Five crisp and fuzzy models for supply chain of an automotive manufacturing system

Supply Chain Management (SCM) is a new approach to production planning. It integrates the components of supply chain in a holistic manner. Modeling this large-scale system, which contains all effective enterprises in production such as raw material suppliers, part manufacturers, assembly plants, distribution organizations, and the like, is challenging for managers, engineers and researchers. This paper concentrates on supply chain system modeling with fuzzy linear programming, and fuzzy expert system for an automobile plant. First, a linear programming model is developed in such a way that while the input data is fuzzy, the constraints are crisp. In the second linear model, the coefficients of the model are crisp while the constraints are fuzzy. In the third model, we aggregate the first and the second models into one fuzzy linear programming where all constraints and coefficients are fuzzy. In each case, we compare the results with those of classical SC models. Finally, a rule based fuzzy expert system for SC is developed and the results are compared with those of the classical and fuzzy LP models. The results of the fuzzy expert system show its superiority over the former crisp and fuzzy linear programming models

Five crisp and fuzzy models for supply chain of an automotive manufacturing system

Supply Chain Management (SCM) is a new approach to production planning. It integrates the components of supply chain in a holistic manner. Modeling this large-scale system, which contains all effective enterprises in production such as raw material suppliers, part manufacturers, assembly plants, distribution organizations, and the like, is challenging for managers, engineers and researchers. This paper concentrates on supply chain system modeling with fuzzy linear programming, and fuzzy expert system for an automobile plant. First, a linear programming model is developed in such a way that while the input data is fuzzy, the constraints are crisp. In the second linear model, the coefficients of the model are crisp while the constraints are fuzzy. In the third model, we aggregate the first and the second models into one fuzzy linear programming where all constraints and coefficients are fuzzy. In each case, we compare the results with those of classical SC models. Finally, a rule based fuzzy expert system for SC is developed and the results are compared with those of the classical and fuzzy LP models. The results of the fuzzy expert system show its superiority over the former crisp and fuzzy linear programming models.</p

The number of undocumented immigrants in the United States: Estimates based on demographic modeling with data from 1990 to 2016.

We apply standard demographic principles of inflows and outflows to estimate the number of undocumented immigrants in the United States, using the best available data, including some that have only recently become available. Our analysis covers the years 1990 to 2016. We develop an estimate of the number of undocumented immigrants based on parameter values that tend to underestimate undocumented immigrant inflows and overstate outflows; we also show the probability distribution for the number of undocumented immigrants based on simulating our model over parameter value ranges. Our conservative estimate is 16.7 million for 2016, nearly fifty percent higher than the most prominent current estimate of 11.3 million, which is based on survey data and thus different sources and methods. The mean estimate based on our simulation analysis is 22.1 million, essentially double the current widely accepted estimate. Our model predicts a similar trajectory of growth in the number of undocumented immigrants over the years of our analysis, but at a higher level. While our analysis delivers different results, we note that it is based on many assumptions. The most critical of these concern border apprehension rates and voluntary emigration rates of undocumented immigrants in the U.S. These rates are uncertain, especially in the 1990's and early 2000's, which is when-both based on our modeling and the very different survey data approach-the number of undocumented immigrants increases most significantly. Our results, while based on a number of assumptions and uncertainties, could help frame debates about policies whose consequences depend on the number of undocumented immigrants in the United States