Complex Decision Situations Research Articles

Intelligent system for measuring and monitoring water levels

Abstract Measuring and controlling water levels at water management facilities is one of the important tasks. In this research work, we, the authors, have studied the available means of measuring water level. Some of their drawbacks were brought to light throughout the investigation. For instance, if you take a water level control gauge, you have to keep an eye on it all the time, and automation is challenging. These water level measurement devices are not up to date with modern standards. At present, there is a need for systems for continuous monitoring of water resources, collection of necessary information and, if necessary, delivery of processed information to the user or decision-making in complex situations. Taking this into account, it was aimed to develop an intelligent system for measuring and controlling the water level that meets the requirements of the time. As the basis of the intelligent system, the JSN SR-04T ultrasound sensor, which is resistant to various aggressive conditions and has high measurement accuracy, and the Arduino Nano board were taken. This intelligent system was tested at the facility and the developed system performed all tasks reliably and qualitatively.

SYNECTICS AS ONE WAY OF DEVELOPING SOFT SKILLS OF HIGH SCHOOL STUDENTS

This article examines the potential for fostering soft skills in high school students, aimed at preparing them for independent living, enabling quick adaptation to changing circumstances, making decisions in complex situations, working in teams, and communicating effectively with others. Many psychological challenges exist regarding the effective development of these skills in this age group.The analysis of various sources has identified synectics as an effective method for developing soft skills in high school students, as it addresses the aforementioned challenges. During a psychological experiment that included surveys and observational studies, the level of development of soft competencies within the "4K" framework—creativity, communication, critical thinking, and cooperation—was assessed using synectics techniques such as "Feedback," "Situational Modeling," "Teamwork," "Creative Thinking,""Brainstorming," "Communication," and quiz games. The implementation of synectics positively influenced the development of all soft skills among high school students in the experimental group, particularly enhancing creativity and communication abilities.Consequently, recommendations were made for the general use of synectics to develop soft skills in high school students.The study holds practical significance, as the findings can be utilized by educators to develop new psychologist methods aimed at enhancing soft skills in high school students.

A simulation optimization approach for weight valuation in analytic hierarchy process

The analytic hierarchy process (AHP) is a structured technique used to analyze complex decision-making situations such as resource allocation, benchmarking, and quality management. In the weight valuation step of using AHP to select the best design, pairwise comparison matrices are used to calculate the local priorities for designs that have contentious and unresolved criticisms. In this study, we propose a Bayesian approach using a Dirichlet-multinomial model to estimate local priorities during weight valuation. Experts are only asked to select the best design with respect to predetermined criterion. Subsequently, local priorities are estimated without pairwise comparison matrices. To improve the efficiency of the AHP, we propose two expert allocation policies (AHP-KG and AHP-AKG) based on the ranking and selection procedures. Our numerical results show that the proposed AHP-KG and AHP-AKG policies outperform pure exploration and proportional allocation policies.

Stochastic calculus-guided reinforcement learning: A probabilistic framework for optimal decision-making

Stochastic Calculus-guided Reinforcement learning (SCRL) is a new way to make decisions in situations where things are uncertain. It uses mathematical principles to make better choices and improve decision-making in complex situations. SCRL works better than traditional Stochastic Reinforcement Learning (SRL) methods. In tests, SCRL showed that it can adapt and perform well. It was better than the SRL methods. SCRL had a lower dispersion value of 63.49 compared to SRL's 65.96. This means SCRL had less variation in its results. SCRL also had lower risks than SRL in the short- and long-term. SCRL's short-term risk value was 0.64, and its long-term risk value was 0.78. SRL's short-term risk value was much higher at 18.64, and its long-term risk value was 10.41. Lower risk values are better because they mean less chance of something going wrong. Overall, SCRL is a better way to make decisions when things are uncertain. It uses math to make smarter choices and has less risk than other methods. Also, different metrics, viz training rewards, learning progress, and rolling averages between SRL and SCRL, were assessed, and the study found that SCRL outperforms well compared to SRL. This makes SCRL very useful for real-world situations where decisions must be made carefully.•By leveraging mathematical principles derived from stochastic calculus, SCRL offers a robust framework for making informed choices and enhancing performance in complex scenarios.•In comparison to traditional SRL methods, SCRL demonstrates superior adaptability and efficacy, as evidenced by empirical tests.

Research and Development of an Embedded Multi-Channel Sensor Data Acquisition Device for Reservoir Dams

The embedded system of intelligent reservoir dam achieves the integration and efficient utilization of water conservancy dam system data through multi-channel data collection and analysis calculated by computer technology, CNC system, and neural network. Compared with traditional data collection and processing methods, both timeliness and accuracy have been greatly improved. This study aims to develop a multi-channel sensor data acquisition device for reservoir dams based on embedded system technology. This device can collect real-time and efficient data from sensors in various parts of the dam, ensuring the safe operation of the reservoir dam. By using advanced embedded system technology, this device has advantages such as low power consumption, high stability, and real-time data transmission. The Analytic Hierarchy Process (AHP) was used to study the embedded multi-channel sensor data acquisition device for reservoir dams in multiple directions and factors. The AHP method provides an effective means for problem decision-making in complex situations. Referring to the AHP method, the factors that affect reservoir dams can be divided into different levels. Compare the importance of two random factors in each level to obtain a specific quantitative expression of the relative important factors on a scale. Then repeat this step to obtain the weight ranking for different levels. At the same time, the device monitors key parameters such as temperature, humidity, displacement, and pressure in various parts of the dam through multiple sensors, providing strong support for early warning and decision-making of reservoir dams. The results of this study have important practical significance and application value for improving the safety and stability of reservoir dams.

Quantenalgorithmen zur Optimierung von Produktions- und Logistikprozessen

Abstract Quantum computing is an important building block for the digitalized future of a company. Successes in recent years – in the development, construction and operation of quantum computers – have led to the achievement of a preliminary stage of industrialization. Quantum-based algorithms have the potential to solve operational decision-making problems more quickly and thus to manage complex decision-making situations dynamically. German industrial companies can already demonstrate the first concrete use cases for quantum algorithms to optimize factory processes.

Penerapan Metode EDAS dan ROC Dalam Rekomendasi Objek Wisata Pantai Terbaik

Indonesia's natural diversity, cultural heritage and history have given rise to attractive tourism destinations, including Central Java Province. Central Java offers a diversity of beaches, attracting millions of tourists every year. However, many tourists have difficulty choosing a beach tourist destination that suits their wishes. Decision Support Systems (DSS) are a solution with various methods, one of which is the Evaluation Method based on Distance from Average Solution (EDAS) which is currently used together with Rank Order Centroid (ROC) weighting. This method has been proven to be effective in complex decision situations. In this context, using 13 Alternative Beaches was taken by taking into account the highest number of visitors in each Regency/City in Central Java Province and applying criteria such as cleanliness, facilities, popularity, rating and ticket prices used for evaluation. This research aims to examine the relationship and integration of the ROC and EDAS weighting methods in recommendations for the best beach tourist attractions in Central Java. It is hoped that this study will be able to provide a better interpretation and obtain a ranking ranking of alternative beach tourist attractions in Central Java, thereby contributing to tourists making more accurate decisions according to the criteria in the context of selecting beach tourist locations. The results of the research show that Jatimalang Beach is the best alternative with the highest score of 1,000. Thus, Jatimalang Beach is declared as the best choice for the best tourist attraction.

Prioritizing factors for the adoption of IoT-based smart irrigation in Saudi Arabia: a GRA/AHP approach

The irrigation sector in the Kingdom of Saudi Arabia (KSA) confronts a range of obstacles, such as scarce water resources, the elevated salinity and alkalinity of irrigation water, inefficient irrigation practices, and inter-sectoral competition for water resources. These challenges have led to diminishing agricultural yields and abandonment of arable lands. Internet of Things (IoT)-based irrigation systems present a promising remedy for these issues. By curbing water wastage and ensuring precise water delivery to crops, IoT-based irrigation systems offer a viable solution to the challenges entrenched in traditional irrigation methodologies in KSA. However, the widespread implementation of an IoT-based Smart Irrigation System (I-SIMS) poses a multifaceted and intricate challenge in KSA. This study is focused on the identification of the factors and challenges through a systematic review and ranking of the challenges/factors that exert a significant influence on the adoption of I-SIMS. Ranking aids in determining the importance of various alternatives. It enables locating the best options that support the required objectives in complex decision situations. The study employs both Grey Relational Analysis (GRA) and Analytical Hierarchical Process (AHP) methodologies to prioritize these factors. The study’s conclusive findings indicate that among the challenges, technical expertise and security measures emerge as the foremost concerns that demand attention.

3D MODEL LOGIČKE INTEGRACIJE METAFIZIKE I UPRAVLJANJA RIZICIMA OD KATASTROFA

Science is built upon the continuous re-evaluation of evidence and empirical research driven by the desire for success and the fear of failure, while religion is based on the unconditional acceptance of original dogmatic traditions and fundamental principles of belief in God and eternal reward, coupled with the fear of divine punishment. Metaphysics, as the foundation of philosophical scientific thought, examines the nature of reality and existence, asking questions about what exists, why, in which time, space, and causality. Disaster risk management is an exact scientific discipline established on metaphysical examinations of causes and phenomena, along with mathematical models of prediction and consequence analysis. This paper explores the intriguing correlation between the logical relationships of metaphysical principles and disaster risk management. By delving into the philosophical realm of metaphysics, we analyze concepts such as uncertainty, causality, and the nature of reality. Drawing correlative connections, we examine the development of a logical matrix of cause and effect to better understand metaphysical principles that can timely alert decision-makers and improve their risk management strategies. Researching the interconnections of fundamental principles in the domains of risk, impact, and response capacity to crises offers a fresh perspective on addressing uncertainty and making informed decisions in complex situations. Conclusions will be defined using analytical-deductive and synthetic methods, comparing the relationship between metaphysics and applied science within the framework of the multimillennial challenges faced by humanity, from the time of Noah's flood to contemporary crises and approaches to resolving them. The paper will scientifically determine risks, impacts, and capacities for more effective responses to climatic and socio-political crises, challenging the professionalscientific community to proactively offer answers to the question: Can the acceptance and understanding of the 3D logical model of integrated application of metaphysics and risk management contribute to reducing the vulnerability of humankind?

Project management approaches and their selection in the digital age: Overview, challenges and decision models

Digital transformation is a challenge that also impacts the selection of tools for implementing projects. Which tools are suitable for handling complex digital twins? Project management must respond to this with suitable approaches. The challenge for decision-makers is to choose the right one. Based on literature research and a case study, influencing factors are derived and practice-relevant project management approaches are collected. Furthermore, a decision model is developed that, on the one hand, supports the decision-maker in selecting tools before and during the project, and on the other hand makes empirical values from past projects usable for future decisions. The results show that the number of influencing factors is large, and the approaches are di-verse. In complex projects, this can lead to complex decision-making situations that require appropriate decision models. The developed “Supervised Decision Model – L5” is based on five levels (L): (L1) Building a database; (L2) Derivation of algorithms; (L3) Initial approach selection; (L4) Review of the initial selection; (L5) Using experiences for future decisions. In practice it turns out that complex projects – like Digital Twins - often fail. Modified decision models for selecting suitable approaches should therefore take the following as-pects into account: (a) decision-makers are actively supported in the initial decision phase; (b) initial decisions once made are checked in the early phase of the project and corrected if necessary; (c) the lessons learned are recorded in the database as empirical value and used for future decisions.

A novel MCDM approach for design concept evaluation based on interval-valued picture fuzzy sets.

The assessment of design concepts presents an efficient and effective strategy for businesses to strengthen their competitive edge and introduce market-worthy products. The widely accepted viewpoint acknowledges this as a intricate multi-criteria decision-making (MCDM) approach, involving a multitude of evaluative criteria and a significant amount of data that is frequently ambiguously defined and subjectively influenced. In order to tackle the problems of uncertainty and fuzziness in design concept evaluation, our research creatively combines interval-valued picture fuzzy set (IVPFS) with an MCDM process of design concept evaluation. Firstly, this study draws on the existing relevant literature and the experience of decision makers to identify some important criteria and corresponding sub-criteria and form a scientific evaluation indicator system. We then introduce the essential operational concepts of interval-valued picture fuzzy numbers (IVPFNs) and the interval-valued picture fuzzy ordered weighted interactive averaging (IVPFOWIA) operator. Thirdly, an entropy weighting method based on IVPFS is proposed in this research to calculate the weights of criteria and sub-criteria, and based on this, an integrated IVPF decision matrix is further constructed based on the presented IVPFOWIA operator. Finally, the best design concept alternative is selected by applying the extended TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) approach with IVPFS. The IVPFS combined with improved MCDM method have been proven to be superior in complex and uncertain decision-making situations through experiments and comparative assessments. The information ambiguity in the evaluation of design concept is well characterized by our augmentation based on IVPFS.

Evaluation of Cloud-Based Cyber Security System

Cloud-based cyber security systems leverage the power of cloud computing to protect digital assets from cyber threats. By utilizing remote servers and advanced algorithms, these systems provide real-time monitoring, threat detection, and incident response. They offer scalable solutions, enabling businesses to adapt to evolving threats and handle increasing data volumes. Cloud-based security systems provide benefits such as reduced infrastructure costs, continuous updates and patches, centralized management, and global threat intelligence. They protect against various attacks, including malware, phishing, DDoS, and unauthorized access. With their flexibility, reliability, and ease of deployment, cloud-based cyber security systems are becoming essential for organizations seeking robust protection in today's interconnected digital landscape. The research significance of cloud-based cyber security systems lies in their ability to address the growing complexity and scale of cyber threats in today's digital landscape. By leveraging cloud computing, these systems offer several key advantages for researchers and organizations: Scalability: Cloud-based systems can scale resources on-demand, allowing researchers to handle large volumes of data and analyze complex threat patterns effectively. Cost-efficiency: The cloud eliminates the need for extensive on-premises infrastructure, reducing costs associated with hardware, maintenance, and upgrades. Researchers can allocate resources based on their needs, optimizing cost-effectiveness. Real-time monitoring and threat detection: Cloud-based systems provide real-time monitoring of network traffic, enabling quick identification of suspicious activities and potential threats. Researchers can leverage advanced analytics and machine learning algorithms to enhance threat detection capabilities. Collaboration and knowledge sharing: Cloud platforms facilitate collaboration among researchers and organizations by enabling the sharing of threat intelligence, best practices, and research findings. Compliance and regulatory requirements: Cloud platforms often offer built-in compliance features and tools to meet regulatory requirements, assisting researchers in adhering to data protection and privacy standards. Overall, the research significance of cloud-based cyber security systems lies in their ability to provide scalable, cost-effective, and advanced security capabilities, empowering researchers to mitigate evolving cyber threats and protect sensitive data and systems effectively. We will be using Weighted Product Methodology (WPM) which is a decision-making technique that assigns weights to various criteria and ranks alternatives based on their weighted scores. It involves multiplying the ratings of each criterion by their corresponding weights and summing them up to determine the overall score. This method helps prioritize options and make informed decisions in complex situations. Taken of Operational, Technological, Organizational Recorded Electronic Delivery, Recorded Electronic Deliver, Blockchain technology, Database security, Software updates, Antivirus and antimalware The Organizational cyber security measures comes in last place, while Technological cyber security measures is ranked top and Operational measures comes in between the above two in second place. In conclusion, a cloud-based cyber security system revolutionizes the way organizations safeguard their digital assets. By utilizing remote servers, advanced algorithms, and real-time monitoring, it offers scalable and robust protection against evolving threats. With features like threat detection, data encryption, and centralized management, it ensures enhanced security, agility, and efficiency. Embracing a cloud-based approach empowers organizations to stay ahead in the ever-changing landscape of cyber security, effectively safeguarding their critical data and infrastructure.

Multicriteria analysis and GIS applied to the selection of agroindustrial waste. A case study contextualized to the Ecuadorian reality

Multi-criteria decision-making is a tool that facilitates decision-making in complex situations, given multiple criteria to consider. This research proposes the integration of multi-criteria analysis and geographic information systems to establish evaluation criteria that guide the appropriate selection of waste of agro-industrial origin for its revaluation in Ecuador. The final objective is to select agricultural waste potentially available as raw material in the production of bioplastics. From the survey of the most frequently used methods in this field, a modified and adapted analytical hierarchy process (AHP) was adopted, with four criteria analyzed: Waste availability, industrialization, social-environmental impact, and geomorphological aspect. As initial materials, databases of 23 waste belonging to 11 items located from geographical coordinates extracted from satellite maps provided by the Ministry of Agriculture and Livestock of Ecuador were considered. When carrying out the weighting and respective data processing, the criterion with the greatest impact on the selection of alternatives is the social-environmental criterion, followed by the availability of the waste. To validate the method contrast a panel of experts was used. Agricultural items make up a ranking of the possibility of success according to this panel, led by bananas, followed by plantain and corn. This conclusion is concomitant with that obtained by the proposed method. The sensitivity analysis carried out indicated that the changes in the score of the industrialization criterion are the ones that most influence the results obtained in the AHP. Although the methodology used focused on the use of agroindustrial waste for the production of bioplastics, it applies to other bioproducts and even in other countries with characteristics similar to the one studied. Although AHP techniques and GIS tools are commonly used their integration as a simple and open-access tool provides local governments (mayors and municipalities) with a tool for decision-making in complex situations such as the one studied here.

A robust algorithmic framework for the evaluation of international cricket batters in ODI format based on q-rung linguistic neutrosophic quantification

This article presents a novel approach for decision-making problems in which the criteria and alternatives are evaluated under a q-rung linguistic neutrosophic set (QRLNS) environment. QRLN sets are an extension of the traditional linguistic variables, which allow more flexibility and accuracy in modeling complex decision-making situations. We introduce several QRLN weighted aggregation operators, including QRLN weighted averaging operator, QRLN weighted geometric operator, and QRLN weighted hybrid operator, which can be used to aggregate the QRLN information provided by decision-makers. The properties and characteristics of these operators are analyzed, and their performance is compared with other existing aggregation operators. Finally, this system is able to handle the uncertainty and imprecision in the data and provide a more reliable assessment of the performance of cricket players. Our study demonstrates the potential of QRLN-based approaches for ranking assessment in other fields and provides insights for future research in this area.

An extended TODIM method for hyperbolic fuzzy environments

Multi-criteria decision-making (MCDM) methods are increasingly being used to solve socio-economic problems; however, many real-world problems are characterised by high complexity and uncertainty. Hyperbolic fuzzy sets (HyFSs), a new extension of fuzzy sets, are able to deal with ambiguity effectively. They are more flexible than other fuzzy sets, and can model uncertainty better. This paper proposes a hybrid MCDM method based on the TODIM (an acronym in Portuguese for interactive and multiple attribute decision-making) approach in a hyperbolic fuzzy environment. One of the main elements of all MCDM methods is the decision matrix, but the construction of this matrix can be a subjective process. To address this issue, we propose a systematic approach based on the Kano model to make the process more objective. More specifically, we build a hyperbolic fuzzy decision matrix based on Kano-clustering of criteria for each alternative. We validate the effectiveness of our method by applying it to a sustainable supplier selection problem in the dairy industry. The results show that the proposed method can effectively deal with uncertainty and make better decisions in complex situations. We also conduct a sensitivity analysis to assess the impact of the decision-maker’s (DM's) psychological behavior on the ranking of the alternatives, and show that the proposed method is robust to these behaviors. The proposed method represents a promising approach to decision-making under conditions of uncertainty; it is easy to understand and implement, and is feasible for real-world applications.

SUPER FLEXIBLE MANAGEMENT OF AN ORGANIZATION IN CONDITIONSOF A TURBULENT ENVIRONMENT

The authors considered the definition of a turbulent environment, the need and problems of super-flexible management of organizations in the conditions of a turbulent environment. Differences between turbulent medium and elasticity, superelasticity have been considered by various scientists. The essence of super-flexible companies is to quickly change the direction of development in a short period of time, quickly adapt to different situations and make the right decisions in complex situations. This article analyzes the concept of "super-flexible" companies studied by H. Bahrami and S. Evans. Taking into account the development of the changes taking place in the world, the impact of changes on the business of Kazakhstan, according to the authors' conclusion, super-flexible companies in a turbulent environment can respond quicklyand effectively.

Exploring the superiority of human expertise over algorithmic expertise in the cognitive and metacognitive processes of decision-making among decision-makers.

Investigating the role of human vs algorithmic expertise on decision-making processes is crucial, especially in the public sector where it can impact millions of people. To better comprehend the underlying cognitive and metacognitive processes, we conducted an experiment to manipulate the influence of human and algorithmic agents on decision-makers' confidence levels. We also studied the resulting impact on decision outcomes and metacognitive awareness. By exploring a theoretical model of serial and interaction effects, we were able to manipulate the complexity and uncertainty of initial data and analyze the role of confidence in decision-making facing human or algorithmic expertise. Results showed that individuals tend to be more confident in their decision-making and less likely to revise their decisions when presented with consistent information. External expertise, whether from an expert or algorithmic analysis, can significantly impact decision outcomes, depending on whether it confirms or contradicts the initial decision. Also, human expertise proved to have a higher impact on decision outcomes than algorithmic expertise, which may demonstrate confirmation bias and other social processes that we further discuss. In conclusion, the study highlights the importance of adopting a holistic perspective in complex decision-making situations. Decision-makers must recognize their biases and the influence of external factors on their confidence and accountability.

Probabilistic dual hesitant fuzzy MAGDM method based on generalized extended power average operator and its application to online teaching platform supplier selection

Probabilistic dual hesitation fuzzy set (PDHFS) is a new tool used to enhance fuzzy and imprecise information in a changing and complex decision-making environment. Compared with the existing fuzzy sets, PDHFS is more suitable for identifying uncertain evaluation data in complex and realistic decision-making situations. The extended power average (EPA) and generalized power average (GPA) operators provide more information for the information aggregation process, reflecting the mutual support among all aggregation values. To solve the evaluation problems related to decision analysis involving extremely complex information, especially in the case of extreme data in the consideration of decision problems. In this paper, the generalized extended power average (GEPA) operator is proposed in the PDHF environment. This research has the following four important contributions. First, we propose a probability splitting algorithm of normalizing PDHFE, and give some new basic operations, score function, distance measures and aggregation operators. Second, to better integrate the advantages of EPA and GPA operators, we developed the GEPA operator, weighted form (WGEPA) and ordered form (GEPOWA). Meanwhile, by adjusting the parameter in three types of operators, we can find that many existing operators are special forms of the three types of operators. Third, extended the three types of operators to the PDHF environment, the PDHFGEPA, PDHFWGEPA, PDHFGEPOWA operators and some special aggregation operators are obtained, and studied their valuable properties in detail. Finally, taking the application of PDHFWGEPA operator as an example, a new multi-attribute group decision-making (MAGDM) technique is developed and used to solve the problem of online teaching platform supplier selection (OTPSS). Through parameter sensitivity analysis, the flexibility and stability of MAGDM technique are illustrated, and compared with some existing decision-making methods, the effectiveness of MAGDM method is proved. In addition, this technique also provides some theoretical references for dealing with other complex MAGDM problems, and provides a new operator for the extension of GEPA operator in other fuzzy environments. The biggest advantage of several operators is that they can pay full attention to the special role of extreme data in decision-making.

Generalized Similarity Operator for Intuitionistic Fuzzy Sets and its Applications Based on Recognition Principle and Multiple Criteria Decision Making Technique

Many complex real-world problems have been resolved based on similarity operators under intuitionistic fuzzy sets (IFSs). Numerous authors have developed intuitionistic fuzzy similarity operators (IFSOs) but with some setbacks, which include imprecise results, omission of hesitation information, misleading interpretations, and outright violations of metric axioms of similarity operator. To this end, this article presents a newly developed similarity operator under IFSs to ameliorate the itemized setbacks noticed with the hitherto similarity operators. To buttress the validity of the new similarity operator, we discuss its properties in alliance with the truisms of similarity. In addition, we discuss some complex decision-making situations involving car purchase selection process, pattern recognition, and emergency management using the new similarity operator based on multiple criteria decision making (MCDM) technique and recognition principle, respectively. Finally, comparative studies are presented to argue the justification of the new similarity operator. In short, the novelty of this work includes the evaluation of the existing IFSOs to isolate their fault lines, development of a new IFSO technique with the capacity to resolve the fault lines in the existing techniques, elaboration of some properties of the newly developed IFSO, and its applications in the solution of disaster control, pattern recognition, and the process of car selection for purchasing purpose based on the recognition principle and MCDM.

Multiple criteria decision analytic methods in management with T-spherical fuzzy information.

With a focus on T-spherical fuzzy (T-SF) sets, the aim of this paper is to create a split-new appraisal mechanism and an innovative decision analytic method for use with multiple-criteria assessment and selection in uncertain situations. The T-SF frame is the latest recent advancement in fuzzy settings and uses four facets (consisting of membership grades of positivity, neutrality, negativity, and refusal) to elucidate complex uncertainties, thereby evidently reducing information loss, in anticipation of fully manifesting indistinct and equivocal information. This paper adds to the body of knowledge regarding multiple criteria choice modeling by raising T-SF correlation-oriented measurements connected to the fixed and displaced ideal/anti-ideal benchmarks and by creating an approachable appraisal mechanism for advancing a T-SF decision analytic methodology. Consider, in particular, the performance ratings of available options in terms of judging criteria under the T-SF type of uncertainties. This research gives correlation-oriented measurements focusing on two varieties of maximum and square root functions in T-SF situations, which serve as a solid foundation for an efficacious appraisal mechanism from two views of anchored judgments corresponding to the fixed and displaced benchmarks. The T-SF Minkowski distance index is generated to integrate the outranking and outranked identifiers relying on correlation-oriented measurements for figuring out the local outranking and outranked indices. The T-SF decision analytic procedures are constructed using a new appraisal significance index that is founded on certain valuable insights of correlation-oriented maximizing and minimizing indices as well as global outranking and outranked indices. Additionally, a concrete location selection dilemma is dealt with in this research to showcase the applicability and efficiency of the suggested T-SF decision analytic methodology. Sensitivity analyses and comparative studies are carried out to investigate substantial modifications in pertinent parameters and to confirm the robustness of the predominance relationships among the available options. The suggested approaches are adaptable, flexible, and reliable, according to the application outcomes and comparison findings. This research provides four scientific contributions: (1) the utilization of T-SF correlation coefficients as the basis for prioritization analysis involving multiple criteria assessments, (2) the evolution of the T-SF Minkowski distance index to model outranking decision-making processes, (3) the creation of a reliable appraisal mechanism based on T-SF correlation-oriented measurements for intelligent decision support, and (4) the advancement of computational tools and procedures (e.g., correlation-oriented maximizing and minimizing indices, global outranking and outranked indices, and appraisal significance indices) to perform the decision analytic procedure in T-SF settings. In terms of managerial implications, the solution findings support the employment of the fixed ideal/anti-ideal benchmarking mechanism, as its measurements and indices are easy to operate and suitably sensitive. Next, in practical implementations of the T-SF decision analytic procedure, it is advised to utilize the T-SF Manhattan distance index for calculating convenience. Finally, the T-SF decision analytic techniques offer fundamental ideas and measurements appropriate for manipulating T-SF information in complex decision situations, thereby increasing the application potential in the area of decision-making with information uncertainty.