Multi-attribute Decision Research Articles

A Relative Projection-Based Multiattribute Group Decision-Making Model With Noncooperative Behavior Management and Application to NEV Supplier Selection.

This article reports a novel consensus model where a group of internal and external experts evaluate alternatives under multiple attributes and provide mutual evaluations. First, different from previous studies, the cognitive and interest conflicts of internal and external experts are considered simultaneously. But interest conflict is emphasized for internal experts, and cognitive conflict is mainly considered for external experts. Second, we explore the categorization and management methods of noncooperative behaviors (NCBs) of experts. The relative projection-based indexes are proposed for the first time to measure the degrees of cognitive and interest conflicts by using multiattribute preference matrices (MAPMs) and the weight vectors of attributes. A group of experts are divided into three categories and the corresponding management strategies are developed. Third, we investigate the consensus mechanism among experts with cognitive and interest conflicts. For reaching an acceptable consensus level, an adjustment process is proposed to revise some local entries in MAPMs and mutual evaluation matrix (MEM). A penalty mechanism is further established to dynamically update the weights of experts. An algorithm is designed to capture the consensus reaching process in multiattribute group decision making, where internal and external experts are distinguished by proposing a parameter. Finally, the high-performance battery supplier selection of new energy vehicle is studied to illustrate the proposed model. The results reveal that the efficiency of reaching consensus can be enhanced by using the developed model with effective management of NCBs.

Bounded rationality consensus reaching process with regret theory and weighted Moment estimation for multi-attribute group decision making

Bounded rationality consensus reaching process with regret theory and weighted Moment estimation for multi-attribute group decision making

Robust Multilinear Target-Based Decision Analysis Considering High-dimensional Interactions

The Multilinear Target-based Preference Functions (MTPFs) support multi-attribute decision problems characterized by attribute interactions and targets. However, existing research falls short in flexibly modeling high-dimensional interactions and lacks robustness in decision-making recommendations when faced with uncertain parameters and targets. The paper proposes a robust multilinear target-based decision analysis framework considering high-dimensional interactions and uncertainty in parameters and targets. First, the necessity of high-dimensional interactions and the limitations of available MTPFs in modeling high-dimensional interactions are demonstrated. Second, the MTPFs based on the 2-interactive fuzzy measure and the Nonmodularity index are proposed to model the high-dimensional interactions, while simultaneously reducing the computational challenges of parameter identification. Third, new descriptive measures are proposed based on the Stochastic Multicriteria Acceptability Analysis to evaluate the robustness of decision recommendations subject to uncertain targets and parameters. The validation and advantages of the framework are illustrated with simulation studies and an application in customer competitive evaluation of smart thermometer patches.

A parametric similarity measure for neutrosophic set and its applications in energy production

As a useful tool for managing ambiguous and inconsistent data, the Single Value Neutrosophic Set (SVNSs) is an extension of both Fuzzy Sets (FSs) and Intuitionistic Fuzzy Sets (IFSs). In the field of information theory, metrics like similarity, entropy, and distance are important. Although a number of entropy measures for SVNSs have been put forth and used in real-world situations, both academic research and real-world applications have pointed out certain drawbacks. Additionally, the Similarity Measures (SMs) is a useful instrument for determining how similar any two fuzzy values are to one another. The distance between the values allows the current SMs to evaluate the similarity. However, due to a few characteristics and intricate value operations, there are irrational and nonsensical cases. To deal with these preposterous cases, this paper proposed a parametric similarity measure in view of three parameters m1,m2,m3 in which decision makers can obtain the appropriate SMs by changing parameters with different decision styles. Furthermore, we analyze some existing SMs from a mathematical perspective and demonstrate the success of the proposed SMs using mathematical models. Ultimately, we apply the suggested SMs to resolve the Multi-Attribute Decision-Making (MADM) problems. We learn from the correlation and analysis that the suggested SM outperforms certain other SMs that are based on the SVNSs.

Optimizing Multi-attributive Decision-making: A Novel Approach through Matrix Theory and Fuzzy Hypersoft Sets

Optimizing Multi-attributive Decision-making: A Novel Approach through Matrix Theory and Fuzzy Hypersoft Sets

Optimizing underwater connectivity through multi-attribute decision-making for underwater IoT deployments using remote sensing technologies

The underwater Internet of Things (UIoT) and remote sensing are significant for biodiversity preservation, environmental protection, national security, disaster assistance, and technological innovation. Assigning tasks to autonomous underwater vehicles (AUVs) is a fundamental challenge in underwater technology and exploration. Remote sensing and AUVs are vital for pollution detection, disaster prevention, marine observation, and ocean monitoring. This work presents an optimized network connectivity using a multi-attribute decision-making approach for underwater IoT deployment. A feature engineering approach highlights the significant characteristics of underwater things, incorporating remote sensing data, and a multi-objective optimization method is used to select optimal UIoT for effective task allocation in deep-sea environments. A balance between data transmission, energy economy, and operational performance is necessary for efficient task distribution. Effective communication algorithms and protocols are needed to maintain environmental sustainability, protect marine ecosystems, and improve underwater monitoring enhanced by remote sensing technologies. Multi-criteria decision-making (MCDM) is beneficial for addressing various challenges in underwater technology, considering factors such as mission objectives, energy efficiency, environmental conditions, vehicle performance, safety, and much more. The proposed criteria importance through intercriteria correlation (CRITIC) methodology will assess technical competencies like communication, resilience, navigation, and safety in an underwater environment, leveraging remote sensing and aiding decision-makers in selecting appropriate undersea devices and vehicles for enhancing communication and transportation. This method prioritizes characteristics and aligns them with specific objectives, improving decision-making quality in the marine environment.

A variant-informed decision support system for tackling COVID-19: a transfer learning and multi-attribute decision-making approach.

The global impact of the COVID-19 pandemic, characterized by its extensive societal, economic, and environmental challenges, escalated with the emergence of variants of concern (VOCs) in 2020. Governments, grappling with the unpredictable evolution of VOCs, faced the need for agile decision support systems to safeguard nations effectively. This article introduces the Variant-Informed Decision Support System (VIDSS), designed to dynamically adapt to each variant of concern's unique characteristics. Utilizing multi-attribute decision-making (MADM) techniques, VIDSS assesses a country's performance by considering improvements relative to its past state and comparing it with others. The study incorporates transfer learning, leveraging insights from forecast models of previous VOCs to enhance predictions for future variants. This proactive approach harnesses historical data, contributing to more accurate forecasting amid evolving COVID-19 challenges. Results reveal that the VIDSS framework, through rigorous K-fold cross-validation, achieves robust predictive accuracy, with neural network models significantly benefiting from transfer learning. The proposed hybrid MADM approach integrated approaches yield insightful scores for each country, highlighting positive and negative criteria influencing COVID-19 spread. Additionally, feature importance, illustrated through SHAP plots, varies across variants, underscoring the evolving nature of the pandemic. Notably, vaccination rates, intensive care unit (ICU) patient numbers, and weekly hospital admissions consistently emerge as critical features, guiding effective pandemic responses. These findings demonstrate that leveraging past VOC data significantly improves future variant predictions, offering valuable insights for policymakers to optimize strategies and allocate resources effectively. VIDSS thus stands as a pivotal tool in navigating the complexities of COVID-19, providing dynamic, data-driven decision support in a continually evolving landscape.

Leveraging online reviews and expert opinions for electric vehicle type prioritization

The rapid expansion of the electric vehicle (EV) market in recent years has presented manufacturers with the challenge of strategically prioritizing the types of EVs in which to invest under uncertain conditions. This study proposes an enhanced multi-attribute decision-making (MADM) framework to address this issue by leveraging online reviews and expert opinions. The proposed framework combines a novel linguistic representation, called calibrated basic uncertain linguistic information (CBULI), to capture uncertainty, a value function from cumulative prospect theory (CPT) with double reference points to model the manufacturers’ psychological preferences, and the Preference Ranking Organization Method for Enrichment of Evaluations II (PROMETHEE II) for prioritizing EVs for investment. It extracts demand attributes from online reviews, applies CBULI to represent uncertain evaluations, and incorporates CPT to capture risk preferences. A case study of an EV manufacturing enterprise in Sichuan, China, was conducted to validate our framework, and the results demonstrated its effectiveness and practicability in identifying the most promising types of EVs for investment. The results of sensitivity and comparative analyses further confirmed the robustness and superiority of the model in comparison with prevalent methods. The work here contributes to methodological advancements in research on the choice of types of EVs in which to invest, and provides valuable insights for EV enterprises to make informed investment-related decisions that are aligned with user demands and enterprise development. The proposed MADM framework supports the strategic development of the EV industry by enabling manufacturers to prioritize investment in the appropriate types of EVs under uncertainty.

A multi attribute decision making framework based on partitioned dual Maclaurin symmetric mean operators under Fermatean fuzzy environment

Abstract In information aggregation, the Maclaurin symmetric mean (MSM) operator has drawn a lot of interest to the researchers. And, partitioned dual MSM (PDMSM) has a precondition that all attributes are grouped into several partitions and the attributes in the same partition are relevant to other attributes in the same group, while the attributes located in different groups have no relation. The Fermatean fuzzy set (FFS), on the other hand, is a potent mathematical model that effectively manages uncertain data. The existing FFS-based multi attribute decision making (MADM) techniques fail to evaluate the partitions of the relative attributes, the interdependencies between various criteria, and the ability to mitigate the detrimental impacts of irrelevant criteria. Motivated by these issues, this paper proposes novel operators named FFPDMSM and weighted FFPDMSM to handle the scenarios where criteria are divided into distinct parts and there are interconnections among multiple criteria within the same part. The proposed operators deal not only with interrelationships between criteria but also with partitioned relationships among criteria. Some properties of the proposed operators are discussed in detail. Further, an MADM approach is developed based on the proposed operators in the FF environment. A realistic numerical illustration with sensitivity analysis is demonstrated to validate the proposed approach. Finally, the method is compared with different existing techniques to demonstrate the proposed method’s applicability and feasibility.

A Generalized Method for Deriving Steady-State Behavior of Consistent Fuzzy Priority for Interdependent Criteria

Interdependent criteria play a crucial role in complex decision-making across various domains. Traditional methods often struggle to evaluate and prioritize criteria with intricate dependencies. This paper introduces a generalized method integrating the analytic network process (ANP), the decision-making trial and evaluation laboratory (DEMATEL), and the consistent fuzzy analytic hierarchy process (CFAHP) in a fuzzy environment. The Drazin inverse technique is applied to derive a fuzzy total priority matrix, and we normalize the row sum to achieve the steady-state fuzzy priorities. A numerical example in the information systems (IS) industry demonstrates the approach’s real-world applications. The proposed method derives narrower fuzzy spreads compared to the past fuzzy analytic network process (FANP) approaches, minimizing objective uncertainty. Total priority interdependent maps provide insights into complex technical and usability criteria relationships. Comparative analysis highlights innovations, including non-iterative convergence of the total priority matrix and the ability to understand interdependencies between criteria. The integration of the FANP’s network structure with the fuzzy DEMATEL’s influence analysis transcends the capabilities of either method in isolation, marking a significant methodological advancement. By addressing challenges such as parameter selection and mathematical complexity, this research offers new perspectives for future research and application in multi-attribute decision-making (MADM).

Intuitionistic Linguistic EDAS Method with New Score Function: Case Study on Evaluating Universities’ Innovation and Entrepreneurship Education

Intuitionistic linguistic numbers (ILNs) describe expert evaluation information by representing semantic assessment values and reflecting the confidence level and hesitation of decision-makers. ILNs are widely used to handle uncertain and incomplete information. The Evaluation Based on Distance from Average Solution (EDAS) method selects the optimal solution based on the distance of each alternative from the average solution, making it suitable for multi-attribute decision-making with conflicting attributes. This study proposes a new scoring function for ILNs and develops an evaluation method combining ILNs with EDAS (IL-EDAS). Experts’ evaluations of each alternative’s indices are expressed using ILNs, and the EDAS method ranks the alternatives to select the optimal solution. We apply this method to assess innovation and entrepreneurship education capabilities in universities, and compare the results with those from other methods to verify their applicability and practicality.

A spherical Z-number multi-attribute group decision making model based on the prospect theory and GLDS method

Multi-attribute group decision-making is an important research field in decision science, and its theories and methods have been widely applied to engineering, economics and management. However, as the information embedded volume and complexity of decision-making expand, the diversity and heterogeneity of decision-making groups present significant challenges to the decision-making process. In order to effectively address these challenges, this paper defines the concept of spherical Z-number, which is a fuzzy number that takes into account a wide range of evaluation and its reliability. Additionally, a group decision-making model in a spherical Z-number environment is proposed. First, an objective phased tracking method is used to determine expert weights, maintain the consistency in decision-making group evaluations. The gained and lost dominance score method is combined with prospect theory to integrate expert psychological behavior when facing risks. The proposed method considers both group utility and individual regret, and balances the gains and losses of various options in the decision-making process. Finally, in response to the 3R principle, the model is employed to address the shared e-bike recycling supplier selection problem and to assess the viability of the decision-making outcomes. The results demonstrate that the model is robust in the context of varying parameter configurations. Moreover, the correlation coefficients between its ranking outcomes and those of alternative methodologies are all above 0.77, and its average superiority degree is 1.121, which is considerably higher than that of other methods. Consequently, the model's effectiveness and superiority are substantiated.

Multi-attribute group decision making based on p, q-quasirung orthopair fuzzy Yager prioritized weighted geometric aggregation operator of p, q-quasirung orthopair fuzzy numbers

Multi-attribute group decision making based on p, q-quasirung orthopair fuzzy Yager prioritized weighted geometric aggregation operator of p, q-quasirung orthopair fuzzy numbers

Multi-Attribute Decision-Making Based on Consistent Bidirectional Projection Measures of Triangular Dual Hesitant Fuzzy Set

Multi-Attribute Decision-Making Based on Consistent Bidirectional Projection Measures of Triangular Dual Hesitant Fuzzy Set

A Group Intuitionistic Fuzzy Exponential TODIM Method Considering Attribute Interactions Applied to Green Building Material Supplier Selection

Green building, driven by the goal of sustainable development, has prompted extensive attention to be paid to the environmental impact of its materials. However, some of the traditional methods of evaluating building material suppliers and attribute systems are not able to adapt to the new issues arising from the green context. This paper aims to provide a new solution for selecting green building material suppliers to enhance the green efficiency of buildings. Specifically, this paper presents a framework for evaluating and selecting suppliers of green building materials that meet the criteria of environmental friendliness and sustainability. A comprehensive evaluation attribute system is established, encompassing cost, quality, service level, delivery capability, and green and sustainable ability. Additionally, a group decision-making method based on the exponential TODIM (an acronym in Portuguese for Interactive and Multi-attribute Decision Making) and intuitionistic fuzzy numbers is developed to integrate expert opinions from diverse domains. Intuitionistic fuzzy numbers represent an extension of traditional fuzzy sets, offering a means of more fully and accurately responding to the inherent vagueness and hesitancy of human thinking. They can often prove invaluable when faced with problems containing uncertainty. Moreover, to obtain more precise attribute weights, the λ-fuzzy measure, Choquet integral, and Shapley value are employed to consider attribute interactions. Subsequently, a selection case involving six timber suppliers was proposed. Subsystem analysis was employed to ascertain the relative strengths and weaknesses of the various suppliers, with a view to facilitating future improvements. The findings indicated that green and sustainability capability attributes exert a considerable influence on the selection of green building material suppliers. Consequently, suppliers distinguished under this standard may encounter challenges in attaining exemplary rankings. Comparative analysis and robustness analysis have demonstrated the efficacy, superiority, and stability of the proposed framework. The findings of this paper can provide a reference for companies engaged in or planning to develop green buildings and help them choose green building material suppliers, which can help them achieve the expected green building efficiency and promote the sustainable development of the industry.

Selection of AI Architecture for Autonomous Vehicles Using Complex Intuitionistic Fuzzy Rough Decision Making

The advancement of artificial intelligence (AI) has become a crucial element in autonomous cars. A well-designed AI architecture will be necessary to attain the full potential of autonomous vehicles and will significantly accelerate the development and deployment of autonomous cars in the transportation sector. Promising autonomous cars for innovating modern transportation systems are anticipated to address many long-standing transporting challenges related to congestion, safety, parking, and energy conservation. Choosing the optimal AI architecture for autonomous vehicles is a multi-attribute decision-making (MADM) dilemma, as it requires making a complicated decision while considering a number of attributes, and these attributes can have two-dimensional uncertainty as well as indiscernibility. Thus, in this framework, we developed a novel mathematical framework “complex intuitionistic fuzzy rough set” for tackling both two-dimensional uncertainties and indiscernibility. We also developed the elementary operations of the deduced complex intuitionistic fuzzy rough set. Moreover, we developed complex intuitionistic fuzzy rough (weighted averaging, ordered weighted averaging, weighted geometric, and ordered weighted geometric) aggregation operators. Afterward, we developed a method of MADM by employing the devised operators and investigated the case study “Selection of optimal AI architecture for autonomous vehicles” to reveal the practicability of the devised method of MADM. Finally, to reveal the dominance and supremacy of our proposed work, a benchmark dilemma was used for comparison with various prevailing techniques.

Multiattribute decision-making based on TOPSIS technique and novel correlation coefficient of q-rung orthopair fuzzy sets

Multiattribute decision-making based on TOPSIS technique and novel correlation coefficient of q-rung orthopair fuzzy sets

Graph model for conflict resolution with hybrid information based on prospect theory and PROMETHEE method and its application in water resources conflict

Water resources are crucial for national economic growth, but industrial development has led to severe pollution, sparking conflicts between government and enterprises over their responsibilities. Addressing these water pollution conflicts is urgent. The Graph Model for Conflict Resolution (GMCR) offers a simple and logical approach to analyzing such conflicts with minimal data. However, current models overlook decision makers’ (DMs) heterogeneity, bounded rationality, and the theoretical foundation for preference ranking. To overcome these limitations, this study integrates hybrid information to account for DMs’ diverse knowledge and experience. Prospect theory is employed to capture DMs’ bounded rationality, enhancing the accuracy of decision outcomes. The PROMETHEE method is introduced for preference ranking during GMCR modeling, guided by multi-attribute decision-making (MADM) theory. Additionally, the four basic stability concepts are redefined and proven, expanding GMCR’s theoretical applications. Finally, a case study of the Huangshui River water conflict and the comparative analysis demonstrate the model’s validity and advantages.

Optimization Design of a Novel Permanent Magnet Linear-Rotary Generator for Offshore Wind-Wave Combined Energy Conversion Based on Multi-Attribute Decision Making

Optimization Design of a Novel Permanent Magnet Linear-Rotary Generator for Offshore Wind-Wave Combined Energy Conversion Based on Multi-Attribute Decision Making

A two-stage emergency supplies procurement model based on prospect multi-attribute three-way decision

A two-stage emergency supplies procurement model based on prospect multi-attribute three-way decision