Weights Of Criteria Research Articles

Application of a Multi-Criterion Decision-Making Method for Solving the Multi-Objective Optimization of a Two-Stage Helical Gearbox

This paper provides a novel application of a multi-criterion decision-making (MCDM) method to the multi-objective optimization problem of designing a two-stage helical gearbox. This study’s goal is to identify the ideal primary design elements that increase gearbox efficiency while reducing the gearbox cross-section area. In this work, three primary design parameters were selected for investigation: the gear ratio of the first stage and the coefficients of wheel face width (CWFW) of the first and second stages. The multi-objective optimization problem was further split into two phases: phase 1 solved the single-objective optimization problem of minimizing the gap between the variable levels, and phase 2 solved the multi-objective optimization issue of identifying the ideal key design factors. Moreover, the multi-objective optimization problem was handled by the SAW method as an MCDM approach, and the weight criteria were computed using the entropy approach. This study’s significant characteristics are as follows: First, a multi-objective optimization problem was successfully solved using the MCDM approach (SAW technique) for the first time. Second, the power losses in idle motion were investigated in this work in order to determine the efficiency of a two-stage helical gearbox. From this study’s findings, the ideal values for three major design parameters can be determined for the design of a two-stage helical gearbox.

Learning the weights using attribute order information for multi-criteria decision making tasks

In multi-criteria decision making, the importance of decision criteria (decision attributes) plays a crucial role. Ranking is a useful technique for expressing the importance of decision criteria in a decision-makers’ preference system. Since weights are commonly utilized for characterizing the importance of criteria, weight determination and assessment are important tasks in multi-criteria decision making and in voting systems as well. In this study, we concentrate on the connection between the preference order of decision criteria and the decision weights. Here, we present an easy-to-use procedure that can be used to produce a sequence of weights corresponding to a decision-makers’ preference order of decision criteria. The proposed method does not require pairwise comparisons, which is an advantageous property especially in cases where the number of criteria is large. This method is based on the application of a class of regular increasing monotone quantifiers, which we refer to as the class of weighting generator functions. We will show that the derivatives of these functions can be used for approximating the criteria weights. Also, we will demonstrate that using weighting generator functions, weights can be inverted in a consistent way. We will deduce the generators for arithmetic and geometric weight sequences, and we will present a one-parameter generator function known as the tau function in continuous-valued logic. We will show that using these weighting generator functions, the weight learning task can be turned into a simple, one-parameter optimization problem.

Agricultural land suitability analysis in Manipur, India using GIS and AHP

This article aims to identify potential sites for agricultural use in the state of Manipur of north east India by employing the analytic hierarchy process in a geographic information system environment in conjunction with the use of remote sensing and soil data. Within the analytic hierarchy process, each terrain variable underwent a pairwise comparison and criteria weights were assigned according to their relative importance. Eight variables were selected and used in land suitability analysis for agriculture. It was found that Manipur had 57% (12,660 km2) of its total geographical area suitable for agriculture. However, 8126 km2 (37%) and 1374 km2 (6%) of the total geographical area was currently and permanently unsuitable land respectively. The distribution of suitable land varied greatly, with highly, moderately and marginally suitable land covering only 8%, 16% and 33% respectively of the total geographical area. The highly suitable agricultural land is predominantly concentrated in the Imphal valley (70%), though 90% of moderately suitable and 96% of marginally suitable land also exist in the hills. The hilly areas constitute 96% and 97% respectively of currently unsuitable and permanently unsuitable land in the state. Suitable land comprises of land with low to medium altitude, gentle to moderate slopes, soil of fine or acceptable quality, and with minimal flood risk. Unsuitable lands tend to be diametrically opposite to these attributes with steep hill slopes. The nature of distribution of land suitability types influences the agricultural pattern in Manipur. Agriculture in the hill areas comprises mainly of shifting cultivation on hill slopes, whereas in the valley region it is irrigated and permanent. This analysis of Manipur has a wider applicability since the shifting cultivation-irrigated agriculture combination is similar to that which exists across much of the highlands of South East Asia.

Three-way decision TOPSIS for food business location under a benefit-opportunity-cost-risk platform

This work holistically evaluates the factors that can aid decision makers in finding the optimal location for food businesses in developing economies. It integrates the analytic hierarchy process (AHP) in determining criteria weights (i.e. benefit, opportunity, cost, risk) and the newly introduced three-way decision – Technique for Order Preference by Similarity to Ideal Solution (3WD-TOPSIS) method in identifying the priority factors. Applying the AHP yields the assignment of more priorities to benefits and opportunities rather than costs and risks, reflecting the benefit – or opportunity-oriented attitudes of food businesses. Meanwhile, the implementation of 3WD-TOPSIS results in the identification of government regulations and restrictions, proximity to consumers, parking capacity, supply chain strategy, and socio-economic status as the most crucial location decision factors. Findings from the comparative analyses show a high agreement between the results and those of other comparable methods. Managerial insights from these findings are outlined in this work.

Evaluation Challenges of Leadership Management in the Energy Sector using Multi-Criteria Decision Making Approach

Leadership management has various benefits and advantages in the energy sector, such as improving energy sector development and obtaining sustainability benefits. However, the leadership management faces multiple challenges in the energy sector that must be evaluated and ranked. So, this study used the fusion multi-criteria decision-making (MCDM) methodology to assess and rank the challenges of leadership management in the energy sector. The WASPAS method is a fusion MCDM method used to rank the alternatives. Five experts are invited to evaluate the criteria and alternatives. Fourteen criteria and 13 alternatives were collected in this study. The results show that alternative 4 is the best and alternative 8 is the worst. A sensitivity analysis was conducted to show the stability of the alternatives' ranks. The sensitivity analysis was conducted in two parts. In the first part, the lambda value of WASPAS changed between 0 and 1. In the second part, 14 cases are introduced in criteria weights. The results show that the rank of alternatives is stable in different cases.

Leveraging text mining and analytic hierarchy process for the automatic evaluation of online courses

This study introduced a multi-criteria decision-making methodology leveraging text mining and analytic hierarchy process (AHP) for online course quality evaluation based on students’ feedback texts. First, a hierarchical structure of online course evaluation criteria was formulated by integrating topics (sub-criteria) identified through topic modeling and interpreted based on transactional distance and technology acceptance theories. Second, the weights of the criteria in the hierarchical structure were determined based on topic proportions. Third, the AHP was employed to determine the overall relative advantage of online courses and their relative advantage within each criterion based on the hierarchical framework and criterion weights. The proposed approach was implemented on the datasets of 6940 reviews for knowledge-seeking courses in Art, Design, and Humanities (D1) and 44,697 reviews for skill-seeking courses in Computer Science, Engineering, and Programming (D2) from Class Central to determine ranking positions of nine courses from both D1 and D2 as alternatives. Results revealed common concerns among knowledge and skill-seeking course learners, encompassing “assessment”, “content”, “effort”, “usefulness”, “enjoyment”, “faculty”, “interaction”, and “structure”. The article provides valuable insights into the online course evaluation and selection processes for learners in D1 and D2 groups. Notably, both groups prioritize “effort” and “faculty”, while D2 learners value “assessment” and “enjoyment”, and D1 learners value “usefulness” more. This study demonstrates the efficacy of leveraging online learner reviews and topic modeling for automating MOOC evaluation and informing learners’ decision-making processes.

E-Commerce Promotional Products Selection Using SWARA and TOPSIS

This research aims to select products that will be used for promotion on e-commerce platforms. The increasing use of e-commerce has led to a high level of competition in the e-commerce field. The company strives to maintain the quality of its services to increase customer satisfaction, one of which is by providing regular promotions. The process of selecting promotional products is a routine activity carried out every week. However, the current promotional product selection process is not effective enough, and there are no criteria to use as a reference for selection. This research was conducted on two e-commerce companies actively operating in Indonesia. The research began with a literature study and expert survey to select important criteria in selecting promotional products. Weighting of important criteria is carried out using the Stepwise Weight Assessment Ratio Analysis (SWARA) method. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to rank the best products to promote. The results showed that products from Soundcore, Lenovo, and Xiaomi were the best products with preference values of 0.83, 0.65, and 0.60 respectively.

An integrated group decision-making method under q-rung orthopair fuzzy 2-tuple linguistic context with partial weight information.

Considering the advantages of q-rung orthopair fuzzy 2-tuple linguistic set (q-RFLS), which includes both linguistic and numeric data to describe evaluations, this article aims to design a new decision-making methodology by integrating Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and qualitative flexible (QUALIFLEX) methods based on the revised aggregation operators to solve multiple criteria group decision making (MCGDM). To accomplish this, we first revise the extant operational laws of q-RFLSs to make up for their shortcomings. Based on novel operational laws, we develop q-rung orthopair fuzzy 2-tuple linguistic (q-RFL) weighted averaging and geometric operators and provide the corresponding results. Next, we develop a maximization deviation model to determine the criterion weights in the decision-making procedure, which accounts for partial weight unknown information. Then, the VIKOR and QUALIFLEX methodologies are combined, which can assess the concordance index of each ranking combination using group utility and individual maximum regret value of alternative and acquire the ranking result based on each permutation's general concordance index values. Consequently, a case study is conducted to select the best bike-sharing recycling supplier utilizing the suggested VIKOR-QUALIFLEX MCGDM method, demonstrating the method's applicability and availability. Finally, through sensitivity and comparative analysis, the validity and superiority of the proposed method are demonstrated.

Assessing the Academic Performance of Turkish Universities in 2023: A MEREC-WEDBA Hybrid Methodology Approach

Research and reporting on university rankings serve as valuable tools for students in evaluating universities and understanding their current performance status. Within academic literature, university rankings are established using diverse criteria across various domains, each carrying varying degrees of importance. This study adopts a multi-criteria decision-making (MCDM) perspective to analyze the academic performance ranking of Turkish Universities in 2023. Data sourced from the 2023 reports of sixty-one universities from Times Higher Education (THE) serve as the basis for this research, with THE indicators—teaching, research, citations, industry income, and international outlook—considered as primary research criteria. The Method based on the Removal Effects of Criteria (MEREC) method is employed to ascertain criterion weights, while the Weighted Euclidean Distance-Based Approach (WEDBA) method is utilized for university ranking. The study identifies "citations" as the criterion of highest significance. Notably, the top-performing universities in the ranking include Çankaya University, Fırat University, and Bahçeşehir University. Furthermore, by comparing the rankings from this study with THE university rankings, the research offers tailored suggestions for universities. This study underscores the importance of deriving criterion weights from university performance datasets rather than relying on fixed weights, facilitating a more nuanced approach to university rankings. Moreover, it presents THE performance rankings for sixty-one Turkish universities, offering valuable insights for strategic planning within the university sector.

Sustainable and smart electric bus charging station deployment via hybrid spherical fuzzy BWM and MULTIMOORA framework

This study aims to assist public bus operators in locating electric bus charging station (EBCS) facilities from a smart and sustainable view. The selection of the most suitable EBCSs from various possible candidates involves a sophisticated decision-making procedure in terms of several contradictory criteria with imprecise information. The novelty of the study resides in exploring the EBCS site selection problem with spherical fuzzy sets (SFSs), which have shown remarkable effectiveness in limiting information loss by seizing ambiguous, and uncertain data. In this regard, a novel best–worst method (BWM) incorporating Multi-objective optimization via full multiplicative form ratio analysis (MULTIMOORA) methodology in the spherical fuzzy context is proposed to choose the optimal locations for EBCSs. The integrated framework combines the adaptability of the spherical fuzzy BWM (SF-BWM) for determining the criteria weights with the convenience of spherical fuzzy MULTIMOORA (SF-MULTIMOORA) approach for ranking the alternatives. A case study for Istanbul is provided to substantiate the propounded technique and to confirm its viability and efficiency. In the course of making a decision, a four-level hierarchical structure consisting of five main and 22 sub-criteria is built and the comparison matrices are reviewed by a panel of seven experts. A sensitivity analysis is executed, and the results demonstrate that the propositioned approach produces outcomes that are quite robust and consistent. Hence, the findings of this research can benefit public bus operators in choosing the ideal sites for electric charging stations. Finally, the formulated generic methodology is also easily applicable to diverse and complex multiple-criteria problems in the spherical fuzzy domain.

A multi-criteria decision-making model for heavy construction equipment replacement in Saudi Arabia

PurposeThis paper aims to propose a novel model that addresses the limitations of current practices, through considering quantitative and qualitative criteria in the decision-making process for equipment replacement.Design/methodology/approachLiterature review and consultation with professionals in the heavy construction industry was conducted to identify the criteria influencing the replacement of construction machines. A questionnaire survey using analytic hierarchy process and multi-attribute utility theory was used to rank these criteria and establish their utility scores. Sensitivity analysis was performed to assess how adjustments in the weights of main criteria would impact equipment replacement decisions.FindingsThe identified criteria were classified into three categories: economic, technical and socioenvironmental, encompassing a total of 15 criteria. The findings indicated that salvage value/meeting payback period/maximizing profitability held the highest importance in the replacement process, followed by considerations like high repair and maintenance cost; working condition and economic conditions. Safety and social benefits scored the least among all criteria and categories.Research limitations/implicationsThis study focuses on earth-moving equipment and involves experts from the Eastern Province of Saudi Arabia. The model introduces a novel methodology to aid decision-makers, particularly contractors and project managers, in determining when to replace heavy construction equipment, which results in resource efficiency and time saving.Originality/valueThe model integrates expertise and knowledge from experts to establish criteria for replacing construction equipment. This research aims to improve the functionality of the decision-making process regarding the acquisition or replacement of equipment throughout its lifespan.

Forecast combination in agricultural economics: Past, present, and the future

AbstractForecasts are common in agricultural settings where they are routinely used for decision‐making. The advent of the computer age has allowed for rapid generation of individual forecasts that can be updated in real time. It is well known that the selection and use of a single forecast can expose the forecaster to serious error as a result of model mis‐specification. Forecast combination avoids this problem by combining information from different forecasts. Although forecast combination can be as simple as averaging across forecasts, advances in machine learning have made it possible to combine forecasts according to more complicated weighting schemes and criteria. We provide an overview of forecast combination techniques, including those at the frontier of current practice and involving machine learning. We also provide a retrospective on the use of forecast combination in agricultural economics and prospects for the future. Several of the techniques are illustrated in an application to forecasting nationwide corn and soybean planted acreage and we demonstrate how forecast combination can improve expert USDA projections.

Analyzing Coastal Vulnerability Using Analytic Hierarchy Process and Best–Worst Method: A Case Study of the Marmara Gulf Region

AbstractCoastal regions, integral to human economic and social frameworks, face increasing threats from disasters and climate change. This situation has made it necessary and a priority to study these areas at an international level. To be able to take precautions, protect, and manage coastal areas, it is essential to identify their coastal vulnerability. In this study, a coastal vulnerability analysis was conducted using best–worst method (BWM) in the Marmara Gulf Region, Türkiye. Moreover, comparison was made with the analytic hierarchy process (AHP) method in the subject of assessment of coastal vulnerability. According to the vulnerability map obtained with AHP, 17% of the region was determined to have a moderate vulnerability risk, 54% had a high risk, and 29% had a very high risk. On the other hand, the analysis results in BWM calculated the vulnerability percentages as follows: 18% moderate vulnerability, 57% high vulnerability, and 25% very high vulnerability. Visual and statistical comparisons revealed that the BWM method provides more consistent results and involves fewer pairwise comparisons than the AHP method. Thus, it offers ease of use and convenience to decision maker while maintaining relatively same level of weights for criteria. This study aims to lay the foundation for a dynamic system designed to assess coastal vulnerability, emphasizing usability for policymakers in decision-making and flexibility across various scenarios. Particularly, the adoption of the BWM provides notable benefits due to its direct approach and ability to yield more uniform and dependable evaluations, thereby efficiently addressing intricate decision-making challenges.

MEREC-WISP(S) Integration Extended with Fermatean Fuzzy Set for Requirement Prioritization

Software projects need efficient requirement prioritization. Time, budget, and quality often limit these projects. MCDM techniques help balance conflicting criteria. However, they struggle to rank options due to multiple parties. Current MCDM methods have drawbacks, like poor uncertainty management. This thesis presents a new technique, WISP-S, for requirement prioritization in software development. The dynamic WISP(S) approach and the MEREC method are merged. Fermatean fuzzy numbers manage qualitative data and uncertainty. This technique surpasses the restrictions of current MCDM methods. It offers an effective way to rank software requirements within limits. The research consists of two separate stages. The first phase introduces an enhanced MEREC technique. It’s designed to compute the objective weights of each criteria within Fermatean Fuzzy Sets (FFS). By leveraging FFS features, this expansion improves the current MEREC technique. It allows a more thorough examination of criteria weights. The second phase integrates the WISP(S) method with the suggested generalized weighted Fermatean fuzzy aggregated operator and MEREC technology. This integration allows ranking and evaluating alternatives in a prioritization context. The study offers a robust strategy to prioritize alternatives. It considers both qualitative and uncertain data by integrating two novel methods. The approach’s validity and robustness are confirmed through comparisons with existing models and sensitivity analysis. A real-world case study demonstrates the proposed approach. The WISP-S approach is a novel technique for integrated Fermatean fuzzy information-based decision-making. It can handle complex decision-making problems in software development. This approach could improve the success of software projects. It addresses the shortcomings of existing MCDM methods.

Development of a decision support system for client acceptance in independent audit process

Intelligent Information Technology (IIT) applications are crucial in the audit process, enhancing quality, effectiveness, and efficiency. The client acceptance process (CAP), one of the critical audit steps, involves subjective evaluations where business managers' claims intersect with independent audit firm managers' expectations. This subjective nature introduces the potential for errors or misjudgments, impacting audit time and costs. In this paper, therefore, we propose a decision support system considering both auditors' subjective judgments and financial data variations for accepting or rejecting a client enterprise. The decision support system consisting of the Fuzzy Analytic Hierarchy Process (AHP), the logistic regression model, and the fuzzy inference system comprises four phases. In the first phase, a logistic regression model is developed using financial ratios to determine the client's probability of being in a close monitoring market (CMM) which represents publicly traded firms that are struggling to meet specific financial indicators or that are exposed to certain risks. In the second phase, the evaluation criteria used by the audit firm to measure the market reputation of the client enterprise are defined, and the weights of the evaluation criteria are obtained by using Fuzzy AHP. In the third phase, the Client Acceptance Score (CAS) representing market reputation of the client is calculated by incorporating the results of a reputation survey and applying the weights assigned to the evaluation criteria obtained in the second phase. Finally, client acceptance risk level (CARL) is obtained by using a fuzzy inference system and a rule-based defined by auditors. The CMM probability value and CAS score obtained in previous phases are used as input values of the fuzzy inference system. The CARL score guides the audit firm in deciding whether to engage with the client. To illustrate the applicability of the proposed model, a case study has been given in the paper.

A Robust Decision-Making Model for Medical Supplies via Selecting Appropriate Unmanned Aerial Vehicle

Recently, Unmanned Aerial Vehicles (UAVs) have been used in many fields, including the field of health care, especially in delivering the necessary medical equipment and supplies, due to the many advantages they have compared to other traditional methods and the presence of different types of UAVs, to improve healthcare and provide it with the medical supplies and equipment necessary to save the lives of patients. Choosing the appropriate UAV for a specific situation represents a problem facing decision-makers, which is considered a multi-criteria decision-making problem. Since the decision-making process is cumbersome and complex, and deals with uncertainty and ambiguity. In this research, we proposed multi-criteria decision-making (MCDM) model using CRITERIA (Criteria Importance through Intercriteria Correlation) and MARICA (Multi-Attribute Rating Analysis with Ideal Concepts) methods integrated with neutrosophic logic, which is considered a powerful tool in dealing with uncertainty and ambiguity. The CRITIC method calculates the weight of criteria, whereas it takes into account the correlations and relationships between the criteria, whether they are positive or negative, unlike other methods that consider the criteria separately, which allows for a more accurate and comprehensive analysis of the decision problem. The MARICA method is used also to rank the alternatives. It allows decision-makers to evaluate alternatives according to how well they perform across multiple criteria by considering several factors at once. This helps increase the effectiveness of judgments by taking into account all relevant factors. Moreover, MARICA is a user-friendly method that doesn't require complex mathematical calculations, making it accessible to anyone who wants to make sound choices. The UAV with the highest ranking is the one that will be chosen and represents the best among the alternatives. The proposed model proved its effectiveness by applying it to an experimental case.

Comparison of multi-criteria decision-making methods for selection of optimum passive design strategy

In the pursuit of achieving high-performance building design, the selection of the most suitable passive design strategies often involves the use of multi-criteria decision-making (MCDM) methods to address multiple conflicting criteria simultaneously. However, identifying the appropriate MCDM method for a specific building design context poses a challenge, as methods commonly effective in other contexts may not yield equivalent results. This study evaluates five MCDM methods (AHP, COPRAS, TOPSIS, VIKOR, and WSM) to understand their sensitivity in recommending the best solution. The considered criteria are energy demand, thermal comfort and daylight availability. The sensitivity analysis involves the impact of the variability of assigned weights on the rank shifting given by the considered MCDM method and the sensitivity of each criterion to weights variability. The findings reveal that implementing a fair-weight allocation leads to similar top 5 solutions among all MCDM methods. However, when a negative shift is applied to each criterion weight, AHP demonstrates greater robustness to weight variability compared to the other methods evaluated, while VIKOR is the most sensitive to weight variation.

A study of entrepreneur and innovative university index by entropy-based grey relational analysis and PROMETHEE

In recent years, the concept of entrepreneurial and innovative universities has gained widespread prominence. Many universities have been paying more attention to being entrepreneurial and innovative by improving their organizational systems, advancing their infrastructure, and increasing financial support. Since numerous criteria with different weights exist, ranking universities based on entrepreneurial and innovative performance can be considered a multi-criteria decision-making (MCDM) problem. This article aims to investigate how different multi-criteria decision-making methods with different criterion weights can affect university rankings and to highlight the reasons that contribute to these differences. In this scope, Grey Relational Analysis (GRA) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) methods were used to rank and compare the universities in Türkiye according to the 2022 Entrepreneur and Innovative University Index (EIUI). In addition to the current weights of each EIUI dimension, entropy-based weights and equal weights were used in MCDM methods. Three ranking approaches with varying weights provided different rankings for universities. The effect of criterion weights was found to be more important in the ranking difference than the method used. The ranks for universities coded U1 and U2 as the most entrepreneurial and innovative universities remained the same. In addition, the performance of each university according to each dimension was evaluated graphically using the GAIA plane to enable them to identify areas for improvement in their rankings.

DMS: Low-overlap Registration of 3D Point Clouds with Double-layer Multi-scale Star-graph.

Registering 3D point clouds with low overlap is challenging in 3D computer vision, primarily due to difficulties in identifying small overlap regions and removing correspondence outliers. We observe that the neighborhood similarity can be utilized to detect point correspondence, and the consistent neighborhood correspondence can be used as a criterion to detect robust overlapping regions. So that a Double-layer Multi-scale Star-graph (DMS) structure is proposed to detect robust correspondences using two different types of multi-scale star-graphs. The first-layer Multi-scale Neighbor Feature Star-graphs (MNFS) takes each point as the center and its multi-scale nearest neighbors as the leaves. The MNFS enables to establish the initial correspondence candidate set between the two point clouds based on multi-scale neighborhood topology and feature similarity. Subsequently, each pair of corresponding points find their nearest neighbors within the correspondence sets to construct a Multi-scale Matching Star-graphs (MMS) on each side, so the mutual correspondence relationships between the MMS vertices are identified. These identified mutual correspondences are treated as vertices to construct the Multi-scale Correspondence Star-graphs (MCS), that indicate the relationships among the correspondences. We design edge weight and vertex weight criterion in MCS to detect only the robust correspondence set that has strong neighborhood consistency, so as to reject the outliers. Finally, the point cloud registration is conducted based on the detected robust correspondence. The experimental results demonstrate clearly that the proposed DMS method exhibits superior robustness when compared to existing state-of-the-art registration algorithms. The code of this study will be available at https://github.com/HualongCao/DMS.

Green, resilient, and inclusive supplier selection using enhanced BWM-TOPSIS with scenario-varying Z-numbers and reversed PageRank

Firms need a reliable decision-making framework to select suppliers who are Green, Resilient, and embrace Inclusive Development (GRID). However, GRID supplier selection is challenged by events that are uncertain and often dependent. Such events affect supplier rankings in a fuzzy decision environment. This paper builds a GRID-based criteria system and applies an enhanced Best Worst Method (BWM) and Technique for Order Preferencing using the Similarity to the Ideal Solution (TOPSIS), with Scenario-Varying Z-numbers (SVZs) and reversed PageRank. The decision criteria are extracted from the literature and confirmed by expert judgement. The SVZs and SVZ Preference Relations (SVZPRs) are developed by combining the Concept of Stratification Theory (CST) with Z-numbers, to reflect the impact of Dependent Uncertain Events (DUEs). Reverse PageRank is then used to find the occurrence probability of the scenarios due to DUEs. BWM is applied in an SVZPR environment to obtain the weights of the decision criteria. TOPSIS, incorporating SVZs, is used to rank the suppliers. An example is used to highlight the enhanced BWM-TOPSIS, to find the priority ranking of the GRID weights. Comparative and sensitivity analyses are conducted to validate the proposed method.