@article{liu_wang_chen_zhu_2022, title={A combination forecasting model based on hybrid interval multi-scale decomposition: Application to interval-valued carbon price forecasting}, volume={191}, ISSN={["1873-6793"]}, DOI={10.1016/j.eswa.2021.116267}, abstractNote={Forecasting carbon price accurately is of great significance to ensure the healthy development of the carbon market. However, due to the non-linearity, non-stationarity, and dynamic uncertainty of interval-valued carbon price, there are many challenges to forecast the interval-valued carbon price precisely and stably. Therefore, this paper proposes a combination forecasting model based on the hybrid interval multi-scale decomposition method and its application to forecasting interval-valued carbon prices. First, three interval multi-scale decomposition methods, including interval discrete wavelet transform method (IDWT), interval empirical mode decomposition method (IEMD), and interval variational mode decomposition method (IVMD), are developed to decompose the interval-valued carbon price into interval trend and residuals. Second, Generalized autoregressive conditional heteroskedasticity (GARCH), auto-regressive integrated moving average model (ARIMA), support vector regression model (SVR), backpropagation neural network (BPNN), and long short-term memory networks (LSTM) are used to forecast the interval trend and residuals. Third, through interval-valued reconstruction, the results of each single forecasting model for three different decomposition methods are obtained respectively. Finally, the combination forecasting results are obtained by the LSTM, which is employed as an ensemble tool. The empirical analysis results show that our proposed model is significantly superior to some benchmark models in terms of accuracy and stability, and is an effective model for forecasting interval-valued carbon prices.}, journal={EXPERT SYSTEMS WITH APPLICATIONS}, author={Liu, Jinpei and Wang, Piao and Chen, Huayou and Zhu, Jiaming}, year={2022}, month={Apr} }
 @article{liu_qiang_wu_du_2022, title={Multiple stage optimization driven group decision making method with interval linguistic fuzzy preference relations based on ordinal consistency and DEA cross-efficiency}, ISSN={["1573-2908"]}, DOI={10.1007/s10700-022-09394-z}, abstractNote={Interval linguistic term (ILT) is highly useful to express decision-makers’ (DMs’) uncertain preferences in the decision-making process. This paper proposes a new group decision-making (GDM) method with interval linguistic fuzzy preference relations (ILFPRs) by integrating ordinal consistency improvement algorithm, cooperative game, Data Envelopment Analysis (DEA) cross-efficiency model, and stochastic simulation. Firstly, the ordinal consistency of ILFPR is developed. For improving the ordinal consistency of an ILFPR, a two-stage integer optimization model is presented to derive an ILFPR with ordinal consistency. Then, a weight-determination method for obtaining DMs’ weights is presented based on cooperative games. Moreover, a DEA cross-efficiency model is presented to obtain the priorities of linguistic preference relation derived from ILFPR. Meanwhile, the expected ranking vector of ILFPR is obtained based on the DEA cross-efficiency model by integrating stochastic preference analysis and Monte Carlo stochastic simulation. Finally, a numerical example of emergency logistics selection illustrates the applicability and credibility of the proposed method.}, journal={FUZZY OPTIMIZATION AND DECISION MAKING}, author={Liu, Jinpei and Qiang, Zijie and Wu, Peng and Du, Pengcheng}, year={2022}, month={Jul} }
 @article{liu_zheng_zhou_jin_chen_2021, title={A novel probabilistic linguistic decision-making method with consistency improvement algorithm and DEA cross-efficiency}, volume={99}, ISSN={["1873-6769"]}, DOI={10.1016/j.engappai.2020.104108}, abstractNote={Probabilistic linguistic term set (PLTS) is highly useful for decision-makers (DMs) to describe qualitative and uncertain information in the decision-making process. This paper proposes a novel probabilistic linguistic decision-making method with consistency improvement algorithm and data envelopment analysis (DEA) cross-efficiency. Firstly, we put forward the concept of order consistency of probabilistic linguistic preference relation (PLPR). The order consistency is helpful for DMs to make quick and efficient decision in certain situations. Then, based on the defined multiplicative consistency of PLPR, we develop a consistency improvement algorithm to transform the unacceptable multiplicative consistent PLPRs into the acceptable ones. Furthermore, a DEA model is established to derive the priority weight vector of alternatives from the acceptable multiplicative consistent PLPR. Meanwhile, for the alternatives that have equal priority weights, we use a DEA cross-efficiency model to further differentiate and obtain the final ranking of alternatives. Finally, a numerical example of emergency logistics distribution selection is given to illustrate the effectiveness and applicability of the proposed method.}, journal={ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE}, author={Liu, Jinpei and Zheng, Yun and Zhou, Ligang and Jin, Feifei and Chen, Huayou}, year={2021}, month={Mar} }
 @article{liu_shao_zhou_jin_2021, title={Consistency Adjustment Strategy and Multiplicative DEA Cross-Efficiency for Group Decision-Making with Probabilistic Linguistic Preference Relations}, ISSN={["2199-3211"]}, DOI={10.1007/s40815-021-01159-x}, journal={INTERNATIONAL JOURNAL OF FUZZY SYSTEMS}, author={Liu, Jinpei and Shao, Longlong and Zhou, Ligang and Jin, Feifei}, year={2021}, month={Aug} }
 @article{jin_li_liu_zhou_2021, title={Distribution Linguistic Fuzzy Group Decision Making Based on Consistency and Consensus Analysis}, volume={9}, ISSN={["2227-7390"]}, DOI={10.3390/math9192457}, abstractNote={The development of distribution linguistic provides a new research idea for linguistic information group decision-making (GDM) problems, which is more flexible and convenient for experts to express their opinions. However, in the process of using distribution linguistic fuzzy preference relations (DLFPRs) to solve linguistic information GDM problems, there are few studies that pay attention to both internal consistency adjustment and external consensus of experts. Therefore, this study proposes a fresh decision support model based on consistency adjustment algorithm and consensus adjustment algorithm to solve GDM problems with distribution linguistic data. Firstly, we review the concept of DLFPRs to describe the fuzzy linguistic evaluation information, and then we present the multiplicative consistency of DLFPRs and a new consistency measurement method based on the distance, and investigate the consistency adjustment algorithm to ameliorate the consistency level of DLFPRs. Subsequently, the consensus degree measurement is carried out, and a new consensus degree calculation method is put forward. At the same time, the consensus degree adjustment is taken the expert cost into account to make it reach the predetermined level. Finally, a distribution linguistic fuzzy group decision making (DLFGDM) method is designed to integrate the evaluation linguistic elements and obtain the final evaluation information. A case of the evaluation of China’s state-owned enterprise equity incentive model is provided, and the validity and superiority of the proposed method are performed by comparative analysis.}, number={19}, journal={MATHEMATICS}, author={Jin, Feifei and Li, Chang and Liu, Jinpei and Zhou, Ligang}, year={2021}, month={Oct} }
 @article{liu_shao_zhou_jin_2021, title={Expected consistency-based model and multiplicative DEA cross-efficiency for group decision-making with incomplete distribution linguistic preference relations}, volume={40}, ISSN={["1875-8967"]}, DOI={10.3233/JIFS-210148}, abstractNote={Faced with complex decision problems, distribution linguistic preference relation (DLPR) is an effective way for decision-makers (DMs) to express preference information. However, due to the complexity of the decision-making environment, DMs may not be able to provide complete linguistic distribution for all linguistic terms in DLPRs, which results in incomplete DLPRs. Therefore, in order to solve group decision-making (GDM) with incomplete DLPRs, this paper proposes expected consistency-based model and multiplicative DEA cross-efficiency. For a given incomplete DLPRs, we first propose an optimization model to obtain complete DLPR. This optimization model can evaluate the missing linguistic distribution and ensure that the obtained DLPR has a high consistency level. And then, we develop a transformation function that can transform DLPRs into multiplicative preference relations (MPRs). Furthermore, we design an improved multiplicative DEA model to obtain the priority vector of MPR for ranking all alternatives. Finally, a numerical example is provided to show the rationality and applicability of the proposed GDM method.}, number={6}, journal={JOURNAL OF INTELLIGENT & FUZZY SYSTEMS}, author={Liu, Jinpei and Shao, Longlong and Zhou, Ligang and Jin, Feifei}, year={2021}, pages={12039–12059} }
 @article{liu_huang_song_du_jin_chen_2021, title={Group decision making based on the modified probability calculation method and DEA cross-efficiency with probabilistic hesitant fuzzy preference relations}, volume={156}, ISSN={["1879-0550"]}, DOI={10.1016/j.cie.2021.107262}, abstractNote={In this paper, we propose a novel group decision making (GDM) method with probabilistic hesitant fuzzy preference relations (PHFPRs) based on the modified probability calculation method and data envelopment analysis (DEA) cross-efficiency. The primary advantage of the method is that it can reduce information loss and ensure the reliability of the decision-making result. A modified probability calculation method for hesitant fuzzy preference relations (HFPRs) is first proposed to obtain complete and normalized PHFPRs. Then, for the consistent additive preference relation (APR), we construct an output-oriented DEA model to derive the priority vector, in which each alternative is regarded as a decision-making unit (DMU). A DEA cross-efficiency model is further proposed to yield the priority vector, irrespective of whether the desired consistency level is achieved. Furthermore, a stochastic analysis method is developed to obtain the expected priority vector for any PHFPR, and a mathematical programming model is established to obtain the weight vector of decision makers based on group consensus. Finally, the procedure of the GDM method based on the modified probability calculation and the DEA cross-efficiency is designed. We also show the applicability and effectiveness of the proposed GDM method using illustrative examples.}, journal={COMPUTERS & INDUSTRIAL ENGINEERING}, author={Liu, Jinpei and Huang, Chong and Song, Jiashu and Du, Pengcheng and Jin, Feifei and Chen, Huayou}, year={2021}, month={Jun} }
 @article{liu_zheng_jin_chen_2021, title={Local consistency adjustment strategy and DEA - driven interval type-2 trapezoidal fuzzy decision-making model and its application for fog-haze factor assessment problem}, ISSN={["1573-7497"]}, DOI={10.1007/s10489-021-02354-x}, journal={APPLIED INTELLIGENCE}, author={Liu, Jinpei and Zheng, Yun and Jin, Feifei and Chen, Huayou}, year={2021}, month={May} }
 @article{liu_fang_jin_tao_chen_du_2021, title={Pythagorean fuzzy linguistic decision support model based on consistency-adjustment strategy and consensus reaching process}, volume={25}, ISSN={["1433-7479"]}, DOI={10.1007/s00500-021-05747-9}, number={13}, journal={SOFT COMPUTING}, author={Liu, Jinpei and Fang, Mengdi and Jin, Feifei and Tao, Zhifu and Chen, Huayou and Du, Pengcheng}, year={2021}, month={Jul}, pages={8205–8221} }
 @article{han_tao_chen_zhou_liu_2020, title={A new computational model based on Archimedean copula for probabilistic unbalanced linguistic term set and its application to multiple attribute group decision making}, volume={140}, ISSN={["1879-0550"]}, DOI={10.1016/j.cie.2019.106264}, abstractNote={This paper proposes the concept of the probabilistic unbalanced linguistic term set which considers not only the probability of linguistic variables but also the non-uniform and non-symmetric distribution of linguistic labels. A new computational model on basis of Archimedean copula and corresponding co-copula is developed to deal with probabilistic unbalanced linguistic information. The most advantage of the model is that it can keep the closure of the operation. Some operational properties and particular cases are further investigated. We present the concepts of Archimedean copula weighted probabilistic unbalanced linguistic arithmetic average aggregation operator and Archimedean copula weighted probabilistic unbalanced linguistic geometric average aggregation operator, some properties are also discussed. Finally, the effectiveness and universality of the developed approach are illustrated by a hospital selection and comparison analysis. A sensitivity analysis is also performed to test the robustness of proposed methods.}, journal={COMPUTERS & INDUSTRIAL ENGINEERING}, author={Han, Bing and Tao, Zhifu and Chen, Huayou and Zhou, Ligang and Liu, Jinpei}, year={2020}, month={Feb} }
 @article{xu_zhang_liu_luo_2020, title={Efficient synthetical clustering validity indexes for hierarchical clustering}, volume={151}, ISSN={["1873-6793"]}, DOI={10.1016/j.eswa.2020.113367}, abstractNote={Clustering validation and identifying the optimal number of clusters are of great importance in expert and intelligent systems. However, the commonly used similarity measures for validating are not versatile to measure the complex data structure, in reality, some of which are not as effective as that of the used clustering algorithm which gives the clustering results. This paper studies the validity indexes for the hierarchical clustering algorithm and proposes a unified validity index framework. For the single-linkage agglomerative hierarchical clustering we propose two efficient synthetical clustering validity (SCV) indexes using the minimum spanning tree to calculate the intra-cluster compactness to overcome the deficiencies of the measurements in the existing validity indexes. For the general hierarchical clustering, a self-adaptive similarity measure strategy and two generalized synthetical clustering validity (GSCV) indexes, which are the extension of the proposed SCV indexes, are developed. The proposed SCV and GSCV indexes constitute a unified validity index framework, where SCV index is a special case of GSCV index, can avoid the incompatibility of the similarity measure between the clustering and validation. The experimental comparisons with the state-of-the-art validity indexes on artificial and real-world data sets demonstrate the efficiency of the proposed validity indexes in discovering the true number of clusters and dealing with various sorts of data sets, including imbalanced data sets.}, journal={EXPERT SYSTEMS WITH APPLICATIONS}, author={Xu, Qin and Zhang, Qiang and Liu, Jinpei and Luo, Bin}, year={2020}, month={Aug} }
 @article{liu_fang_jin_wu_chen_2020, title={Multi-Attribute Decision Making Based on Stochastic DEA Cross-Efficiency with Ordinal Variable and Its Application to Evaluation of Banks' Sustainable Development}, volume={12}, ISBN={2071-1050}, DOI={10.3390/su12062375}, abstractNote={Multi-attribute decision making (MADM) is a cognitive process for evaluating data with different attributes in order to select the optimal alternative from a finite number of alternatives. In the real world, a lot of MADM problems involve some random and ordinal variables. Therefore, in this paper, a MADM method based on stochastic data envelopment analysis (DEA) cross-efficiency with ordinal variable is proposed. First, we develop a stochastic DEA model with ordinal variable, which can derive self-efficiency and the optimal weight of each attribute for all decision making units (DMUs). To further improve its discrimination power, cross-efficiency as a significant extension is proposed, which utilizes peer DMUs’ optimal weight to evaluate the relative efficiency of each alternative. Then, based on self-efficiency and cross-efficiency of all DMUs, we construct corresponding fuzzy preference relations (FPRs) and consistent fuzzy preference relations (FPRs). In addition, we obtain the priority weight vector of all DMUs by utilizing the row wise summation technique according to the consistent FPRs. Finally, we provide a numerical example for evaluating operation performance of sustainable development of 15 listed banks in China, which illustrates the feasibility and applicability of the proposed MADM method based on stochastic DEA cross-efficiency with ordinal variable.}, number={6}, journal={SUSTAINABILITY}, author={Liu, Jinpei and Fang, Mengdi and Jin, Feifei and Wu, Chengsong and Chen, Huayou}, year={2020} }
 @article{liu_fang_chen_2020, title={Multiplicative data envelopment analysis cross-efficiency and stochastic weight space acceptability analysis for group decision making with interval multiplicative preference relations}, volume={514}, ISSN={0020-0255}, url={http://dx.doi.org/10.1016/j.ins.2019.11.032}, DOI={10.1016/j.ins.2019.11.032}, abstractNote={To deal with group decision making (GDM) with interval multiplicative preference relations (IMPRs), this paper proposes a novel method based on multiplicative data envelopment analysis (DEA) cross-efficiency and stochastic weight space acceptability analysis. We first develop a multiplicative DEA model to evaluate the relative efficiency of all alternatives of a given multiplicative preference relation (MPR). Then, we present a method, free from consistency adjustment, to derive a priority vector using the multiplicative DEA cross-efficiency with respect to the given MPR. For GDM with IMPRs, we consider the decision makers’ weights as a uniform distribution for acceptability analysis. A modified unacceptability index is further defined to measure the unlikeliness for a particular alternative in a particular rank. Finally, we develop an assignment problem model to achieve an optimal ranking by minimizing the total rank unacceptability, and to compute the expected priority vector of all alternatives. Numerical examples are provided to show the applicability and justifications of the proposed GDM method.}, journal={Information Sciences}, publisher={Elsevier BV}, author={Liu, Jinpei and Fang, Shu-Cherng and Chen, Huayou}, year={2020}, month={Apr}, pages={319–332} }
 @article{liu_song_xu_tao_chen_2019, title={Group decision making based on DEA cross-efficiency with intuitionistic fuzzy preference relations}, volume={18}, ISSN={["1573-2908"]}, DOI={10.1007/s10700-018-9297-0}, abstractNote={The aim of this paper is to investigate a novel approach to group decision making based on DEA cross-efficiency with intuitionistic fuzzy preference relations, which can avoid information distortion and obtain more credible decision making results. An interval transform function is defined, which can transform an intuitionistic fuzzy preference relation into an interval multiplicative preference relation. Then, an interval transform function based data envelopment analysis model is developed to obtain the ranking vector of consistent intuitionistic fuzzy preference relation, in which each of the alternatives is viewed as a decision making unit. Moreover, for any intuitionistic fuzzy preference relations, we propose two DEA cross-efficiency models to get the cross-efficiency values of all alternatives, and we can calculate the normalized intuitionistic fuzzy priority weight vector of the intuitionistic fuzzy preference relation based on the cross-efficiency values. A goal programming model is investigated to derive the weight vector of decision makers. A step-by-step procedure for group decision making approach based on DEA cross-efficiency with intuitionistic fuzzy preference relations is presented. Finally, numerical examples are given to illustrate the validity and applicability of the proposed method. This is the first attempt of employing the DEA cross-efficiency to the group decision making with intuitionistic fuzzy preference relations.}, number={3}, journal={FUZZY OPTIMIZATION AND DECISION MAKING}, author={Liu, Jinpei and Song, Jingmiao and Xu, Qin and Tao, Zhifu and Chen, Huayou}, year={2019}, month={Sep}, pages={345–370} }
 @article{liu_wang_huang_wu_xu_chen_2019, title={Power load combination forecasting based on triangular fuzzy discrete difference equation forecasting model and PSO-SVR}, volume={36}, ISSN={["1875-8967"]}, DOI={10.3233/JIFS-181717}, abstractNote={In this paper, we develop a new triangular fuzzy series combination forecasting method based on triangular fuzzy discrete difference equation forecasting model and PSO-SVR, and use the developed forecasting method to power load forecasting. First, we propose a triangular fuzzy discrete difference equation (TFDDE) forecasting model to predict the triangular fuzzy series, which can accurately predict the fluctuating trend and is suitable for small sample data. Then, the support vector regression optimized by particle swarm optimization (PSO-SVR) is adopted to further improve the forecast result of TFDDE forecasting model, in which the parameters of support vector regression are optimally obtained by particle swarm optimization algorithm so as to avoid the blindness of artificial selection. Finally, the practical example of load forecasting of US PJM power market is employed to illustrate the proposed forecasting method. The experimental results show that the proposed forecasting method produces much better forecasting performance than some existing triangular fuzzy series models. The proposed combination forecasting method, which fully capitalizes on the time series forecasting model and intelligent algorithm, makes the triangular fuzzy series prediction more accurate than before and has good applicability. This is the first attempt of employing discrete difference equation theory for the triangular fuzzy series forecasting.}, number={6}, journal={JOURNAL OF INTELLIGENT & FUZZY SYSTEMS}, author={Liu, Jinpei and Wang, Piao and Huang, Yanyan and Wu, Peng and Xu, Qin and Chen, Huayou}, year={2019}, pages={5889–5898} }
 @article{han_chen_zhu_liu_2018, title={An Approach to Linguistic Multiple Attribute Decision-Making Based on Unbalanced Linguistic Generalized Heronian Mean Aggregation Operator}, volume={2018}, ISSN={["1687-5273"]}, DOI={10.1155/2018/1404067}, abstractNote={This paper proposes an approach to linguistic multiple attribute decision-making problems with interactive unbalanced linguistic assessment information by unbalanced linguistic generalized Heronian mean aggregation operators. First, some generalized Heronian mean aggregation operators with unbalanced linguistic information are proposed, involving the unbalanced linguistic generalized arithmetic Heronian mean operator and the unbalanced linguistic generalized geometric Heronian mean operator. For the situation that the input arguments have different degrees of importance, the unbalanced linguistic generalized weighted arithmetic Heronian mean operator and the unbalanced linguistic generalized weighted geometric Heronian mean operator are developed. Then we investigate their properties and some particular cases. Finally, the effectiveness and universality of the developed approach are illustrated by a low-carbon tourist instance and comparison analysis. A sensitivity analysis is performed as well to test the robustness of proposed methods.}, journal={COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE}, author={Han, Bing and Chen, Huayou and Zhu, Jiaming and Liu, Jinpei}, year={2018} }
 @article{xu_zhang_hu_liu_2018, title={Removal of Salt and Pepper Noise in Corrupted Image Based on Multilevel Weighted Graphs and IGOWA Operator}, volume={2018}, ISSN={["1563-5147"]}, DOI={10.1155/2018/7975248}, abstractNote={This paper proposes a novel iterative two-stage method to suppress salt and pepper noise. In the first phase, a multilevel weighted graphs model for image representation is built to characterize the gray or color difference between the pixels and their neighbouring pixels at different scales. Then the noise detection is cast into finding the node with minimum node strength in the graphs. In the second phase, we develop a method to determine the order-inducing variables and weighted vectors of the induced generalized order weighted average (IGOWA) operator to restore the detected noise candidate. In the proposed method, the two stages are not separate, but rather alternate. Simulated experiments on gray and color images demonstrate that the proposed method can remove the noise effectively and keep the image details well in comparison to other state-of-the-art methods.}, journal={MATHEMATICAL PROBLEMS IN ENGINEERING}, author={Xu, Qin and Zhang, Qiang and Hu, Duo and Liu, Jinpei}, year={2018} }