Optimization of Reentrant Hybrid Flow Shop Scheduling Problem Based on Improved Grey Wolf Algorithm: Taking automatic chemiluminescence immunoassay analyzer as an example

The research on reentrant hybrid flow shop scheduling problem is of great significance for the semiconductor and medical fields. Taking automatic chemiluminescence immunoassay analyzer (ACIA) as an example, this paper establishes an ACIA scheduling model with the optimization objective of minimizing the maximum completion time. In addition, this article introduces dynamic disturbance factor strategy and somersault foraging strategy to improve the traditional grey wolf algorithm, enhancing its global search ability and convergence speed. Considering the uncertainty of parameters such as chip type and incubation time in actual production, this paper simulates the parameters and uses k-means cluster algorithm to reduce the scene, obtaining the most representative scenario. The improved grey wolf algorithm is used to solve the typical scenario.