Intelligent order flexible processing production scheduling management method

Abstract

The invention discloses an intelligent order flexible processing production scheduling management method, and relates to the technical field of order management. The method comprises the steps of obtaining demand data from a baking order management system and performing priority classification on orders to ensure timely delivery of important orders and improve customer satisfaction. The future consumption of the baking raw materials is predicted through a time sequence prediction algorithm, the inventory cost is reduced, and the production continuity is guaranteed. And configuration of production equipment, workers and raw materials is optimized through mixed integer linear programming, reasonable utilization of resources is achieved, and the production efficiency is improved. The production progress and the variation of raw material consumption are controlled and monitored through the statistical process, abnormal conditions are recognized in time, and stable product quality is ensured. And flexible processing scheduling adjustment is carried out according to the production abnormity index, and the adaptability of the production system is enhanced. In conclusion, according to the method, the intelligent level of production management is effectively improved, the production process is optimized, and powerful support is provided for enterprises in the market with fierce competition.

Description

Technical Field

[0001] The present invention relates to the technical field of order management, and in particular to an intelligent order flexible processing production scheduling management method. Background Art

[0002] With the rapid development of the economy and the diversification of consumer demand, the baking industry is facing increasingly fierce market competition. Consumers' requirements for product quality and delivery time are constantly increasing, and companies need to respond quickly to market changes to ensure timely delivery and customer satisfaction. In this context, the intelligence and flexibility of the production process have become key factors in enhancing corporate competitiveness. Many baking companies have gradually realized the importance of data-driven and intelligent algorithms in optimizing production management to achieve efficient production processes and resource utilization.

[0003] However, traditional baking production management methods often rely on manual scheduling and empirical judgment, which makes it difficult to adapt to rapidly changing market demands. These methods usually lack real-time analysis of production data, resulting in waste of raw materials and increased inventory costs. In addition, traditional production scheduling systems lack flexibility when handling diversified orders, which can easily lead to production bottlenecks, affecting delivery cycles and product quality. Due to the lack of an effective abnormal monitoring mechanism, it is also difficult to detect and correct problems in the production process in a timely manner, further affecting overall production efficiency and customer satisfaction. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an intelligent order flexible processing scheduling management method, which solves the problems of the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent order flexible processing scheduling management method, comprising the following steps: S1. Obtaining baking order demand data from a baking order management system, and prioritizing and preliminarily scheduling production tasks for baking orders according to baking order types and delivery times; S2. Predicting the future consumption of baking raw materials based on the historical consumption rate of baking raw materials through a time series prediction algorithm, analyzing the consumption trend of baking raw materials, and determining the safety inventory level of each baking raw material; S3. According to the priority classification of baking orders and the preliminary scheduling of production tasks, allocating production equipment and baking raw materials through mixed integer linear programming for processing scheduling; S4. In the scheduling process, obtaining baking raw material consumption data and production progress data, monitoring the variation of production progress and raw material consumption through SPC, and obtaining a production anomaly index; S5. Based on the production anomaly index, determining whether processing scheduling adjustment is required, and if processing scheduling adjustment is required, adjusting the processing scheduling of production equipment and baking raw materials through a fuzzy control algorithm.

[0006] Furthermore, the baking order demand data includes order number, order type, delivery time, delivery type requirement, and delivery quantity requirement.

[0007] Furthermore, the specific process of priority classification of baking orders and preliminary scheduling of production tasks according to baking order types and delivery times is as follows: based on the baking order demand data, the orders are preliminarily sorted according to the delivery time of the orders; for orders with the same delivery time, the production complexity and profit distribution weights of the orders are calculated, the order production complexity weight factor and the order profit weight factor are calculated, and the priority production index of each order is obtained; based on the priority production index of each order, the orders are sorted in order from large to small, and a preliminary production schedule is generated.

[0008] Furthermore, based on the historical consumption rate of baking raw materials, the future consumption of baking raw materials is predicted through a time series prediction algorithm. The specific process of analyzing the consumption trend of baking raw materials is as follows: based on the historical consumption rate of baking raw materials, the periodic change pattern of raw material consumption is identified through a time series prediction algorithm, the consumption rates in different time periods are classified, and a baking raw material prediction model is constructed; based on the baking raw material prediction model, the future consumption of each type of raw materials is predicted.

[0009] Furthermore, the specific process of analyzing the consumption trend of baking raw materials and determining the safety stock level of each baking raw material is as follows: according to the future consumption of raw materials output by the time series prediction model, the consumption trend is classified and the minimum safety stock level of each type of raw material is set; an early warning mechanism for safety stock is established to dynamically determine whether the raw material inventory is close to the safety stock threshold by real-time monitoring of inventory data; when the predicted future consumption exceeds the minimum safety stock level, a replenishment reminder is automatically generated.

[0010] Furthermore, the specific process of allocating production equipment and baking raw materials for processing scheduling through mixed integer linear programming is as follows: define the allocation variables of production equipment and baking raw materials, set minimizing production time and maximizing equipment utilization as the objective function, take the supply of raw materials as a constraint, and establish a mixed integer linear programming model; solve the mixed integer linear programming model through the cutting plane method to obtain the allocation plan of production equipment and baking raw materials.

[0011] Furthermore, the specific process of obtaining the production anomaly index by monitoring the variation of production progress and raw material consumption through SPC is as follows: determine the type of control chart in the statistical process according to the baking raw material consumption data and the production progress data; calculate the control limits of production progress and raw material consumption based on the historical baking raw material consumption data and the historical production progress data, and establish the baseline of the control chart; draw the real-time monitoring baking raw material consumption data and production progress data onto the control chart to monitor whether the actual fluctuations of production progress and raw material consumption are within the control limits; when it is found that the data exceeds the control limits or shows irregular variation, the irregular variation includes the baking raw material consumption data and the production progress data exceeding the control limits, and the baking raw material consumption data and the production progress data showing a non-random trend, automatically generate a production anomaly index to quantify the degree of abnormality in the current production or raw material consumption process.

[0012] Furthermore, the specific process of determining whether processing scheduling adjustment is required is as follows: based on the production abnormality index, when the production abnormality index exceeds the set production abnormality index threshold, processing scheduling adjustment is required.

[0013] Furthermore, the specific process of adjusting the processing schedule of production equipment and baking raw materials through fuzzy control algorithm is as follows: setting the production abnormality index as the input fuzzy variable, and setting the production equipment operation rate and the adjustment amount of the baking raw material safety inventory level as the output fuzzy variable; setting fuzzy rules to define the relationship between the production abnormality index and the production equipment operation rate and the adjustment amount of the baking raw material safety inventory level; according to the input production abnormality index, reasoning is performed through fuzzy control rules to obtain the corresponding production equipment operation rate and the adjustment amount of the baking raw material safety inventory level, and fuzzifying the production abnormality index by using fuzzy logic; through the fuzzy reasoning mechanism, the membership of each fuzzy set is comprehensively considered to output the corresponding production equipment operation rate and the adjustment amount of the baking raw material safety inventory level; the production equipment operation rate and the adjustment amount of the baking raw material safety inventory level obtained by fuzzy reasoning are defuzzified, and according to the defuzzified results, the operating efficiency of the production equipment and the safety inventory level of baking raw materials are dynamically adjusted.

[0014] The present invention has the following beneficial effects:

[0015] (1) This intelligent order flexible processing scheduling management method can ensure that important orders are processed first by classifying baking orders according to their priorities, thereby improving customer satisfaction and the timeliness of order delivery and avoiding waste of production resources. Through the time series prediction algorithm, the future consumption of raw materials can be accurately predicted, which can effectively avoid shortages or surpluses of raw materials, thereby reducing inventory costs and ensuring the continuity and stability of production. In addition, determining the safety stock level helps to maintain production flexibility when demand fluctuates.

[0016] (2) This is an intelligent order flexible processing scheduling management method. Mixed integer linear programming optimizes the allocation of production resources, ensures the rational use of production equipment, workers and raw materials, improves production efficiency and resource utilization, shortens the production cycle, and implements statistical process control (SPC) to timely identify abnormal situations in the production process, enhance the transparency of the production process, ensure stable product quality, and help managers adjust production strategies in a timely manner to reduce losses. Obtain the production abnormality index, and use the fuzzy control algorithm to adjust the processing scheduling of production equipment and baking raw materials. Managers can judge the production status in a timely manner and make flexible adjustments when necessary to ensure the feasibility and response speed of the production plan. This intelligent adjustment method can improve the adaptability of the overall production system and optimize production efficiency.

[0017] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of an intelligent order flexible processing scheduling management method of the present invention. DETAILED DESCRIPTION

[0019] The embodiment of the present application solves the problems of slow order response, unreasonable resource allocation, waste of raw materials and insufficient monitoring of production abnormalities in traditional baking production management through an intelligent order flexible processing and scheduling management method, significantly improves production efficiency and flexibility, and ensures the timely delivery of important orders and stable product quality.

[0020] The overall idea of ​​the problem in the embodiment of this application is as follows:

[0021] Obtain baking order demand data from the baking order management system, and prioritize and preliminarily schedule the baking orders based on the baking order type and delivery time.

[0022] According to the historical consumption rate of baking raw materials, the future consumption of baking raw materials is predicted through time series prediction algorithm, the consumption trend of baking raw materials is analyzed, and the safety inventory level of each baking raw material is determined.

[0023] According to the priority classification of baking orders and the preliminary scheduling of production tasks, production equipment and baking raw materials are allocated for processing scheduling through mixed integer linear programming.

[0024] During the production scheduling process, obtain baking raw material consumption data and production progress data, monitor the variation of production progress and raw material consumption through SPC, and obtain the production abnormality index.

[0025] Based on the production abnormality index, it is determined whether processing scheduling adjustment is needed. If processing scheduling adjustment is needed, the processing scheduling of production equipment and baking raw materials is adjusted through fuzzy control algorithm.

[0026] See also Figure 1 , an embodiment of the present invention provides a technical solution: an intelligent order flexible processing scheduling management method, comprising the following steps: S1. Obtaining baking order demand data from a baking order management system, and prioritizing and preliminarily scheduling production tasks for baking orders according to baking order types and delivery times; S2. Predicting future consumption of baking raw materials through a time series prediction algorithm based on historical baking raw material consumption rates, analyzing consumption trends of baking raw materials, and determining safety inventory levels of each baking raw material; S3. Allocating production equipment and baking raw materials for processing scheduling through mixed integer linear programming based on priority classification of baking orders and preliminary scheduling of production tasks; S4. In the scheduling process, obtaining baking raw material consumption data and production progress data, monitoring production progress and raw material consumption variations through SPC, and obtaining a production anomaly index; S5. Judging whether processing scheduling adjustment is required based on the production anomaly index, if processing scheduling adjustment is required, adjusting the processing scheduling of production equipment and baking raw materials through a fuzzy control algorithm.

[0027] In this embodiment, S1. extracts relevant order demand data from the baking order management system. These data include order types (such as different baking products) and delivery times. Based on this information, the orders are prioritized to ensure that the delivery needs of important or urgent orders can be met first during the production process, thereby improving customer satisfaction. S2. Analyze the consumption rate of baking raw materials based on historical data. Use time series prediction algorithms (such as ARIMA or exponential smoothing) to predict future raw material consumption and analyze its trend. Determine the safety inventory level of each raw material through these data to prevent shortages or surpluses of raw materials and improve production continuity and cost control. S3. According to the order priority and preliminary production schedule obtained in S1, use the mixed integer linear programming (MILP) model to optimize the configuration of production equipment, workers and raw materials. This method can efficiently utilize production resources to reduce production costs and improve overall production efficiency. S4. During the production process, continuously obtain consumption data and production progress data of baking raw materials. Monitor the variation of these data through the statistical process control (SPC) method to obtain the production abnormality index. SPC can help identify possible problems in the production process, thereby ensuring the stability of product quality. S5. According to the production abnormality index obtained in S4, determine whether it is necessary to adjust the processing schedule. If necessary, the fuzzy control algorithm is used to adjust the production equipment and baking raw materials. The fuzzy control algorithm allows decision-making under uncertainty and ambiguity, so as to flexibly respond to various changes encountered in the production process. Order management system: a comprehensive system for managing customer orders, production plans and logistics to improve the efficiency and accuracy of order processing. Priority classification: orders are divided into different priorities according to their importance and urgency, so as to arrange the production sequence reasonably. Time series prediction algorithm: a statistical method that predicts future data trends by analyzing the trend, seasonality and periodicity of historical data sequences. Safety stock level: a certain amount of raw material inventory maintained to avoid production interruptions in response to demand fluctuations or supply chain problems. Mixed integer linear programming (MILP): a mathematical optimization method that contains both integer decision variables (such as production quantity) and continuous decision variables (such as resource utilization), and is widely used in resource allocation and scheduling problems. Statistical process control (SPC): a method used to monitor and control the production process. By analyzing production data, the production process is ensured to be under control, thereby ensuring product quality. Production anomaly index: An indicator used to quantify anomalies that occur during the production process, such as production delays, abnormal material consumption, etc. Fuzzy control algorithm: A control algorithm based on fuzzy logic that can handle uncertainty and ambiguity to flexibly respond to complex decision-making problems.

[0028] Specifically, the specific process of priority classification of baking orders and preliminary scheduling of production tasks according to baking order types and delivery times is as follows: based on the baking order demand data, the orders are preliminarily sorted according to the delivery time of the orders; for orders with the same delivery time, the production complexity and profit distribution weights of the orders are calculated, the order production complexity weight factor and the order profit weight factor are calculated, and the priority production index of each order is obtained; based on the priority production index of each order, the orders are sorted in order from large to small to generate a preliminary production schedule.

[0029] In this implementation scheme, orders are preliminarily sorted according to the delivery time of each order. Orders with earlier delivery times have higher priorities. This sorting ensures that customer needs can be met in a timely manner during the production process, thereby improving customer satisfaction. Calculate the production complexity and profit weights. For orders with the same delivery time, further analysis is required to determine the priority. The specific steps include: Production complexity weight factor: Evaluate the production complexity of the order (such as process flow, difficulty in preparing raw materials, etc.) and assign a weight factor. The higher the production complexity of the order, the larger the weight factor, indicating that it has a higher demand for production resources. Profit weight factor: Calculate the corresponding weight factor based on the expected profit of each order. The higher the profit of the order, the larger the weight factor, indicating that it is more important to the economic benefits of the enterprise. Obtain the priority production index, combine the production complexity weight factor and the profit weight factor of each order, and obtain the priority production index of each order. It can be calculated by the following formula: P i =α·C i +β·R i ; Among them: P i represents the priority production index of the i-th order, C i represents the production complexity weight factor of the i-th order, R i represents the profit weight factor of the i-th order, α and β are the weight coefficients of production complexity and profit respectively, and α+β=1. This index is used to comprehensively evaluate the importance of orders. All orders are sorted from large to small according to the priority production index. Orders with high priority production index will be arranged for priority execution in the production plan. Based on the above sorting, a preliminary production schedule is generated. This plan includes information such as the production sequence, estimated production time, and required resource allocation of each order, providing a basis for subsequent production arrangements.

[0030] Specifically, based on the historical consumption rate of baking raw materials, the future consumption of baking raw materials is predicted through a time series prediction algorithm. The specific process of analyzing the consumption trend of baking raw materials is as follows: based on the historical consumption rate of baking raw materials, the periodic change pattern of raw material consumption is identified through a time series prediction algorithm, the consumption rates in different time periods are classified, and a baking raw material prediction model is constructed; based on the baking raw material prediction model, the future consumption of each type of raw materials is predicted.

[0031] In this implementation scheme, historical consumption data analysis: collect historical consumption data of baking raw materials. These data usually include daily, weekly or monthly raw material usage. Through data analysis, identify the periodic change pattern of raw material consumption, such as seasonal fluctuations (such as increased demand during holidays or special events) and periodic trends (such as weekly sales peaks). Use time series prediction algorithms (such as ARIMA, seasonal decomposition or exponential smoothing) to build a forecasting model for baking raw materials. The model extracts key features by analyzing the trends, seasonality and random fluctuations of historical data to better predict future consumption. Classify the consumption rate according to time periods (such as days, weeks, and months), and group similar consumption patterns to better capture the consumption characteristics of different time periods in the forecast. For example, before the holidays, the demand for some raw materials may increase significantly, while it is relatively stable in other periods. Baking raw material forecasting model This model will take into account the historical consumption patterns of various raw materials to help predict the future demand for different types of raw materials. Future consumption forecast: Use the constructed forecasting model to predict the future consumption of various raw materials. These forecast results will provide a basis for production planning, inventory management and raw material procurement to ensure a smooth production process and avoid production delays caused by raw material shortages.

[0032] Specifically, the specific process of analyzing the consumption trend of baking raw materials and determining the safety stock level of each baking raw material is as follows: classify the consumption trend according to the future consumption of raw materials output by the time series prediction model, and set the minimum safety stock level of each type of raw material; establish an early warning mechanism for safety stock, and dynamically determine whether the raw material inventory is close to the safety stock threshold by real-time monitoring of inventory data. When the predicted future consumption exceeds the minimum safety stock level, a replenishment reminder is automatically generated.

[0033] In this implementation scheme, based on the previously established time series forecasting model, the estimated consumption of various raw materials in the future is obtained. These data help enterprises understand the changes in demand for different raw materials in a specific time period. According to the predicted future consumption, the consumption trend of raw materials is classified. For example, some raw materials may show a stable consumption trend, while others may show seasonal fluctuations or sudden high demand. Through this classification, enterprises can formulate corresponding inventory strategies more effectively. For different categories of raw materials, the minimum safety stock of each type of raw material is set according to its consumption trend and predicted consumption. The safety stock is to ensure that normal production operations can be maintained in the event of a sudden increase in demand or supply delay. The setting of safety stock usually considers the following factors: Consumption rate: calculated based on historical data and forecast data. Supply cycle: the time required for raw materials to arrive from suppliers. Demand fluctuations: taking into account the possible fluctuation range of demand. Safety stock early warning mechanism: establish an early warning mechanism for safety stock, monitor inventory data in real time, and ensure that the inventory level is within a safe range. When the actual inventory is close to or lower than the set safety stock threshold, the system will trigger an early warning prompt to notify relevant personnel to take replenishment measures. When the predicted future consumption exceeds the minimum safety stock level, the system automatically generates a replenishment reminder. This automated mechanism ensures that the company can respond to changing demand in a timely manner and avoid production interruptions caused by raw material shortages.

[0034] Specifically, the specific process of allocating production equipment and baking raw materials for processing scheduling through mixed integer linear programming is as follows: define the allocation variables of production equipment and baking raw materials, set minimizing production time and maximizing equipment utilization as the objective function, take the supply of raw materials as a constraint, and establish a mixed integer linear programming model; solve the mixed integer linear programming model through the cutting plane method to obtain the allocation plan of production equipment and baking raw materials.

[0035] In this implementation scheme, the specific process of allocating production equipment and baking raw materials for processing scheduling through mixed integer linear programming is as follows: Allocating variables: Set the decision variables related to the allocation of production equipment and baking raw materials. Define x ij Define y as the time allocated to device i to process order j k is the quantity allocated to the baking raw material k. These variables will be used in the subsequent construction of the objective function and constraints. Objective function: Minimize production time: The objective function can be set to minimize the total production time of all orders to improve production efficiency. The objective function can be expressed as: MinimizeZ = ∑ j T j ; Where, Z: the value of the objective function, which represents the total production time. The goal is to minimize this value to improve the overall production efficiency, T jRepresents the total time required to process order j. Maximize equipment utilization: Consider the utilization of equipment at the same time to ensure that resources are used reasonably. The objective function of equipment utilization can be expressed as: Where, U: the total value of equipment utilization, the goal is to maximize this value to ensure the efficiency of each device, T max is the maximum available time of the equipment. Constraints: Raw material supply constraint: During the planning process, it must be ensured that the raw materials allocated to each order do not exceed the available inventory. This can be expressed by the following formula: Where S k is the total supply of raw material k, y jk is the quantity of raw material k assigned to order j. Equipment and production time constraints: Ensure that the number of orders and production time processed by each equipment in any time period does not exceed the capacity of the equipment: Among them C i is the maximum processing capacity of device i. The above objective function and constraints are combined to form a complete mixed integer linear programming model. This model contains integer and continuous variables and is suitable for optimizing resource allocation problems. Cutting plane method: Use algorithms such as the cutting plane method to solve the mixed integer linear programming model and find the optimal solution. This method eliminates infeasible solutions and optimizes the solution space by gradually adding constraints (cutting planes). Ultimately, the solution will provide an optimal allocation plan for production equipment and baking raw materials, thereby achieving more efficient processing scheduling.

[0036] Specifically, the specific process of obtaining the production anomaly index by monitoring the variation of production progress and raw material consumption through SPC is as follows: determine the type of control chart in the statistical process according to the baking raw material consumption data and the production progress data; calculate the control limits of production progress and raw material consumption based on the historical baking raw material consumption data and the historical production progress data, and establish the baseline of the control chart; draw the real-time monitoring baking raw material consumption data and production progress data onto the control chart to monitor whether the actual fluctuations of production progress and raw material consumption are within the control limits; when it is found that the data exceeds the control limits or shows irregular variation, the irregular variation includes the baking raw material consumption data and the production progress data exceeding the control limits, and the baking raw material consumption data and the production progress data showing a non-random trend, automatically generate a production anomaly index to quantify the degree of abnormality in the current production or raw material consumption process.

[0037] In this embodiment, the process of obtaining the production abnormality index by monitoring the variation of production progress and raw material consumption through statistical process control (SPC) specifically includes the following steps: Determine the control chart type: According to the characteristics of the baking raw material consumption data and the production progress data, select the appropriate control chart type, such as the X control chart, R control chart or p control chart, so as to effectively monitor the fluctuation of different data. Calculate the control limit: Based on the historical data, calculate the control limit UCL (upper control limit) and LCL (lower control limit) to establish the baseline of the control chart. The calculation formula of the control limit is usually as follows: in: is the average value of historical data, and σ is the standard deviation of historical data. k is the control coefficient selected by the control chart (usually 3, Table 3σ control limit). Real-time monitoring and drawing of control charts: Plot the real-time monitored baking raw material consumption data and production progress data on the control chart to observe whether the data points are within the control limits. If the data points fall within the control limits, it indicates that the production process is under control. Identify irregular variations: When it is monitored that the data points exceed the control limits or irregular variations occur (for example, the baking raw material consumption data and the production progress data exceed the control limits at the same time, or the data show a non-random trend), these abnormal conditions need to be recorded. Generate production abnormality index: Automatically generate the production abnormality index AEI (Abnormal Event Index), which is used to quantify the degree of abnormality in the current production or raw material consumption process. The calculation formula of the abnormal index can be defined as: Where: D is the number of data points that exceed the control limit. C is the total number of monitored data points. Through the above steps, the variation of production progress and raw material consumption can be effectively monitored, abnormalities in the production process can be discovered and quantified in a timely manner, and the sensitivity and response speed of production management can be improved.

[0038] Specifically, the specific process of adjusting the processing schedule of production equipment and baking raw materials through fuzzy control algorithm is as follows: setting the production abnormality index as the input fuzzy variable, and setting the production equipment operation rate and the adjustment amount of the baking raw material safety inventory level as the output fuzzy variable; setting fuzzy rules to define the relationship between the production abnormality index and the production equipment operation rate and the adjustment amount of the baking raw material safety inventory level; according to the input production abnormality index, reasoning is performed through fuzzy control rules to obtain the corresponding production equipment operation rate and the adjustment amount of the baking raw material safety inventory level, and fuzzifying the production abnormality index by using fuzzy logic; through the fuzzy reasoning mechanism, the membership of each fuzzy set is comprehensively considered to output the corresponding production equipment operation rate and the adjustment amount of the baking raw material safety inventory level; the production equipment operation rate and the adjustment amount of the baking raw material safety inventory level obtained by fuzzy reasoning are defuzzified, and according to the defuzzified results, the operating efficiency of the production equipment and the safety inventory level of baking raw materials are dynamically adjusted.

[0039] In this implementation scheme, 1. Input and output variable definition: The specific process of adjusting the processing schedule of production equipment and baking raw materials through fuzzy control algorithm can be divided into the following steps: The production abnormality index (AEI) is set as the input fuzzy variable, representing the degree of abnormality in the current production process. The production equipment operating rate (PDR) and the baking raw material safety stock level adjustment (SRA) are set as output fuzzy variables, reflecting the operating status of the equipment and the adjustment requirements of the raw material inventory respectively. Fuzzy rule setting: According to the relationship between the production abnormality index and the production equipment operating rate and the baking raw material safety stock level adjustment, a set of fuzzy control rules is set. The example rules are as follows: If AEI is "low", then PDR is "low" and SRA is "low". If AEI is "medium", then PDR is "medium" and SRA is "moderate". If AEI is "high", then PDR is "high" and SRA is "high". Fuzzification processing: Apply fuzzy logic to fuzzify the input production abnormality index so that it can be combined with the set fuzzy rules. The process of fuzzification involves determining the membership of the input value in various fuzzy sets (such as "low", "medium", and "high"), as follows: Among them, μ A (PEI): The membership of the production anomaly index PEI in the fuzzy set A. a: The lower limit of the fuzzy set A. b: The upper limit of the fuzzy set A. According to the input fuzzy value, the membership of each fuzzy set is comprehensively considered through the fuzzy reasoning mechanism to obtain the corresponding production equipment operation rate and the adjustment of the safety inventory level of baking raw materials. Fuzzy reasoning usually adopts the Mamdani reasoning method, combining the membership of the input fuzzy set and fuzzy rules for reasoning. The output results obtained by fuzzy reasoning are defuzzified to generate specific production equipment operation rates and adjustments to the safety inventory level of baking raw materials. Commonly used defuzzification methods include the centroid method, and the calculation formula is: where x i is the specific value of the output variable, μ(x i ) is the corresponding membership degree. According to the defuzzified results, the operating efficiency of the production equipment and the safety inventory level of baking raw materials are dynamically adjusted to ensure that the production system can flexibly respond to abnormal situations and improve production efficiency and resource utilization.

[0040] In summary, this application has at least the following effects:

[0041] An intelligent order-based flexible processing and scheduling management method ensures timely delivery of important orders and improves customer satisfaction by obtaining demand data and prioritizing orders. The future consumption of baking raw materials is predicted through a time series prediction algorithm to reduce inventory costs and ensure production continuity. The configuration of production equipment, workers, and raw materials is optimized through mixed integer linear programming to achieve rational resource utilization and improve production efficiency. The production progress and raw material consumption variation are monitored through statistical process control to identify abnormal situations in a timely manner and ensure stable product quality. Flexible processing scheduling adjustments are made according to the production abnormality index to enhance the adaptability of the production system. In summary, this method effectively improves the intelligent level of production management, optimizes the production process, and provides strong support for enterprises in the fiercely competitive market.

[0042] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0043] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0044] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0045] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0046] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0047] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent order flexible processing scheduling management method, characterized in that: The following steps are involved: S1. Obtain baking order demand data from the baking order management system, and prioritize and schedule the baking orders according to the baking order type and delivery time; S2. Based on the historical consumption rate of baking raw materials, the future consumption of baking raw materials is predicted by time series prediction algorithm, the consumption trend of baking raw materials is analyzed, and the safety stock level of each baking raw material is determined; S3. According to the priority classification of baking orders and the preliminary scheduling of production tasks, the production equipment and baking raw materials are allocated through mixed integer linear programming for processing scheduling; S4. During the production scheduling process, obtain the baking raw material consumption data and production progress data, monitor the variation of production progress and raw material consumption through SPC, and obtain the production abnormality index; S5. Based on the production abnormality index, determine whether processing scheduling adjustment is needed. If processing scheduling adjustment is needed, adjust the processing scheduling of production equipment and baking raw materials through fuzzy control algorithm.

2. The intelligent order flexible processing scheduling management method according to claim 1 is characterized by: Baking order demand data includes order number, order type, delivery time, delivery type requirement, and delivery quantity requirement.

3. The intelligent order flexible processing scheduling management method according to claim 2 is characterized by: The specific process of prioritizing baking orders and preliminarily scheduling production tasks according to baking order types and delivery times is as follows: Based on the baking order demand data, the orders are preliminarily sorted according to their delivery time; For orders with the same delivery time, the production complexity and profit distribution weights of the orders are calculated, and the order production complexity weight factor and order profit weight factor are calculated to obtain the priority production index of each order; Based on the production priority index of each order, the orders are sorted from large to small and a preliminary production schedule is generated.

4. The intelligent order flexible processing scheduling management method according to claim 3 is characterized by: According to the historical consumption rate of baking raw materials, the future consumption of baking raw materials is predicted by time series prediction algorithm. The specific process of analyzing the consumption trend of baking raw materials is as follows: According to the historical consumption rate of baking raw materials, the periodic change pattern of raw material consumption is identified through the time series prediction algorithm, the consumption rate in different time periods is classified, and the baking raw material prediction model is constructed; Based on the baking raw material prediction model, the future consumption of various raw materials is predicted.

5. The intelligent order flexible processing scheduling management method according to claim 4 is characterized by: The specific process of analyzing the consumption trend of baking raw materials and determining the safety stock level of each baking raw material is as follows: Based on the future consumption of raw materials output by the time series forecasting model, the consumption trends are classified and the minimum safety inventory of each type of raw materials is set; Establish an early warning mechanism for safety stock. By real-time monitoring of inventory data, dynamically determine whether the raw material inventory is close to the safety stock threshold. When the predicted future consumption exceeds the minimum safety stock, automatically generate a replenishment reminder.

6. The intelligent order flexible processing scheduling management method according to claim 5 is characterized by: The specific process of allocating production equipment and baking raw materials for processing scheduling through mixed integer linear programming is as follows: Define the allocation variables of production equipment and baking raw materials, set minimizing production time and maximizing equipment utilization as the objective function, take the supply of raw materials as the constraint condition, and establish a mixed integer linear programming model; The mixed integer linear programming model is solved by the cutting plane method to obtain the allocation plan of production equipment and baking raw materials.

7. The intelligent order flexible processing scheduling management method according to claim 6 is characterized by: The specific process of obtaining the production abnormality index by monitoring the production progress and raw material consumption variation through SPC is as follows: Determine the control chart type in the statistical process based on the baking raw material consumption data and production progress data; Based on historical baking raw material consumption data and historical production progress data, calculate the control limits of production progress and raw material consumption, and establish the baseline of the control chart; Plot the real-time monitoring data of baking raw material consumption and production progress on the control chart to monitor whether the actual fluctuations of production progress and raw material consumption are within the control limit; When it is found that the data exceeds the control limit or exhibits irregular variation, the irregular variation includes the baking raw material consumption data and the production progress data exceeding the control limit, the baking raw material consumption data and the production progress data showing a non-random trend, the production anomaly index is automatically generated to quantify the degree of abnormality in the current production or raw material consumption process.

8. The intelligent order flexible processing scheduling management method according to claim 7 is characterized by: The specific process of determining whether processing scheduling adjustment is needed is as follows: Based on the production abnormality index, when the production abnormality index exceeds the set production abnormality index threshold, processing scheduling adjustment is required.

9. The intelligent order flexible processing scheduling management method according to claim 8 is characterized by: The specific process of adjusting the production equipment and baking raw materials through fuzzy control algorithm is as follows: The production abnormality index is set as the input fuzzy variable, and the production equipment operating rate and the adjustment amount of the baking raw material safety inventory level are set as the output fuzzy variables; Set fuzzy rules to define the relationship between the production abnormality index and the production equipment utilization rate and the adjustment amount of the baking raw material safety inventory level; According to the input production abnormality index, the corresponding production equipment operation rate and baking raw material safety inventory level adjustment are obtained through fuzzy control rules, and the production abnormality index is fuzzified by fuzzy logic. Through the fuzzy reasoning mechanism, the membership degree of each fuzzy set is comprehensively considered to output the corresponding production equipment operation rate and the adjustment amount of the safety inventory level of baking raw materials; The production equipment utilization rate and the adjustment amount of the safety inventory level of baking raw materials obtained by fuzzy reasoning are defuzzified. According to the defuzzified results, the operating efficiency of the production equipment and the safety inventory level of baking raw materials are dynamically adjusted.

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