Modeling And Optimizing Methods Research On Hybrid Scheduling Of R&D And Batch Production

The development of globalization and intellectualization bring new opportunity and challenge to manufacturing.The ability to increase productivity and meet the variable market need has become a key factor for the development of an enterprise.As one of the important strategic industries,the improvement of military industry's capacity is quite important.Production scheduling problem is quite complex for its uncertainty.The existing research are mostly focused on the schedule problem with certain process time,and few uncertain schedule research are based on the relatively simple flow shop or inflexible Job shop,which failed to meet the need of military enterprise.Therefore,a research on military enterprise's hybrid scheduling problem of R&D and batch production is developed.First of all,a model of static scheduling problem is established.The relationship between due time and makespan is considered,then a method to calculate the tardiness and earliness publish interval is presented.To minimize the earliness/tardiness publish interval,a scheduling model is established.Then,based on the characteristic of hybrid system,a modified GA is developed.The main improvement of the MGA is that an interval based fitness chose method is developed to solve this problem with high efficiency.Then,based on the static scheduling model,a dynamic scheduling is established to solve the random event of production system(such as new orders arrive,emergency inserted single,equipment failure,etc.).The paper puts forward a rolling scheduling strategy,while an events and cycle driven strategy is designed to help the system to give a quick feedback to the arrived affairs.The solving the model,a dynamic scheduling genetic algorithm is developed.Finally,with the example validations,the effectiveness of the model and the algorithm is proved.This case study results show the effectiveness of the model and algorithm,which have can give an important guiding to the military enterprises' production.