Abstract
Most Process-Aware Information Systems (PAIS) and resource allocation approaches do the selection of the resource to be allocated to a certain process activity at run time, when the activity must be executed. This results in cumulative (activity per activity) local optimal allocations for which assumptions (e.g. on loop repetitions) are not needed beforehand, but which altogether might incur in an increase of cycle time and/or cost. Global optimal allocation approaches take all the process-, organization- and time-related constraints into account at once before process execution, handling better the optimization objectives. However, a number of assumptions must be made upfront on the decisions made at run time. When an assumption does not hold at run time, a resource reallocation must be triggered. Aiming at achieving a compromise between the pros and cons of these two methods, in this paper we introduce a novel approach that fragments the process dynamically for the purpose of risk-aware resource allocation. Given historical execution data and a process fragmentation threshold, our method enhances the feasibility of the resource allocations by dynamically generating the process fragments (i.e. execution horizons) that satisfy the given probabilistic threshold. Our evaluation with simulations demonstrates the advantages in terms of reduction in reallocation efforts.
Funded by the Austrian Science Fund (FWF) Elise Richter programme under agreement V 569-N31 (PRAIS).
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Notes
- 1.
In this work we assume human resources but the concept could be adapted for non-human resource allocation, too.
- 2.
With the Fundamental Modeling Concepts (FMC) notation (www.fmc-modeling.org/).
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Havur, G., Cabanillas, C. (2019). History-Aware Dynamic Process Fragmentation for Risk-Aware Resource Allocation. In: Panetto, H., Debruyne, C., Hepp, M., Lewis, D., Ardagna, C., Meersman, R. (eds) On the Move to Meaningful Internet Systems: OTM 2019 Conferences. OTM 2019. Lecture Notes in Computer Science(), vol 11877. Springer, Cham. https://doi.org/10.1007/978-3-030-33246-4_33
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