A Fuzzy Goal Programming Approach to Fully Fuzzy Linear Regression

Pérez‐Cañedo, Boris; Rosete, Alejandro; Verdegay, J. L.

doi:10.1007/978-3-030-50143-3_53

Cited by 2 publications

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“…Shafi et al [46] proposed a hybrid approach by augmenting FLR with symmetric parameter model and fuzzy c-means method. Pérez-Cañedo et al [40] proposed two new fuzzy goal programming methods based on linear and Chebyshev scalarisations with a view to solve the problem of fully fuzzy multi-objective linear programming.…”

Section: Related Workmentioning

confidence: 99%

Comparative Assessment of Multiple Linear Regression and Fuzzy Linear Regression Models

2021

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Prognosticating crop yield still remains as one of the challenging tasks in agriculture. Even though multiple linear regression methodology has dominated the area of predictive modelling, it is constrained to the assumption that the underlying relationship is assumed to be crisp or precise. Consequently, it often fails to provide satisfactory results when this assumption is violated in realistic situations. Fuzzy linear regression methodology is one of the promising and potential techniques to overcome this lacuna. Moreover, this fuzzy methodology can efficiently handle the problem of multicollinearity. In this paper, an attempt has been made to comparatively assess the efficiency of conventional regression models with their fuzzy counterparts using data on sweet corn yield (t/ha), total weed dry matter (g/m 2 ) at 30 DAS and total weed density (no./m 2 ) at 30 DAS. Model efficiency is computed in terms of average width of the prediction intervals. Efficiency of the models is also assessed in the presence of correlated explanatory variables. Outcomes emanated from the study clearly show the higher relative efficiency of fuzzy linear regression technique in comparison with the widely used simple and multiple linear regression techniques. This study also reveals that the fuzzy methodology has clear advantages over the conventional regression methodology in dealing with correlated explanatory variables.

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Section: Related Workmentioning

confidence: 99%

Comparative Assessment of Multiple Linear Regression and Fuzzy Linear Regression Models

2021

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Lexicographic Methods for Fuzzy Linear Programming

2020

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Fuzzy Linear Programming (FLP) has addressed the increasing complexity of real-world decision-making problems that arise in uncertain and ever-changing environments since its introduction in the 1970s. Built upon the Fuzzy Sets theory and classical Linear Programming (LP) theory, FLP encompasses an extensive area of theoretical research and algorithmic development. Unlike classical LP, there is not a unique model for the FLP problem, since fuzziness can appear in the model components in different ways. Hence, despite fifty years of research, new formulations of FLP problems and solution methods are still being proposed. Among the existing formulations, those using fuzzy numbers (FNs) as parameters and/or decision variables for handling inexactness and vagueness in data have experienced a remarkable development in recent years. Here, a long-standing issue has been how to deal with FN-valued objective functions and with constraints whose left- and right-hand sides are FNs. The main objective of this paper is to present an updated review of advances in this particular area. Consequently, the paper briefly examines well-known models and methods for FLP, and expands on methods for fuzzy single- and multi-objective LP that use lexicographic criteria for ranking FNs. A lexicographic approach to the fuzzy linear assignment (FLA) problem is discussed in detail due to the theoretical and practical relevance. For this case, computer codes are provided that can be used to reproduce results presented in the paper and for practical applications. The paper demonstrates that FLP that is focused on lexicographic methods is an active area with promising research lines and practical implications.

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A Fuzzy Goal Programming Approach to Fully Fuzzy Linear Regression

Cited by 2 publications

References 30 publications

Comparative Assessment of Multiple Linear Regression and Fuzzy Linear Regression Models

Comparative Assessment of Multiple Linear Regression and Fuzzy Linear Regression Models

Lexicographic Methods for Fuzzy Linear Programming

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