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This paper presents a mixed integer programming model that aims to optimize the inventory and transportation costs for several drugs across three echelons: hospital pharmacy, warehouse and manufacturer. A continuous inventory review system is adopted to meet the rapid nature of the pharmaceutical supply chain. The model decides the quantities to be ordered of each drug in every echelon, in addition to the selection of warehouses and manufacturers that would best reduce the total cost of the supply chain.

This paper explores the utility of supervised machine learning algorithms in predicting the tensile strength of high density polyethylene film produced by extrusion-blown molding process. Three algorithms were used: Artificial Neural Networks, Decision Tree, and kNearest Neighbors. Eleven input parameters, five materials related and six process related; were modeled in the algorithms. The application of algorithms demonstrated their capability in predicting the intended property of the extrusion-blown process products.

This study aims to optimally set the operational parameters of pulsed green laser welding with copper. Multi input parameters and critical-to-quality outputs were considered. The chosen inputs were power, pulse duration and pressured air flow, while the outputs were the maximum force handled by the welding joint and the penetration level of the laser weld. Design and analyses for single-response and two-response optimization were presented using response surface methodology, Analysis of variance (ANOVA), and overlaid contour plots. The results indicated that power and pulse duration have the most significant effect on both the maximum force handled and the penetration level. Air flow showed little to no effect on the force handled and critical effect on the penetration level.