Current Issue : July-September Volume : 2023 Issue Number : 3 Articles : 5 Articles
This paper investigates single-period inventory management problems with uncertain market demand, where the exact possibility distribution of demand is unavailable. In this condition, it is important to order a reliable quantity which can immunize against distribution uncertainty. To model this type of single-period inventory management problem, this paper characterizes the uncertain demand by generalized interval-valued possibility distributions. We present a novel concept about an uncertain distribution set to describe distribution perturbation characterization. First, we introduce a lambda selection of the interval-valued fuzzy variable, and the uncertain distribution set is a collection of all generalized possibility distributions of lambda selection variables. According to the uncertain distribution set, a new distributionally robust fuzzy optimization method is developed for single-period inventory management problems. Under mild assumptions, the robust counterpart of the proposed fuzzy singleperiod inventory management model is formulated, which is an optimization program with certain linear objectives and infinitely many integral constraints. We discuss the computational issue of integral constraints and reformulate equivalently the robust counterpart as three deterministic inventory submodels under generalized interval-valued trapezoidal possibility distributions. According to the characteristics of three submodels, a domain decomposition method is designed to find the robust optimal solution that can immunize against uncertainty in our single-period inventory management problem. Finally, some computational results demonstrate the efficiency of the proposed distributionally robust fuzzy optimization method....
This study utilizes the advantages of soft calculations, represented as intelligent combined methods and computational intelligence methods, to enhance credit risk management. For this purpose, the proposed method contains the fuzzy regression and artificial neural network (ANN). In this way, the parameters of neural network are fuzzy, encompassing weights and errors to model under uncertain conditions. Then, fuzzy neural networks form the system where the optimal decision is obtained using the highest degree of superiority by fuzzy inferences. Finally, using the credit information of some countries, the efficiency of the proposed combined model in credit scoring analysis has been shown....
Multigranulation rough set theory is one of the most effective tools for data analysis and mining in multicriteria information systems. Six types of covering-based multigranulation fuzzy rough set (CMFRS) models have been constructed through fuzzy β -neighborhoods or multigranulation fuzzy measures. However, it is often time-consuming to compute these CMFRS models with a large fuzzy covering using set representation approaches. Hence, presenting novel methods to compute them quickly is our motivation for this paper. In this article, we study the matrix representations of CMFRS models to save time in data processing. Firstly, some new matrices and matrix operations are proposed. Then, matrix representations of optimistic CMFRSs are presented. Moreover, matrix approaches for computing pessimistic CMFRSs are also proposed. Finally, some experiments are proposed to illustrate the effectiveness of our approaches....
The design of analog circuits is a complex and repetitive process aimed at finding the best design variant. It is characterized by uncertainty and multivariate approaches. The designer has to make different choices to satisfy a predefined specification with required parameters. This paper proposes a method for facilitating the design of analog amplifiers based on m-polar fuzzy graphs theory and deep learning. M-polar fuzzy graphs are used because of their flexibility and the possibility to model different real-life multi-attribute problems. Deep learning is applied to solve a regression task and to predict the membership functions of the m-polar fuzzy graph vertices (the solutions), taking on the role of domain experts. The performance of the learner is high since the obtained errors are very small: Root Mean Squared Error is from 0.0032 to 0.0187, Absolute Error is from 0.022 to 0.098 and Relative Error is between 0.27% and 1.57%. The proposed method is verified through the design of three amplifiers: summing amplifier, subtracting amplifier, and summing/subtracting amplifier. The method can be used for improving the design process of electronic circuits with the possibility of automating some tasks....
The control performance of the fixed-depth motion is related to the performance of the underwater vehicle. Due to the complex and changing environment underwater and various potential risks, the variety of underwater operations, and the variability of the structural parameters and environmental parameters of the underwater vehicle, the control performance is compromised when performing constant depth motion. It is of much significance to study the control method of the fixed-depth motion to improve the performance of the underwater vehicle. The transfer function of underwater vehicle’s depth-setting motion was established, and the fuzzy-PID controller was established for simulating the control of depth-setting movements of underwater vehicles. The interaction law between PID initial parameters and controller performance was studied, and the interaction law between the change of underwater vehicle mass and the hydrodynamic coefficient and controller performance was also studied. The results show that the fuzzy-PID controller can realize the control of the underwater vehicle’s depth-setting motion, and the control effect was independent of the initial PID parameters, thus avoiding the dependence of the formulation of PID parameters on manual control experience. When the mass and hydrodynamic coefficient of the underwater vehicle change, the fuzzy-PID controller can still maintain good control performance and has strong adaptive ability....
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