Bot Scheduling Prioritization Under Shared RPA Resource Constraints
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Solution Overview
Problem
The increasing demand for robotic process automation (RPA) exceeds resource availability, making it difficult to prioritize and manage RPA operations effectively.
Innovation Solution
A system that dynamically optimizes bot schedules by receiving input data for transactions, generating bot schedules, automatically determining priorities based on input data and business configurations, and assigning shared resources according to resource availability and priority values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If more bot runs are scheduled to meet increasing transaction demand, then transaction processing capacity is improved, but resource availability becomes insufficient and management complexity increases
Solution Approach 1:
The system implements dynamic scheduling that automatically adjusts bot run priorities and resource allocation based on real-time transaction demand and resource availability. The scheduler continuously re-evaluates and re-optimizes the bot run queue, transforming static resource allocation into a dynamic adaptive system that responds to changing conditions without manual intervention.
Solution Approach 2:
The system employs self-service mechanisms where the automated scheduler independently manages bot run prioritization and resource assignment. The intelligent agent autonomously monitors transaction queues, evaluates resource status, and makes scheduling decisions without human input, enabling the system to self-regulate and reduce management complexity.
2Productivity
If bot schedules are optimized to improve resource utilization, then resource efficiency is improved, but the system complexity and computational overhead increase
Solution Approach 1:
The system introduces an intelligent scheduling agent as an intermediary layer between transaction requests and bot execution. This agent serves as a mediator that abstracts the complexity of resource allocation algorithms, handling prioritization and scheduling decisions while presenting a simplified interface to users and systems.
Solution Approach 2:
The optimizer dynamically changes scheduling parameters such as priority weights, time windows, and resource allocation thresholds based on system conditions. By adjusting these parameters adaptively, the system achieves improved resource efficiency without requiring complete redesign of the scheduling architecture.
3Ease of operation
If automated priority determination is implemented to improve scheduling efficiency, then operational effectiveness is improved, but computational processing requirements increase
Solution Approach 1:
The system applies partial optimization by focusing computational efforts on critical scheduling decisions rather than re-evaluating all parameters continuously. The intelligent agent performs selective prioritization based on key transaction attributes and resource constraints, reducing unnecessary computational overhead while maintaining scheduling effectiveness.
Data Source
AI summary
In an embodiment, a method includes receiving input data for a transaction that is to be executed robotically, at least in part, via a plurality of bot runs. The method also includes generating a plurality of bot schedules for the plurality of bot runs based, at least in part, on the input data. The method also includes automatically determining priorities for the plurality of bot schedules based, at least in part, on the input data and business configurations. The method also includes assigning shared resources to the plurality of bot runs according to the plurality of bot schedules, where the assigning is at least partly based on resource availability and values of the automatically determined priorities relative to priority values for other bot schedules for other transactions.