Solving scheduling problems by untimed model checking

Abstract

In this paper, we show how scheduling problems can be modelled in untimed process algebra, by using special tick-actions. As a result, we can use efficient, distributed state space generators to solve scheduling problems. Also, we can use more flexible data specifications than timed model checkers usually provide. We propose a variant on breadth-first search, which visits the states per time slice between ticks. We applied our approach to find optimal schedules for test batches of a realistic clinical chemical analyser, which performs several kinds of tests on patient samples.