Study For Flexible Flow Shop Scheduling Problem With Limited Buffer With Advanced Hopfield Neural Network Algorithm

The main characteristics of the optimization problem of the limited buffer scheduling in flexible flow shop are:Each process can have multiple parallel machines,and there are buffers between the operations and the buffer capacity is limited,the workpiece in the current machine processing,if the next process of all the stations are occupied,the workpiece into the buffer waiting,Due to the low level of technology and the limitations of storage equipment and production process,the number of buffers between adjacent processes in the flow shop is strictly limited,and if the buffer is full,the workpiece waits on the current machine until the buffer is idle.The limitation of buffer capacity can lead to blocking problem,so the optimization problem of limited buffer scheduling in flexible flow shop is a kind of complicated-problem.The processing sequence,the choice of the working position and the capacity of the limited buffer are directly related to the production efficiency of the enterprise,in addition,with the diversification and individuation of the market demands of various kinds of products,the workshop not only produces a large quantity of task,but also has a wide variety of production tasks,which leads to The selection of the station and the capacity of intermediate buffer are further improved.Therefore,the research content of this paper has important theoretical significance and practical application value.The research content of this paper has important theoretical significance and practical application value.Optimization problem of limited buffer scheduling in flexible flow workshop(flexible flow shop scheduling Problem FFSP),is in the traditional water workshop scheduling optimization problem(flows shop scheduling Problem FSP)The constraints of limited buffers are combined to make the optimization of scheduling more complex.The contents of this paper are as follows:(1)Firstly,the mathematical programming model is put forward for the optimization problem of the finite buffer line in flexible flow shop.(2)According to the LBFFFSP problem model,the Hopfield Neural network algorithm is proposed as the global optimization algorithm for the LBFFSP problem,and the finite buffer number is estimated by using the discrete estimation probability theory,and the optimization target is the minimum completion time,and this algorithm proposes the transposition matrix of FFSP problem.The energy function of LBFFSP problem is constructed,and the maximum completion time of LBFFSP problem is solved by using the asymptotic stability characteristic of energy function.(3)Because the standard Hopfield neural network algorithm is very easy to get into the local optimum when solving the LBFFSP problem,this paper uses a Hopfield neural network algorithm based on simulated annealing to act as the Global optimization algorithm,which can find the fast speed and accept the poor solution,Simulation results show that the Hopfield Neural network algorithm for simulated annealing is an effective method to solve FFSP problems.