An empirical study of policies to integrate reactive scheduling and control in just-in-time job shop environments

In this paper, we compare the performance of policies for integrating reactive scheduling and control that differ in the way they interpret and dynamically reoptimize schedules in the face of contingencies. We conduct our analysis in the context of just-in-time job shop environments ( job shop problems with an objective of minimizing the sum of tardiness and inventory costs), subject to machine failures. We empirically evaluate the tradeoffs in schedule quality and computational time of different scheduling policies under different load conditions and different levels of uncertainty. Our results show that reactive procedures that selectively reoptimize a subset of the scheduling problems are capable of producing high-quality solutions in a fraction of the time required to generate brand new schedules.

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