Abstract
Synthesis is the generation of a process model that fulfills a set of declarative constraints, a. k. a. properties. In this article, we study synthesis in the presence of both so-called sequential and parallel constraints. Sequential constraints state that certain tasks must occur in a specific ordering. Parallel constraints specify the maximal degree of parallelization at a certain position in a process model. Combining both sequential and parallel constraints in one approach is difficult, because their interference is complex and hard to foresee. Besides this, with large specifications, solutions which do not scale are not viable either. Our synthesis approach consists of two steps. First, we generate a model fulfilling only the sequential constraints. We then apply a novel algorithm that deparallelizes the process to fulfill the parallel constraints as well as any additional optimization criteria. We evaluate our approach using the real-world use case of commissioning in vehicle manufacturing. In particular, we compare our synthesized models to ones domain experts have generated by hand. It turns out that our synthesized models are significantly better than these reference points.
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Mrasek, R., Mülle, J., Böhm, K. (2016). Process Synthesis with Sequential and Parallel Constraints. In: Debruyne, C., et al. On the Move to Meaningful Internet Systems: OTM 2016 Conferences. OTM 2016. Lecture Notes in Computer Science(), vol 10033. Springer, Cham. https://doi.org/10.1007/978-3-319-48472-3_3
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