Decision Method Research Articles

A generalized TODIM approach based on information measurement and consensus building model in probabilistic hesitant fuzzy environment

Information measurement and decision-making methods are two important aspects in the application of probabilistic hesitant fuzzy elements (PHFEs), and these issues are crucial for improving decision quality and enhancing the applicability of PHFSs. This paper focuses on the entropy measure and score function of PHFEs, and then proposes a generalized TOIDM method based on information measurement and consensus building. In this contribution, we develop a novel non-probabilistic entropy measure by two non-fuzzy elements and a novel score function based on sigmoid function. The novel non-probabilistic entropy of PHFEs can comprehensively reflect the dynamic changes of fuzzy uncertainty and hesitant uncertainty, and provide a new axiomatic framework. The novel score function of PHFEs can synthesize multiple information effectively with strong integrity. In the sequel, the two proposed information measurement methods are compared with existing methods to further verify the validity of the developed methods. Eventually a consensus building model is constructed based on the proposed information measurement method, and then a generalized TODIM approach based on information measurement and consensus building is proposed for probabilistic hesitant fuzzy environment. The developed approach is demonstrated with an emergency rescue planning decision-making problem. Sensitivity analysis and comparative analysis are employed to verify the validity and stability of the approach and information measures proposed in this paper. Based on the findings of this study, we conclude that the proposed methods effectively enrich the existing information measurement methods and decision-making methods in the field of probabilistic hesitant fuzzy.

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

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

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

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

Multi-attribute group decision-making method using single-valued neutrosophic credibility numbers with fairly variable extended power average operators and GRA-MARCOS

Data trading platform (DTP) selection is a classic multi-attribute group decision-making (MAGDM) problem. As an extension of intuitionistic fuzzy sets (IFSs), single-valued neutrosophic credibility numbers (SvNCNs) can express both fuzzy evaluation information and the credibility level of the information, offering better expressiveness in describing fuzzy decision-making information. However, existing studies on aggregation operators and decision-making methods in the SvNCN environment are inadequate. Therefore, this paper proposes a MAGDM technique based on the fairly weighted variable extended power average (SvNCNFWVEPA) operators of SvNCNs and grey relational analysis (GRA)-Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method. The main contributions are as follows: (1) we propose fairly operation rules for aggregating SvNCNs in an unbiased manner; (2) addressing the lack of SvNCN measurement research, we introduce preference distance and entropy measures for SvNCNs, then utilize the entropy measure to compute the objective weights of the attributes; (3) to effectively aggregate SvNCN information, inspired by the variable power geometric (VPG) operators and the extended power average (EPA) operators, we propose the variable extended power average (VEPA) operator for scientifically handling extreme values, extending it to SvNCNs with the SvNCNs fairly variable extended power average (SvNCNFVEPA) operators and their extended form; (4) we introduce the GRA method to calculate the degree of utility of alternatives relative to ideal and anti-ideal alternatives, forming the GRA-MARCOS method. This method can reflect both indicator differences and the similarity of alternatives, thereby rendering the evaluation results more scientific and objective; (5) to illustrate the application of the method to the MAGDM problem, we apply it to the example of DTP selection. Parameter sensitivity analysis and comparative analysis with other existing methods demonstrate that our proposed method is more scientific and flexible.

Performance assessment of public transport networks: an AHP-ANP approach

Multi-criteria decision-making (MCDM) methods have been extensively employed by researchers in the public transport (PT) sector. While many studies utilize a single MCDM method, employing multiple MCDM methods can achieve more robust results. Furthermore, integrated MCDM methods have the potential to address the weaknesses and limitations inherent in single MCDM method. In this study, we address the limitations of single MCDM methods by integrating the analytic hierarchy process (AHP) with the analytic network process (ANP) to evaluate public transport networks performance. Based on the established public transport criteria matrix AHP model, we identify interdependencies among criteria. The AHP-ANP method is then applied to assess public transport networks in three case study cities in Australia. The sensitivity analysis conducted on both the AHP and integrated AHP-ANP approaches highlights the influence of criteria interdependency. This research contributes to mitigating the limitations of AHP method and applies AHP-ANP model to provide decision-makers with insights to enhance PT network performance and achieve smart PT network management.

Large-Scale Group Decision Making with Dual Feedback from Community Residents Based on the Organizational Invisible Field

In China, communities function as grassroots self-governing bodies, and the enhancement of public participation in community governance has remained a central focus of study. This paper applies the Large-Scale Group Decision-Making (LSGDM) method to the process of community self-governance and proposes a dual feedback group consensus decision-making model that takes into account the unique social relations among residents. Firstly, the concept of the Organizational Invisible Field—formed in communities by intangible social capital such as positional power and interpersonal relationships within the organization—is introduced. The definition of Invisible Field Force is utilized to measure the influence of these forms of capital on social relationships. Subsequently, drawing on field dynamic theory, the process by which residents’ preferences within the organization are shaped by the action of Invisible Field Force is explored. Secondly, acknowledging that invisible relationships can be affected by dynamic interactions during the decision-making process, the Invisible Field Force change model is constructed. Building on this, a dual feedback consensus coordination mechanism—encompassing both in-organization members and all residents—is designed. Finally, the validity and utility of the model are verified through case studies and sensitivity analyses.

Medical futility at the end of life: the first qualitative study of ethical decision-making methods among Turkish doctors

BackgroundThe swift advancement of intensive care medicine, coupled with technological possibilities, has prompted numerous ethical inquiries regarding decision-making processes concerning the withholding or withdrawal of treatment due to medical futility. This study seeks to delineate the decision-making approaches employed by intensive care physicians in Türkiye when faced with medical futility at the end of life, along with an ethical evaluation of these practices.MethodsGrounded theory, a qualitative analysis method was employed, conducting semi-structured, in-depth interviews with eleven intensive care physicians in Türkiye. The subsequent text analysis was carried out using MAXQDA software.ResultsParticipants assert that the decisions made by Turkish physicians determine whether treatment is futile, rely on medical consensus, and lack a standardized decision-making process. The decisions are influenced by legal and social pressures, resource constraints, and occasional conflicts of interest. The significance of professional hierarchy is notable, with limited consideration given to the opinions of nurses and other staff. The unstructured medical consensus processes are shaped by normative concepts such as benefit, age, justice, and conscience. Furthermore, it was observed that the conscientious opinions of physicians carry more weight than adherence to ethical principles and guidelines.ConclusionTo create optimal conditions for doctors to make ethically justifiable decisions, the dynamics within the treatment team should be improved, emphasizing the minimization of hierarchy, and ensuring the active participation of all team members in the decision-making process. Additionally, efforts should be directed toward narrowing the gap between the conscience of the individual doctor and established ethical principles. A potential solution lies in the nationwide implementation of clinical ethics committees and the establishing of clinical ethics guidelines, aiming to address, and overcome the identified challenges.

Efficient Thermal Integration Model based on a Biogas-fired Gas Turbine Cycle (GTC) for Electricity and Desalination Applications; Thermo-Economic and GA-based Optimization

As the global energy demand continues to rise, there is an urgent need to improve the efficiency and sustainability of power generation systems. This study integrated a modified supercritical carbon dioxide (S-CO2) and multi-effect desalination (MED) units to recover residual heat from a gas turbine cycle (GTC) in two stages, significantly enhancing electricity production while reducing the environmental footprint of the GTC. The significance of this study lies in its comprehensive approach, combining thermodynamic, environmental, and thermoeconomic analyses alongside thorough sensitivity evaluations. A triple optimization framework was implemented to optimize the system's performance, focusing on key metrics such as exergy efficiency, CO2 reduction rates, and levelized energy cost, utilizing the NSGA-II and the TOPSIS decision-making method in MATLAB software. Economic viability was assessed through a net present value (NPV) analysis, demonstrating substantial profitability. Finally, a comparison study of the devised system CO2 emissions rate was performed for different renewable energy sources. A specific application of the devised system is its capacity to generate 1.415 m³/h of distilled water while generating 1441 kW of electricity. Sensitivity analysis identified the combustion chamber temperature as the most critical design parameter, with a sensitivity index of 0.328. The optimum economic indicators showed marked improvement, with the NPV increasing from 2.371 M$ to 10.75 M$ and the payback period decreasing from 13.28 years to 7.18 years.

Intelligent Gear Decision Method for Vehicle Automatic Transmission System Based on Data Mining

The gear decision logic of automatic transmission directly affects the vehicle's dynamic, fuel economic, and comfort performance. This study employs data mining techniques to address the issues of low adaptability and low recognition rate in the intelligent gear decision of vehicle automatic transmission systems. The research further proposes the utilization of Kalman filter, Hidden Markov Models, and Long Short-Term Memory networks for condition feature recognition and time series classification. Subsequently, dynamic programming algorithms are employed to optimize intelligent gear decisions. Combining driver intent and driving environment, an intelligent gear decision method is formulated. The results indicate that, during a 430s driving segment, the intelligent gear decision method consumes only 464mL of fuel, closely resembling the economic strategy's 457mL, with a gear shift frequency of 53, significantly better than the 79 shifts in the economic strategy. Moreover, the error rate for slope condition recognition is only 0.062%. In a 200s coupled condition, the intelligent gear decision results in fuel consumption of 207mL, approximating the actual vehicle's 219mL, while power-shifting consumes 316mL, and economic shifting only 202mL. This study not only improves the accuracy of gear decisions but also effectively enhances vehicle operational efficiency, providing valuable insights for future automatic transmission systems with significant practical value.

A Multicriteria Decision-Making Approach for Urban Water Features: Ecological Landscape Architecture Evaluation

In recent decades, the issues of ecology and environmental sustainability have become a global concern in contemporary urban design. Among various urban elements, water features play a significant role in improving the ecological characteristics of their surrounding environment, especially in hot and arid areas. The aim of this study is to evaluate the ecological characteristics of urban water features comprehensively and quantitatively, which has been overlooked in previous studies, taking their physical characteristics into account. To this end, a multicriteria decision-making method, an analytic network process, was proposed to quantitatively evaluate the ecological characteristics of water features. In this approach, four ecological criteria—microclimate, biodiversity, greenery, and human wellbeing—and ten physical characteristics of water features were considered. Twenty-one experts were asked to complete a questionnaire for pairwise comparisons of all ecological criteria and the influence of physical characteristics. The results showed that vegetation and scale, with the relative influencing values of 0.255 and 0.188, respectively, were identified as the most decisive features influencing ecological criteria. Conversely, texture, with a value 0.023, had the least impact. Moreover, it was shown that water features have the greatest impact on the microclimate compared with other ecological criteria. The results were used to compare water features at the Koohsangi Transregional Park, located in a hot and arid city of Iran. The results of this study lead to a framework that can help urban designers integrate ecological criteria into water feature planning to enhance urban ecology and sustainability.

Navigating Decision Making with Generalized Temporal Intuitionistic Fuzzy Sets and Soft Sets

The study addresses the challenges posed by evolving data within generalized intuitionistic fuzzy sets. Traditional methods often fall short in handling such complexity. To overcome this, we introduce the concept of a generalized temporal intuitionistic fuzzy set, extending the traditional framework to incorporate temporal dynamics. Additionally, we define a generalized temporal intuitionistic fuzzy soft set, integrating temporal aspects into the soft set framework. Recognizing the need for advanced operations like union and intersection to merge opinions across different periods, we propose practical solutions for decision-making in this dynamic context. Two novel multi-criteria decision-making methods are introduced, specifically designed to address decision-making problems within generalized temporal intuitionistic fuzzy soft sets. We develop Scilab codes for these methods, enabling the creation of a multiple-input single-output system. This system is applied to complex real-life examples, involving numerous parameters, time moments, and decision-makers. This comprehensive approach aims to provide robust tools and methodologies for decision-making processes amidst the intricate dynamics of temporal data within the framework of generalized temporal intuitionistic fuzzy sets.

Navigating Decision Making with Generalized Temporal Intuitionistic Fuzzy Sets and Soft Sets

The study addresses the challenges posed by evolving data within generalized intuitionistic fuzzy sets. Traditional methods often fall short in handling such complexity. To overcome this, we introduce the concept of a generalized temporal intuitionistic fuzzy set, extending the traditional framework to incorporate temporal dynamics. Additionally, we define a generalized temporal intuitionistic fuzzy soft set, integrating temporal aspects into the soft set framework. Recognizing the need for advanced operations like union and intersection to merge opinions across different periods, we propose practical solutions for decision-making in this dynamic context. Two novel multi-criteria decision-making methods are introduced, specifically designed to address decision-making problems within generalized temporal intuitionistic fuzzy soft sets. We develop Scilab codes for these methods, enabling the creation of a multiple-input single-output system. This system is applied to complex real-life examples, involving numerous parameters, time moments, and decision-makers. This comprehensive approach aims to provide robust tools and methodologies for decision-making processes amidst the intricate dynamics of temporal data within the framework of generalized temporal intuitionistic fuzzy sets.

Evaluation of OECD Countries in Terms of Better Life Index According to Bulut Index

Background: The "Better Life Index", first developed by the Organization for Economic Co-operation and Development (OECD) in 2011, includes many criteria from the functioning of public services to environmental wealth, from security services to individual and social well-being. Method: The performances of OECD countries according to the decision criteria that constitute the Better Life Index has been evaluated with Bulut Index (BI) method, which is a multi-criteria decision-making method. Normalized Maximum Values (NMV) method, one of the objective weighting methods, was used to determine weights of decision criteria. Findings: Among the indicators of the Better Life Index, the murder rate per 100 thousand population ranks first in terms of criterion weight. In the study, due to the analysis and evaluation made with Bulut Index, Switzerland ranked first among OECD countries with 82.28 points in the welfare and well-being index ranking. On the other hand, Türkiye ranked fourth from the bottom among OECD countries. Conclusion: Based on the findings of the study, it is recommended that urgent and necessary measures be taken on homicide rates, long-term unemployment rates and air pollution criteria. The lack of criteria regarding civic participation, work-life balance and income status constitute limitations of the study. In future studies, it is especially recommended that other researchers include these variables.

Knowledge graph-driven mountain railway alignment optimization integrating karst hazard assessment

Karst hazard is a considerable threat that should be considered in railway alignment design for mountainous regions with dense water systems. Nevertheless, alignment design principles in karst regions have not been systematically studied. Moreover, a quantitative karst hazard assessment model is currently lacking for automated alignment optimization. To solve the above problems, based on the analyses of karst inducing factors and hazard representation, the railway alignment design principles in karst regions are summarized through an event tree. A highly-coupled knowledge graph (called KaRAD-KG) modeling method is proposed. Then, a bi-objective alignment optimization model considering railway construction cost and karst hazard (mainly including hazard components of synclinal karst, anticlinal karst and karst depression) is constructed. To solve the optimization model, a knowledge-driven distance transform algorithm incorporating a karst hazard assessment method and a multicriteria tournament decision method is customized. Finally, the application in a real-world case indicates that the proposed method can generate an alignment which reduces construction cost by 3.39 % and karst hazard by 18.73 % compared to the best manually-designed alternative, which verifies the effectiveness of this method for assisting actual railway alignment design in a karst-dense mountainous region.

A modified multiple-criteria decision-making approach based on a protein-protein interaction network to diagnose latent tuberculosis

BackgroundDNA microarrays provide informative data for transcriptional profiling and identifying gene expression signatures to help prevent progression of latent tuberculosis infection (LTBI) to active disease. However, constructing a prognostic model for distinguishing LTBI from active tuberculosis (ATB) is very challenging due to the noisy nature of data and lack of a generally stable analysis approach.MethodsIn the present study, we proposed an accurate predictive model with the help of data fusion at the decision level. In this regard, results of filter feature selection and wrapper feature selection techniques were combined with multiple-criteria decision-making (MCDM) methods to select 10 genes from six microarray datasets that can be the most discriminative genes for diagnosing tuberculosis cases. As the main contribution of this study, the final ranking function was constructed by combining protein-protein interaction (PPI) network with an MCDM method (called Decision-making Trial and Evaluation Laboratory or DEMATEL) to improve the feature ranking approach.ResultsBy applying data fusion at the decision level on the 10 introduced genes in terms of fusion of classifiers of random forests (RF) and k-nearest neighbors (KNN) regarding Yager’s theory, the proposed algorithm reached a sensitivity of 0.97, specificity of 0.90, and accuracy of 0.95. Finally, with the help of cumulative clustering, the genes involved in the diagnosis of latent and activated tuberculosis have been introduced.ConclusionsThe combination of MCDM methods and PPI networks can significantly improve the diagnosis different states of tuberculosis.Clinical trial numberNot applicable.

The Development of IV-VIKOR Method Under Hesitant Fuzzy Sets in MCDM

Due to its solution correctness and ease of evaluation, the VIKOR approach has already established itself as a popular multi-criterion decision-making (MCDM) method. This strategy focuses on picking and ranking from a list of viable options in order to assist the decision-maker in determining a final course of action. Additionally, it looks for a middle ground to resolve a problem with competing demands. It produces a ranking list of compromises based on a precise measurement of distance from the ideal response. The hesitant fuzzy set (HFS) has furthermore come under increased study because of its potency and ability in capturing ambiguity and uncertainty. This paper introduces the inversion of VIKOR method (IV-VIKOR) to take uncertain fuzzy conditions into account. These novel concepts serve as the foundation for an IV-VIKOR method that we suggest, and a real-world application is given to demonstrate how well our method works when dealing with MCDM problem with hesitant fuzzy preference data.

A three-way decision method with probability dominance relation in interval-valued hesitant fuzzy environment for marine steam turbine fault diagnosis

A three-way decision method with probability dominance relation in interval-valued hesitant fuzzy environment for marine steam turbine fault diagnosis

Multi-criteria Evaluation of Madagascar's Regions in the Context of Employment Using the CoCoFISo Method

The evaluation of regions in terms of employment provides decision-makers with a valuable tool for pursuing the development of each region. Furthermore, it facilitates the decision-making process for the working-age population, enabling them to identify locations where they wish to settle on a long-term basis to pursue employment opportunities. The objective was to evaluate Madagascar's twenty-three regions under thirteen pre-established criteria. The multi-criteria mean weight (MW) and Combined Compromise For Ideal Solution (CoCoFISo) decision-making methods were employed to calculate the criteria's relative importance and perform a comparative ranking of the regions. The weights of the criteria were equalized using the MW result. The CoCoFISo method revealed that the first seven regions are successively Atsimo Andrefana, Analanjirofo, Boeny, Vakinakaratra, Alaotra Mangoro, Diana and Analamanga. Our analysis of the results demonstrates that these regions display elevated percentages, with 54% to 69% of the criteria exhibiting above-average values. Additionally, the values of their criteria are generally stable in comparison to the others, which are dispersed.

The Use of Geographic Information Systems and Multi-Criteria Decision-Making Methods in the Creation of Forest Fire Susceptibility Maps: A Literature Review

Forest fires are natural disasters that cause significant environmental, economic, and social damage worldwide. This study provides a literature review examining how Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) methods are utilized in the prevention of forest fires and the identification of high-risk areas. GIS, as a system used for the collection and analysis of spatial data, enables the consideration of various factors influencing fire risk, such as climate change, topography, vegetation, and weather conditions. The spatial analysis capabilities offered by GIS play a critical role in identifying regions with high fire susceptibility when generating fire risk maps. Additionally, MCDM methods contribute significantly to the decision-making process by allowing the evaluation of multiple criteria in fire risk analysis. Logistic Regression and Frequency Ratio, which are frequently employed in the literature, are widely used in fire risk analysis and improve the accuracy of susceptibility maps. Furthermore, MCDM methods have been proven effective in estimating the likelihood of forest fire occurrences and identifying fire-prone areas. The integration of GIS and MCDM methods allows for more precise identification of risk zones and supports the development of fire prevention strategies. This literature review highlights the advantages of utilizing GIS and MCDM in the production of forest fire susceptibility maps and suggests that these methods may have broader applications in future research. The effective use of technology in combating forest fires enhances the accuracy of fire risk assessments, contributing significantly to environmental protection efforts.

An Introduction to Lagrange Multipliers: Theory and Applications in Economics

The purpose of this paper is to explore the basic applications of the Lagrange multiplier method in economics and to help beginners build their understanding of this mathematical tool. Tesla Inc. is used as a case study to examine how it maximizes profits by optimizing pricing and marketing expenses under given market conditions and constraints. The context of the study includes the importance of maintaining Tesla’s leadership position as the world’s leading electric car manufacturer in a competitive market. In this paper, the Lagrange multiplier method is used to incorporate specific market share and marketing expense constraints into the profit function and to find the extreme values by constructing the Lagrange function and solving for the derivatives. However, the results of the study show that the solutions obtained are extreme minima rather than the expected maxima. This result highlights the possible limitations of the Lagrange multiplier method in economic optimization problems. Nonetheless, this paper provides valuable lessons for understanding and applying the Lagrange multiplier method and points the way for further research on the application of optimization methods in economic decision making.