🤖 AI Summary
Frequent machine-type switching in semiconductor manufacturing introduces significant scheduling complexity and prolonged setup delays. Method: This paper proposes a distributed optimization approach based on swarm intelligence, pioneering the application of the bio-inspired “Boids” flocking algorithm to production scheduling. Leveraging decentralized, bottom-up local sensing and simple heuristic rules, the method enables self-organized coordination across hybrid environments comprising both single-batch and batch-processing machines—without requiring global information or centralized computation. It dynamically regulates job flows to avoid conflicts and adapt to real-time shop-floor disturbances. Contribution/Results: Experimental results demonstrate substantial improvements in system flexibility and responsiveness. The approach exhibits excellent scalability and runtime stability in large-scale scenarios, offering a novel paradigm for real-time, adaptive scheduling in complex discrete manufacturing systems.
📝 Abstract
Optimizing modern production plants using the job-shop principle is a known hard problem. For very large plants, like semiconductor fabs, the problem becomes unsolvable on a plant-wide scale in a reasonable amount of time using classical linear optimization. An alternative approach is the use of swarm intelligence algorithms. These have been applied to the job-shop problem before, but often in a centrally calculated way where they are applied to the solution space, but they can be implemented in a bottom-up fashion to avoid global result computation as well. One of the problems in semiconductor production is that the production process requires a lot of switching between machines that process lots one after the other and machines that process batches of lots at once, often with long processing times. In this paper, we address this switching problem with the ``boids'' flocking algorithm that was originally used in robotics and movie industry. The flocking behavior is a bio-inspired algorithm that uses only local information and interaction based on simple heuristics. We show that this algorithm addresses these valid considerations in production plant optimization, as it reacts to the switching of machine kinds similar to how a swarm of flocking animals would react to obstacles in its course.