The Intelligence Of The Production Line Adjusts One Degree Method And It Is Applied

Production scheduling is a key link in Campsite is gave attention to their theory and practice in sphere of learning and business circles. Production scheduling problems are stochastic optimal problems with multi-constraints, multi-objectives. It ranks among the most difficultkn own to mathematical community, since it has proved to belong to thec lass of NP hard problems. The most practical solution algorithms aban don the goal of finding the optimal solution, and instead attempt to find an approximate, useful solution in a reasonable amount of time.In this paper, some intelligence methods solving production sched uling problems such as genetic algorithm, artificial neural network and fuzzy theory are studied in a systematic way. Some algorithm are improved and applied to the practice. The achievements in the research work of this dissertation include:1.Two modeling methods for continuous process production scheduling based on uniform discrimination time model and non-uniform discrimination time model are studied in a systematic way. The relations and their transform each other of two modeling methods are discussed. The are expended to the processes coexistence of continuous and batch process. The cyclic scheduling model for parallel continuous multi-product plants are studied. Linearization models for production scheduling are studied.2.The production scheduling methods based on genetic algorithm are studied in a systematic way, some correlative technique of genetic algorithm are exhaustive studied. Fuzzy flow-slop scheduling methods are proposed for much uncertainty existing widely in practical production scheduling processes. An example of production scheduling problem for metalworking workshop in a car engine plant is given.3.The paper improves the Hopfield neural networks approach for job-scheduling problems. To avoid Hopfield neural networks many converge to local minimum, simulated annealing is applied to Hopfield neural netwoks.Compared with existing methods, modified method can kee p the steady outputs of Hopfield neural networks as feasible for job-shop scheduling problems.4.The production scheduling approach for continuous process, industry is studied based on genetic algorithm. Coding methods of production scheduling problem for continuous process industry including continuous and discrete variables is given. The modeling method of the dynamic scheduling problems caused by the order forms for continuous production processes of the single production line are proposed based on fuzzy logical theory.Finally,the production planning and scheduling sub-system in CIMS for certain cement limited company is realized. It can play directive function for enterprise operation and management.