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Photo of Avishai Mandelbaum
Queue mining for delay prediction in multi-class service processes

Information recorded by service systems (e.g., in the telecommunication, finance, and health sectors) during their operation provides an angle for operational process analysis, commonly referred to as process mining. Here we establish a queueing perspective in process mining to address the online delay prediction problem, which refers to the time that the execution of an activity for a running instance of a service process is delayed due to queueing effects. We develop predictors for waiting-times from event logs recorded by an information system during process execution. Based on large datasets from the telecommunications and financial sectors, our evaluation demonstrate accurate online predictions, which drastically improve over predictors neglecting the queueing perspective.

Data-Driven Appointment-Scheduling Under Uncertainty

Service systems are often stochastic and preplanned by appointments, yet implementations of their appointment systems are prevalently deterministic. We address this gap, between planned and reality, by developing data-driven methods for appointment scheduling and sequencing – the result are tractable and scalable solutions that accommodate hundreds of jobs and servers. To test for practical performance, we leverage a unique data set from a cancer center that combines real-time locations, electronic health records, and appointments log. Focusing on one of the center’s infusion units, we reduce cost (waiting plus overtime) on the order of 15%–40% consistently.