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

This article presents initial results in the search for analytical models that can predict the performance of one-machine systems under periodic and event-driven rescheduling strategies in an environment where different job types arrive dynamically for processing and set-up must incur when production changes from one product type to another. The scheduling algorithm considered uses a first-in first-out dispatching rule to sequence jobs and it also groups jobs with similar types to save set-up time. The analytical models can estimate important performance measures like average flow time and machine utilization, which can then be used to determine optimal rescheduling parameters. Simulation experiments are used to show that the analytical models accurately predict the performance of the single machine under the scheduling algorithm proposed.