Multi-attribute Decision Research Articles

Bonferroni mean aggregation operators under q-rung linear diophantine fuzzy hypersoft set environment and its application in multi-attribute decision making

Bonferroni mean aggregation operators under q-rung linear diophantine fuzzy hypersoft set environment and its application in multi-attribute decision making

Multi-attribute quantum group interference decision model considering minimum constraint and information entropy

About multi-attribute group decision making process, the opinions of different decisions will interfere with each other, or be affected by the decision-making environment, resulting in an unobserved interference effect, which is regarded as quantum entanglement in quantum theory. In this paper, an interference effect model based on minimum constraint and information entropy is proposed to measure the interference value in quantum decision models. Firstly, considering the interference effects of decision makers’ opinions, the evaluation opinions of decision makers are aggregated using Quantum-like Bayesian Network (QLBN), which are regarded as wave functions superimposed in the quantum model. Secondly, due to the uncertainty of decision process, information entropy and minimum constraint are combined to solve the interference effect. The minimum constraint is used to limit the range of interference values and the information entropy represents the uncertainty in the decision process. The grey relational degree represents the conditional probability of the decision maker about the alternatives. Thirdly, the weights of attributes and the probabilities of the alternatives are aggregated about decision makers by the proposed interference effect model, and then the alternatives are ranked by the aggregated probabilities. Subsequently, a numerical example is used to verify the validity and rationality of the model. Comparative analyses and sensitivity analyses are also implemented to demonstrate the stability and reliability of our method.

Multi-UAV Cooperative Air Combat Target Assignment Method Based on VNS-IBPSO in Complex Dynamic Environment

This paper investigates the threat assessment method and target assignment algorithm in multi-UAV cooperative air combat decision-making. To address the uncertainty and dynamic changes in multiple threat attributes and attribute information of UAV targets, we propose a UAV target dynamic threat assessment method based on intuitionistic fuzzy multiattribute decision-making. Firstly, we propose a mixed situation information representation method to represent interval-valued fuzzy data appropriately. Secondly, we employ the normal distribution weight assignment method to fuse the multi-time situation information. Then, by incorporating the analytic hierarchy process and entropy method, we determine the normalized threat value of the target considering both objective situation data characteristics and decision-maker preferences. Finally, a simulation example is provided to validate the rationality of our proposed algorithm. For solving the multi-weapon multi-target assignment problem, a target assignment method based on the VNS-IBPSO algorithm is introduced. This method improves upon the limitations of the BPSO algorithm, such as limited local search capability and premature convergence, by combining variable neighborhood search and an improved binary particle swarm optimization algorithm. Simulation results show that the proposed threat assessment method can obtain reasonable threat assessment results under complex dynamic environments. The proposed VNS-IBPSO algorithm can solve the target assignment model quickly and efficiently based on the assessment results, therefore ensuring that the UAV mission planning system makes the correct combat plan.

Fuzzy analytic hierarchy process based generation management for interconnected power system

Decision makers consistently face the challenge of simultaneously assessing numerous attributes, determining their respective importance, and selecting an appropriate method for calculating their weights. This article addresses the problem of automatic generation control (AGC) in a two area power system (2-APS) by proposing fuzzy analytic hierarchy process (FAHP), an multi-attribute decision-making (MADM) technique, to determine weights for sub-objective functions. The integral-time-absolute-errors (ITAE) of tie-line power fluctuation, frequency deviations and area control errors, are defined as the sub-objectives. Each of these is given a weight by the FAHP method, which then combines them into an single final objective function. This objective function is then used to design a PID controller. To improve the optimization of the objective function, the Jaya optimization algorithm (JOA) is used in conjunction with other optimization techniques such as sine cosine algorithm (SCA), Luus–Jaakola algorithm (LJA), Nelder–Mead simplex algorithm (NMSA), symbiotic organism search algorithm (SOSA) and elephant herding optimization algorithm (EHOA). Six distinct experimental cases are conducted to evaluate the controller’s performance under various load conditions, with data plotted to show responses corresponding to fluctuations in frequency and tie-line exchange. Furthermore, statistical analysis is performed to gain a better understanding of the effectiveness of the JOA-based PID controller. For non-parametric evaluation, Friedman rank test is also used to validate the performance of the proposed JOA-based controller.

An enhanced fuzzy IDOCRIW-COCOSO multi-attribute decision making algorithm for decisive electric vehicle battery recycling method

An adaptation to electric mobility quickens waste management tasks for recyclers to end-to-end processing of marketed electric vehicle batteries. Especially lithium-ion batteries play a prominent role in electrifying the world for e-transport technology innovation. This research offers a multi-attribute decision-making (MADM) structure for finding the best performance e-vehicle recycling techniques. The structured algorithm combines an advanced stratified MADM strategy with e-transportation recycling techniques. The optimal algorithm evaluates the results of qualitative attributes and alternatives using a weighted-ranking MADM approach. The importance of attributes is calculated using a blending of dual objective-weighted approaches: entropy and CILOS methods, viz., the aggregated IDOCRIW approach. The ranking of alternatives is determined through the COCOSO method in a hesitation environment. The q-rung orthopair picture fuzzy set (q-ROPFS) is used to cope with uncertainty and vagueness in decision analysis. The feasibility and robustness of the suggested algorithm were validated through different MADM methods and by altering crucial ranking-dependent parameters in the problem.

Consensus adjustment for multi-attribute group decision making based on cross-allocation

Consensus adjustment is crucial in group decision making (GDM), aiming to reduce discrepancies in decision makers’ (DMs’) judgments for final decision results. The consensus adjustments made by DMs are interactive rather than independent. However, few studies investigate the consensus adjustment problem from the perspective of interactions among DMs. This study first constructs a model to determine the total minimum consensus adjustment under the requirements of consensus threshold and individual-collective judgment bias. Then, a consensus adjustment approach based on cross-allocation scheme is proposed to address the inconsistency between individual and collective minimum adjustments, which concerns all DMs’ individual favorable allocation results under the total minimum consensus adjustment. The cross-allocation scheme ensures unanimous support for the same allocation result among all rational DMs, thereby eliminating the discrepancies among them. Subsequently, the optimization model-based minimum consensus judgment penalty is formulated to handle the non-cooperative behaviors of DMs. Furthermore, we show how to determine the unique consensus adjustment result for the non-cooperative DMs using the cross-allocation scheme. Therefore, our study contributes by developing a new GDM method that conducts consensus adjustment allocation by considering interactions among DMs. Finally, a case study is employed to demonstrate the specific application of the new method and discuss its efficiency. Meanwhile, comparison analysis and simulation experiments are provided.

Use of measurement results from a mobile laboratory in the diagnostics of anthropogenic environment - linear ordering of objects using the TOPSIS method

The measurements of the concentrations of gaseous and dust pollutants in the anthropogenic environment are an important element of environmental monitoring and for determining directions of preventive activities in the field of health protection. The article presents the results involving the concentrations of suspended dust and gaseous pollutants in the outdoor air, which were recorded at three measuring stations of air quality in the Silesian and Opole voivodeships (Wodzisław Śląski, Zabrze, Kędzierzyn-Koźle). The results were supplemented with the values recorded by the mobile laboratory located at the Center for Continuing Education - Branch of the Silesian University of Technology in Rybnik. The research results were used for a synthetic assessment of the threat level to the anthropogenic environment. In the computational layer, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed, which is included in the group of methods for solving multi-criteria decision-making problems (Multi Attribute Decision Making).

Selection of safety equipment with choquet integral operators and q-rung orthopair fuzzy information

The q-rung orthopair fuzzy set (q-ROFS) is a moderate mathematical model, that has diverse capabilities to handle uncertain and ambiguous information of human opinion during the decision analysis process. The Aczel Alsina operations are more flexible and valuable aggregating tools with parameter values ϻ ⩾ 1, reflecting smooth and accurate information by aggregating awkward and redundant information. The theory of the Choquet integral operator is also used to express the interaction between preferences or criteria by incorporating certain values of preferences. The primary features of this article are to derive some dominant mathematical approaches by combining two different theories like Choquet integral operators and operations of Aczel Alsina tools namely “q-rung orthopair fuzzy Choquet integral Aczel Alsina average” (q-ROFCIAAA), and “q-rung orthopair fuzzy Choquet integral Aczel Alsina geometric” (q-ROFCIAAG) operators. Some special cases and notable characteristics are also demonstrated to show the feasibility of derived approaches. Based on our derived aggregation approaches, a multi-attribute decision-making (MADM) technique aggregates redundant and unpredictable information. In light of developed approaches, a numerical example study to evaluate suitable safety equipment in the construction sector. To reveal the intensity and applicability of derived approaches by contrasting the results of prevailing approaches with currently developed AOs.

A Hybrid Multi-Attribute Decision-Making Approach Considering Correlations Among Indicators

Considering the fuzzy and uncertainty of the evaluation information of the evaluation object, the type of indicators are expanded into five types of mixed evaluation information, namely, exact number, interval number, triangular fuzzy number, hesitant fuzzy number, and probabilistic linguistic term sets, and different types of evaluation information are adopted according to the different characteristics and types of indicators, respectively. When the evaluation information is hybrid information, the generalized Shapley function based on fuzzy measurement is used to analyze the interaction between indicators and determine the weights of indicators, considering that the interaction between indicators is more complicated. In view of the fact that the conversion of mixed information into the same kind of information will lead to the problem of complicated calculation and missing information, the VIKOR method is used to comprehensively evaluate the evaluation objects and select the best ones. Finally, the validity and feasibility of the proposed method is verified by taking the assessment of data literacy level and competence of teachers in a university in a western province of China as an example.

An enhanced MADM with Lq* q-Rung orthopair multi-fuzzy soft set in healthcare supplier selection

The global healthcare systems have encountered unparalleled difficulties due to the COVID-19 pandemic, underscoring the crucial significance of effective management within healthcare supply chains. This research contributes to the field of healthcare supply chain management by presenting a robust MADM methodology called lattice ordered(Lq*) q-rung orthopair multi-fuzzy soft set(Lq* q-ROMFS-MADM) for supplier evaluation and ranking amidst the challenges posed by the COVID-19 pandemic. Taking inspiration from multi-fuzzy soft set and q-rung orthopair fuzzy set, the present research article proposes a novel framework known as Lq* q-rung orthopair multi-fuzzy soft set (Lq* qROMFSS), which incorporates lattice ordering in q-rung orthopair multi-fuzzy soft set. The effectiveness of the proposed model is confirmed through successful experimentation on various important operations, including union, intersection, complement, restricted union and intersection. Moreover, the verification of De Morgan’s laws for Lq* qROMFSS is carried out specifically for these operations mentioned above. To highlight the significance of the proposed Lq* qROMFSS, a multi-attribute decision-making (MADM) problem is presented, showcasing its application in the domain of healthcare supply chain management. Furthermore, a comparative analysis is conducted to elucidate the advantages of this model in comparison to existing models.

Multi-attribute decision making method based on the generalised spherical fuzzy Heronian operators

Traditional fuzzy decision-making methods still have certain limitations in practical applications, such as the problem of the sum of attribute memberships and non-memberships possibly exceeding 1. Additionally, due to the attributes in real decision-making processes often not being mutually independent but rather exhibiting a certain degree of correlation, traditional fuzzy decision-making methods may not fully capture and express this complexity. To overcome these limitations, this paper proposes a new multi-attribute decision-making method addressing the problem of integrating information with correlated attributes in the generalized spherical fuzzy environment. Initially, by combining the generalized spherical fuzzy set with the Heronian averaging operator, the paper introduces the generalized spherical fuzzy weighted Heronian averaging operator and thoroughly discusses some valuable properties of both operators, providing corresponding proofs. Furthermore, the paper proposes the multi-attribute decision-making method using the generalized spherical fuzzy weighted Heronian averaging operator, enriching not only the theoretical framework of multi-attribute decision-making methods but also offering more possibilities for practical applications. Finally, the application of this method in the field of commercial bank lending decision-making will be further explored to enhance the accuracy and efficiency of credit decisions, reduce risks, and promote the healthy development of the banking industry.

Multi-objective optimization of wire electrical discharge machining process using multi-attribute decision making techniques and regression analysis

Wire electrical discharge machining (WEDM) is one of the most important non-traditional machining methods that is widely used in various industries. The present research work is concerned with the influences of process variables on quality of machined specimen obtained from WEDM process. The process parameters to manufacture mold structure included wire feed speed, wire tension and generator power, and in the current research, the effects of these variables on the aim factors, namely dimensional accuracy, hardness and roughness of product surface have been investigated, simultaneously. In order to obtain the optimal experiment, the multi-objective optimization with discrete solution area has been employed. Method based on the removal effects of criteria (MEREC) and weighted aggregates sum product assessment (WASPAS) techniques have been used with the aim of weighting the objective functions and discovering the best practical experiment. In the following, the regression analysis has been employed to study the effects of variables on response factors. A good correlation between the results gained from two analysis methods was observed. Based on MEREC-WASPAS hybrid technique, the weights of roughness, hardness and dimensional accuracy of machined part were calculated to about 89%, 9% and 2%, respectively. In the selected optimal experiment, the amount of wire feed speed, wire tension and generator power variables were considered to, in turn, 2 cm/s, 2.5 kg, and 10%.

Optimal Parking Space Selection and Vehicle Driving Decision for Autonomous Parking System Based on Multi-Attribute Decision

Autonomous parking systems (APSs) can help drivers complete the task of finding a parking space and the parking operation, which improves driving comfort. Current research on APSs focus on the perception, localization, planning, and control modules, while few pay attention to the decision modules. This paper proposes a method for optimal parking space selection and vehicle driving decisions. In terms of selecting the optimal parking space, a multi-attribute decision method is designed considering the type of parking space, walking distance, and other factors. In terms of vehicle driving decisions, we first predict the behavior and trajectory of the target vehicle in a specific scenario, and then use a combination of rule-based and learning-based decision methods for safe and comfortable vehicle driving behavior decisions. Simulation results show that the proposed methods can find the optimal parking space according to the parking lot map and improve the efficiency and smoothness of vehicle driving while ensuring driving safety.

Application of Interval-valued T-spherical Fuzzy Dombi Hamy Mean Operators in the antiviral mask selection against COVID-19

This study introduces Interval-valued T-Spherical Fuzzy Dombi Hamy Mean (IVT-SPFDHM) Operators as a powerful tool for group decision-making. The IVT-SPFDHM operators allow for prioritization of fuzzy data effectively managing uncertainty. Its framework is applied in diverse group decision-making contexts, presenting its adaptability and robustness in addressing complex real-world problems. This study examines the Multi-Attribute Decision-Making (MADM) issue in IVT-SPFDHM environment where the qualifications and expertise are at varying levels of necessity. We regard the novel Aczel-Alsina aggregation operators (AOs) as the most recently created AOs, capable of handling considerable uncertainty. To propose some AOs we investigated the Hamy mean (HM) operator in following environment: Interval-valued T-Spherical Fuzzy weighted Dombi Hamy mean (IVT-SPWDHM) operator, stretch esteemed interval valued T-Spherical Dual Dombi Hamy Mean (IVT-SPFDDHM), and Interval-valued T-Spherical Fuzzy weighted Dual Dombi Hamy Mean (IVT-SPFWDDHM). The weights for prioritization are derived from the knowledge of experts, and the proposed operators can capture the phenomenon of prioritization among the aggregated arguments. The MADM models are then planned using the IVPWDHM and IVPWDDHM operators. Finally, we provided a sample example within the prioritized ones to select the best antiviral mask for fighting COVID.

Permanent Employee Assessment Decision Support System using the Simple Multi Attribute Rating Technique (SMART) Method

Every agency or organization must be able to select and determine competent employees to fill vacant positions so that the main duties of that position can be carried out. To obtain information quickly and accurately about the assessment of permanent employees in accordance with the expected criteria, a decision support system is needed. Objective decisions are usually fair because they are in accordance with the facts and supported by data to produce a decision/recommendation that can make things easier for agency or organization leaders. By using the Simple Multi Attribute Rating Technique (SMART) method, it is hoped that it can help in making decisions about evaluating permanent employees. The SMART method uses a multiattribute decision technique which is used to support decision makers in choosing between several alternatives. This research aims to assist the leadership of the West Sumatra Ministry of Religion in making decisions regarding permanent employee recommendations for employees. The method used in this research is qualitative. Based on the test results, a weighting system is produced that takes into account the assessment factors that exist in employees. The results of the assessments that have been carried out will immediately produce data on employees who have competencies worthy of being promoted to a better career level. In the research, 6 criteria were used, namely cooperation , work ability, work discipline, loyalty, responsibility and communication. With 5 alternative data, calculations were carried out using the SMART method, the result that got the highest score was Endang Mesfitra with a final result of 0.7425. With the research results, the application can help leaders in providing assessments and the results of these assessments produce objective decisions so that leaders can recommend employees according to the criteria

Efficacy of Induced Complex Aggregation Operators in Multi-Attribute Decision-Making with Confidence Levels

Efficacy of Induced Complex Aggregation Operators in Multi-Attribute Decision-Making with Confidence Levels

GHF-COPRAS Multiple Attribute Decision-Making Method Based on Cumulative Prospect Theory and Its Application to Enterprise Digital Asset Valuation

With the rapid development of the economy, data have become a new production factor and strategic asset, enhancing efficiency and energy for technological innovation and industrial upgrading in enterprises. The evaluation of enterprise digital asset value (EDAV) is a typical multi-attribute decision-making (MADM) problem. Generalized hesitant fuzzy numbers (GHFNs) can better express the uncertainty and fuzziness of evaluation indexes, thus finding wide applications in MADM problems. In this paper, we first propose the Kullback–Leibler (K-L) divergence distance of GHFNs and prove its mathematical properties. Second, recognizing that decision-makers often have finite rationality in practical problems, we combine the cumulative prospect theory (CPT) with the Complex Proportional Assessment (COPRAS) method to propose the GHF-CPT-COPRAS model for solving MADM problems. Simultaneously, we extend the distance correlation-based Criteria Importance Through Intercriteria Correlation (D-CRITIC) method to the GHF environment to rationally calculate the weights of attributes in the EDAV evaluation problem. Finally, we apply the proposed GHF-CPT-COPRAS model to the EDAV evaluation problem and compare it with existing GHF decision-making methods to verify its effectiveness and feasibility. This study provides an important reference for addressing the EDAV assessment problem within an uncertain fuzzy environment and extends its application methods in the decision-making field.

Multi-Attribute Decision-Making Method Based on Dispersion Maximization and its Practical Application

Multi-attribute decision making is an important part of modern decision science, which has wide and practical applications in engineering design, economy, management, military and other fields. This paper first introduces the theory and sequencing steps of multi-attribute decision making method based on deviation maximization, and then makes specific application to the example of civil servant recruitment in government departments by using MATLAB programming. It makes the operation process of recruitment work clear and clear, and realizes scientific, normative and fair.

Text Mining and Multi-Attribute Decision-Making-Based Course Improvement in Massive Open Online Courses

As the leading platform of online education, MOOCs provide learners with rich course resources, but course designers are still faced with the challenge of how to accurately improve the quality of courses. Current research mainly focuses on learners’ emotional feedback on different course attributes, neglecting non-emotional content as well as the costs required to improve these attributes. This limitation makes it difficult for course designers to fully grasp the real needs of learners and to accurately locate the key issues in the course. To overcome the above challenges, this study proposes an MOOC improvement method based on text mining and multi-attribute decision-making. Firstly, we utilize word vectors and clustering techniques to extract course attributes that learners focus on from their comments. Secondly, with the help of some deep learning methods based on BERT, we conduct a sentiment analysis on these comments to reveal learners’ emotional tendencies and non-emotional content towards course attributes. Finally, we adopt the multi-attribute decision-making method TOPSIS to comprehensively consider the emotional score, attention, non-emotional content, and improvement costs of the attributes, providing course designers with a priority ranking for attribute improvement. We applied this method to two typical MOOC programming courses—C language and Java language. The experimental findings demonstrate that our approach effectively identifies course attributes from reviews, assesses learners’ satisfaction, attention, and cost of improvement, and ultimately generates a prioritized list of course attributes for improvement. This study provides a new approach for improving the quality of online courses and contributes to the sustainable development of online course quality.

An integrated multi-attribute decision framework for sustainability assessment of renewable diesel fuel production pathways

Ascertaining the sustainability of a fuel production technology in the presence of conflicting factors out of different existing production pathways is a typical and time-consuming exercise. Thus, it is necessary to follow a resourceful and systematic methodology to decide the best fuel production method for any application. The present work proposes a systematic decision framework that utilizes various MCDM schemes for identifying suitable renewable diesel production technology to replace conventional diesel fuel. Sustainability assessment for a total of five production methods was conducted, based on fifteen criteria. The MOORA, VIKOR, and COPRAS techniques are utilized to compute ranking schemes for different alternatives according to integrated criteria weights based on AHP and CRITIC methods. Qualitative assessment in terms of rank reversal test and sensitivity analysis employed to compare and check the robustness of applied methodologies. The results demonstrated that FT diesel is the most suitable alternative followed by green diesel-I and feedstock price as the most persuasive criteria followed by PM2.5.The qualitative assessment revealed that the integrated weight-based MOORA technique is the most robust framework for the present decision problem, followed by VIKOR and COPRAS strategies respectively. Further, the proposed MCDM techniques can be suggested independently to encounter similar types of decision-making problems.