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In this paper, we show the practical application of two recently proposed Multi-Criteria Decision Analysis (MCDA) methods, namely the Stable Preference Ordering Towards the Ideal Solution (SPOTIS) and the Reference Ideal Method (RIM) methods. Both of these methods can utilize the Expected Solution Point (ESP) concept, which allows it to easily reflect the decision-maker's or the customer's preferences and expectations. We show the comparison of those methods in the practical study case of laptop choice for the customer with specific needs and expectations for the hardware expressed using ESP. The comparison also includes rankings built toward an optimal solution to underline the necessity of such an approach as the Expected Solution Point. We also use the recently developed Ranking Comparison (RANCOM) method to identify criteria weights to include customer preferences in the weights. The paper also contains a broad discussion of the obtained results and limitations of the presented methods. [ABSTRACT FROM AUTHOR]

Enterprise resource planning (ERP) adoption literature has a consensus that selecting the right ERP system is one of the most critical success factors in the ERP adoption lifecycle. While choosing a non-fitting ERP system may lead to adoption failures, however very few papers focus solely on this selection phase. Hence, given the criticality of the ERP selection phase, this paper aims to identify and review the different ERP selection methods in extant literature. This research also presents the factors and variables included in each identified selection method in ERP literature. As a result, each method identified was reviewed, analyzed, and summarized. Our main findings suggest that ERP selection is a multi-criteria decision-making (MCDM) problem, with various methods and techniques that can be utilized for such problems. Several MCDM methods have been used in literature, but often complementing more than one method combined at a time. This is since some methods excel in considering factors in uncertain environments, and other methods are best in evaluating qualitative and quantitative factors. Finally, while there are some methods that were used for cloud-ERP selections, there is no clear consensus in extant literature if some methods could best fit specifically cloud-ERP contexts in contrast to on-premises counterparts. [ABSTRACT FROM AUTHOR]

This paper aims to perform a literature review on the use of multi-criteria methods for assessing agricultural sustainability, focusing on the distribution of papers according to year, journal, and countries, the most productive authors, the most frequently used multi-criteria methods and their characteristics, the type of system and the type of agriculture in which these methods are being performed, the methodological approaches and assessment types, and the sustainability dimensions considered. The data collection has been carried out through the Web of Science™ platform on September 3rd, 2021. After a refinement process, 41 papers were selected. The descriptive analysis was carried out through Bibliometrix tool, while content analysis was performed using Nvivo. The descriptive analysis shows that from 2016 to 2021 the scientific production addressing multi-criteria methods to assess agricultural sustainability started to grow markedly in a very rapid matter, reaching an average of 6 papers per year. France and China are the most scientifically productive countries. The content analysis points out that the most used multi-criteria method is the AHP that was used 11 times. The outranking methods, instead, were used only 3 times. In 68% of the papers the Triple Bottom Line was used as dimensions, and in 41% of the papers the spatial applicability was the farming system. The results highlight that doesn't exist many MCDM/A methods for assessing agricultural sustainability, and most of them are compensatory. These results point out that the use of multi-criteria methods in assessing agricultural sustainability is still underexplored and can be improved. [Display omitted] • Multicriteria methods are powerful tools for assessing agricultural sustainability. • A literature review on agricultural sustainability evaluation was performed. • The number of studies has been increasing in the last five years. • Most of the approaches are based on compensatory multicriteria methods. [ABSTRACT FROM AUTHOR]

Machine Learning as a Service, MLaaS refers to cloud-based platforms that provide machine learning tools and infrastructure to users, allowing them to access and utilize machine learning capabilities without managing the underlying hardware and software. Cloud service providers offer scalable and flexible resources, enabling businesses to train and deploy machine learning models efficiently and cost-effectively. Multi-Criteria Decision making, MCDM helps individuals or organizations make choices when multiple criteria or objectives are involved. It aims to find the best alternative among a set of available options by considering various criteria or factors that are relevant to the decision. Machine learning as a service (MLaaS) and MCDM are two distinct concepts that can be combined to create powerful decision-making solutions. The present paper proposed an integrated approach based on the Analytic Hierarchy Process and TOPSIS method for choosing the best MLaaS cloud. Four alternatives for MLaaS cloud were analyzed based on seventeen shortlisted criteria. Based on the calculations, conclusions have been reached and potential study areas have been identified. [ABSTRACT FROM AUTHOR]

Decision analysis is a key aspect in improving slums intervention strategies utilizing a variety of tools to evaluate all relevant information that support decision-making process for dealing with the complexity of slums interventions policies. This paper presents a framework integrating Multi Criterion Decision Analysis (MCDA) with (GIS) based decision support tool to quantitatively assess land suitability for site relocation of an urban slum site utilizing Analytical Hierarchy Process (AHP), which is commonly used for fastest growing decision-analytic techniques in several disciplines for the determination of various aspects weights. Herein Batn Al Baqara region (which is located in south of Cairo) was selected as study area to be relocated as it was considered among the most difficult to deal with, three proposed relocation sites were selected and analyzed using MCDA-GIS Model. Twelve different criteria were utilized to evaluate the degree of suitability of the relocation sites. A five degree of suitability were employed according to the different criteria scores for each site. The results supported that Al Asmarat is the most suitable site for the relocation of slum since it achieved the highest evaluation score (84.92 %) when the model was applied to the relocation sites. [ABSTRACT FROM AUTHOR]

Multi-attribute decision-making problem (MADM) is a kind of multi-criteria decision-making (MCDM), which is often used to solve decision-making problems where the decision variables are discrete and the number of decision-making schemes is limited. This paper reviews the basic definition of multi-attribute decision making (MADM) through the performance evaluation of science and technology funds in a city, and then deduces the TODIM decision making method and TOPSIS decision making method in detail. Finally, the clue case is decided by combining these two decision making methods. This paper specifically shows the usage scenarios and limitations of the TODIM decision method and the TOPSIS decision method through the clue case, analyzes the advantages and disadvantages of the two methods through the comparison of the two methods, and puts forward suggestions for the clue case based on the results of the two schemes. [ABSTRACT FROM AUTHOR]

Most Multi-Criteria Decision Analysis (MCDA) methods identify only discrete, defined sets of alternatives. Such identification does not provide an opportunity to consider a more significant number of samples acquired over long periods, which is not a desirable effect. An additional aspect of identification is the frequent application of expert knowledge, whereby a continuous model would have to be defined to consider further alternatives that would be suitable for further use. This paper considers a new approach for identifying continuous decision models named SITCOM. The purpose of this paper was to conduct a study on the accuracy of the proposed approach. The accuracy of Stochastic IdenTifiCation Of Models (SITCOM) was tested using sample evaluations obtained from fuzzy reference models (FRM). Spearman's weighted correlation coefficient was used to measure the accuracy of the created models. The experiments showed the dependence of accuracy on the number of characteristic objects. Furthermore, the obtained results prove the reliability of the obtained results, where the trends of accuracy decrease based on the number of characteristic values. [ABSTRACT FROM AUTHOR]

In contemporary healthcare systems, the selection and judicious choice of the right medical device becomes paramount consideration for accurate diagnosis, patient care, mitigate risk and cost incurred on diagnosis of disease. Moreover, medical devices are requisite indispensable tools to help healthcare professionals for effective diagnosis, selection of the right treatment amongst different options, and continuous monitoring of the patient. Thus, to select the best manufacturing company is as equally important to achieve the desired goals. This paper presents a decision making algorithm based on fuzzy theory that helps the decision maker in selecting the most suitable worldwide medical device manufacturer. Fuzzy theory is extensively used in managing databases and analyzing data to summarize information and draw meaningful conclusions. This paper utilizes the aggregation operators within the context of the complex q-rung orthopair fuzzy environment by utilizing Yager t-norm and t-conorm operations. Some aggregation operators are applied, namely, complex q-rung orthopair fuzzy Yager weighted geometric operator, complex q-rung orthopair fuzzy Yager ordered weighted geometric operator, complex q-rung orthopair fuzzy Yager weighted averaging operator and complex q-rung orthopair fuzzy Yager ordered weighted averaging operator. Additionally, the notable properties of these operators are proved. These aggregation operators are generated specifically for multi-criteria decision-making. Decision making algorithm is used to choose the most relevant medical device manufacturer (alternative) to validate its efficacy. The results demonstrate the versatility of parameters, adaptability to multi-criteria decision making, and efficiency of the proposed aggregation operators and algorithm. Test with specific criteria is also conducted to verify the legality and validity of the decision making algorithm. At last, the acquired results underwent a comparative analysis against various established methodologies, confirming the method's capacity to produce precise and accurate outcomes. • Multi criteria decision making algorithm is devised in fuzzy environment. • Aggregation operators are introduced and notable properties are investigated. • Test with specific criteria is conducted for the validity of proposed operators. • The algorithm is utilized in medical industry to choose best device manufacturer. [ABSTRACT FROM AUTHOR]

• Life Cycle Engineering has been demonstrated as a viable approach for space mission design. • The life cycle sustainability impacts of SmallSats have been benchmarked for the first time. • Multi-Criteria Decision Analysis is a useful technique for identifying significant hotspots. • Improvements measures are more successful with increased levels of participant interaction. The application of Life Cycle Engineering (LCE) within the concurrent engineering process presents a viable method for assessing environmental, social and economic impacts of space missions. Despite this, the novelty of the concept within space mission design has meant that the approach has not yet been widely implemented. This paper successfully demonstrates this technique for the first time and presents LCE results of three SmallSat missions designed at the University of Strathclyde using the concurrent engineering approach. The Strathclyde Space Systems Database (SSSD) was deployed to calculate the total life cycle impacts of each mission, including the identification of common design hotspots. A novel technique called Multi-Criteria Decision Analysis (MCDA) was also trialled, whereby several impact categories were converted into single scores as a test case to reduce the learning curve for engineers. Overall, the LCE results indicate that the manufacturing & production of the launcher dominate the majority of impact categories. Other common hotspots were found to relate to the use of germanium as a substrate as well as the launch event. As an additional observation, in terms of the behavioural aspects, it was clear that study participants were more open to the concept of LCE with each new concurrent engineering session, evidenced by increasing levels of interaction amongst study participants. These findings are intended to provide industrial stakeholders with a preliminary benchmark relating to the general sustainability footprint of SmallSats, whilst demonstrating the viability of integrating LCE within the concurrent engineering process of space missions. [ABSTRACT FROM AUTHOR]

As an important carrier of multi-energy coupling of regional integrated energy system, the integrated energy station integrates a variety of energy coupling devices, which can fulfill interconnection and complementarity of energy between regions and enhance the economic and environmental benefits of energy use. In the planning of integrated energy stations, it is necessary to consider the characteristics of regional resource endowments and load energy consumption. In this paper geographic information system (GIS) is introduced into the site selection planning of integrated energy stations, and a location decision model is built based on GIS & improved TOPSIS. In order to verify this for the feasibility of the model, Tianjin, China is selected as a representative case for empirical research. First, according to Chinese national standards and local planning requirements, GIS is adopted to initially screen the distribution and area of exclusion areas and viable areas for construction in the study area. Secondly, a comprehensive evaluation index system is established for nature, economy and society, in which the AHP method is used for calculate the weight of each index to obtain the final optimal alternative with the integration of the GIS geographic layer overlay function. Finally, the improved TOPSIS model is used to rank the alternatives and obtain the ranking results. Sensitivity analysis and comparative analysis is conducted on the ranking results, indicating a higher priority of selecting sites A1, A4, A5 and A11. Analysis result effectively verifies that the improved TOPSIS method can improve the accuracy of the site selection decision for integrated energy stations. This paper can provide support for the site selection and layout of integrated energy stations, effectively improve the decision-making level and work efficiency of decision-makers, and enrich the application space of GIS methods in the energy field. •Introduction of GIS method into the site selection of integrated energy stations. •Identify the best location and construction area of the integrated energy stations. •Establish a MCDM index system for siting integrated energy stations. •Construction of "GIS-AHP-improved TOPSIS" siting decision model. •Meet the integrated energy stations current and future transformation needs. [ABSTRACT FROM AUTHOR]

• Study analyzes 1374 papers on sensitivity analysis in Scopus. • RStudio, CiteSpace, and VOSviewer are used. • China has most articles published; India leads in international cooperation. • Top three cited journals: J. Clean. Prod., Expert Syst. Appl., and Comput. Ind. Eng. • Most cited authors are Pamučar D, Kahraman C, and Zavadskas EK. In the present era, the implementation of scientifically grounded Multi-Criteria Decision Making (MCDM) has emerged as a pivotal solution to diverse decision-making challenges across various domains. Although a substantial body of exploratory, conceptual, and experimental studies exists, only 9.457% studies have incorporated sensitivity analyses to assess the robustness of MCDM methods. An exhaustive scientific exploration of sensitivity analysis within the scope of MCDM is thus lacking. This research aims to address this gap through Bibliometric Analysis while examining 1374 articles published between January 2000 and March 2023 from the Scopus database. Using RStudio (Biblioshiny), CiteSpace, and VOSviewer software, the study constructs a visual representation of the most prolific countries, institutions, and authors. Impressively, China takes the lead in article publications, while India excels in international collaboration. "An extended TODIM multi-criteria group decision-making method for green supplier selection in an interval type-2 fuzzy environment," featured in the Journal of Environmental Management, emerges as the most cited paper with a total citation of 455. The study also identifies the top three most cited journals, namely "Journal of Cleaner Production," "Expert Systems With Applications," and "Computers and Industrial Engineering." "North China Electric Power University" is the leading institute with the highest research outputs. "Pamučar D" is the most cited author, with 2594 citations and 39 articles, followed by "Kahraman C" and "Zavadskas EK. This study sheds light on trends in scientific developments and collaborations, providing a model for the application of sensitivity analysis in MCDM research and highlighting global trends. An understanding of the current state of sensitivity analysis research can assist researchers working in the entire domain of MCDM. Additionally, the visualization provides prescriptive data for future work and applications related to sensitivity analysis. [ABSTRACT FROM AUTHOR]

Over the last twenty years, patient opinions of the received healthcare services have emerged as a crucial quality indicator and a valuable instrument for evaluating the service quality of health organizations. However, many surveyors still utilized uncertain information for conducting this evaluation process. Whereas, in the past few years, the issues of inconsistency, indeterminacy, and uncertainty in data have been growing and have become a matter of concern in dealing with decision-making issues. This paper proposes an improved RAFSI method based on a single-valued trapezoidal neutrosophic number (SVTraNN) as a multi-attribute framework for dealing with a healthcare service quality evaluation. The proposed framework consists of two features: a feature for determining numerical criteria weights involving the procedures of the Ordinal Priority Approach (OPA) and a feature for selecting the best polyclinic through contributing our new SVTraNN Harmonic and arithmetic mean to yield criterion functions in the RAFSI method. This paper then compares the results obtained by the proposed method with other SVTraNN decision-making methods. A comparative analysis and rank reversal testing of the improved method demonstrated that the multi-attribute framework proffers the option of capability and stability in overcoming inconsistency, indeterminacy, and uncertainty information efficiently during the healthcare service quality evaluation. [ABSTRACT FROM AUTHOR]

• The centroid distance of PFNs is proposed and its properties are discussed. • Propose a score function based on the centroid distance and discuss its properties. • Applications of Pythagorean fuzzy clustering, ranking and MCDM are developed. • Some advantages of the proposed methods are showed through comparative analysis. By using Pythagorean fuzzy set (PFS), people can quantitatively describe and process information with fuzziness, and then make reasonable and effective judgments or decisions. In this process, distance measure often serves as a useful and important tool. The purpose of this paper is to explore some information processing methods and techniques in Pythagorean fuzzy environment. Firstly, the research progress of PFS is reviewed, and the main research directions and some hot issues in relevant research fields are discussed. Secondly, by analyzing the information of hesitation degree of Pythagorean fuzzy number (PFN), definitions of hesitation factor and centroid distance measure based on the representation of centroid coordinates of the hesitation regions are proposed, and properties of hesitation factor and centroid distance measure are discussed. Finally, the novel clustering algorithm, ranking method and multi criteria decision making (MCDM) method are developed in Pythagorean fuzzy environment, which are based on the new centroid distance measure. The effectiveness of the proposed algorithm and methods are verified through numerical examples and comparative analysis. The research results indicate that the centroid distance measure proposed in this paper can effectively characterize the distance between PFNs, thus making the clustering algorithm of PFNs, the ranking method of PFNs, and the Pythagorean fuzzy MCDM method have obvious superiorities and advantages. [ABSTRACT FROM AUTHOR]

It is common practice in the MCDA to use several multi-criteria decision methods and then compares obtained rankings with one or two different rank correlation coefficients. The problem is that different rank correlation coefficient gives different values for the same pair of rankings, and the number of studies which tries to investigate it is small. Studying the similarity of rankings is a very important challenge in multi-criteria decision support, and the coefficients themselves seem to be the most practical ways of evaluating rankings. This paper compares chosen rank correlation coefficients to show how much different they are. Spearman's, Weighted Spearman's, Kendall Tau and Rank similarity correlation coefficient are compared statistically. The paper confirms that the coefficients are closely related, and their dependence is graphically represented, which initiates research towards allows for their better selection in the future. In conclusions, directions of further development are indicated. [ABSTRACT FROM AUTHOR]

Models of Fuzzy Multi-Criteria Decision Analysis (FMCDA) are based, as a rule, on different approaches to fuzzy extension of source MCDA methods. For this, simplified models are used to approximate the functions of fuzzy variables with propagation of parametric fuzzy numbers (FNs) through all calculations. In this paper, authors suggest a novel approach to fuzzy extension of MCDA methods, for PROMETHEE-I/II, through development of fuzzy PROMETHEE-I/II (FPOMETHEE-I/II) models of different complexity: in addition to simplified models, the standard fuzzy arithmetic (SFA), and transformation methods (TMs) are implemented for assessing functions of FNs corresponding to these models. For ranking of alternatives, two defuzzification based, and one pairwise comparison ranking methods are implemented within the developed models. Special attention is paid to analysis of the overestimation problem, which can occur when using SFA in the presence of dependent variables in corresponding expressions, and to "proper fuzzy extensions" of PROMETHEE-I/II (i.e., results of all functions of FNs within the model are in accordance with the extension principle) based on TMs and, for some models, on the SFA. One of the key goals of this contribution is comparison of the distinctions in ranking alternatives by different FPROMETHEE-II models. It is demonstrated by evaluating a large number of scenarios based on Monte Carlo simulation that the probability of distinction in ranking alternatives by "proper" and "approximated" FPROMETHEE-II models may be considered as significant for ranking multicriteria problems. Another goal of this paper is analysis of the correctness of FPROMETHEE-I/II models with respect to the basic MCDA axiom related to ranking of dominated and dominating alternatives. Authors demonstrate that the basic axiom can be violated, in the general case, by all developed FPROMETHEE-I/II models and suggest an approach to fix this problem. [ABSTRACT FROM AUTHOR]

The probabilistic hesitant fuzzy sets (P-HFSs) allow experts to express their preferences regarding different membership degrees with probabilistic information. Fuzzy entropy is an important concept to measure the uncertainty of probabilistic hesitant fuzzy information. In this paper, we propose a novel fuzzy entropy named as the exponential probabilistic hesitant fuzzy entropy for the P-HFEs. This novel fuzzy entropy not only observes the basic property of fuzzy entropy, but also has the same characteristics and change tends with the existing fuzzy entropy proposed and studied by other scholars. Then, based on the cardinal reconciliation method and TODIM(Tomada de decisao interativa multicriterio) method, we propose a multi-criteria decision-making (MCDM) problem, where the attribute weight is determined by the exponential probabilistic hesitant fuzzy entropy of the integration results. Finally, a application case in green building is given for the sake of illustrating the efficiency of the MCDM problem. Numerical and theoretical results show that a MCDM problem based on the proposed novel fuzzy entropy and the TODIM method have a wide range of application. [ABSTRACT FROM AUTHOR]

Health-care waste (HCW) contains numerous hazardous, infectious, and radioactive contents that may cause serious health issues for society and drastic environmental problems. Therefore, it is crucial to dispose of HCW safely and appropriately. Four commonly used HCW disposal methods are incineration, steam sterilization, microwave, and landfill disposal. To select an appropriate HCW disposal method, there are several conflicting criteria. Furthermore, when information is lacking due to uncertainty and criteria weighing conditions are also unknown, the decision to choose an appropriate HCW disposal method becomes more challenging. So, the objective of this paper is to introduce a novel integrated multi-criteria group decision-making (MCGDM) approach to evaluate HCW disposal methods in which incomplete information is quantified using intuitionistic fuzzy sets (IFSs), unknown criteria weights are evaluated using the maximizing deviation method, and the evaluation of HCW disposal methods is done by applying the Archimedean t -norm and t -conorm-based interactive intuitionistic fuzzy weighted aggregated sum product assessment (WASPAS) method. Herein, the Archimedean t -norm and t -conorm cover a wide range of t -norms and t -conorms, including the Algebraic, Einstein, Hamacher, and Frank, etc., by adopting prescribed combinations of additive generators. The proposed MCGDM approach has been applied to evaluate the above-listed four HCW disposal methods for a case study conducted by Mishra et al. (2020) in the context of India. A sensitivity analysis is performed to examine the impact of changes in the parameters involved in the proposed MCGDM approach. A comparative study is also performed with some well-known multi-criteria decision making(MCDM) and MCGDM methods to demonstrate the consistency and stability of the proposed approach. Findings suggest that the newly developed MCGDM approach is more general, flexible, and provides realistic results to evaluate HCW disposal methods under uncertainty and unknown criteria weight information. • The paper proposes an Archimedean t-norm and t-conorm based IF-WASPAS method with unknown criteria weights to evaluate HCW disposal techniques. • The method applies IFS to quantify uncertainty while unknown criteria weights are evaluated using the maximizing deviation method. • Method has been applied to evaluate HCW disposal alternatives in context of India. • Sensitivity and comparative analyses are also conducted to examine the consistency and stability of the method. • The proposed MCDM approach is general,flexible, and provides realistic results under uncertainty and unknown criteria weights. [ABSTRACT FROM AUTHOR]

Due to the unavailability of time series data for newly listed stocks or products, it is a challenge for investors to make rational portfolio selection under uncertain circumstances. To solve the problem, a new approach is put forward in this paper. Firstly, the problem is considered as a multi-criteria decision making (MCDM) problem based on the assumption that the assessments are given in intuitionistic fuzzy set (IFS) form, which can better describe the uncertainty. Then, the TOPSIS method, which is widely used in MCDM problem, has been modified in two aspects. On the one hand, the new definitions of Absolute Positive Ideal Solution (APIS) and Absolute Negative Ideal Solution (ANIS) are proposed to represent the best returns and the greatest cost/risk in portfolio selection. They are more meaningful than the definitions of Positive Ideal Solution (PIS) and Negative Ideal Solution (NIS), which could only express the best and the worst case of portfolio but cannot achieve a balance between returns and risk. On the other hand, the weighted closeness coefficient is refined to offer the more appropriate results that are consistent with investors' demands or preferences. In addition, based on distance measure of IFSs, several novel linear programming models with different constraints are proposed to allocate the investment ratios according to investors' demands. The models can make up for the disadvantage of TOPSIS method which only considers the ranking of investments but neglects the investment ratios. Finally, compared with the conventional TOPSIS method and the IF ELECTRE Method in a numerical example, our new approach is demonstrated to be more effective and more flexible. Particularly, it can provide a more appropriate strategy in accordance with investors' preferences and demands. [ABSTRACT FROM AUTHOR]

• Investigating traditional village human settlement suitability by multi-source data. • Propose a model for evaluating human settlement suitability at the village scale. • Exploring the drivers of human settlement suitability by geographical detectors. • Investigating influencing mechanisms of human settlement suitability by MGWR method. Traditional villages are invaluable cultural heritage sites in China, reflecting a rich historical and cultural legacy. However, rapid urbanization has led to the destruction and loss of numerous traditional villages. The remaining traditional villages also face challenges in preserving their original living environment. In order to assess human settlement suitability and identify potential factors, this paper proposes a village-scale model for assessing human settlement suitability. This paper used an eight-factor evaluation system, multi-criteria decision analysis and spatial visualization methods to evaluate the scores and spatial characteristics of human settlement suitability of 688 traditional villages in Hunan Province. This paper also examines the factors affecting cluster suitability using geographic detectors and multi-scale geographically weighted regression (MGWR) models. This study found that the quality of human settlement suitability varied widely. The overall quality is low and very few areas achieve medium or higher scores. Among them, the traditional villages in Huaihua city, Zhangjiajie junction, southern Hengyang city, northwestern Yongzhou city, central Chenzhou city and Changsha city exhibit higher scores. Secondly, the commercial facility density and normalized difference vegetation index (NDVI) are important factors driving the spatial changes. And there is a significant interaction effect between them. Additional factors impacting spatial variation include annual average temperature, per capita disposable income, and average house price. This comprehensive assessment reveals spatial characteristics and potential influencing factors. The results of the study provide an empirical basis and decision-making foundation for the protection and development of traditional villages, which will ultimately contribute to the sustainable development of China's rural areas. [ABSTRACT FROM AUTHOR]

Due to more and more awareness of environmental conservancy and sustainable development, automated container terminals (ACTs) have gained increasing attention and become popular worldwide. However, there is a crucial challenge in implementing ACTs, i.e., how to develop terminal layout planning (including terminal layout and facility selection) to realize performance goals. Focusing on this challenge, the aim of this paper is to resolve how to appropriately select terminal layout and facilities in ACTs with respect to various performance criteria. Several research limitations can be gathered from the existing literature: (i) Most of the existing works mainly develop optimization technologies to tackle layout optimization and facility comparison issues within a few types of terminal layout and facilities, yet few researchers have proposed evaluation methods for making selection decisions on terminal planning. (ii) Only a few types of criteria are considered, and requirements and preferences of different participants are ignored, not to say realizing an optimal compromise by employing comprehensive criteria weights in evaluation processes. (iii) New technological improvement in ACTs has not received much attention, and there still lacks a method to precisely quantify the concerned criteria. Accordingly, this paper introduces a criteria system comprising economic, environment, social, resource and performance groups and develops an agent-based simulation method considering different terminal layout and facilities to determine criteria values. A multi-criteria decision making (MCDM) framework is proposed for making the most reasonable and appropriate selection decisions, which employs a variation coefficient weighted method to calculate comprehensive criteria weights, which integrate subjective and objective weights obtained from AHP (analytic hierarchy process) method and entropy weight method. To overcome rank reversal problems in the TOPSIS (technique for order preference by similarity to an ideal solution) method, a new improved rank reversal robust TOPSIS method, called NR-TOPSIS, is utilized to rank and select alternatives, which is ranking stable in the experiments performed. An illustrative application on a container terminal in China demonstrates the effectiveness of the proposed MCDM method and sensitivity and comparative analysis verifies the robustness and stability of the method. [ABSTRACT FROM AUTHOR]

This study presents a review of literature on the usage of artificial neural networks (ANNs) architecture contribution method and structural equation modeling (SEM), and proposes a new selection process in the context of algorithm -based SEM-ANNs schemes. This study enriches academic literature by providing a review of all the main aspects of customization in ANNs and contribution methods in combination with SEM. Academic databases are examined for exhibition findings, yielding 253 papers published between 2016 and 2022. The retrieved papers are categorized according to inclusion criteria, and the final set of 73 articles are discussed based on two directions, namely, 'Sector-based' and 'Algorithm-based' as a new representation of taxonomy research. A state-of-the-art bibliographic analysis is presented. This review also identifies modern challenges and open issues in terms of multiple evaluation criteria, importance criteria, and data variations related to the selection of customizations in ANNs and contribution methods combined with SEM in different industrial cases. Several issues fall under multicriteria decision making for handling complexity problems in different ANNs and contribution methods. Thus, this study also presents a research proposal and recommends a solution based on a three-phase methodology for handling the selection and overcoming the identified issues, subsequently completing a strategic guideline solution. [ABSTRACT FROM AUTHOR]

Multi-criteria decision making (MCDM) methods have become increasingly popular in site selection decision-making of renewable energy power plants because they consider multiple conflicting goals and decision-maker preferences. In this paper, a systematic literature review of MCDM applications for renewable energy site selection is performed, covering a total of 85 papers published from 2001 to 2018 in high-level journals (chosen from the ScienceDirect database). This paper has summarized the exclusion criteria and evaluation criteria of site selection for five energy sources. The five site selection stages, criteria selection, data normalization, criteria weighting, alternative evaluation and result validation, are revealed by content analysis. All papers are further classified by the date, energy source, journal of publication, country of author affiliation, and study area. It is found that different energy sources emphasize different criteria; however, some similarities exist. Literature surveys and expert opinions are most common criteria selection method. Reclassification is most frequently used data normalization method. The analytic hierarchy process is popular for weighting. In alternative evaluation, geographic information systems and weighted linear combination are the most popular tools. Varying the criteria weights is most commonly used result validation method. • This is the first review of MCDM applications for renewable energy site selection. • It covers five energy sources: solar, onshore wind, offshore wind, wave and tidal. • It focuses on two types of criteria and methods in the five site selection stages. • It also involves methods for data normalization and result validation. • The combination of GISs and MCDM is the most popular tool in this research area. [ABSTRACT FROM AUTHOR]

Complex environments, conflicting criteria, uncertainties, and inaccurate information are some characteristics of many nowadays real-world decision-making problems. The Multi-Criteria Decision-Making (MCDM) methodology is one of the most used, perhaps because it drives decision-making processes to be more rational, efficient, and effective. Thus, the Armed Forces of several countries have used MCDM methods in the Life Cycle Management of Defense Systems (LCM-DS) to deal with military systems' complexity, breadth, and sensitivity. However, despite the rich and vast literature available on several MCDM methods applied to LCM-DS problems, there is a lack of a recent bibliometric study on this relevant subject. This paper presents an analysis of a quantitative approach based on bibliometric metrics to evaluate the application of MCDM methods in LCM-DS. By using the Scopus and Web of Science databases as references, this study focuses on the identification of the following aspects related to the research in this field: distribution of scientific publications, most relevant sources, interaction dynamics among these sources, most prominent subjects in recent research (trend topics), and application of MCDM methods in LCM-DS. Regarding the latter, this study identified the Analytic Hierarchy Process (AHP) as the most used method in the LCM-DS since almost a quarter of the analyzed works applied it in the original, adapted, or hybrid (combined with other methods) format. Finally, our results show that MCDM methods are commonly applied to LCM-DS problems in all life cycle stages, mainly in development, utilization, and support. [ABSTRACT FROM AUTHOR]

TOPSIS and VIKOR are two well-known and widely-used multiple attribute decision making methods. Many extensions of these methods have been proposed that either use different techniques to rank alternatives or utilize fuzzy logic to handle alternatives and criteria that are unquantifiable and/or incomplete. In this paper, we present the implementation of a web-based decision support system that incorporates TOPSIS and VIKOR to solve multicriteria decision making problems either in a nonfuzzy or in a fuzzy environment. The aim of this paper is to present a tool that will be used by decision makers to compare various alternative solutions and understand how robust a decision will be. The proposed system can be used both in single and in group decision making problems. In addition, we review several variations for each step of these methods and implement different techniques when applicable. Hence, decision makers can experiment with different techniques in each method. We implement ten normalization techniques, three methods for the calculation of the ideal and anti-ideal solutions, fifteen distance metrics, five fuzzy distance metrics, and eight defuzzification techniques. An illustrative example is presented to highlight the key features of the implemented system and the different scenarios that can be built using the proposed DSS. [ABSTRACT FROM AUTHOR]

Hydrogen production from renewable energy sources appears to be an interesting solution for reducing greenhouse gas emissions and ensuring the energy security supply. This paper develops an integrated framework to evaluate land suitability for hydrogen production from solar energy site selection that combines multi-criteria decision making (MCDM) with geographical information systems (GIS); an application of the proposed framework for Algerian country. In GIS two types of criteria will be taken: constraints and weighting criteria. Constraints criteria will make it possible to reduce the area of study by discarding those areas that prevent the implementation of installing solar hydrogen production systems. These criteria will be obtained from the legislation (land use, water bodies, waterways, roads, railways, power lines, and also their buffer around them). Weighting criteria will be chosen according to the objective to be reached, in this case they will be the hydrogen demand, potential solar hydrogen production, digital elevation models (DEMs), slope, proximity to roads, railways, and power lines. Through the use of MCDM the criteria mentioned will be weighted in order to evaluate potential sites to locate a solar hydrogen production installation system. Analysis and calculation of the weights of these criteria will be conducted using Analytic Hierarchy Process (AHP). As a result, the final index model was grouped into four categories as "very low suitability", "low suitability", "moderate suitability" and "high suitability" with a manual interval classification method. The results indicate that 10.34% (246,272.02 km2), of the study area has very low suitability, 60.75% (1,446,907.65 km2) has low suitability, 6.68% (159,100.3 km2) has moderate suitability and 0.49% (11,669.21 km2) has high suitability for a solar-powered hydrogen production installation system. The other 21.74% (517,790.5 km2) of the study area is not suitable for such projects. The sensitivity analysis highlights that the suitable sites for solar hydrogen production installation system are dependent on the weights of the criteria that influence the decision. The MCDM methodology integrated with GIS is a powerful tool for effective evaluation of the solar-powered hydrogen production sites selection. • The paper addresses the problem of site selection for solar hydrogen projects. • Land suitability index is computed to determine the best sites. • MCDM coupled with GIS is a powerful tool for effective evaluation of the solar hydrogen production sites selection. [ABSTRACT FROM AUTHOR]

The practical capabilities of decision support systems are beneficial in many complex problems. They aim to equip the decision-maker with knowledge about the preferred choices from the set under consideration. Often, there are minor differences between the alternatives analyzed, making it necessary to determine whether changes in the parameters characterizing them can influence their final position. For this purpose, a sensitivity analysis of the solutions can be used to examine the resistance of the results to changes. In this paper, we address the problem of performing the sensitivity analysis using MCDA methods. Most studies focus on testing the sensitivity of rankings of popular methods. However, their main disadvantage is their susceptibility to the phenomenon of ranking reversal. It may distort the results obtained in a sensitivity analysis. Therefore, it is worth using methods resistant to the ranking reversal phenomenon for this purpose. The SPOTIS and COMET methods were used in a theoretical multi-criteria problem, where the calculated results were then subjected to sensitivity analysis. The study showed that the SPOTIS method tends to be more robust to the computed results. [ABSTRACT FROM AUTHOR]

Multi-Criteria Decision Analysis (MCDA) methods enable a comprehensive analysis of decision options to identify the most preferred values. However, the abundance of these techniques, using different transformations in the calculations, can cause a divergence of results. However, the resulting rankings should be as consistent and reliable as possible. In this paper, we used the TOPSIS, COPRAS, SPOTIS, and COMET methods to examine the consistency of their results in the practical problem of laptop evaluation. The rankings were then compared with the reference ranking. The research showed that among the methods listed, the COPRAS method's results differed the most from the others. In turn, this technique ensured a ranking in line with the reference ranking. Based on the results, it can be established that the methods used are characterized by high consistency of results. [ABSTRACT FROM AUTHOR]

Assessment of the sustainability of countries for afordable and clean Energy requires a framework with relevant criteria, simultaneous inclusion of multiple criteria in the evaluation, and consideration of the dynamics of changes in performance over time. A wide variety of multi-criteria decision analysis (MCDA) approaches using different algorithms for multi-criteria aggregation are currently available. These methods are suitable for sustainability assessment because they allow the consideration of multiple criteria, the construction of a hierarchical structure of criteria, and the assignment of significance to criteria weights. However, MCDA methods have the limitation of being able to evaluate a data set for a single moment in time, which is insufficient for the sustainability paradigm. The mentioned limitation of MCDA motivates researchers to develop approaches that adapt MCDA methods to temporal assessment. The purpose of this paper is to present the application of a method called Data vARIability Assessment Technique for Order of Preference by Similarity to Ideal Solution (the DARIA-TOPSIS method) for multi-criteria temporal assessment of European countries for affordable and clean energy. This aspect of sustainability is included in Sustainable Development Goal 7 proposed by United Nations in the 2030 Agenda for Sustainable Development. The DARIA-TOPSIS method combines the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for multi-criteria evaluation with a Gini coefficient-based measure of result variability. The method has identified the best-scored countries in terms of implementing sustainable energy policies focused on affordable and clean energy, including Norway, Iceland, and Sweden. [ABSTRACT FROM AUTHOR]

This paper presented optimization of machining parameters for high pressure abrasive water jet (AWJ) cutting of Hardox 500 steel utilizing WASPAS approach. Weighted aggregated sum product assessment (WASPAS) method is explored as an effective MCDM tool while solving advanced manufacturing decision making problems. It is a unique combination of two well-known MCDM approaches, i.e. weighted sum model (WSM) and weighted product model (WPM). The tests were caried out according to the orthogonal matrix (Taguchi) L27. The control parameters of process such pressure, abrasive flow rate and traverse speed was optimized under multi-response conditions namely cutting depth and surface roughness. The optimal set of control parameters was established on the ground of the overall weighted criterium. Effects shows that optimal set for high cutting depth and small surface roughness are high pressure, high abrasive flow rate and small traverse speed. Results of research have shown that machining efficiency at keeping satisfactory level quality of cut surface can be improved this approach. [ABSTRACT FROM AUTHOR]

While uncertainty quantification (UQ) has served a prominent role in ensuring the safety of dynamical engineering systems, the lack of an integrated approach to handle the aleatory nature of ground motion records, a.k.a., record-to-record (RTR) variability, remains a bottleneck in seismic design. This paper presents a novel approach with two key features. First, on the multi-criteria decision-making (MCDM) front, a general-purpose collective decision support system is introduced by integrating Monte Carlo simulation, automated machine learning (AutoML), and a hybrid fuzzy-outranking MCDM technique. This allows for robust uncertainty capture at a group level without compromising computational efficiency—a departure from the traditional trade-off between MCDM competency in uncertainty handling and computational burden. Second, from an Earthquake Engineering perspective, the established AutoML-based community-level MCDM approach is combined with an efficient metamodel-aided reliability-based design optimization technique and a novel probabilistic-fuzzy seismic-design safety index. Conflicting criteria are classified into structural performance, design safety, and construction cost metrics. The results indicate that the developed intelligent seismic design paradigm properly captures uncertainties rooted in the RTR variability, input data, criterion weighting, and decision-makers' preferences from a community-level standpoint. It also offers acceptable error metrics during the ranking procedure in a user-friendly environment, at a significantly reduced computational expenditure. Moreover, it can be concluded that there is no imperative need for developing complex decision tools for robust-to-uncertainty seismic design. The implementation of the proposed MCDM-based UQ framework has reduced the design cost of an existing dam by 17%. [Display omitted] • A general-purpose stochastic-fuzzy multi-criteria decision-making tool is introduced. • An MCDM-based uncertainty quantification tool is suggested for robust seismic design. • A probabilistic-fuzzy seismic-design safety index captures ground motion variability. • An automated machine-learning pipeline reduces computational decision-making costs. • A hybrid stochastic-fuzzy MCDM dataset for earthquake/risk engineers is developed. [ABSTRACT FROM AUTHOR]

With the constant changes in the decision-making environment, various limitations have emerged in traditional decision-making methods, such as the inability to simultaneously consider multiple factors such as randomness, hesitation, and ambiguity, thus limiting a comprehensive grasp of the decision-maker's cognitive inputs. To address the above problems, we propose a multi-criteria decision-making (MCDM) model based on a new fuzzy set (FS). Firstly, we define a new Z hesitant fuzzy linguistic term set (ZHFLTS) and propose a visualization metric for mapping ZHFLTS into T-spherical fuzzy (T-SF) space to simplify the computation. The relevant properties of ZHFLTS are further investigated. In order to accurately measure the uncertainty of FSs, we propose the probabilistic-hesitation-fuzzy (PHF) entropy and use it as a basis to derive the potential probability distribution using the fruit fly optimization algorithm (FOA). Subsequently, an MCDM model is established based on the proposed ZHFLTS, visualization metric, and PHF entropy. The model can better solve complex decision-making problems such as TCM through example studies and comparative analyses. The research in this paper aims to cope with today's increasingly complex decision-making environments, and the introduced ZHFLTS framework and the corresponding methodology provide new ways to solve complex decision problems. • ZHFLTS is defined and a mapping method to visualize ZHFLTS in T-SF space is studied. • PHF entropy is proposed based on FOA for obtaining potential probability distribution. • A Novel MCDM model based on ZHFLTS is presented. • It proves the effectiveness of the model through instances and comparative analysis. [ABSTRACT FROM AUTHOR]

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. • General normalization methods and distance measures are considered in MCDM methods. • The causes of the same solutions from different MCDM methods are examined. • The characteristics of various MCDM methods are discussed by category. • The relationship between different MCDM methods is summarized. [ABSTRACT FROM AUTHOR]

The major obstacle in designing of the wind turbine/photovoltaic/battery storage system lies in the task of choosing the most optimum solution while simultaneously considering techno-economic objectives. Consequently, this study provides a distinctive amalgamation of multi-objective multi-perspective and group multi-criteria decision-making methodologies to ascertain collections of Pareto optimum configurations and to weight, rank, and pick up the most desirable optimum solutions that represent best trade-off between conflicting objectives. In this paper, the multi-objective Dragon fly algorithm is developed based on mutation scheme and crossover tactic algorithm is advanced to handle a lack of diversity and poor convergence, while non-dominated sorting and crowding distance strategy is addressed to store the best found optimum solutions in the achieve. Loss of load probability, excess energy, and life cycle cost are conflicting techno-economic objectives solved by multi-objective method. The optimum Pareto front solutions are ranked based on hybrid multi-criteria decision-making method for determining the most favorable solution for the WT/PV/BS system. The experimental outcomes indicated that the developed approach has a distinct performance in constructing a collection of Pareto front solutions in terms of diversity, converge, and convergence. Moreover, it demonstrates outstanding results when compared to well-organized multi-objective optimization methods. The future direction can be accomplished by applying the proposed hybrid sizing methodology for solving gird-connected system along with electric vehicle based on techno-economic scenarios. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) technology provides new possibilities for a variety of applications, but it also comes with challenges influenced by distinct aspects, such as the size of organizations that use such technology. Therefore, it is important to understand which architecture of 5G-radio access networks (RANs) is best for a given purpose; this requires an evaluation platform for assessment. This paper tackles this problem by presenting a novel multi-criteria decision-making (MCDM) solution based on a new integrated fuzzy set. The proposed integrated approach, which is based on a Type-2 neutrosophic fuzzy environment, is developed to address the application challenges of 5G-RANs architecture evaluation, as also to face the MCDM theoretical challenge represented by ambiguities and inconsistencies among decision makers within the decision making context of the presented case study. Many MCDM techniques for weighting and selection were presented from the literature, yet many of them still suffer from inconsistencies and uncertainty. Therefore, the chosen methods in this research are unique in a way that previous issues are addressed, making them suitable for integration with Type-2 neutrosophic fuzzy environment, and therefore creating a more robust decision platform for the presented challenge in this research, as a theoretical contribution. First, a new "Type-2 Neutrosophic Fuzzy-Weighted Zero-Inconsistency" (T2NN-FWZIC) technique is formulated for weighting the evaluation criteria of RAN architectures. Second, another new method, namely, "Type-2 Neutrosophic Fuzzy Decision by Opinion Score Method" (T2NN-FDOSM), was formulated to select the optimal RAN architecture using the obtained weights. The weighting results by T2NN-FWZIC for the (n = 25) evaluation criteria revealed that (C 21 latency and C 22 reliability) as the most important criteria, with 0.06 value for each as opposed to (C 15 Data Processing) as the lowest weighted criteria with 0.0186 value. As for T2NN-FDOSM, a total of four 5G-RAN architectures were evaluated, including "virtualized cloud RAN" coming as the optimal one, followed by "fog RAN," "cloud RAN," and finally "heterogeneous cloud RAN." The results were confirmed by carrying out a sensitivity analysis. The outcome of this study can be used to assist future 5G-RAN developments according to business needs and to establish an assessment platform for 5G technology in different domains and applications. [ABSTRACT FROM AUTHOR]

Abstract Failure mode and effect analysis (FMEA) is a forward-looking risk-management technique used in various industries for promoting the reliability and safety of products, processes, structures, systems, and services. However, FMEA has many defects in practical experimentation. Therefore, this paper proposes a new model that uses multiple-criteria decision-making in combination with grey theory for FMEA. This approach has several advantages, such as being able to add the expected cost into the original risk priority number (RPN) to reflect the actual resource limitations, consider the different weights of severity, occurrence, detectability, and cost based on the best–worst method in RPN element calculation, and use the grey interval linguistic variables to manage information uncertainty. Furthermore, this study applied probability-based grey relational analysis to calculate the RPN, which preserves the information of prioritized failure modes through interval analysis. To demonstrate the usefulness and effectiveness of the proposed model, real data from an international electronics company were applied. The proposed model can provide an alternative risk priority solution for product development. Graphical abstract Highlights • This paper proposed a novel hybrid model for failure mode and effect analysis. • The expected cost was added in the original risk priority number (RPN) calculation. • We applied BWM in RPN element calculation and grey interval to manage information uncertainty. • The real data from an international electronics company were applied to demonstrate the model. [ABSTRACT FROM AUTHOR]

Highlights • This paper proposes a novel MCGDM method under dual hesitant fuzzy information. • Several professional degree measures are proposed in this paper. • This paper proposes a quasi-order for dual hesitant fuzzy sets. Abstract Dual hesitant fuzzy set (DHFS) is a useful tool that can assist the decision makers (DMs) to express their evaluation information more flexibly from two contrary points of view, which are the membership degrees and the non-membership degrees. In this paper, we mainly focus on the quasi-order for DHFSs and the professional degrees of the DMs in multi-criteria group decision-making (MCGDM) under dual hesitant fuzzy information. First, we define a quasi-order for DHFSs based on pairwise comparisons. The quasi-order could distinguish two different DHFSs more clearly and rationally. Second, we propose the professional degrees of the DMs based on dual hesitant fuzzy information and use the professional degrees to determine the weights of the DMs. The professional degrees could indicate the weights of the DMs from different viewpoints in MCGDM more reliably. In the process of decision-making, the professional degree of each DM to the decision-making problem could influence the final decision directly. On the one hand, the evaluation information has a close-knit relationship with the professional degree of each DM. On the other hand, the professional degrees of the DMs should be a very important aspect to determine the weights of the DMs directly. In addition, we combine the quasi-order and the professional degrees in MCGDM. Finally, we use a practical numerical example on the rural land conversion to illustrate the availability and practicability of the proposed MCGDM model. [ABSTRACT FROM AUTHOR]