Multi-attribute Decision Research Articles

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

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.

Divergence and Similarity Characteristics for Two Fuzzy Measures Based on Associated Probabilities

Abstract: The article deals with the definitions of the distance, divergence, and similarity characteristics between two finite fuzzy measures, which are generalizations of the same definitions between two finite probability distributions. As is known, a fuzzy measure can be uniquely represented by the so-called its associated probability class (APC). The idea of generalization is that new definitions of distance, divergence, and similarity between fuzzy measures are reduced to the definitions of distance, divergence, and similarity between the APCs of fuzzy measures. These definitions are based on the concept of distance generator. The proof of the correctness of generalizations is provided. Constructed distance, similarity, and divergence relations can be used in such applied problems as: determining the difference between Dempster-Shafer belief structures; Constructions of collaborative filtering similarity relations; non-additive and interactive parameters of machine learning in phase space metrics definition, object clustering, classification and other tasks. In this work, a new concept is used in the fuzzy measure identification problem for a certain multi-attribute decision-making (MADM) environment. For this, a conditional optimization problem with one objective function representing the distance, divergence or similarity index is formulated. Numerical examples are discussed and a comparative analysis of the obtained results is presented.

A Synergistic Multi-Attribute Decision-Making Strategy for Selecting Sustainable Educational Institution Site Using Parameterized Single-Valued Neutrosophic Soft Sets

Selecting the ideal site for an educational institution is a challenging task that calls for a deep understanding of the available resources, the educational needs of society, and projected development patterns. To cope with such intricacies, this study proposes a novel mathematical context: A single-valued neutrosophic parameterized single-valued neutrosophic soft set ([Formula: see text]-set) that merges the versatility and adaptability of single-valued neutrosophic sets with parameterized single-valued neutrosophic soft settings. Using [Formula: see text]-set-based information, the study emphasizes the intrinsic uncertainty and inaccuracies in the decision-making process and enables a more thorough portrayal of the many criteria involved in site selection. In the first stage, the elementary notions of [Formula: see text]-set are explored with the description of understandable examples. In the second stage of the study, a decision-assistance mechanism is presented, and a strong algorithm is developed and evaluated using a case study prototype that focuses on choosing an appropriate location for an educational institution to be established. By guaranteeing that the chosen site satisfies the essential requirements for ideal placement, the algorithm proves its efficacy and improves the decision-making process.

Assessing metacognitive knowledge in subjective decisions: The knowledge of weights paradigm

Subjective, multi-attribute choice decisions – such as whom to marry or which college to attend – play a substantial role in decision makers’ long-term well-being. However, the metacognition literature lacks tools for assessing metacognitive capacities in subjective decisions. We present three studies in which we propose and validate the Knowledge of Weights (KoW) paradigm, a novel method for assessing metacognitive knowledge of attribute weights in subjective, multi-attribute choice decisions. In Study 1, we demonstrate the test-retest reliability of metrics generated by the KoW paradigm. In Study 2, we apply the KoW paradigm in four domains and show that it generates consistent results. In Study 3, we demonstrate that participants who perform better on the KoW paradigm make choices with which they are more satisfied, providing suggestive evidence of predictive validity. Use cases in cognitive psychology and beyond are discussed.

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.

Fairly Aggregation Operators Based on Complex p, q-Rung Orthopair Fuzzy Sets and Their Application in Decision-Making Problems

The decision-making technique is used to evaluate the best optimal among the collection of finite alternatives. Further, the technique of complex p, q-rung orthopair fuzzy (CPQROF) set is very reliable and dominant due to parameters “p” and “q”. In contrast, the technique of simple q-rung orthopair fuzzy sets is the special case of the CPQROF set. The major theme of this article is to expose the novel theory of fairly operational laws based on CPQROF numbers (CPQROFNs). Further, we evaluate the weighted fairly aggregation operators based on CPQROF information, called CPQROF weighted fairly averaging (CPQROFWFA) operator and CPQROF ordered weighted fairly averaging (CPQROFOWFA) operator. Some properties are also discussed for the above operators. Additionally, we validate the system of multi-attribute decision-making (MADM) difficulties based on initiated operators. Finally, we associate our suggested ranking consequences with approximately prevailing techniques to demonstrate the proposed theory's sovereignty and validity.

Evolutive multi-attribute decision making with online consumer reviews

In the digital age, the sheer volume of online consumer reviews imposes a cognitive burden on consumers, complicating their purchasing decisions. Many studies have integrated consumer opinions to provide consumers with clear and concise information. However, these studies often prioritize mainstream opinions, overlooking the diversity and timeliness of other important perspectives. To address this challenge, we propose an evolutive decision-making method. Firstly, we propose an attribute rating evolution algorithm to address the online reviews based on the iterative self-organizing data analysis technique and time decay. This algorithm enables real-time analysis of the diverse opinions expressed in review data. Then, taking into account consumer attribute preferences and decision-making psychology, we formulate multiple product ranking strategies to offer personalized decisions based on the evolutive opinions. Our method decreases the bias towards review quantity, ensuring that significant opinions are not overshadowed by more frequent ones. Through data experiments and an application on OpenTable.com, we demonstrate that our method can provides effective decision recommendation for consumers.

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.

Application of Multi Attribute Decision Making Methods for the Selection of Throttling Valve in Refrigeration System

Application of Multi Attribute Decision Making Methods for the Selection of Throttling Valve in Refrigeration System

Bipolar Complex Fuzzy Rough Sets and Their Applications in Multicriteria Decision Making

Bipolar complex fuzzy set (BCFS) is a more advanced and powerful phenomenon as it consists of two-dimensional data with positive and negative impacts of an element. It can solve the data consisting of the positive and negative impacts of an element which is a bipolar fuzzy set (BFS). It also covers the two-dimensional complex data which is a complex fuzzy set (CFS). Due to these attributes, BFS and CFS are less useful in comparison with BCFS to capture vagueness, complexity, and ambiguity in the data. Furthermore, lower and upper approximations based on equivalency relations constitute another significant phenomenon known as rough set (RS). This structure is also more powerful in dealing with real-life dilemmas. Rather than comparing the RS and BCFS, we combine both phenomena to handle the complexity more powerfully to deal with such types of phenomena that are not handled by other structures. So, by combining both phenomena, we introduce a novel structure known to be bipolar complex fuzzy rough set (BCFRS) in this manuscript. After that, we define some important operations, some significant properties related to this structure, and some aggregation operators (AOs) to solve decision-making (DM) problems related to cyber security. We address a practical application of cyber security (C-S) in computing for the protection of critical data to demonstrate the usefulness of the multi-attribute DM(MADM) approach. Based on the various criteria and attributes given by the experts, we find the best and better alternative to the C-S by applying the MADM approach. We get the A4 as the best and finest alternative by using bipolar complex fuzzy rough (BCFR) weighted arithmetic averaging (BCFRWAA), BCFR ordered weighted arithmetic averaging (BCFROWAA), and BCFR ordered weighted geometric averaging (BCFROWGA) operators. And, by using BCFR weighted geometric averaging (BCFRWGA), we get the A3 as the finest alternative. Lastly, to prove the superiority, validity, and generalization of our unique established theory, we give a detailed comparative study of our established work with several prevalent theories.

Fuzzy Multi Attribute Decision Making Determining House DSS

The property industry in Indonesia is experiencing rapid development along with improving economic conditions, causing consumers to face difficulties in choosing a house that suits their preferences. Various factors such as price, location, facilities, and payment methods often make decisions complex. The Fuzzy Multi Attribute Decision Making (FMADM) method is used to determine and evaluate the best options based on the degree of suitability of all criteria, with ranking carried out using the defuzzy method. The results of this study are expected to provide practical solutions for consumers in choosing a house that suits their needs and improve the quality of property developer services in Indonesia. The author's test results show that 72% of users feel helped by this solution method.

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.

Application of intuitionistic fuzzy set in social life cycle impact assessment

Abstract Purpose Intuitionistic fuzzy set (IFS) has been found promising for handling ambiguity forms of uncertainty in social data. This paper undertakes an application of an intuitionistic fuzzy set in the social life cycle impact assessment (S-LCIA) of a public building project. This work proposes to combine an intuitionistic fuzzy set (IFS) with multi-attribute decision-making in converting qualitative data into quantitative social impacts of the building project. This research utilises IFS to accommodate and handle experts’ imprecise cognitions in S-LCIA to facilitate the identification and prioritisation of the most pressing indicators accompanying social impacts in a project. Methods Data were collected using questionnaire(s), structured interviews, and through targeted focus group sessions. Intuitionistic fuzzy set (IFS) approach was used to model the obtained data from structured interviews. Using IFS, this research also accounts for missing or ambiguous data that emerged during the data elicitation process. In combining IFS with multi-attribute decision-making techniques, the social impacts of selected stakeholders were evaluated using fuzzy set and IFS approaches. Sensitivity analysis was then used to test the robustness of the results and ranking was conducted based on each social stakeholder subcategory. Results and discussion Results revealed that approximately 23% of missing datapoints in the public case study building were incorporated in the S-LCIA using IFS. The issues of highest priority in each of the considered subcategory in the public case study building project according to IFS are (i) consumer privacy by adopting more functional planning (0.27), (ii) public commitment to sustainability (0.33), and (iii) education provided in the local community by having an indigenous botanical element for children (0.39), respectively. Conversely, the FS technique inferred that the issue of highest priority in the consumer stakeholder is the feedback mechanism (0.26). The overall degree of correlation between the IFS and FS is only 0.234, revealing that IFS provides a different perspective from conventional FS when used in modelling social data. Conclusion The IFS method provides an objective and systematic approach for dealing with a heterogeneous scope of imprecise and inexact social data in achieving holistic social life cycle assessment results. Furthermore, expansive stakeholder involvement would rely on a robust approach for improving social well-being in public buildings, thereby leaving no one behind in accomplishing a sustainable world.

Maintaining Accuracy While Reducing Effort in Online Decision Making: A New Quantitative Approach for Multi-Attribute Decision Problems Based on Principal Component Analysis

This paper explores consumer decision making, particularly focusing on the increasing prevalence of choices on the Internet such as online shopping. Examining the fundamental question of how individuals decide how to decide, our paper draws upon the effort–accuracy framework. This framework indicates that people typically consider both the cognitive effort associated with employing a specific decision strategy and the decision quality (i.e., accuracy) implied by using a particular strategy. However, decision strategies with high accuracy imply high effort. Empirical evidence shows that people often use decision strategies that require little effort. As a result, accuracy is often not high. Against this backdrop, this paper introduces a quantitative approach leveraging principal component analysis (PCA) as a decision support tool. Based on a simulation study, the approach demonstrates that it is possible to maintain high accuracy with significantly reduced effort in multi-attribute decision situations where attribute information is available in a quantitative format. This demonstration is based on the example of two decision strategies, which are both theoretically and practically highly relevant: the multi-attribute utility model (MAU) and the elimination-by-aspects strategy (EBA). By employing PCA for dimensionality reduction, the approach becomes particularly advantageous for online shops and online comparison portals, presenting users with concise yet accurate information. It is important to emphasize that our PCA approach is designed for data with a natural ordering, primarily focusing on quantitative variables. Consequently, decision situations where qualitative variables (e.g., product design or color) play a role in the decision-making process will need further exploration in future studies. However, we present a first solution to this problem so that our approach, based on this solution, can be implemented by online shops and online comparison portals in the near future.

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.

Application of New Fuzzy Measure in Multi-Attribute Decision-Making

The implementation of multi-attribute decision-making (MADM) and the different approaches that facilitate it are addressed in this article. We focus on a recently developed fuzzy divergence measure, which is critical in improving decision accuracy when faced with several conflicting criteria. To highlight its real-world relevance, we present a detailed case study focusing on picking the best market for investment. In this case study, the previously studied Fuzzy divergence measure that is used to evaluate and prioritize various market possibilities based on important characteristics such as risk, return, and market potential. In this example, we demonstrate how this unique measure improves decision-making processes by providing a more precise and comprehensive method to selecting the greatest investment possibilities in uncertain and complicated contexts. The findings highlight the measure's usefulness in guiding investment decisions and enhancing the overall efficacy of MADM applications. JEL Codes: C44, D80, D81, D11 Received: 19/07/2024. Accepted: 06/10/2024. Published: 20/10/2024.

Democrats and Republicans choose solar panels in very similar ways.

Appealing to individuals' social identity is a powerful form of social influence, capable of changing the way people process information, the information they think about, and how they evaluate other individuals. The purpose of this study is to explore the idea that Democrat and Republican environmental norms may impact the attributes and strategies partisans use when choosing whether to have solar panels on a house. An online study with N = 363 participants was conducted to examine these possible effects through multi-attribute decision making, applying predefined decision process models to participant behavior to test which attribute-based models best describe participants' decision making. A choice task was combined with an experimental manipulation of political affiliation salience to examine whether the norms of political groups would have influence on decision behavior. Results of the study show remarkable similarities between political parties in their strategies for choosing solar panels. Members of both political parties appeared to use similar strategies and similar attributes for the formation of their decisions. Recommendations are made that science communicators and policy makers avoid polarizing language so as not to create unnecessary polarization where ideological gaps may not currently exist.

Peer-to-peer energy trading with decentralized bidirectional matching of multipreference community prosumers

Peer-to-peer (P2P) energy trading of community prosumers is effective at coordinating grid-connected distributed energy resources. The existing methods fail to address the multiple preferences of prosumers in energy trading, and the information privacy and autonomy of prosumers are not respected. These issues hinder the effective and applicable P2P energy trading of community prosumers. To bridge this gap, this paper proposes a decentralized bidirectional matching method (DBMM) based on multiattribute decision making (MADM) to match the P2P energy trades between sellers and buyers. This method enables the bidirectional evaluation, selection, and matching of buyers/sellers, allowing prosumers to make decisions on energy trading autonomously without disclosing private information. The numerical analysis indicates that compared with prevalent trading methods, the proposed DBMM is more cost-effective and generalized for handling multiple preferences while preserving information privacy and autonomy. In the four-preference case, the total revenue of the proposed DBMM is 32.30 % greater than that of the other compared methods. Furthermore, the computational efficiency and scalability of this method are also validated.

Green dielectric exploration: spectrographic analysis and technical feasibility of vegetable oils in EDM via PIV, TOPSIS, and MOORA evaluation methods

Achieving a sustainable dielectric medium for Electrical Discharge Machining (EDM) has now drawn the attention of researchers and industries alike. Increased awareness among the Government and NGOs paved the way for the manufacturing industries to move towards a green environment and sustainability in the production process. To search for sustainable dielectric, 5 different types of oils viz., Jatropha, coconut, groundnut, sunflower, and EDM 4 oil (conventional oil) are experimented in this paper and revealed its suitability of potential replacement for the conventional dielectric. The properties such as viscosity, density, flash point, electrical conductivity, and BDV (Break down Voltage) are tested as per the standards. Since the number of output response is more than, Multi Attribute Decision Making (MADM) algorithms namely PIV, TOPSIS, and MOORA are used for the ranking and optimization. Entropy weight method is adopted to endow criteria weight to each of the MADM techniques. Jatropha oil has been ascertained to be rank 1 followed by groundnut, coconut, sunflower, and EDM 4 oil. Subsequently, the Gas Chromatography & Mass Spectrometry (GC–MS) analysis has revealed the quantitative constituents present in Jatropha as well as remaining oils. Furthermore GC–MS analysis reports that the retention time of Jatropha (43.042 min) is relatively longer than other oils except coconut oil, to release Ricinoleic acids. Groundnut and EDM 4 oils took relatively less retention time to release the alkanes. Machining performance was also conducted with the obtained dielectric and compared with conventional type oil (EDM 4 oil).