Multi-attribute Decision-making Method Research Articles

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 novel multi-attribute decision-making method for supplier selection in the health care industry using cosine similarity measures of single-valued neutrosophic cubic hypersoft sets

A novel multi-attribute decision-making method for supplier selection in the health care industry using cosine similarity measures of single-valued neutrosophic cubic hypersoft sets

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

Optimization of the Metal Injection Molding Process with 3l6L Stainless Steel Powder and Influence Analysis of Process Parameters Using the Taguchi-MADM-Based Hybrid Method.

Metal injection molding (MIM) is an advanced manufacturing technology for producing complex metal parts with precise dimensions. Multiattribute decision making (MADM) can convert multiple quality attributes into a single overall quality score (OQS). To improve multiple quality attributes of the MIM compacts, a reasonable multiobjective optimization method should be applied. This paper proposes a Taguchi-MADM-based hybrid optimization method of the MIM process. It consists of the following main steps: (1) designing the experiment using a Taguchi orthogonal array, (2) conducting the experiment and measuring multiple quality attributes at each experimental trial, (3) calculating the OQS values at every experimental trial using some popular MADM methods, (4) selecting the best suitable MADM method from among some MADMs, and (5) determining optimal MIM process parameters to maximize the OQS obtained from the selected MADM using the Taguchi method. By using this method, this work determines the optimal MIM process parameters (injection pressure, injection temperature, powder loading, mold temperature, holding pressure, and injection speed) for improving multiple quality attributes (flexural strength, density, and surface appearance) of the MIM compacts with 3l6L stainless steel powder. The proposed method could be actively applied to many practical multiple quality attribute optimization problems in the manufacturing industry.

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.

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 three-way decision with prospect-regret theory under Pythagorean fuzzy environments

As science and technology continue to advance at a rapid pace, people are facing increasingly complex data, and research on uncertain information technology has become a hot field. Compared to intuitionistic fuzzy sets, Pythagorean fuzzy sets are more tolerant and flexible in representing uncertain information. Therefore, this article mainly focuses on the study of three-way decisions based on Pythagorean fuzzy β covering. First, considering the disadvantages of other methods in comparing the magnitudes of Pythagorean fuzzy numbers, we propose the concept of the ideal positive degree in a Pythagorean fuzzy environment and determine a method for calculating conditional probabilities based on this newly defined ideal positive degree. Second, regret theory and prospect theory are combined to obtain a relative utility function. Then, based on the aforementioned conditional probabilities and relative utility function, a three-way multi-attribute decision-making method based on the Pythagorean fuzzy environment is established. Finally, the three-way multi-attribute decision-making method is utilized to solve the problem of selecting the optimal choice for rural revitalization industrial projects. Through experimental comparison and analysis, the effectiveness and reliability of this method are verified.

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.

Prioritizing Watersheds for Intervention Design Using GIS and Remote Sensing

In many developing countries with poorly managed landscapes, soil erosion threatens the sustainability of water bodies. The main limitations of this study are the lack of daily sediment data, lithology, higher-resolution DEM data, and socioeconomic factors. Poor land use policy and resource management in the Upper Awash Sub-basin lead to soil erosion and sedimentation of hydrological infrastructure, Effective watershed prioritization requires integrating land use, hydrology, sediment load, and morphometric factors but often faces gaps, especially in the study area. This research aims to prioritise the Upper Awash Sub-Basin by its morphometric, land use and cover (LULC), and sediment yield characteristics. We used the integrated AHP-VIKOR multi-attribute decision-making method to prioritise watersheds, incorporating morphometry, LULC, and sediment load attributes in the simple matrix approach. The findings showed the following classes of erosion: exceedingly high (2722.14 km2), high (2524.46 km2), moderate (2205.48 km2), low (1611.43 km2), and extremely low (854.35 km2). Sub-watersheds WS6, WS8, WS10, WS13, and WS24 are the top priority for watershed management. The study ranked watersheds based on various attributes but encountered limitations such as the lack of daily sediment data, geological structure, and lithology. It can be concluded that this approach is very important to identify and categorize hotspots of soil erosion sub-watersheds for planners and decision-makers for conserving water and soil and for different environmental management purposes.

Application of multi attribute decision making methods for the selection of throttling valve in refrigeration system

The throttling valve is an essential component of any refrigeration system and its proper functioning is crucial for the successful operation of the refrigeration cycle. Its primary function is to expand the pressurized hot refrigerant and convert it into low-pressure cold refrigerant while maintaining a constant enthalpy and controlling the flow rate of the refrigerant. However, selecting the appropriate throttling valve is challenging, and a wrong decision can harm the system's performance. To overcome this challenge, decision-makers rely on multi-attribute decision-making methods to select the best throttle valve from a wide range of high-quality options available in the market. In this study, the three selected decision-making methods viz: analytical hierarchy process, technique of order preference by similarity to ideal solution and preference selection index are used for ranking appropriate throttle valves. The seven alternatives and seven attributes are considered to determine the best ranks. The results are compared with solutions obtained using selected methods, and it is observed that the QCX-2034BUC throttling valve has been determined to be the best choice, and the QCX-1534BUC is the last choice. The results of the investigation can potentially enhance the process and performance of refrigeration systems. Thus, the present study assists decision-makers in evaluating and ranking the best solution for their refrigeration system.

Grey Relation System Applications in Multi-Attribute Decision-Making and Forecasting in Financial Management

Objective: The objective of the paper is to verify the Grey relation analysis methods and compare them with known models in financial management. The first part is devoted to MADM (multi-attribute decision methods) and the second one to grey forecasting methods. Method: Localization GRA and globalization GRA multi-criteria models are described and compared with the TOPSIS method. The forecasting GRA(1,1) model based on cumulative data is described, derived and compared with the linear growth model. The models are verified on the data set. Results and Discussion: The globalisation model based on a pair-wise comparison of alternatives is different, and the ranking of alternatives is fully different in comparison with the TOPSIS model. It was found that the localisation GRA model and TOPSIS, both constructed on similarity measures, present similar and comparable results. So, the grey relation approach considers the uncertainty of input data. The forecasting model GRA(1,1) is computed and compared with the growth model. Results show that the growth model is the trendy model, and the non-trendy series is overestimated or underestimated, so the GRA model looks more robust. Research Implications: The problem of imprecise data is investigated. And it is a realistic condition for practical decision-making. The problem of lack of data is often a phenomenon in decision-making. Traditional statistical methods cannot be applied. Therefore, grey relation methodology is one approach to overcoming the problem of imprecise data and lack of data. Uncertainty and lack of data problems are topics important for research and the managerial community. Originality/Value: Sometimes, in financial decision-making, high-quality data or long-term time series is not at our disposal, and on the other hand, decisions have to be made. This problem is often neglected and solved by not-relevant traditional statistical models. Therefore, methods that allow the solving of such problems are useful. It was shown that a grey relation methodology for multicriteria financial performance evaluation and forecasting shows more robust and stable results and could be more relevant for imprecise data or short time series data in financial decision-making problems.

Multi-objective optimization and multi-attribute decision-making support for optimal operation of multi stakeholder integrated energy systems

To efficiently tackle the optimal operation problem of multi-stakeholder integrated energy systems (IESs), this paper develops a multi-objective optimization and multi-attribute decision-making support method. Mathematically, The optimal operation of IESs interconnected with distributed district heating and cooling units (DHCs) via the power grid and gas network, can be formulated as a multi-objective optimization problem considering both economic, reliability and environment-friendly objectives with numerous constraints of each energy stakeholder. Firstly, a multi-objective group search optimizer with probabilistic operator and chaotic local search (MPGSO) is proposed to balance global and local optimality during the random search iteration. The MPGSO utilizes a crowding probabilistic operator to select producers to explore areas with higher potential but less crowding and reduce the number of fitness function calculations. Moreover, a new parameter selection strategy based on chaotic sequences with limited computational complexity is adopted to escape the local optimal solutions. Consequently, a set of superior Pareto-optimal fronts could be obtained by the MPGSO. Subsequently, a multi-attribute decision-making support method based on the interval evidential reasoning (IER) approach is used to determine a final optimal solution from the Pareto-optimal solutions, taking multiple attributes of each stakeholder into consideration. To verify the effectiveness of the MPGSO, the DTLZ suite of benchmark problems are tested compared with the original GSOMP, NSGA-II and SPEA2. Additionally, simulation studies are conducted on a modified IEEE 30-bus system connected with distributed DHCs and a 15-node gas network to verify the proposed approach. The quality of the obtained Pareto-optimal solutions is assessed using a set of criteria, including hypervolume (HV), generational distance (GD), and Spacing index, among others. Simulation results show that the number of Pareto-optimal solutions (NPS) of MPGSO are higher by about 32.6 %-62.1%, computation time (CT) are lower by about 2.94 %-46.1 % compared with other algorithms. Besides, to further evaluate the performance of the proposed approach in addressing larger-scale issues, the study employs the modified IEEE 118-bus system of greater magnitude. The proposed MPGSO algorithm effectively handles multi-objective and non-convex optimization problems with Pareto sets in terms of better convergence and distributivity.

A Kansei engineering-based decision-making method for offline medical service quality evaluation with multidimensional attributes

The evaluation of doctors’ offline medical service quality plays a vital role in evaluating hospital performance. There are two challenges that need to be addressed: (1) Currently, telephone follow-up and questionnaire surveys are the main methods for evaluating the quality of offline (mainly outpatient and inpatient) medical services in hospitals. However, these methods suffer from drawbacks such as low efficiency and resource consumption. (2) There are certain limitations to determining attribute dimensions and weighting patient evaluation attributes based solely on word frequency. To this end, this study proposes a Kansei engineering-based decision-making method for offline medical service quality evaluation with multidimensional attributes. First, this study constructs a medical Kansei lexicon based on speed and cosine similarity. Second, a concept of Kansei utility values is been defined to represent attribute values in medical service quality evaluation. Additionally, this study builds upon the Kano model to obtain the Kano classification of each attribute. On that basis, a novel approach to obtaining the implicit importance of the attributes is proposed. Thus, the weight vector of the attributes can be calculated as the combination of explicit and implicit importance. Finally, the evaluation result is obtained by using the multiattribute decision-making method. The effectiveness of the proposed method is verified through an analysis of online reviews from Haodf.com. The case study reveals that offline patients consider medical ethics and communication skills as the ”must-be” dimension of attributes, medical competence as a one-dimensional attribute, and medical advice and prescriptions as the attractive dimension of attributes. A comparative analysis with the traditional method is conducted to demonstrate the importance of dimensional analysis for offline medical service quality evaluation. The main contributions of this study include the following: (1) A medical Kansei lexicon is constructed based on Kansei engineering, and (2) a novel decision-making framework for medical service evaluation based on both appearance and implicit importance is constructed.

A multi-attribute decision making approach considering economic psychology

The magnitude of the utility of a good depends on the subjective psychological evaluation of the good by the consumer, and the difference in the obtained utility will affect consumer behavior, taking into account that consumers are not completely rational in the actual decision-making process, and that under uncertain conditions, consumers’ perception of loss is much greater than gain. This paper investigates an improved scoring function based on consumer utility and loss aversion, which takes into account the effects of different decision makers’ preferences and loss aversion on the decision outcome. First, the existing score function of intuitionistic fuzzy sets is analyzed in depth, an improved score function is defined, and its properties and special cases are studied in detail. Then, a Multi-Attribute Decision-Making (MADM) method is proposed based on the improved scoring function combined with the intuitionistic fuzzy hybrid average (IFHA) operator. Finally, a real case of new energy-used car transaction decision-making is given, and the proposed method is validated by Spearman’s correlation coefficient, WS ranking similarity coefficient, and RW coefficient to prove its practicality and effectiveness.

A Hybrid Multi-Attribute Decision-Making Method Considering Tourists' Behavioral Preferences for Customized Tours

A decision-making analysis method considering tourists' behavioral preferences is proposed to address the problem of hybrid multi-attribute decision making (HMDM) for customized tours in which attribute values are intuitionistic fuzzy numbers, interval numbers and real numbers. This method is presented through step-by-step instructions:1) construct an evaluation index system of the HMDM for customized tours; 2) convert the attribute values of different data types into normalized intuitionistic fuzzy numbers, and obtain their objective weights by calculating their intuitionistic fuzzy entropies; 3) use the intuitionistic fuzzy weighted average operator to aggregate all the converted attribute values of each customized scheme;4) calculate scores of aggregation values for customized schemes and rank these schemes accordingly under the scenarios where tourists express such behavioral preferences as caution, mildness and recklessness; and 5) apply the proposed method to an example case to prove its feasibility.

Determining method of optimal process parameters using Taguchi method and technique for order preference by similarity to ideal solution in plastic injection molding

Many plastic injection molding (PIM) process optimization problems are multi-objective optimization problems that optimize multiple mechanical properties. This paper proposed a determining method of optimal process parameters and their effect ranks using Taguchi method and TOPSIS in the PIM. TOPSIS was used to convert multiple mechanical properties into single comprehensive response. It was selected as a reasonable multi-attribute decision-making (MADM) method from among some well-known MADM methods based on mean rank correlation coefficient and mean absolute rank deviation. Taguchi method was used to design experiment and find the optimal process parameters. The proposed method was applied to determine the optimal values of the process parameters: melt temperature (MT), packing pressure (PP), cooling time (CT), and injection pressure (IP) for improving tensile strength, elasticity module, flexural modulus, and impact strength in the PIM with Acrylonitrile-Butadiene-Styrene compound as plastic materials and AISI 1020 as mold materials. The optimal values of the process parameters were MT of 280°C, PP of 28 MPa, CT of 22 s, and IP of 50 MPa, and their effect ranking was IP (51.941%), PP (32.280%), CT (9.045%), and MT (6.734%). The method could be widely applied to not only the PIM process optimization but also various manufacturing process optimization problems.

A Crowd-Intelligence-Driven, Multi-Attribute Decision-Making Approach for Product Form Design in the Cloud Environment

In the traditional decision-making process for product form design, designers and experts often prioritize schemes based on their own knowledge and experience. This approach can lead to an oversight of user preferences, ultimately affecting decision outcomes. In contrast, crowd-intelligence-driven, multi-attribute decision-making for product form design in the cloud environment builds upon traditional approaches by leveraging the vast and diverse expertise of individuals on cloud platforms, engaging participants from various fields and roles in the decision-making process to enhance comprehensiveness and accuracy. To address the issue of a single decision-maker and limited user participation in the decision-making process for product form design schemes in the cloud environment, a multi-attribute decision-making method integrating expert knowledge and user preferences is proposed. This method aims to select a product form design scheme that optimally balances expert and user satisfaction. Initially, the Pythagorean Hesitant Fuzzy Set (PHFS) is used to quantify qualitative product attributes and to establish a comprehensive multi-attribute evaluation system. In the aspect of expert decision-making, a gray correlation coefficient decision matrix based on expert knowledge is established and the overall score of the base alternative is calculated by the ViseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method and the Improved Osculating Value method. In terms of user decision-making, weights are determined by calculating the similarity between user evaluation matrices, and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is used to calculate scores for product form designs based on user preferences. Ultimately, optimal selection is achieved by aggregating the aforementioned expert evaluation values and user preference values. The method’s effectiveness and feasibility are confirmed through a case study of coffee machine product form design schemes.

Intuitionistic Fuzzy Ordinal Priority Approach with Grey Relational Analysis

Multi-attribute decision-making (MADM) is a methodology for solving decision problems with a finite set of alternatives. The several methods of MADM require weights for the criteria and the alternatives to provide a solution. The Ordinal Priority Approach (OPA) is a recently proposed method for MADM that innovates; it does not require these inputs, just the rankings of criteria and alternatives. This article introduces a new hybrid method for MADM: the Intuitionistic Fuzzy Ordinal Priority Approach with Grey Relational Analysis (OPA-IF-GRA). OPA-IF-GRA combines GRA with OPA-IF, a newer extension of OPA that includes intuitionistic fuzzy sets to incorporate uncertainty into the decision-making process. The article presents an OPA-IF-GRA application for solving an electronics engineering problem, considering four criteria and six alternatives. The solution of OPA-IF-GRA is compared with the solutions obtained with three other MADM methods.

Multi-attribute decision-making methods based on Aczel-Alsina power aggregation operators for English blended teaching quality evaluation under single-valued neutrosophic sets

As a new form of teaching, blended teaching can integrate the advantages of classroom teaching and online teaching and improve the overall quality and effectiveness of English teaching under the premise of changing the orientation of teachers and students. The English blended teaching quality evaluation is a multiple attribute decision making (MADM) issue. Aczel-Alsina t-norm and t-conorm operations have the true advantage of changeability by adjusting a decision parameter. power averaging (PA) operator allowed decision attributes to strengthen each other through weight information during fusion, thereby eliminating the influence of subjective weights on the decision results. Motivate through classical Aczel-Alsina operators, PA operator and the single-valued neutrosophic sets (SVNSs), in this paper, the single-valued neutrosophic number Aczel-Alsina power averaging (SVNNAAPA) operator and the single-valued neutrosophic number Aczel-Alsina power weighted averaging (SVNNAAPWA) operator are constructed under SVNSs. The SVNNAAPWA operator is built for MADM. Eventually, an example about English blended teaching quality evaluation and some selected comparative decision analysis is used to depict the SVNNAAPWA method.

Multi-Attribute Three-Way Decision Approach Based on Ideal Solutions under Interval-Valued Fuzzy Soft Environment

The interval-valued fuzzy soft set (IVFSS) model, which combines the benefits of the soft set model with the interval-valued fuzzy set (IVFS) model, is a growing and effective mathematical tool for processing hazy data. In detail, this model is characterized by symmetry, which has the lower and upper membership degree. The study of decision-making based on IVFSS has picked up more steam recently. However, existing multi-attribute decision-making (MADM) methods can only sort alternative schemes, but are not able to classify them, which is detrimental to decision-makers’ efficient decision-making. In this paper, we propose a multi-attribute three-way decision-making (MATWDM) algorithm based on ideal solutions for IVFSS. MATWDM is extended to the IVFSS environment by incorporating the concept of the ideal solution, offering a more adaptable and comprehensive approach for addressing uncertain MADM issues. The method not only obtains the ranking results of the alternatives, but also divides them into acceptance domain, rejection domain, and delayed-decision domain, which makes the decision results more reasonable and effective, facilitating decision-makers to make better decisions. We apply the proposed three-way decision algorithm to two practical cases as diverse as mine emergency decision and Homestay selection decision. Additionally, the effectiveness and viability of the suggested method are confirmed by experimental findings.