Multiple attribute decision making method based on DEMATEL and fuzzy distance of trapezoidal fuzzy neutrosophic numbers and its application in typhoon disaster evaluation

Some aggregation operators of credibility trapezoidal fuzzy neutrosophic values and their decision-making application in the selection of slope design schemes

Modern decision-making (DM) systems are becoming more and more complex and sophisticated in their demands for information expressions and credibility levels. In the existing literature, a trapezoidal fuzzy neutrosophic value (TFNV) that combines trapezoidal fuzzy numbers with neutrosophic values can be better depicted by truth, indeterminacy, and falsity membership functions. Unfortunately, TFNV implies its defect since it lacks a measure of credibility. To make TFNV more creditable, TFNV should be related to its credibility level. Regarding the motivation for combining TFNV with its credibility level, this paper first proposes the concept of a credibility TFNV (C-TFNV) as a new framework of TFNV associated with the measure of credibility. The advantage of its information expression is that C-TFNV has a more creditable ability to describe indeterminate and inconsistent knowledge and judgments of human beings by the mixed information of a TFNV and a related credibility level (an ordered pair of TFNVs). Next, we propose the operational laws of C-TFNVs and the score function of C-TFNV. Furthermore, we present a C-TFNV weighted arithmetic averaging (C-TFNVWAA) and a C-TFNV weighted geometric averaging (C-TFNVWGA) operators and their properties. Then, a multicriteria DM model based on the C-TFNVWAA and C-TFNVWGA operators and the score function is established in the case of C-TFNVs. Finally, an actual DM example of slope decision schemes is provided to show the applicability and efficiency of the established DM model in the case of C-TFNVs.

A mixed 0-1 programming approach for multiple attribute strategic weight manipulation based on uncertainty theory

In practical multiple attribute decision making (MADM) problems, the interest groups or individuals intentionally set attribute weights to achieve their own benefits. In this case, the rankings of different alternatives are changed strategically, which is called the strategic weight manipulation in MADM. Sometimes, the attribute values are given with imprecise forms. Several theories and methods have been developed to deal with uncertainty, such as probability theory, interval values, intuitionistic fuzzy sets, hesitant fuzzy sets, etc. In this paper, we study the strategic weight manipulation based on the belief degree of uncertainty theory, with uncertain attribute values obeying linear uncertain distributions. It allows the attribute values to be considered as a whole in the operation process. A series of mixed 0-1 programming models are constructed to set a strategic weight vector for a desired ranking of a particular alternative. Finally, an example based on the assessment of the performance of COVID-19 vaccines illustrates the validity of the proposed models. Comparison analysis shows that, compared to the deterministic case, it is easier to manipulate attribute weights when the attribute values obey the linear uncertain distribution. And a further comparative analysis highlights the performance of different aggregation operators in defending against the strategic manipulation, and highlights the impacts on ranking range under different belief degrees.

A dynamic network-based decision architecture for performance evaluation and improvement

This study introduces a dynamic decision architecture that involves three steps for corporate performance forecasting as such bad performance has been widely recognized as the main trigger for a financial crisis. Step-1: performance evaluation and integration; Step-2: forecasting model construction; and Step-3: knowledge generation. First, the decision making trial and evaluation laboratory (DEMATEL) is incorporated with balanced scorecards (BSC) to discover the complicated/intertwined relationships among BSC’s four perspectives. To overcome the problem of BSC that cannot yield a specific direction, the study then employs data envelopment analysis (DEA). Apart from previous studies that utilize an all embracing one-stage model, this set-up extends it to a two-stage model that calculates the performance scores for each BSC perspective. By doing so, users can realize a company’s weaknesses and strengths and identify possible paths toward efficiency. VIKOR is subsequently used to summarize all scores into a synthesized one. Second, the analyzed outcomes are then fed into random vector functional-link (RVFL) networks to establish the forecasting model. To handle the opaque nature of RVFL, the instance learning method is conducted to extract the implicit decision logics. Finally, the introduced architecture, tested by real cases, offers a promising alternative for performance evaluation and forecasting.