(530h) Scheduling and Analytics – Towards Better Planning
With sophisticated data analytics methods, one can embed to the overall key performance indicators (KPI) also all information about the process, e.g., tracking abnormal situations (anomaly detection), individual process equipment performance degradations (predictive maintenance), anticipated process timings (prediction of process behavior) and scenario simulation (e.g., artificial intelligence AI planning). Such an approach will help to select the best production strategies in order to maintain, e.g., production and energy efficiency as well as sustainability in rapidly changing market situations through data-driven self-adaptive scheduling models. The topic of data-driven models have already been investigated in other domains (Van der Aalst et al., 2004) and tools become available for the process industry (Wilson and Sahinidis, 2017). It can be expected that Industrial Internet of Things (IIoT) provides the needed seamless connectivity, cloud computing infrastructure and service-based business models to realize this vision.
Qin, S. J. (2014), Process data analytics in the era of big data. AIChE J., 60, 3092-3100. doi:10.1002/aic.14523
Yidan Shu, Liang Ming, Feifan Cheng, Zhanpeng Zhang, Jinsong Zhao (2016), Abnormal situation management: Challenges and opportunities in the big data era. Computers & Chemical Engineering, 91m 104-113. doi:10.1016/j.compchemeng.2016.04.011
Touretzky, C. R., Harjunkoski, I., & Baldea, M. (2017). Dynamic models and fault diagnosis-based triggers for closed-loop scheduling. AIChE Journal, 63(6), 1959-1973. doi:10.1002/aic.15564
Harjunkoski, I., Maravelias, C. T., Bongers, P., Castro, P. M., Engell, S., Grossmann, I. E., . . . Wassick, J. (2014). Scope for industrial applications of production scheduling models and solution methods. Computers and Chemical Engineering, 62, 161-193. doi:10.1016/j.compchemeng.2013.12.001
Van der Aalst, W., T. Weijters, L. Maruster, (2004). Workflow mining: Discovering process models from event logs", Knowledge and Data Engineering IEEE Transactions on, vol. 16, no. 9, 1128-1142
Wilson, Z. T., & Sahinidis, N. V. (2017). The ALAMO approach to machine learning. Computers and Chemical Engineering, 106, 785-795. doi:10.1016/j.compchemeng.2017.02.010