Research on Job Shop Scheduling based on Smart Prediction of Disturbance

Abstract

In order to solve the problems of equipment failure and cross process production and collaborative scheduling in discrete manufacturing, this paper proposed a method about a disturbance intelligent reasoning mechanism based on manufacturing big data and improved case-based reasoning. The probability probability of disturbance was analyzed, which the corresponding disturbance similarity threshold was calculated combined with the hierarchical attributes of cases. Combined with prediction of disturbance, in order to minimize the maximum completion time and minimum carbon emission, a hybrid discrete bat algorithm combined with simulated annealing algorithm was designed. Taking the production of engine cylinder head as an example, the proposed algorithm may reduce the production cycle of cylinder and the total carbon emission in the manufacturing process.