Large Number Of Decision Makers Research Articles

A hierarchical consensus reaching model based on trust and uncertain degree for heterogeneous large-scale group decision making and application to product design

Nowadays, due to the fierce market competition, companies in many industries needs to constantly introduce new products. How to select an optimal product design has become a widespread problem. Due to complexity and a large number of decision makers (DMs) participating in decision-making, this paper formulates the product design selection as a type of large-scale group decision making (LSGDM). Because of the differences in occupation, purpose, and personality of DMs in LSGDM, they prefer to provide evaluations by heterogeneous representation formats. A hierarchical consensus reaching model is developed based on trust and uncertain degree for heterogeneous LSGDM. Firstly, the heterogeneous evaluations of DMs are transformed into fuzzy preference relations (FPRs) through possibility degrees. According to trust relationships and original evaluations of DMs, the trust and uncertain degrees of DMs are extracted considering the inherent personalities and psychological traits implied in the heterogeneous evaluations. Thereby, this paper (1) constructs an optimization model to determine the weights of DMs and (2) divides the feedback mechanism into four cases. Case study of product design selection is provided to illustrate the applicability of the proposed model. Several comparisons validate the advantages of the proposed model.

Consensus methods with Nash and Kalai-Smorodinsky bargaining game for large-scale group decision-making

With the significant advancements in communication technology, group decision-making (GDM) can now be implemented online, allowing a large number of decision-makers (DMs) to participate concurrently. However, current methods for large-scale group decision-making (LSGDM) are primarily suitable for 20 to 50 DMs, and their effectiveness in scenarios involving thousands or even tens of thousands of participants has yet to be fully validated. Furthermore, as the number of participants increases, the evaluation information becomes increasingly diverse and complex. At the same time, the social networks associated with the DMs typically become sparse, making information sharing and consensus building more challenging. In light of these challenges, we develop two new methods based on cooperative games to effectively address the challenges in super LSGDM. First, we propose a two-stage semi-supervised fuzzy C-means (FCM) clustering method with trust constraints, which aims to address the issue of sparsity in relationships within large-scale social networks. This method utilizes trust relationships as reliable resources and prior knowledge to guide and supervise the clustering process. On this basis, we discuss three scenarios from the perspective of cooperative games: (i) subgroup optimal consensus adjustments in non-cooperative situations, (ii) group optimal consensus adjustments in cooperative situations, and (iii) subgroup optimal consensus adjustments in cooperative situations. Subsequently, we view the consensus adjustment allocation as a cost cooperative game problem and propose two new LSGDM consensus methods based on Nash Bargaining (NB) and Kalai-Smorodinsky Bargaining (KSB). Finally, experiments on real datasets demonstrate the superiority and reliability of our proposed LSGDM methods.

A global optimization feedback model with PSO for large scale group decision making in hesitant fuzzy linguistic environments

In an increasingly complex and uncertain decision-making environment, large-scale group decision-making (LSGDM) can offer a more efficient method, allowing a large number of decision-makers (DMs) to truly participate in the decision-making process. The consensus-reaching process (CRP) is an effective method for resolving conflicting opinions among large-scale DMs. However, in the existing CRP of LSGDM, the new consensus state and the adjustment cost borne by inconsistent DMs after implementing feedback suggestions are not taken into consideration. To address this issue, this paper proposes a global optimization feedback model with particle swarm optimization (PSO) for LSGDM in hesitant fuzzy linguistic environments. An improved density-based spatial clustering of applications with noise (DBSCAN) on hesitant fuzzy linguistic term sets (HFLTSs) is introduced to classify large-scale DMs into several clusters, and a weight determination method that combines cluster size and intra-cluster tightness is also presented. The consensus degree of clusters is calculated at two levels: intra-consensus and inter-consensus. To improve the global consensus level with minimum cost, a global optimization feedback model is established to generate recommendation advice for inconsistent DMs, and the model is solved by PSO. A numerical example related to “COVID-19” and some comparisons are provided to verify the feasibility and advantages of the proposed method.

Opinion Dynamics and Minimum Adjustment-Driven Consensus Model for Multi-Criteria Large-Scale Group Decision Making Under a Novel Social Trust Propagation Mechanism

Compared with group decision making (GDM), it is more difficult to reach a consensus in large-scale group decision making (LSGDM) owing to the large number of decision makers (DMs). Moreover, studies on LSGDM under social trust networks are obviously fewer than those on GDM, and rarely utilize opinion dynamics to analyze the interaction of DMs. Therefore, in the context of multi-criteria large-scale group decision making (MCLSGDM), an MCLSGDM consensus decision framework and a bounded confidence-based consensus optimization model are proposed. First, a trust propagation method considering the relative importance of the trust degrees (TDs) is proposed. Then, a two-stage process of obtaining DMs’ opinions based on opinion dynamics is developed to analyze the interaction of DMs before clustering. Furthermore, a new method for determining the heterogeneous weights of DMs and subgroups is discussed. Finally, to consider the adjustment willingness of DMs, this study proposes a two-stage optimization consensus model based on bounded confidence. In addition, a numerical example is used to further elaborate the above methods and models, and highlight their rationality and superiority through a series of simulation experiments and comparative analysis.

ASSESSMENT OF RISK FOR THE INTEGRATION OF RENEWABLE ENERGY SOURCES INTO THE ELECTRICITY SUPPLY SYSTEM

The work is devoted to the development of a methodology that allows combining qualitative and quantitative assessments of different risk factors to make an integrated decision when renewable energy sources connect to the electricity supply system. The integration of quantitative information and qualitatively presented knowledge through the creation of appropriate algorithms can significantly expand the possibilities of risk analysis of renewable energy sources, established energy systems and justification of measures to manage them.
 The key features of large-scale and complex systems are the diversity and interdependence of the elements included in them, the large number of decision-makers in conditions of high uncertainty and the uniqueness of these objects. Therefore, the study describes the methods and approaches that can be used to analyze the risks of complex systems. In conditions of high uncertainty and complexity of large-scale systems, many solutions can be obtained by using qualitative assessments based on the knowledge and experience of experts.
 Maintaining the reliability, stability and efficiency of the electrical system is becoming a difficult issue for distribution networks with variable energy resources. Further movement in the direction of increasing the share of solar power plants (SPP) in the generation of the power system is a great challenge for effective management of the distribution network and a serious threat to its normal functioning.
 The article identifies the main factors that lead to the problems of integration of SPP. Also, the probability of occurrence and the level of possible consequences of the implementation of each of the identified risk factors were assessed by using the method of expert assessments.
 According to the results of the study, the main problems in integrating SPP into the power supply system are increasing shunting power, deteriorating electricity quality, the need to modernize lines, increasing debt on the "green" tariff, higher harmonics and reactive power flow.

Expert Component as Element of Content Strategy of Industrial Enterprise

Internet marketing in the industrial market has a number of features and differs from that in the consumer market. The first is characterized by specialized and personalized approaches, as well as it must take into account the long cycle of the transaction, and the presence of a large number of decision makers. Marketers at industrial enterprises must have a range of knowledge about the market and industry trends, possess information about the organization of procurement processes and the operation of distribution channels, and be able to build a communication process with a certain type of customer. The paper presents methods for online promotion of industrial products and a study of the features of choosing of internet marketing tools for industrial enterprises. The main idea of the paper is to highlight the expert component for such enterprises as the main tool for communication with customers. To solve this problem, the internet marketing tools of an industrial enterprise has been considered and the expert component in its content strategy has been shown, approaches to the systematization of internet marketing tools have been studied. In the course of the study, general scientific methods have been used: analysis, synthesis, generalization. The result of the research is a model of product promotion in the logic of the industrial consumer's journey, which is based on the connection of the consumer's journey map with internet marketing tools.

The methodology of using multicriteria analysis methods choosing the optimal architecture of the GLONASS space segment

The project presents a methodology for choosing the optimal architecture using, as an example, global navigation satellite system, namely its space segment. Several architectures of the GLONASS system were taken as an example for testing the methodology. The usage of traditional methods of multi-criteria analysis in this case is too way difficult due to the presence of a large number of particular navigation tasks, that often put forward contradictory and uncertain requirements for their resolution, the presence of a large number of private criteria, the need to involve a large number of decision makers (DM), and as a consequence, a conflict of interests, difficulty in setting weights, determining preferences, etc. The confident judgment method was used to implement the task. The system of private criteria was structured, taking into account the requirements of specific narrow segments, and their preferences were formed. After that, tables were built for each structure, according to the required number of criteria and for three different particular tasks, as well as to normalize and collapse the criteria for each task into one criterion. Then a set of Pareto-rational solutions and a rating of alternatives were formed. The final appearance of the system satisfied the requirements imposed by the consumer segment. Keyword

A Novel Approach for Group Decision Making Based on the Best–Worst Method (G-BWM): Application to Supply Chain Management

Due to the complexity of real-world multi-criteria decision-making (MCDM) issues, analyzing different opinions from a group of decision makers needs to ensure appropriate decision making. The group decision-making methods collect preferences of the decision makers and present the best preferences using mathematical equations. The best–worst method (BWM) is one of the recently introduced MCDM methods that requires fewer pairwise comparisons to obtain the criteria weights than the other MCDM methods. In this research, we develop a novel approach to group decision-making problems based on the BWM called G-BWM. This approach helps us to analyze the preferences of decision makers to carry out democratic decision making using the BWM structure. In order to assess the applicability of the proposed methodology and represent its novelty, two numerical examples from the literature with the application to supply chain management (SCM) (i.e., green supplier selection and supplier development/segmentation) are examined and discussed. The results demonstrate the performance of our proposed G-BWM for group decision making in terms of a large number of decision makers, ease of use and achieving democratic decisions in the decision-making process.

Consensus-Based Linguistic Distribution Large-Scale Group Decision Making Using Statistical Inference and Regret Theory.

Large-scale group decision-making (LSGDM) deals with complex decision- making problems which involve a large number of decision makers (DMs). Such a complex scenario leads to uncertain contexts in which DMs elicit their knowledge using linguistic information that can be modelled using different representations. However, current processes for solving LSGDM problems commonly neglect a key concept in many real-world decision-making problems, such as DMs’ regret aversion psychological behavior. Therefore, this paper introduces a novel consensus based linguistic distribution LSGDM (CLDLSGDM) approach based on a statistical inference principle that considers DMs’ regret aversion psychological characteristics using regret theory and which aims at obtaining agreed solutions. Specifically, the CLDLSGDM approach applies the statistical inference principle to the consensual information obtained in the consensus process, in order to derive the weights of DMs and attributes using the consensus matrix and adjusted decision-making matrices to solve the decision-making problem. Afterwards, by using regret theory, the comprehensive perceived utility values of alternatives are derived and their ranking determined. Finally, a performance evaluation of public hospitals in China is given as an example in order to illustrate the implementation of the designed method. The stability and advantages of the designed method are analyzed by a sensitivity and a comparative analysis.

Distance-Based Large-Scale Group Decision-Making Method with Group Influence

Large-scale group decision making (LSGDM), which involves a large number of decision makers, has become a hot topic in the field of decision making. To address LSGDM problems with hesitant information, in this paper, a distance-based LSGDM method is proposed by considering group influence. In the method, a generalized distance measure between two pieces of hesitant information is designed to overcome the limitations of existing distance measures. To accelerate the convergence of consensus reaching process with the consideration of the interaction among decision makers, decision makers are divided into several subgroups. The group leader is selected in terms of the contributions of decision makers to group consensus. In order to help generate a satisfactory solution by adequately considering the decision makers’ diverse opinions, the group discussion is introduced to clarify their opinions and minimize bias under the organization of the selected group leader. Based on the changes in the preference information before and after group discussion, the influence of each subgroup is generated and further applied to determine the weights of subgroups. Simulation and comparison experiments are conducted to demonstrate the applicability and effectiveness of the proposed method.

Big data-enabled large-scale group decision making for circular economy: An emerging market context

This study is focused on presenting a unique landscape for big data-enabled circular economy that involves stakeholders as important decision makers. This research is designed based on five case studies from emerging markets with a focus on circular models to propose a framework for large scale decision making. In these cases, different linear economy problems are addressed that further utilizes the integration of big data and large-scale group decision making by stakeholders to achieve circularity. The findings of our study indicate a four-step design (enabling technologies, business significance, deriving value, and circular goals) to implement the 10R's of the circular economy through emerging technologies such as big data and related mobile applications along with cloud-based platforms. The study highlights how cases from emerging markets can be useful for other firms and ecosystems, ranging from e-commerce to manufacturing, that employ large number of decision makers with the aim of creating a circular economy. At the end, the study presents theoretical and practical implications along with the scope for future research.

Capturing attitudinal characteristics of decision-makers in group decision making: application to select policy recommendations to enhance supply chain resilience under COVID-19 outbreak

The impact of COVID-19 on the global outbreak of supply chain is enormous. It is crucial for governments to take policy recommendations to enhance the supply chain resilience to mitigate the negative impact of COVID-19. For such a major issue, it is a common occurrence that a large number of decision-makers (DMs) are invited to participate in the decision-making process so as to ensure the comprehensiveness and reliability of decision results. Since the attitudinal characteristics of DMs are important factors affecting decision results, this study focuses on capturing the attitudinal characteristics of DMs in the large-scale group decision making process. The capturing process combines the ordinal k-means clustering algorithm, gained and lost dominance score method and personalized quantifiers. To enable DMs to express their cognitions in depth, we use the probabilistic linguistic term set to express the evaluation information of DMs. A case study on selecting the optimal policy recommendation for improving the integration capability of supply chain is given to illustrate the applicability of the proposed process. The superiority of the proposed algorithm is highlighted through sensitive analysis and comparative analysis.

Hierarchical group decision‐making approach for information technology project evaluation and prioritization

AbstractThe purpose of this study is to define a novel hierarchical group decision‐making (HGDM) problem. For this problem, a practical solution approach is proposed for information technology (IT) project evaluation and prioritization. HGDM is a decision‐making problem with a large number of decision makers (DMs) or experts organized in a hierarchical structure. For this problem, a practical solution approach based on pairwise comparisons is developed. For evaluating the projects in organizations, specifically for the IT projects, a hierarchy of many DMs make project proposals and assess proposed projects using various sets of criteria. The proposed HGDM approach is applied to the evaluation and prioritization of IT projects. A problem with 22 DMs and experts in a three‐level hierarchy, 29 criteria and 15 projects is solved using the proposed HGDM approach. The HGDM approach can deal with more complex real‐life problems such as problems where more than 500 projects, 100 DMs and experts, and 100 criteria exist. The originality of the study is the introduction of HGDM in the group decision‐making environment.

An Energy Optimal Dispatching Model of an Integrated Energy System Based on Uncertain Bilevel Programming

An integrated energy system (IES) involving a large number of decision-makers causes problems of bad coordination between energy sub-networks and the IES and it is not able to fully consider the multi-energy complementarity among multiple decision-makers. In this context, firstly, this paper constructs an energy optimal dispatching model of an IES based on uncertain bilevel programming. The upper model takes the transformation matrix of energy hubs as the upper decision-maker, taking the minimum operation cost of the IES in the form of confidence as the objective function; the lower model takes each optimal operation plan of the electric power sub-network, the thermal energy sub-network, and the gas energy sub-network as the lower decision-makers, aiming at the operation economy of each sub-network and considering their operation as necessary constraints. Secondly, a firefly algorithm with chaotic search and an improved light intensity coefficient is designed to improve the proposed model. An empirical analysis was conducted on a pilot area of an integrated energy system in Hebei Province. The results show the following: (1) The typical daily operating cost of the integrated energy system in winter is lower than that in summer; (2) under the same load level, the typical winter and summer running costs of the integrated energy system are lower than that of the traditional microgrid; (3) compared with the particle swarm optimization algorithm, the improved firefly algorithm proposed in the paper has obvious advantages both in terms of running cost and solution time; and (4) when the confidence of the objective function and the constraints increases, the operating cost of various schemes also increase. The above results validate the effectiveness of the energy optimal dispatching model of the IES and the economy of the system operation under the multiple decision-maker hierarchy.

Ordinal consensus measure with objective threshold for heterogeneous large-scale group decision making

Because of the increasing complexity of real-world decision-making environment, there is a trend that a large number of decision-makers are becoming involved in group decision making problems. In large-scale group decision making problems, owing to various backgrounds and psychological cognition, it is natural to use heterogeneous representation forms (quantitative or qualitative) to express distinct preference information for different decision-makers. In this paper, we investigate the consensus reaching process in the environment of heterogeneous large-scale group decision making. A novel ordinal consensus measure with an objective threshold based on preference orderings is proposed. This process contains five parts: (1) obtaining ordinal preferences; (2) classifying all decision-makers into several subgroups using the ordinal k-means clustering algorithm; (3) measuring consensus levels of subgroups and the global group using novel ordinal consensus indexes; (4) providing suggestions for decision-makers to revise preferences using feedback strategies; (5) obtaining final decision result. An illustrative example is provided to verify the implementation of the proposed consensus model. Lastly, we discuss the approaches to determine the appropriate number of clusters and the initialization center points for the proposed ordinal k-means clustering algorithm.

The determinants for adoption of energy supply contracting: Empirical evidence from the Swiss market

Energy supply contracting, the outsourcing of energy-related services, is considered a promising tool to induce investment in energy efficiency and renewable technologies. Yet, some energy contracting markets grow slowly and some segments remain untapped. In order to assess whether the potential is underexploited and policy support is needed, the determinants of energy supply contracting adoption are analyzed using a dataset of 2003 accepted and rejected contracts offered in the Swiss market. The results show that the advantages of risk sharing and economies of scale brought by contracting, as well as trust in the supplier and the technology, are important determinants of the client's choice. A large number of decision-makers, inducing higher expected transaction costs, negatively impacts the likelihood of adoption. Less specific contracts involving residential or new buildings are more likely to be signed. Policy intervention is needed to reduce the entailed transaction costs, by clarifying the regulatory framework regarding tenants and subsidizing the costs of facilitators. Trust may be enhanced by the government's information campaigns on renewable technologies and certifications of the suppliers. This paper provides guidelines on how to adapt the business model to better exploit the market.

Mental Models Analysis and Comparison Based on Fuzzy Rules: A Case Study of the Protests of June and July 2013 in Brazil

Collective decision processes involve a large number of decision makers, demanding the consensus of different points of view about problems of several knowledge areas. The analysis and comparison of these points of view can contribute to this consensus, but they depend on the representation of each decision maker's individual knowledge about the problem. Mental models (MMs) are diagrammatic artifacts based on natural language which can be used to represent such knowledge. These models comprise logical cause-effect loops that are used to describe a problem as understood by each decision maker. This paper proposes an innovative tool based on a knowledge-based system of fuzzy rules which identifies MMs that best represent the consensus about the causes of a specific problem. Fuzzy rules were created, taking into account both, qualitative and quantitative variables. The tool was applied to the analysis and comparison of MMs of university students to describe the protests that occurred in Brazil between June and July 2013. A comparison of results using Pareto analysis indicated that the tool identifies those MMs that best indicate the probable causes of the protests.

消費者の購買地選択行動における選択肢集合の形成方法

This paper discusses choice sets in consumer behavior. We have many works analyzing individual choice behavior. These objectives are path choice, household choice, commercial area choice and so forth. Almost all these works take the way that is to express choice behaviors as a mathematical model to analyze and most works assume individuals with the complete information. The assumption of the complete information is the precondition that all individuals have all information about alternatives and relates deeply to individual choice set. In case that there are a large number of decision-makers and alternatives in the area applied the model, the larger size of the area, the larger size of choice set of decision-maker, the assumption, that is decision-maker with complete information, is not valid and gets unreal. There are two ways to consider choice sets in previous papers. The first is the way to determine sets a priori and the next is the probabilistic approach. These approaches are considered multi-steps choice structure. That is the choice an alternative from subset continued from the choice a subset from the set of subsets. The deterministic approach considers that the choice set is consist of available alternatives that the decision makers consider actually. There are a few considerations in terms of the methodology of generation of choice set in the papers that take its approach. The another approach is that the choice probability derives from the joint probability that the choice probability of alternative and subset. There is the representative model that is the Nested Logit model described in Ben-Akiva(1985). The choice sets in the NL model are considered similarity in alternatives in the master choice set therefore it is the subset characterized by similarity. It assumes the individuals with the complete information. In this paper, in the sake of applying the consumer behavior model to a large area such as Tokyo, we at first propose the choice set generation methodology that is deterministic and a priori sets and extend the previous model. The methodology defines the scale of commercial area and distance. The next, we apply the models on Tokyo area and calibrate the models statistically. Then, we assess the proposed model comparing the previous model. Finally, we discuss the distribution of the attractiveness of the choice sets in Tokyo area using the calibrated parameters.

A Large Group Decision Making Approach Based on TOPSIS Framework with Unknown Weights Information

Large group decision making considering multiple attributes is imperative in many decision areas. The weights of the decision makers (DMs) is difficult to obtain for the large number of DMs. To cope with this issue, an integrated multiple-attributes large group decision making framework is proposed in this article. The fuzziness and hesitation of the linguistic decision variables are described by interval-valued intuitionistic fuzzy sets. The weights of the DMs are optimized by constructing a non-linear programming model, in which the original decision matrices are aggregated by using the interval-valued intuitionistic fuzzy weighted average operator. By solving the non-linear programming model with MATLAB ® , the weights of the DMs and the fuzzy comprehensive decision matrix are determined. Then the weights of the criteria are calculated based on the information entropy theory. At last, the TOPSIS framework is employed to establish the decision process. The divergence between interval-valued intuitionistic fuzzy numbers is calculated by interval-valued intuitionistic fuzzy cross entropy. A real-world case study is constructed to elaborate the feasibility and effectiveness of the proposed methodology.

An improved consensus-based group decision making model with heterogeneous information

In group decision making (GDM) problems, it is natural for decision makers (DMs) to provide different preferences and evaluations owing to varying domain knowledge and cultural values. When the number of DMs is large, a higher degree of heterogeneity is expected, and it is difficult to translate heterogeneous information into one unified preference without loss of context. In this aspect, the current GDM models face two main challenges, i.e., handling the complexity pertaining to the unification of heterogeneous information from a large number of DMs, and providing optimal solutions based on unification methods. This paper presents a new consensus-based GDM model to manage heterogeneous information. In the new GDM model, an aggregation of individual priority (AIP)-based aggregation mechanism, which is able to employ flexible methods for deriving each DM's individual priority and to avoid information loss caused by unifying heterogeneous information, is utilized to aggregate the individual preferences. To reach a consensus more efficiently, different revision schemes are employed to reward/penalize the cooperative/non-cooperative DMs, respectively. The temporary collective opinion used to guide the revision process is derived by aggregating only those non-conflicting opinions at each round of revision. In order to measure the consensus in a robust manner, a position-based dissimilarity measure is developed. Compared with the existing GDM models, the proposed GDM model is more effective and flexible in processing heterogeneous information. It can be used to handle different types of information with different degrees of granularity. Six types of information are exemplified in this paper, i.e., ordinal, interval, fuzzy number, linguistic, intuitionistic fuzzy set, and real number. The results indicate that the position-based consensus measure is able to overcome possible distortions of the results in large-scale GDM problems.