Modeling lot-size with time-dependent demand based on stochastic programming and case study of drug supply in Chile.

The objective of this paper is to propose a lot-sizing methodology for an inventory system that faces time-dependent random demands and that seeks to minimize total cost as a function of order, purchase, holding and shortage costs. A two-stage stochastic programming framework is derived to optimize lot-sizing decisions over a time horizon. To this end, we simulate a demand time-series by using a generalized autoregressive moving average structure. The modeling includes covariates of the demand, which are used as predictors of this. We describe an algorithm that summarizes the methodology and we discuss its computational framework. A case study with unpublished real-world data is presented to illustrate the potential of this methodology. We report that the accuracy of the demand variance estimator improves when a temporal structure is considered, instead of assuming time-independent demand. The methodology is useful in decisions related to inventory logistics management when the demand shows patterns of temporal dependence.

Sarcocornia neei as an Indicator of Environmental Pollution: A Comparative Study in Coastal Wetlands of Central Chile.

Being adapted to saline environments, halophytes are plant species that have received considerable attention due to their ability to cope with environmental stress factors, such as high concentrations of soluble salts and heavy metals. In this work, we focused on determining if the Sarcocornia neei (S. neei) plant can be considered as an indicator of heavy metal pollution in soil. This was done by analyzing the concentration of cadmium (Cd), lead (Pb), copper (Cu), and arsenic (As) in plants and soil sampled from two wetlands in the central zone of Chile: a wetland contaminated by industrial activities and a wetland protected by the Chilean government. In addition, 14 fertility parameters (pH, electrical conductivity, organic matter, nitrogen (N), phosphorus (P), potassium (K), sodium (Na), Pb, calcium (Ca), magnesium (Mg), Manganese (Mn), zinc (Zn), iron (Fe), and boron (B)) were analyzed for soil samples in both wetlands. This was done to differentiate between available elements and contamination by heavy metals. Plant and soil samples in the contaminated wetland exhibited significantly higher heavy metal concentrations in comparison to samples analyzed from the protected wetland. This indicates that the S. neei plant can be further researched as an indicator of heavy metal pollution in saline soils and possibly for phytoremediation purposes.