Browsing by Author "Wang, Junjie"

Now showing 1 - 6 of 6
  • Thumbnail Image
    ItemEmbargo
    A GRA-based heterogeneous multi-attribute group decision-making method with attribute interactions
    (Elsevier, 2025-01-30) Feng, Yu; Dang, Yaoguo; Wang, Junjie; Du, Junliang; Chiclana, Francisco
    In the era of VUCA (Volatility, Uncertainty, Complexity, Ambiguity), multi-attribute group decision-making (MAGDM) problems face the challenges of heterogeneous uncertainty in decision information and complex interactions between attributes, which greatly affect the reliability of decision-making outcomes. To address these challenges, this paper proposes a novel heterogeneous MAGDM method based on grey relational analysis (GRA) that considers attribute interactions. First, the heterogeneous information is integrated, including crisp numbers, generalized grey numbers, intuitionistic fuzzy numbers, hesitant fuzzy numbers, and probabilistic linguistic term sets. Then, by incorporating the 2-additive Choquet integral into GRA, we establish a heterogeneous grey interactive relational model and explore its properties. Subsequently, a heterogeneous grey relational Mahalanobis-Taguchi System is designed to estimate the Shapley values of attributes. Additionally, a two-stage resolution mechanism, comprising a consensus reaching process followed by a grey relational multi-objective programming model, is devised to determine the interaction indices. Finally, the effectiveness of the proposed method is demonstrated through a case study from China’s aviation manufacturing industry, along with sensitivity analysis and comparison analyses.
  • Thumbnail Image
    ItemOpen Access
    Grey difference incidence model of panel data and its application. Control and Decision
    (Northeastern University Press, China, 2024-01) Sun, Jing; Dang, Yaoguo; Yang, Shengxiang; Wang, Junjie; Yang, Shaowen
    There are three problems such as order preservation, stability and positive and negative in the grey incidence model applicable to panel data. To solve these problems, this paper has the following efforts. Firstly, the grey incidence coefficient was constructed based on the difference characteristics and exponential function, and the direction judgment factor was proposed to represent the positive and negative correlation. Secondly, the fluctuation adjustment factor was designed to describe the relative fluctuation degree between sequences. Thirdly, indicator weights are determined by entropy method. A grey difference incidence model for panel data is proposed. In addition, antisymmetry and order-preserving property are defined in grey incidence analysis, and the new model is proved to have antisymmetry, order-preserving property. Finally, the model is applied to analyze the trend of air pollution in the Beijing-Tianjin-Hebei region from 2013 to 2021, which shows the practicability of the model. Through two kinds of Monte Carlo experiments, the robustness and order-preserving property of the model are verified. The model is more suitable for the panel data with non-fixed object arrangement.
  • Thumbnail Image
    ItemEmbargo
    Grey prediction model of interval grey numbers based on a novel compound function transformation
    (Research Information Ltd., 2017) Ye, Jing; Dang, Yaoguo; Wang, Junjie; Yang, Yingjie
    Focusing on the prediction accuracy of interval grey numbers, the primary goal of this paper is to investigate novel grey prediction models to predict four typical kinds of interval grey numbers sequences respectively. The models used in this study can be briefly described as a combination of function transformation and Grey Model (GM) of interval grey numbers. According to different interval grey numbers sequences, the approaches can be applied to the upper bound and lower bound sequences of interval grey numbers or the kernel and measurement sequences of interval grey numbers. Finally, these new improved models have been verified by numerical examples and cases to demonstrate their validity and practicability. It proves these new models are not only applicable for increasingly high growth sequences where traditional grey models are not effective, but also control the enlargement of grey degrees of interval grey numbers which is very important for interval grey numbers’ forecasting. In summary, the paper effectively extends function transformation technology to the field of interval grey numbers for grey prediction.
  • Thumbnail Image
    ItemOpen Access
    An improved grey multivariable time-delay prediction model with application to the value of high-tech industry. Expert Systems with Applications
    (Elsevier, 2022-10-20) Zhou, Huimin; Dang, Yaoguo; Yang, Deling; Wang, Junjie; Yang, Yingjie
    To analyse the time lag effects between independent variables and dependent variables, we propose a discrete time-delay grey multivariable model . There are three improvements in this new model compared to the existing models. First, the time lag parameters are assigned different values for each independent variable. A linear correction term expands the new model. Second, with the given time lag, the least square method can be used to calculate the parameter vector. The time response function of is generated, which has the advantage of eliminating the jumping errors between discrete and continuous functions over the existing grey forecasting models. Third, all of the feasible combinations of the time lag parameters are compared by using a traversal algorithm to identify the best values with the minimized mean absolute percentage error (MAPE). In three different case studies, the performance of the new model is evaluated and compared to that of a number of mainstream grey models as well as non-grey models. According to the findings, the newly designed model performs significantly better than the compared models.
  • Thumbnail Image
    ItemEmbargo
    Minimum Cost Consensus-Based Social Network Group Decision Making With Altruism-Fairness Preferences and Ordered Trust Propagation
    (IEEE, 2024-09-17) Feng, Yu; Dang, Yaoguo; Wang, Junjie; Du, Junliang; Chiclana, Francisco
    Different from conventional decision-making environments, decision makers (DMs) in a community setting usually exhibit the complex social preferences and intricate social interactions, which may lead to high-decision costs for group consensus reaching. To address this challenge, we design a minimum cost consensus-based social network group decision making (SNGDM) approach considering altruism-fairness preferences and ordered trust propagation. First, a trust propagation method with order effect and path length is proposed to estimate the completed trust relationships among DMs in order to determine the weights of DMs. Then, inspired by the interaction of altruism and fairness preferences, we define the individual altruism-fairness preference utility function and utility level for cost consensus, and explore some properties. Afterwards, a new minimum cost consensus-based SNGDM with individual altruism-fairness preference utility is constructed. Finally, the validity of the proposed consensus framework is confirmed through the carbon reduction consensus problem of China’s aviation enterprises. Moreover, the sensitivity studies and comparative analysis are conducted to further demonstrate the merits of our proposal.
  • Thumbnail Image
    ItemOpen Access
    An optimized nonlinear time-varying grey Bernoulli model and its application in forecasting the stock and sales of electric vehicles
    (Elsevier, 2022-10-27) Zhou, Huimin; Dang, Yaoguo; Yang, Yingjie; Wang, Junjie; Yang, Shaowen
    An accurate prediction of electric vehicles stock and sales is a prerequisite for planning industrial policies for renewable sources to be used by a transportation system. We propose a novel time-varying grey Bernoulli model to investigate the nonlinear, complexity, and time-varying characteristics associated with electric vehicles stock and sales. We first design the time-varying parameters and a power exponent to explore the nonlinear developing trends of sequences. Subsequently, the cuckoo search algorithm determines optimum solutions because of its competence in dealing with complex optimization problems. Furthermore, its relationship with existing grey prediction models is presented, which demonstrates the flexibility and practicality of the newly-designed model. In order to validate this new model, the global electric vehicles stock and electric vehicles sales in France are predicted in comparison with six benchmark models. As demonstrated by the empirical findings, the proposed model is superior in terms of its capacity for forecasting, confirming its significant potential as a promising tool for electric vehicles stock and sales prediction.