Polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) are a group of dangerous compounds, emitted mostly from anthropogenic sources, that have negative effects on human health. Therefore, it is interesting to analyze the emission patterns of dioxins proceeding from different sources around the world, to observe the actual trend of the transmission of dioxins and furans into the atmosphere.For that reason, the main objective of the present document is to provide a general assessment about the dioxin problematic, analyzing the main parameters that influence the ambient concentration of dioxins worldwide, and describing the most characteristic features of the fingerprint from different sources, while making emphasis in the importance that non-industrial sources are gaining over the last years in front of the decreasing tendency of industrial sources. The description of the most important abatement technologies for dioxins is also included in this review. Given the negative effects of dioxins in human health, it is important to depict and locate the main sources of these dangerous compounds. Emissions proceeding from industrial facilities have decreased over the last years; however, other zones where nonindustrial sources used to be relevant contributors do not show the same decreasing tendency because it is more difficult to control this type of emissions. For that reason, future studies should focus on measuring and regulating this highly uncontrolled source of dioxins.

A common way of dynamically scheduling jobs in a manufacturing system is by implementing dispatching rules. The issues with this method are that the performance of these rules depends on the state the system is in at each moment and also that no “ideal” single rule exists for all the possible states that the system may be in. Therefore, it would be interesting to use the most appropriate dispatching rule for each instance. To achieve this goal, a scheduling approach that uses machine learning can be used. Analyzing the previous performance of the system (training examples) by means of this technique, knowledge is obtained that can be used to decide which is the most appropriate dispatching rule at each moment in time. In this paper, a literature review of the main machine learning based scheduling approaches from the last decade is presented.