Simulation model risk-based scheduling tool for high-mix and low-volume manufacturing facility with sequence-dependent setup times
Linares-Blasini, José E.
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Production scheduling in most real-world scenarios is very complex. Many of the methodologies used for creating production schedules are often deterministic which means that variation is not considered. The use of deterministic scheduling for high-mix and low-volume manufacturing facility is inefficient and obsolete due to the inherent variability and occurrence of uncertain events in the manufacturing floor. This project develops a robust scheduling tool for a high-mix low-volume manufacturing facility with sequence-dependent setup times taking into consideration some of the inherent variability of the manufacturing floor. This robust scheduling tool is created using the Simio software package to enable the creation of schedules that adjust to schedulers’ needs while incorporating manufacturing constraints. This analytical tool was created to solve an existing problem for a local industry partner and it was investigated further through a case study (i.e in-silico analysis using 10 machines). The manufacturing floor was simulated taking into consideration real-world information and simulated variability depending on which model was explored. The accuracy of the industry-driven model was verified and validated while the case study model was properly verified and studied. Experimentation with the case study model provided much-needed knowledge and understanding of the effect of dispatching rules on the completion time of manufacturing orders and the optimization of resources given a particular dispatching rule. The dispatching rule that performed the best to minimize the completion time of orders in the case study model was the Least Setup Time which prioritizes orders with the smallest setup time. Ultimately, this work developed a simulation tool that models inherent variability in a high-mix low-volume manufacturing facility with sequence-dependent setup times and feeds into the generation of risk-based schedules allowing to forecast possible tardiness before it happens and update schedules dynamically as needed.