Weight Determination Method Research Articles

Ensemble model for risk status evaluation of excavation

This study develops a risk status evaluation model by integrating the technique for order preference by similarity to an ideal solution (TOPSIS) and Monte Carlo simulation (MCS) methods. The excavation system is divided into subsystems based on the monitoring scheme. The TOPSIS method integrates the information on the influential factors, and MCS overcomes the uncertainty, fuzziness, and human errors in data collection. A comprehensive weight determination method is proposed to determine the weights of the influential factors. The developed model was applied to evaluate the risk status of excavation projects in Tianjin. The results were consistent with the actual conditions of the excavation system. The higher correlation factors in the evaluation results can be identified through correlation analysis. Finally, a value for the ideal parameter λ in the membership function is recommended through sensitivity analysis. The developed model provides guidelines for establishing early risk warnings and management for excavation engineering.

Punishment-driven consensus reaching model in social network large-scale decision-making with application to social capital selection

Clustering and consensus reaching are two crucial processes for solving social network large-scale decision-making (SN-LSDM) problems. This paper explores the moderating effect of trust on the decision-making process, which is decomposed into examining the influence of trust constraint on clustering and the influence of trust loss on consensus reaching. We hold that low-trust experts are not suitable to be assigned to the same subgroup, even though their preferences are sufficiently similar. We propose a punishment-driven consensus reaching model for SN-LSDM problems. The model contains two main stages. In Stage 1, a trust-constrained K-means clustering algorithm is presented, which can overcome the defect of grouping low-trust experts caused by traditional clustering methods based on preference similarity. Then, a weight-determining method combining the size, cohesion, and overall trust degree of a subgroup is proposed. In Stage 2, a punishment-driven consensus reaching process is designed, which identifies four categories of consensus scenarios (high–high, high–low, low–high, low–low) through consensus measures and trust measures. Different adjustment strategies are established, and the moderating effect of trust loss on consensus reaching and consensus cost is investigated. Finally, both a case study on social capital selection in public–private partnership projects and a comparative analysis are implemented to reveal the feasibility and advantages of the clustering algorithm and consensus reaching model proposed in this paper.

Weighted aggregation systems and an expectation level-based weighting and scoring procedure

This paper presents a novel approach to the weighted aggregation and to determination of weights in an aggregation procedure. In our study, we introduce the concept of a weighted aggregation system that consists of two components: (1) a weighting transformation and (2) an aggregation operator, both induced by a common generator function. We provide the necessary and sufficient condition for the form of a generator function-based weighted aggregation system. We show that the weighted quasi-arithmetic means on the non-negative extended real line are none other than the aggregation functions induced by weighted aggregation systems, i.e., these means are compositions of an n-ary aggregation operator and n weighting transformations (n∈N, n≥1). Next, using weighted quasi-arithmetic means on the unit interval, we introduce a new, expectation level-based weight determination method and a scoring procedure. In this method, the decision-maker’s expectation levels for the input variables are directly transformed into weights by making use of the generator function of a weighted quasi-arithmetic mean. We utilize this mean as a scoring function to evaluate the decision alternatives. Lastly, by the means of illustrative numerical examples, we present a novel decision model, in which the expectation levels can be even intervals, i.e., the weights are also intervals. Finally, we get an interval-valued score for each alternative.

Validation of an Analytical Method for the Simultaneous Determination of Hyaluronic Acid Concentration and Molecular Weight by Size-Exclusion Chromatography.

The hyaluronic acid (HA) global market growth can be attributed to its use in medical, cosmetic, and pharmaceutical applications; thus, it is important to have validated, analytical methods to ensure confidence and security of its use (and to save time and resources). In this work, a size-exclusion chromatography method (HPLC-SEC) was validated to determine the concentration and molecular distribution of HA simultaneously. Analytical curves were developed for concentration and molecular weight in the ranges of 100–1000 mg/L and 0.011–2.200 MDa, respectively. The HPLC-SEC method showed repeatability and reproducibility greater than 98% and limits of detection and quantification of 12 and 42 mg/L, respectively, and was successfully applied to the analysis of HA from a bacterial culture, as well as cosmetic, and pharmaceutical products.

A Recommendation for a Pre-Standardized Marine Microalgal Dry Weight Determination Protocol for Laboratory Scale Culture Using Ammonium Formate as a Washing Agent.

Simple SummaryMicroalgae are increasingly recognized as a source of valuable biomass with numerous health benefits. Cleaning of marine microalgal biomass is very crucial for microalgal studies as the salt on the microalgae cells will lead to overestimation of biomass determination. Incomplete washing of salt from microalgae could also interfere with the nutritional analyses. The biomass, especially dry weight, has been utilized for nutritional or compositional evaluation. Although standard methods of marine microalgal dry weight determination are available, these methods did not provide comprehensive details, and the parameters vary among themselves. Without a standard method, a comparison of results among previous studies can be misleading and unreliable. Therefore, the current study aimed to investigate and determine the ideal setting of several parameters in the marine microalgal dry weight determination for laboratory-scale culture. The present findings could assist in developing a standardized protocol to ensure a high quality of biomass for microalgal studies. Microalgal biomass is one of the crucial criteria in microalgal studies. Many reported methods, even the well-established protocol on microalgal dry weight (DW) determination, vary greatly, and reliable comparative assessment amongst published results could be problematic. This study aimed to determine the best condition of critical parameters in marine microalgal DW determination for laboratory-scale culture using four different marine microalgal species. These parameters included the washing process, grades of glass microfiber filter (GMF), GMF pretreatment conditions, washing agent (ammonium formate) concentrations, culture: washing agent ratios (v:v) and washing cycles. GMF grade GF/A with precombustion at 450 °C provided the most satisfactory DW and the highest ash-free dry weight (AFDW)/DW ratio. Furthermore, 0.05 M ammonium formate with 1:2 culture: washing agent ratio and a minimum of two washing cycles appeared to be the best settings of microalgal DW determination. The present treatment increased the AFDW/DW ratio of the four respective microalgae by a minimum of 19%. The findings of this study could serve as a pivotal reference in developing a standardized protocol of marine microalgal DW determination to obtain veracious and reliable marine microalgal DW.

Projection-based probabilistic linguistic multi-criteria decision-making method for new energy project selection

The development of new energy industry is a pressing issue due to the deterioration of the environment. The selection of new energy projects is a critical problem for decision makers. Incomplete and uncertain information appears in the process of new energy project selection. Compared with other linguistic expressions, probabilistic linguistic term set (PLTS) simultaneously reflects all possible linguistic terms and their corresponding weights, which conforms to the cognitive habits of people. Thus, a multi-criteria decision-making framework under PLTS environment is constructed for energy project selection. Firstly, a normalised projection model of PLTS, which considers the distance and the angle between two objects, is proposed to overcome the limitations of distance measurement. Secondly, a comprehensive weight-determination method combining the maximum deviation and expert scoring methods is developed to calculate the weight vector of the criteria. Furthermore, a projection-based VIKOR (Višekriterijumska optimizacija i kompromisno rešenje) method is established to select new energy projects, which can reflect the preferences of decision makers for group utility and individual regret. Finally, a numerical study on new energy project selection is performed to determine the validity and applicability of this method. Sensitive and comparative analyses are also conducted to reflect the rationality and feasibility of the method.

A sentiment analysis-based expert weight determination method for large-scale group decision-making driven by social media data

In the big data era, it is practical and generalizable to use social media data for decision-making. Thus, determining how to effectively use this kind of data to support large-scale group decision-making (LSGDM) is a direction worth studying. In this paper, for the first time, the sentiment analysis of social media data is used to evaluate the quality of LSGDM and then to dynamically determine the weights of experts from the statistical perspective. The proposed method helps improve the objectivity, accuracy, and acceptability of LSGDM. A case application demonstrates the feasibility of this method, and the results of comparative analyses reveal its superior accuracy and stability to a certain extent.

Fire Safety Evaluation for Scenic Spots: An Evidential Best-Worst Method

Fire safety plays a vital role in tourism management, which can cause significant loss of life and property. It is necessary to present an efficient fire safety evaluation for scenic spots. However, some key issues are not well addressed in existing methods. For example, how to model experts’ opinions and how to combine them are still open problems. To address these issues, a new evaluation method based on the Dempster–Shafer evidence theory and best-worst method is presented. First, a fire safety evaluation index system is constructed using the domino model. Domain experts can evaluate different factors with linguistic assessments. The best-worst method is used to determine the weights of different factors. Then, these weighted linguistic assessments are efficiently fused by Dempster’s combination rule to obtain the evaluation result. Finally, a case study is illustrated to demonstrate the efficiency of the proposed method in fire safety evaluation for scenic spots. The main contribution of the proposed method is to represent and handle the uncertainty in experts’ linguistic assessments, so as to decrease the uncertainty and improve decision making. In addition, the weight determination method BWM is easier and more reliable than the existing method AHP.

Hierarchical Bayesian approach for improving weights for solving multi-objective route optimization problem

The weighted sum method is a simple and widely used technique that scalarizes multiple conflicting objectives into a single objective function. It suffers from the problem of determining the appropriate weights corresponding to the objectives. This paper proposes a novel Hierarchical Bayesian model based on multinomial distribution and Dirichlet prior to refine the weights for solving such multi-objective route optimization problems. The model and methodologies revolve around data obtained from a small-scale pilot survey. The method aims at improving the existing methods of weight determination in the field of Intelligent Transport Systems as data driven choice of weights through appropriate probabilistic modelling ensures, on an average, much reliable results than non-probabilistic techniques. Application of this model and methodologies to simulated as well as real data sets revealed quite encouraging performances with respect to stabilizing the estimates of weights. Generation of weights using the proposed Bayesian methodology can be used to develop a bona-fide Bayesian posterior distribution for the optima, thus properly and coherently quantifying the uncertainty about the optima.

Wasserstein distance-based probabilistic linguistic TODIM method with application to the evaluation of sustainable rural tourism potential

The evaluation of sustainable rural tourism potential is a key work in sustainable rural tourism development. Due to the complexity of the rural tourism development situation and the limited cognition of people, most of the assessment problems for sustainable rural tourism potential are highly uncertain, which brings challenges to the characterisation and measurement of evaluation information. Besides, decision-makers (DMs) usually do not exhibit complete rationality in the practical evaluation process. To tackle such problems, this paper proposes a new behaviour multi-attribute group decision-making (MAGDM) method with probabilistic linguistic terms sets (PLTSs) by integrating Wasserstein distance measure into TODIM (an acronym in Portuguese of interactive and multicriteria decision making) method. Firstly, a new Wasserstein-based distance measure with PLTSs is defined, and some properties of the proposed distance are developed. Secondly, based on the correlation coefficient among attributes and standard deviation of each attribute, an attribute weight determination method (called PL-CRITIC method) is proposed. Subsequently, a Wasserstein distance-based probabilistic linguistic TODIM method is developed. Finally, the proposed method is applied to the evaluation of sustainable rural tourism potential, along with sensitivity and comparative analyses, as a means of illustrating the effectiveness and advantages of the new method.

DEA Cross-Efficiency Aggregation with Deviation Degree Based on Standardized Euclidean Distance

Data envelopment analysis (DEA) has been extended to cross-efficiency to provide better discrimination and ranking of decision-making units (DMUs). Current researches about cross-efficiency mainly focus on the non-uniqueness of optimal solution of linear programming and information aggregation. As a common distance metric, standardized Euclidean distance is introduced to define the discrimination power between two vectors and the deviation degree for measuring the difference between the individual preference and group ideal preference. Based on above definitions, an alternative method is presented to compare multiple optimal solutions, and further, a universal weighted cross-efficiency model considering both dynamic adjustment of weights and preference formulation is constructed for evaluation and ranking. Two numerical examples are given to illustrate the effectiveness of the comparison method for multiple optimal solutions and weights determination method of DMUs, respectively. At last, a practical application aimed at evaluating environmental treatment efficiency in western area of China is given. Comparative analysis shows that our model could be more moderate, flexible, and general than some available models and methods, which can extend the theoretical research of cross-efficiency evaluation.

Altman Model Verification Using a Multi-Criteria Approach for Slovakian Agricultural Enterprises

The Altman model is still one of the most widely used predictive models in the 21st century, and it aims to highlight the differences between bankrupt and healthy enterprises. This model has been modified several times; its most well-known forms are from 1968, 1983 and 1995. However, the use of the Altman Z-score for Slovak enterprises is more than questionable. The unsuitability of the model for the conditions of Slovak companies has been confirmed by several empirical surveys. The objective of this study was to verify the validation of these three variants of the Altman model, depending on how an unprosperous company is identified, using a sample of 996 agricultural enterprises operating in the Slovak Republic. Four indicators were selected for the identification of an unprosperous enterprise – economic results, total liquidity, equity, and economic value added – and they were monitored over the last year or, as the case may be, over the last three years from 2014 to 2016. Using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Coefficient of variation (CV) methods as an objective method for weight determination, a combination of the Altman model from 1968 and the negative total liquidity in the last reference year was determined to be the best. One of our main findings is that the way in which an unprosperous enterprise is identified is a significant factor affecting the overall reliability of the Altman model. The Altman model from 1968 and 1983 confirmed the differences resulting from the natural conditions in which the enterprises operate. The economic results and economic value added (EVA) proved to be inappropriate as indicators for defining an unprosperous enterprise in the conditions of the Slovak Republic.

Combined Geometric Positioning and Performance Analysis of Multi-Resolution Optical Imageries from Satellite and Aerial Platforms Based on Weighted RFM Bundle Adjustment

Combined geometric positioning using images with different resolutions and imaging sensors is being increasingly widely utilized in practical engineering applications. In this work, we attempt to perform the combined geometric positioning and performance analysis of multi-resolution optical images from satellite and aerial platforms based on weighted rational function model (RFM) bundle adjustment without using ground control points (GCPs). Firstly, we introduced an integrated image matching method combining least squares and phase correlation. Next, for bundle adjustment, a combined model of the geometric positioning based on weighted RFM bundle adjustment was derived, and a method for weight determination was given to make the weights of all image points variable. Finally, we conducted experiments using a case study in Shanghai with ZiYuan-3 (ZY-3) satellite imagery, GeoEye-1 satellite imagery, and Digital Mapping Camera (DMC) aerial imagery to validate the effectiveness of the proposed weighted method, and to investigate the positioning accuracy by using different combination scenarios of multi-resolution heterogeneous images. The experimental results indicate that the proposed weighted method is effective, and the positioning accuracy of different combination scenarios can give a good reference for the combined geometric positioning of multi-stereo heterogeneous images in future practical engineering applications.

Consensus convergence in large-group social network environment: Coordination between trust relationship and opinion similarity

Group decision-making (GDM) in large-group social network environment (LGSNE) has attracted considerable attention in the field of decision science. Social relationships exist among decision-makers, and individual decisions are often influenced by others they are connected with. Opinions among large-scale decision-makers can easily be controversial and conflicting. Reaching consensus is necessary, but it requires the adjustment of some individual opinions. Due to differences in self-interest and perception, some decision-makers are noncooperative with regard to adjusting their opinions to promote consensus. This may delay consensus convergence and ultimately affect decision quality. This study proposes a two-dimensional consensus convergence model considering noncooperative behaviors. We first describe the characteristics of GDM problems in LGSNE. Two measurement attributes – trust relationship and opinion similarity – are identified as important factors throughout the decision-making process. Then, we propose a hierarchical clustering method based on the trust–similarity measure. A weight-determining method for clusters is presented that considers the internal and external features of a cluster. Based on these, a two-dimensional consensus convergence process is designed to reduce opinion differences and manage noncooperative behaviors. Finally, a numerical experiment is used to illustrate the feasibility and efficacy of the proposed model, and comparative analysis reveals its features and advantages.

Q ‐Rung orthopair fuzzy decision‐making framework for integrating mobile edge caching scheme preferences

Mobile edge caching scheme (MECS) can determine where, how, and what to cache on user equipment by employing its own storage. When considering the performance of MECS, it is often full of uncertainty. The q-rung orthopair fuzzy set (q-ROFS), characterized by membership and nonmembership degrees with adjustable parameter q, is quite a high-efficiency way to capture uncertainty. In this paper, first, information measure (entropy, distance measure, and similarity measure)-based area difference under the q-rung orthopair fuzzy (q-ROF) circumstance is studied along with their detailed proofs. Then, we present a comprehensive weight-determination method by combining objective weights (determining by entropy) and subjective weights (given by experts) as combined weights, which can effectually alleviate the unconscionable influence of extreme data on evaluation results and simultaneously reflect objective data and subjective emotion. Moreover, q-ROF score function-based distance measure is presented for dealing with a value comparison problem. Later, q-ROF multicriteria decision-making (MCDM) method called total area based on orthogonal vector (TAOV) is introduced. Moreover, its feasibility is illustrated by MECS selection problem. Finally, a comparison of some existing MCDM methods and the proposed method is constructed for displaying their effectiveness. This proposed method can effectively avoid counterintuitive phenomena, eliminate antilogarithm by negative and zero issue, and has no division by zero issue.

Supplier performance evaluation for 500 kV main transformer

As an important state-owned energy backbone enterprise related to national energy security and the lifeline of national economy, power grid enterprises are responsible for ensuring the quality and safety of equipment and promoting the development of equipment quality. Therefore, continuously improving the quality evaluation of main transformer equipment is very important to improve the quality of power grid main transformer equipment. This paper first constructs the main transformer equipment quality evaluation index system from equipment procurement, equipment installation, equipment use and equipment decommissioning and scrapping; on this basis, the scoring method of each evaluation index is given, and the final main transformer equipment quality evaluation score is calculated by combining subjective and objective weight determination method; Finally, according to the evaluation of 500kV main transformer equipment of provincial power grid company price score, to evaluate the performance of equipment quality provided by suppliers.

A probabilistic linguistic TODIM method considering cumulative probability-based Hellinger distance and its application in waste mobile phone recycling

The prosperity of online mobile phone recycling platforms has brought convenience to waste mobile phone recycling. This study aims to propose a novel decision-making method to evaluate the recycling channels of mobile phones in an uncertain environment using the probabilistic linguistic term set (PLTS) to represent uncertain information. Given that each PLTS is associated with a probability distribution, the differences of different PLTSs can be represented by comparing different probability distributions. The Hellinger distance is a widely-used index to measure the similarity of two probability distributions, but it cannot be directly used to express the semantic differences of PLTSs. In this regard, we introduce a cumulative probability-based Hellinger distance measure of PLTSs. Then, we propose an extension of TODIM (an acronym in Portuguese of Interactive and Multicriteria Decision Making) method considering the bounded rationality and psychology of decision-makers based on the proposed distance measure of PLTSs and a criterion weight determination method, the Simos-Roy-Figueira method. Finally, an illustrative example of evaluating the recycling channels of mobile phones is given with sensitive and comparative analyses, showing the efficiency of the proposed method. This method can also be applied to other various fields.

Probabilistic double hierarchy linguistic alternative queuing method for real economy development evaluation under the perspective of economic financialization

With the development of science and technology, the new road of scientific economic and financial development has played a decisive role in supporting the financial undertaking. To accelerate the economic development, it is very important to increase the guiding role of financial undertaking in the real economy. Therefore, it is necessary to promote the development of the real economy under the perspective of economic financialization based on some actions. To judge the implementation effect of these actions, this paper develops a multiple criteria decision-making (MCDM) method to evaluate them. First, the decision-making matrices are established with the probabilistic double hierarchy linguistic term set in which the probabilities are added to all double hierarchy linguistic terms. Additionally, a weight-determining method is developed to obtain the weight vector of criteria, and we develop a MCDM method named the probabilistic double hierarchy linguistic alternative queuing method (PDHL-AQM), where the decision-making result is intuitive by a directed graph or a 0–1 precedence relationship matrix. Furthermore, we apply the PDHL-AQM to solve a practical MCDM problem involving the real economy development evaluation under the perspective of economic financialization. Finally, some comparative analyses are made to show the advantages and reasonableness of the PDHL-AQM.

Contribution Modeling on Condition Evaluation of Asphalt Pavement Using Uncertainty Measurement and Entropy Theory

In view of the deficiency that traditional pavement performance evaluation index did not consider the influence of their difference on weight, the grade of the evaluation index also did not take into account intermediate state and the impact of uncertainty on the evaluation results, a determination method of pavement performance evaluation index weight based on entropy theory was developed. The unascertained measurement function of evaluation index was performed by left‐half ladder distribution, and unascertained measurement matrix was obtained. The index weight was calculated by minimum entropy theory, and the practicability of this method was verified through a concrete example finally. The results show that there were different weights in different samples, which depended on index measurement function and were the overall characterization of comprehensive measurement of every index. The method which is based on the given weighting factor did not conform to the engineering facts. It was difficult to identify the importance of the pavement performance evaluation index in different samples. The balance of the various indexes is better to be considered in the proposed method, and the comprehensive situation of pavement performance is really reflected, which improves the evaluation of the reliability.

A novel parking-lot intelligent selection algorithm based on gray relational analysis considering user preferences

In order to select and recommend the optimal parking lot for the users, a novel intelligent selection algorithm based on the grey relational analysis considering user preferences is proposed. The parking-lot evaluation index system consisting of indicators in effectiveness, convenience, economy, environmental attribute and agreeableness is firstly established. The comprehensive weight determination method combining the expert estimation method with the user preference settings is proposed to determine the comprehensive weight of each evaluation index, and this method can meet the personalized needs of individual user to the greatest extent. The evaluation indexes considering various representative objective and subjective influential factors including the driving distance between the current location and the target parking lot, the walking distance between the target parking lot and the destination, the remaining parking spaces, the parking fee, the condition of the hardware facilities and service quality are determined, and the corresponding comprehensive weight of each evaluation index is calculated. Then the parking lots near the destination are evaluated by the grey relational analysis in order to select the optimal one to the user for parking reservation. This algorithm has been proven to be effective by the APP on Android mobile phones.