Reproducible decision support for industrial decision making using a knowledge extraction platform on multi-objective optimisation data

Simulation-based optimisation enables companies to take decisions based on data, and allows prescriptive analysis of current and future production scenarios, creating a competitive edge. However, effectively visualising and extracting knowledge from the vast amounts of data generated by many-objective optimisation algorithms can be challenging. We present an open-source, web-based application in the R language to extract knowledge from data generated from simulation-based optimisation. For the tool to be useful for real-world industrial decision-making support, several decision makers gave their requirements for such a tool. This information was used to augment the tool to provide the desired features for decision support in the industry. The open-source tool is then used to extract knowledge from two industrial use cases. Furthermore, we discuss future work, including planned additions to the open-source tool and the exploration of automatic model generation.