用深度强化学习优化企业流程中的任务分配与执行决策
GymPN: A Library for Decision-Making in Process Management Systems
- 基于深度强化学习构建可处理部分可观测性的流程决策库
- 支持多类型决策建模，在8类典型场景中学习到最优策略
- 适合流程自动化、智能调度方向的研究与开发人员
流程管理系统支持组织中工作分配的关键决策，包括下一步执行的任务、执行时机及任务负责人。为实现组织最优的决策支持，本文提出一个名为GymPN的软件库，利用深度强化学习实现业务流程中的最优决策。GymPN在已有研究基础上引入两项关键创新：支持部分流程可观测性，以及能够建模业务流程中的多重决策。这些新特性解决了以往方法的根本局限，使更真实的流程决策得以表示。我们在八种典型的业务流程决策模式上对库进行了评估，结果表明GymPN能简便建模目标问题，并成功学习出最优决策策略。
原文摘要 · Abstract (English)
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.
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