OPTIMIZING ROBOTICS AND AUTOMATION WORKFLOWS IN CLOUD MANUFACTURING FRAMEWORKS WITH NP- COMPLEXITY SOLUTIONS
Keywords:
Cloud manufacturing, NP-complexity, getting stuff done without people, divvying up resources, planning out tasks, robot stuff, making things better, doing things as they come, mixed- up methods, the cloud in computingAbstract
Background: Managing how robots and automatic systems in cloud manufacturing organize stuff choose jobs and make the quick smart update is tough. Using NP-complexity models is super important to get good at this stuff. Objectives: Our goal is to enhance robot and automation processes using NP-complexity tricks that boost how we manage cloud resources, task assignment, and monitoring factories for improvements in performance. Methods: Our research puts NP-complexity models to work mixing different algorithms and managing resources in the cloud. We break down tasks and figure out when to do them to make things run smoother and faster. Results: The proposed NP-complexity-based optimization framework improved the efficiency of the robotic task by 38%, reduced latency by 45%, and enhanced resource utilization by 29% in cloud manufacturing. It ensures real-time task scheduling and adaptive workflow management.Conclusion: Models of NP-complexity improve the workflows in cloud manufacturing. They are
able to improve the dole-out mechanism of tasks through resource more efficiently, making everything run more smoothly. Findings show such models have a lot of promise for systems that automate stuff on the fly.
