A Model for Determining Weight Coefficients by Forming a Non-Decreasing Series at Criteria Significance Levels (NDSL)

Abstract

In this paper, a new method for determining weight coefficients by forming a non-decreasing series at criteria significance levels (the NDSL method) is presented. The NDLS method includes the identification of the best criterion (i.e., the most significant and most influential criterion) and the ranking of criteria in a decreasing series from the most significant to the least significant criterion. Criteria are then grouped as per the levels of significance within the framework of which experts express their preferences in compliance with the significance of such criteria. By employing this procedure, fully consistent results are obtained. In this paper, the advantages of the NDSL model are singled out through a comparison with the Best Worst Method (BWM) and Analytic Hierarchy Process (AHP) models. The advantages include the following: (1) the NDSL model requires a significantly smaller number of pairwise comparisons of criteria, only involving an n − 1 comparison, whereas the AHP requires an n(n − 1)/2 comparison and the BWM a 2n − 3 comparison; (2) it enables us to obtain reliable (consistent) results, even in the case of a larger number of criteria (more than nine criteria); (3) the NDSL model applies an original algorithm for grouping criteria according to the levels of significance, through which the deficiencies of the 9-degree scale applied in the BWM and AHP models are eliminated. By doing so, the small range and inconsistency of the 9-degree scale are eliminated; (4) while the BWM includes the defining of one unique best/worst criterion, the NDSL model eliminates this limitation and gives decision-makers the freedom to express the relationships between criteria in accordance with their preferences. In order to demonstrate the performance of the developed model, it was tested on a real-world problem and the results were validated through a comparison with the BWM and AHP models.

Dynamics under Uncertainty: Modeling Simulation and Complexity

“…This issue contains the successful invited submissions [1][2][3][4][5][6][7][8][9][10][11] to a Special Issue of Mathematics on the subject area of "Dynamics under Uncertainty: Modeling Simulation and Complexity".…”

“…Li et al [4] investigated the problems of state feedback and the static output feedback preview controller for uncertain discrete-time multiple-input multiple-output The geographical distribution of the authors (published papers) is presented in Table 1. Published submissions are related to road traffic risk analysis [1], dual-rotor systems [2], multi-criteria decision making [3,5,6,8,9], MIMO discrete-time systems [4], the classification and diagnosis of brain disease [7], data mining [10], and empathic building [11].…”

“…Fu et al [2] investigated the non-probabilistic steadystate dynamics of a dual-rotor system with parametric uncertainties under two-frequency excitations. Žižović et al [3] presented a new method for determining weight coefficients by forming a non-decreasing series at criteria significance levels (the NDSL method). Li et al [4] investigated the problems of state feedback and the static output feedback preview controller for uncertain discrete-time multiple-input multiple-output systems based on the parameter-dependent Lyapunov function and the linear matrix inequality technique.…”