Study For The Permutation Flow Shop Problem Of Awatermeter Manufacturing Enterprise Based On Multi-population GA Algorithm

Planning and scheduling are the core of discrete manufacturing enterprises' production management technology. Planning is the goals made for production activities in a longer period of time of manufacturing enterprises.Production scheduling mainly coordinates the information of production plan, production process, order delivery, the process of real-time status data and so on. What's more, it makes operation plan of manufacturing process in a shorter period of time, including critical operations such as delivery scheduling, job sequencing and machine selection plans, as well as the critical scheduling decisions and other auxiliary scheduling decisions such as the sequence ofoperation of the processing group and the work order or processing start time.This thesis aims at a water meter manufacturing enterprise's technology, variety and diverse requirements of customer. Based on real-time enterprise's ERP data, this thesisstudies modeling, optimizing and application of thepermutation flow shop problem for the real-world enterprise.The main research works are as follows:(1) This thesis summarizes the classification and characteristics of discrete industrial production scheduling problem, and emphatically analyzes the production scheduling problem model of permutation flow shop and the corresponding optimization algorithm of intelligent algorithm.(2) The research on the production process of the water meter manufacturing enterprise is carried out in detail. And water meter manufacturing enterprise's ERP data has beencollected, it also makes a specific introduction of the related technology.(3) The multi-population genetic algorithm is used to solve the eight Car problems (Carl Car8) in the standard test of thepermutation flow shop, and compared with the standard genetic algorithm, the result proves the feasibility of the multi-population genetic algorithm.(4) According to the water meter manufacturing's process data obtained by the survey, this paper abstracts it into permutation flow shop problem. With the objective of minimizing the maximum completion time, the proposed multi-population genetic algorithm is used to optimize the problem. The optimized and unoptimized maximum completion times are then compared, which verifies the validity and reliability of the algorithm. By drawing on this idea, the author developed the copyright of computer software, and the results can be used in water meter manufacturing enterprises to reduce costs and improve production efficiency.