Current Issue : October-December Volume : 2023 Issue Number : 4 Articles : 5 Articles
With the development of the cloud computing era, the decision-making environment and algorithm models have become increasingly complex, and traditional decision-making methods have been unable to meet the needs of large group decision-making (LGDM) problems. Firstly, in order to solve this problem, the concept of double hierarchy interval hesitant fuzzy language (DHIHFL) is proposed. Compared with the traditional double hierarchy hesitant fuzzy language (DHHFL), it contains all elements from the lower limit to the upper limit and more comprehensively characterizes the hesitation of language information. Secondly, for LGDM problems, a self-confident double hierarchy interval hesitant fuzzy language (SC-DHIHFL) is developed, and the integration of self-confident degree can better enrich the evaluation information and promote the achievement of group consensus. Thirdly, a new two-stage LGDM method is proposed. The first stage is clustering and grouping and reaching consensus within the group, and the second stage is the integration of LGDM information. The two-stage method contains novel methods such as expert clustering algorithm, subjective and objective comprehensive weight, consensus degree, and deviation weight considering minority opinions. Finally, the proposed LGDM consensus method is applied to a practical LGDM problem, and the effectiveness is verified by comparative analysis with existing methods....
Renewable energy sources (RESs) such as solar photovoltaic (PV) systems are increasingly used as distributed generation for replacing the conventional energy. At the same time, energy storage systems like battery (BAT) must be applied for maintaining the balance between fluctuating energy production and load consumption. BAT’s state of charge (SOC) should be maintained within their design limits unaffected by RES intermittency and/or load power variations. This necessitates advanced power control and management methodologies for overcoming challenging conditions. This paper discusses and evaluates an optimal DC bus voltage regulation approach: an intelligent controller using an adaptive fuzzy logic controller (FLC) and a novel supervisory power management strategy for PV systems with BAT. The objectives are to keep a stable power flow in the system and guarantee the continuity of service by ensuring that the system components do not exceed their limits. In this manner, the DC bus voltage regulation of the PV/BAT system can be improved in comparison with conventional regulation. Therefore, the most important contributions of this work are as follows. (1) Development of comprehensive and modular novel energy management system (EMS): its originality is related to the inclusion of the control system limits with faster SOC balancing and smaller DC bus voltage fluctuation. (2) Providing a simple power flow management implementation that considers the optimal energy flow between PV system, BAT system, and load: a balance between minimal energy flow in the connecting line and the least requirements of BAT capacity is kept, reducing component constraints with a very straightforward structure. (3) Furthermore, FLC offers high robustness and smooth performances. FLC is added to the control strategy design requirements to reduce DC bus voltage deviation. (4) Real-time simulation/experimentation-based complete cases utilizing Matlab/Simulink and DSpace are illustrated to testify the effectiveness of the proposed FLC and EMS....
Any company must constantly innovate if they want to maintain its market share in the present cutthroat and unstable industry. Innovation has a big influence on consumer behavior, yet it goes against the principles of sustainability. The issue of sustainability has become crucial to their company’s growth. In order to evaluate a business firm’s sustainability performance statistically, a new and effective fuzzy logic tool is created. Evolution and assessment are performed by a novel interval type-2 fuzzy logic inference system. The judgment of the inference system is carried out on the basis of type-2 fuzzy logic (T2FL), principal component analysis (PCA), and statistical data analysis. The main input variables include corporate environmental performance (CEP) and corporate financial performance (CFP). The suggested approach can effectively examine a corporation’s sustainable performance, according to experimental findings. A unique approach that makes use of language variables and if-then logic to assist quantitative business sustainability events is the link between CEP and CFP. The recommended test will provide senior administrative leaders with useful information to supervise natural concerns correctly and gauge their commitment to company success....
The use of interval-valued hesitant fuzzy sets (IVHFS) can aid decision-makers in evaluating a variable using multiple interval numbers, making it a valuable tool for addressing decision-making problems. However, it fails to obtain information with greyness. The grey fuzzy set (GFS) can improve this problem but studies on it have lost the advantages of IVHFS. In order to improve the accuracy of decision-making and obtain more reasonable results, it is important to enhance the description of real-life information. We combined IVHFS and GFS and defined a novel fuzzy set named interval grey hesitant fuzzy set (IGHFS), in which possible degrees of grey numbers are designed to indicate the upper and lower limits of the interval number. Meanwhile, its basic operational laws, score function, entropy method, and distance measures are proposed. And then, a multicriteria decisionmaking (MCDM) model IGHFS-TOPSIS is developed based on them. Finally, an example of MOOC platform selection issues for teaching courses illustrates the effectiveness and feasibility of the decision model under the IGHFS....
Accurately predicting short-term congestions in ship traffic flow is important for water traffic safety and intelligent shipping. We propose a method for predicting the traffic flow of ships by applying the whale optimization algorithm to an extreme learning machine. The method considers external environmental uncertainty and complexity of ships navigating in traffic-intensive waters. First, the parameters of ship traffic flow are divided into multiple modal components using variational mode decomposition and extreme learning machine. The machine and the whale optimization algorithm constitute a hybrid modelling approach for predicting individual modal components and integrating the results of individual components. Considering a map between ship traffic flow parameters and congestion, fuzzy c-means clustering is used to predict the level of ship traffic congestion. To verify the effectiveness of the proposed method, ship traffic flow data of the Yangtze River estuary were selected for evaluation. Results from the proposed method for predicting ship traffic flow parameters are consistent with measurements. Specifically, the prediction accuracy of the ship traffic congestion reaches 76.04%, which is reasonable and practical for predicting ship traffic congestion....
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