This work has been motivated by the needs we discovered when analyzing real-world processes from the
healthcare domain that have revealed high flexibility demands and complex temporal constraints. When trying
to model these processes with existing languages, we learned that none of the latter was able to fully address
these needs. This motivated us to design TConDec-R, a declarative process modeling language enabling the
specification of complex temporal constraints. Enacting business processes based on declarative process models,
however, introduces a high complexity due to the required optimization of objective functions, the handling of
various temporal constraints, the concurrent execution of multiple process instances, the management of crossinstance
constraints, and complex resource allocations. Consequently, advanced user support through optimized
schedules is required when executing the instances of such models. In previous work, we suggested a method for
generating an optimized enactment plan for a given set of process instances created from a TConDec-R model.
However, this approach was not applicable to scenarios with uncertain demands in which the enactment of
newly created process instances starts continuously over time, as in the considered healthcare scenarios. Here,
the process instances to be planned within a specific timeframe cannot be considered in isolation from the ones
planned for future timeframes. To be able to support such scenarios, this article significantly extends our previous
work by generating optimized enactment plans under a rolling planning horizon. We evaluate the approach
by applying it to a particularly challenging healthcare process scenario, i.e., the diagnostic procedures required
for treating patients with ovarian carcinoma in a Woman Hospital. The application of the approach to this sophisticated
scenario allows avoiding constraint violations and effectively managing shared resources, which
contributes to reduce the length of patient stays in the hospital.Ministerio de Economía y Competitividad TIN2016-76956-C3-2-RMinisterio de Ciencia e Innovación PID2019-105455 GB-C3
