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AbstractExisting research on multistate system reliability has mainly focused on one‐dimensional systems such as parallel systems, linear sliding window systems, and linearly consecutively connected systems. However, two‐dimensional networked systems widely exist in real‐world applications such as lighting systems, monitoring systems, and computer network systems. This research considers a two‐dimensional networked system consisting of multistate components. The system fails if the cumulative performance of any row or any column cannot meet a predetermined demand. A novel reliability evaluation algorithm is proposed for the considered two‐dimensional networked system by extending the universal generating function technique. Furthermore, the proposed model and reliability evaluation algorithm are extended to a two‐dimensional networked system with phased missions. The proposed models and algorithms are illustrated by a matrix heating system in a thermoforming machine.

The authors thank the editor and anonymous referees for their valuable comments and insightful recommendations, and thank Prof. Yucheng Dong for the helpful suggestions. This research was supported in part by grants from the National Natural Science Foundation of China (#71874023, #U1811462, #71725001, #71771037, #71971042, #71910107002) and Major project of the National Social Science Foundation of China (#19ZDA092). Enrique Herrera-Viedma is supported by the FEDER funds in the project TIN2016-75850-R. ; Non-cooperative behavior is a common situation in large-scale group decision-making (LSGDM) problems. In addition, decision makers in LSGDM often use different preference formats to express their opinions, due to their educational backgrounds, knowledge, and experiences. Heterogeneous preference information and non-cooperative behaviors bring challenges to LSGDM. This study develops a consensus reaching model to address heterogeneous LSGDM with non-cooperative behaviors and discuss its application in financial inclusion. Specifically, the cosine similarity degree is introduced to build a distance measure for different preference structures. Clustering analysis is employed to divide large-scale groups and handle non-cooperative behaviors in LSGDM. A consensus degree and a weighting process are proposed to decrease the influence of non-cooperative behaviors and facilitate the consensus reaching process. The convergence of the proposed approach is proven by theoretical and simulation analyses. Experimental studies are carried out to compare the performances of the proposed approach with existing methods. Finally, a real-life example from the "targeted poverty reduction project" in China is presented to validate the proposed approach. The selection of beneficiaries in finance inclusion is difficult due to the lack of credit history, the large number of participants, and the conflicting views of participants. The results showed that the proposed consensus model can integrate opinions of participants using diverse preference formats and reach an agreement efficiently. ; National Natural Science Foundation of China (NSFC) 71874023 U1811462 71725001 71771037 71971042 71910107002 ; Major project of the National Social Science Foundation of China 19ZDA092 ; European Union (EU) TIN2016-75850-R

AbstractIn many real‐world applications, designs can only be evaluated pairwise, relative to each other. Nevertheless, in the simulation literature, almost all the ranking and selection procedures are developed based on the individual performances of each design. This research considers the statistical ranking and selection problem when the design performance can only be simulated pairwise. We formulate this new problem using the optimal computing budget allocation approach and derive the asymptotic optimality condition based on some approximations. The numerical study indicates that our approach can reduce the number of simulations required to confidently identify the best design.

To avoid credit fraud, social credit within an economic system has become an increasingly important criterion for the evaluation of economic agent activity and guaranteeing the development of a market economy with minimal supervision costs. This paper provides a comprehensive review of the social credit literature from the perspectives of theoretical foundation, scoring methods, and regulatory mechanisms. The study considers the credit of various economic agents within the social credit system such as countries (or governments), corporations, and individuals and their credit variations in online markets (i.e., network credit). A historical review of the theoretical (or model) development of economic agents is presented together with significant works and future research directions. Some interesting conclusions are summarized from the literature review. (1) Credit theory studies can be categorized into traditional and emerging schools both focusing on the economic explanation of social credit in conjunction with creation and evolution mechanisms. (2) The most popular credit scoring methods include expert systems, econometric models, artificial intelligence (AI) techniques, and their hybrid forms. Evaluation indexes should vary across different target agents. (3) The most pressing task for regulatory mechanisms that supervise social credit to avoid credit fraud is the establishment of shared credit databases with consistent data standards.

ABSTRACTThe reciprocal pairwise comparison matrix is a well‐established technique and widely used in multiple criteria decision making methods. However, some entries in a pairwise comparison matrix may not be available in many real‐world decision problems. The goal of this paper is to propose a new method for estimating missing elements of an incomplete pairwise comparison matrix. A bias induced matrix model (BIMM), which combines the matrix multiplication and the properties of the original reciprocal pairwise comparison matrix, is used to calculate the missing entries in an incomplete pairwise comparison matrix. The proposed BIMM minimizes all bias values of the bias induced matrix to keep the global consistency. The missing value(s) can be estimated by solving the system of equations from the bias induced matrix. The theorems of the BIMM and the related corollaries are developed, and three numerical examples are introduced to illustrate the proposed model. Copyright © 2011 John Wiley & Sons, Ltd.