Planning and Scheduling Business Processes under Control-Flow Uncertainty

The paper addresses the challenge of scheduling business process activities when the exact sequence of required tasks is uncertain due to data-driven decisions made during execution. It proposes framing the problem as a chance-constrained optimization and introduces two formulations: a decomposed two-stage approach that first minimizes expected superfluous activities under a feasibility constraint and then schedules to minimize makespan, and an integrated approach that combines planning and scheduling into a single model. Experiments on two real-world and one synthetic dataset show that the integrated approach achieves better makespans but struggles with scalability, whereas the decomposed approach scales to larger settings.

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