Abstract: Fuzzy regression models are useful for investigating
the relationship between explanatory variables and responses in fuzzy
environments. To overcome the deficiencies of previous models and
increase the explanatory power of fuzzy data, the graded mean
integration (GMI) representation is applied to determine
representative crisp regression coefficients. A fuzzy regression model
is constructed based on the modified dissemblance index (MDI),
which can precisely measure the actual total error. Compared with
previous studies based on the proposed MDI and distance criterion, the
results from commonly used test examples show that the proposed
fuzzy linear regression model has higher explanatory power and
forecasting accuracy.
Abstract: This paper investigates the optimization problem of
multi-product aggregate production planning (APP) with fuzzy data.
From a comprehensive viewpoint of conserving the fuzziness of input
information, this paper proposes a method that can completely
describe the membership function of the performance measure. The
idea is based on the well-known Zadeh-s extension principle which
plays an important role in fuzzy theory. In the proposed solution
procedure, a pair of mathematical programs parameterized by
possibility level a is formulated to calculate the bounds of the
optimal performance measure at a . Then the membership function of
the optimal performance measure is constructed by enumerating
different values of a . Solutions obtained from the proposed method
contain more information, and can offer more chance to achieve the
feasible disaggregate plan. This is helpful to the decision-maker in
practical applications.