Analytical models to predict the performance of a single-machine system under periodic and event-driven rescheduling strategies

This article presents initial results in the search for analytical models t hat can predict the performance of one-machine systems under periodic and e vent-driven rescheduling strategies in an environment where different job t ypes arrive dynamically for processing and set-up must incur when productio n changes from one product type to another. The scheduling algorithm consid ered uses a first-in first-out dispatching rule to sequence jobs and it als o groups jobs with similar types to save set-up time. The analytical models can estimate important performance measures like average how time and mach ine utilization, which can then be used to determine optimal rescheduling p arameters. Simulation experiments are used to show that the analytical mode ls accurately predict the performance of the single machine under the sched uling algorithm proposed.