CompuMath Citation Index

Hydrogen is the new age alternative energy source to combat energy demand and climate change. Storage of hydrogen is vital for a nation’s growth. Works of literature provide different methods for storing the produced hydrogen, and the rational selection of a viable method is crucial for promoting sustainability and green practices. Typically, hydrogen storage is associated with diverse sustainable and circular economy (SCE) criteria. As a result, the authors consider the situation a multi-criteria decision-making (MCDM) problem. Studies infer that previous models for hydrogen storage method (HSM) selection (i) do not consider preferences in the natural language form; (ii) weights of experts are not methodically determined; (iii) hesitation of experts during criteria weight assessment is not effectively explored; and (iv) three-stage solution of a suitable selection of HSM is unexplored. Driven by these gaps, in this paper, authors put forward a new integrated framework, which considers double hierarchy linguistic information for rating, criteria importance through inter-criteria correlation (CRITIC) for expert weight calculation, evidence-based Bayesian method for criteria weight estimation, and combined compromise solution (CoCoSo) for ranking HSMs. The applicability of the developed framework is testified by using a case example of HSM selection in India. Sensitivity and comparative analysis reveal the merits and limitations of the developed framework.

The selection and prioritization of appropriate actions to improve the performance of the railway sector pose a common challenge for policymakers and transport planners. This challenge is further complicated by factors such as political power shifts, policy changes, and socio-economic and environmental impacts, introducing various uncertainties. To address this issue, a novel multi-criteria decision-making framework was developed in this study. The framework introduces the weighted geometric Dombi Maclaurin Symmetric Mean operator within an intuitionistic fuzzy environment, enabling the identification of the most appropriate action from nine different alternatives to enhance the performance of railway sector. Moreover, the study makes an additional contribution by combining this approach with an interval rough PIvot Pairwise RElative Criteria Importance Assessment to determine the weight coefficients of the criteria. To assess the robustness of the proposed methodology, sensitivity analysis and a comparative study were conducted. The findings of the study indicate that “enhancing the governance of the transport sector” emerged as the most appropriate action to improve the performance of the railway sector.

Hospital performance evaluation (HPE) has a major role in improving the quality, safety, and effectiveness of health care services, so it is indispensable for proper and continuous operation of hospitals. Although several studies have been performed on HPE, few have used group decision-making (GDM). This study presents a comprehensive multi-criteria GDM model for HPE under uncertain conditions. In this model, we have combined the group best–worst method (GBWM) and fuzzy preference programming (FPP) method to create an applicable framework for GDM in which members of a decision-making group including decision-makers (DMs) have different expertise and the importance of criteria and DMs opinions are determined by a supervisor. The advantages of the proposed method include the integration of the GDM process in the form of a single model and there is no need to calculate separately the consistency of the decisions of the decision-making team members. Finally, a case study conducted on 5 hospitals in Tehran is presented to demonstrate the applicability and effectiveness of the proposed method. The results show that Sina Hospital, Baharloo Hospital, and Tehran Heart Center were ranked first to third, respectively. Also, we can conclude from this study that the proposed integrated framework is capable to address the HPE problem by using a GDM and considering the uncertainty of the comparisons made by decision-making team members.

The article presents performance evaluation of parallel haemodynamic flow computations on heterogeneous resources of the OpenStack cloud infrastructure. The main focus is on the parallel performance analysis, energy consumption and virtualization overhead of the developed software service based on ANSYS Fluent platform, which runs on Docker containers of the private university cloud. The haemodynamic aortic valve flow described by incompressible Navier-Stokes equations is considered as a target application of the hosted cloud infrastructure. The parallel performance of the developed software service is assessed measuring the parallel speedup of computations carried out on virtualized heterogeneous resources. The performance measured on Docker containers is compared with that obtained by using the native hardware. The alternative solution algorithms are explored in terms of the parallel performance and power consumption. The investigation of a trade-off between the computing speed and the consumed energy is performed by using Pareto front analysis and a linear scalarization method.

Sharp falls or explosive growths in exchange markets, whether expected or not, generates new challenges for investors who want to protect their investments or achieve an optimum benefit during and after the turmoil. An anomaly of the exchange market, instigated by the Swiss National Bank, occurred when the Swiss Franc decoupled from the euro unexpectedly. The United Kingdom (UK) vote to withdraw from the European Union (Brexit), in contrast, was feared but expected. A comparison of the consequences of the anomalies gives us an unprecedented opportunity to investigate prediction capabilities of the EVOLINO Recurrent Neural Network Ensemble (ERNN) model following an anomaly. By introducing this new information to the ERNN model and analyzing its response, we increase investor resources during large exchange rate fluctuations; this will provide them with additional information that will help them construct different portfolios. Reaction to the anomaly was visible only after the anomaly occurred, this is when the model began to acquire data influenced by the extreme change. Comparing different strategies which are related or unrelated to the anomaly and orthogonal or not orthogonal for conservative, moderate, or aggressive trading shows that in order to profit from the anomaly, speculation depends on prediction-accuracy and on the sets of exchange-rate associated with the anomaly.

Credit to personal consumption is an important activity of the financial system and crucial to the socio-economic development of a country. It is important, therefore, that the methods and techniques used to evaluate consumer credit risk should be as efficient and informative as possible, in order to strengthen decisions to approve or reject credit and promote sustainable economic growth. This study aims to create a multiple criteria expert system which integrates cognitive maps and the measuring attractiveness by a categorical based evaluation technique (MACBETH) to create a complementary framework for consumer credit risk assessment. The results show that this integrated approach allows the evaluation process of consumer credit risk to be more informed and transparent, providing value for the evaluation processes of this type of credit application as a result of the privileged contact established with a panel of credit analysts. Advantages, limitations and managerial implications are also discussed.

In many real-life decision making (DM) situations, the available information is vague or imprecise. To adequately solve decision problems with vague or imprecise information, fuzzy set theory and aggregation operator theory have become powerful tools. In last three decades, DM theories and methods under fuzzy aggregation operator have been proposed and developed for effectively solving the DM problems and numerous applications have been reported in the literature. While various aggregation operators have been suggested and developed, there is a lack of research regarding systematic literature review and classification of study in this field. Regarding this, Web of Science database has been nominated and systematic and meta-analysis method called “PRISMA” has been proposed. Accordingly, a review of 312 published articles appearing in 33 popular journals related to fuzzy set theory, aggregation operator theory and DM approaches between July 1986 and June 2017 have been attained to reach a comprehensive review of DM methods and aggregation operator environment. Consequently, the selected published articles have been categorized by name of author(s), the publication year, technique, application area, country, research contribution and journals in which they appeared. The findings of this study found that, ordered weighted averaging (OWA) has been the highest frequently accessed more than other areas. This systematic review shows that the DM theories under fuzzy aggregation operator environment have received a great deal of interest from researchers and practitioners in many disciplines.

Multi-objective programming became more and more popular in real world decision making problems in recent decades. There is an underlying and fundamental uncertainty in almost all of these problems. Among different frameworks of dealing with uncertainty, probability and statistic-based schemes are well-known. In this paper, a method is developed to find some efficient solutions of a multi-objective stochastic programming problem. The method composed a process of transforming the stochastic multi-objective problem to a bi-objective equivalent using the concept of Chebyshev inequality bounds and then solving the obtained problem with a fuzzy set based approach. Application of the proposed method is examined on two numerical examples and the results are compared with different methods. These comparisons illustrated that the results are satisfying.

Websites are one of the most widely distributed information resources. Educational institutions use this resource to ensure that the best quality of information transmission is achieved. As such, academic sites have become a very important aspect of academic institutions, one that affects their overall quality. Bearing in mind the importance of university websites’ quality, the authors of this paper presented a multi-criteria model for evaluating the quality of university websites. This paper presents the hybrid IR-AHP-MABAC (Interval Rough Analytic Hierarchy Process - MultiAttributive Border Approximation Area Comparison) model. The model is adapted to group decision making as based on the application of a new approach to treating uncertainties through the use of interval rough numbers (IRN). The modified IR-AHP method was used to determine the weight coefficients of the criteria in the group decision-making process. The results of the IR-AHP model are compared with results provided by the traditional AHP method and the fuzzy AHP approach. The IR-MABAC model was used for the evaluation of university websites. In order to verify the results of the IRN based approach, the IR-MABAC model was compared to the F-TOPSIS (Fuzzy Technique for Order of Preference by Similarity to Ideal Solution), F-VIKOR (Fuzzy MultiCriterion Optimization and Compromise Solution), F-COPRAS (Fuzzy COmpressed PRoportional ASsessment), F-MAIRCA (Fuzzy MultiAtributive Ideal-Real Comparative Analysis), and F-TODIM (an acronym of Interactive and Multi Criteria Decision Making in Portuguese) models. The credibility of the IR-AHP-MABAC model was demonstrated by comparing the results of different multi-criteria techniques and analyzing viability. The results of the IRN approach and fuzzy comparison have shown that the new approach to dealing with imprecision yields credible, reputable ranks.

In the paper, an algorithm that allows to detect and reject outliers in a self-organizing map (SOM) has been proposed. SOM is used for data clustering as well as dimensionality reduction and the results obtained are presented in a special graphical form. To detect outliers in SOM, a genetic algorithm-based traveling salesman approach has been applied. After outliers are detected and removed, the SOM quality has to be estimated. A measure has been proposed to evaluate the coincidence of data classes and clusters obtained in SOM. A larger value of the measure means that the distance between centers of different classes in SOM is longer and the clusters corresponding to the data classes separate better. With a view to illustrate the proposed algorithm, two datasets (numerical and textual) are used in this investigation.