Traditional Multi-criteria Decision Research Articles

Evaluation of Renewable Energy Sources for a Sustainable Future: A Multi-Criteria Decision-Making Approach

The urgent global challenges of climate change, energy security, and environmental degradation highlight the need for sustainable energy solutions. Renewable energy sources (RES) present a viable pathway towards sustainability by mitigating greenhouse gas emissions, reducing reliance on fossil fuels, and fostering economic resilience. Purpose: The purpose of this paper is to propose an advanced Multi-Criteria Decision-Making (MCDM) approach to evaluate various RES by integrating environmental, economic, technological, social acceptance, and resource availability criteria, to identify the most suitable RES for sustainable energy solutions. Methodology: The study employs a hybrid method combining Type-2 Neutrosophic Numbers (T2NN) with LOPCOW (Logarithmic Percentage Change Operator Weighting) and MAIRCA (Multi-Attributive Ideal-Real Comparative Assessment) to rank the suitability of different RES, including solar, wind, hydropower, and geothermal energy. Findings: The case study results reveal wind energy as the top-ranked alternative, supported by consistent findings across comparative methods such as COPRAS, MABAC, EDAS, and TOPSIS. Sensitivity analysis further confirms the stability of the proposed model under various scenarios. Originality: The originality of this study lies in the integration of T2NN, LOPCOW, and MAIRCA to address the limitations of traditional MCDM approaches in handling uncertainty and imprecision in data. The study demonstrates the efficacy of the proposed framework in providing a robust evaluation of RES, and its value lies in its potential to inform decision-making in the field of sustainable energy solutions.

Enhancing industrial decision-making through Multi-Criteria Decision-Making approaches and ML-Integrated Frameworks

Decision-making in current industrial contexts has shifted from intuition to a data-driven approach, requiring prompt processing of huge datasets. However, conventional Multi-Criteria Decision Making (MCDM) methodologies fall short of navigating the intricacy of large datasets. This paper introduces an innovative decision-support system integrating multi-criteria methods with machine learning techniques such as artificial neural networks. The proposed six-step framework aims to optimize operational decisions by analyzing real-time performance data. The research contributes to the advancement of decision-making methodologies in the industrial field, offering dynamic responsiveness and improved recommendations compared to traditional MCDM methods. While results are promising, future work should focus on robustness testing particularly in terms of its dependence on real-time data, to ensure sustained efficacy and mitigate potential biases in recommendations over time.

Development of RAMDOE: a new method for rapidly ranking alternatives with supplementary options and considering changes in criteria values

This paper delves into the development and validation of the RAMDOE method, a pioneering approach in multi-criteria decision making (MCDM) that seamlessly integrates the root assessment method (RAM) and design of experiments (DOE) techniques, addressing the inflexibility of traditional MCDM methods in accommodating adjustments in criteria ranges and the addition of new alternatives without necessitating a complete overhaul of the decision framework. Through empirical analysis, the study demonstrates the RAMDOE method's remarkable efficacy in precisely ranking alternatives, as illustrated through a practical case study focused on the selection of a supplier from a pool of seven candidates. One of the most notable aspects of the RAMDOE method lies in its capacity to formulate a regression equation that accurately captures the intricate relationship between alternative scores and criteria values, enabling decision-makers to seamlessly integrate new alternatives into the decision-making process without the cumbersome task of recalibration, thereby distinguishing it from conventional MCDM techniques such as TOPSIS (technique for order of preference by similarity to ideal solution), COPRAS (complex proportional assessment), MOORA (multiobjective optimization on the basis of ratio analysis), EDAS (evaluation based on distance from average solution) and CODAS (combinative distance-based assessment). The practical implications of these findings are profound, offering decision-makers across various domains a more efficient and adaptable framework to navigate complex decision scenarios. Particularly in contexts like supplier selection, where criteria ranges may vary significantly, the RAMDOE method provides decision-makers with a robust toolset to make informed decisions, presenting a promising avenue for addressing the dynamic nature of decision-making environments and enhancing the overall robustness and flexibility of MCDM processes in real-world applications

A Decision-Making Approach Incorporating TODIM Method and Sine Entropy in q-Rung Picture Fuzzy Set Setting

In this study, we propose a new approach based on fuzzy TODIM (Portuguese acronym for interactive and multicriteria decision-making) for decision-making problems in uncertain environments. Our method incorporates group utility and individual regret, which are often ignored in traditional multicriteria decision-making (MCDM) methods. To enhance the analysis and application of fuzzy sets in decision-making processes, we introduce novel entropy and distance measures for q-rung picture fuzzy sets. These measures include an entropy measure based on the sine function and a distance measure derived from the Jensen-Shannon divergence. In our methodology, incorporating the sine function into the entropy measure stands out as a distinctive decision, grounded in a profound understanding of the inherent characteristics of fuzzy sets. Utilizing the sine function proves especially advantageous when handling fuzzy sets that exhibit cyclical variations or fluctuations in their membership degrees. We effectively weight the criteria for an improved evaluation by using this new entropy measure. The introduced distance measure finds application in the TODIM approach, allowing the execution of TODIM method steps within a fuzzy environment until the determination of one alternative’s dominance over another—an advancement beyond traditional approaches. We apply our enhanced fuzzy TODIM method to a real-life construction project management problem from the literature and compare the results with those in the literature and obtained from other MCDM methods. Our proposed measures are robust, as demonstrated by the sensitivity analysis that varied the weights of group utility and individual regret, with the results visualized in a 3D sensitivity plot. The findings demonstrate the superiority of our method in providing a more comprehensive evaluation of alternatives, making it a useful tool for decision-makers facing complex and uncertain decision-making problems.

Proportional intuitionistic fuzzy AHP method: Wind turbine selection application

The direct assignment of decimal numbers for membership and non-membership degrees of an element in intuitionistic fuzzy sets is not practical. The problem is that the expert cannot assign the same values to the degrees of membership, non-membership and hesitancy in decimal numbers for the same proposition in every attempt. Rather than the former, the assignment of proportional relationships between membership and non-membership degrees is more appropriate. We propose proportion-based models for intuitionistic fuzzy sets that include arithmetic and aggregation operators. Proportional intuitionistic fuzzy (PIF) sets require only the proportion relations between an intuitionistic fuzzy set’s parameters. These models will make it easier to define intuitionistic fuzzy sets with more accurate data that better represents expert judgments. We transform AHP method, one of the traditional multi-criteria decision making methods, to PIF AHP using PIF sets. We compare the proposed PIF AHP method by interval-valued intuitionistic fuzzy AHP method existing in the literature. A wind turbine selection problem is handled to show the validity of the proposed PIF AHP method.

Comparative analysis of three categories of multi-criteria decision-making methods

Multi-criteria decision-making (MCDM) is a popular branch of decision theory in which many methods have been proposed to solve MCDM real-world problems. However, in spite of the multiple methods, it is common that different of them provide the same solutions. Therefore, this paper analyzes the causes of the same solutions from different MCDM methods, studies why different normalization methods can achieve the same solutions in MCDM methods through the theory of MCDM methods, and discusses the relationship between different MCDM methods by category. To achieve such goals, MCDM methods are classified into three categories according to their ranking methods. Some traditional MCDM methods are then extended by considering general normalization methods and distance measures. Finally, the characteristics of various MCDM methods are discussed by category and their evaluation indexes are also compared. The comparison results are represented by tables and flowchart models. The relationship between different categories of MCDM methods is shown through the comparison of the evaluation indexes. The aim of the proposed method is not to replace existing MCDM methods, but to fill some present research gaps about MCDM methods.

Attention-based deep learning model to improving multi-criteria decision-making for customer loyalty

Understanding the factors that influence product loyalty is crucial for businesses to effectively attract and retain customers. This study suggests a novel approach to assess the importance and weight of criteria that lead to product loyalty by considering the Halo effect in customer decision-making. The suggested method utilizes an attention-based deep learning model to analyze customer feedback collected through the Net Promoter Score (NPS) scale, incorporating the insights of a large number of customers. The proposed method overcomes the limitations of traditional methods that rely on expert judgments or data mining, providing a more comprehensive and customer-centric perspective. By considering the Halo effect, which can lead to biased perceptions of product features, the method offers a more accurate assessment of criteria weights and their impact on product loyalty. A case study focusing on mobile phone selection and loyalty is conducted to explain the applicability and efficiency of the suggested method. The outcomes are compared with the NPS index and several common multi-criteria decision-making (MCDM) techniques. The findings highlight the superiority of the suggested method in capturing the complex relationships between criteria and product loyalty, surpassing the limitations of expert-based approaches and outperforming traditional MCDM methods. The suggested technique provides valuable insights for companies seeking to enhance customer loyalty and optimize product development strategies. However, it is important to acknowledge limitations related to the reliance on customer feedback and the contextual specificity of the results.

Data Scientist Selection in IT Industry: SMCDM Approach

Traditional Multi-Criteria Decision Making (MCDM) methods cannot provide solutions to problems that may be encountered in the near future. Basically, MCDM methods have a very rich algorithmliterature for decision making problems. Traditional MCDM methods do not take short-term changes into account. In a new development, it is necessary to start the process from the beginning and solve the problem from the beginning. This leads to huge loss of money and time. The purpose of developing the MCDM method is to make decision-making problems more efficient and to prevent losses in the process. However, the MCDM method is a method that has been developed based on the changes in the process, which is a problem that has not been addressed until now. Since it will be tiring and difficult to solve the problem by assigning new weights from the beginning, the Stratified Multi-Criteria Decision Making (SMCDM) Method has been developed for the possibilities that may occur in the near future. In this study, an exemplary study of the SMCDM method was carried out in a IT company. In the data scientist selectionproblem, the best alternative was selected with the SMCDM method, taking into account the events that may occur in the near future.

Resilience and sustainability-informed probabilistic multi-criteria decision-making framework for design solutions selection

In this paper, a novel probabilistic multi-criteria decision-making (MCDM) framework for sustainable and resilient building design solutions selection is developed to optimize alternatives or rehabilitation strategies in a life cycle context. The proposed framework holistically takes into account the buildings' resilience metrics, environmental sustainability, and energy consumption. The life cycle economic cost, global warming potential (GWP), primary energy use (PEU), and social impact are used to represent the buildings’ resilience and sustainability. The second-generation Performance-based Earthquake Engineering procedure and FEMA P-58 method for time-based seismic performance analyses are used to evaluate earthquake-induced impact. The whole-building energy modeling (BEM) is adopted to assess energy use during the operational phase. In addition, life cycle analysis (LCA) and life cycle cost analysis (LCCA) are carried out to assess the life cycle performance of different design alternatives regarding environmental impacts and cost, respectively. Aiming to consider the various uncertainties in the decision-making process, a novel MCS-MCDM method is developed by combining the Monte Carlo simulation (MCS) with the traditional MCDM method. The proposed framework is applied to analyze hypothetical cases considering different geographic locations, structural systems, and building envelope systems, and recommendations are given according to the analysis results. The case study demonstrates the advantages of the proposed framework over traditional frameworks and its guidance for architectural design decisions.

A Novel Crowdsourcing Task Recommendation Method Considering Workers’ Fuzzy Expectations: A Case of ZBJ.COM

Crowdsourcing recommendation is necessary to surmount information overload and assist workers to identify tasks of interest efficiently. However, existing studies mainly concentrate on investigating workers’ qualifications or requirements, neglecting their fuzzy expectations and psychological behavior. Besides, workers can only passively accept the results and do not have any right to change them even if they are not satisfactory. As workers will pay different attention to task aspects, we regard the crowdsourcing recommendation as a multi-criteria decision-making (MCDM) problem. In this paper, we propose an MCDM-based method for automated and interactive crowdsourcing task recommendations while comprehensively considering workers’ fuzzy expectations and psychological behavior. Given a large volume of available tasks on a crowdsourcing platform, the requirement of massive human efforts in traditional MCDM is not applicable in such situation. Therefore, we formulate a set of quantitative task evaluation criteria for automated task assessments based on the attributes that have the most impact on workers’ participation behavior. Further, to capture workers’ fuzziness in expectations, an intuitionistic fuzzy 2-tuple linguistic (IF2L) term is introduced, which are further leveraged as reference points to evaluate their “gains” and “losses” based on the prospect theory. Finally, the D-S evidence theory is adopted to gather workers’ uncertain prospect values. A case of ZBJ.COM, a popular crowdsourcing platform in China, shows that the proposed method is efficient and effective in recommending suitable and personalized tasks to workers.

Use of Data Envelopment Analysis (DEA) to assess management alternatives in the presence of multiple objectives

Marine ecosystem-based management often aims to satisfy a wide range of economic, social and environmental objectives. These are often measured in non-commensurable units, making identification of a preferred option difficult when outcomes and measures vary considerably. In this paper, we examine the potential for data envelopment analysis (DEA) as an alternative approach to traditional multi-criteria decision analysis (MCDA). We derive multi-output composite indicator scores for three case study examples. The management alternatives are assessed both with and without exogenously determined objective preference weights. The frontier-based analysis does not require weights to be assigned to each objective, instead generating flexible “benefit of the doubt” weights that allow for different objectives to be influencing each management option. Applying stakeholder objective importance weights allows the assessment of the objective alignment of the different stakeholder groups, providing additional information on management option performance. The DEA results were found to be consistent with those from an earlier study that used a more traditional MCDA approach. DEA has the additional advantage of not requiring measures to be commensurable and can also directly incorporate undesirable outcomes into the analysis.

Learning the thresholds in the ORESTE method from historical preference information

The ORESTE (organísation, rangement et Synthèse de données relarionnelles, in French, meaning organization, storage and synthesis of relevant data in English) method is an effective multi-criteria decision-making method which takes into account the importance of both criteria and alternatives. Nevertheless, how to objectively determine the thresholds used in the ORESTE method is still an unsolved issue. In this regard, this study proposes a model that combines the threshold learning method based on historical data and the traditional MCDM (multi-criteria decision-making) method, to learn the thresholds used in the ORESTE method from the preference information provided by stakeholders. We implement a simulation analysis to verify the feasibility of this model and reflect the relation between the weights balancing the importance of alternatives and criteria and the thresholds, and the relation among different thresholds. Finally, an illustration concerning the selection of lung cancer screening methods is given to show the applicability of the threshold learning model. Sensitivity analysis, comparative analysis, and managerial insights are also provided.

Selection of optimal seismic intensity measures using fuzzy-probabilistic seismic demand analysis and fuzzy multi-criteria decision approach

A fuzzy decision-theoretic computational approach for selecting optimal intensity measures (IMs) under the conditions of randomness, fuzziness and uncertainty is proposed in this paper. The main objectives of this study are twofold: (a) to quantify the contribution of multiple evaluation criteria, and (b) to improve the credibility of decision-making results by selecting the appropriate multi-criteria decision making (MCDM) method. To achieve these, three components of the methodology for this approach are utilized: (1) Fuzzy-probabilistic seismic demand analysis (FPSDA) is employed to consider the aleatory and epistemic uncertainties in the seismic demand analysis. (2) Fuzzy analytical hierarchical process (FAHP) is used to determine the importance weights of multiple evaluation criteria. (3) A combination of FAHP and fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) is utilized to determine the optimal IM alternatives. First, the FPSDA method in which the interval PSDA is applied to evaluate the IM performance is developed. The interval results are transformed into triangular fuzzy numbers to establish a fuzzy decision-making matrix. Subsequently, the FAHP is employed to estimate the importance of each evaluation criterion. Then, FAHP–FTOPSIS is utilized to rank the orders of the IM alternatives and select the optimal IM. An illustrative application of the proposed methodology to a five-story reinforced concrete frame structure is presented. The decision results demonstrate that by considering the maximum inter-story displacement angle (θmax), top displacement (Dt), maximum floor velocity (Vmax) and maximum floor acceleration (Amax) as engineering demand parameters (EDPs), the average spectral displacement (Sdam), Sdam, average spectral velocity (Svam), and Vamvatsikos spectral acceleration (VSa2) are the most optimal IMs, respectively. The decision-making results for multiple EDPs indicate that Svam is the optimal IM. Furthermore, the Cordova spectral acceleration (CSa) considering period prolongation has the highest closeness coefficient among the acceleration-related IMs. To verify the effectiveness of the new framework and methodology, the performance of the proposed approach is compared with that of the traditional MCDM method while ignoring fuzziness. The proposed methodology can be applied to other engineering structures and provide a reference for solving similar problems.

Fuzzy VIKOR approach to identify COVID-19 vulnerability region to control third wave in Assam, India

These days, the appraisal of the COVID-19 vulnerability has become a difficult errand for the whole world. The COVID-19 administration dynamic issue frequently includes numerous elective arrangements clashing standards. In this paper, we present a multi-criteria decision-making (MCDM) procedure based on the fuzzy VIKOR method to survey the COVID-19 vulnerability in the state of Assam, India. The trapezoidal fuzzy number is utilized to evaluate the rating of the loads for the set-up models. We have observed environment, social, and Medical factors after observing the spread of COVID-19. To study and to have comments, a committee of five experts has been formed from a different region of Assam to observe and comment to identify Coronavirus’s weakest factors. For a better survey, we have divided the state into four areas namely Rural Area, Urban Area, Market Area in Rural Area, and Market Area in Urban Area. The current research looked at how the fuzzy VIKOR selects provinces for urgent adaptation needs differently than a traditional MCDM technique.

New multi-criteria decision-making technique based on neutrosophic axiomatic design

There are several multicriteria decision-making (MCDM) approaches presented in the literature with their characteristics. Although traditional MCDM approaches are considered a proper implementation to select the best alternative from available types, they failed to consider uncertainty which is quite high and desires to be thoughtfully measured in the selection process. This research focuses on extending MCDM in the neutrosophic environment using axiomatic design (AD) as a novel contribution to selecting appropriate Computed Tomography (CT) devices. We present a new linguistic scale for evaluating criteria and alternatives based on single-valued triangular neutrosophic numbers (SVTrN). The proposed approach is superior to other existing approaches due to its simplicity and ability to simulate natural human thinking via considering truth, indeterminacy, and falsity degrees. Then, applying it will increase the value of imaging for medical decision-making and decrease needless costs. So, this study can be valuable to researchers by helping them consider the appropriate medical imaging system selection problem theoretically under uncertainty, and for governments and organizations to design better satisfying medical imaging evaluation systems.

Multi-Criteria Usability Evaluation of mHealth Applications on Type 2 Diabetes Mellitus Using Two Hybrid MCDM Models: CODAS-FAHP and MOORA-FAHP

People use mHealth applications to help manage and keep track of their health conditions more effectively. With the increase of mHealth applications, it has become more difficult to choose the best applications that are user-friendly and provide user satisfaction. The best techniques for any decision-making challenge are multi-criteria decision-making (MCDM) methodologies. However, traditional MCDM methods cannot provide accurate results in complex situations. Currently, researchers are focusing on the use of hybrid MCDM methods to provide accurate decisions for complex problems. Thus, the authors in this paper proposed two hybrid MCDM methods, CODAS-FAHP and MOORA-FAHP, to assess the usability of the five most familiar mHealth applications that focus on type 2 diabetes mellitus (T2DM), based on ten criteria. The fuzzy Analytic Hierarchy Process (FAHP) is applied for efficient weight estimation by removing the vagueness and ambiguity of expert judgment. The CODAS and MOORA MCDM methods are used to rank the mHealth applications, depending on the usability parameter, and to select the best application. The resulting analysis shows that the ranking from both hybrid models is sufficiently consistent. To assess the proposed framework’s stability and validity, a sensitivity analysis was performed. It showed that the result is consistent with the proposed hybrid model.

Analysis of a Robot Selection Problem Using Two Newly Developed Hybrid MCDM Models of TOPSIS-ARAS and COPRAS-ARAS

Traditional Multi-Criteria Decision Making (MCDM) methods have now become outdated; therefore, most researchers are focusing on more robust hybrid MCDM models that combine two or more MCDM techniques to address decision-making problems. The authors attempted to create two novel hybrid MCDM systems in this paper by integrating Additive Ratio ASsessment (ARAS) with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Complex PRoportional ASsessment (COPRAS). To demonstrate the ability and effectiveness of these two hybrid models i.e., TOPSIS-ARAS and COPRAS-ARAS were applied to solve a real-time robot selection problem with 12 alternative robots and five selection criteria, while evaluating the parametric importance using the CRiteria Importance Through Inter criteria Correlation (CRITIC) objective weighting estimation tool. The rankings of the robot alternatives gained from these two hybrid models were also compared to the obtained results from eight other solo MCDM tools. Although the rankings by the applied methods slightly differ from each other, the final outcomes from all of the adopted techniques are consistent enough to suggest that robot 12 is the best choice followed by robot 11, and robot 4 is the worst one among these 12 alternatives. Spearman Correlation Coefficient (SCC) also reveals that the proposed rankings derived from various methods have a strong ranking relationship with one another. Finally, sensitivity analysis was performed to investigate the effects of weight variation and to validate the robustness of the implemented MCDM approaches.

Multicriteria decision making approach using an efficient novel similarity measure for generalized trapezoidal fuzzy numbers.

Multicriteria Decision Making (MCDM) has a huge role to play while ruling out one suitable alternative among a pool of alternatives governed by predefined multiple criteria. Some of the factors like imprecision, lack of information/data, etc., which are present in traditional MCDM processes have showcased their lack of efficiency and hence eventually it has paved the ways for the development of Fuzzy multicriteria decision making (FMCDM). In FMCDM processes, the decision makers can model most of the real-life phenomena by fuzzy information-based preferences. The availability of a wide literature on similarity measure (SM) emphasizes the vital role of SM of generalized fuzzy numbers (GFNs) to conduct accurate and precise decision making in FMCDM problems. Despite having few advantages, most of the existing approaches possessed a certain degree of counter intuitiveness and discrepancies. Thus, we have attempted to propose a novel SM for generalized trapezoidal fuzzy numbers (GTrFNs) which could deliberately overcome the impediments associated with the earlier existing approaches. Moreover, a meticulous comparative study with the existing approaches is also presented. This paper provides us with an improved method to obtain the similarity values between GTrFNs and the proposed SM consists of calculating the prominent features of fuzzy numbers such as expected value and variance. We use fourteen different sets of GTrFNs, to compare the fruition of the present approach with the existing SM approaches. Furthermore, to show the utility and applicability of our proposed measure, we illustrate few practical scenarios such as the launching of an electronic gadget by a company, a problem of medical diagnosis and finally, a proper anti-virus mask selection in light of the recent COVID-19 pandemic. The obtained results with our proposed SM, for the mentioned FMCDM problems, are analytically correct and they depict the efficiency and novelty of the present article.

A literature review of interval-valued intuitionistic fuzzy multi-criteria decision-making methodologies

Multi criteria decision-making (MCDM) is one of the most popular problems handled by researchers in the literature. Since the interval-valued intuitionistic fuzzy set (IVIFS) theory generates as realistic as possible evaluation of linguistic expressions, researchers have been expanding traditional MCDM methods to the IVIF environment, especially in the last decade. This study provides a literature review of the relevant articles from several academic databases on applications of IVIF-MCDM methods. The review of 131 publications addresses specific research questions. To understand the research publication trend, this review offers a visual analysis that examines the studies from different perspectives, such as application areas, IVIF-MCDM methods, citations, most relevant journals, and validation methods. One of the most remarkable results of the literature review is that most publications in this field are published in SCIE indexed journals. Another noteworthy issue is that China is the country that produces the most articles in this field. In addition, English journals are mostly selected for the publication of articles. While it is seen that the investment selection problem is chosen mostly as the application area, the TOPSIS method is preferred mostly in the applications. This study stands out as the most comprehensive one that compiles publications containing extended traditional MCDM methods for IVIF sets. This review will be an important reference for future researchers and decision-makers involved in advancing MCDM methods considering vagueness and ambiguity.

Criteria Making in Role Negotiation

Role negotiation is a pivotal step in the role-based collaboration (RBC) process. There are many factors to be considered when decision makers set up criteria to evaluate group performance and agents’ abilities. In this paper, we investigate the applications of three traditional multicriteria decision making methods into role negotiation of RBC, i.e., simple additive weighting, multiplication exponent weighting, and weighted distance. The goal is to acquire ideal and satisfactory methods to set up the evaluation criteria for the role negotiation task. The main contributions of this paper include: 1) a method of generating the matrix of agent qualifications, with a discrete, usually, limited, and finite set of prespecified attributes and values; 2) the upper and lower thresholds of a role’s ability requirements, which are critical parameters for a workable team and its performance; and 3) the potential influences of specialist agents and generalist agents on the group performance. The proposed methods are verified by simulation experiments. The simulation results show that the proposed methods are reasonable, feasible, and practical.