Liangqing Feng's research while affiliated with Nanchang Hangkong University and other places

The additive ratio assessment system (ARAS) method is an effective technique for simplifying complex decision problems by determining the optimal alternative through the relative index (utility degree) to the ideal solution. However, there are still some shortcomings in the existing researches on the extension of this method when it is utilized in different decision environments, such as ignoring the correlation relationship between attributes, the lack of flexibility in the utilization of the decision process, and the relative index to the ideal solution may be scaled up or down with the ratio form. In order to overcome these disadvantages, this paper proposes the novel T-spherical fuzzy (TSF) cross entropy (TSFCE) measure and T-spherical Aczel-Alsina Heronian mean (TSFAAHM) aggregation operators and uses them to improve the ARAS method in the TSF environment. For the TSF multiple attribute group decision-making (MAGDM) problems, a group decision making model based on the improved ARAS is designed. In this model, the experts’ weights are obtained by the TSFCE-based similarity measure. The attribute combined weights are calculated by fusing the objective weights obtained by TSFCE-based entropy measure and the subjective weights got by the extended stepwise weight assessment ratio analysis (SWARA) integrated with TSFCE. In the improved ARAS method, the T-spherical Aczel-Alsina Weighted Heronian mean (TSFAAWHM) operator can capture the correlation relationship between the attributes. Compared with the relative index, the TSFCE can reflect the difference between the alternatives and the ideal solution to obtain a more stable solution ranking. Lastly, an illustrative example about the sustainable supplier selection of power battery echelon utilization (PBEU) for a 5G base station is used to demonstrate the proposed method. The effectiveness, practicability and superiority of proposed method are illustrated by parameters influence and methods comparison analysis.

With the rapid development of technological informatization, competition among enterprises is gradually transitioning from being "production-centered" to being "customer-centric," making service-oriented enterprises increasingly important. In addition to this, as global manufacturing advances in the process of intelligent manufacturing (IM), there is growing attention on the integration of manufacturing and the service industry, which has garnered the interest of numerous experts and scholars in the field of intelligent manufacturing services (IMS). This article combines intelligent manufacturing enterprises, intelligent service nodes, and consumers. Based on the background of intelligent manufacturing services, it collected risk factors within the smart supply chain (SSC) that connect different service nodes. These factors were evaluated by experts using a proposed linguistic q-rung orthopair fuzzy weighted averaging (Lq-ROFWA) operator in combination with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method for aggregation operations. Finally, we obtain the conclusions that the most influential factor affecting other risk factors is the inadequate identification of core customer needs; and the most important risk factor for smart supply chains oriented to intelligent manufacturing services is the leakage of customer information. After analyzing the relevant data, we will provide some theoretical and managerial implications for IM enterprises.

A T-spherical uncertain linguistic set (TSULS) is not only an expanded form of the T-spherical fuzzy set and the uncertain linguistic set but can also integrate the quantitative judging ideas and qualitative assessing information of decision-makers. For the description of complex and uncertain assessment data, TSULS is a powerful tool for the precise description and reliable processing of information data. However, the existing multi-attribute border approximation area comparison (MABAC) method has not been studied in TSULS. Thus, the goal of this paper is to extend and improve the MABAC method to tackle group decision-making problems with completely unknown weight information in the TSUL context. First, the cross-entropy measure and the interactive operation laws for the TSUL numbers are defined, respectively. Then, the two interactive aggregation operators for TSUL numbers are developed, namely T-spherical uncertain linguistic interactive weighted averaging and T-spherical uncertain linguistic interactive weighted geometric operators. Their effective properties and some special cases are also investigated. Subsequently, a new TSULMAGDM model considering the DM’s behavioral preference and psychology is built by integrating the interactive aggregation operators, the cross-entropy measure, prospect theory, and the MABAC method. To explore the effectiveness and practicability of the proposed model, an illustrative example of Sustainable Waste Clothing Recycling Partner selection is presented, and the results show that the optimal solution is h 3 . Finally, the reliable, valid, and generalized nature of the method is further verified through sensitivity analysis and comparative studies with existing methods.

T-spherical fuzzy sets (TSFSs) are more flexible and efficient tools to deal with ambiguous, uncertain and vague information in complex real-world decision-making problems than various extended intuitionistic fuzzy sets. This paper aims to develop a novel T-spherical fuzzy (TSF) Combinative Distance-Based ASsessment (CODAS) based on the Heronian Minkowski distance aggregation operator, this new method can capture interrelationship between input arguments. Some TSF weighted Heronian Minkowski distance (TSFWHMD) aggregation operators with generalization are developed based on Heronian mean and Minkowski-type distance, their properties are discussed as well as their families are analyzed. Furthermore, the TSF MAGDM methodology based on the improved CODAS is designed, where the Minkowski-type distance is used to define the TSF similarity for computing the expert weights and to construct the maximizing deviation method (MDM) for determining the attribute weights, respectively. The TSF ordered weighted Heronian Hamming distance (TSFOWHHD) and TSF ordered weighted Heronian Euclidean distance (TSFOWHED) operators derived from the TSFOWHMD operator are integrated into the CODAS method, which is an improved method for both measuring the deviation of the negative ideal solution from each alternative and capturing the correlation between attributes. Finally, the feasibility and practicality of developed methodology are illustrated with an example of CAE (Computer Aided Engineering) software selection for lithium-ion power battery (LiPB) design, sensitivity analysis and method comparisons are performed to elucidate the reliability and validity of the developed methodology.

The purpose of this manuscript is to develop a novel MAIRCA (Multi-Attribute Ideal-Real Comparative Analysis) method to solve the MCDM (Multiple Criteria Decision-Making) problems with completely unknown weights in the q-rung orthopair fuzzy (q-ROF) setting. Firstly, the new concepts of q-ROF Lance distance are defined and some related properties are discussed in this paper, from which we establish the maximizing deviation method (MDM) model for q-ROF numbers to determine the optimal criteria weight. Then, the Lance distance-based MAIRCA (MAIRCA-L) method is designed. In it, the preference, theoretical and real evaluation matrices are calculated considering the interaction relationship in q-ROF numbers, and the q-ROF Lance distance is applied to obtain the gap matrix. Finally, we manifest the effectiveness and advantage of the q-ROF MAIRCA-L method by two numerical examples.

A T-spherical fuzzy set is a more powerful mathematical tool to handle uncertain and vague information than several fuzzy sets, such as fuzzy set, intuitionistic fuzzy set, Pythagorean fuzzy set, q-rung orthopair fuzzy set, and picture fuzzy set. The Aczel–Alsina t-norm and s-norm are significant mathematical operations with a high premium on affectability with parameter activity, which are extremely conducive to handling imprecise and undetermined data. On the other hand, the Hamy mean operator is able to catch the interconnection among multiple input data and achieve great results in the fusion process of evaluation information. Based on the above advantages, the purpose of this study is to propose some novel aggregation operators (AOs) integrated by the Hamy mean and Aczel–Alsina operations to settle T-spherical fuzzy multi-criteria decision-making (MCDM) issues. First, a series of T-spherical fuzzy Aczel–Alsina Hamy mean AOs are advanced, including the T-spherical fuzzy Aczel–Alsina Hamy mean (TSFAAHM) operator, T-spherical fuzzy Aczel–Alsina dual Hamy mean (TSFAADHM) operator, and their weighted forms, i.e., the T-spherical fuzzy Aczel–Alsina-weighted Hamy mean (TSFAAWHM) and T-spherical fuzzy Aczel–Alsina-weighted dual Hamy mean (TSFAAWDHM) operators. Moreover, some related properties are discussed. Then, a MCDM model based on the proposed AOs is built. Lastly, a numerical example is provided to show the applicability and feasibility of the developed AOs, and the effectiveness of this study is verified by the implementation of a parameters influence test and comparison with available methods.