In the 21st century, with rapid technological advancements and a constantly changing market, enterprises must respond quickly to customer and market demands to remain competitive. Improving enterprise informatization is an effective way for companies to deliver products to customers with low cost, high quality, and short delivery times, through advanced production operation and management models. APS (Advanced Planning & Scheduling) and MES (Manufacturing Execution System), as advanced enterprise production planning and management models and advanced execution systems, are being increasingly adopted by enterprises. Production is the primary activity of an enterprise, and production management is the most important part of enterprise management, with most of its work ultimately focused on implementation. Shop floor production planning and scheduling play a crucial role in enterprise production management, acting as a bridge between upstream and downstream processes. Reasonable production planning and scheduling methods can deepen an enterprise's understanding of production process mechanisms and key data, improve production capacity, and reduce inventory costs. Therefore, it is necessary to strengthen the management and control of shop floor production through integrated research on APS and MES, making shop floor production planning and scheduling activities faster and more accurate. 1. Introduction to APS and MES 1.1 Advanced Planning and Scheduling (APS) Advanced Planning and Scheduling System (APS) is a revolutionary management technology developed in the last 50 years of the 20th century. It is an advanced planning and scheduling tool based on supply chain management and constraint theory, incorporating numerous mathematical models, optimization, and simulation techniques. Its functional advantages lie in real-time constraint-based replanning and alarm functions. During the planning and scheduling process, APS encompasses both internal and external resource and capability constraints, using complex intelligent algorithms to perform resident memory calculations. APS has enormous potential to improve enterprise economic efficiency. It can respond promptly to customer requirements, quickly synchronize plans, provide accurate delivery dates, reduce work-in-process and finished goods inventory, and simultaneously consider all supply chain constraints, automatically identify potential bottlenecks, and improve resource utilization, thereby improving the overall production management level of the enterprise. As shown in Figure 1, the APS planning hierarchy model covers three planning levels: strategic, tactical, and operational levels of supply chain management. The strategic layer includes supply chain strategy and supply chain planning; the tactical layer includes demand planning and forecasting, manufacturing planning, operations planning, and distribution planning; the operations layer includes Capable to Promise (CTP), shop floor scheduling, transportation planning, and Available to Promise (ATP). For short-term production planning within the shop floor, such as arranging the production sequence of multiple products on a single machine, APS often uses Constraint Programming (CP) to solve this problem. CP represents each existing resource constraint as a variable, and then uses the logical relationships between the constraint variables to find a solution that satisfies all constraints. APS simulates workflows based on a large amount of accurate data input and provides real-time monitoring functions. APS can output the simulation scheduling results in the form of a Gantt chart to a visual planning dashboard, which can be dragged and dropped according to established rules to achieve optimization. 1.2 Manufacturing Execution System (MES) Manufacturing Execution System is a new concept proposed by the American management community in the 1990s. MES is the execution layer between the planning layer and the shop floor operation control system, playing a crucial role in the entire enterprise information system. MES (Manufacturing Execution System) monitors, diagnoses, and controls the production process in real time, integrating production units and optimizing the system. At the production process level, it performs material balancing, production planning, real-time scheduling, and optimized dispatching. Furthermore, it monitors, analyzes, controls, and optimizes materials, energy, quality, equipment, capital, and even human resources throughout the production process, achieving optimized management of the entire production process from order placement to product completion. When real-time events occur in the factory, MES can react and report them promptly, using accurate current data to guide and process them. This rapid response to changes in status allows MES to reduce non-value-added activities within the enterprise, effectively guiding the factory's production operations, thereby improving on-time delivery capabilities, material flow performance, and production return on investment. MESA (Mechanical Engineering and Automation) outlines 11 main functional modules of MES, with a general functional model shown in Figure 2. However, actual MES system products may only include one or a few of these modules. MESA also defines the technical model of the current MES system, as shown in Figure 3. Compared to APS, MES is a complete solution offering many functional modules. However, from an optimization perspective, MES cannot reach the level of optimization capabilities provided by APS. Therefore, integrating APS with MES is more suitable for complex workshop production planning and scheduling activities with strong real-time requirements. 2. Analysis of Current Workshop Production Planning and Scheduling Methods As the specific execution department for product processing, workshop production management involves all aspects of the product production process, such as: production process monitoring, production scheduling, on-site equipment management, personnel arrangement, consumption statistics, working hour statistics, dynamic cost accounting, material management, work-in-process management, and product data management, etc. A typical manufacturing workshop production activity model is shown in Figure 4. Its main workflow is: first, the received master production plan is decomposed into tasks; then, according to certain rules, the processing equipment for each sub-task is determined; and the start time of the task order is uniformly determined based on the order placement date, the planned warehousing date of the task order, relevant process information, and the current processing plan of each processing unit. After the theoretical plan is compiled, the load on each processing unit is formed. Next, the processing capacity and workload of each processing equipment need to be balanced, and work assignment plans and related material preparation plans need to be formulated. Simultaneously, based on material and tooling conditions and feedback information from each processing unit, a formal work plan is formulated, and work assignment begins. After the plan is issued, during actual production, real-time scheduling must be continuously performed based on changes in various parameters to ensure the smooth implementation of the master production plan. In current traditional production management systems, the production decision-making layer and the workshop execution layer, as well as the planning, scheduling, and control layers, are independent. Information collection is mostly done manually, resulting in a long feedback cycle for workshop production information. This leads to a lack of necessary on-site information during production scheduling and control, making it impossible to reschedule in a timely manner based on changes in actual production conditions, let alone revise production plans in real time. Furthermore, when actual production cannot meet the original production plan, traditional production management systems often fail to detect and resolve these problems in a timely manner, ultimately leading to delays in product delivery. 3. Workshop Production Planning and Scheduling Based on APS and MES Integration As discussed in the previous two sections, in complex workshop production activities, the detailed production planning and scheduling modules in the MES system have limited functionality and cannot be rescheduled according to changes in actual production conditions. Using the enterprise's ERP system as the information foundation, we adopt an integrated APS and MES approach. MES provides real-time workshop production progress, work-in-process information, and on-site equipment operating status. Then, utilizing the advanced optimization algorithms of APS, we can initially solve the optimization problem of current workshop production planning and scheduling. 3.1 Architecture Analysis of APS and MES Integration In modern manufacturing enterprises, ERP has become an essential business and data platform. Both APS and MES need to interact and share data with the ERP system to obtain the necessary data for their own operation. When integrating APS and MES, the ERP system must also be considered, meaning the integration of ERP, APS, and MES inevitably leads to overlap and interoperability. Therefore, the framework for the ERP-based APS and MES integrated system is determined by considering factors such as the enterprise's products, production mode, planning mode, and differences between existing and newly constructed systems. Currently, scholars both domestically and internationally have proposed frameworks for the integration of APS, MES, and ERP. However, existing integration frameworks still use supply chain management platforms as interfaces, with APS applications focusing on supply chain planning management, primarily targeting enterprise production environments with complex materials and significant outsourcing. Building upon previous research, this paper proposes a system integration framework for workshop production planning and scheduling, as shown in Figure 5. This framework, centered on the closed-loop system integration of APS, MES, and ERP, solves the optimization problem of workshop production planning and scheduling within the enterprise. 3.2 Functional Model Analysis of Workshop Production Planning and Scheduling Integrated with APS and MES The system integration framework proposed in the previous section mainly addresses the positioning and interface issues of each system. However, how the planning modules of each system interact, and how their scope and functions are redefined, requires further analysis. The integrated system presented in this paper includes functional modules such as demand planning and global ATP in the APS system; BOM, master production schedule, and MRP in the ERP system; and production scheduling and dispatching in the MES system. For the functional modules that overlap or intersect with each other in various systems, the optimization tools selected the original functional modules of APS, and the entity functions selected the original functional modules of ERP and MES. Among these, Master Production Schedule (MPS), MRP, and production scheduling constitute the main body of the enterprise production planning system, with MRP and production scheduling modules playing a decisive role in shop floor production planning and scheduling methods. In the integrated system, the algorithms for these two parts were modified according to the characteristics of shop floor production activities, as shown in Figure 6. 3.3 Implementation Method of APS and MES System Integration In the implementation of APS and MES system integration, we adopted the "Common Object Request Broker Architecture" (CORBA) proposed by the Object Management Group (OMG). CORBA provides a mechanism that allows objects to transparently request services and receive information from other objects locally or online. ORB, as its core, provides interoperability between different applications in a distributed heterogeneous environment and enables seamless connections between multiple object systems. Furthermore, CORBA provides an Interface Definition Language (IDL), independent of programming languages, to describe objects and operations, enabling remotely distributed applications to request operational services on these objects through the ORB. The integration of APS and MES systems using the CORBA architecture consists of five parts: the information resource layer, the access interface layer, the request service proxy layer, the object communication service layer, and the network transport layer. Its integrated layered architecture is shown in Figure 7. When implementing system integration, the main content of the application objects and related functional proxies is first determined; then, based on the specific application system (APS, MES, or ERP), its encapsulation is implemented, extracting the necessary services; finally, a series of functional proxies are formed, realizing all functions of workshop production planning and scheduling based on the integration of APS and MES. 4 Conclusion With increasingly fierce competition in domestic and international markets, manufacturing enterprises are facing unprecedented challenges. To quickly adapt to rapid changes in the internal and external environment, leveraging advanced enterprise production management models and information systems such as APS and MES, fully utilizing enterprise manufacturing resources, optimizing workshop production planning and scheduling algorithms, and improving productivity by changing traditional production models are crucial means to enhance enterprise competitiveness and respond quickly to market demands. Practice has proven that in heterogeneous enterprise environments, using CORBA to reconstruct and integrate complex enterprise systems such as APS, MES, and ERP is effective and feasible.