Triggering Proactive Business Process Adaptations via Online Reinforcement Learning

Metzger, Andreas; Kley, Tristan; Palm, Alexander

doi:10.1007/978-3-030-58666-9_16

Cited by 33 publications

(37 citation statements)

References 29 publications

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“…Metzger et al [9] propose ensemble methods to compute predictions and reliability estimates to optimize the threshold instead of optimizing it empirically. They introduce policy-based reinforcement learning to find and learn when to trigger proactive process adaptation.…”

Section: Prescriptive Process Monitoringmentioning

confidence: 99%

“…Prescriptive Process Monitoring (PrPM) [5,9] is a set of techniques to recommend or to trigger actions (herein called interventions) during the execution of a process in order to optimize its performance. PrPM techniques use business process execution logs (a.k.a.…”

Section: Introductionmentioning

confidence: 99%

“…Several PrPM techniques have been proposed in the literature [5,9,2]. These techniques, however, assume that it is possible to trigger any number of interventions at any point in time.…”

Section: Introductionmentioning

confidence: 99%

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Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach

Shoush¹,

Dumas²

2021

Preprint

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Prescriptive process monitoring is a family of techniques to optimize the performance of a business process by triggering interventions at runtime. Existing prescriptive process monitoring techniques assume that the number of interventions that may be triggered is unbounded. In practice, though, specific interventions consume resources with finite capacity. For example, in a loan origination process, an intervention may consist of preparing an alternative loan offer to increase the applicant's chances of taking a loan. This intervention requires a certain amount of time from a credit officer, and thus, it is not possible to trigger this intervention in all cases. This paper proposes a prescriptive process monitoring technique that triggers interventions to optimize a cost function under fixed resource constraints. The proposed technique relies on predictive modeling to identify cases that are likely to lead to a negative outcome, in combination with causal inference to estimate the effect of an intervention on the outcome of the case. These outputs are then used to allocate resources to interventions to maximize a cost function. A preliminary empirical evaluation suggests that the proposed approach produces a higher net gain than a purely predictive (non-causal) baseline.

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Section: Prescriptive Process Monitoringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach

Shoush¹,

Dumas²

2021

Preprint

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“…Reinforcement learning has been also used for the task of proactive business process adaptation [17] [18]. The goal there is to monitor the particular business process case while it is running and intervene in case of any detected upcoming problems.…”

Section: Resource Allocationmentioning

confidence: 99%

“…The evaluations conducted in aforementioned works are either based on simulations ( [18], [16]) or on analysis of historical data, mostly from Business Process Intelligence Challenge ( [17], [15], [19]). The latter has an obvious advantage of being real-world based dataset while at the same time being limited by the number of available cases.…”

Section: Resource Allocationmentioning

confidence: 99%

Deep Reinforcement Learning for Resource Allocation in Business Processes

Żbikowski¹,

Ostapowicz²,

Gawrysiak³

2021

Preprint

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Assigning resources in business processes execution is a repetitive task that can be effectively automated. However, different automation methods may give varying results that may not be optimal. Proper resource allocation is crucial as it may lead to significant cost reductions or increased effectiveness that results in increased revenues. In this work, we first propose a novel representation that allows modeling of a multi-process environment with different process-based rewards. These processes can share resources that differ in their eligibility. Then, we use double deep reinforcement learning to look for optimal resource allocation policy. We compare those results with two popular strategies that are widely used in the industry. Learning optimal policy through reinforcement learning requires frequent interactions with the environment, so we also designed and developed a simulation engine that can mimic real-world processes. The results obtained are promising. Deep reinforcement learning based resource allocation achieved significantly better results compared to two commonly used techniques.

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ProGAN: Toward a Framework for Process Monitoring and Flexibility by Change via Generative Adversarial Networks

Hoffmann

Malburg

Bergmann

2022

Business Process Management Workshops

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Triggering Proactive Business Process Adaptations via Online Reinforcement Learning

Cited by 33 publications

References 29 publications

Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach

Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach

Deep Reinforcement Learning for Resource Allocation in Business Processes

ProGAN: Toward a Framework for Process Monitoring and Flexibility by Change via Generative Adversarial Networks

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