analysis for researchers in this field, and to provide a study on preliminary understanding of PFSs.

The research topic of Pythagorean fuzzy fields, through keyword extraction and describing the changes in characteristic themes over the past eight years, are firstly examined. Main path analysis, including local and global main paths and key route paths, is then used to reveal the most influential relationships between papers and to explore the trajectory and structure of knowledge transmission.

The application of Pythagorean fuzzy theory to the field of decision-making has been popular, and combinations of the traditional Pythagorean fuzzy decision-making method with other fuzzy sets have attracted widespread attention in recent years. In addition, over the past eight years, research interest has shifted to different types of PFSs, such as interval-valued PFSs.

This paper implicates to investigate the growth in certain trends in the literature and to explore the main paths of knowledge dissemination in the domain of PFSs in recent years.

This paper aims to identify the topics in which researchers are currently interested, to help scholars to keep abreast of the latest research on PFSs.

The purpose of this paper is to put forward a multi-stage information aggregation method based on grey inspiriting control lines to evaluate the objects dynamically and comprehensively.

According to the evaluation value of the objects, the positive and negative incentive lines were set up and the predicted values were solved based on the grey GM(1, 1) model, so the value with expected information could be evaluated. In the evaluation, the part above the positive incentive line should be “rewarded” and that below the negative incentive line should be “punished” appropriately. Thereby the double incentive effects of “the current development situation and future development trend” to objects could be implemented on the basis of control.

This method can primarily describe the decision maker's expectancy of the development of evaluation objects and make the evaluation results have better practical application value.

Many comprehensive evaluations were always based on the past information. However, the future development trend of the evaluated object is also very important. This study can be used in the evaluation for future application and development.

The paper succeeds in providing not only a method of multi-phase information aggregation with expectancy information, but also a simple and convenient method solving nonlinear inspiring lines objectively.