Weights Of Decision Makers Research Articles

Interval-valued intuitionistic fuzzy multi-attribute group decision-making method considering risk preference of decision-makers and its application

An improved interval-valued intuitionistic fuzzy multi-attribute group decision-making method considering the risk preference of decision-makers is proposed to solve the multi-attribute group decision-making problem with interval-valued intuitionistic fuzzy numbers and the condition that the attribute weight information is completely unknown. Firstly, the decision-maker weight of each attribute is determined by combining similarity and proximity. In order to consider the influence of the decision-maker's risk preference on the decision result and avoid the asymptotic behavior of interval-valued intuitionistic fuzzy matrix, the risk aversion coefficient of the decision-maker is introduced and combined with the determined decision-maker's weight aggregation to form a group decision matrix. Then, the information of group decision matrix is mined, and the interval-valued intuitionistic fuzzy entropy is used to determine the attribute weight and relative weight. Based on the interval-valued intuitionistic fuzzy distance measure formula and the TODIM method, the overall superiority of each scheme relative to other schemes is obtained by calculating the superiority between schemes, and the optimal scheme is determined by comparing and sequencing. Finally, the rationality and effectiveness of the proposed method are verified by an example of mechanical assembly supplier selection decision.

Multiple-attribute group decision making using a modified TOPSIS method in the presence of interval data

TOPSIS is a well-known technique in multiple criteria decisionmaking and has found several applications in recent years.However, as mentioned in literature TOPSIS has severalshortcomings. In this paper, we present an extension of TOPSISmethod to determine the weight of decision makers (DMs) inmultiple attribute group decision making problems with intervalinformation. Our method is based on the concept that the bestalternative is closer to the positive ideal solution and far awayfrom the negative ideal solution, simultaneously. The contributionof the proposed method is that while it overcomes the shortcomingsof the TOPSIS method it can be used to weight the decision makingteam and rankingthe alternatives, as well. The method is illustrated through two examples

A genetic algorithm based on dual hesitant fuzzy preference relations for consensus group decision making

Consensus is an important issue in group decision making as it aims to avoid future contestation by the decision makers. The elicitation of criteria and decision makers’ weights is an essential part of decision-making problems. This study proposes a new consensus model for group decision making based on dual hesitant fuzzy and evolutionary algorithm for the definition of unknown criteria and decision makers’ weights. The Dual Hesitant Fuzzy Preference Relations combines the advantages of intuitionistic and hesitant fuzzy representations, and it is used to deal with the imprecision in decision makers’ judgments. In order to find decision makers weights to reach a better level of consensus without the need to modify initial assessments, the proposed Genetic Algorithm (GA) is applied. An illustrative application case is presented in a large steel company considering sustainable criteria. An instance generator was proposed to create different scenarios of decision making varying the number of criteria, decision makers, and level of hesitation. Several instances were used in the computational tests for the evaluation of the GA performance. The effectiveness of the proposed GA was verified by comparing its results with the results of the implemented Particle Swarm Optimization (PSO) algorithm. The GA yielded solutions with improved consensus levels in a reasonable runtime, especially for a small or medium number of decision makers.

EVALUATION OF INFRASTRUCTURE PROJECTS BY A DECISION MODEL BASED ON RPR, MABAC, AND WASPAS METHODS WITH INTERVAL-VALUED INTUITIONISTIC FUZZY SETS

Infrastructure projects (IPs) face numerous challenges to reach the predefined aims over their life-cycle. There are many difficulties in projects because of the variety of elements in project’s tendency and the dependency of the project on mainly national factors. Due to these difficulties and their practices, the projects meet with uncertainty. In this paper, an interval-valued intuitionistic fuzzy set (IVIFS) is used at identifying ambiguity in IPs. Also, a new multi-criteria decision-making (MCDM) model is presented to evaluate and select the suitable alternative in IPs. Hence, a new IVIF-relative preference alternative-multi-attributive border approximation area comparison (IVIF-RPR-MABAC), and IVIF-weighted aggregated sum product assessment (IVIF-WASPAS) are proposed in order to obtain the weights of decision makers (DMs) and criteria, and a new IVIF-RPR-MABAC method is proposed to rank the alternatives. In this paper, a combination of the three mentioned approaches creates proposed new hybrid model to evaluate the main factors and the projects. Furthermore, a real case study is applied from the literature to validate the efficiency and performance of the proposed model. Afterward, a comparative analysis is presented to validate the proposed approach by comparing the hybrid proposed model with two IVIF-TOPSIS and IVIF-extended-VIKOR methods. The final results confirm the efficiency of the proposed model in ranking the main alternatives of an MCDM problem. Moreover, the sensitivity analysis is reported to determine the affection of parameters on the final weighting and ranking outcomes.

Evaluating and selecting the supplier in prefabricated megaprojects using extended fuzzy TOPSIS under hesitant environment: a case study from China

PurposePrefabricated components sustainable supplier (PCSS) selection is critical to the success of prefabricated projects. However, limited studies have addressed the uncertainty and complexities during the selection process, particularly in multi-criterion group decision-making (MCGDM) circumstances. Hence, the research aims to develop a group decision-making model using a modified fuzzy MCGDM approach for PCSS selection under uncertain situation.Design/methodology/approachThe proposed study develops a framework for sorting decisions in PCSS selection by using the hesitant fuzzy technique for order preference by similarity to ideal solution (HF-TOPSIS) method. The maximum consistency (MC) model is used to calculate the weights of decision makers (DMs) based on the cardinality and sequence of decision data.FindingsThe proposed framework has been successfully applied and illustrated in the case example of CB01 contract section in Hong Kong-Zhuhai-Macao Bridge (HZMB) megaproject. The results show various complicated decision-making scenarios can be addressed through the proposed approach. The MC model is able to calculate the weights of DMs based on the cardinality and sequence of decision data.Originality/valueThe research contributes to improving accuracy and reliability decision-making processes for PCSS selection, especially under hesitant and fuzzy situations in prefabricated megaprojects.

Addressing resiliency in supply chains through a multi-criteria group evaluation approach under interval type-2 fuzzy uncertainty

In real-world supply chain problems because of the non-existence of exact information and various fluctuating conditions, resilient supplier selection problems (RSSPs) are considered. In this paper, interval type-2 fuzzy sets (IT2FSs) are used in a modified VIKOR method to achieve more accuracy in the results. Moreover, a new method for computing weights of decision makers and a new method for computing weight of each evaluation criterion based on subjective and objective data are introduced. Finally, to verify the presented method a numerical example from the existing literature is presented and solved.

A Fermatean Fuzzy ELECTRE Method for Multi-Criteria Group Decision-Making

This paper aims to develop a Fermatean fuzzy ELECTRE method for solving multi-criteria group decision-making problems with unknown weights of decision makers and incomplete weights of criteria. First, a new distance measure between Fermatean fuzzy sets is proposed based on the Jensen–Shannon divergence. The cross entropy for Fermatean fuzzy sets is defined. Three kinds of dominance relationships for Fermatean fuzzy sets are proposed. Then, two optimization models are constructed to obtain positive ideal decision-making information and negative ideal decision-making information, respectively. Accordingly, the credibility degree of each decision maker is calculated. Decision makers’ dynamic weights are determined by their credibility degrees. Besides, to obtain the weights of criteria, an optimization model is constructed based on grey relational analysis for Fermatean fuzzy numbers. Finally, the strong, medium and weak Fermatean fuzzy concordance and discordance sets are identified to construct the Fermatean fuzzy concordance and discordance matrices, respectively. A practical case study is carried out to illustrate the feasibility and applicability of the proposed ELECTRE method. Comparative analyses are performed to demonstrate the superiority and effectiveness of the proposed ELECTRE method.

A Novel Evaluation and Decision-Making Approach to Prioritizing the Service Quality Criteria in Road Transportation Systems

The passengers’ expectations of road transportation systems' service quality lead transportation policy-makers to determine the technical requirements to meet these expectations. It means that Road trip Designs (RDs) as technical requirements should be translated based on Road users’ Requirements (RRs) as passengers’ expectations. We classified the RRs and RDs to 8 and 10 requirements, respectively. The Quality Function Deployment (QFD) method can translate the RRs to RDs in road transportation systems. On the one hand, due to the inherent uncertainty in decision-makers viewpoints in such systems, the Fuzzy QFD (FQFD) can be applied as a more accurate translation of RRs to RDs. On the other hand, the Evidential Reasoning (ER) approach, as one of the best evidence-based decision analysis methods, deals with the raised ambiguity and chaos by decision makers’ viewpoints. Accordingly, a novel hybrid FQFD-ER approach to prioritize the service quality criteria in road transportation systems has been provided in this paper. Moreover, a novel mathematical lemma is provided to hybridize and link the decision-making approaches of the FQFD and ER. Considering the weights of decision makers and fuzzy trapezoidal numbers to achieve the better results are other innovations of this paper. Totally, this study aims to integrate the FQFD with the ER approach to prioritize the RDs based on RRs in related road transportation systems to the Arba'een ceremony as a real case study. The obtained results by hybrid FQFD-ER revealed that the suitable road lanes in terms of width and number mostly improve the service quality.

A circular economy model for waste management in India.

Management of waste is one of the major challenges faced by many developing countries. This study therefore attempts to develop a circular economy (CE) model to manage wastes and closing the loop and reducing the generation of residual wastes in Indian municipalities. Through extant literature review, the researchers found 30 success factors of CE implementation. Using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) SIMOS approach, the rating and weight of decision makers (DMs) for each factor were collected. A structured questionnaire has been developed incorporating all these 30 factors, to extract the most important factors. The data was collected from top 10 officials (DMs) from the Chennai municipality, who handle three regions (metropolitan, suburbia and industrial). Based on the TOPSIS SIMOS analysis, nine CE implementing factors (critical success factors (CSFs)) among the 30 variables that were significant based on the cut-off value was identified. A CE model has been proposed based on these nine CSFs for waste management in India.

Group decision making with incomplete interval‐valued q‐rung orthopair fuzzy preference relations

In this paper, we focus on group decision making (GDM) with incomplete interval-valued q-rung orthopair fuzzy preference relations (IV q-ROFPRs). On the basis of interval-valued q-rung orthopair fuzzy set (IV q-ROFS), the novel concepts of IV q-ROFPR, incomplete IV q-ROFPR, additive consistent IV q-ROFPR, and acceptably additive consistent IV q-ROFPR are proposed. Subsequently, two optimization models are established for deriving the complete IV q-ROFPRs and acceptably additive consistent IV q-ROFPRs. Inspired by the Markov model, the weight-generating method is designed to obtain the weights of decision makers. For the purpose of improving the consensus level, a modification process is constructed under the consideration of minimizing the deviations between the adjusted IV q-ROFPRs and the original ones. Afterward, a goal programming model is constructed to derive the normalized q-rung orthopair fuzzy ( q-ROF) priority weights. The interval-valued q-rung orthopair fuzzy (IV q-ROF) priority weights of alternatives are further determined. Thus, an algorithm for the GDM method with IV q-ROFPRs is completed. Finally, the applications to examples and comparisons with previous approaches are conducted to illustrate the validity and effectiveness of the proposed method.

Budget optimized dynamic virtual machine provisioning in hybrid cloud using fuzzy analytic hierarchy process

Among various deployment models, Hybrid cloud is the preferred model allowing customers to maximize cost savings and performance by leveraging advantage of quick provisioning capabilities of public cloud. However, due to vast diversity of cloud services, today’s struggle for the customers is to evaluate and discover the best fit Cloud Service Provider (CSP) for an efficient Virtual Machine (VM) provisioning in various hybrid cloud contracts of multi cloud environment. Currently there is no framework that allows customers to evaluate CSPs based on application workload, application environment and employ an budget optimized VM provisioning with dynamically varying factors while adhering to Service Level Agreement (SLA). Hence, the proposed VM Provisioning framework is a Multi Criteria Decision-Making (MCDM) model with an objective to provision the VMs from the well suited CSP in hybrid cloud. The framework initially derives weights for each decision-making input criteria considered as Quality of Service (QoS) Service Measurement Index (SMI) attributes using extent analysis method of Fuzzy Analytical processing (FHP). It ranks the CSP by aggregating weights of decision makers among the alternatives. The framework then proposes a budget optimized algorithm called BOPVM to provision VMs in the hybrid cloud based on the order of assigned rank to CSPs. The framework is evaluated and implemented on real time workload among the CSPs – Amazon, Azure and Openstack. The evaluation results shows significant provisioning cost savings of 50% with reduced provisioning time by 65% when compared with existing algorithms.

Symmetric projection group approach for promoting homogeneity in the analytic hierarchy process

The complexity of the socio-economic environment requires the involvement of a group of decision makers in the decision-making process. They can contribute the diversity of knowledge and experience that allow all the important aspects of the decision-making problem to be taken into account. In the aggregation process, the weights of the decision makers play an important role. In this paper we have focused on the group analytic hierarchy process (AHP). We emphasized the homogeneity of the opinions of the decision makers as the basis for determining the importance weights of the decision makers. We reviewed the projection method and its upgrades for determining decision makers weights and demonstrated with examples that they are not suitable for application in AHP. We proposed a new symmetric projection method that eliminates the imperfections of the previous projection methods. We proposed a Symmetric Projection Group Approach (SPGA) to derive group weights in AHP. To validate the SPGA, we selected four evaluation measures. The evaluation of the examples shows that the SPGA achieves good evaluation results compared to the other methods for deriving decision makers weights and is suitable for real-world applications.

Three New Models for Ranking of Candidates In the Preferential Voting Systems

Election is the main challenge to the political and social science. In the meantime, in the literature, several methods to decide the winner of elections have been proposed; theoretically there is no reason to be limited to these models. Hence, in this paper, we assume three new approaches (1. election result prediction by pre-election preference information using Markov chain model [to identify the efficient electoral strategy for each candidate]. 2. Improved Borda’s function method using the weights of decision makers [or voters]. And 3. A new interval TOPSIS-based approach applying ordinal set of preferences [so, data is ordinal form that first convert to interval value and then inject them into the conventional interval TOPSIS model]) for ranking candidates in voting systems. Ultimately, three numerical examples in social choice context are given to depict the feasibility and practability of the proposed methods. In sum, this paper suggests a mind line for decreasing the wrong choice winner risks correlated with voting systems.

Linguistic Einstein aggregation operator‐based TOPSIS for multicriteria group decision making in linguistic Pythagorean fuzzy environment

This article presents a new family of linguistic Pythagorean fuzzy aggregation operations based on Einstein t-norms and t-conorms. Some of their necessary properties are also discussed. In the proposed method, a generalized weighted distance measure is developed using a linguistic-scale function to evaluate differences among linguistic Pythagorean fuzzy sets (LPFSs). An entropy measure is also introduced for LPFS to measure fuzziness associated with linguistic decision information. Moreover, a technique for ordering preference by similarity to ideal solution (TOPSIS)-based methodology is constructed through the newly defined concepts to address linguistic Pythagorean fuzzy multicriteria group decision-making problems, where the information about weight is either completely known or unknown. Subsequently, weights of decision makers are computed through a TOPSIS-based algorithm, and weights of criteria are evaluated by introducing a linguistic Pythagorean fuzzy entropy weight model. To minimize information loss throughout the decision-making procedure, aggregation is done at the final stage for obtaining the final ranking of alternatives. Finally, several practical examples are considered, solved, and compared with existing methods to exhibit the robustness of the proposed methodology. Also, a sensitivity analysis is performed to establish the dynamic nature of the proposed model.

A multi-attribute group decision making method considering both the correlation coefficient and hesitancy degrees under interval-valued intuitionistic fuzzy environment

In this paper, we propose a novel multiple attribute group decision making (MAGDM) method based on the correlation coefficient and hesitancy degrees under interval-valued intuitionistic fuzzy environment. Firstly, the conception of individual hesitancy degree and group hesitancy degree are defined to ensure the effective communication of information among group members and overcome the restrictions of the methodology of “majority rule” that some critical information of decision-making would be treated as conflicting opinions to be modified. Secondly, the correlation coefficients of interval-valued intuitionistic fuzzy set (IVIFS) are utilized to measure the similarity instead of distance functions in order to alleviate the drawbacks that do not consider the parameters of hesitancy. Thirdly, we extend the subjective assignment methods on utilizing the IVIFS to assign the weights of different decision makers in order to better address uncertainty in the MAGDM problem. Finally, TOPSIS and Linear programming optimization method are used to calculate the optimal attribute weight, which results in more accurate weights. A real-word application example has been presented to demonstrate the working of the proposed methodology. Moreover, a thorough comparison has been done with related existing works in order to show the validity of this methodology.

Air Pollution Assessment in China: A Novel Group Multiple Criteria Decision Making Model under Uncertain Information

Assessment of and controlling air pollution are urgent global issues where international cooperation is deemed necessary. Although a very relevant data source can be obtained through continuous monitoring of air quality, measuring air pollutant concentrations is quite difficult when compared to other environmental indicators. We mainly have three different aims for the current study: (1) we propose the computation of the interval weights of decision makers (DMs) based on a group multiple criteria decision making (GMCDM) model; (2) we aim to rank the overall preferences of DMs by the possibility concepts; (3) we aim to evaluate the air quality in China using the most recent data based on our proposed method. We consider three monitoring stations, namely Luhu Park, Wanqingsha, and Tianhu, and the data for SO2, NO2, and PM10 are collected for November 2017, 2018, and 2019. The results from our innovative model show that November 2019 had the best air quality. Finally, robustness analyses are also performed to confirm the discriminatory power of the proposed approach.

A dynamic group MCDM model with intuitionistic fuzzy set: Perspective of alternative queuing method

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.

An integrated interval type-2 fuzzy technique for democratic–autocratic​ multi-criteria decision making

This paper develops an integrated trapezoidal interval type-2 fuzzy (TrIT2F) technique for democratic–autocratic multi-criteria group decision making based on best–worstmethod (BWM) and VIKOR (VIsekriterijumska optimizacija i KOm-promisno Resenje), which is called TrIT2F-BW-VIKOR. In this technique, the pairwise comparisons and evaluations are represented by trapezoidal interval type-2 fuzzy sets (TrIT2FSs). The existing definition of TrIT2FS is perfected by adding two rational constraints proposed in this paper. A weight-normalizing theorem is initiated to normalize the TrIT2F weights. To determine the TrIT2F weights of junior decision makers (JDMs) and criteria, the classical BWM is extended into TrIT2F environment, which is called TrIT2F-BWM. In this TrIT2F-BWM, the weight-normalizing theorem is applied to normalize the TrIT2F weights, a consistency ratio is designed to check the reliability of the obtained TrIT2F weights. Based on the determined weights of JDMs and criteria, an extended VIKOR is developed to rank alternatives. The proposed technique can not only effectively retain the inherent fuzzy information of TrIT2FSs, but also flexibly handle different decision situations. The validity of the proposed technique is demonstrated with a makeshift (fangcang) hospital selection example on COVID-19. Some sensitivity and comparison analyses are provided to show the stability, flexibility, and superiorities of the proposed technique.

An evidence theory based approach for group decision making under uncertainty

With the changing business environment and the active participation of various stakeholders in the decision making process, it plays an increasingly important role to the weight of decision makers and the preference information given by decision makers. This paper presents a novel approach for group decision making under uncertainty with the involvement of the third-party evaluator in the decision making process. Recognizing the challenge in adequately determining the weight of decision makers in group decision making, the evidence theory is appropriately used with the involvement of the third-party evaluator. To effectively model the uncertainty and imprecision in the decision making process, fuzzy preference relations are used for better representing the subjective assessment of individual decision makers. To adequately determine the ranking of available alternatives, the logarithmic least square method is applied for appropriately aggregating the fuzzy preference relation of individual decision makers. A group consensus index is developed for facilitating consensus building in group decision making. This leads to better group decisions being made. A real-world application is presented that shows the proposed approach is effective in solving group decision making problems under uncertainty.

Solution of multi-response optimization: Case study in turning of AISI D2 steel using utility-fuzzy approach

The current experimental study emphasizes on examination of different turning parameters like feed rate (f), depth of cut (DOC), and spindle speed (S) and their effect on the performance of dry turning of AISI D2 steel bar using a PVC coated tool of carbide. Experiments were conducted on the basis of L9 orthogonal array to examine the characteristics like surface roughness (Ra), material removal rate (MRR), that measure the responses of turning operation. A further attempt was made to convert the multi objective to an equivalent single response and the utility function approach was used for this. The subjective opinion (for response weight) of Decision Makers (DMs) which was in linguistic terminology, was changed to fuzzy numbers. Thereafter the multi judgments in terms of the fuzzy number were defuzzyfied finally to get the crisp weight. The obtained response data from the experiments and the crisp weight were utilized in the Utility function approach. In last the Taguchi approach was implemented to determine the parameter combination of optimum values.