Production planning and control (PPC) systems have to deal with rising complexity and dynamics. The complexity of planning tasks is due to some existing multiple variables and dynamic factors derived from uncertainties surrounding the PPC. Although literatures on exact scheduling algorithms, simulation approaches, and heuristic methods are extensive in production planning, they seem to be ineff...

The so-called Functional Linear Regression model consists in explaining a scalar response by a regressor which is a random function observed on a compact subset of R: in this context, the “parameter” of linear model is a function of the weights. In order to estimate this functional coefficient some estimators such as Functional Principal Component Regression Estimator, Smooth Principal Componen...

Crop yield forecasting is becoming more essential in the current scenario when food security must be assured, despite problems posed by an increasingly globalized community and other environmental challenges such as climate change natural disasters. Several factors influence crop prediction, which has complex non-linear relationships. Hence, to study these relationships, machine learning method...

In this study, a odor concentration estimation model in the grow to finish swine pen was derived using principal component analysis and multiple regression models. 14 kinds of compounds were used. Prior statistical analysis, missing value (less than MDL)for each imputed with MDL/2, unit log-transformed follow normal distribution. The three components extracted through explained for 77.03% initi...