Fuzzy clustering methods are conveniently employed in constructing a fuzzy model of a system, but they need to tune some parameters. In this research, FCM is chosen for fuzzy clustering. Parameters such as the number of clusters and the value of fuzzifier significantly influence the extent of generalization of the fuzzy model. These two parameters require tuning to reduce the overfitting in the...

in this paper, the researchers present a multi-objective linear fractional inventory model using fuzzy pro-gramming. the proposed method in this paper is applied to a problem with two objective functions. the re-sulted fuzzy model is transformed into an ordinary linear programming. the implementation of the develop-ing model presented in this paper is demonstrated through the use of some numeri...

this paper presents a new mathematical model for a bi-objective job shop scheduling problem with sequence-dependent setup times that minimizes the weighted mean completion time and the weighted mean tardiness time. for solving this multi-objective model, we develop a fuzzy multi-objective linear programming (fmolp) model. in this problem, a proposed fmolp method is applied with respect to the o...

appropriate scheduling and sequencing of tasks on machines is one of the basic and significant problems that a shop or a factory manager encounters; this is why in recent decades extensive studies have been done on scheduling issues. one type of scheduling problems is just-in-time (jit) scheduling and in this area, motivated by jit manufacturing, this study investigates a mathematical model for...

In this paper, the researchers present a multi-objective linear fractional inventory model using fuzzy pro-gramming. The proposed method in this paper is applied to a problem with two objective functions. The re-sulted fuzzy model is transformed into an ordinary linear programming. The implementation of the develop-ing model presented in this paper is demonstrated through the use of some numeri...

in this study, a multi-objective genetic algorithm (moga) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear multi-input multi-output (mimo) systems. in the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. furthermore, se- curing low-level and high-level ...

in this paper, the portfolio selection problem is considered, where fuzziness and randomness appear simultaneously in optimization process. since return and dividend play an important role in such problems, a new model is developed in a mixed environment by incorporating fuzzy random variable as multi-objective nonlinear model. then a novel interactive approach is proposed to determine the pref...

In this research, the multi-objective project management decision problem with fuzzy goals and fuzzy constraints are considered. We constitute α-cut approach and two various fuzzy goal programming solution methods for solving the Multi-Objective Project Management (MOPM) decision problem under fuzzy environments. The Interactive fuzzy multi-objective linear programming (i-FMOLP) and Weighted Ad...

This paper proposes a compromise model, based on a new method, to solve the multi-objective large-scale linear programming (MOLSLP) problems with block angular structure involving fuzzy parameters. The problem involves fuzzy parameters in the objective functions and constraints. In this compromise programming method, two concepts are considered simultaneously. First of them is that the optimal ...