Weight Calculation Method Research Articles

A combination weighting method for debris flow risk assessment based on t-distribution and linear programming optimization algorithm.

Debris flow risk assessment can provide some reference for debris flow prevention and control projects. In risk assessment, researchers often only focus on the impact of objective or subjective indicators. For this purpose, this paper proposed a weight calculation method based on t-distribution and linear programming optimization algorithm (LPOA). Taking 72 mudslides in Beichuan County as an example, this paper used analytic hierarchy process (AHP), entropy weight method (EWM) and variation coefficient method (VCM) to obtain the initial weights. Based on the initial weights, weight intervals with different confidence levels were obtained by t-distribution. Subsequently, the final weights were obtained by LOPA in the 90% confidence interval. Finally, the final weights were used to calculate the risk score for each debris flow, thus delineating the level of risk for each debris flow. The results showed that this paper's method can avoid overemphasizing the importance of a particular indicator compared to EWM and VCM. In contrast, EWM and VCM ignored the effect of debris flow frequency on debris flow risk. The assessment results showed that the 72 debris flows in Beichuan County were mainly dominated by moderate and light risks. Of these, there were 8 high risk debris flows, 24 medium risk debris flows, and 40 light risk debris flows. The excellent triggering conditions provide favorable conditions for the formation of high-risk debris flows. Slightly and moderate risk debris flows are mainly located on both sides of highways and rivers, still posing a minor threat to Beichuan County. The proposed fusion weighting method effectively avoids the limitations of single weight calculating method. Through comparison and data analysis, the rationality of the proposed method is verified, which can provide some reference for combination weighting method and debris flow risk assessment.

Health status evaluation of highway tunnel inverted arch based on variable weight and extension cloud model

Accurate assessment of the inverted arch's health status in tunnels is pivotal for preventing floor heave and ensuring traffic safety. This study investigated the condition of inverted arches in 237 highway tunnels in Gansu Province, China. An evaluation system was developed to assess the health of inverted arches, focusing on the disease characteristics and occurrence frequencies of tunnel pavements. Additionally, a new variable weight calculation method was established, incorporating the extension cloud and variable weight theory based on cloud correlation degrees. Established grading standards were used to assign scores for determining the health status of the inverted arches. The completed health status evaluation model was applied to a case study of the Taiyangshan tunnel, demonstrating its effectiveness and applicability. The results show that the evaluation method, applying variable weights based on health levels of indicators, effectively uncovers potential risks to deteriorating inverted arches. In the Taiyangshan tunnel, three inverted arches were classified as sub-healthy, relatively dangerous, and dangerous. Intensified inspection is required for sub-healthy sections. For areas categorized as relatively dangerous, employing 'miniature steel pipe piles + H-shaped steel support' has proven effective. This methodology yields essential insights for developing effective treatment strategies for inverted arches in similar tunnel engineering projects.

NSGA-T: A novel evaluation method for renewable energy plans

In the background of exhaustion of the traditional fossil energy sources and environmental deterioration, developing renewable energy has become a strategic choice for countries to achieve energy sustainable utilization and carbon neutrality target. Different renewable energy technical plans have different characteristics under multiple criteria. Therefore, before the further exploitation of renewable energy sources, it is of great significance to evaluate the comprehensive performance of different plans and then determine the best renewable energy sources. However, the commonly used weight calculation methods in the existing renewable energy evaluation have obvious shortcomings, such as the randomness of the subjective method is strong and the gap in the weight allocation of the objective method is too large, which affect the reliability and accuracy of the sorting result. In this paper, a novel weight calculation method based on non-dominated sorting genetic algorithm (NSGA-II) is proposed, and is applied to renewable energy evaluation. The study in this paper focuses on methodology. Firstly, for the linear normalized and Zeros normalized evaluation data, the differences between renewable energy plans in the two normalized evaluation data are measured, and the ranking deviations of the two normalized data are calculated. Then, the multi-objective weight solving model is constructed in terms of plans differences minimization and ranking vectors deviations minimization. The NSGA-II algorithm is applied to the optimization of multi-objective functions to find the Pareto non-dominated solution, so as to determine the weight values of each evaluation index. Finally, the TOPSIS method is used to determine the relative closeness value of each plan, and the rankings of renewable energy technical plans are achieved. Based on the actual renewable energy development data of a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some popular renewable energy evaluation methods.

A novel evaluation method for renewable energy development based on improved sparrow search algorithm and projection pursuit model

With global climate change posing a major threat to human society, a growing number of countries have taken “carbon-neutral” as a national strategy and proposed a vision of carbon-free future. As an important supplement to traditional fossil energy, renewable energy is the main force to reduce the use of high-carbon energy and carbon dioxide emissions, which will become the trend of social development in the future. Finding the optimal renewable energy source is of particular significance for achieving the net zero emissions. However, the existing evaluation methods of renewable energy sources have obvious shortcomings. In terms of weight calculation methods, such as the randomness of the subjective method is strong and the index weights do not reflect the small changes of the evaluation matrix, which affect the reliability and accuracy of the evaluation result. The existing ranking methods can only achieve the complete ranking of the different objects, but cannot classify the renewable energy technical alternatives into different grades. Given this background, this paper proposes a novel evaluation method for renewable energy plans based on improved sparrow search algorithm and projection pursuit model. Firstly, this paper improves the traditional sparrow search algorithm from three aspects: population initialization, population update and population variation. Then, the projection pursuit model is constructed, and the improved sparrow search algorithm is applied to optimize the projection target to find the optimal projection direction, so as to determine the weight values of each evaluation index. Finally, the weighted rank-sum ratio method is used to select the best renewable energy technical plan, which can not only realize the complete ranking of different plans, but also classify the technical plans into different levels. Based on the actual renewable energy development data from a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some existing evaluation methods of renewable energy technical plans.

Research on the Evaluation Method of Online Learning Satisfaction of College Students Based on Hierarchical Analysis Approach

Abstract This paper adopts the hierarchical analysis method as the weight calculation method for the evaluation of online learning satisfaction, calculates the weights between the evaluation indexes, and constructs the evaluation system of college students’ online learning satisfaction. Combining the teaching experience, the characteristics of online teaching and the items recorded in the web logs in the online teaching platform, the judgment matrix for online learning satisfaction evaluation is established, and the reasonableness of the matrix is judged by the consistency checking method. Evaluation standards are formulated based on the online learning evaluation information, and the research on online learning satisfaction evaluation is designed, while the online learning satisfaction evaluation of college students is analyzed by using the method of statistical analysis. The results show that the comprehensive learning evaluation scores derived from the use of hierarchical analysis are significantly more objective than the traditional evaluation of students’ learning effectiveness using only the end-of-term test scores, with a score difference of 15, and more than 85.24% of the students based on the hierarchical analysis method of college students’ online learning satisfaction evaluation method, which is effective in promoting the students’ regular participation in online learning. The correlation coefficients of online learning experience with clarity of teaching goals and expectations, teacher-student communication and exchange, learning support services, learning motivation, learning hardware equipment and operability, and teaching methods are 0.491, 0.524, 0.435, 0.596, 0.542, 0.391, respectively, which are all significant at 1% level, i.e., only by guaranteeing a higher level of learning effect can we improve students’ satisfaction with online learning.

An integrated linguistic Pythagorean fuzzy decision-making approach for risk analysis of offshore wind turbine

The evaluation of risk factors within the context of condition-based maintenance (CBM) for offshore wind turbines has been a challenging issue. In this paper, a novel risk analysis method that combines the fuzzy weighted with zero inconsistency (FWZIC) technique and the DEMATEL method under the linguistic Pythagorean fuzzy environment is proposed. Firstly, 75 risk factors linked to offshore wind turbines are identified by leveraging analysis and prior literature, which are modeled using linguistic Pythagorean fuzzy sets (LPFSs) to facilitate the complexity of expert opinions in the face of uncertainty. Then, the hybrid expert weight calculation method and the FWZIC-based criteria weight calculation method are introduced to determine the weights of the experts and the criteria while considering uncertainty. Next, the extended DEMATEL method under the linguistic Pythagorean fuzzy environment is adopted to determine the significance and influence of the pinpointed risk factors, and an illustrative cause–effect diagram is crafted to dissect the interconnections between these factors. Through this intricate analysis, the cause factors and effect factors specific to offshore wind turbines are identified, and the results obtained from the proposed method are validated through sensitivity analysis and comparative analysis. The results of this study could provide valuable recommendations and references for the implementation of CBM in offshore wind turbines.

Wheat Quantity Monitoring Methods Based on Inventory Measurement and SVR Prediction Model

Due to the influences of the storage environment, water content change, particle settlement, natural loss, and other factors, the distribution density of wheat and the volume of grain pile in the storage process are gradually changed so that the single weight calculation method cannot objectively evaluate the storage quantity of wheat and also causes difficulties to the regular inspection of the quantity of wheat stock. To meet the practical needs of wheat inventory monitoring, a wheat inventory monitoring method based on inventory measurement and the support vector machine regression (SVR) prediction model is proposed. By collecting the working papers for the physical inspection of wheat in grain warehouses in Shanxi province, Hebei province, Henan province, Jiangsu province, and other places, the storage time, storage weight, storage moisture content, measured moisture content, measured volume weight, measured net volume, and measured weight for inspection were selected as training samples for the SVR prediction model, and kernel function selection and parameter optimization were carried out. We developed an optimal prediction model for the amount of wheat in the grain depots. In the actual grain store measurement process, the net volume of wheat in the current grain store was obtained by a laser volumetric measuring apparatus, the actual bulk density of wheat was sampled, and the actual moisture content of wheat was measured by sampling. The three samples, their storage time, their storage moisture content, and their storage weight were fed into the trained SVR prediction model as new samples, and the predicted weight of the wheat in the current grain store was obtained from the output. The error rate calculation procedure was introduced to achieve an anomalous judgment error rate for grain depots. The experimental results showed that the SVR prediction model based on the linear kernel function had a very low mean squared error and high determination coefficient, and the average prediction accuracy of the grain stock error rate reached 93.2 percent, which can meet the requirements of wheat quantity monitoring in grain warehouses.

Development of Visual Culture in Product Design with Application of Digital Mining Technology

Abstract The development and use of digital mining technology provide new trends for product visual design. This paper identifies the product design system using the product appearance design framework. Digital technology is integrated into the product’s visual style, and the empathy design method is utilized to communicate the product’s culture. Apply fuzzy language summarization algorithm of digital mining technology to calculate user demand data, propose weight calculation method based on fuzzy language summarization results, and set objective weights of product user demand. The design of product visual culture is based on the results of acquiring and parameterizing product users’ perceptual demands, and the design results are evaluated in terms of three-party satisfaction. The average satisfaction values of industrial design experts, automobile appearance researchers, and mass consumers are 4.09, 4.23, and 4.53, respectively, indicating that the visual design results of automobile appearance have a high degree of user satisfaction, which is in line with the expectations of design practice.

A novel Fermatean fuzzy BWM-VIKOR based multi-criteria decision-making approach for selecting health care waste treatment technology

Medical waste management (MWM) is a challenging issue for medical facility managers owing to its potential to prevent environmental and health risks. Many treatment technologies (TTs) have been used for MWM, and selecting appropriate TTs is a complex multi-criteria decision-making (MCDM) problem. In this paper, we propose a novel integrated MCDM method based on the best-worst method (BWM) and the VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method under the Fermatean fuzzy environment to evaluate, rank, and select treatment technologies for medical waste management. First, novel distance measure and entropy measure for Fermatean fuzzy sets are developed, and their properties are examined. Second, different treatment technologies are evaluated by experts using Fermatean fuzzy sets. Third, the experts’ weights are determined using a novel method based on the entropy measure, and the criteria weights are calculated by a novel hybrid criteria weight calculation method. Finally, the ranking of different treatment technologies is determined by the proposed method. To validate the proposed method, a real case study in Jinan, China is presented, where the proposed method is applied to determine the optimal health care waste (HCW) treatment technologies from five TTs with eight criteria. The results are compared to other MCDM methods, which shows the effectiveness and reliability of the proposed method. Moreover, sensitivity analysis is also carried out to show the robustness of the proposed method. From the results, it can be found that the proposed method could provide reliable and robust results for obtaining the optimal health care waste treatment technology.

Research on cloud-CRITIC-PDR method for hybrid multi-criteria decision making

In order to solve the problems of insufficient uncertainty information measure, inaccuracy of weight calculation and incommensurability of indices in hybrid multi-criteria decision making, this paper introduces the Cloud-CRITIC weight calculation method and Cloud-CRITIC-PDR method, which combine cloud model, CRITIC method and Probabilistic Dominance Relation (PDR). In these two methods, the cloud model is used to characterize uncertainty, the Comprehensive information of CRITIC method has been modified in order to adapt to uncertain situation, the PDR method is used to compare schemes. A case study concerning supplier evaluation is given to demonstrate the merits of the cloud-CRITIC and cloud-CRITIC-PDR. The effectiveness and superiority of the developed methods are further illustrated through method comparison and sensitivity analysis. These combined methods can be used for dealing with decision-making problems with complex index types and strong data uncertainty, such as supplier evaluation and risk assessment. There are few papers about combining the cloud model, CRITIC method, and PDR method under hybrid indices decision-making situation at present, so this paper can provide a new perspective on hybrid MCDM.

Improvement of motor imagery electroencephalogram decoding by iterative weighted Sparse-Group Lasso

Discriminative feature selection is vital for enhancing motor imagery decoding performance in electroencephalogram (EEG) signals. However, existing feature optimization methods have not sufficiently explored the intrinsic attribute distribution of features and their associations with the target class, which could result in spurious correlations between optimized features and class labels, yielding suboptimal performance. Therefore, this study proposed an iterative Weighted Sparse-Group Lasso (iWSGL) model for optimizing Common Spatial Pattern (CSP)-based high-dimensional features, thus further enhancing the decoding accuracy of motor imagery. Specifically, the affinity propagation (AP) clustering algorithm was utilized to adaptively partition the high-dimensional features into multiple groups based on the underlying relationships among them. To evaluate the significance of individual feature within each group and the overall significance of the groups themselves, a weight calculation method was proposed based on conditional entropy. With the weights and feature structural information, a weighted sparse regression model was devised within the iterative Sparse-Group Lasso (iSGL) framework to jointly optimize the CSP-based high-dimensional features. The performance of the proposed method was validated on three datasets using the support vector machine (SVM). The experimental results exhibited the exceptional superiority of the proposed method over the current CSP and its variants, demonstrating its remarkable performance. These findings imply that the proposed model can offer a novel optimization strategy for enhancing pattern recognition of brain intentions in Brain-Computer Interface (BCI) applications.

Indoor High Precision Positioning System Based on Visible Light Communication and Location Fingerprinting

Indoor positioning system based on visible light communication and location fingerprinting can achieve cm-level positioning accuracy, which has become an important candidate for high precision positioning. However, in the case of non-uniform and low-density location fingerprinting distribution, it will still lead to large errors and poor robustness. To solve this problem, an accurate visible light positioning (VLP) method based on location fingerprinting and meta-heuristic is proposed, and all possible cases of uneven distribution of location fingerprinting are considered by introducing the random selection rate. The effectiveness of the proposed VLP method is compared with other six VLP methods based on location fingerprinting. Simulation results show that when the signal-to-noise ratio is 20 dB, the average positioning error of the proposed VLP method is 0.51 cm, which is at least 80.23% lower than the other six VLP methods. Experimental results show that when the random selection rate is 50%, the average positioning error of the proposed VLP method is 3.11 cm, which is at least 41.87% lower than the other six VLP methods. The proposed VLP method can be applied to positioning scenarios with different location fingerprinting distributions. The research results can provide a new research idea for the fingerprinting weight calculation method.

A Pointwise-Optimal Ensemble of Surrogate Models

AbstractThe ensemble of surrogate models is commonly used to replace computationally expensive simulations due to their superior prediction accuracy and robustness compared to individual models. This paper proposes a new pointwise ensemble of surrogate models, namely, a pointwise-optimal ensemble of surrogate models (POEMs). To address the limitations of the cross-validation (CV) error in evaluating the performance of regression surrogate models, this paper introduces the compensated cross-validation error, which is more reliable in selecting better individual surrogate models and improving the accuracy of surrogate model ensembles. To overcome the limitations of CV error in calculating pointwise weight factors, this paper designs and solves an optimization problem at training points to obtain corresponding pointwise weight factors. Additionally, this paper proposes two weight calculation methods to be applied in the interpolation and extrapolation regions, respectively, to reduce the instability of ensembles caused by extrapolation. Thirty test functions are employed to investigate the appropriate hyperparameters of POEM and the Friedman test is used to verify the rationality of the α value. The thirty test functions are also used to examine the performance of POEM and compare it with state-of-the-art ensemble surrogate models. Furthermore, POEM is applied to a large-aperture mirror holder optimization case to verify its superiority. The results demonstrate that POEM presents better accuracy and robustness than individual surrogates and other compared ensembles of surrogate models.

Prioritization of key practices for marine diesel engine maintenance activities using 2-tuple linguistic term set and DEMATEL

Marine diesel engines frequently operate in challenging environments that necessitate regular maintenance, encompassing a diverse range of practices. Identifying and prioritizing these practices is of paramount importance due to potential impact on marine diesel engine maintenance activities. However, there has been limited research on how different practices influence such maintenance activities. This paper proposes an integrated decision-making approach that combines the 2-tuple linguistic term and DEMATEL method to prioritize key practices for marine diesel engine maintenance. Firstly, 32 practices for marine diesel engine maintenance are identified. The 2-tuple linguistic term is then employed to capture the expert judgments regarding the importance of these practices. Next, the judgments of experts are aggregated using the 2-tuple weighted average operator, where an objective weight calculation method is used. Finally, the 2-tuple linguistic DEMATEL (2TL-DEMATEL) method is proposed to analyze the importance and interrelationships among the different practices, and a cause–effect relation diagram is constructed. Based on the results, the key practices can be identified and prioritized, with 17 practices identified as causal factors. The findings of this study serve as valuable guidance for maintenance personnel, enabling them to focus on the critical practices essential for marine diesel engine maintenance.

Evaluation Model of Urban Resilience in the Face of Public Health Emergencies: A Case Study of Xi’an

In recent years, there have been frequent outbreaks of public health emergencies worldwide, especially resulting from the COVID-19 pandemic, which has seriously affected social and economic development and people’s production and life. In order to avoid or minimize the harm caused by these emergencies to society and the public, this article constructs a resilience indicator system for urban emergency response capabilities based on resistance, adaptation, and recovery. We establish a dynamic model of urban emergency response resilience, select the infectious disease dynamics method as the index weight calculation method, analyze the correlations among various indicators and parameters of the urban emergency response resilience system, and conduct sensitivity analysis on the model parameters to determine the importance sequence of each model parameter. Combined with the fuzzy hierarchy analysis method, we evaluate the model and use the 2021 year-end epidemic in Xi’an as an example to evaluate the urban emergency response resilience level using the evaluation model. According to the maximum membership degree principle, the results show that the emergency resilience levels of Xi’an were “moderately strong”, “moderately strong”, and “strong” in the first, second, and third stages of the epidemic, respectively. The results demonstrate that the method proposed in this article can objectively reflect the current level of urban emergency resilience and provide some references and guidance for improving the resilience of urban emergency response to public health emergencies.

Study of Light Pollution Risk Measurement Based on GIS And EWM-AHP

Light pollution has serious negative impacts on all aspects of human health and biological circadian rhythms. To gain insight into the determinants of light pollution risk and to establish a widely applicable metric to accurately assess the light pollution risk level of an area, an evaluation model of light pollution risk level is developed in this paper. In terms of data acquisition, this paper uses ArcGIS to obtain 12 impact evaluation factors of light pollution risk level. In terms of model building, the metrics of each influencing factor were selected during the process of exploring each influencing factor and the mathematical model of light pollution risk level, and the entropy weight method-hierarchical analysis method (EWM-AHP) combined weight calculation method was used, and finally a model based on logical relationships was established to bring in the data to get the light pollution risk level of the area.

Safety risk assessment of aircraft EWIS based on the improved combined weight and cloud model

Purpose This study aims to conduct the aircraft electrical wiring interconnection system (EWIS) safety risk assessment process abundantly and hierarchically and establish the assessment index system considering the weights and interrelationships of different levels of indices. Design/methodology/approach Due to the failure of EWIS being multifactorial, hidden and diverse, this paper divides the factors influencing the failure of EWIS into 3 primary indices, 13 secondary indices and 38 tertiary indices. Taking open circuit failure (OCF) and short circuit failure (SCF) as examples, calculate the weights of assessment indices based on the triangular fuzzy number analytic hierarchy process (TFNAHP) and triangular fuzzy number decision-making trial and evaluation laboratory (TFNDEMATEL). The cloud model (CM) divides the risk levels and obtains the safety risk assessment results. The comparative analyses of different weight calculation methods, different failure modes and different aircraft EWIS zones verify the effectiveness and practicability of the proposed method. Findings The results show that the proposed method aligns more with the actual situation than other methods. Also, the results identify key focus objects in EWIS safety risk assessment, such as the surrounding environmental factors among the primary indices having the most significant influence on OCF and SCF, the risk level of SCF being higher than that of OCF, etc. Originality/value This paper proposes a safety risk assessment index system for aircraft EWIS based on the cable parameters, surrounding environmental factors, installation and protection methods. The weight assignment is added to the assessment index system, and the safety risk assessment model is constructed by combining TFNAHP, TFNDEMATEL and CM.

A new multi-objective decision-making method with diversified weights and Pythagorean fuzzy rough sets

In reality, there are many multi-objective decision-making problems with fuzziness and uncertainty, such as the choice of clinical treatment schemes. To effectively cope with such problems, it is urgent to scientifically handle fuzzy and uncertain information. Given the fact that the Pythagorean fuzzy set is a useful tool to tackle fuzzy information, and the rough set theory is superior in dealing with uncertain data, this paper aims to establish a novel decision-making method for scheme selection from the perspective of Pythagorean fuzzy rough sets. Pythagorean fuzzy numbers (PFNs) are first employed to express fuzzy preference information, and then multi-objective Pythagorean fuzzy information systems are introduced. Subsequently, we develop a new fuzzy rough set using an improved correlation coefficient method to deal with the uncertainty of PFNs. Furthermore, considering the diversified weights, we present a weight calculation method based on approximate accuracies. Meanwhile, a weighted fuzzy rough set model is constructed to achieve multi-objective information fusion. Accordingly, a multi-objective decision-making method is put forward with the help of overlap functions. Finally, we apply the built method to three cases from different decision scenarios to illustrate the applicability of the method. By some comparative analyses, the effectiveness and superiority of the method are also exhibited. Moreover, we discuss the establishment of decision support systems regarding scheme selection. On the whole, the work of this paper is helpful to support the solution of complex multi-objective decision-making problems.

A new method for estimating ore grade based on sample length weighting

Estimation of ore grade is very important for the value evaluation of ore deposits, and it directly affects the development of mineral resources. To improve the accuracy of the inverse distance weighting (IDW) method in ore grade estimation and reduce the smoothing effect of the IDW method in grade estimation, the weight calculation method involved in the IDW method was improved. The length parameter of the ore sample was used to calculate the weight of the IDW method. The length of the ore samples was used as a new factor of the weighting calculation. A new method of IDW integrated with sample length weighting (IDWW) was proposed. The grade estimation of Li, Al, and Fe in porcelain clay ore was used as a case study. A comparative protocol for grade estimation via the IDWW method was designed and implemented. The number of samples involved in the estimation, sample combination, sample grade distribution, and other factors affecting the grade estimation were considered in the experimental scheme. The grade estimation results of the IDWW and the IDW methods were used for comparative analysis of grades of the original and combined samples. The estimated results of the IDWW method were also compared with those of the IDW method. The deviation analysis of the estimated grade mainly included the minimum, maximum, mean, and coefficient of variation of the ore grade. The estimation effect of IDWW method was verified. The minimum deviations of the estimated grade of Li, Al, and Fe were between 9.129% and 59.554%. The maximum deviations were between 4.210 and 22.375%. The mean deviations were between − 1.068 and 7.187%. The deviations in the coefficient of variation were between 3.076 and 36.186%. The deviations in the maximum, minimum, mean, and coefficients of variation of the IDWW were consistent with those of the IDW, demonstrating the accuracy and stability of the IDWW method. The more the samples involved in the estimation, the greater the estimation deviations of IDW and IDWW methods. The estimated deviations of Li, Al, and Fe were affected by the shape of the grade distribution, when the same estimation parameters were used. The grade distribution pattern of the samples significantly influenced the grade estimation results. The IDWW method offers significant theoretical advantages and addresses the adverse effects of uneven sample lengths on the estimates. The IDWW method can effectively reduce the smoothing effect and improves the utilization efficiency of the original samples.

The m-polar fuzzy set ELECTRE-I with revised Simos’ and AHP weight calculation methods for selection of non-traditional machining processes

Using improvements to the recently published m-polar fuzzy set (mFS) elimination and choice translating reality-I (ELECTRE-I) approach for calculating criteria weights, the selection of a Non-Traditional Machining (NTM) process problem from the industry is solved in this research. The criteria weights for the m-polar fuzzy ELECTRE-I method are evaluated using the Analytical Hierarchy Process (AHP) approach and the Revised Simos' method. For the ELECTRE family's criteria weight calculations, the Simos’ approach has been revised. Many researchers calculated the weight of the criteria in the selection of the NTM process using the AHP approach. Problems with both physical and intangible properties can be solved using the m-polar fuzzy ELECTRE-I approach. Additionally, it has the ability to solve MCDM issues with more variables. The improved Simos' technique is used in this work because it incorporates user choices for the criteria, or user voting for the criterion. Using expert assistance, the AHP technique prioritizes the criterion based on pair-by-pair comparisons of the criteria. The AHP approach makes compromises between the criteria. The ultimate selection of the process based on the needed aim is affected by both tangible and intangible features in the NTM selection dilemma. The impact of criteria weight techniques on the choice of the NTM process is examined using a single dimensional sensitivity analysis. AHP approach is proven to be less stable for criteria weight variation than the improved Simos' weight calculation method. The updated Simos' method, which takes into account user preferences, performs better for the m-polar fuzzy ELECTRE-I algorithm than the AHP weight calculation method.