Multi-objective evolutionary search for optimal Robotic Process Automation architectures
Publication Name
IEEE Transactions on Services Computing
Abstract
Robotic Process Automation (RPA) design and implementation requires an architecture which facilitates the seamless transition between human agents, robotic agents, and intelligent agents to automate information acquisition tasks and decision-making tasks. Coordination of those agents must consider various factors, such as efficiency of a resource when completing tasks, the quality of completed complex tasks, and the cost of the used resources. This paper proposes a novel approach for generating an optimal architecture based on distinct types of resources, including human agents, intelligent agents, and robotic agents. An optimal architecture is the optimal enactment of process instances executed by a combination of human and automation agents based on their characteristics. The architecture provides a set of resources and their characteristics that are tailored to meet multiple objectives for process execution. The proposed approach is validated through an empirical evaluation based on a real-world business process. An empirical evaluation demonstrates that, given equal computational time, our approach outperforms conventional constraint optimization ILOG CPLEX [1].
Open Access Status
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