A rolling horizon stochastic programming approach for the integrated planning of production and utility systems

• Published in: Chemical engineering research & design Vol. 139; pp. 224 - 247
• Main Authors: Zulkafli, Nur I., Kopanos, Georgios M.
• Format: Journal Article
• Language: English
• Published: Rugby Elsevier B.V 01.11.2018; Elsevier Science Ltd
• Subjects: Cleaning; Continuity (mathematics); Dependent variables; Horizon; Independent variables; Optimization; Performance degradation; Production planning; Rolling horizon; Shutdowns; Stochastic programming; Terminal constraints; Uncertainty; Utilities; Utility system; Cleaning; Production planning; Utility system; Optimization; Stochastic programming; Rolling horizon
• ISSN: 0263-8762; 1744-3563
• DOI: 10.1016/j.cherd.2018.09.024

•Integrated condition-based planning of production and utility systems under uncertainty.•Operational and cleaning plans are simultaneously optimized for both systems.•A rolling horizon two-stage scenario-based stochastic programming model is presented.•Improved performance degradation and recovery models for units are introduced.•Cumulative time of operation and operating level deviation affect the unit performance. This study focuses on the operational and resource-constrained condition-based cleaning planning problem of integrated production and utility systems under uncertainty. For the problem under consideration, a two-stage scenario-based stochastic programming model that follows a rolling horizon modeling representation is introduced; resulting in a hybrid reactive-proactive planning approach. In the stochastic programming model, all the binary variables related to the operational status (i.e., startup, operating, shutdown, under online or offline cleaning) of the production and utility units are considered as first-stage variables (i.e., scenario independent), and most of the remaining continuous variables are second-stage variables (i.e., scenario dependent). In addition, enhanced unit performance degradation and recovery models due to the cumulative operating level deviation and cumulative operating times are presented. Terminal constraints for minimum inventory levels for utilities and products as well as maximum unit performance degradation levels are also introduced. Two case studies are presented to highlight the applicability and the particular features of the proposed approach as an effective means of dealing with the sophisticated integrated planning problem considered in highly dynamic environments.