Titlebook: Business Process Management; 22nd International C Andrea Marrella,Manuel Resinas,Michael Rosemann Conference proceedings 2024 The Editor(s)

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https://doi.org/10.1007/978-3-031-70396-6business process management; business process modeling; business process monitoring; cross-organization

nonsensical

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Optimizing Resource-Driven Process Configuration Through Genetic Algorithmsels with optimization-focused resource allocation modeling. It is shown how the validity of the resource allocation and the soundness of the resulting process instance can be checked based on the constructed RA-PST. For the combinatorial optimization problem of resource allocation, we adopt a geneti

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Arresting

Glocal Conformance Checkingnd show how it can be tackled by “acting locally, and thinking globally”, that is, pairing local alignments with a suitable global compatibility condition. We then observe that in this setting, cost functions must take the context of activities in the global trace into account, which is realised thr

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Repairing Process Models Through Simulation and?Explainable AIe employed to make the distinguishing features explicit, which are then used to repair the original process model. Our framework has been implemented and evaluated on four processes and various models, proving the effectiveness of enhancing the original process model, in the harmonic means of fitnes

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Optimizing Resource Allocation Policies in?Real-World Business Processes Using Hybrid Process Simulaprocesses. We evaluate our method on four real-world and two elaborate synthetic business processes, constrained by temporal resource availability and a restricted number of resources. An empirical evaluation shows that . outperforms the benchmarks by, on average, 45%, up to 307%, in 14 out of 24 co