Weights Of Decision Makers Research Articles

Self-confidence and consensus-based group decision making methods and applications

As the field of intelligent decision making continues to evolve, research on multi-attribute group decision making (MAGDM) is advancing at a steady pace. Nevertheless, existing research seldom addresses the aggregated solution of self-confidence and consensus in decision making information with diverse possibilities, despite its relevance to uncertain decision making environments. Consequently, this paper presents an approach to self-confidence and consensus-based group decision making. Firstly, the method provides a way for transforming three-parameter interval grey numbers into randomized decision making information. This establishes the foundation for the computation of possibility decision making information for uncertain decisions. Then, a method based on self-confidence and consensus is developed to determine the decision makers (DMs) weights, which is derived from the randomized decision making information. Next, a novel self-confidence index (SCI) for computing self-confidence and a new group consensus index (GCI) for computing consensus are demonstrated, and a reliability index (REI) method for randomized decision making information is given, which is used to identify high self-confidence and high consensus decision making information to improve the quality of decisions. Subsequently, a possibility ranking method is constructed based on pairwise comparisons of alternatives, taking into account density preferences, in order to obtain final possibility ranking conclusions. Finally, an illustrative example is provided to illustrate the concrete implementation process of the methods employed in this research. The efficacy of the proposed method is also demonstrated through a comparative analysis with the other methods.

A New Fuzzy Joint Choquet Integral Method Under Interval-Valued Function

A new fuzzy group decision-making method considering multi-attributes correlation under interval-valued function is presented, which mainly includes (1) acquiring the group fuzzy preference matrix and (2) handling the interactions between multiple evaluation attributes. To do that, firstly, the fuzzy joint Choquet integral based on an interval-valued function is proposed, which not only reflects the interaction between multiple attributes in a complex and uncertain environment, but also retains the initial preference of the decision maker. Secondly, a Shapley value with fuzzy measure is applied to assign each decision maker's weight, and the fuzzy group preference matrix is acquired by fusing the fuzzy preference matrices of all decision makers. Finally, a nursing home selection case is depicted to explain the effectiveness of the proposed technique. The corresponding sensitivity analysis is operated, which clarifies the reliability and flexibility of the proposed technique.

A New Framework for Sustainable Supplier Selection Based on a Plant Growth Simulation Algorithm

With the intensification of global competition and the increasing awareness of reducing energy consumption, sustainable supplier selection is crucial for establishing a solid cooperative relationship in sustainable supply chain management. This paper proposes a new framework that considers both the effective expression of uncertain information and the objective weights of decision makers to select sustainable suppliers. We first apply an interval-valued intuitionistic fuzzy set to express the information of decision makers. Moreover, this paper applies a plant growth simulation algorithm to aggregate decision makers’ information. Next, we adopt the similarity measure method to derive the target weight of each decision maker. Then, we apply the score function to rank the candidate sustainable suppliers. Finally, two practical cases are presented to verify the effectiveness of the proposed framework. The outcomes and comparative discussion show that the developed framework is efficient for sustainable supplier selection. Therefore, the proposed framework can be used to establish a solid cooperative relationship in the process of sustainable supply chain management.

Higher-order interaction of stability simplicial complex driven group consensus reaching in social network

To group decision-making (GDM) under social network, the interactions are no longer limited to pairwise but can take higher-order interaction, which can affect the consensus process. The higher-order interaction can be represented in simplicial complex. Therefore, under social network, this paper deeply explores a new higher-order interaction-based adjustment model in the simplicial complex. Firstly, we identify simplicial complex by utilizing the consensual objective and subjective trust information, and determine the decision maker weights. Then, we design the stability of simplex from the system structure and select the adjustment alternative and decision maker to construct the adjustment model based on the consensus and the stability of simplex. Considering the simplex structure, inspired by spreading dynamics model of simplex, we propose strengthening mechanism and competing mechanism of higher-order evaluation interaction. Further, this paper establishes a higher-order interaction-based minimum adjustment model. Because of the connectedness of simplicial complex, decision makers between simplices may occur the spontaneous interaction. In this case, we construct a global stability of simplicial complex to improve the minimum adjustment model. Finally, we use an example to expound the feasibility of our method, and design also a comparative analysis to demonstrates its advantages. In general, our method can significantly improve the consensus level and changes the adjustment decision maker selection from the system structure.

Design and evaluation of a sustainable entropy-weighted and VIKOR-based method for offshore oil collecting

With the expansion of marine oil exploitation, offshore oil leakage has become an urgent problem that cannot be ignored in marine ecological protection. To address the problem, this paper proposes a comprehensive design and evaluation approach aiming at the design of offshore oil collection. This approach combines the entropy weight and VIKOR method with three principles of sustainable design. Specifically, to establish the evaluation model, this paper transforms these principles of sustainability into three aspects of practicality, economy, and environmental protection, thus constructing a multi-level evaluation index system. Subsequently, entropy weights are then used to decline the one's own element in the decision maker's weights. Besides this, the VIKOR method is applied to acquire the optimal ranking of design alternatives for offshore oil collection to choose the optimal product design strategy. According to the obtained experimental results, the proposed design method combining the entropy weight and the VIKOR method is feasible and reasonable. To be exact, it improves the comprehensive index of the product in the design concept stage by over 20%, and it effectively optimizes the resource allocation of the design process.

An entropy-based group decision-making approach for software quality evaluation

Software quality evaluation (SQE) is a multi-dimensional decision-making problem. The increasingly complex socioeconomic environment has made group decision making (GDM) more important. Determination of the relative contribution (weight) of individual decision makers is an important part of the GDM process. Calculation of the similarity between decision matrices, potentially using a comprehensive projection measure, is also critical. This paper presents: (1) a new entropy-based approach to determine the weight of decision makers; (2) a novel normalized projection approach to measuring the similarity between two decision matrices; and (3) new group regret and satisfaction reference matrices that are modeled in a GDM setting. A comprehensive GDM method is proposed and adopted for SQE. An experimental analysis is conducted to investigate the practicability, feasibility, and validity of the proposed GDM method. The experimental results confirm the proposed GDM method as practicable, producing stable rankings of alternatives, with the same ranking of alternatives being produced in almost all of the dynamic experimental cases.

Heterogeneous multi-attribute group decision making based on a fuzzy data envelopment analysis cross-efficiency model

Heterogeneous multi-attribute group decision making (HMAGDM) is a kind of complex decision making problem, which exists widely in reality. The purpose of this paper is to extend data envelopment analysis (DEA) for solving the HMAGDM problems whose attribute values include real numbers, intervals, intuitionistic fuzzy numbers, triangular fuzzy numbers, trapezoidal fuzzy numbers and linguistic variables. For this purpose, a method to HMAGDM based on a fuzzy DEA cross-efficiency model is proposed. In the proposed method, heterogeneous information is uniformly transformed into intervals, which are expressed as variables with unknown parameters. On this basis, a fuzzy DEA cross-efficiency model is established to calculate the individual cross-efficiency intervals of decision making units (DMUs). The model takes into consideration all possible attribute values of each DMU in the intervals, and obtains stable evaluation results through peer evaluation mode and iteration. Then, a maximizing group consensus model is established to calculate the weights of decision makers (DMs). Given the weights of DMs, the individual cross-efficiency intervals can be aggregated into the collective cross-efficiency interval, and DMU can be ranked based on this. An example of supplier selection is presented to illustrate the practicability and effectiveness of the proposed method.

MAGDM Model Using an Intuitionistic Fuzzy Matrix Energy Method and Its Application in the Selection Issue of Hospital Locations

Matrix energy is a valid mathematical tool for representing collective information. However, it is not used in the existing literature for fuzzy sets, intuitionistic fuzzy sets, and multi-attribute group decision making (MAGDM) problems, which highlights research gaps. Motivated by both matrix energy and the research gaps, this study aims to extend matrix energy to the energy of an intuitionistic fuzzy matrix (IFM) and to utilize the IFM energy method in the MAGDM problem, which fully contains all the IFM information on attribute weights, decision maker weights, and attribute values. To achieve these objectives, this paper first proposes IFM energy in terms of the true matrix energy and false matrix energy; then, it develops a MAGDM model using the IFM energy method and the score and accuracy equations of IFM energy. Then, the developed MAGDM model is applied to the selection problem of hospital locations in Shaoxing City, China to demonstrate the practicality and validity of the developed model. Compared with existing intuitionistic fuzzy MAGDM methods, the developed MAGDM model using the IFM energy method reveals its superiority and novelty in complete IFM information expressions and the MAGDM method because the existing MAGDM methods containing intuitionistic fuzzy set information have difficulty tackling MAGDM problems containing all the IFM information on attribute weights, decision maker weights, and attribute values.

Projection-Based Approach for Deriving Decision Makers’ Weights in DEMATEL

To successfully solve complex real-world problems, decision makers with broad knowledge and experience should be involved in the decision-making process. In this paper, we address the question of how to determine weights of decision makers. The aim of our study is to propose a new approach that derives objective weights for decision makers based on projection method, which emphasizes their agreement by assigning higher weights to similar opinions. The new projection method DProj is specifically adapted for Decision Making Trial and Evaluation Laboratory (DEMATEL), which allows to determine the relationship between cause and effect factors of the system, while we show that some projection methods from the literature are not suitable for DEMATEL. To validate DProj-DEMATEL, we performed two analyses based on Manhattan and Euclidean distances on three examples from the literature. The validation results show that DProj-DEMATEL is a reliable method that can contribute to satisfactory group decision making.

Multiattribute group decision making based on novel score function considering both the angle and fuzzy information under interval-valued intuitionistic fuzzy environment

The interval-valued intuitionistic fuzzy values (IVIFVs) have been widely employed for multiattribute group decision making (MAGDM). However, the existing decision making method using IVIFVs has some drawbacks. The recognition degree and information utilization suffer from a gap in the evaluation of alternatives. Therefore, the need for a reliable and effective decision method is obvious. In this paper, a novel score function considering both the angle and fuzzy information of IVIFVs is proposed, which ensures the effective fusion of the fuzzy information measure and angle measure and overcomes the shortcomings of the existing methods. Then, the weights of decision makers (DMs) are set as IVIFVs and subjective values are determined by the optimization model to ensure the objectivity and authenticity of the weights of DMs. Furthermore, a novel MAGDM method based on the proposed score function is proposed. Finally, the proposed method is implemented on the regional innovation resource allocation efficiency evaluation. The results show that our method possesses a higher recognition degree than the other methods, and it ensures relatively high ranking accuracy. The ideology and results of this research will help the DMs to evaluate the alternatives effectively.

A framework for defining weights of decision makers in group decision-making, using consistency between different multicriteria weighting methods

ABSTRACT Most forest operations are complex problems that require the weights of relevant criteria – representing trade-offs between various economic, ecological, and social aspects of the problem – to be defined. Usually this is done by using multicriteria weighting method(s) in a group (participatory) context in order to include different opinions and to minimize risk of poor individual judgments. Furthermore, in group decision-making, the weights of decision makers (DMs) must be defined. However, no consensus exists on the best way to determine related weights assigned to DMs. For that purpose, we propose the consistency-based group decision-making framework (CGDF), which uses the expertise of a DM to weight the responses of the DM when deriving an overall group decision. The novel part of CGDF is the inter-weights consistency method (ICM) for evaluating the expertise of a DM based on the consistency of the weights the DM assigns to different criteria using different multicriteria weighting methods. We demonstrate the utility of ICM and CGDF by applying them to a decision-making problem from Swedish forest operations – defining weights of criteria relevant for designing the machine-trail network for driving in the forest terrain.

Reinforcement Learning-Based Feedback and Weight-Adjustment Mechanisms for Consensus Reaching in Group Decision Making

The number of discussion rounds and harmony degree of decision makers are two crucial efficiency measures to be considered in the design of the consensus-reaching process for the group decision-making problems. Adjusting the feedback parameter and importance weights of the decision makers in the recommendation mechanism has a great impact on these efficiency measures. This work aims to propose novel and efficient reinforcement learning-based adjustment mechanisms to address the tradeoff between the aforementioned measures. To employ these adjustment mechanisms, we propose to extract the dynamics of state transition from consensus models based on the distributed trust functions and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Z$ </tex-math></inline-formula> -Numbers in order to convert the decision environment into a Markov decision process. Two independent reinforcement learning agents are then trained via a deep deterministic policy gradient algorithm to adjust the feedback parameter and importance weights of decision makers. The first agent is trained toward reducing the number of discussion rounds while ensuring the highest possible level of harmony degree among the decision makers. The second agent merely speeds up the consensus reaching process by adjusting the importance weights of the decision makers. Various experiments are designed to verify the applicability and scalability of the proposed feedback and weight-adjustment mechanisms in different decision environments.

A Novel GDMD-PROMETHEE Algorithm Based on the Maximizing Deviation Method and Social Media Data Mining for Large Group Decision Making

Multi-attribute group decision making is widely used in the real world, and many scholars have done a lot of research on it. The public’s focus on emergencies can provide an important reference for emergency handling decision making in the social media big data environment. Due to the complexity of emergency handling decision making, the asymmetry of user evaluation information is easy to cause the loss of important information. It is very important to mine valuable information for decision making through online reviews. Then, a generalized extended hybrid distance measure method between the probabilistic linguistic term sets is proposed. Based on this, an extended GDMD-PROMETHEE large-scale multi-attribute group decision-making method is proposed as well, which can be used to decision making under symmetric information and asymmetric information. Firstly, web crawler technology is used to explore the topics of public concern of emergency handling on social media platforms, and use k-means cluster analysis to classify the crawling variables, then the attributes and subjective weights of emergency handling plans are obtained by TF-IDF and Word2vec technology. Secondly, in order to better retain the linguistic evaluation information from decision-makers, a new generalized probabilistic hybrid distance measure method based on Hamming distance is proposed. Considering the difference of decision makers’ evaluation, the objective weight of decision makers is calculated by combining the maximum deviation method with the new extended hybrid Euclidean distance. On this basis, the comprehensive weights of the attributes are calculated by combining subjective and objective factors. Meanwhile, this paper realizes the distance measures and information fusion of probabilistic linguistic term sets under cumulative prospect theory, and the ranking results of the emergency handling plans based on the extended GDMD-PROMETHEE algorithm are given. Finally, the feasibility and effectiveness of the extended GDMD-PROMETHEE algorithm are verified by the case study of the explosion accident handling decision making of Shanghai “6.18” Petrochemical, and the comparative analyses between the several traditional algorithms demonstrate the extended GDMD-PROMETHEE algorithm is more scientific and superior in this paper.

Ordinal-cardinal consensus analysis for large-scale group decision making with uncertain self-confidence

Consensus analysis is necessary for large-scale group decision making (LSGDM) for ensuring reasonable decision results. This paper offers a new method for LSGDM with uncertain self-confidence that follows ordinal-cardinal consensus analysis. For this purpose, a new interval ranking method is first proposed to compare alternatives. Then, an improved ordinal clustering method on criteria is introduced using the deviation between individual ranking positions. In view of opinion deviation, uncertain self-confidence deviation, and ranking deviation, the weights of decision makers (DMs) are defined. Similarly, the weights of clusters are determined by further combining cluster cardinality. Further, an ordinal-cardinal consensus procedure is offered, which contains two algorithms: the first algorithm is about the ordinal consensus improvement in view of three aspects: ranking adjustment, opinion adjustment, and uncertain self-confidence; the second algorithm studies the cardinal consensus improvement under the ordinal consensus requirement, which also mainly contains three aspects: opinion adjustment, the number of adjusted judgments, and uncertain self-confidence. Moreover, a new algorithm for LSGDM is presented. Finally, an example is provided to check the feasibility and efficiency of the new method, and a comparison analysis is also made.

A group decision-making method that links changes in experts’ preferences and weights: Application to site selection of waste-to-energy plants

The selection of a waste-to-energy (WTE) plant site is the core issue that determines whether the WTE project can effectively treat municipal solid waste, reduce environmental pollution, and promote the development of a circular economy, and is often determined through group decision-making. The complexity of this group decision problem makes the opinions of decision makers often with uncertainty. The single-valued neutrosophic set (SVNS) can reduce the loss of information that contains uncertainty by quantitatively describing the information through three functions. In addition, existing studies on group decision-making for WTE plant siting suffer from the problem that decision maker weights do not change in concert with those decision makers’ decision information. Therefore, this study proposes a group decision-making method based on SVNSs. First, a group consensus strategy is proposed to improve group consensus by removing the evaluation value of the corresponding solution for decision makers who do not reach consensus and are unwilling to modify their preferences. Second, a decision maker weight determination and adjustment method is proposed to readjust the decision maker weights from the solution level according to their respective consensus degree when the decision makers’ preference information changes. This method enables the decision makers’ preferences and weights to be changed jointly. An illustrative example and a comparative analysis of WTE plant siting decisions demonstrate the feasibility and superiority of the method. The experimental results show that the method is effective in helping decision makers to select the optimal WTE plant site more accurately.

Information Fusion Model of Group Decision Making Based on a Combinatorial Ordered Weighted Average Operator

Aiming at the problem that the existing aggregation operators only consider the weight of the data itself or the weight of the data position when aggregating data, a group decision information fusion model based on the combinatorial ordered weighted average operator is proposed, which considers the incompleteness, uncertainty and plurality of the decision information, and effectively improves the accuracy of group consensus. In this paper, an interval intuitionistic fuzzy combinatorically weighted average operator is proposed, which embodies the weight of the set data itself and the weight of data position, and can adjust the importance of the two weights in the operator through parameters, and prove that the operator has the properties of boundedness, monotonicity, idempotency and permutation invariance. Secondly, a position weight solving model of interval intuitionistic fuzzy combinatorial ordered weighted average operator is established, and the position weight is solved with the help of cross-entropy and Orness measures. Finally, a group decision information fusion process based on IVIFCOWA operators is designed, and the decision maker weight information is obtained according to the decision matrix, and the comprehensive decision matrix is obtained by fusion of IVIFCOWA operators, and then the mean similarity is obtained. Through specific case analysis, it is proved that the proposed operator can make full use of the decision data, and can better reach group consensus after data fusion than the existing operator.

Serious Games as a Validation Tool for PREDIS: A Decision Support System for Disaster Management

In this paper, we validate PREDIS, a decision support system for disaster management using serious games to collect experts' judgments on its performance. PREDIS is a model for DISaster response supplier selection (PREDIS). It has a PREDictive component (PRED) for predicting the disaster human impact and an estimation component to Estimate the DISaster (EDIS) needs to optimise supplier-based resource allocation. A quasi-experiment design embedded in a participatory simulation game is conducted to compare the opinions of equal samples of 22 experts and non-experts. The following questions are put forward. First, "Does PREDIS model assists the decision makers to make the same decisions faster?" Second, "Does the PREDIS model assist the non-experts as simulated decision makers to decide like an expert?" Using AHP weights of decision makers' preferences as well as Borda counts, the decisions are compared. The result shows that PREDIS helps to reduce the decision-making time by experts and non-experts to 6 h after the disaster strike, instead of the usual 72 h. It also assists 71% of the non-experts to make decisions similar to those made by experts. In summary, the PREDIS model has two major capabilities. It enables the experts and non-experts to predict the disaster results immediately using widely available data. It also enables the non-experts to decide almost the same as the experts; either in predicting the human impact of a disaster and estimating the needs or in selecting suitable suppliers.

A Large Group Emergency Decision-Making Method Based on Uncertain Linguistic Cloud Similarity Method

In recent years, the consensus-reaching process of large group decision making has attracted much attention in the research society, especially in emergency environment area. However, the decision information is always limited and inaccurate. The trust relationship among decision makers has been proven to exert important impacts on group consensus. In this study, we proposed a novel uncertain linguistic cloud similarity method based on trust update and the opinion interaction mechanism. Firstly, we transformed the linguistic preferences into clouds and used cloud similarity to divide large-scale decision makers into several groups. Secondly, an improved PageRank algorithm based on the trust relationship was developed to calculate the weights of decision makers. A combined weighting method considering the similarity and group size was also presented to calculate the weights of groups. Thirdly, a trust updating mechanism based on cloud similarity, consensus level, and cooperation willingness was developed to speed up the consensus-reaching process, and an opinion interaction mechanism was constructed to measure the consensus level of decision makers. Finally, a numerical experiment effectively illustrated the feasibility of the proposed method. The proposed method was proven to maximally retain the randomness and fuzziness of the decision information during a consensus-reaching process with fast convergent speed and good practicality.

Introducing MOWSCER Method for Multiple Criteria Group Decision-Making: A New Method of Weighting in the Structure of Cause and Effect Relationships

One of the most remarkable subjects in multi-criteria group decision-making (MCGDM) is determining the weight and importance of criteria. The weighting methods based on inputs are categorized in the manifold group. This paper presents a novel method for weighting the criteria in a network structure. This approach, namely MOWSCER, is used when the relationships among the criteria are modeled by a cause-and-effect directed graph. The directed graph demonstrates the cause and effect relationship among criteria. In the presented method, the criteria are divided into three groups. The basic idea of the criteria segmentation is derived from European Foundation for Quality Management (EFQM). These groups include (1) effect criteria, (2) cause criteria, and (3) connector criteria. Then, the connector criteria are allocated fewer weights than the other two types. In other words, the introduced method follows two purposes of decreasing criteria number and appropriate allocating of weights among remaining criteria. Accordingly, first, the connector criteria are detected, so they are assigned less weight, and remained criteria are allocated a proper weight according to their importance. Furthermore, a new weighting method for determining the weights of decision makers (DMs) in group decision-making problems is presented to achieve a comprehensive manner. In the end, to prove the practicality of the proposed method, the weights of criteria and DMs are computed in a case study and two illustrative examples. Besides, to confirm the accuracy of that, it is compared with the DEMATEL method.

A dynamic large-scale multiple attribute group decision-making method with probabilistic linguistic term sets based on trust relationship and opinion correlation

Dynamic large-scale multiple attribute group decision making (DLMAGDM) is ubiquitous in many areas of the real world. It is composed of large numbers of decision makers, several continuous periods, alternative set and attribute set changed with time. Given the characteristics implicited in decision-making elements and the advantages of probabilistic linguistic term sets (PLTSs) in modelling uncertainty and complexity of decision makers’ subjective opinions, this paper constructs a probabilistic linguistic DLMAGDM method. First of all, a dynamic weight determination model based on trust relationships and evidential conflicts between decision makers is proposed to obtain current dynamic weights of decision makers. Then, a comprehensive hierarchical clustering method that divides large numbers of decision makers into several subgroups is constructed based on three clustering constrains. Moreover, some probabilistic linguistic extended evidential power aggregation operators are proposed to aggregate PLTSs. These operators can appropriately handle the extreme PLTSs and fully consider the role of incomplete probabilistic distributions in PLTSs. In addition, a dynamic decision-making method based on PROMETHEE is developed to determine the final priority order of alternatives according to preferences between alternatives over several periods. Lastly, a case study for supply chain finance risk assessment for several firms in Chinese household appliance industry is utilized to illustrate the practicality and effectiveness of the probabilistic linguistic DLMAGDM method. Furthermore, the comparative analysis with some other existing methods and the sensitivity analysis are made to verify its advantages.