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Found 5 papers, 5 papers with code
Prescriptive Process Monitoring Under Resource Constraints: A Reinforcement Learning Approach
This paper argues that, in the presence of resource constraints, a key dilemma in the field of prescriptive process monitoring is to trigger interventions based not only on predictions of their necessity, timeliness, or effect but also on the uncertainty of these predictions and the level of resource utilization.
Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learning
Increasing the success rate of a process, i. e. the percentage of cases that end in a positive outcome, is a recurrent process improvement goal.
Intervening With Confidence: Conformal Prescriptive Monitoring of Business Processes
Prescriptive process monitoring methods seek to improve the performance of a process by selectively triggering interventions at runtime (e. g., offering a discount to a customer) to increase the probability of a desired case outcome (e. g., a customer making a purchase).
When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints
Prescriptive process monitoring approaches leverage historical data to prescribe runtime interventions that will likely prevent negative case outcomes or improve a process's performance.
Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach
This paper proposes a prescriptive process monitoring technique that triggers interventions to optimize a cost function under fixed resource constraints.
