Multilayer Game Collaborative Optimization Based on Elman Neural Network System Diagnosis in Shared Manufacturing Mode

Abstract

With the dynamic sharing of global resources shifted from the independent production of a single enterprise to and the development of collaborative manufacturing, today’s manufacturing industry has collaborative production among multiple enterprises. This paper extends the optimization method of the multienterprise dynamic equipment collaborative scheduling system with the dynamic manufacturing network theory approach, combining the neural network system and model predictive control theory to continuously and dynamically diagnose the changes of demand and capacity in the actual production process and design the corresponding multilayer game collaborative optimization robust model according to the uncertainty relationship. The study considers the production characteristics and management needs of multiple enterprises within the manufacturing industry, as well as the optimization of the dynamic equipment collaborative scheduling system, uncertain demand, dynamic equipment matching effectiveness, and equipment failure under the shared manufacturing model, deduces the market capacity demand for manufacturing enterprises by inverse prediction of dynamic market demand through the simple recurrent neural network (Elman network), and then maximizes the idle equipment utilization and load capacity under the shared manufacturing model in model rolling optimization. This paper gives model solutions based on big data analysis and combines improved population intelligence algorithms to determine the dynamic matching of manufacturing resources, product supply and demand paths, and transportation flows. The simulation results show that the application of model predictive control theory to the model of Elman neural network system diagnosis can effectively reduce the adverse effects of the two-way uncertainty environment on the profitability of enterprises. Meanwhile, considering the maximum performance matching of dynamic equipment scheduling and combining with robust optimization methods will reduce the cost loss of manufacturing enterprises in the worst case, and the cost loss reduction can reach 24.73%. The analysis results verify that the method is of great practical significance in the direction of intelligent production collaboration and industrial digital transformation.

Machine learning in TCM with natural products and molecules: current status and future perspectives

“…By the recurrent layer, the output of the hidden layer can self-connected to the input through delay and storage facilitated, and this self-connection makes the network can capture historical information. The addition of the internal feedback network increases the capacity of the network to handle dynamic information, thereby allowing dynamic modeling [ 21 ] (Fig. 2 ).…”

“…The Elman RNN has shown good ability in capturing the dynamics of sequences via recurrent connection, e.g., as in natural language processing [ 22 ]. The Elman RNN is effective and shows good generalization ability and has been widely used for solving practical problems [ 21 ]. However, it is prone to show gradient explosion and gradient vanishing, and cannot address the problems of long-term dependencies and parallel training [ 42 ].…”

A Digital Twin-Assisted Collaborative Capability Optimization Model for Smart Manufacturing System Based on Elman-IVIF-TOPSIS

“…By the existence of profit model analysis within the closed-loop supply chain system, the corresponding and values need to be established. The specific inferences are as follows [1]: Definition 5: Combining the data, the corresponding…”

“…Since the proposed method requires too much data information, we choose to compare it with actual manufacturing cases and perform multiple comparisons to verify the feasibility and validity of the model in both normal and significant risk situations. Referring to the actual case presented by Tang and Wu [1], there are three existing steel enterprises, A, B and C, with multiple subsidiaries existing within each enterprise. In order to better meet customer demand, each subsidiary within enterprises A, B and C decides to adopt an intermediarybased sharing platform to share idle equipment in order to enhance its production capacity.…”

“…The application of the Internet and big data has changed the way enterprises discover and utilize resources and demands, expanding the scope of resource utilization and breaking through the time and space constraints of matching resources and demands across borders and regions. In order to gain advantages in the fierce competitive environment and achieve sustainable development, each manufacturing company needs to focus on customer value and explore new manufacturing models and manufacturing concepts to match them [1]. With the further development of digital information technology, manufacturing is becoming increasingly smart at all levels, from physical equipment to plant management to production networks, thus acquiring the ability to learn, configure, and perform cognitive intelligence [2].…”