Robotic Process Automation in Business Process Modelling using Improved Sine Cosine Algorithm with Chaotic Theory

Robotic Process Automation (RPA) is a transformative technology that automates rule-based tasks, improving operational accuracy and efficiency by eliminating repetitive processes. However, high maintenance overhead often arises due to system dependencies and evolving workflows, leading to scalability challenges and frequent bot reconfigurations. To address this, this research proposes an Improved Sine Cosine Algorithm (ISCA) based on chaos theory, encompassing stages such as design, modeling, execution, monitoring and optimization. In the optimization phase, chaos theory introduces non-repetitive and unpredictable behavior into ISCA through chaotic maps, enhancing the randomness of position updates, helping to avoid local optima and enabling more reliable and faster convergence. The RPA workflow is designed and developed to perform documented tasks such as data entry, approval routing and decision logic, with bots configured accordingly. Automation scripts are built based on the designed workflow. The modeling phase, often referred to as the design phase, involves creating a detailed blueprint of the process to be automated. The execution phase involves deploying validated bots into the production environment, ensuring proper configuration using Infrastructure as Code (IaC) and automated pipelines. Monitoring is used to assess bot performance by continuously tracking key metrics such as errors and execution time, ensuring smooth operation and enabling regular maintenance and updates. Finally, in the optimization phase, the ISCA algorithm analyzes performance data to identify areas for improvement, optimize bot performance, and refine processes. The proposed ISCA consumes a power of 1120 joules with a computation cost of 48.98 and an automation efficiency of 99.85%.