Robot Allocation Control for Legacy System Processor Limits
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Solution Overview
Problem
Legacy systems face challenges in scaling with the number of robots deployed for task automation, leading to potential process degradation or system failure due to unbalanced processor utilization and physical constraints, such as limited robot interfaces.
Innovation Solution
A method and system that dynamically optimize the number of robots by monitoring processor utilization and balancing process constraints with physical system constraints, using a relationship between the number of tasks and processor utilization to determine the optimal number of robots, which can be fractional to avoid overwhelming the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of robots is increased to improve task processing speed, then productivity increases, but processor utilization becomes unbalanced and system performance degrades
Solution Approach 1:
The system dynamically adjusts the number of robots based on real-time processor utilization monitoring. The optimization module continuously receives processor utilization data and automatically modifies robot allocation to maintain balanced system performance while maximizing productivity, transforming the static robot allocation into a dynamic adaptive system.
Solution Approach 2:
The system implements a feedback mechanism where processor utilization is continuously monitored and fed back to the optimization module. This feedback loop enables the system to detect when processor utilization becomes unbalanced and automatically adjust robot allocation accordingly, preventing system performance degradation while maintaining high productivity.
2Productivity
If more robots are deployed to handle increasing tasks, then task completion rate improves, but physical system constraints such as limited robot interfaces are exceeded
Solution Approach 1:
The optimization module dynamically determines the maximum number of robots that can be supported by the target system by monitoring physical constraints such as robot interfaces. This dynamic adjustment allows the system to adapt robot allocation to current physical capacity, enabling increased task completion rates without exceeding interface limitations.
Solution Approach 2:
The system changes the parameter of robot allocation based on monitored physical constraints. By continuously assessing available robot interfaces and other physical system limits, the optimization module adjusts the number of deployed robots to match actual system capacity, maximizing productivity within physical boundaries.
3Reliability
If the number of robots is optimized to balance processor utilization, then system reliability improves, but the complexity of monitoring and adjustment increases
Solution Approach 1:
The optimization module operates autonomously to balance processor utilization without requiring manual intervention. The system self-monitors processor utilization, automatically calculates optimal robot allocation, and implements adjustments independently, reducing operational complexity while maintaining reliable processor utilization balance.
Solution Approach 2:
The automated feedback mechanism continuously monitors processor utilization and triggers automatic optimization adjustments. This closed-loop system eliminates the need for complex manual monitoring and adjustment processes, achieving reliable processor utilization balance through automated feedback-driven optimization.
4Productivity
If fractional robots are used to precisely optimize resource allocation, then productivity is maximized, but the concept of fractional robots adds system complexity
Solution Approach 1:
The optimization module segments robot allocation into fractional units, allowing precise allocation of robot capacity rather than requiring whole robots. This segmentation enables fine-grained resource allocation that maximizes productivity by matching robot capacity precisely to task requirements, even when full robots are not needed.
Solution Approach 2:
The system changes the allocation parameter from discrete whole robots to continuous fractional values. This parameter transformation enables precise optimization of resource allocation, allowing the system to allocate exactly the right amount of robot capacity to each task, thereby maximizing overall productivity with minimal waste.
Data Source
AI summary
A method, computer system, and computer program product for optimizing a number of robots for operation of a process at a target system. The method may include providing a plurality of available robots to carry out tasks in the process at the target system. The method may monitor the target system by carrying out the process or part of the process with a varying number of robots to determine the processor utilization whilst the robots are executing a varying number of tasks. The method may balance process constraints of the execution of the process with physical system constraints of the target system by measuring a relationship between a number of tasks at a transactional level and the processor utilization. The method may output the optimized number of robots to be allocated for the process or part of the process.