Multiple Attribute Decision Making Research Articles

A novel perspective on the selection of an effective approach to reduce road traffic accidents under Fermatean fuzzy settings.

Road traffic accidents (RTAs) pose a significant hazard to the security of the general public, especially in developing nations. A daily average of more than three thousand fatalities is recorded worldwide, rating it as the second most prevalent cause of death among people aged 5-29. Precise and reliable decisionmaking techniques are essential for identifying the most effective approach to mitigate road traffic incidents. This research endeavors to investigate this specific concern. The Fermatean fuzzy set (FFS) is a strong and efficient method for addressing ambiguity, particularly when the concept of Pythagorean fuzzy set fails to provide a solution. This research presents two innovative aggregation operators: the Fermatean fuzzy ordered weighted averaging (FFOWA) operator and the Fermatean fuzzy dynamic ordered weighted geometric (FFOWG) operator. The salient characteristics of these operators are discussed and important exceptional scenarios are thoroughly delineated. Furthermore, by implementing the suggested operators, we develop a systematic approach to handle multiple attribute decisionmaking (MADM) scenarios that involve Fermatean fuzzy (FF) data. In order to show the viability of the developed method, we provide a numerical illustration encompassing the determination of the most effective approach to alleviate road traffic accidents. Lastly, we conduct a comparative evaluation of the proposed approach in relation to a number of established methodologies.

Fuzzy-AHP based optimal RSU deployment (Fuzzy-AHP-ORD) approach using road and traffic analysis in VANET

Vehicular Ad Hoc Network (VANET) is a proven technology in Intelligent Transportation Systems (ITS) that provides various safety and non-safety applications. The Roadside Unit (RSU) is one of the essential components of the VANET, which allows vehicles to disseminate and exchange safety and infotainment information in a broader range. RSUs are also valuable for offloading the internet data traffic of vehicular users from cellular networks. However, due to high deployment and maintenance costs and security overhead, it is essential to optimally deploy these RSUs and place them in prominent places to increase the overall network coverage and efficiency. The existing deployment strategies either analyzed the road network on one or a few topological parameters or vehicular traffic parameters and considered equal or random weights for each parameter. None of them deal with the uncertainty of parameter weightage. Thus, this paper proposes a Fuzzy-Analytic Hierarchy Process-based optimal RSU deployment (Fuzzy-AHP-ORD) approach to finding the highly influential intersection nodes for RSU deployment. The Fuzzy-AHP-ORD considers different static and dynamic parameters concerning road topology and the vehicle’s traffic behavior. The weights of the parameter are calculated through Fuzzy-AHP, then the various intersection nodes of the road networks are ranked using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) based MADM (Multiple Attribute Decision Making) method. The simulation results reveal the effectiveness of the proposed method in comparison to other benchmark methods in terms of coverage ratio, connection time, packet delivery ratio, and delay.

A hybrid multi-attribute decision-making model for the systematic evaluation of exoticism-themed retail spaces from the perspective of consumer experience

Previous studies have provided abundant evidence confirming the significant impact of the physical environment in retail spaces themed with exoticism on brand establishment, consumer attraction, and stimulation of consumption behavior. However, there has been limited research employing systematic approaches to examine the influence of physical environmental elements on consumer experiences in Exoticism-themed retail spaces. Therefore, this study aims to propose a comprehensive and systematic evaluation framework from the perspective of consumer experience. It integrates the Caption Evaluation Method (CEM), Kano model, and Decision Making Trial and Evaluation Laboratory (DEMATEL) technique into a Multiple Attribute Decision Making (MADM) model. This study selects cases from China to demonstrate the superiority of the model. The research findings suggest that in the development and renovation process of integrating exoticism elements into retail environments, more attention should be paid to infrastructure such as street layout and facilities. Real plants, in particular, have the greatest primary influence on other elements. Theoretically, it offers a novel perspective on decision-making concerning the enhancement of the physical environment system in Exoticism-themed retail spaces. Practically, the analytical model developed in this study can provide decision-making insights and experiential references for similar themed retail spaces globally.

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.

A novel framework for single-valued neutrosophic MADM and applications to English-blended teaching quality evaluation

Abstract In the context of “Internet plus,” college English-teaching resources are increasingly rich. Research has found that implementing a blended teaching model for college English based on the Super Star Learning Communication and Rain Classroom online teaching platform is beneficial for improving students’ enthusiasm and continuity in English learning. The English-blended teaching quality evaluation is a multiple attribute decision making (MADM). The single-valued neutrosophic set (SVNS) is a useful tool to depict uncertain information during the English-blended teaching quality evaluation. In such an article, the single-valued neutrosophic number Aczel–Alsina power geometric (SVNNAAPG) operator is produced based on the Aczel–Alsina operations and classical power geometric operator under SVNSs. The SVNNAAPG operator is built for MADM. Eventually, an example about English-blended teaching quality evaluation and some selected comparative analysis was used to depict the SVNNAAPG technique.

Navigating the Maze of Urban Voids: A Hybrid MCDM Approach for Site Selection for Urban Poor in PCMC, India

The rapid pace of migration has incurred a higher demand and a lower supply ratio of economically weaker section (EWS) housing, prompting the emergence of illegal squatters across several Indian cities, especially on public reservation lands, turning them into urban voids. Numerous government initiatives, at the central, state, and urban local body levels, have been attempted to provide housing to the urban poor living in slums, predominantly through in-situ redevelopment. However, the efforts lagged for several reasons. One of the major reasons is the lack of a methodical process for the logical selection of available slum sites for rehabilitation or redevelopment. This creates a challenging situation for the decision makers to prioritize these sites, as currently it is based on the notification date of slums and is majorly driven by political will. Hence, this research attempted to formulate a prioritization model for the selection of slum sites in the PCMC area by evaluating them with parameters derived from expert opinion. The Hybrid Multiple Attribute Decision Making (MADM) model, using weights derived from Shannon’s entropy, and ranking performed using the TOPSIS method were considered for prioritization of the slum sites. This unbiased scientific process will guide the decision makers in the appropriate allocation of available resources to uplift the urban poor living in slums, which is crucial for the sustainable urban development of India.

Multi-Attribute Decision-Making Method in Preventive Maintenance of Asphalt Pavement Based on Optimized Triangular Fuzzy Number

By choosing the right pavement maintenance plan, we can reduce resource utilization, reduce environmental pollution, and extend road life, which is important for improving social sustainability. At present, the selection of road maintenance programs mostly adopts multiple attribute decision-making (MADA), in particular, the analytic hierarchy process (AHP) is often used. However, this method needs to use expert scoring data, which leads to strong subjectivity and poor reliability. Therefore, it reduces the science of road maintenance scheme selection. In order to reduce the subjectivity of the score and obtain a more suitable road maintenance scheme, this paper applies a multi-criteria decision-making method that characterizes attribute information by triangular fuzzy numbers (TFN) in the discrete decision space. Firstly, we invite experts to score the importance of the selection of pavement preventive maintenance technical solutions with respect to the indicators affecting the selection of solutions. Secondly, the two indicators of similarity and reliability are used to quantitatively evaluate the indicators and programs, respectively. Finally, we compare the weighted programs according to the overall possibility degree of each program. In actual cases, the overall possibility degree of each scheme obtained by this method is 1.0002–0.0477, and the optimal solution is fog sealing technology. The decision-making model applied in this paper can be considered in multiple dimensions, which can scientifically reduce the subjectivity of expert scoring. The best maintenance plan can also be quickly obtained through the simple calculation method in this paper.

A Review on Picture Fuzzy Aggregation Operators

Multiple Attribute Decision Making (MADM) is a fundamental concept in the scope of the decision sciences, serving as a structured method for evaluating and selecting the most appropriate alternative option from a pool of available alternatives. MADM methods have gained prominence over the years and are frequently applied in real-world scenarios. Nevertheless, decision-making in practical situations often involves information that is imprecise and uncertain, especially conflicting criteria or attributes. Therefore, Picture Fuzzy Sets (PFSs) and Aggregation Operators (AOs) have proven invaluable in effectively addressing decision challenges characterized by impression and uncertainty. During the past few years, various Picture Fuzzy Aggregation Operators (PFAOs) have been suggested and established but have not been thoroughly reviewed. The primary highlight of this research is to analyse as well as review the development and proposals surrounding PFAOs and their diverse applications within the decision-making paradigm. Regarding this, a review of 140 published articles from 2017 to 2022 appeared in 48 high-ranking journals cited from the "Scopus" and "Web of Science" databases. Other than that, all these articles have been classified by the nationalities of authors, publication year, published journal, research area, operators and methods. The findings of this study discovered that PFAOs have been increasingly applied for supporting decisions due to their frequent implications and applications in different managerial domains, either profit or non-profit organizations. This literature survey's significant contribution provides a platform for researchers to identify future dimensions of works as improvements for decision-making in picture fuzzy environments while also promoting future application of the approaches.

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.

Research on the urban green transportation development level evaluation based on the triangular pythagorean fuzzy multiple attribute decision making

With the acceleration of urbanization and the significant improvement of people’s living standards, the motorization of urban transportation in China has developed rapidly, and the number of urban motor vehicles has sharply increased. This has also caused a series of problems such as increasingly severe urban road traffic congestion, increased traffic energy consumption, and atmospheric environmental pollution. Unprecedented social and environmental pressures have put forward higher requirements for the development model of urban transportation. Against the backdrop of increasingly severe conflicts between urban transportation and resource environment in China, green transportation with the goal of “meeting maximum demand with minimum consumption” has gradually received widespread attention from the academic community. The urban green transportation development level evaluation is a classical multiple attribute decision making (MADM). In this paper, we define the triangular Pythagorean fuzzy sets (TPFSs) and investigate the MADM problems under TPFSs. Based on the traditional geometric BM (GBM) operator and generalized weighted GBM (GWGBM) operator, some triangular Pythagorean fuzzy operators are proposed: triangular Pythagorean fuzzy generalized GBM (TPFGGBM) operator and triangular Pythagorean fuzzy generalized WGBM (TPFGWGBM) operator. Accordingly, we have took advantage of these operators to develop some approaches to work out the triangular Pythagorean fuzzy MADM. Ultimately, a practical example for urban green transportation development level evaluation is took advantage of to validate the developed approach, and an influence analysis of the parameter on the final results is been presented to attest its availability and validity.

Modified TODIM-TOPSIS technique for type-2 neutrosophic number multiple-attribute decision-making and applications to innovation and entrepreneurship education evaluation in vocational colleges

Innovation and entrepreneurship education is an important component of cultivating the comprehensive quality of college students and an important force in promoting economic and social development. Meanwhile, due to changes in the social environment and economic structure, traditional university education is no longer able to meet the needs of contemporary society. Therefore, innovation and reform of innovation and entrepreneurship education for college students are urgent. Innovation and entrepreneurship education for college students needs to keep up with the times, constantly update concepts and techniques, in order to adapt to the ever-changing social and economic environment. The innovation and entrepreneurship education evaluation in the application-oriented vocational colleges is a multiple-attribute decision-making (MADM) problem. Recently, the TODIM and TOPSIS technique has been used to cope with MADM issues. The Type-2 neutrosophic numbers (T2NNs) are employed as a technique for characterizing uncertain information during the innovation and entrepreneurship education evaluation in the application-oriented vocational colleges. In this paper, the Type-2 neutrosophic number TODIM-TOPSIS (T2NN-TODIM-TOPSIS) technique is implemented to solve the MADM under T2NNs. Finally, a numerical case study for innovation and entrepreneurship education evaluation in the application-oriented vocational colleges and several comparative analysis is implemented to validate the proposed T2NN-TODIM-TOPSIS technique. The main research contribution of this paper is managed: (1) the TODIM and TOPSIS technique was enhanced with T2NNs; (2) Entropy technique is enhanced to manage the weight values with T2NNs. (3) the T2NN-TODIM-TOPSIS technique is founded to manage the MADM with T2NNs; (4) Algorithm framework for innovation and entrepreneurship education evaluation in the application-oriented vocational colleges and several comparative analysis are constructed based on one numerical example to verify the effectiveness of the T2NN-TODIM-TOPSIS technique.

Enhanced cross-entropy framework for multiple-attribute decision-making with type-2 neutrosophic number and applications to cross-border e-commerce logistics service providers evaluation

In recent years, the lack of coordination in cross-border logistics has been one of the challenges and challenges faced by cross-border e-commerce. As the primary link in cross-border logistics, the selection of logistics service providers is an important foundation for promoting the development of cross-border e-commerce, and also a key link in improving the competitiveness of cross-border e-commerce enterprises. How to choose suitable and effective cross-border e-commerce logistics service providers has important theoretical significance and practical application value. The cross-border e-commerce logistics service providers evaluation is a multiple-attributed decision-making (MADM) problem. In this paper, the Type-2 neutrosophic number cross-entropy (T2NN-CE) technique is designed with help of cross-entropy and Type-2 neutrosophic number (T2NN). Furthermore, Then, T2NN-CE technique is built to solve the MADM. Finally, a numerical example for cross-border e-commerce logistics service providers evaluation is given and some comparisons are conducted to illustrate advantages of the designed T2NN-CE technique. The research contribution of the paper is outlined: (1) The T2NN-CE is managed under T2NNs; (2) the T2NN-CE method is implemented for MADM under T2NNs; (3) the T2NN-CE technique for cross-border e-commerce logistics service providers evaluation is constructed and were compared with some existing techniques; (4) Through the comparison, it is known that T2NN-CE technique for cross-border e-commerce logistics service providers evaluation is effective.

Evaluation of Node Importance in Collaborative Network of Traditional Manufacturing Enterprises Based on Multiple Attribute Decision Making

The construction and operation of collaborative production networks based on multi-subject collaboration is an important path and means for enterprises to adapt to personalized, diversified, and differentiated market demand. It is of great practical significance to identify the key collaborative subjects in the collaborative network and protect and maintain them to ensure its normal operation. To identify the key collaborative subjects in the collaborative network of traditional manufacturing enterprises, this paper proposes a method for identifying and evaluating the importance of nodes in traditional manufacturing enterprise collaborative networks. Firstly, the method uses four parameters, degree centrality, betweenness centrality, closeness centrality, and subgraph centrality, as node importance evaluation indexes, based on complex network theory. Secondly, the coefficient of variation method (CVM) is used to calculate the weights of evaluation indexes. The Multiple Attribute Decision Making (MADM) based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is then used to comprehensively evaluate node importance and identify key nodes (key collaborative subjects) in the network. Finally, the proposed method's effectiveness, rationality, and scientific nature are verified by using the measurement index of network connectivity in combination with specific enterprise cases. The results show that the failure of key nodes has a more significant impact on network connectivity. Therefore, the node importance evaluation method based on Multiple Attribute Decision Making has better performance. It helps traditional manufacturing enterprises to focus on the protection and maintenance of the key collaborative subjects when coping with the competitive environment of the external market and provides a valuable reference for the normal operation of collaborative network organizations.

Risk measurement of aggregation approaches in multiple attribute decision making under uncertain information

The decision-maker's judgment deviates from uncertain attribute information will lead to decision risk in multiple attribute decision making (MADM), and different aggregation approaches result in different risk levels. This paper aims to study the risk levels of aggregation operators in MADM with uncertain attribute information. We use the signal detection theory to characterize the decision-maker's noisy perceptions of multiple attributes to present his/her judgment deviation. Then, we establish the aggregation models to aggregate these perceptions based on the weighted averaging (WA) and the ordered weighted averaging (OWA) operators. Furthermore, a risk measurement model of each aggregation approach is constructed to measure the risk levels of the commission risk (CR), the omission risk (OR), and the overall risk. A numerical example is used to verify the validity of the proposed model, while simulation experiments are designed to compare the risk levels of the WA and OWA operators. The results reveal that the overall risk level of the WA operator is higher than that of the OWA operator when judging the quality of the alternative with high standards; otherwise, the WA operator is lower. This finding provides a scientific reference for aggregation approach selection under uncertain information. Code metadataPermanent link to reproducible Capsule: https://doi.org/10.24433/CO.0911610.v1.

Decision algorithm for educational institute selection with spherical fuzzy heronian mean operators and Aczel-Alsina triangular norm

This article presents a novel study of spherical fuzzy sets (SFSs), a more comprehensive framework of intuitionistic fuzzy sets and picture fuzzy sets. The SFS allows the decision-makers (DMs) to cope with complicated and insufficient information during the aggregation process. The Heronian mean (HrM) model theory is also utilized to express correlation among different input arguments or characteristics. Recently, the theory of Aczel Alsina triangular norms gained a lot of attention from various research scholars and has many capabilities to provide smooth approximations during decision analysis. In this article, we developed some appropriate operations of Aczel Alsina t-norms and t-conorms in light of spherical fuzzy (SF) information. We develop new mathematical ways to look at SF data to keep clarity and sufficient information. These are the SF Aczel Alsina Heronian mean (SFAAHrM) and SF Aczel Alsina weighted Heronian mean (SFAAWHrM) operators. Furthermore, we also present a list of new strategies based on Aczel Alsina operations, such as SF Aczel Alsina geometric Heronian mean (SFAAGHrM) and SF Aczel Alsina weighted geometric Heronian mean (SFAAWGHrM) operators. Some notable properties are also characterized to show the validity and effectiveness of our derived mathematical approaches. Considering our derived strategies, an algorithm for the multiple attribute decision-making (MADM) problem is established to resolve complicated real-life applications. A numerical example presents the compatibility of derived approaches and provides a solid mechanism to improve the performance of educational institutes. A comparison technique is also demonstrated to show the applicability and consistency of diagnosed approaches by contrasting the findings of pioneered approaches with existing methodologies.

An advanced approach to operational quality evaluation for industry-finance integration enterprises based on integrated interval-valued intuitionistic fuzzy multi-attribute decision making

With the acceleration of economic globalization, enterprises are facing fierce competition and huge challenges, requiring deep financial management transformation. In this context, the integration of industry and finance has gradually demonstrated extremely important value. The integration of industry and finance can not only effectively improve the efficiency of financial management, prevent business risks, and improve operational efficiency, but also enhance the comprehensive ability of enterprise financial management, providing a more flexible, transparent, and efficient financial management system for enterprises. The operational quality evaluation of industry-finance integration enterprises under lean management accounting is a multiple-attribute decision-making (MADM). In this paper, some calculating laws on IVIFSs, Hamacher sum, Hamacher product are introduced, and the interval-valued intuitionistic fuzzy Hamacher interactive power averaging (IVIFHIPA) technique is proposed based on the interval-valued intuitionistic fuzzy (IVIF) Hamacher interactive weighted averaging (IVIFHIWA) technique and power average (PA) technique. Meanwhile, some ideal properties of IVIFHIPA technique are studied. Then, the IVIFHIPA technique is employed to cope with the MADM under IVIFSs. Finally, an example for operational quality evaluation of industry-finance integration enterprises under lean management accounting is employed to test the IVIFHIPA technique. Thus, the main research aim of this paper is concluded as follows: (1) the IVIFHIPA technique is constructed based on IVIFHIWA technique and classical power average (PA) technique; (2) the IVIFHIPA technique is put forward to cope with the MADM under IVIFSs; (3) an empirical example for operational quality evaluation of industry-finance integration enterprises under lean management accounting has been put forward to show the IVIFHIPA technique.

Clustering analysis for Pythagorean fuzzy sets and its application in multiple attribute decision making

Pythagorean fuzzy sets, as a generalization of intuitionistic fuzzy sets, have a wide range of applications in many fields including image recognition, data mining, decision making, etc. However, there is little research on clustering algorithms of Pythagorean fuzzy sets. In this paper, a novel clustering idea under Pythagorean fuzzy environment is presented. Firstly, the concept of feature vector of Pythagorean fuzzy number (PFN) is presented by taking into account five parameters of PFN, and some new methods to compute the similarity measure of PFNs by applying the feature vector are proposed. Furthermore, a fuzzy similarity matrix by utilizing similarity measure of PFNs is established. Later, the fuzzy similarity matrix is transformed into a fuzzy equivalent matrix which is utilized to establish a novel Pythagorean fuzzy clustering algorithm. Based on the proposed clustering algorithm, a novel multiple attribute decision making (MADM) method under Pythagorean fuzzy environment is presented. To illustrate the effectiveness and feasibility of the proposed technique, an application example is offered.

Optimizing market risk evaluation of small and medium sized enterprises through hamacher interactive power geometric technique under uncertainty

Compared with large enterprises, the development scale and organizational structure of small and medium-sized enterprises are insufficient, which brings certain limitations to the development of small and medium-sized enterprises in China. In order to promote the long-term development of small and medium-sized enterprises in the new era, it is necessary to require enterprise leaders to innovate marketing plans, strengthen risk management of enterprises, and enhance their strength in market competition. The market risk evaluation of small and medium sized enterprises (SMSEs) in the new era is a multiple-attribute decision-making (MADM). The IVIFSs are employed as the tool for portraying uncertain information during the market risk evaluation of SMSEs in the new era. In this paper, the interval-valued intuitionistic fuzzy (IVIF) Hamacher interactive power geometric (IVIFHIPG) technique is addressed based on IVIF Hamacher interactive weighted geometric (IVIFHIWG) technique and power geometric (PG) technique. Some properties of IVIFHIPG technique were addressed. Then, the IVIFHIPG technique is employed to manage MADM under IVIFSs. Finally, an example for market risk evaluation of SMSEs in the new era is employed to verify the IVIFHIPG technique. Thus, the main contributions of this paper are addressed: (1) the IVIFHIPG technique is addressed based on IVIFHIWG technique and PG technique; (2) the IVIFHIPG technique is came up with to manage the MADM under IVIFSs; (3) a numerical example for market risk evaluation of SMSEs in the new era has been came up with to show the IVIFHIPG technique; and (4) some comparative analysis is addressed to verify the I IVIFHIPG technique.

Method for Triangular Fuzzy Multiple Attribute Decision Making Based on Two-Dimensional Density Operator Method

Method for Triangular Fuzzy Multiple Attribute Decision Making Based on Two-Dimensional Density Operator Method

A comprehensive study for selecting optimal treatment modalities for blood cancer in a Fermatean fuzzy dynamic environment.

Cancer is characterized by uncontrolled cell proliferation, leading to cellular damage or death. Acute lymphoblastic leukemia (ALL), a kind of blood cancer, that affects lymphoid cells and is a challenging malignancy to treat. The Fermatean fuzzy set (FFS) theory is highly effective at capturing imprecision due to its capacity to incorporate extensive problem descriptions that are unclear and periodic. Within the framework of this study, two innovative aggregation operators: The Fermatean fuzzy Dynamic Weighted Averaging (FFDWA) operator and the Fermatean fuzzy Dynamic Weighted Geometric (FFDWG) operator are presented. The important attributes of these operators, providing a comprehensive elucidation of their significant special cases has been discussed in details. Moreover, these operators are utilized in the development of a systematic approach for addressing scenarios involving multiple attribute decision-making (MADM) problems with Fermatean fuzzy (FF) data. A numerical example concerning on finding the optimal treatment approach for ALL using the proposed operators,is provided. At the end, the validity and merits of the new method to illustrate by comparing it with the existing methods.