G.J. Imanov, A.Z. Aliyev, K.A. Hasanli.
Fuzzy logic extensions-based simulation model for control of logistic performance index
In this paper, an approach is proposed based on fuzzy logic extensions-based instruments to evaluate Logistic Performance Index (LPI). İnterval-valued intuitionistic fuzzy techniques are suitable in this regard to solve this kind of Weighted Linear Combination problems and simulate possible change extent in the overall index. Considering the fact that there is no generalized way for computation of global indices, and Principal Component Analysis is the common method, dealing with data uncertainty requires application of fuzzy logic and its extensions-based methods. The novelties of this study are: taking into account the fuzziness of crisp input data, and simulation of input data that conveys the possible change extent in LPI expressed as interval-valued intuitionistic fuzzy numbers. The methodology elaborated in this study can be considerable in the generalization of LPI computing methodology and for the any index control purposes.
Keywords: Signal, Recognition method, Accuracy, Comparative assessment
DOI: https://doi.org/10.54381/icp.2023.2.03
Fuzzy logic extensions-based simulation model for control of logistic performance index
In this paper, an approach is proposed based on fuzzy logic extensions-based instruments to evaluate Logistic Performance Index (LPI). İnterval-valued intuitionistic fuzzy techniques are suitable in this regard to solve this kind of Weighted Linear Combination problems and simulate possible change extent in the overall index. Considering the fact that there is no generalized way for computation of global indices, and Principal Component Analysis is the common method, dealing with data uncertainty requires application of fuzzy logic and its extensions-based methods. The novelties of this study are: taking into account the fuzziness of crisp input data, and simulation of input data that conveys the possible change extent in LPI expressed as interval-valued intuitionistic fuzzy numbers. The methodology elaborated in this study can be considerable in the generalization of LPI computing methodology and for the any index control purposes.
Keywords: Signal, Recognition method, Accuracy, Comparative assessment
DOI: https://doi.org/10.54381/icp.2023.2.03