Multi-attribute Decision Research Articles

Stock Market Prediction Based Multi-Attribute Decision Making Model Using Picture Fuzzy Z^-Information

As demonstrated in the section above, the stock market place is a dynamic factor, which makes it possible for traders and investors to make good decisions based on the information acquired through accurate prediction. This research aims at improving the prediction of stock market by applying a new method to Multi-attribute Group Decision making (MAGDM). MAGDM goes through a cycle of evaluating and ranking several criteria hence enhancing the decision-making aspects further. To overcome the shortcomings of prior models, some EU and FU is incorporated by combining Zadeh’s Z^-numbers with Picture Fuzzy Sets (PFSs). This integration is to enhance the ability of the model to address completely unclear decisions utilizing the peculiarities of Z^-numbers. To compare decisions between decision-makers, we proposed picture fuzzy Z^-numbers (PFZ^N) and for their aggregation, introduced picture fuzzy weighted averaging, picture fuzzy ordered weighted averaging, picture fuzzy hybrid averaging, picture fuzzy weighted geometric, picture fuzzy ordered weighted geometric and picture fuzzy hybrid geometric operators based algebraic T-norm (T-N) and T-conorm (T-CNs) To verify the efficiency of our suggested technique, we compare these operators with the Combined Compromised Solution (CoCoSo) model focusing on the stock market analysis. Our results, therefore, show how these operators are important in improving decision making accuracy and precision in conditions of risk. This research laid down the basis for enhancing decision-making and dealing with uncertainty in different fields especially in the application of stock market prediction. The proposed methodology can be attributed to providing a systematic and a more efficient way of dealing with uncertainty which in one way or the other has an outcome of enhancing the credibility of the decision making process in the financial sector.

Automated selection of urban road network by fusion of PageRank algorithm and attribute importance metrics

ABSTRACT Selection of urban road networks has always been the focus of map generalization. Previous research has shown that machine learning techniques effectively address multi-attribute weighting challenges. However, these studies primarily focus on supervised learning that requires large amounts of labeled data and scale-specific training, limiting the automation of road network selection processes. To address these challenges, this study proposes an automated selection of urban road network. The method improves the automation level of the selection process by fusing the PageRank algorithm and attribute importance metrics and considering both the structural and attribute information of roads. The proposed method was tested on four typical road network cases from China and compared with the single-attribute decision method, multi-attribute decision method and expert-selected results. The results show that this method can preserve the connectivity and density distribution of the source map.

A Synergistic Multi-Attribute Decision-Making Method for Educational Institutions Evaluation Using Similarity Measures of Possibility Pythagorean Fuzzy Hypersoft Sets

A Synergistic Multi-Attribute Decision-Making Method for Educational Institutions Evaluation Using Similarity Measures of Possibility Pythagorean Fuzzy Hypersoft Sets

Spontaneous Symmetry Breaking in Group Decision-Making with Complex Polytopic Fuzzy System

Beginning with a symmetrical multiple-choice individual as the foundation, I develop a sociophysics model of decision-making. By simplifying the range of choices, the framework incorporates the complex Polytopic fuzzy model to capture nuanced dynamics. This approach enables a deeper analysis of decision-making processes within social systems. Decision-making problems commonly involve uncertainty and complexity, posing considerable challenges for organizations and individuals. Due to their structure and variable parameters, the Einstein t-norm (ETN) and t-conorm (ETCN) offer more elasticity than the algebraic t-norm (ATN) and t-conorm (ATCN). This flexibility makes them commonly effective and valuable in fuzzy multi-attribute decision-making (MADM) problems, where nuanced valuations are critical. Their application enhances the ability to model and analyze vagueness and uncertain information, eventually leading to more informed decision outcomes. The complex Polytopic fuzzy set (CPFS) improves the Polytopic fuzzy set (PFS) and complex fuzzy set (CPFS), allowing for a more precise valuation of attributes in complex (MADM) problems. This study aims to propose a MADM scheme using the ETN and ETCN within the framework of a complex Polytopic fuzzy environment. It begins by presenting the Einstein product and sum operations for complex Polytopic fuzzy numbers (CPFNs) and explores their necessary properties. This method enhances the accuracy and applicability of DM processes in ambiguous environments. Subsequently, three complex Polytopic fuzzy operators with known weighted vectors are developed: the complex Polytopic fuzzy Einstein weighted averaging (CPFEWA) operator, complex Polytopic fuzzy Einstein ordered weighted averaging (CPFEOWA) operator, complex Polytopic fuzzy Einstein hybrid averaging (CPFEHA) operator. Moreover, some substantial properties of the operators are studied. Finally, a method based on novel operators is planned, and a numerical example is provided to prove the practicality and effectiveness of the new proposed methods.

Multi-attribute decision making approach for energy efficient sensor placement and clustering in wireless sensor networks

Multi-attribute decision making approach for energy efficient sensor placement and clustering in wireless sensor networks

A Multi-Attribute Decision-Making Approach for Critical Node Identification in Complex Networks.

Correctly identifying influential nodes in a complex network and implementing targeted protection measures can significantly enhance the overall security of the network. Currently, indicators such as degree centrality, closeness centrality, betweenness centrality, H-index, and K-shell are commonly used to measure node influence. Although these indicators can identify critical nodes to some extent, they often consider node attributes from a narrow perspective and have certain limitations. Therefore, evaluating the importance of nodes using most existing indicators remains incomplete. In this paper, we propose the multi-attribute CRITIC-TOPSIS network decision indicator, or MCTNDI, which integrates closeness centrality, betweenness centrality, H-index, and network constraint coefficients to identify critical nodes in a network. This indicator combines information from multiple perspectives, including local neighborhood importance, network topological location, path centrality, and node mutual information, thereby solving the issue of the one-sided perspective of single indicators and providing a more comprehensive measure of node importance. Additionally, MCTNDI is validated through the analysis of several real-world networks, including the Contiguous USA network, Dolphins network, USAir97 network, and Tech-routers-rf network. The validation is conducted from four aspects: the results of simulated network attacks, the distribution of node importance, the monotonicity of rankings, and the similarity of indicators, illustrating MCTNDI's effectiveness in real networks.

A Hesitation-Associated Multi-Attribute Decision-Making Method Based on Generalized Interval-Valued Hesitation Fuzzy Weighted Heronian Averaging Operator

In multi-attribute decision making (MADM), complex situations often arise where decision attributes are interval-valued hesitant fuzzy numbers (IVHFNs) and the attributes are interrelated. Traditional decision-making methods may be ineffective in handling such cases, highlighting the practical importance of seeking more effective approaches. Therefore, finding a more effective decision-making approach has important practical significance. By combining the theories of Archimedean S-norms and T-norms, we innovatively propose a multi-attribute decision-making method based on the generalized interval-valued hesitant fuzzy weighted Heronian mean (GIVHFWHM) operator to address the aforementioned issues. Initially, based on the operational laws of IVHFNs and the Heronian mean (HM) operator, we introduce the generalized interval-valued hesitant fuzzy Heronian mean (GIVHFHM) operator and the GIVHFWHM operator. We then examine properties of the GIVHFHM operator, including permutation invariance, idempotency, monotonicity, boundedness, and parameter symmetry. A multi-attribute decision-making model is constructed based on the GIVHFWHM operator. Finally, we validate the proposed model through numerical experiments in MADM. The results demonstrate that the new decision-making method, based on the GIVHFWHM operator, is feasible and effective in handling multi-attribute decision problems involving IVHFNs with interdependent attributes. This approach provides a novel perspective and method for solving MADM problems under interval-valued hesitant fuzzy conditions with interdependent attributes. It enriches the theoretical framework of multi-attribute hesitant decision models and expands the application of the Heronian mean operator within interval-valued hesitant fuzzy environments. This methodology assists decision makers in making more accurate decisions within complex decision-making contexts, enhancing both the scientific rigor and reliability of decision-making processes.

Investigating the influence of plate geometry and detonation variations on structural responses under explosion loading: A nonlinear finite-element analysis with sensitivity analysis

Abstract This study presents the results of a numerical analysis of the response of Domex 700 and Dormex 1100 steel plates with varying geometries, combined with several different parameters such as a thickness of up to 6 mm and a trinitrotoluene (TNT) mass. Using ABAQUS software, finite-element analysis was run to examine the structural response ability of the steel plates to explosions. In terms of deformation and energy dissipation, the results showed a large differentiation. A sensitivity analysis was used to examine each simulation result in terms of the structural response performance to explosions and identify the variables that had the greatest impact on variations in the thickness, material, geometry, and TNT mass. The best numerical simulation results were found using the multi-attribute decision-making (MADM) approach. Annotations are utilized to assist in identifying the various modifications made during testing. Annotations LXXI to LVIII achieved the lowest value of 6.176 × 10−09, signifying the best results according to calculations made using the MADM approach. This is evidenced by the structural events, demonstrating that the deformation, von Mises stress, and energy dissipation in the circular plate structure were not significantly impacted by the explosion. The variables that most significantly affect variations in deformation, von Mises stress, and dissipated energy – variables that significantly impact the structural response to explosions – are identified through the sensitivity analysis approach. The results of this research can be used to optimize the structural response performance of circular plates.

Advanced Integration of ES-MADM II in HRM: A Balanced Approach to Appraisal and Promotion Decisions

Personnel appraisal and promotion are fundamental processes in Human Resource Management (HRM), requiring advanced methodologies that adeptly combine objective data with subjective assessments. This paper introduces ES-MADM II, an enhanced iteration of the Entropy Synergy Multi-Attribute Decision-Making model, designed to strengthen decision-making robustness and stability. The model incorporates key entropy-based indices such as Normalized Mutual Information (NMI), Criteria Effectiveness Score (CES), Conditional Stability Factor (CSF), and the newly introduced Alternatives Distinction Index (ADI). Together, these indices offer a comprehensive framework for assessing not only decision accuracy but also the overall resilience and clarity of the evaluation process. The effectiveness of ES-MADM II is showcased through military HRM case studies, illustrating how the model enhances personnel performance appraisals and promotion decisions by harmonizing subjective judgments with objective metrics. A detailed sensitivity analysis further demonstrates the model’s adaptability to variations in input data while preserving decision integrity. ES-MADM II ultimately fosters a more transparent, balanced, and equitable decision-making process, making it an indispensable tool for HR decision makers in complex organizational settings. This refined approach underscores the model’s capacity to navigate the complexities of HR evaluations with rigor and fairness.

A compromise-based MADM approach for prioritizing failures: Integrating the RADAR method within the FMEA framework

Multi-Attribute Decision-Making (MADM) methods are essential in decision-making processes, particularly in solving problems related to ranking and classifying alternatives. Among the MADM methods frequently utilized in the literature for ranking alternatives are distance-based or compromise-based methods. These methods have been widely applied for decades, with ongoing development leading to new approaches. One such approach is RAnking, based on the Distances And Range (RADAR) method. This novel distance-based method evaluates alternatives by considering their distance relative to the best and worst alternative values for a given criterion and the range between them. This paper applies the RADAR method to rank failure modes identified through a standard Failure Modes and Effects Analysis (FMEA) in an automotive industry company that produces rubber and plastic products. The results obtained from the RADAR method are compared with those derived from the traditional Risk Priority Number (RPN) approach. The comparison demonstrates that the RADAR method provides more distinct rankings, reducing the occurrence of ties between alternatives and thus offering a more nuanced and reliable decision-making tool in the context of failure mode prioritization.

A Combined DEAV-BWM Approach for Effective Evaluation and Ranking of Biomass Materials in Charcoal Briquette Production

The utilization of agricultural waste for producing charcoal briquettes is gaining significant attention as a sustainable alternative energy source. Converting these residues into charcoal briquettes not only addresses energy shortages but also provides an efficient solution for managing agricultural waste, contributing to environmental sustainability. This study proposes a novel methodology integrating a Data Envelopment Analysis Variant (DEAV) with the Best-Worst Method (BWM) to assess and rank biomass materials for charcoal briquette production. The DEAV-BWM model enhances the evaluation process by considering multiple criteria simultaneously and incorporating both efficiency and consensus among different evaluation methods. The key highlights of the methodology are as follows:• A novel method, called the DEAV model, for evaluating efficiency scores has been established, incorporating both Data Envelopment Analysis (DEA) and Multi-Attribute Decision Making (MADM) concepts.• This paper introduces a novel hybrid method that combines the DEAV model with the BWM to evaluate and rank biomass materials for charcoal briquette production, enhancing the reliability and robustness of the decision-making process.• The proposed method can be adapted and applied to other areas of biomass utilization and beyond, providing a versatile tool for researchers and practitioners in the field of sustainable energy, engineering, and operations research.

Multi-valued Choquet integral based on a couple of set functions with an application in multi-attribute decision-making

Multi-valued Choquet integral based on a couple of set functions with an application in multi-attribute decision-making

A new three-way multi-attribute decision-making with objective risk avoidance coefficients based on q-rung orthopair fuzzy pre-order relations

A new three-way multi-attribute decision-making with objective risk avoidance coefficients based on q-rung orthopair fuzzy pre-order relations

Evaluation of Motorcycle Brands Using Multi-attribute Decision-Making Method Under Single-Valued Neutrosophic Cubic Hypersoft Set Environment

The process of motorcycles evaluation is intricate and influenced by several factors that are uncertain, indeterminate and vague. It is difficult to estimate to what extent the choice will satisfy all requirements and change with the times. The theoretical frameworks such as hypersoft sets, interval and single-valued neutrosophic sets are useful in managing such vagueness and uncertainty. This study introduces a novel theoretical framework; the single-valued neutrosophic cubic hypersoft set (svNCHSS), to mitigate the loss of useful data. In comparison to the frameworks mentioned above, the svNCHSS is more adaptable in handling ambiguous and unpredictable circumstances because it incorporates all of their limitations as a single model. The set-theoretic operations like P-union (⊔P)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(\\sqcup _{P})$$\\end{document}, P-intersection (⊓P)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$( \\sqcap _{P})$$\\end{document}, R-union (⊔R)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$( \\sqcup _{R})$$\\end{document}, R-intersection (⊓R)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(\\sqcap _{R})$$\\end{document}, AND- and OR-operations are investigated for svNCHSS environment. Two potent algorithms are proposed in the context of a decision-assisted mechanism that employs AND- and OR-operations for the evaluation of motorbikes taking into account multi-argument-based factors. To evaluate the suggested algorithms’ effectiveness, a prototype case study is used for validation. The computed results are then compared. The structural comparison reveals the flexibility of svNCHSS, making its aggregation operations a useful tool for a variety of industrial applications, including risk assessment, process improvement, and decision-making.

Study on Emergency Decision-Making of Mine External Fires Based on Deduction of Precursory Scenarios

External mine fires are known for their unpredictability, rapid spread, and difficulty in terms of extinguishment, often resulting in severe casualties and property damage when not managed swiftly. This study examines the progression of coal mine fire incidents through scenario deduction and presents an emergency decision-making model based on precursor scenario analysis. We classify precursor elements according to the causes of coal mine fires, organizing scenario elements into states, precursors, and emergency activities using knowledge meta-theory. A dynamic Bayesian network forms the core of the decision-making model, enabling calculation of scenario node probabilities and the development of expert-driven response strategies for critical scenarios. Additionally, we design a comprehensive evaluation index system, utilizing multi-attribute decision-making to establish decision matrices and attribute weights. An improved entropy-weighting TOPSIS method is used to select the optimal emergency decision scheme. The model’s effectiveness is demonstrated through a case study of the “9–27” fire incident at the Chongqing Songzao Coal Mine, where findings affirm the model’s practicality and accuracy in supporting timely, effective emergency responses to external coal mine fires.

Sistem Pendukung Keputusan Dengan Menggunakan Metode ELECTRE II Dalam Menentukan Faktor Yang Mempengaruhi Pemilihan Sabun Mandi

Reviewed and with the increasing public awareness of skin health, soap manufacturers are competing to offer their products with various fragrances, forms, and benefits to the public. Each individual certainly has different criteria in choosing bath soap products, such as packaging, product price, product fragrance, type and form, and so on. The purpose of this research is to determine the factors that serve as the basis for each individual in choosing bath soap products by implementing the Elimination et Choix Traduisant la Réalité II (ELECTRE II) method. The ELECTRE II method uses simple criteria, namely setting the same threshold value for all criteria. In contrast, other methods use pseudo criteria where the threshold value is not the same for all criteria. The ELECTRE II method is one of the Multi-Attribute Decision Making (MADM) methods that is effective in determining ranking, as it involves both qualitative and quantitative criteria. Based on the research results, product quality becomes the priority factor in choosing bath soap products, with a Concordance value of 1.375 and a Discordance value of -1.80063, as well as the highest average rank, which is 1. The product price factor ranks second, followed by the skin condition factor in third place, and the product fragrance factor, which ranks last with an average rank of 4.

Attention-dependent attribute comparisons underlie multi-attribute decision-making in orbitofrontal cortex.

Economic decisions often require weighing multiple dimensions, or attributes. The orbitofrontal cortex FC) is thought to be important for computing the integrated value of an option from its attributes and comparing lues to make a choice. Although OFC neurons are known to encode integrated values, evidence for value mparison has been limited. Here, we used a multi-attribute choice task for monkeys to investigate how OFC eurons integrate and compare multi-attribute options. Attributes were represented separately and eye tracking as used to measure attention. We found that OFC neurons encode the value of attended attributes, dependent of other attributes in the same option. Encoding was negatively weighted by the value of the same tribute in the other option, consistent with a comparison between the two like attributes. These results indicate at OFC computes comparisons among attributes rather than integrated values, and does so dynamically, ifting with the focus of attention.

Connecting the Numerical Scale Model With Assessing Attitudes and its Application to Hesitant Fuzzy Linguistic Multi-attribute Decision Making

Connecting the Numerical Scale Model With Assessing Attitudes and its Application to Hesitant Fuzzy Linguistic Multi-attribute Decision Making

Cross-functional group decision making with heterogeneous cooperation for digital transformation in supply chain resilience

Supply chain resilience plays a critical role in gaining competitive advantages for companies. The resilience of supply chains can be achieved by leveraging emerging digital technologies to realize digital transformation. It is necessary to select an appropriate digitalization technology under such background. The wide-spanning of digital transformation and technology selection needs cross-functional integration of various expertise. However, in the process of making decisions by leveraging expert wisdom, differences in experts’ willingness to cooperate lead to difficulties in reaching a consensus. The existing literature fails to incorporate both non-cooperation and proactive-cooperation into the consensus reaching process. Thus, in this study, we introduce a cross-functional multi-attribute group decision making model for digitalization technology selection. To manage potential non-cooperative behaviors in the group consensus reaching process, the proposed model allows experts to have proactive cooperation, i.e., making more contributions than recommended feedback suggestions provided by the moderator. Proactive cooperation can make up for the loss caused by the non-cooperative behaviors of experts. A knowledge mining method is proposed to mine academic and practical preferences for attributes. Two consensus mechanisms are put forward for the meso decision-making process in functional teams and the macro decision-making process in the whole group, respectively. An illustrative example regarding the technology selection in shipbuilding industry is provided to verify the applicability of our model. Numerical experiments suggest that our model will improve the efficiency of consensus reaching process.

Chemical and sensory quality of citrus-flavored vegetable oils

Citrus-flavored vegetable oils (CFVO) demand has grown rapidly in the food industry due to increasing consumer demand for salads as a healthy food choice. Salad dressing has a distinctive taste from sour-tasting citrus that when added to salads to increase acceptance. This study primarily aimed to characterize antioxidant activity, total flavonoid content, and sensory quality by hedonic and organoleptic test between all unflavored oil and CFVO from three different vegetable oils (coconut oil (Cocos nucifera L.), palm oil (Elaeis guineensis L.), sesame oil (Sesamum indicum L.)) and three citruses (key lime (Citrus aurantifolia), kaffir lime (Citrus hystrix), and nasnaran (Citrus ambylcarpa)). One-Way ANOVA with post hoc Tukey were conducted to assess antioxidant and flavonoid differences. ANOVA with Tukey HSD and HSD, PCA and HCA, and a Spearman correlation analysis were conducted to assess the sensory qualities of citrus-flavored vegetable oils. There were different hedonic responses towards citruses in addition to the vegetable oils for each hedonic attribute. There is also a different response to the antioxidant and total flavonoids content of each treatment. The best formula according the multi-attribute decision using a compensatory model and additive weighting technique (MADCAW) score describes key lime (140%) flavored coconut oil as the best formula as having 56.58% antioxidant, 37.00 mg QE/100 g flavonoid and color liking 1.84, aroma liking 3.56, and taste liking 1.88. In conclusion, citrus-flavored vegetable oil has the potential to be a high-antioxidant vegetable oil with good acceptance by consumers.