My MASc research studies focused on the development of an algorithm to schedule operations in an actual large scale analytical services plant using models based on multi-commodity flow (MCF) and integer linear programming (IP) techniques. The proposed scheduling algorithm aims to minimize the total turnaround time of the operations subject to capacity, resource and flow constraints. The working principles of the optimization-based algorithm are illustrated with a real large-scale plant. The algorithm’s results were compared against historical data and results obtained by simulating the current policy implemented in the real plant, i.e., first-come first-served. As part of these research studies, I also developed a new methodology that can address three aspects of the economy of the multiproduct processes together; i.e. simultaneous scheduling, design and control. The proposed methodology takes into account the influence of disturbances in the system by the identification of the critical frequency from the disturbances, which is used to quantify the worst-case variability in the controlled variables via frequency response analysis. The uncertainty in the demands of products has also been addressed by creating critical demand scenarios with different probabilities of occurrence, while the nominal stability of the system has been ensured. Case studies involving large-scale nonlinear systems have been developed as applications of the methodology.
Thesis: Optimal Scheduling for Chemical Processes and its Integration with Design and Control (PDF)