GymPN: A Library for Decision-Making in Process Management Systems
This work addresses decision-making challenges in process management systems for organizations, but it is incremental as it builds on previous task assignment methods.
The paper tackles the problem of optimal decision-making in business process management by introducing GymPN, a software library that uses Deep Reinforcement Learning to support decisions on task allocation, timing, and assignment, and it demonstrates the library's effectiveness on eight typical problem patterns.
Process management systems support key decisions about the way work is allocated in organizations. This includes decisions on which task to perform next, when to execute the task, and who to assign the task to. Suitable software tools are required to support these decisions in a way that is optimal for the organization. This paper presents a software library, called GymPN, that supports optimal decision-making in business processes using Deep Reinforcement Learning. GymPN builds on previous work that supports task assignment in business processes, introducing two key novelties: support for partial process observability and the ability to model multiple decisions in a business process. These novel elements address fundamental limitations of previous work and thus enable the representation of more realistic process decisions. We evaluate the library on eight typical business process decision-making problem patterns, showing that GymPN allows for easy modeling of the desired problems, as well as learning optimal decision policies.