The power Heronian aggregation (PHA) operator can use the advantages of power average and the Heronian mean operator, which together could take into account the interrelationship of the aggregated arguments, and therefore alleviate the e ects caused by unreasonable data through considering the support degree between input arguments. However, PHA operators cannot be used to process single-valued neutrosophic numbers (SVNNs), which is significant for extending it to SVNNs.

The power average (PA) has the property that it can eliminate the influence of inconvenient data and the Muirhead mean (MM) operator takes the correlations among the input arguments, and the single valued neutrosophic (SVN) set is a better tool to deal with incomplete, inconsistent and indeterminate information than fuzzy set (FS) and intuitionistic FS (IFS). Thus the main goal of this article is to develop a few new operators for aggregating SVN information and apply them to multiple-attribute group decision making (MAGDM). To fully utilize the advantages ofMMoperator and PA operator, we develop the single-valued neutrosophic power MM (SVNPMM) operator, weighted single-valued neutrosophic power MM (WSVNPMM) operator, single-valued neutrosophic power dual MM (SVNPDMM) operator and weighted single-valued neutrosophic power dual MM (WSVNPDMM) operator, and discuss their essential properties, particular cases about the parameter vector. The obvious advantages of the proposed operators are that it can eliminate the influence of inconvenient data and can take the correlation among input data at the same time. Moreover, based on the developed aggregation operators, a novel technique to MAGDM problem is proposed. Lastly, a numerical example is provided to show the efficiency and realism of the proposed technique.

Single-valued neutrosophic set (SVN) can valid depict the incompleteness, nondeterminacy and inconsistency of evaluation opinion, and the Power average (PA) operator can take into account the correlation of multiple discussed data. Meanwhile, Archimedean copula and co-copula (ACC) can signicant generate operational laws based upon diverse copulas.

Neutrosophic cubic sets can deal with the complex information by combining the neutrosophic sets and cubic sets, the power average (PA) can weaken some effects of awkward data from biased decision makers, and Heronian mean (HM) can deal with the interrelationship between the aggregated attributes or arguments. In this article, in order to consider the advantages of the PA and HM, we combined and extended them to process neutrosophic cubic information. Firstly, we defined a distance measure for neutrosophic cubic numbers, then we presented the neutrosophic cubic power Heronian aggregation operator and neutrosophic cubic power weighted Heronian aggregation operator, and some characters and special cases of these new aggregation operators were investigated. Furthermore, we gave a new approach for multiattribute group decision making based on new proposed operators. Finally, two examples were given to explain the validity and advantages of the developed approach by comparing with the existing method.

The purpose of this paper is to introduce new aggregation operators based on logarithmic operations and to develop a multi-criteria decision-making approach to study the interaction between the input argument under the single valued neutrosophic (SVN) environment. The main advantage of the proposed operator is that it can deal with the situations of the positive interaction, negative interaction or non-interaction among the criteria, during decision-making process.

This book is a printed edition of the Special Issue "Sustainability in Construction Engineering" that was published in Sustainability

This book introduces readers to the fundamentals of transportation problems under the fuzzy environment and its extensions. It also discusses the limitations and drawbacks of (1) recently proposed aggregation operators under the fuzzy environment and its various extensions; (2) recently proposed methods for solving transportation problems under the fuzzy environment; and (3) recently proposed methods for solving transportation problems under the intuitionistic fuzzy environment. In turn, the book proposes simplified methods to overcome these limitations.