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An entropy-based clustering ensemble method to support resource allocation in business process management

机译：在业务流程管理中支持资源分配的基于熵的聚类集成方法

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摘要

Resource allocation, as a crucial task of business process management, has been widely acknowledged by its importance for process performance improvement. Although some methods have been proposed to support resource allocation, there is little effort to allocate resources from the task preference perspective. This paper proposes a novel mechanism in which resource allocation is considered as a multi-criteria decision problem and solved by a new entropy-based clustering ensemble approach. By mining resource characteristics and task preference patterns from past process executions, the "right" resources could be recommended to improve resource utility. Further, to support dynamic resource allocation in the context of multiple process instances running concurrently, a heuristic method is devised to deal with resource conflicts caused by the interplay between various instances. The effectiveness of this study is evaluated with a real-life scenario, and the simulation results indicate that resource utility can be improved and resource workload can be balanced with the support of resource recommendation.
机译：资源分配作为业务流程管理的关键任务，已被广泛认可，它对提高流程性能具有重要意义。尽管已经提出了一些支持资源分配的方法，但是从任务偏好的角度来看，几乎没有分配资源的工作。本文提出了一种新颖的机制，其中资源分配被视为多准则决策问题，并通过一种新的基于熵的聚类集成方法来解决。通过从过去的流程执行中挖掘资源特征和任务偏好模式，可以建议使用“正确的”资源来提高资源利用率。此外，为了在同时运行的多个流程实例的环境中支持动态资源分配，设计了一种启发式方法来处理由各种实例之间的相互作用引起的资源冲突。结合实际情况对本研究的有效性进行了评估，仿真结果表明，在资源推荐的支持下，可以提高资源的利用率，可以平衡资源的工作量。

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