Logistics plan scheduling management method based on control theory

Abstract

The invention discloses a logistics plan scheduling management method based on a control theory. The method comprises the steps that a production plan, production data and historical data are acquired, the production data are used for representing the current production condition on a production line, and the historical data are used for representing the historical production condition on the production line; determining part data on the production line based on the production plan and the production data; determining post data on the production line based on the production plan and the historical data; based on the part data or the post data, a scheduling strategy is determined, and the scheduling strategy is used for scheduling parts and posts on the production line. According to the invention, the technical problem of poor flexibility of logistics plan scheduling in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of scheduling management, in particular to a logistics plan scheduling management method based on control theory. BACKGROUND

[0002] In the field of logistics in modern manufacturing, efficient scheduling of parts and posts is an important link to ensure the orderly progress of production.

[0003] However, in the related art, the scheduling of resources on the production line often relies on static scheduling methods, but as the order demand fluctuates, static scheduling methods are difficult to adapt to dynamic changes in the production environment. Therefore, the production plan scheduling in the related art has the defect of poor flexibility.

[0004] In view of the above problems, no effective solution has been proposed so far. SUMMARY

[0005] The embodiments of the present application provide a logistics plan scheduling management method based on control theory to at least solve the technical problem of poor flexibility of logistics plan scheduling in the related art.

[0006] According to an aspect of an embodiment of the present application, a logistics plan scheduling management method based on control theory is provided, comprising: obtaining a production plan, production data and historical data, wherein the production data is used to represent the current production situation on the production line, and the historical data is used to represent the historical production situation on the production line; determining part data on the production line based on the production plan and the production data; determining post data on the production line based on the production plan and the historical data; determining a scheduling strategy based on the part data or the post data, wherein the scheduling strategy is used to schedule the parts and the posts on the production line.

[0007] Optionally, the production data includes initial inventory and part consumption data; determining the part data on the production line based on the production plan and the production data comprises: obtaining arrival data of the parts; determining order data and safety stock based on the production plan, the initial inventory and the part consumption data; obtaining the part data based on the arrival data, the order data and the safety stock.

[0008] Optionally, obtaining the part data based on the arrival data, the order data and the safety stock comprises: obtaining the product of the arrival data and a first coefficient to determine the arrival quantity, wherein the first coefficient represents whether the parts are arrived according to the arrival data; determining a first difference based on the arrival quantity and the order data; obtaining the part data based on the sum of the first difference and the safety stock.

[0009] Optionally, the historical data comprises historical post vacancy data; the post data on the production line is determined based on the production plan and the historical data, comprising: determining post demand based on the production plan; determining the post data based on the post demand and the historical post vacancy data.

[0010] Optionally, the scheduling strategy is determined based on the part data or the post data, comprising: comparing the part data with a preset inventory range to obtain a comparison result, wherein the comparison result is used to represent whether the part data is within the preset inventory range; determining the scheduling strategy based on the comparison result or the post data.

[0011] Optionally, the scheduling strategy is determined based on the comparison result, comprising: in response to the comparison result being that the part data exceeds a maximum value in the preset inventory range, determining the scheduling strategy to be reducing the part data; and in response to the comparison result being that the part data is less than a minimum value in the preset inventory range, determining the scheduling strategy to be increasing the part data.

[0012] Optionally, the method further comprises: determining a demand quantity and a storage time of the part based on the production plan; and calculating a preset inventory range of the part based on the demand quantity and the storage time.

[0013] According to another aspect of the embodiments of the present application, a control theory-based logistics plan scheduling management device is also provided, comprising: an acquisition module configured to acquire a production plan, production data and historical data, wherein the production data is used to represent a current production situation on a production line, and the historical data is used to represent a historical production situation on the production line; an inventory module configured to determine part data on the production line based on the production plan and the production data; a post module configured to determine post data on the production line based on the production plan and the historical data; and a scheduling module configured to determine a scheduling strategy based on the part data or the post data, wherein the scheduling strategy is used to schedule parts and posts on the production line.

[0014] According to another aspect of the embodiments of the present application, an electronic device is also provided, comprising: a memory storing an executable program; and a processor configured to run the program, wherein the program, when running, executes the method in the embodiments of the present application.

[0015] According to another aspect of the embodiments of the present application, a computer readable storage medium is also provided, comprising a stored executable program, wherein the executable program, when running, controls a device where the computer readable storage medium is located to execute the method in the embodiments of the present application.

[0016] According to another aspect of the embodiments of the present application, a computer program product is also provided, comprising a computer program, wherein the computer program, when executed by a processor, implements the method in the embodiments of the present application.

[0017] According to another aspect of the embodiments of the present application, there is also provided a computer program product comprising a non-transitory computer readable medium storing a computer program which, when executed by a processor, implements the method in any of the embodiments of the present application.

[0018] According to another aspect of the embodiments of the present application, there is also provided a computer program which, when executed by a processor, implements the method in any of the embodiments of the present application.

[0019] In the embodiments of the present application, the production plan, production data and historical data are acquired, then the part data on the production line is determined based on the production plan and the production data, and the post data on the production line is determined based on the production plan and the historical data, so that the scheduling strategy is determined based on the part data or the post data. The present application can timely grasp the production situation of the production line by integrating the production plan and the production data, and can foresee the post data on the production line based on the understanding of the production plan and the analysis of the historical data, so that the scheduling strategy can be accurately determined based on the part data or the post data to adapt to the changes of the parts and the posts, thereby achieving the purpose of improving the scheduling response speed, and achieving the technical effect of improving the flexibility of logistics plan scheduling, and further solving the technical problem of poor flexibility of logistics plan scheduling in the related art. BRIEF DESCRIPTION OF DRAWINGS

[0020] The accompanying drawings, which are included to provide a further understanding of the present application and are incorporated in and constitute a part of this application, illustrate embodiments of the present application and together with the description serve to explain the present application. In the drawings:

[0021] Figure 1 is a flow chart of a logistics plan scheduling management method based on control theory according to an embodiment of the present application;

[0022] Figure 2 is a schematic diagram of inventory control according to an embodiment of the present application;

[0023] Figure 3 is a schematic diagram of a logistics plan scheduling management device based on control theory according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the personnel in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work should be within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", and the like in the description and in the claims of the present application and in the above-described drawings are intended to distinguish similar objects and not necessarily to describe a particular chronological or sequential order. It should be understood that the use of such terms is interchangeable under appropriate circumstances so that the embodiments of the application described herein can be practiced in other than the illustrative order depicted or described herein. Furthermore, the terms "comprise" and "include", and variations thereof, are intended to cover a non-exclusive inclusion such that a process, method, system, product, or apparatus that comprises, includes, or consists of a list of steps or units can include not only those steps or units but also other steps or units not expressly listed or inherent to such process, method, system, product, or apparatus.

[0026] According to an embodiment of the present application, a method embodiment of a control theory-based logistics plan scheduling management method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0027] Figure 1 is a flowchart of a control theory-based logistics plan scheduling management method according to an embodiment of the present application, as shown in Figure 1 , the method comprises the following steps:

[0028] Step S102, obtaining production plan, production data and historical data.

[0029] Among them, the production data is used to represent the current production situation on the production line, and the historical data is used to represent the historical production situation on the production line.

[0030] The production plan mentioned above can be a guideline for production activities prepared in advance to meet market demand. The production plan can be a dynamic and comprehensive document covering data for different vehicle models, different parts, and different production lines. The production plan can include but is not limited to forecasted product demand, required production of various product models, quantity, production schedule, and bill of materials. The production plan can be divided according to time span, including long-term plan covering months to years, focusing on capacity planning, equipment investment and long-term material procurement; medium-term plan covering weeks to months, focusing on product portfolio, production batch and production line improvement; short-term plan focusing on daily or weekly, involving specific work scheduling, material preparation and production execution. Production planning helps enterprises allocate resources reasonably, avoid overcapacity or insufficient capacity, and ensure the continuity of the supply chain and production efficiency. Through accurate production planning, inventory costs can be reduced, production line flexibility and response speed can be improved, and market changes and customer demand can be better addressed.

[0031] The production data mentioned above can be generated during production operation, which is used to monitor and analyze key information such as production efficiency, product quality and equipment status. Production data can include but is not limited to production progress, equipment running time, fault record, material consumption rate, finished product qualification rate and personnel performance on the production line. Production data is the basis for real-time production monitoring and scheduling management, helping to identify bottlenecks, predict equipment maintenance needs, and analyze production process efficiency. Through in-depth analysis of production data, production processes can be continuously improved, product quality can be improved, costs can be reduced, and overall production management level can be improved.

[0032] The historical data mentioned above can refer to the data records accumulated in the past operation of the enterprise. Historical data can include but is not limited to past production data, operating costs, inventory levels and personnel allocation. Historical data can reflect past trends and patterns, providing a basis for future decision-making. In logistics scheduling, historical data can reveal the periodic characteristics of demand fluctuations, so as to establish more accurate demand forecasting models, reasonably adjust inventory strategies and scheduling plans, and improve service response level. In addition, historical data is also used to evaluate and improve production and logistics processes, and identify inventory replenishment space.

[0033] The cybernetics mentioned above can realize self-regulation and control through the mechanism of input, feedback and output. In logistics planning and scheduling management, cybernetics is used to design systems that can respond to external changes (such as market fluctuations, production plan changes) in a timely manner, maintain stability and efficiency. The cybernetics of the present embodiment is applied to logistics planning and scheduling management, and through the construction of a closed-loop control system and the introduction of a feedback mechanism, it can monitor and adjust logistics resources in real time to cope with uncertainty, ensure timely supply of parts, reduce waste, and improve the overall service level of logistics scheduling.

[0034] The production line described above can be an industrial production system, which can be composed of a series of workstations and production equipment arranged according to a specific process flow. The production line can be used for continuous or step-by-step production of parts. An efficient production line can improve production speed, reduce production cycle time, and reduce production cost and waste rate. The layout of the production line, equipment selection, and technology upgrade are key factors for continuous improvement of production efficiency.

[0035] In an alternative embodiment, cloud services and cloud databases provide an efficient and flexible solution for the storage, access, and analysis of production plans, production data, and historical data. Cloud databases can store massive amounts of production data. Cloud services also support real-time synchronization and sharing of data, so that changes to production plans can be immediately reflected on the production line, and production data can be quickly transmitted between departments. The high availability and security of cloud services ensure the continuity of production data.

[0036] In another alternative embodiment, various sensors such as temperature sensors, vibration sensors, radio frequency identification, and barcode scanners can be deployed on the production line to collect production data in real time. The sensor network can monitor equipment status, material consumption, and production progress, and other key indicators, so that sensor data can be called to obtain production data and historical data when logistics planning and scheduling is performed. In order to perform logistics planning and scheduling management, an automated request and notification mechanism can also be used to ensure that the logistics team can automatically receive a request for production plans on a regular basis or when changes occur in the production plan. The request can be automatically generated based on a preset time, or a notification can be triggered when the production planning system detects a specific condition change (such as an increase in production orders exceeding a certain threshold).

[0037] Step S104, based on the production plan and the production data, determines the part data on the production line.

[0038] The part data described above refers to a set of detailed information associated with each part in the production process. Part data can include but is not limited to part geometry, material properties, supplier information, inventory status, consumption rate, and demand forecast. Part data is the basis for material procurement, inventory management, and production planning, ensuring accurate calculation of material demand and avoiding the risk of excessive inventory and stockout. By analyzing part data, logistics scheduling can be improved, procurement costs can be reduced, and production process stability can be improved.

[0039] In one alternative embodiment, production data provides real-time feedback on parts demand, serving as the basis for determining discrepancies between production plans and actual execution. By analyzing production data and comparing it with parts-related data in the production plan, deviations between the production plan and actual execution are identified, including increases or decreases in consumption, to determine precise parts data for each component. This step ensures a match between parts inventory and actual production demand. With accurate parts data, companies can develop reasonable inventory replenishment plans to ensure that parts inventory matches actual production needs.

[0040] In another alternative embodiment, a predictive model can be built using time series analysis, regression analysis, or neural networks, random forests, etc. This predictive model can identify the correlation between production plans and production data and parts data based on production plans and production data, thereby accurately predicting parts data to facilitate the assessment of inventory levels, the development of reasonable replenishment plans, and the avoidance of parts shortages or surpluses.

[0041] Step S106: Determine job data on the production line based on the production plan and historical data.

[0042] The job data mentioned above can be a detailed description of different job positions on the production line. Job data may include, but is not limited to, information such as job responsibilities, required skills, staffing levels, workload, and performance indicators. Job data can be used for human resource planning, recruitment, training, and scheduling. Detailed management of job data can help allocate appropriate job resources at the right time, ensuring the smooth operation of production activities.

[0043] In one alternative embodiment, time series analysis is a statistical method for predicting future trends based on historical data. When determining production line job data, production plans and historical data can be collected, and an autoregressive integral moving average model can be used to analyze the relationship between personnel demand and production plans. By establishing a time series model, the required job data, such as the number of personnel, skill sets, and job allocation, can be predicted for different future production plans.

[0044] In another alternative embodiment, machine learning methods, such as random forests, support vector machines, and neural networks, can be used to build a job prediction model to predict future job data. Supervised learning algorithms can be used to train the model on historical data, learning the complex relationship between production plans and required job data. After successful training, the production plan is input into the job prediction model to predict job data. Machine learning algorithms can capture the complex relationships in historical data, providing more accurate job data predictions, helping to reduce the uncertainty of job allocation, improve the execution efficiency of production plans, and, through the prediction of skill requirements, adjust personnel training plans to ensure that there are enough personnel with specific skills on the production line.

[0045] Step S108: Determine the scheduling strategy based on part data or job data.

[0046] The scheduling strategy is used to schedule parts and positions on the production line.

[0047] The aforementioned scheduling strategies can be a set of rules or algorithms used to guide the allocation of logistics or production resources and the execution of tasks. Scheduling strategies can be static or dynamic, adjusting based on real-time data. By scheduling parts and work positions, scheduling strategies can ensure resource utilization and reduce waste. Customized scheduling strategies can lead to the construction of more robust and intelligent production logistics systems.

[0048] In one alternative embodiment, dynamic scheduling algorithms (such as rule-based scheduling algorithms or evolutionary algorithms) can be used to adjust the procurement, warehousing, and distribution strategies for parts based on part data to ensure timely supply. Specifically, rule-based scheduling algorithms design a series of logical rules, such as "initiate emergency procurement when inventory falls below a safety threshold" or "adjust warehouse layout in advance based on predicted demand." These rules automatically trigger corresponding actions based on real-time data and forecast results to maintain the stability and efficiency of parts supply. Evolutionary algorithms, such as genetic algorithms, gradually improve scheduling strategies by simulating natural selection and genetic processes. Evolutionary algorithms can explore and test various combinations of procurement, warehousing, and distribution strategies to find solutions that can adapt to demand fluctuations and changes in production plans.

[0049] In another alternative embodiment, a job scheduling algorithm, such as one based on linear programming or a genetic algorithm, can be used to formulate a scheduling strategy based on job data. For example, linear programming can be used to solve for the objective function under the constraints of the job data, which is then used to determine job allocation. A linear programming solver can be used to solve the objective function, and based on the solution results, it can be understood which nodes need more job allocation and which time periods need to increase the number of jobs. Then, these allocation schemes can be applied to actual production scheduling. Genetic algorithms can also search for solutions, iterating continuously through "heredity" and "mutation" operations to find job allocation schemes that satisfy the job data.

[0050] In this embodiment of the invention, production plans, production data, and historical data are acquired. Then, based on the production plans and production data, part data on the production line is determined, and job data on the production line is determined based on the production plans and historical data. Thus, a scheduling strategy is determined based on the part data or job data. This application integrates production plans and production data to promptly grasp the production status of the production line. Based on an understanding of the production plan and analysis of historical data, it predictively determines job data on the production line. This enables accurate determination of scheduling strategies based on part data or job data to adapt to changes in parts and jobs, thereby improving scheduling response speed and achieving the technical effect of enhancing the flexibility of logistics planning and scheduling. This solves the technical problem of poor flexibility in logistics planning and scheduling in related technologies.

[0051] Optionally, production data includes initial inventory and parts consumption data; based on production plans and production data, parts data on the production line is determined, including: obtaining parts arrival data; based on production plans, initial inventory, and parts consumption data, ordering data and safety stock levels are determined; and based on arrival data, ordering data, and safety stock levels, parts data are obtained.

[0052] The aforementioned arrival data refers to the specific data in the logistics or supply chain regarding the arrival of goods from suppliers to the company's warehouse for production line use. Arrival data may include, but is not limited to, the arrival time, quantity, type of parts, and whether they meet predetermined quality standards. Arrival data is used to adjust subsequent ordering strategies and improve inventory management. By tracking arrival data in real time, potential supply chain disruptions can be predicted, allowing for timely measures to ensure production continuity. Arrival data can be automatically acquired through logistics management systems, barcode scanning, or electronic data interchange, ensuring the accuracy and timeliness of the information.

[0053] The initial inventory mentioned above refers to the quantity and status of existing parts in the warehouse for a particular production line at a given moment, and it forms the basis for inventory management. Initial inventory can be categorized by type, storage location, and status. Initial inventory data helps planners understand the availability of existing parts, providing a basis for developing production plans and ordering strategies. Regularly taking inventory of initial inventory can also identify management problems, such as material backlog and losses.

[0054] The aforementioned parts consumption data refers to the actual parts used in production on the production line. Parts consumption data may include, but is not limited to, usage quantity, usage frequency, and consumption rate. Parts consumption data is a key element for supply chain management, inventory control, and production planning. It helps in understanding and predicting parts demand and forms the basis for scheduling management.

[0055] The ordering data mentioned above refers to the specific information of a company's parts purchase requests to suppliers. Ordering data may include, but is not limited to, order number, order date, estimated delivery time, type, quantity, and price of the ordered materials. Ordering data can be divided into regular ordering data (based on a stable production plan) and emergency ordering data (an immediate response to sudden increases in demand or supply chain disruptions). Ordering data is a key input for production planning and inventory control, used to ensure an adequate supply of required parts and balance inventory costs with production needs. Ordering data can reflect the actual demand for parts on the production line.

[0056] The aforementioned safety stock level is an extra quantity of spare parts stored to cope with demand forecasting errors or uncertainties in the supply chain, such as delayed deliveries, equipment failures, or sudden increases in order demand. Safety stock can be static (a fixed value) or dynamic (adjusted according to demand fluctuations and supply chain risks). The presence of safety stock reduces the risk of stockouts and ensures uninterrupted production, but may also increase inventory holding costs. Safety stock can be calculated based on historical data, supply chain response time, service level targets, and other parameters using statistical methods or simulation models.

[0057] In one alternative embodiment, understanding currently available parts resources is fundamental to developing parts demand and ordering plans. Initial inventory helps determine existing parts stock, avoiding duplicate orders or excessive inventory, thus saving costs. Ordering data can then be determined based on production plans, initial inventory, and parts consumption data, such as adding the required parts quantity in the production plan to the initial inventory and subtracting parts consumption data, to ensure sufficient parts supply on the production line while controlling inventory costs. Ordering data determines how many parts need to be ordered from suppliers and when to place orders to meet production plan requirements. This involves forecasting future demand, subtracting current inventory and the quantity ordered but not yet received to calculate net demand, and then determining the order quantity based on factors such as procurement cycle time and batch costs. Furthermore, determining safety stock levels through production planning can be used to handle unforeseen circumstances, such as sudden changes in production plans or supplier delays. Obtaining parts arrival data allows understanding of parts delivery status and predicting future inventory levels. Arrival data helps adjust ordering strategies to ensure parts arrive on time and avoid production line downtime. This allows for the integration of arrival data, order data, and safety stock levels to form parts data, reflecting the actual inventory and demand for parts and providing a basis for production scheduling.

[0058] Optionally, based on arrival data, order data, and safety stock, part data is obtained, including: obtaining the product of arrival data and a first coefficient to determine the arrival quantity, wherein the first coefficient indicates whether the parts are delivered according to the arrival data; determining a first difference based on the arrival quantity and order data; and obtaining the part data based on the sum of the first difference and the safety stock.

[0059] The aforementioned first coefficient can refer to a proportional constant used to adjust the weights of variables. The first coefficient reflects whether the parts have actually arrived; it can be either 0 or 1, where 0 indicates no arrival and 1 indicates that the arrival quantity meets the demand data. The first coefficient ensures that the scheduling strategy closely matches the actual situation, enabling more accurate prediction of parts supply fluctuations and improving the accuracy of parts scheduling and allocation.

[0060] The aforementioned arrival volume refers to the total number of parts received within a specific time period, and is a core indicator of logistics management. Arrival volume can be categorized by material type, supplier, or arrival batch to track the supply status of different parts. Arrival volume can assess the reliability of the supply chain, promptly identify supply shortages, and allow for adjustments to production plans or the implementation of emergency measures.

[0061] In one alternative embodiment, ensuring timely supply and reasonable inventory of parts is crucial for improving production efficiency. Considering that delivery data may not perfectly match expectations due to supplier delays, transportation issues, or human error, a first coefficient is used to adjust the actual delivery quantity. Then, the actual delivery quantity of parts is compared with the expected order data to understand whether the current inventory status meets production needs. The first difference can identify inventory shortages or surpluses. If the first difference is positive, it indicates that more parts have arrived than ordered, and it may be necessary to check for over-ordering. If the first difference is negative, it indicates an inventory shortage, requiring urgent action to ensure the smooth execution of the production plan.

[0062] Therefore, based on the sum of the first difference and the safety stock, the parts data is obtained, ensuring that there is sufficient safety stock to cope with uncertainties, even considering inventory gaps or surpluses. The parts data ultimately reflects the actual number of parts required by the production line based on current ordering, delivery status, and safety stock requirements. By adding the first difference to the safety stock, it ensures that the production line can continue to operate even in the event of supply chain fluctuations or production plan adjustments, avoiding the risk of downtime due to insufficient inventory.

[0063] By following the steps above, the inventory management strategy for parts can be dynamically adjusted based on real-time arrival data, ordering plans, and safety stock levels. This ensures timely supply of parts while avoiding excessive inventory and improves the flexibility of parts scheduling.

[0064] Optionally, historical data includes historical job vacancy data; determining job data on the production line based on production plans and historical data includes: determining job requirements based on production plans; and determining job data based on job requirements and historical job vacancy data.

[0065] The aforementioned job requirements refer to the number of personnel and skill levels required for each job position based on the production plan. Accurately determining job requirements is fundamental to ensuring the stable operation of the production line, facilitating understanding of the necessary production activities, and avoiding the cost burden of over-employment.

[0066] The aforementioned historical job vacancy data refers to the record of job vacancies within a company over a specific period, including the type of vacant position, duration of vacancy, and frequency of occurrence. This historical job vacancy data can be categorized by department, time period, or job type and used for human resource planning and scheduling. Historical job vacancy data helps predict future fluctuations in human resource demand, improve staffing and training plans, and reduce production delays and cost increases caused by job vacancies.

[0067] In one alternative embodiment, by analyzing historical job vacancy data and considering production plans, the rational allocation and efficient utilization of human resources can be ensured. Specifically, the number of personnel and skill levels required for each production position can be predicted based on production tasks. This step is the foundation of the entire scheduling process, ensuring that production resources match the production plan, avoiding overstaffing or shortages, improving production efficiency, and reducing waiting time. Furthermore, considering past job vacancy situations, job requirements are adjusted to prevent potential future vacancy problems. Historical job vacancy data can reveal which production stages and positions were prone to personnel shortages or turnover in the past, allowing for advance preparation in new production cycles, such as increasing training reserves, improving shift arrangements, or strengthening recruitment. For example, by analyzing historical job vacancy data, a predictive model can be built to predict job data under the current job requirements. Alternatively, a linear programming algorithm can be used, using the job requirements of the production plan and historical job vacancy data as constraints, to adjust the allocation of personnel to minimize total costs (including recruitment, training, and overtime costs) and optimize job data.

[0068] Optionally, a scheduling strategy is determined based on part data or job data, including: comparing part data with a preset inventory range to obtain a comparison result, wherein the comparison result is used to characterize whether the part data is within the preset inventory range; and determining a scheduling strategy based on the comparison result or job data.

[0069] The aforementioned preset inventory range can be the upper and lower limits of inventory levels set according to the production plan, used to guide inventory management. Preset inventory ranges can be divided into maximum and minimum inventory levels; the former prevents inventory buildup, while the latter avoids the risk of stockouts. Preset inventory ranges help balance inventory costs, and by monitoring the difference between actual inventory and the preset range, logistics scheduling strategies can be adjusted in a timely manner to ensure that production needs are met.

[0070] In one alternative embodiment, determining scheduling strategies based on parts data or job data is a key step in ensuring production continuity, improving efficiency, and reducing costs. Specifically, parts data can be compared with preset inventory ranges to assess whether the current parts inventory status meets production and inventory management needs. The comparison results reveal whether parts inventory is within the expected range—neither excessive nor insufficient. Therefore, scheduling plans can be adjusted based on parts inventory status or job data to ensure efficient and stable production line operation. If the comparison results indicate a parts shortage, scheduling strategies may include emergency procurement, rapid internal allocation, or adjustments to the production plan (such as reducing production volume until parts are replenished). Based on job data, if a vacancy is predicted for a particular position due to employee turnover or sick leave, scheduling strategies may include pre-training reserve personnel, adjusting shift schedules, or introducing temporary workers.

[0071] Optionally, based on the comparison results, a scheduling strategy is determined, including: in response to the comparison result that the part data exceeds the maximum value in the preset inventory range, determining the scheduling strategy to reduce the part data; in response to the comparison result that the part data is less than the minimum value in the preset inventory range, determining the scheduling strategy to increase the part data.

[0072] In one alternative embodiment, ensuring that parts inventory meets production needs without excessively occupying storage space is crucial in logistics scheduling management. Therefore, when comparison results show that parts inventory exceeds the upper limit of demand or reasonable storage, the scheduling strategy can be adjusted to reduce order quantities, accelerate parts turnover (e.g., through promotions or internal allocation to other production lines), or improve warehouse management to reduce inventory levels. If the comparison results show that parts data is less than the minimum value in the preset inventory range, to ensure that the production line does not stop due to parts shortages and to maintain production continuity and efficiency, the scheduling strategy can be adjusted to emergency procurement, accelerated delivery speed, or adjustment of the production plan to replenish parts inventory and avoid production line interruptions. Through the above steps, the scheduling strategy can be dynamically adjusted based on the real-time parts inventory status and the preset inventory range to improve production efficiency, reduce production interruptions or cost waste caused by parts shortages or surpluses, and also improve the responsiveness and flexibility of parts inventory.

[0073] Optionally, the above method further includes: determining the demand and storage time of parts based on the production plan; and calculating the preset inventory range of parts based on the demand and storage time.

[0074] The aforementioned demand refers to the quantity of parts needed to produce one car within a specific timeframe, according to the production plan. Demand data is central to production planning and inventory management, helping companies formulate reasonable production strategies, procurement plans, and pricing policies to meet market demands.

[0075] The storage time mentioned above refers to the average time from when materials enter the warehouse until they are consumed or removed. Storage time can be further broken down by material category, storage conditions, or the nature of logistics activities to assess inventory efficiency and the effectiveness of warehousing strategies. Storage time helps to standardize inventory planning, reduce material aging or expiration, and lower warehousing costs.

[0076] In one alternative embodiment, to achieve inventory control in the storage phase, the production plan can be used to determine the number of parts required during production and the timeframe for their availability, thus accurately assessing the demand intensity for different parts within a specific time period. This prevents resource waste and production delays, ensuring a consistent supply of parts to the production line. A reasonable inventory level can then be determined based on demand and storage time. By pre-setting inventory ranges, inventory can be managed more effectively, reducing inventory holding costs while ensuring production efficiency. This minimizes inventory-related costs without impacting production. Pre-setting inventory ranges can include a minimum inventory level (to prevent stockouts) and a maximum inventory level (to prevent overstocking). High and low inventory standards for each part can be standardized through calculations of per-vehicle quotas and planned storage times.

[0077] The technical solution proposed in this application will be described below with reference to an optional embodiment. This application proposes a novel logistics planning and scheduling management method based on cybernetics. Through innovative improvements to the planning and scheduling function, it enhances the logistics management capabilities of enterprises and has significant positive implications for enterprise development and industry progress.

[0078] Cybernetics, originally a science studying the general laws of control and communication in machines and living organisms, has since been applied to mechanical and electrical research and development, manufacturing, and socio-economic organization and management. Cybernetics classifies systems into two categories: open-loop control systems and closed-loop control systems, emphasizing the functional relationship between system inputs and outputs.

[0079] In an open-loop control system, the output directly responds to the input, resulting in a more direct correspondence but lacking stability. In the context of point-to-point handling in logistics, although the process is relatively efficient, it lacks transitional elements such as storage and conveyor belts, making it difficult to achieve stable control over the logistics process.

[0080] The closed-loop control system introduces the concept of "feedback", compares the system output with the set value, and uses the deviation between the two as input to act on the controller again, so as to achieve a dynamic stable state in which the output approaches the set value. Corresponding to the storage, sequential construction and other processes in the logistics process, the system provides feedback on the delivery plan through real-time consumption, ensuring the stable, efficient and sustainable operation of the logistics process.

[0081] In specific logistics operations, the above control methods are mainly applied to the scheduling of two types of inventory resources and human resources.

[0082] For the control of parts inventory, in the field of logistics planning and scheduling, the inventory fluctuation status of parts can be controlled by a closed-loop control system. The production plan is used as the system input, and the parts inventory status is used as the system output, and an inventory controller is designed accordingly. For example, if the production plan is released one to two days in advance, an ordering plan is formulated based on the production plan and the initial inventory, and the inventory at the replenishment point is replenished through external logistics transportation modes such as milk run. In order to respond to market fluctuations and actual emergencies, there are often adjustments in the actual production compared to the ordering plan. Therefore, the actual production data is used as the disturbance of the control system, that is, the influence of the disturbance on the system stability is eliminated through the action of the controller. Specifically, in terms of implementation, the inventory control focuses on the storage link. By calculating the single-vehicle quota and the planned storage time, the high and low storage inventory standards for each part are standardized. Through the comparison of the actual inventory monitored by automated equipment such as the automated storage and retrieval system with the ideal high and low storage levels, the impact of inventory changes caused by planned fluctuations and disturbances is demonstrated. That is, for parts with actual inventory greater than the theoretical inventory, the disturbance causes insufficient unloading and storage space, resulting in adverse effects such as vehicle congestion and even不畅 return of containers. In this case, it is necessary to reduce the subsequent kanban to cut the inventory. For parts with actual inventory less than the theoretical inventory, the disturbance causes insufficient subsequent arrivals to meet subsequent consumption. When it is determined that the inventory status is true, the kanban room needs to issue an urgent kanban to replenish the inventory in a timely manner within the safety inventory time, and finally achieve the dynamic stability of parts inventory. As<![CDATA[ Figure 2 ]]shown, it shows a schematic diagram of inventory control. In<![CDATA[ Figure 2 ]]it, the inventory within the high and low storage ranges is the safety inventory. The inventory circled by the dashed circle in the figure indicates that it is lower than the safety inventory and needs to give an alarm.<![CDATA[ ]]<![CDATA[

[0083] ]]The mathematical model of the inventory control system can be expressed by the following formula:<![CDATA[ ]]<![CDATA[

[0084] ]]<![CDATA[ ]];<![CDATA[ ]]<![CDATA[

[0085] ]]where<![CDATA[ ]]is the inventory level at time t, D is the demand rate per unit time, R is the safety inventory,<![CDATA[ ]]is the quantity of one arrival, C is whether there is an arrival, C can be 0 or 1, and t is the time.<![CDATA[ ]]<![CDATA[

[0086] ]]Regarding human resource control, besides the need for parts inventory, operators also play a crucial role in actual logistics operations. In today's highly competitive logistics industry, employee turnover, training, and proficiency are becoming sensitive issues for logistics company managers. To address staff shortages caused by unexpected departures or leave and avoid production fluctuations, a "reservoir" human resource management model is proposed based on cybernetics. Employee turnover information can be used as system input. For planned departures, there is approximately a one-month window between the announcement of the departure plan and the actual departure, allowing for re-recruitment based on a replacement plan without affecting production. However, for unexpected departures, information is only available on the start of the training period or within a shorter training cycle (e.g., 7 days for position A, 5 days for position B), making timely replacement impossible and leading to staff shortages that significantly impact production stability—a system disturbance. To eliminate the impact of disturbances, this method introduces process control concepts, using the number of unexpected departures or leave from the previous year as a reference to maintain a weekly workforce reserve, replenishing the reservoir weekly. Therefore, based on the abnormal departures or leave in week N last year, training should be conducted in week N-1 this year, and recruitment should begin in week N-2. If the reserve personnel trained in week N-1 are not used in week N, training for other positions should be conducted in week N, with personnel to be used in week N+1. By setting up a "safety stock" of human resources, the disruption caused by abnormal departures can be reduced, ensuring stable logistics operations. Furthermore, this method can also be applied to procurement applications such as component supplier selection, demonstrating good robustness. Table 1 illustrates the human resource control situation, using a 5-week example and week N+2 as an example for explanation. Based on historical data, 5 abnormal departures or leaves are identified. Therefore, 5 personnel need to be reserved in week N+2 to cope with possible abnormal personnel situations this year. Since recruitment needs to begin two weeks in advance, one week needs to be allocated for training. 5 people are recruited in week N, and 5 are trained in week N-1, thus meeting the requirement of a reserve of 5 people in week N+2.

[0087]

[0088] According to an embodiment of the present invention, a device embodiment of a logistics planning and scheduling management device based on cybernetics is provided. It should be noted that the device can be used to execute the above-described logistics planning and scheduling management method based on cybernetics. The specific implementation scheme and application scenario of this embodiment are the same as those of the above embodiments, and will not be repeated here.

[0089] Figure 3 This is a schematic diagram of a cybernetics-based logistics planning, scheduling, and management device according to an embodiment of this application, such as... Figure 3 As shown, the device includes the following:

[0090] The acquisition module 40 is used to acquire production plans, production data, and historical data. The production data is used to characterize the current production status on the production line, and the historical data is used to characterize the historical production status on the production line.

[0091] Inventory module 42 is used to determine the parts data on the production line based on production plans and production data.

[0092] Job module 44 is used to determine job data on the production line based on production plans and historical data.

[0093] The scheduling module 46 is used to determine the scheduling strategy based on part data or job data, wherein the scheduling strategy is used to schedule parts and jobs on the production line.

[0094] Optionally, production data includes initial inventory and parts consumption data; the inventory module is also used to obtain parts arrival data; based on the production plan, initial inventory, and parts consumption data, order data and safety stock levels are determined; and based on the arrival data, order data, and safety stock levels, parts data are obtained.

[0095] Optionally, the inventory module is also used to obtain the product of the arrival data and the first coefficient to determine the arrival quantity, wherein the first coefficient indicates whether the parts are delivered according to the arrival data; to determine the first difference based on the arrival quantity and the order data; and to obtain the parts data based on the sum of the first difference and the safety stock quantity.

[0096] Optionally, historical data includes historical job vacancy data; the job module is also used to determine job requirements based on production plans; and job data is determined based on job requirements and historical job vacancy data.

[0097] Optionally, the scheduling module is also used to compare part data with a preset inventory range to obtain a comparison result, wherein the comparison result is used to characterize whether the part data is within the preset inventory range; and to determine a scheduling strategy based on the comparison result or job data.

[0098] Optionally, the scheduling module is also configured to determine a scheduling strategy of reducing the part data in response to a comparison result that the part data exceeds the maximum value in the preset inventory range; and to determine a scheduling strategy of increasing the part data in response to a comparison result that the part data is less than the minimum value in the preset inventory range.

[0099] Optionally, the above-mentioned device also includes a preset module for determining the demand and storage time of parts based on the production plan; and calculating the preset inventory range of parts based on the demand and storage time.

[0100] Embodiments of this application also provide an electronic device, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of the present invention during runtime.

[0101] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0102] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0103] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of the present invention.

[0104] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of the present invention.

[0105] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0106] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0107] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0108] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0109] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0110] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A control theory-based logistics plan scheduling management method, characterized by, The method comprises: obtaining a production plan, production data and historical data, wherein the production data is used to represent a current production situation on a production line, and the historical data is used to represent a historical production situation on the production line; determining part data on the production line based on the production plan and the production data; determining post data on the production line based on the production plan and the historical data; determining a scheduling strategy based on the part data or the post data, wherein the scheduling strategy is used to schedule parts and posts on the production line.

2. The method of claim 1, wherein, The production data comprises initial inventory and part consumption data; determining the part data on the production line based on the production plan and the production data comprises: obtaining arrival data of the part; determining order data and safety inventory based on the production plan, the initial inventory and the part consumption data; obtaining the part data based on the arrival data, the order data and the safety inventory.

3. The method of claim 2, wherein, Obtaining the product data based on the arrival data, the order data and the safety inventory comprises: obtaining a product of the arrival data and a first coefficient to determine an arrival quantity, wherein the first coefficient represents whether the part is arrived according to the arrival data; determining a first difference based on the arrival quantity and the order data; obtaining the part data based on a sum of the first difference and the safety inventory.

4. The method of claim 1, wherein, The historical data comprises historical post vacancy data; determining the post data on the production line based on the production plan and the historical data comprises: determining post demand based on the production plan; determining the post data based on the post demand and the historical post vacancy data.

5. The method of claim 1, wherein, Determining the scheduling strategy based on the part data or the post data comprises: comparing the part data with a preset inventory range to obtain a comparison result, wherein the comparison result is used to represent whether the part data is in the preset inventory range; determining the scheduling strategy based on the comparison result or the post data.

6. The method of claim 5, wherein, Determining the scheduling strategy based on the comparison result comprises: in response to the comparison result being that the part data exceeds a maximum value in the preset inventory range, determining the scheduling strategy as reducing the part data; in response to the comparison result being that the part data is less than a minimum value in the preset inventory range, determining the scheduling strategy as increasing the part data.

7. The method according to claim 5 or 6, characterized in that, The method further comprises: determining a demand quantity and a storage time of the part based on the production plan; calculating a preset inventory range of the part based on the demand quantity and the storage time.

8. A cybernetics-based logistics plan scheduling management apparatus characterized by comprising: The method comprises: an obtaining module, configured to obtain a production plan, production data and historical data, wherein the production data is used to represent a current production situation on a production line, and the historical data is used to represent a historical production situation on the production line; an inventory module, configured to determine part data on the production line based on the production plan and the production data; a post module, configured to determine post data on the production line based on the production plan and the historical data; A scheduling module is configured to determine a scheduling strategy based on the part data or the post data, wherein the scheduling strategy is used to schedule the parts and the posts on the production line.

9. An electronic device, comprising: The method comprises the following steps: A memory stores an executable program; A processor is configured to run the program, wherein the program performs the method of any one of claims 1 to 8 when running.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein the executable program controls the device where the storage medium is located to perform the method of any one of claims 1 to 8 when running.

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