Genetic Algorithm Based Scheduling Optimization for Design and Manufacturing Integration

AA127 at multi-task job shop scheduling problem, the improved dynamic scheduling method based on capability model of manufacturing unit was proposed. Through analyzing the characteristic of the multi-task scheduling in the actual working environment, the capacity model of the manufacturing unit was established. With the minimum completion time, delay time and total load of the machine as the target function, the genetic algorithm was used here to generate initial schedule as well as new ones. The double chains structure coding method based on ability selection chain and processing sequence chain was proposed to provide suitable simulation rules for the GA algorithm. The correctness was verified with experiments.