Packages to encode Machine Learned models into optimization problems is an underdeveloped area, despite the advantages is could provide. The main draw of implementing Machine Learned models into optimization models, is that it allows the optimizer to better account for the human experience.
Maragno D., Wiberg H. et al. constructed an implementation of the encoding with their package OptiCL. In order to verify their implementation and provide principles for (re)designing packages with similar functions, an amount of components of OptiCL were replicated within this paper. The requirements for
the program were first constructed before detailing the implementation process. After the program was implemented, both OptiCL and the found program were tested in order to compare performances. Using the results and an investigation of the two implementations, a framework for encoding similar packages
was provided using the insights gained. Using mathematical formulations supplied by Maragno D., Wiberg H. et al., design principles outlined in this report and research into the encoding of other Machine Learned models, other developers could construct robust packages that allow for easy integration of
valuable information gained from Machine Learning into optimization problems. This in turn allows for frequently used optimization models to account for more human understanding.