Neutrosophic Completion Technique For Incomplete Higher Order Ahp Comparison Matrices

The present paper proposes a neutrosophic AHP completion methodology to reduce the number of judgments required to be emitted by the decision maker. This increases the consistency of their responses, while accounting for uncertainties associated to the fuzziness of human thinking. The method is applied to a sustainable-design problem, resulting in weight estimations that allow for a reduction of up to 22% of the conventionally required comparisons, with an average accuracy below 10% between estimates and the weights resulting from a conventionally completed AHP matrix, and a root mean standard error below 15%.

Every organization seeks to set strategies for its development and growth and to do this, it must take into account the factors that affect its success or failure.

We introduce for the first time the concept of plithogeny in philosophy and, as a derivative, the concepts of plithogenic set / logic / probability / statistics in mathematics and engineering – and the degrees of contradiction (dissimilarity) between the attributes’ values that contribute to a more accurate construction of plithogenic aggregation operators and to the plithogenic relationship of inclusion (partial ordering).

This study aims at identifying and assessing supply chain risks and developing criteria for managing these risks.

Internet of Things (IoT) connects billion of devices to afford inventive opportunities between things and people. The rapid development of products related to IoT is a new challenge to keep security issues, lack of confidence, and understanding of IoT. Analytical hierarchy process (AHP) is a classic multi criteria decision making (MCDM) method used to analyze and scale complex problems and to obtain weights for the selected criteria. The vague and inconsistent information in real situations can lead to decision maker's confusion.

The best-worst method (BWM) is a multiple criteria decision-making (MCDM) method for evaluating a set of alternatives based on a set of decision criteria where two vectors of pairwise comparisons are used to calculate the importance weight of decision criteria. The BWM is an efficient and mathematically sound method used to solve a wide range of MCDM problems by reducing the number of pairwise comparisons and identifying the inconsistencies derived from the comparison process. In spite of its simplicity and efficiency, the BWM does not consider the decision-makers’ (DMs’) confidence about their pairwise comparisons. We propose a neutrosophic enhancement to the original BWM by introducing two new parameters as the DMs’ confidence in the best-to-others preferences and the DMs’ confidence in the others-to-worst preferences. We present two real-world cases to illustrate the applicability of the proposed neutrosophic enhanced BWM (NE-BWM) by considering confidence rating levels of the DMs.

This book is the first in the literature to present the state of the art and some interesting and relevant applications of the Fuzzy Analytic Hierarchy Process (FAHP). The AHP is a conceptually and mathematically simple, easily implementable, yet extremely powerful tool for group decision making and is used around the world in a wide variety of decision situations, in fields such as government, business, industry, healthcare, and education. The aim of this book is to study various fuzzy methods for dealing with the imprecise and ambiguous data in AHP. Features: First book available on FAHP. Showcases state-of-the-art developments. Contains several novel real-life applications. Provides useful insights to both academics and practitioners in making group decisions under uncertainty This book provides the necessary background to work with existing fuzzy AHP models. Once the material in this book has been mastered, the reader will be able to apply fuzzy AHP models to his or her problems for making decisions with imprecise data.

1. The increasing number of research papers appeared in the last years that either make use of aggregation functions or contribute to its theoretieal study asses its growing importance in the field of Fuzzy Logie and in others where uncertainty and imprecision play a relevant role. Since these papers are pub lished in many journals, few books and several proceedings of conferences, books on aggregation are partieularly welcome. To my knowledge, "Agrega tion Operators. New Trends and Applications" is the first book aiming at generality , and I take it as a honour to write this Foreword in response to the gentle demand of its editors, Radko Mesiar, Tomasa Calvo and Gaspar Mayor. My pleasure also derives from the fact that twenty years aga I was one of the first Spaniards interested in the study of aggregation functions, and this book includes work by several Spanish authors. The book contains nice and relevant original papers, authored by some of the most outstanding researchers in the field, and since it can serve, as the editors point out in the Preface, as a small handbook on aggregation, the book is very useful for those entering the subject for the first time. The book also contains apart dealing with potential areas of application, so it can be helpful in gaining insight on the future developments.

This book offers a comprehensive guide to the use of neutrosophic sets in multiple criteria decision making problems. It shows how neutrosophic sets, which have been developed as an extension of fuzzy and paraconsistent logic, can help in dealing with certain types of uncertainty that classical methods could not cope with. The chapters, written by well-known researchers, report on cutting-edge methodologies they have been developing and testing on a variety of engineering problems. The book is unique in its kind as it reports for the first time and in a comprehensive manner on the joint use of neutrosophic sets together with existing decision making methods to solve multi-criteria decision-making problems, as well as other engineering problems that are complex, hard to model and/or include incomplete and vague data. By providing new ideas, suggestions and directions for the solution of complex problems in engineering and decision making, it represents an excellent guide for researchers, lecturers and postgraduate students pursuing research on neutrosophic decision making, and more in general in the area of industrial and management engineering.

Information in a signal is often followed by undesirable disturbance which is termed as noise. Preventing noise in the signal leads to signal integrity, which also leads to better signal quality. The previous related works have the major issues while reducing noise in signals regarding assumptions, frequency and time domain, etc. This paper proposes a new Neutrosophic approach to reduce noises and errors in signal transmission. In the proposed method, confidence function is used as the truth membership function, which is associated with sampled time intervals.