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Production data analytics for production scheduling

Nowadays, production scheduling researches mainly focus on developing various optimization models and algorithms, but the problem how to determine parameters in the models and algorithms has not attracted enough attention from researchers. It leads to that scheduling results do not fit to the manufacturing practice very well nor are accepted by the workers. As IT technology develops, abundant low-level data has been collected. By analyzing low-level operation data, more accurate values for the parameters in production scheduling models and algorithms can be estimated, making the scheduling results fit the actual manufacturing implementation better. Moreover, workers' preference can also be generalized and reused in scheduling, making the scheduling results accepted by the workers more easily. Therefore, this paper proposes a production data analytics method for estimating parameter values and generalizing humanized scheduling rules for production scheduling. The research is implemented on the basis of data from an oil refinery.