Application of Improved Best Worst Method (BWM) in Real-World Problems

Abstract

The Best Worst Method (BWM) represents a powerful tool for multi-criteria decision-making and defining criteria weight coefficients. However, while solving real-world problems, there are specific multi-criteria problems where several criteria exert the same influence on decision-making. In such situations, the traditional postulates of the BWM imply the defining of one best criterion and one worst criterion from within a set of observed criteria. In this paper, an improvement of the traditional BWM that eliminates this problem is presented. The improved BWM (BWM-I) offers the possibility for decision-makers to express their preferences even in cases where there is more than one best and worst criterion. The development enables the following: (1) the BWM-I enables us to express experts’ preferences irrespective of the number of the best/worst criteria in a set of evaluation criteria; (2) the application of the BWM-I reduces the possibility of making a mistake while comparing pairs of criteria, which increases the reliability of the results; and (3) the BWM-I is characterized by its flexibility, which is expressed through the possibility of the realistic processing of experts’ preferences irrespective of the number of the criteria that have the same significance and the possibility of the transformation of the BWM-I into the traditional BWM (should there be a unique best/worst criterion). To present the applicability of the BWM-I, it was applied to defining the weight coefficients of the criteria in the field of renewable energy and their ranking.

Combining the Suitability-Feasibility-Acceptability (Sfa) Strategy With the McDm Approach

“…Torkayesh et al applied it for the assessment of healthcare sectors in Eastern European countries [27]. Pamucar et al addressed BWM to select the most preferred renewable energy source for a developing country [28]. Ecer performed it for the sustainability evaluation of wind plants [29].…”

BW-MaxEnt: A Novel MCDM Method for Limited Knowledge

“…To address this issue, researchers extended the BWM to the fuzzy environment by describing DMs' preferences with various fuzzy information types, such as fuzzy sets [19], intuitionistic fuzzy sets [20], interval type-2 fuzzy sets [21], probabilistic hesitant fuzzy sets [22], Z-numbers [23], rough fuzzy sets [24], etc. Although the fuzzy extensions of the BWM can handle the ambiguity and uncertainty of expert judgement, the collection of DMs' preferences in these methods is still based on the assumption that DMs are familiar with all criteria [25]. However, sometimes, the DMs do not have enough time or energy to study all criteria before making a decision, and they cannot express the preference relation between some criteria, even with fuzzy information.…”

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].…”

“…Hamzenejad et al [7] introduced a new robust algorithm using three methods for the classification of brain disease: (1) the Wavelet-Generalized Autoregressive Conditional Heteroscedasticity-K-Nearest Neighbor method; (2) the Wavelet-GARCH-KNN method; and (3) the Wavelet Local Linear Approximation. Pamučar et al [8] presented an improved Best Worst Method for determining criteria weights in multi-criteria decision making. Ulutaş et al [9] proposed a multiple-criteria decision-making approach for the selection of the optimal equipment for performing logistics activity.…”