## 1 Introduction  
The rapid development of information and communication technology has significantly facilitated the digital transformation of enterprises. This process has also promoted the rapid development of enabling technologies, such as the industrial Internet, digital twins, and industrial big data. Moreover, the increasing demand for personalized products will affect the system, production lines, and products. The enterprise digital transformation is developing towards multi-variety, small-batch, and personalized production, which promotes the realization of intelligent manufacturing, product life cycle monitoring, and \"Made in China 2025\" [1]. Aiming at multi-level spans of time/space scales of components in the production line cyber-physical system, order-driven and random disturbances interact in the production process. Moreover, the physical resource allocation/execution and information decision-making reconstruction are loosely related to the control link [2]. The production mode in discrete manufacturing workshops is different from process production. The production process is usually decomposed into multiple processing tasks to complete, and each of them is applied to specific equipment or resources within the enterprise. It should be noted that tasks with the same process are assigned to one device for processing. At the same time, the production process of discrete manufacturing workshops needs to adapt to changes in customer needs, product forms, and production factors. Indiscrete workshops, many factors affect production planning, including changes in product types, production delivery dates, and equipment status, thereby adding complexity to the formulation of production plans and the prediction of actual factory capacity. In the past production, factory scheduling and delivery forecasts were mainly evaluated by scheduling personnel based on experience and historical data. However, with the ever-changing demand for product production, the management of production factors, production activity planning, and production process control are becoming increasingly complex. Managing, controlling, predicting, analyzing, and optimizing efficiently, reasonably, and in real time are among the important issues facing discrete manufacturing workshops. Furthermore, before a company builds a new production base and processes new products, realizing the company's production capacity forecast, delivery date evaluation, and rational production arrangement must be the focus of the company's work. Discrete manufacturing workshop production activity planning and production capacity evaluation typically involve two stages [3; 4]. In the first stage, experienced engineers manage personnel, materials, equipment, processes, and other information in the discrete manufacturing workshop. Material storage, planning arrangements, capacity forecasting, etc., are all evaluated based on personnel experience and historical information and stored on paper media. Information statistics, transmission, query, and analysis are difficult, leading to a lower accuracy rate. However, with the introduction of Enterprise Resource Planning (ERP), Manufacturing Execution System (MES) systems, and computer technology, data statistics, archival, and transmission capabilities have been significantly improved. Since information processing and analysis capabilities are continuously improved, factory information has entered the second stage [5]. According to the material requirement plan, process route, and man-hour quota, the time and workload required for production capacity are given and compared with the capacity provided by the manufacturing workshop. This evaluation shows whether completing the material requirement plan matches the ability. If the utilization rate of the equipment is unacceptable, the master production plan should be adjusted, and the material requirements plan should be regenerated.