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Scheduling is a decision-making mechanism that enables the sharing of resources among several activities by determining their execution order on the set of available resources. In distributed systems, it is a great challenge to schedule multiple workflows submitted at different times. In particular, concurrent workflow scheduling with time constraints makes the problem more complex in the cloud due to the dynamics of the cloud such as elasticity, non-homogeneous resource types, various pricing schemes, and virtualization. A well-managed deadline workflow scheduling is required to improve end-user satisfaction and system performance. In the meantime, the intrinsic uncertainty in the cloud increases the difficulties of scheduling problems. Therefore, it is a great challenge to improve system performance and optimize several scheduling criteria simultaneously. To address the above issues, a novel concurrent workflow scheduling method for heterogeneous distributed environments based on the new Multi-Criteria Decision Making (MCDM) method i.e., TOPSIS (Technique of Order Preference by Similarity to Ideal Solution) is presented. A weighted sum of execution time, cost and communication time are used to find out the optimal resource among the existing resources as per the workflow task requirements. The proposed method minimizes the makespan and execution cost of the workflow and improves the resource efficiency under uncertain environment. The performance of the proposed work is compared with the state-of-the-art algorithms such as Cloud-based Workflow Scheduling Algorithm (CWSA), Earliest Finish Time-Maximum Effective Reduction (EFT-MER) and Heterogeneous Earliest-Finish-Time (HEFT) algorithms based on deadline constraint and resource utilization. Our experimental results demonstrate that the proposed T-CCWSA outperforms current state-of-the-art heuristics with the criteria of achieving the deadline constraint, minimizing the cost of execution and resource efficiency.

Cloud service selection assists cloud users to find the best cloud services as per their needs and minimizes the loss occurring due to improper selection of services. This paper aims to develop a cloud service selection framework for the neutrosophic environment using single-valued neutrosophic set (SVNS) theory and multi-criteria decision making (MCDM) based technique for order of preference by similarity to ideal solution (TOPSIS). The SVNS helps cloud users and experts to express their opinion in linguistic terms rather than crisp value due to partial knowledge involving some degree of truth, indeterminacy and falsehood. TOPSIS is used to rank cloud services efficiently. A case study has been performed on a real dataset obtained from CloudHarmony to demonstrate its practicality and usefulness. Sensitivity analysis has been carried out with the addition and deletion of cloud services to rank them and found that the framework is consistent and robust to rank reversal problem. The framework is also capable to strongly handle a fuzzy environment without rank reversal phenomenon in comparison with other MCDM based cloud service selection frameworks available in the literature.

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.

The capacities and the Choquet integral are powerful tools to represent decision problems with dependencies and aggregate correlated decision criteria. Random generation of suitable capacities is a vital and challenging task in this decision model because of an exponential number of the involved parameters as well as the associated monotonicity restrictions. In this paper we present various approaches to representing decision makers’ preferences on aggregation through linear constraints , random generation of capacities from the selected polytope , testing the uniformity of the resulting distribution, and constructing the dominance relations between the alternatives, which are subsequently used to get the most credible ranking of the alternatives.

Web service composition allows developers to create applications via reusing available services that are interoperable to each other. The process of selecting relevant Web services for a composite service satisfying the developer requirements is commonly acknowledged to be hard and challenging, especially with the exponentially increasing number of available Web services on the Internet. The majority of existing approaches on Web Services Selection are merely based on the Quality of Service (QoS) as a basic criterion to guide the selection process. However, existing approaches tend to ignore the service design quality, which plays a crucial role in discovering, understanding, and reusing service functionalities. Indeed, poorly designed Web service interfaces result in service anti-patterns, which are symptoms of bad design and implementation practices. The existence of anti-pattern instances in Web service interfaces typically complicates their reuse in real-world service-based systems and may lead to several maintenance and evolution problems. To address this issue, we introduce a new approach based on the Multi-Objective and Optimization on the basis of Ratio Analysis method (MOORA) as a multi-criteria decision making (MCDM) method to select Web services based on a combination of their (1) QoS attributes and (2) QoS design. The proposed approach aims to help developers to maintain the soundness and quality of their service composite development processes. We conduct a quantitative and qualitative empirical study to evaluate our approach on a Quality of Web Service dataset. We compare our MOORA-based approach against four commonly used MCDM methods as well as a recent state-of-the-art Web service selection approach. The obtained results show that our approach outperforms state-of-the-art approaches by significantly improving the service selection quality of top- k selected services while providing the best trade-off between both service design quality and desired QoS values. Furthermore, we conducted a qualitative evaluation with developers. The obtained results provide evidence that our approach generates a good trade-off for what developers need regarding both QoS and quality of design. Our selection approach was evaluated as “relevant” from developers point of view, in improving the service selection task with an average score of 3.93, compared to an average of 2.62 for the traditional QoS-based approach.

As the Covid-19 pandemic also proved, access to health care plays a crucial role in our lives. Public officials, managers and investors should consider many criteria such as public infrastructure, environment, accessibility and demand for selecting the most appropriate site for a new hospital. Thus, the hospital site selection problem is a critical multi-criteria decision-making (MCDM) problem. This paper is a case study for Istanbul where a recent MCDM methodology, the fuzzy Evaluation based on Distance from Average Solution (EDAS) method, is applied to this problem for the first time. We used a comprehensive set of five main criteria and 17 sub-criteria found in the relevant literature regarding hospital site selection. These criteria were evaluated by three decision-makers to choose the hospital site from three districts. The recommendation of the fuzzy EDAS method was then compared to the outcome of a frequently used fuzzy MCDM method. The methods resulted in different site recommendations. © 2021-IOS Press. All rights reserved.

This paper proposes a fuzzy best-worst method (BWM), called the GITrF BWM, based on generalized interval-valued trapezoidal fuzzy (GITrF) numbers (GITrFNs) for multi-criteria decision-making (MCDM). The reference comparisons between criteria are represented by GITrFNs and the weights of criteria are also taken the form of GITrFNs. The concept of normalized GITrF weight vector is proposed and a new graded mean integration representation (GMIR) of GITrFN is given. A goal programming model is built to obtain the optimal normalized GITrF weights of criteria. Furthermore, the GITrF consistency index and the GITrF consistency ratio are proposed. The GMIR of the GITrF consistency ratio is calculated to measure the acceptable consistency of all the reference comparisons between criteria. For the unacceptable consistent reference comparisons, we propose an approach to improve the consistency of reference comparisons between criteria. Finally, a GITrF BWM is proposed for MCDM. Three real examples are analyzed to illustrate the proposed GITrF BWM. The comparison analyses show that the proposed GITrF BWM outperforms the existing methods for MCDM in GITrF environments.

In this article, we investigate the multi-criteria decision-making complications under Pythagorean fuzzy soft information. The Pythagorean fuzzy soft set (PFSS) is a proper extension of the Pythagorean fuzzy set (PFS) which discusses the parametrization of the attributes of alternatives. It is also a generalization of the intuitionistic fuzzy soft set (IFSS). The PFSS is used to precisely evaluate the deficiencies, anxiety, and hesitation in decision-making (DM). The most essential determination of the current study is to advance some operational laws along with aggregation operators (AOs) within the Pythagorean fuzzy soft environs such as Pythagorean fuzzy soft interaction weighted average (PFSIWA) and Pythagorean fuzzy soft interaction weighted geometric (PFSIWG) operators with their desirable features. Furthermore, a DM technique has been established based on the developed operators to solve multi-criteria decision-making (MCDM) problems. Moreover, an application of the projected method is presented for the selection of an effective hand sanitizer during the COVID-19 pandemic. A comparative analysis with the merits, effectivity, tractability, along with some available research deduces the effectiveness of this approach.

Numerous multicriteria decision aiding (MCDA) methods have been developed over the last decades and are now applied in various domains, sometimes using facilitated group workshops to create models. These models are all designed to improve decision processes. However, the lack of follow-up and post-project evaluations limit the understanding of how the participants experienced the group workshops and how the results were subsequently used within the organization. This is in contrast with the public participation research field, where a rich literature was developed for a posteriori evaluation of projects. Based on this literature, our research proposes a framework to evaluate, ex-post, MCDA projects. In order to illustrate this framework, we apply it to an MCDA project in Quebec City where a spatial decision support system to prioritize the redesign of streets as Complete Streets was built. Individual interviews were conducted with the Quebec City professionals that currently use, were leaders of the project, or have participated in the development of the decision support system. This research has identified that the need for change of practices within the workplace, communication problems, and the requirement for multidisciplinary work were at the root of the various challenges encountered during the workshops. Based on our experience, we propose some lessons learned and potential solutions that can enhance the body of literature in MCDA.

As a typical methodology to deal with uncertain issues, the three-way decision (3WD) has been developed rapidly in nearly ten years, both in theories and applications. Three-way classification is one of the important research fields of 3WD, which utilizes the idea of 3WD to solve classification problems. In this paper, we focus on investigating the effectiveness of three-way classification through two evaluation indicators: the classification quality ( Precision , Recall , Accuracy and F 1 ) and the decision cost. The comparisons between two-way classification and three-way classification are concretely analyzed, some mathematical properties , judging conditions and decision criteria of these two classification methods are also discussed in detail. Finally, the experimental results on eight UCI data sets validate the mathematical analysis, which reveal the effectiveness of three-way classification.

Nowadays, more scholars and practitioners are committed to improving and applying type-2 fuzzy sets (T2FSs) in various domains because of the stronger ability to handle the uncertainty of fuzzy complex systems. To explore the status and internal laws of development of this field, this paper presents the development overview based on the bibliometric analysis, then the dynamic evolution of main topics and the knowledge diffusion trajectory are also displayed on the basis of the strategy diagram and main path analysis (MPA). From 1997 to 2019, 1749 documents are retrieved from Web of Science (WoS) repository for the analysis. The results show that there are four stable collaborative communities existing in the countries/regions’ collaborative network and the collaboration is affected by geographical factors to some degree. Three main evolution paths of hot topics are also presented in this paper and the multi-criteria decision-making (MCDM) has gradually developed into the moto theme. Furthermore, articles appearing on the main path mainly focus on the research of basic concept and framework, self-optimization and applications in various domains of T2FSs. In general, this paper provides a new landscape in the longitudinal research based on the development overview, thematic evolution and the knowledge diffusion trajectory.

Purpose Selection of fabrics for particular purposy-e has created lots of research interest over the years, and the said problems have been addressed by several researchers using various multi-criteria decision-making (MCDM) methods. The main purpose of this paper is to highlight a maiden approach to handle one such fabric selection problem using multi-objective optimization by ratio analysis (MOORA) as a simple yet potent MCDM tool. Design/methodology/approach Two approaches of MOORA method (namely, ratio system and reference point) have been demonstrated for ranking of 13 candidate fabrics based on four fabric attributes, namely, fabric cover, thickness, areal density and porosity. Findings In both the approaches, candidate fabric F3 secures rank 1 (the best alternative) whereas fabric F6 occupies rank 13 (the worst alternative). Moreover, ranking orders of these two approaches are alike and also show very high level of congruence with those of other approaches reported by earlier researchers, as evidenced from extremely high rank correlation coefficients (Rs > 0.89). During sensitivity analysis, each of the ranking results obtained from the four simulated weight sets do demonstrate very high degree of correlation (Rs > 0.90 for ratio system, and Rs > 0.81 for reference point). Besides, no occurrence of rank reversal is observed even when the initial decision-making matrix is changed. Originality/value Most the methods adopted so far for fabric selection purpose involve huge mathematical equations, complex computation and/or logic. The uniqueness of the MOORA method is that it involves minimal and thus very simple mathematical operations although possesses higher level of robustness and reliability.

Nowadays, customers are not only interested in the quality of products, but they also want to have these products in a timely manner. The managers of an organization are faced with two problems when the distribution of products is in question, namely: (1) customers are usually geographically dispersed and (2) transportation should be performed in a cost-effective way. Although managers may have a significant experience and formal knowledge, decisions connected with the selection of an appropriate transportation company may very often be biased. For the purpose of avoiding making the inadequate decisions that might harm the operation of the organization, the application of a hybrid MCDM model is proposed in this paper. The proposed model consists of three fuzzy MCDM methods, including: the PIPRECIA, the PSI, and the CoCoSo methods. The fuzzy-PIPRECIA method is used to achieve the subjective weights of criteria, whereas the fuzzy-PSI method is used to obtain the objective weights of criteria. Fuzzy-CoCoSo is utilized to rank alternative transportation companies according to their performances. The possibilities of the proposed hybrid model are tested on a real case study pointed at the selection of an appropriate company for the transportation of ready-garments to retailers in Turkey. First published online 07 July 2021

Internet of Things (IoT) may be defined as a network of smart devices that are involved in data collection and exchange. This technology has automated the day-to-day jobs and thus made our lives easier. But, real-time analysis of data is not always possible in a typical cloud-IoT architecture, especially for latency-sensitive applications. This led to the introduction of fog computing. On one side, fog layer has the capability of data processing and computation at the network edge and thus provides faster results. But, on the other hand, it also brings the attack surface closer to the devices. This makes the sensitive data on the layer vulnerable to attacks. Thus, considering Fog-IoT security is of prime importance. The security of a system or platform depends upon multiple factors. The order of selection of these factors plays a vital role in efficient assessment of security. This makes the problem of assessment of Fog-IoT security a Multi-Criteria Decision-Making (MCDM) problem. Therefore, the authors have deployed an Interval-Valued Intuitionistic Fuzzy Set (IVIFS) based Analytical Hierarchy Process (AHP) for the said environment. Using this integrated approach, the Fog-IoT security factors and their sub-factors are prioritized and ranked. The results obtained using above hybrid approach are validated by comparing them with Fuzzy-AHP (F-AHP) and Classical- AHP (C-AHP) results and are found to statistically correlated. The ideology and results of this research will help the security practitioners in accessing the security of Fog-IoT environment effectively. Moreover, the outcome of this analysis will help in paving a path for researchers by shifting their focus towards the most prioritized factor thereby assuring security in the environment.

Hesitant fuzzy preference relations (HFPRs) have been widely applied in multicriteria decision-making (MCDM) for their ability to efficiently express hesitant information. To address the situation where HFPRs are necessary, this paper develops several decision-making models integrating HFPRs with the best worst method (BWM). First, consistency measures from the perspectives of additive/multiplicative consistent hesitant fuzzy best worst preference relations (HFBWPRs) are introduced. Second, several decision-making models are developed in view of the proposed additive/multiplicatively consistent HFBWPRs. The main characteristic of the constructed models is that they consider all the values included in the HFBWPRs and consider the same and different compromise limit constraints. Third, an absolute programming model is developed to obtain the decision-makers’ objective weights utilizing the information of optimal priority weight vectors and provides the calculation of decision-makers’ comprehensive weights. Finally, a framework of the MCDM procedure based on hesitant fuzzy BWM is introduced, and an illustrative example in conjunction with comparative analysis is provided to demonstrate the feasibility and efficiency of the proposed models.

Hybridised classification and prioritisation of patients with chronic heart diseases (CHDs) can save lives by categorising them on the basis of disease severity and determining priority patients. Such hybridisation is challenging and thus has not been reported in the literature on telemedicine. This paper presents an intelligent classification and prioritisation framework for patients with CHDs who engage in telemedicine. The emergency status of 500 patients with CHDs was evaluated on the basis of multiple heterogeneous clinical parameters, such as electrocardiogram, oxygen saturation, blood pressure and non-sensory measurements (i.e. text frame), by using wearable sensors. In the first stage, the patients were classified according to Dempster–Shafer theory and separated into five categories, namely, at high risk, requires urgent care, sick, in a cold state and normal. In the second stage, hybridised multi-criteria decision-making models, namely, multi-layer analytic hierarchy process (MLAHP) and technique for order performance by similarity to ideal solution (TOPSIS), were used to prioritise patients according to their emergency status. Then, the priority patients were queued in each emergency category according to the results of the first stage. Results demonstrated that Dempster–Shafer theory and the hybridised MLAHP and TOPSIS model are suitable for classifying and prioritising patients with CHDs. Moreover, the groups’ scores in each category showed remarkable differences, indicating that the framework results were identical. The proposed framework has an advantage over other benchmark classification frameworks by 33.33% and 50%, and an advantage over earlier benchmark prioritisation by 50%. This framework should be considered in future studies on telemedicine architecture to improve healthcare management.

The COVID-19 pandemic has affected public transportation worldwide, and its implications need to be evaluated and study deeply on all public transportation aspects. Therefore, an analysis has been created to examine the effects of the pandemic on public transportation service quality decisions to have a better vision of the different stakeholders’ needs to keep the system functioning in a profitable way. Stakeholder participation in complex, multi-criteria decision-making often produces very different results in prioritizing the decision attributes. Rank correlation techniques generally measure the degree of agreement or non-agreement among the evaluator groups. However, the multi-criteria methodology can determine not only ordinal but also cardinal priorities. Consequently, except for the attributes’ positions, the weight values are also significant in the final decision. This paper aims to apply a more sophisticated measure of group agreement than rank correlation. First, the Fuzzy-hierarchical analytical process (FAHP) has been used to find out the aggregated weights, then the Kendall correlation values are computed to reveal stakeholder opinions. Finally, the agreement measure approach has been tested in a real-world case study: the public transport development decision of Amman, Jordan. The analysis shows that by applying the Kendall technique, Kendall could gain a more profound insight into the priority characteristics of different evaluator groups.

Agriculture is a crucial and strategic sector for developing countries. The agricultural sector in Turkey has been suffering from regression in recent years due to several reasons. In an attempt to reverse this process, we analyze the cultivation possibilities of high profit-margin crops in Turkish lands and develop a ranking among eight alternative crops. To perform a comprehensive analysis encompassing several dimensions, three MCDM methods are utilized; namely fuzzy AHP to determine the weights of evaluation criteria, and TOPSIS and PROMETHEE to develop a ranking among the crop alternatives. The crop alternatives are evaluated against several economic, technical, social and environmental criteria. The results favor the cultivation of soy bean, goji berry and buckwheat, while tamarind appears to be the least favored crop among the considered alternatives. The analysis results are enhanced with a sensitivity analysis.

Nonmodularity is a prominent property of capacity that deeply links to the internal interaction phenomenon of multiple decision criteria. Following the common architectures of the simultaneous interaction indices as well as of the bipartition interaction indices, in this paper, we construct and study the notion of probabilistic nonmodularity index and also its particular cases, such as Shapely and Banzhaf nonmodularity indices, which can be used to describe the comprehensive interaction situations of decision criteria. The connections and differences among three categories of interaction indices are also investigated and compared theoretically and empirically. It is shown that three types of interaction indices have the same roots in their first and second orders, but meanwhile the nonmodularity indices have involved less amount of subsets and can be adopted to describe the interaction phenomenon in decision analysis.

The use of games in daily life, especially in education, has been in an incline during the COVID-2019 pandemic. Thus, game-based learning environments have caused an increase in the need of game contents, but generation of the game contents and levels is a time-consuming and costly process. Generated game contents and levels should be balanced, dense, aesthetic and reachable. Also, the time as well as the costs spent should be decreased. In order to overcome this problem, automatic and intelligent game content and level generation methods have emerged, and procedural content generation (PCG) is the most popular one of these methods. Artificial intelligence techniques are used for procedural game level generation instead of traditional methods. In this study, bidirectional long short-term memory (BiLSTM) and fuzzy analytic hierarchy process-genetic algorithm (FAHP-GA) methods were used to generate procedural game levels. This proposed hybrid system was used in a developed educational game as a case study to create game levels. The performance of the proposed study was compared to the other multi-criteria decision-making (MCDM) methods, and also further statistical analyses were investigated. The results showed that the BiLSTM-based FAHP-GA method can be used for procedural game level generation effectively.

One of the most powerful tools to operate imprecision is bipolar complex fuzzy sets (BCFSs), which is an enlargement of bipolar fuzzy sets (BFSs) as well as complex fuzzy sets (CFSs). This paper deals with an integrated MULTIMOORA (multi-objective optimization on the basis of ratio analysis plus full multiplicative form) framework as a generalization of fuzzy MULTIMOORA procedure to assess the multi-criteria decision-making (MCDM) problems with BCFSs. We develop BCF-Archimedean power weighted (ordered weighted) arithmetic and geometric aggregation operators (AOs) and discuss their properties from this point of view. The proposed Archimedean power-weighted AOs can eliminate the influence of extreme evaluating criteria values from some biased experts with different preference attitudes under the BCF setting. Afterward, we put forward an integrated MULTIMOORA algorithm based on the proposed AOs, where criteria weights are estimated using the CRITIC (criteria importance through inter-criteria correlation) method, which is a well-known objective weighting method based on aggregated score values of options, intensity contrast of every criteria and conflict among attributes. In the proposed methodology, criteria values are aggregated based on the MULTIMOORA method that involves three sub-methods: the ‘ratio system’, the ‘reference point’ and the ‘full multiplicative form’ and thus takes less computational time, minimum mathematical evaluations and bears good stability. In the following, third-party reverse logistics providers' (3PRLP) selection problem is brought into consideration to manifest the sufficiency of the developed methodology. At the end of this study, we draw attention to a comparison between the proposed decision-making approach with the corresponding BCF-CRITIC-TOPSIS and BCF-CRITIC-WASPAS methods.

In a real or practical situation, there often exist different priority levels and interactions among the criteria of the MCDM problems. This paper combines the prioritized average operator with the normalized weighted geometric Bonferroni mean operator under the Einstein operational law of interval neutrosophic numbers (INNs) to propose the interval neutrosophic Einstein prioritized normalized weighted geometric Bonferroni mean (INEPNWGBM) operator to deal with the prioritization and correlation among the criteria in the real-life decision making problems. Then, some desired properties of the proposed aggregation operator are discussed. Furthermore, an approach to multicriteria decision making based on the Einstein prioritized normalized weighted geometric Bonferroni mean is developed. Finally, a numerical example is provided to illustrate the proposed approach.

The emergency response to the health care management in the hospital do not have enough systems for providing medical service to the COVID19 patients (e.g., scheduled or nonemergency). Therefore, in this paper, we developed an emergency decision support model for consideration of patients care and admission scheduling (PCAS). The complex decision support model assigns a set of patients into a number of restricted resources like rooms, time slots, and beds depending on satisfying a number of predefined constraints such as disease severity, waiting time, and disease types. This is a crucial issue with multi-criteria decision making (MCDM). In this paper, we first begin an assessment into the admission and care to tackle this issue and collect four factors effecting the admission and care of COVID-19 patients that form a system of criteria. While there is a lot of vague and uncertain data that can be effectively depicted for these indicators by the spherical hesitant fuzzy set, then, we implement a strong MCDM method based on list of aggregation operators to address the patients' hospital admission and care. Last of all, a numerical real-life application about PCAS is provided to demonstrate the validity of the proposed approaches along with relevant discussions, the merits of proposed approaches are also analyzed by validity test. The proposed methodology has been shown to help hospitals manage the admissions and care of COVID-19 patients in a flexible manner.

The world has been challenged since late 2019 by COVID-19. Higher education institutions have faced various challenges in adapting online education to control the pandemic spread of COVID-19. The present study aims to conduct a survey study through the interview and scrutinizing the literature to find the key challenges. Subsequently, an integrated MCDM framework, including Stepwise Weight Assessment Ratio Analysis (SWARA) and Multiple Objective Optimization based on Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), is developed. The SWARA procedure is applied to the analysis and assesses the challenges to adapt the online education during the COVID-19 outbreak, and the MULTIMOORA approach is utilized to rank the higher education institutions on hesitant fuzzy sets. Further, an illustrative case study is considered to express the proposed idea's feasibility and efficacy in real-world decision-making. Finally, the obtained result is compared with other existing approaches, confirming the proposed framework's strength and steadiness. The identified challenges were systemic, pedagogical, and psychological challenges, while the analysis results found that the pedagogical challenges, including the lack of experience and student engagement, were the main essential challenges to adapting online education in higher education institutions during the COVID-19 outbreak.

For the first time, the ensemble feature selection is modeled as a Multi-Criteria Decision-Making (MCDM) process in this paper. For this purpose, we used the VIKOR method as a famous MCDM algorithm to rank the features based on the evaluation of several feature selection methods as different decision-making criteria. Our proposed method, EFS-MCDM, first obtains a decision matrix using the ranks of every feature according to various rankers. The VIKOR approach is then used to assign a score to each feature based on the decision matrix. Finally, a rank vector for the features generates as an output in which the user can select a desired number of features. We have compared our approach with some ensemble feature selection methods using feature ranking strategy and basic feature selection algorithms to illustrate the proposed method's optimality and efficiency. The results show that our approach in terms of accuracy, F-score, and algorithm run-time is superior to other similar methods and performs in a short time, and it is more efficient than the other methods.

We propose heuristics for constructing a compromise incomplete ranking based on partial rankings admitting incomparability. We consider the utilitarian and egalitarian perspectives oriented toward minimizing an average or a maximal distance from any input ranking. The proposed algorithms incorporate genetic algorithms, simulated annealing, tabu search, local search, and intuitive, dedicated procedures. We demonstrate their efficiency in a real-world case study concerning the ranking of insulating materials based on the conflicting, incomplete preferences of a few tens of Decision Makers (DMs). For each DM, we consider a single representative ranking consistent with his/her preferences or thousands of such rankings following incorporation of the robustness concern. The experimental comparison is generalized to artificially generated problems that differ in terms of the numbers of alternatives and input rankings, and diversity levels. The results are quantified with the quality of obtained rankings and computation time.

Purpose World Cup tournament is one of the most popular international organizations in football. The purpose of this paper is to investigate the overall performance of World Cup 2018 teams via multi-criteria decision-making (MCDM) approaches. Design/methodology/approach The presented approach adopts entropy integrated Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Weighted Aggregated Sum Product Assessment (WASPAS) approaches to weight the criteria and evaluate the performance of World Cup 2018 teams. Initially, the authors compute weight criteria via Shannon Entropy method. Then, the authors compute and compare the results of TOPSIS and WASPAS methods so as to evaluate the performance of World Cup 2018 teams. Findings According to TOPSIS and WASPAS results, Belgium has demonstrated better performance in comparison to other teams. As per to the empirical results, both methods have shown a significant harmony in terms of performance figures. There is also strong positive correlation between TOPSIS and WASPAS method results. This result confirms the stability of the analysis. Originality/value This paper contributes to sport performance management literature by using MCDM methods in FIFA World Cup 2018 teams. To the best of the authors’ knowledge, this is the first paper to measure performance of an international football organization via MCDM methods.

Normalization is one of the stages that have an impact on the results of MCDM problems. Choosing the right normalization technique leads the decision maker to the right results. Accordingly, the purpose of this study is to determine the most appropriate normalization technique for the ROV method. In this study, a real case is analyzed, eight different normalization methods are compared with each other on the basis of a multi-stage framework. The findings show that the model used in this study can be successfully applied in the selection of normalization technique. This study provides a decision support and reference for the selection of nomalization technique for MCDM methods in terms of the framework used. Another importance of this study is the first testing the suitability of different normalization techniques for the ROV method.

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.

We consider a multicriteria decision analysis (MCDA) problem where importance of criteria, and evaluations of alternatives with respect to the criteria, are expressed on a qualitative ordinal scale. Using the extreme-point principle of Data Envelopment Analysis (DEA), we develop a two-parameter method for obtaining overall ratings of the alternatives when preferences and evaluations are made on an ordinal scale. We assume no parametric setup other than the two parameters that reflect minimum intensities of discriminating among rank positions: one parameter for the alternatives’ ranking and one for the criteria ranking. These parameters are bounded by the ordinal input data, and they imply a universal tie among the alternatives when both parameters are selected to be zero. We describe the model, discuss its theoretical underpinning, and demonstrate its application.

The theory of Multi-Criteria Decision Making (MCDM) was introduced in the second half of the twentieth century and aids the decision maker to resolve problems when interacting criteria are involved and need to be evaluated. In this paper, we apply MCDM on the problem of the best drug for rheumatoid arthritis disease. Then, we solve the MCDM problem via -Sugeno measure and the Choquet integral to provide realistic values in the process of selecting the most appropriate drug. The approach confirms the proper interpretation of multi-criteria decision making in the drug ranking for rheumatoid arthritis.

In this study, the flood hazards susceptibility map of an area in Turkey which is frequently exposed to flooding was predicted by training 70% of inventory data. For this, statistical, and hybrid methods such as frequency ratio (FR), evidential belief function (EBF), weight of evidence (WoE), index of entropy (IoE), fuzzy logic (FL), principal component analysis (PCA), analytical hierarchy process (AHP), technique for order preference by similarity to an ideal solution (TOPSIS), and VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR) were adapted. Values at both 70% and 30% of inventory data from the generated maps were extracted to validate the training and testing processes by receiver operating characteristics (ROC) analysis and seed cell area index (SCAI). Sensitivity, specificity, accuracy, and kappa index were calculated from ROC analysis, and SCAI was computed from the classification of map by natural break method and flood pixels in that classification. Since the predicted results of the methods applied did not point out the same model for each criterion, a simple method was selected to determine the most preferable method. Analysis showed that, IoE model was found to be the best model considering the ROC parameters, while PCA and AHP methods gave more reliable results considering SCAI. This study may be considered as a comprehensive contribution to the hybridization methods in predicting accurate flood hazards susceptibility maps.

The more attached a customer becomes toward a specific brand, the stronger the extent of customer loyalty he or she will present. However, studies on brand attachment (BA) have been limited to the perspectives of enterprises about the manner in which to consolidate customer loyalty while opinions and views of customers are absent. This study integrates the views of enterprises and those of customers to build a BA-based multi-criteria decision-making (MCDM) model and adopts consistent fuzzy linguistic preference relations (CFLPR) to conduct empirical research on three car brands, namely Lexus, Mercedes-Benz, and BMW, by analyzing differences in customers’ BA preference to the three brands and assessing management performance. Findings reveal that the customers of the two German brands, Mercedes-Benz and BMW, emphasize hedonic needs while customers of Lexus, the Japanese brand, stress on bonds of personal emotional factors. Apart from being able to measure the precise strength of customers’ BAs, this analytic model can also help enterprises to examine changes of factors concerning customers’ BA on a regular basis as well as serving as the foundation for evoking consumers’ identification about brand personality, consolidating brand loyalty, and developing marketing communications strategies.

Multi-criteria decision-making (MCDM) methods deeply assist decision-makers in evaluating and analyzing input data as accurately as possible in the area of safety and reliability. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) can be used to rank a set of alternatives based on the different types of criteria. However, this method still needs to be improved; fuzzy set theory and its corresponding extensions are bold in the line of TOPSIS improvement. In this study, the original form of TOPSIS method is extended using Pythagorean fuzzy-based Chebyshev distance measures. Pythagorean fuzzy set has better reflection compared to the conventional fuzzy set theory and Chebyshev distance measures can provide more accurate results, which are used to compute the separation measures in the TOPSIS method. Therefore, the novel improved TOPSIS methods have been developed underlying the ideal of Pythagorean fuzzy-based Chebyshev distance measures. The developed method is applied on a humanitarian case-study to show the efficiency of the approach in an emergency response situation. Results show that the proposed method is efficiently applicable in real-life decision-making situations.

Public blockchain enables decentralized trust models in distributed systems. The programmable features such as smart contracts and decentralized applications are attracting application developers, systems integrators, and users to adopt public blockchain systems for a large plethora of applications across all industries. However, the abundance and variety of features in immature blockchain ecosystems make it hard to select an appropriate and useful public blockchain platform. This paper employs a multi-criteria decision-making (MCDM) approach to rank and outline the suitable public blockchain platforms. To this end, we present ECWM, a new weight assignment technique, which is a combination of Entropy and CRITIC method. We applied ECWM on a diverse dataset curated with 16 features (i.e., indicators representing different criteria for blockchain adoption) from 30 public blockchain systems. We apply three MCDM techniques, namely WSM, TOPSIS, and VIKOR, to generate ranks. These techniques produce divergent rankings; therefore, we use Spearman’s rank correlation coefficient to resolve disagreements and outline the best possible ranking with the given dataset. We also rank blockchains according to three categories: popularity, sustainability, and profitability. The ranks are meticulously evaluated by domain experts and are deemed quite effective to guide future researchers and system developers.

This paper develops fuzzy multi-criteria decision making (MCDM) approach to evaluate performance of airports according to the passengers’ perspective. Assessments for qualitative measures and quantitative measures are represented by intuitionistic cubic fuzzy numbers (ICFNs), and crisp numbers represents criteria weights, which are obtained via surveys. To construct a strategy for solving MCDM problems, we have developed extended forms of VIKOR method and TOPSIS method for ICFNs and constructed ICF-VIKOR and ICF-TOPSIS algorithm under ICF information in group decision environment. We have applied these methods for an experimental analysis of 7 significant international airports in the Asia-Pacific region to evaluate their performance with conflicting criteria under ICF environment. To show the supremacy of the proposed techniques, we have presented comparative analysis with the existing techniques.

Often, data in multi-criteria decision-making (MCDM) problems are imprecise and changeable due to the mandatory participation of human judgement, which is often unclear and vague. Besides, different MCDM methods may produce different results under different levels of uncertainty and require divergent levels of computational resources. Therefore, the quality of the decision and the amount of effort are heavily affected by selection of the MCDM method. With the regular proliferation of such methods and their modifications, it is important to carry out a comparative study that provides comprehensive insight into their performances under uncertain conditions. In this study, we use the randomized quasi-Monte Carlo simulation approach to compare empirically the results produced by 12 classic and contemporary fuzzy MCDM (FMCDM) approaches with rank-reversal perspective over increasing uncertainty in various decision scenarios. Furthermore, this study also investigates the similarity between ranks produced by each pair of methods for the same decision problems. The study further compares the results obtained by quasi-Monte Carlo simulation with the results obtained by Monte Carlo simulation. The findings of this study will assist decision-makers in the selection of most appropriate fuzzy MCDM approach for different decision scenarios. The results of this research are significant additions to the current repository of knowledge in the multi-criteria decision analysis as well as the literature pertaining to the Information Systems. It also provides insights for many managerial applications of these MCDM methods.

Dynamic multi-criteria decision making (DMCDM) is prevalent in real life, where the evaluations are given sequentially across time and earlier evaluations will influence later ones. Moreover, alternatives and criteria are allowed to vary with time. However, on the one hand, such dynamic change is rarely considered under the situation of group decision making. On the other hand, recent DMCDM methods just aggregate the evaluations from different periods, which can not accurately reflect the relative relation between alternatives due to constant change of alternatives and criteria. Motivated by these cases, we provide an insight with alternative queuing method (AQM) and intuitionistic fuzzy set (IFS) into dynamic group MCDM (DGMCDM), which ranks the alternatives based on preference relation. In the proposed model, we generate current weights of decision makers (DMs) by introducing induced ordered weighted averaging (IOWA) operatorwhich acts as a link among the weights collected across time. Moreover, we extend classical AQM by using fuzzy preference relation (FPR) instead of 0–1 preference relationship in paired comparison between alternatives. Additionally, a feedback mechanism is defined within the framework of extended AQM, where later FPRs will be influenced by the earlier ones as well as carried to the next period. Our method is of great effectiveness and flexibility with complicated and changeable environment, which has been demonstrated in the field of supplier selection undergoing three periods. Furthermore, we make a comparative analysis of the method with classical AQM and static MCDM.

To deal with multicriteria decision making (MCDM) problems with interaction criteria, the Choquet integral (CI) is one of effective tools. This paper first proposes the reverse Choquet integral (RCI), which defines the importance of the ordered elements in an opposite principle to the CI. To show the principle of the RCI, we offer its concrete expression in view of the Mobius representation by which one can clearly see the difference and the relationship between the CI and the RCI. Then, we propose the “bi-direction Choquet integral” (BDCI), which is a convex combination of the CI and the RCI. To get the interactions of ordered coalitions comprehensively, this paper further proposes the generalized Shapley bi-direction Choquet integral (GSBDCI). Furthermore, the hybrid generalized Shapley bi-direction Choquet integral (HGSBDCI) is proposed, which defines the importance of ordered positions and the criteria with interactions simultaneously. With respect to these types of CIs, their exponent forms are also discussed. Finally, we use an application case to show the utilization of the proposed new CIs for MCDM. The proposed new Choquet integrals provide us a very useful way to deal with MCDM problems.

Refereed/Peer-reviewed Multicriteria decision analysis evaluates multiple conflicting criteria in decision making, but conflicting criteria are typical in evaluating options. As the existing ordering operations involved in multicriteria decision making cannot easily be implemented with intervals, we assume that scalar representative values with intervals can effectively avoid this issue. To deal with interval-valued criteria, we propose a generalized golden rule representative value approach, which involves the sigmoid function of backpropagation neural networks to tune parameters. Our approach considers the uncertainties and side effects of the interval variables to improve individual scalar representative values. Based on numerical examples, we address the effectiveness of the proposed approach, and we provide a specific application concerning multicriteria decision making with interval criteria satisfaction. usc

Background Despite the proven value of multicriteria decision analysis in the health field, there is a lack of studies focused on prioritising victims in the Emergency Medical Service, EMS. With this, and knowing that the decision maker needs a direction on which choice may be the most appropriate, based on different and often conflicting criteria. The current work developed a new model for prioritizing victims of SAMU/192, based on the multicriteria decision methodology, taking into account the scarcity of resources. Methods An expert panel and a discussion group were formed, which defined the limits of the problem, and identified the evaluation criteria for choosing a victim, amongst four alternatives illustrated from hypothetical scenarios of emergency situations—clinical and traumatic diseases of absolute priority. For prioritization, an additive mathematical method was used that aggregates criteria in a flexible and interactive version, FITradeoff. Results The structuring of the problem led the researchers to identify twenty-five evaluation criteria, amongst which ten were essential to guide decisions. As a result, in the simulation of prioritization of four requesting victims in view of the availability of only one ambulance, the proposed model supported the decision by suggesting the prioritization of one of the victims. Conclusions This work contributed to the prioritization of victims using multicriteria decision support methodology. Selecting and weighing the criteria in this study indicated that the protocols that guide regulatory physicians do not consider all the criteria for prioritizing victims in an environment of scarcity of resources. Finally, the proposed model can support crucial decision based on a rational and transparent decision-making process that can be applied in other EMS.

PurposeIn the cold supply chain (SC), effective risk management is regarded as an essential component to address the risky and uncertain SC environment in handling time- and temperature-sensitive products. However, existing multi-criteria decision-making (MCDM) approaches greatly rely on expert opinions for pairwise comparisons. Despite the fact that machine learning models can be customised to conduct pairwise comparisons, it is difficult for small and medium enterprises (SMEs) to intelligently measure the ratings between risk criteria without sufficiently large datasets. Therefore, this paper aims at developing an enterprise-wide solution to identify and assess cold chain risks.Design/methodology/approachA novel federated learning (FL)-enabled multi-criteria risk evaluation system (FMRES) is proposed, which integrates FL and the best–worst method (BWM) to measure firm-level cold chain risks under the suggested risk hierarchical structure. The factors of technologies and equipment, operations, external environment, and personnel and organisation are considered. Furthermore, a case analysis of an e-grocery SC in Australia is conducted to examine the feasibility of the proposed approach.FindingsThroughout this study, it is found that embedding the FL mechanism into the MCDM process is effective in acquiring knowledge of pairwise comparisons from experts. A trusted federation in a cold chain network is therefore formulated to identify and assess cold SC risks in a systematic manner.Originality/valueA novel hybridisation between horizontal FL and MCDM process is explored, which enhances the autonomy of the MCDM approaches to evaluate cold chain risks under the structured hierarchy.

With the emergence of outsourcing logistics and the rapid development of the e-commerce business, Third Party Logistics (TPL) plays an indispensable role in modern business. In the TPL provider selection process, uncertain information brings more challenges to decision makers. This paper uses probabilistic linguistic term sets (PLTSs) to describe uncertain decision making information. Firstly, we propose an improved Decision Making Trial and Evaluation Laboratory method, which allows a certain relationship between decision criteria and calculates criteria weights in multi-criteria decision making (MCDM) problems. Then, in order to make full use of uncertain TPL provider information and maximize the values of data, the probabilistic linguistic complex proportional assessment method is proposed and applied to solve the MCDM problems under probabilistic linguistic environment, which needs much less computation than other MCDM methods. Finally, an application example of TPL provider selection is presented to demonstrate the proposed method. A comparative analysis is further conducted to validate the effectiveness of the proposed method.

Hesitant fuzzy (HF) entropy and HF-knowledge measures are two dual concepts that have similar practical applications despite different mathematical structures. In some real-life scenarios, one particular entropy/knowledge measure may not be reasonable due to some undesirable and counter-intuitive situations. In this paper, we introduce a one-parametric generalized knowledge measure in the HF-setting. Such a generalization provides a class of HF-knowledge measures and hence the flexibility in the practical problems. We show the advantages of the generalized measure over the existing HF-entropy/knowledge measures in view of weight computation in the decision-making problems and ambiguity computation of two different hesitant fuzzy elements. At last, we apply the proposed generalized knowledge measure of HFSs in multi-criteria decision-making (MCDM) using the bidirectional projection method in the hesitant fuzzy environment.

Online reviews have become an increasingly popular information source in consumer’s decision making process. To help consumers make informed decisions, how to select products through online reviews is a valuable research topic. This work deals with a personized product selection problem with review sentiments under probabilistic linguistic circumstances. To this end, we propose a multi-criteria decision making (MCDM) method incorporating personalized heuristic judgments in the prospect theory (PT). We focus on the role of personalized heuristic judgments on review helpfulness in the final decision outcomes. We demonstrate the consistency between the three common heuristic judgments (with respect to review valence, sentiment extremity, and aspiration levels) and the three behavioral principles of the PT. Then, the products are ranked with the probabilistic linguistic term set (PLTS) input, based on the proposed adjustable PT framework, in which the coefficients of negativity bias are derived from the consumer’s heuristic judgments. Finally, a real case on TripAdvisor.com and two simulation experiments are given to illustrate the validity of the proposed method.

In regard to multicriteria decision making (MCDM) problems where the values of the criteria are expressed by 2-D uncertain linguistic variables (2DULVs), where the criteria are interactive and the criteria weights are incompletely known, two novel MCDM methods are proposed in this paper. First, we offer some novel operational laws of 2DULVs, which can avoid the operational results exceeding the boundary of linguistic term sets. Then, we propose four operators to capture the interactions over the criteria, namely, the 2-D uncertain linguistic Choquet averaging (2DULCA) operator, the 2-D uncertain linguistic Choquet geometric (2DULCG) operator, the Shapley 2DULCA (S2DULCA) operator, and the Shapley 2DULCG (S2DULCG) operator. In addition, we establish the models based on the maximization deviation approach and the Shapley function to get the criteria weights. Finally, we propose two novel MCDM methods under 2-D uncertain linguistic environments, where four examples are used to explain the created MCDM methods. Comparative experimental results are presented to highlight the superiorities of the created approaches.

Thermal energy is the energy from a substance in which molecules and atoms vibrate faster because of an increase in temperature. Thermal energy storage (TES) is an available energy resource for renewable energy platforms that enables them to meet sustainable technical requirements. The TES technique is divided into three categories; sensible TES, latent-heat TES, and thermo-chemical TES. The best of these techniques is selected in this research paper. Here the Interval-Valued Hesitant Pythagorean Fuzzy Set (IVPHFS) under the Normal Wiggly Mathematical Methodology is proposed and described for application to multi-criteria decision making (MCDM) technology. The MCDM methods, the Step-wise Weight Assessment Ratio Analysis (SWARA) method for determining weight values, and the Weighted Aggregated Sum Product Assessment (WASPAS) method for ranking alternative values are used employed here. The alternative values are selected based on the following criteria: capacity, efficiency, storage period, charging and discharging times, and cost

In recent years, several concepts such as fuzzy sets, Z-numbers, and D-numbers have been proposed to handle real-world decision-making problems. Despite the desirable properties of these types of numbers, they do not consider the concept of Necessity. On the other hand, recently, the Best-Worst Method (BWM) has been introduced as a technique based on a systematic pairwise comparison of decision criteria. The advantage of this method is that it reduces the level of inconsistency or ambiguity in the results. Since ambiguity is associated with information, it is important to consider it in the decision-making process to boost the accuracy of findings. The main aim of this study is to reduce the ambiguity in attributing weights to the criteria by incorporating the BWM method and the Importance-Necessity concept (G-number), and to present a novel method, namely The GBWM method. By decreasing levels of ambiguity in the final results through the addition of the Necessity and Importance concepts, this method can be applied to an extensive range of practical and complex decision-making problems. To express the feasibility and usefulness of the proposed method in the real-world, two case studies have been investigated. Finally, the sensitivity analysis and a conceptual comparison with other methods have been conducted to confirm the strength and stability of this method.

The main contribution of this paper is to give a new axiomatic definition of entropy measure and provide a constructing approach in the context of interval-valued intuitionistic fuzzy set (IVIFS). We give a new idea to define entropy on IVIFS: From the graphical representation, we consider the difference between a given IVIFS and its corresponding two interval fuzzy sets (IVFSs) by introducing a distance function that meets some specific conditions. The relationship between the distance function and the distance measure has also been illustrated. Based on distance functions, we give an approach to construct entropy measures on IVIFS. Then, a plenty of new entropies on IVIFS are introduced. Furthermore, we use a comparative example to show the proposed measures outperform the existing measures and utilize a demonstrative example to explain the application of the entropy measure in the multi-criteria decision making (MCDM), which verify the feasibility of our entropy construction method.

This study aims to extend classic VIKOR technique for multicriteria decision-making (MCDM) problems within Pythagorean fuzzy (PF) scenario. First, judgments from decision makers (DMs) are expressed by PF sets that can describe more uncertain and ambiguous information than available fuzzy sets. Second, PF point operators are applied to denote the risk preference of the DM who may express an attitude toward an emerging science and technology. Third, a new generalized distance measurement formula considering all the characteristics of PF sets is proposed, and some attractive properties of distance measure, which outperforms available distance measures, are proved. Fourth, the novel generalized distance measure is employed to relative distance to identify the optimum and worst PF values and then employed in $$L_{p}$$ -metric VIKOR formula to accurately gain the group utility, individual regret, and compromise index. The novel PF-VIKOR algorithm considering DM’s risk preference and a novel distance measure is described in detail, and a blockchain technology solution selection problem is utilized to validate the feasibility of our technique. Then, the sensitivity analysis is implemented to test stability of our PF-VIKOR technique when the parameters in risk preferences and generalized distance measure are adjusted. Fifth, the comparison among various PF-MCDM techniques is performed to validate superiority and practicability of our presented technique.