Production line dynamic collaborative scheduling methods, devices, electronic equipment and readable storage media

Abstract

This application provides a method, apparatus, electronic device, and readable storage medium for dynamic collaborative production line scheduling. The method includes: determining the material availability constraint time for a production order based on its material requirements, real-time inventory levels, and in-transit procurement information; determining the available production line time window for each production process based on its occupancy status; generating a production plan by performing a flow-line time-connection scheduling for each production process based on the material availability constraint time, available production line time window, and employee capacity information for each production process; collecting daily data on the actual output of each production process and employee attendance for the following day during production according to the production plan; and dynamically adjusting subsequent production plans based on the deviation between the actual output of each process and the daily planned output, employee attendance information for the following day, and employee capacity information. This method improves the accuracy of the production plan itself.

Description

Technical Field

[0001] This application relates to the field of production management technology, and in particular to a method, apparatus, electronic device and readable storage medium for dynamic collaborative production scheduling of production lines. Background Technology

[0002] In the medical device manufacturing industry, the rationality of production line scheduling directly affects production efficiency, material utilization, and order delivery timeliness, and is a core element in ensuring factory cash flow and meeting customer needs. Medical device manufacturing is characterized by a wide variety of materials, complex product processes, and significant differences in employee skills, placing extremely high demands on the accuracy and flexibility of production scheduling.

[0003] Currently, most factories rely on a production line scheduling method based on fixed cycles and human experience. The generation of the production plan typically begins with the production manager, who manually compiles estimated "total capacity per person" data based on historical experience, submitted by each production line team leader weekly or at the beginning of each month. Combined with known sales orders (i.e., production orders), the manager manually creates a static production schedule covering the next few days or even weeks. This schedule must be reviewed and verified offline by the respective production line leaders before it is finalized and implemented. This process reveals several limitations when dealing with dynamically changing production environments.

[0004] First, production planning adjustments are extremely slow, lacking daily dynamic response capabilities. Specifically, once a plan is issued on a weekly or monthly basis, it becomes a relatively rigid execution framework. When employees take temporary leave, equipment malfunctions, or the actual daily output deviates from the plan, existing methods cannot automatically incorporate these real-time changes and quickly rearrange subsequent processes. Production managers often can only make centralized adjustments in the next planning cycle or respond through emergency manual communication and coordination, which makes it easy for production plans to become disconnected from the actual execution process, resulting in a severe lack of scheduling flexibility.

[0005] Secondly, the "total capacity per employee" currently relied upon for production scheduling decisions is an overall average of a production line or team. This masks the efficiency differences of the same employee handling different products and fails to distinguish the differences in proficiency among different employees in the same process. For example, an employee might assemble 20 units of product A per hour, but only 15 units of product B; another employee's speed in the same process could also be completely different. Plans based on this coarse-grained average capacity data often deviate from actual figures in terms of daily output, precise inter-process coordination time, and overall order lead time, directly affecting the reliability of production scheduling plans.

[0006] Finally, this static and coarse-grained scheduling method, unable to predict material consumption based on daily and process-specific production plans, leads factories to tend to purchase high-frequency materials in large quantities to ensure production continuity. This results in significant inventory build-up of some materials, tying up substantial working capital. Simultaneously, procurement of low-frequency materials or for unexpected demands may be delayed, impacting production line operations. Furthermore, due to the inherent inaccuracy of the production schedule, factories often proactively commit to longer delivery cycles when accepting orders to mitigate delivery risks. This not only affects customer satisfaction but also disrupts the rhythm of subsequent processes such as internal quality inspection and warehousing, potentially causing finished products to remain in warehouses and hindering flexible batch shipment arrangements. Summary of the Invention

[0007] In view of this, the purpose of this application is to provide a method, apparatus, electronic equipment and readable storage medium for dynamic collaborative production scheduling of production lines, so as to improve the accuracy of the scheduling plan itself, production flexibility and order delivery reliability.

[0008] In a first aspect, embodiments of this application provide a method for dynamic collaborative production scheduling on a production line, including: Upon receiving a production order, the material availability constraint time for the production order is determined based on the material requirements of the production order, the real-time inventory of materials, and the material procurement in-transit information. The available time window for each production process is determined based on the production line occupancy status of each production process of the products involved in the production order. Based on the material kitting constraint time, the production line available time window, and the employee capacity information of each production process, with the goal of minimizing the production order completion time, a production line-style time connection scheduling is performed on each production process to generate a production schedule that includes the planned start time, planned end time, and daily planned output of each production process. During the production process according to the aforementioned production schedule, the actual output of each production process and the employee attendance information for the next day are collected daily. Based on the deviation between the actual output of each process and the daily planned output, employee attendance information for the next day, and employee capacity information, the planned end time of each production process in the subsequent production scheduling plan, as well as the planned start time of the unexecuted production process, are dynamically adjusted.

[0009] In conjunction with the first aspect, this application provides a first possible implementation of the first aspect, wherein the material requirements include the required target materials and their required quantities; determining the material availability constraint time for the production order based on the material requirements of the production order, the real-time inventory of materials, and the material procurement in-transit information includes: For each of the target materials, calculate the difference between the real-time inventory of the target material and the quantity already occupied by other production orders to obtain the current available inventory of the target material; If the current available inventory meets the quantity required for the target material in the production order, then the preparation time of the target material is determined to be the current time plus the time required for the target material to be issued and pre-processed. If the current available inventory does not meet the quantity required for the target material in the production order, then determine whether the target material is included in the materials being procured based on the material procurement in-transit information. When the target material is included in the procurement of materials in transit, if the sum of the procurement quantity of the target material and the current available inventory quantity meets the quantity of the target material required in the production order, then the preparation time of the target material is based on the estimated arrival time of the target material plus the time required for the outbound and pre-processing of the target material. When the target material is not included in the materials being procured, a procurement plan for the target material is generated based on the current available inventory and required quantity of the target material. The preparation time of the target material is then calculated by adding the promised delivery time of the procurement plan to the time required for the material to be issued and pre-processed. Take the latest value among the preparation times of all target materials as the material kitting constraint time for the production order.

[0010] In conjunction with the first possible implementation of the first aspect, this application provides a second possible implementation of the first aspect, wherein the method further includes: Establish a material substitution relationship database; the material substitution relationship database records the correspondence between materials and substitute materials that can be used to replace them; If the current available inventory does not meet the quantity required for the target material in the production order, then based on the material procurement in-transit information, it is determined whether the target material is included in the procurement in-transit materials, including: If the current available inventory does not meet the quantity required for the target material, then the material substitution relationship database is queried to determine whether there are any substitute materials that can be used to replace the target material; If it exists, obtain the current available inventory of the substitute material, and determine whether the sum of the current available inventory of the substitute material and the current available inventory of the target material meets the quantity required by the target material. If the required quantity of the target material is met, the preparation time of the target material is determined to be the current time plus the target duration; the target duration is the longer of the time required for the replacement material to be released and pre-processed and the time required for the target material to be released and pre-processed. If the required quantity of the target material is not met, then the material procurement in transit information is used to determine whether the target material is included in the procurement in transit.

[0011] In conjunction with the first possible implementation of the first aspect, this application provides a third possible implementation of the first aspect, wherein the method further includes: For each material stored in the material library, calculate the supply risk score based on at least two of the following dimensions: the material's basic attributes, usage frequency, number of suppliers, and the supplier's historical on-time delivery rate. Then, classify the material into a supply risk level, such as high risk, medium risk, or low risk, based on the supply risk score. Real-time monitoring of the current available inventory of each material in the material warehouse; For high-risk materials, when the current available inventory of the material drops to the first warning threshold, a purchase warning message for the material is generated; when it drops to the second warning threshold, a purchase order for the material is generated; the first warning threshold is greater than the second warning threshold and the safety stock threshold. For materials with a medium risk level, when the current available inventory of the material drops to the third warning threshold, a purchase order for the material is generated; the third warning threshold is greater than the safety stock threshold and less than the second warning threshold. For materials with low risk levels, when the current available inventory of the material drops to the safety stock threshold, a purchase request for the material is generated and submitted for approval.

[0012] In conjunction with the first aspect, this application provides a fourth possible implementation of the first aspect, wherein the method further includes determining employee productivity information in the following manner: For each production process, the number of qualified products produced and the actual production time consumed by the employee in that production process are collected daily within a historical time period. Based on the number of qualified products produced and the actual production time consumed in that production process, the historical baseline capacity of the employee in that production process is calculated. Assign a proficiency coefficient to the employee based on their proficiency in the production process. Assign a complexity coefficient to the production process based on its complexity. Based on the operating conditions of the production equipment required for this production process, configure the equipment operating condition coefficient for this production process; Based on the shift schedule and environment of this production process, assign a shift environment coefficient to this production process; Based on the employee's historical baseline capacity, proficiency coefficient, complexity coefficient, equipment condition coefficient, scheduling environment coefficient, and their respective weights for that production process, predict the employee's capacity information for that production process.

[0013] In conjunction with the fourth possible implementation of the first aspect, this application provides a fifth possible implementation of the first aspect, wherein, after predicting the employee's productivity information for that production process, the method further includes: According to a preset time interval, the actual production capacity information of each employee for each production process is periodically collected, and the deviation rate between the actual production capacity information and the predicted employee production capacity information is compared. If the deviation rate exceeds the preset deviation rate, the weighting ratio of historical benchmark capacity, proficiency coefficient, complexity coefficient, equipment condition coefficient, and scheduling environment coefficient will be dynamically adjusted.

[0014] In conjunction with the first aspect, this application provides a sixth possible implementation of the first aspect, wherein the dynamic adjustment of the planned end time of each production process and the planned start time of unexecuted production processes in the subsequent production scheduling plan based on the deviation between the actual output of each process and the daily planned output, employee attendance information for the next day, and employee capacity information includes: When an employee's leave request is detected based on their attendance information for the following day, it is determined whether the production order is allowed to be delivered late: If delayed delivery is permitted, the remaining production quantity for each production process of the production order is calculated based on the deviation between the actual output of each process and the daily planned output. Excluding the production output of employees on leave during their leave period, and combining the production output information of employees on duty, the daily planned output of each production process is recalculated. Based on the recalculated daily planned output of each production process, the planned start time of the affected unexecuted production processes and the planned end time of the affected executed production processes will be postponed. If on-time delivery is required, check the production line employee occupancy plan corresponding to other production orders in the factory that are in a deferred delivery status, and filter out on-duty employees who have the ability to operate the corresponding production process of the employee on leave. The selected on-duty employees will be temporarily reassigned to the production process corresponding to the production order of the employee on leave. Based on the employee capacity information of the reassigned employees and the remaining production quantity of each production process, the daily planned output of each production process is reallocated. Based on the daily planned output of each production process that has been reallocated, the planned start time of the production process that has not been executed for the production order, the planned end time of the affected production process, and the subsequent production schedule of the production line corresponding to the original production order of the reassigned employee are adjusted simultaneously.

[0015] Secondly, embodiments of this application also provide a dynamic collaborative production scheduling device for production lines, comprising: The first determining module is used to determine the material fulfillment constraint time of the production order after receiving the production order, based on the material requirements of the production order, the real-time inventory of materials, and the material procurement in-transit information, and to determine the production line availability time window of each production process based on the production line occupancy status of each production process of the product involved in the production order. The first generation module is used to perform a flow-line time connection scheduling for each production process based on the material kitting constraint time, the production line available time window, and the employee capacity information of each production process, with the goal of minimizing the production order completion time, and generate a production schedule that includes the planned start time, planned end time, and daily planned output of each production process. The data acquisition module is used to collect the actual output of each production process and the employee attendance information for the next day during the production process according to the production schedule. The adjustment module is used to dynamically adjust the planned end time of each production process and the planned start time of unexecuted production processes in the subsequent production schedule based on the deviation between the actual output of each process and the daily planned output, the employee attendance information for the next day, and the employee capacity information.

[0016] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor, the steps in any of the possible implementations of the first aspect described above are performed.

[0017] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps in any of the possible implementations of the first aspect described above.

[0018] This application provides a method, apparatus, electronic device, and readable storage medium for dynamic collaborative production scheduling. When generating a production schedule, it does not rely on coarse-grained "total capacity per employee," but rather on employee capacity information specific to each production process. This is combined with precisely calculated material fulfillment constraints and the available time windows for each process, aiming to minimize production order completion time through a streamlined time-connected scheduling approach. This method fully considers the efficiency differences of individual employees in different processes and ensures the feasibility of the production schedule in terms of both material supply and equipment resources. The resulting production schedule contains more accurate and reliable start and end times for each process, as well as daily planned output. This provides a detailed data foundation for material consumption forecasting, helping to address the problem of blind material procurement caused by inaccurate planning at its source, and creating conditions for rationally arranging quality inspection, warehousing, and delivery schedules.

[0019] Furthermore, by collecting actual output data for each production process and employee attendance information for the following day, and based on the deviation between actual output and daily planned output, employee attendance information, and employee capacity information, the system dynamically adjusts subsequent production schedules, effectively solving the problems of lagging adjustments and lack of daily responsiveness in traditional scheduling methods. This mechanism enables production schedules to respond in real time to changes such as employee leave and output fluctuations on the production line, automatically and quickly rearranging the planned start times for subsequent unexecuted production processes and the adjusted planned end times for executed processes. This transforms the production schedule from a static execution framework into a dynamic guide that closely aligns with daily actual production conditions, significantly improving the flexibility and adaptability of production scheduling.

[0020] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart of a dynamic collaborative production scheduling method for production lines provided in an embodiment of this application is shown; Figure 2 A flowchart illustrating an automatic replenishment process for inventory materials provided in an embodiment of this application is shown; Figure 3This illustration shows a structural schematic diagram of a dynamic collaborative production scheduling device for production lines provided in an embodiment of this application; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0024] In the medical device manufacturing industry, the rationality of production line scheduling directly affects production efficiency, material utilization, and order delivery timeliness, and is a core element in ensuring factory cash flow and meeting customer needs. Medical device manufacturing is characterized by a wide variety of materials, complex product processes, and significant differences in employee skills, placing extremely high demands on the accuracy and flexibility of production scheduling.

[0025] Currently, most factories rely on a production line scheduling method based on fixed cycles and human experience. The generation of the production plan typically begins with the production manager, who manually compiles estimated "total capacity per person" data based on historical experience, submitted by each production line team leader weekly or at the beginning of each month. Combined with known sales orders (i.e., production orders), the manager manually creates a static production schedule covering the next few days or even weeks. This schedule must be reviewed and verified offline by the respective production line leaders before it is finalized and implemented. This process reveals several limitations when dealing with dynamically changing production environments.

[0026] First, production planning adjustments are extremely slow, lacking daily dynamic response capabilities. Specifically, once a plan is issued on a weekly or monthly basis, it becomes a relatively rigid execution framework. When employees take temporary leave, equipment malfunctions, or the actual daily output deviates from the plan, existing methods cannot automatically incorporate these real-time changes and quickly rearrange subsequent processes. Production managers often can only make centralized adjustments in the next planning cycle or respond through emergency manual communication and coordination, which makes it easy for production plans to become disconnected from the actual execution process, resulting in a severe lack of scheduling flexibility.

[0027] Secondly, the "total capacity per employee" currently relied upon for production scheduling decisions is an overall average of a production line or team. This masks the efficiency differences of the same employee handling different products and fails to distinguish the differences in proficiency among different employees in the same process. For example, an employee might assemble 20 units of product A per hour, but only 15 units of product B; another employee's speed in the same process could also be completely different. Plans based on this coarse-grained average capacity data often deviate from actual figures in terms of daily output, precise inter-process coordination time, and overall order lead time, directly affecting the reliability of production scheduling plans.

[0028] Finally, this static and coarse-grained scheduling method, unable to predict material consumption based on daily and process-specific production plans, leads factories to tend to purchase high-frequency materials in large quantities to ensure production continuity. This results in significant inventory build-up of some materials, tying up substantial working capital. Simultaneously, procurement of low-frequency materials or for unexpected demands may be delayed, impacting production line operations. Furthermore, due to the inherent inaccuracy of the production schedule, factories often proactively commit to longer delivery cycles when accepting orders to mitigate delivery risks. This not only affects customer satisfaction but also disrupts the rhythm of subsequent processes such as internal quality inspection and warehousing, potentially causing finished products to remain in warehouses and hindering flexible batch shipment arrangements.

[0029] In view of the above problems, this application provides a method, apparatus, electronic device and readable storage medium for dynamic collaborative production scheduling of production lines, so as to improve the accuracy of the production scheduling plan itself, the production flexibility and the reliability of order delivery. The following is a description through embodiments.

[0030] To facilitate understanding of this embodiment, a dynamic collaborative production scheduling method for production lines disclosed in this application will first be described in detail. For example... Figure 1 As shown, the process includes the following steps S101-S104: S101: After receiving a production order, determine the material availability constraint time for the production order based on the material requirements of the production order, the real-time inventory of materials, and the material procurement in-transit information. Also, determine the available time window for each production process based on the production line occupancy status of each production process involved in the production order.

[0031] In this step, a production order is an order received by the factory, such as to produce 10,000 units of a target product. The material requirements for a production order include the various target materials needed to produce the product and their respective quantities. Real-time material inventory refers to the current quantity of each material in the inventory. Material procurement in transit information includes materials procured by the factory and their corresponding procurement quantities. The material fulfillment constraint time for a production order refers to the earliest point in time when all target materials required for the production order are ready.

[0032] A production order refers to the product to be manufactured. Manufacturing this product requires multiple production processes, each corresponding to its own production line. Production line occupancy status refers to whether the production line is idle or occupied at various times. Based on the production processes involved in the production order and the production line occupancy status of each process at different times, the available time window for each production process can be determined. The available time window for a production line refers to the idle time period during which the production line is not occupied by other production orders.

[0033] In one possible implementation, the material requirements include the target materials and their required quantities; when performing step S101 to determine the material fulfillment constraint time for the production order based on the material requirements of the production order, the real-time inventory of materials, and the material procurement in-transit information, the specific steps S1011-S1016 can be performed as follows: S1011: For each target material, calculate the difference between the real-time inventory of the target material and the quantity already occupied by other production orders to obtain the current available inventory of the target material.

[0034] In this step, the real-time inventory level of the target material refers to the total real-time inventory of the target material. For each target material required in the production process of the product involved in the production order, the total real-time inventory of that target material is obtained from the warehouse management system, and all quantities already used by other scheduled production orders are deducted to obtain the available inventory level that can be used by this new production order.

[0035] S1012: If the current available inventory meets the quantity required for the target material in the production order, then the preparation time of the target material is determined to be the current time plus the time required for the target material to be issued and pre-processed.

[0036] For example, if the target material requires 100 units, the current available inventory is 150 units, and it is known that it takes 2 hours to retrieve the material from the warehouse and complete the necessary pre-processing such as cleaning and labeling, then the material's readiness time is 2 hours after the current time.

[0037] S1013: If the current available inventory does not meet the quantity required for the target material in the production order, then determine whether the target material is included in the materials being purchased based on the material procurement in-transit information.

[0038] In this step, query all purchase orders that have been placed but not yet received, and check whether they contain the target material.

[0039] S1014: When the target material is included in the procurement of materials in transit, if the sum of the procurement quantity of the target material and the current available inventory quantity meets the quantity of the target material required in the production order, then the preparation time of the target material shall be based on the estimated arrival time of the target material plus the time required for the outbound and pre-processing of the target material.

[0040] For example, suppose the target material requirement is 100 units, and the current available inventory is only 30 units. A query reveals an in-transit purchase order containing 80 units of the target material, expected to arrive at 14:00 tomorrow. 30 units + 80 units = 110 units > 100 units, satisfying the required quantity of the target material. If the outbound and pre-processing of the target material takes 2 hours, then the ready time for the target material is 16:00 tomorrow.

[0041] S1015: When the target material is not included in the materials being procured, a procurement plan for the target material is generated based on the current available inventory and required quantity of the target material. The preparation time of the target material is calculated by adding the promised delivery time of the procurement plan to the time required for the material to be issued and pre-processed.

[0042] In this step, if the current inventory and materials in transit cannot meet the demand, a procurement plan is automatically generated based on the supplier ledger (which records suppliers and procurement lead times). For example, if the required quantity is to be procured from the preferred supplier, and the supplier promises delivery in 5 days, then the readiness time for the target material is the date 5 days later plus the time required for the target material to be issued and pre-processed.

[0043] S1016: Take the latest value among the preparation times of all target materials as the material kitting constraint time for the production order.

[0044] In this step, the preparation time of each target material required for the production order is calculated. The earliest time when all target materials are ready depends on the preparation time of the latest target material to be ready, which is defined as the material kitting constraint time.

[0045] In one possible implementation, the following step S201 can also be performed: S201: Establish a material substitution relationship database; the material substitution relationship database records the correspondence between materials and substitute materials that can be used to replace them.

[0046] In this step, the following relationships are maintained in the material substitution relationship library: the substitute materials for each material, for example, the substitute material for material A can be A1 or A2. Each material can have one or more substitute materials.

[0047] Based on this, the specific execution process of the aforementioned step S1013 can be performed according to the following steps S10131 to S10134.

[0048] S10131: If the current available inventory does not meet the quantity required for the target material, query the material substitution relationship database to determine if there are any substitute materials that can be used to replace the target material.

[0049] In this step, when it is found that the current available inventory of the target material is insufficient, the material substitution relationship database is first queried to determine whether there are any defined substitute materials for the target material.

[0050] S10132: If it exists, obtain the current available inventory of the substitute material, and determine whether the sum of the current available inventory of the substitute material and the current available inventory of the target material meets the quantity required by the target material.

[0051] In this step, if there are substitutes for the target material, the total current available inventory of the target material and its substitutes is calculated.

[0052] S10133: If the required quantity of the target material is met, the preparation time of the target material is determined to be the current time plus the target duration; the target duration is the longer of the time required for the release and pre-processing of the substitute material and the time required for the release and pre-processing of the target material.

[0053] For example, suppose 100 units of target material B are required, the current available inventory of target material B is 50 units, and the current available inventory of its substitute material B1 is 60 units, totaling 110 units, which meets the quantity required for the target material in the production order. If the outbound and pre-processing time for target material B is 1 hour, and the outbound and pre-processing time for substitute material B1 is 1.5 hours, then the longer 1.5 hours is taken as the required preparation time (i.e., the target time). In this case, the ready time of the target material is 1.5 hours after the current time. This avoids the additional processing time that might be required due to the use of substitute materials.

[0054] S10134: If the required quantity of the target material is not met, determine whether the target material is included in the materials being procured based on the material procurement information.

[0055] If the sum of the current available inventory of the target material and its substitute material still does not meet the demand in this step, then proceed to step S1014 and subsequent steps.

[0056] In this embodiment, by introducing a material substitution relationship check, alternative solutions can be proactively sought when inventory is insufficient, thereby effectively advancing the material availability constraint time and increasing the feasibility and flexibility of production scheduling.

[0057] S102: Based on material kitting constraints, production line availability windows, and employee capacity information for each production process, with the goal of minimizing production order completion time, perform a flow-line time-connection scheduling for each production process to generate a production schedule that includes the planned start time, planned end time, and daily planned output for each production process.

[0058] In this step, after obtaining the material fulfillment constraints and the available production line time windows for each production process, specific scheduling calculations are performed in conjunction with the employee capacity information for each production process. The core objective is to complete the entire production order as early as possible while meeting the hard constraints of material supply and production line availability.

[0059] In specific scheduling, the logic of assembly line production is followed, meaning that the next production process can only begin processing these semi-finished products after the previous one has completed a certain quantity. Starting with the first process, the material kitting constraint time is taken as the earliest possible start time for that process, combined with the available time window of the production line for that process, to schedule production. Then, based on the planned daily output of that process (derived from the sum of employee capacity information assigned to that process), the estimated end time of that process is calculated; this time is the earliest possible start time for the next process. This process iterates until the last process, resulting in a preliminary production schedule for the entire order, including the planned start and end times for each process, as well as the daily planned output calculated based on employee allocation and capacity.

[0060] In this step, the employee productivity information includes the daily standard output of each employee for each production process.

[0061] In one possible implementation, accurate employee productivity information needs to be obtained in advance to perform the above scheduling. Specifically, employee productivity information can be determined through the dynamic forecasting method in steps S301-S306: S301: For each production process, collect the number of qualified products produced by the employee each day and the actual production time consumed in that production process within a historical time period. Based on the number of qualified products produced each day and the actual production time consumed in that production process, calculate the employee's historical baseline capacity for that production process.

[0062] In this step, the number of qualified products produced daily and the corresponding actual production time for this employee in this production process over a past period (e.g., the last three months) are obtained from the work report records. The historical baseline capacity of this employee in this production process is calculated using the following formula:

[0063] The historical baseline capacity is measured in "pieces / hour", representing the average productivity of the employee in that process.

[0064] S302: Assign a proficiency coefficient to the employee based on their proficiency in the production process.

[0065] In this step, based on the employee's start date, past performance, skill level assessment, or supervisor evaluation, their proficiency in a specific production process is categorized into levels 1 to 5, with corresponding proficiency coefficients: Level 1 = 0.7, Level 2 = 0.9, Level 3 = 1.0, Level 4 = 1.1, and Level 5 = 1.2. This proficiency coefficient reflects the impact of the employee's skill level on production capacity.

[0066] S303: Assign a complexity coefficient to the production process based on its complexity.

[0067] In this step, based on the Bill of Materials (BOM) structure, technological difficulty, or historical production data analysis of the production process, the complexity of the production process is divided into three levels: A (simple), B (medium), and C (complex), with corresponding complexity coefficients: A = 1.0, B = 0.9, and C = 0.6. This complexity coefficient reflects the adjustment of the task's inherent difficulty to production capacity.

[0068] S304: Configure equipment condition coefficients for the production process based on the operating conditions of the production equipment required for that production process.

[0069] In this step, based on equipment maintenance records and real-time status monitoring, the current condition of the production equipment is determined, and equipment operating condition coefficients are configured: Normal = 1.0, Under Maintenance = 0.9, Recent Failure = 0.7. These equipment operating condition coefficients reflect the impact of production equipment reliability on employee productivity.

[0070] S305: Configure the shift scheduling environment coefficient for the production process based on the shift schedule and environment.

[0071] In this step, based on the production schedule (day shift / night shift) and the workshop environment (such as whether it is in a high or low temperature environment), the scheduling environment coefficient is configured as follows: day shift = 1.0, night shift = 0.9, high or low temperature environment = 0.85. This scheduling environment coefficient reflects the impact of working hours and environmental conditions on efficiency.

[0072] S306: Based on the employee's historical baseline capacity, proficiency coefficient, complexity coefficient, equipment condition coefficient, scheduling environment coefficient, and their respective weights in the production process, predict the employee's capacity information for the production process.

[0073] In this step, the aforementioned influencing factors are comprehensively calculated according to preset weights. An example of the weighting is: historical baseline capacity weight 0.5, proficiency coefficient weight 0.15, complexity coefficient weight 0.15, equipment operating condition coefficient weight 0.1, and scheduling environment coefficient weight 0.1. The formula for predicting employee productivity information is: Employee productivity information = historical baseline productivity × 0.5 + historical baseline productivity × proficiency coefficient × 0.15 + historical baseline productivity × complexity coefficient × 0.15 + historical baseline productivity × equipment condition coefficient × 0.1 + historical baseline productivity × scheduling environment coefficient × 0.1.

[0074] The calculated employee productivity information is the expected output per unit time (i.e., daily standard output) that the employee can achieve in the production process in the future.

[0075] Through steps S301 to S306, personalized, multi-factor comprehensive forecasts of employee productivity can be generated for each employee at each production process. Compared to traditional average productivity data, this employee productivity information more accurately reflects the actual production capacity of employees, laying a solid foundation for the subsequent generation of high-precision production scheduling plans.

[0076] In one possible implementation, after performing step S306 to predict the employee's production capacity information for that production process, in order to ensure the accuracy of the employee production capacity information prediction, the method further includes steps S307-S308: periodically calibrating the weights of the aforementioned influencing factors. S307: Periodically collect the actual production capacity information of each employee for each production process according to a preset time interval, and compare the deviation rate between the actual production capacity information and the predicted employee production capacity information. In this step, calibration is performed once every preset time period (e.g., monthly). First, the actual production quantity and corresponding time reported daily by each employee for each production process within that period are recorded. The employee's actual average productivity for that process within that period is then calculated using the following formula:

[0077] The actual average production capacity is used as the actual production capacity information. Then, this actual production capacity information is compared with the employee production capacity information predicted for that employee at the beginning of the current cycle (or the end of the previous calibration cycle) according to step S306, and the deviation rate is calculated. The formula for calculating the deviation rate is as follows:

[0078] S308: If the deviation rate exceeds the preset deviation rate, the weighting ratio of historical benchmark capacity, proficiency coefficient, complexity coefficient, equipment condition coefficient, and scheduling environment coefficient will be dynamically adjusted.

[0079] In this step, a pre-set deviation rate threshold (e.g., ±10%) is established. When the calculated deviation rate exceeds this pre-set deviation rate, it indicates that the current weighting of the influencing factors no longer accurately reflects actual production efficiency and requires adjustment. At this point, the main causes of the deviation are analyzed (e.g., whether the increased skill level of new employees leads to a greater impact, or whether equipment upgrades weaken the impact of equipment condition). Based on this, the weighting of the five influencing factors in step S306—historical baseline capacity, skill level coefficient, complexity coefficient, equipment condition coefficient, and scheduling environment coefficient—is dynamically optimized and redistributed. The adjusted weighting will be used for the employee capacity forecast calculation in the next cycle, thereby enabling self-learning and continuous optimization of the employee capacity information forecasting process, gradually narrowing the gap between forecasts and actual results, and ensuring the long-term reliability of the employee capacity information upon which production scheduling relies.

[0080] S103: During the production process according to the production schedule, collect the actual output of each production process and the employee attendance information for the next day.

[0081] In this step, the production scheduling plan enters the execution phase. Each day (usually at the end of a shift or after the day's production is completed), the server automatically collects the actual number of qualified products completed for each production process from the reporting terminals on the production floor, i.e., the actual output. Simultaneously, it interfaces with the factory's human resources or attendance system to obtain the planned attendance status of all employees for the following day, especially leave information, thus forming the employee attendance information for the next day.

[0082] S104: Based on the deviation between the actual output of each process and the daily planned output, the employee attendance information for the next day, and the employee capacity information, dynamically adjust the planned end time of each production process in the subsequent production scheduling plan, as well as the planned start time of the unexecuted production process.

[0083] In this step, based on the deviation between the actual output of each process and the daily planned output, the employee attendance information for the next day, and the employee capacity information, the planned end time of each production process in the subsequent production schedule and the planned start time of the unexecuted production process are dynamically adjusted, and the adjusted production schedule is used as the new production schedule. Step S103 continues to be executed. During the production process according to the production schedule, the actual output of each production process, the employee attendance information for the next day, and the subsequent steps are collected daily.

[0084] In this step, based on the actual output of each production process collected by S103 and the employee attendance information for the following day, dynamic optimization of the subsequent production schedule is initiated. First, the difference (i.e., deviation) between the actual daily output of each production process and the original planned daily output in the production schedule is calculated. Combined with the known employee capacity information for each production process, the current production progress is assessed as being ahead of schedule or behind schedule. Second, the employee attendance information for the following day is verified to predict changes in available manpower for the next day. Combining this information, the remaining workload of each production process and the achievable daily output are recalculated. This automatically and dynamically adjusts the planned start time of unexecuted production processes and the adjusted planned end time of all incomplete processes (including those currently being executed and those not yet executed), ensuring that the production schedule remains synchronized with actual production resources and progress.

[0085] In one possible implementation, when performing step S104, for the common disturbance event of employee leave, the following steps S1041-S1048 can be performed: S1041: When an employee's leave event is detected based on the employee's attendance information for the next day, distinguish whether the production order is allowed to be delivered late.

[0086] In this step, after obtaining the employee's leave information, the first step is to determine whether the production order in which the employee is involved has an identifier or attribute that allows delayed delivery.

[0087] S1042: If delayed delivery is permitted, calculate the remaining production quantity for each production process of the production order based on the deviation between the actual output of each process and the planned daily output.

[0088] In this step, based on the daily planned output in the production schedule, the total demand for each process can be derived cumulatively. By summing the actual output of each process collected daily since production began, the cumulative total actual output completed for each production process can be obtained. For each production process, the difference between the total demand and the cumulative total actual output completed is calculated; this difference represents the remaining production quantity to be completed for that process at the current moment.

[0089] S1043: Exclude the production output of employees on leave during their leave period, and recalculate the daily planned output of each production process by combining the employee production output information of on-duty employees.

[0090] In this step, during rescheduling, the server treats the planned capacity output of employees on leave as zero during their leave period. Only based on the remaining on-duty employees and their corresponding employee capacity information, the planned output that the production process can achieve each day in the future is recalculated.

[0091] S1044: Based on the recalculated daily planned output of each production process, postpone the planned start time of the affected unexecuted production processes and the planned end time of the affected executed production processes.

[0092] In this step, the number of days required to complete the remaining workload will increase due to the reduction in total available capacity. Therefore, the scheduling of affected production processes will be postponed sequentially based on the new daily planned output. For production processes that have not yet started, their planned start time will be delayed; for production processes that have started but not yet completed, their planned end time will be postponed. The overall delivery time of production orders will be adjusted accordingly.

[0093] S1045: If on-time delivery is required, query the production line employee occupancy plan corresponding to other production orders in the factory that are in a deferred delivery status, and filter out on-duty employees who have the ability to operate the corresponding production process of the employee on leave.

[0094] In this step, for urgent orders that cannot be delayed, a cross-production line resource coordination mechanism is activated. First, within the factory, other production orders marked as "delayable" are searched, and their production schedules are reviewed to see which employees are assigned tasks in the near future (especially during leave periods). Then, from these employees, on-duty staff with the same skills as the employee on leave and capable of operating the same production process are further screened as potential support personnel.

[0095] S1046: Temporarily reassign the selected on-duty employees to the production process corresponding to the production order of the employee on leave.

[0096] In this step, a temporary employee allocation plan is generated. Selected employees are temporarily removed from their original planned tasks and assigned to the corresponding processes of urgent orders with staff shortages in order to fill the capacity gap.

[0097] S1047: Based on the employee capacity information of the reassigned employees and the remaining production quantity of each production process, reallocate the daily planned output of each production process.

[0098] In this step, based on the newly added support staff and their staff capacity information, combined with the original on-duty staff, a new and higher daily planned output for the production process is recalculated.

[0099] S1048: Based on the daily planned output of each production process that has been reallocated, the planned start time of the production process that has not been executed for the production order, the planned end time of the affected production process, and the subsequent production schedule of the production line corresponding to the original production order of the reassigned employee are adjusted simultaneously.

[0100] In this step, firstly, for orders that urgently need to be delivered, the server recalculates the schedule of each process due to the additional capacity obtained. This may allow the planned end time of some production processes to be brought forward or remain unchanged, and the planned start time of subsequent production processes will be adjusted accordingly to ensure that the orders are completed on schedule.

[0101] Simultaneously, the server must process "deferred delivery" orders affected by the employee's reassignment. The server reduces the planned production output of the reassigned employee and recalculates and postpones all subsequent production schedules for the production line corresponding to the employee's original order, including the start and end times of each process, thereby maintaining the consistency and feasibility of the plant-wide production plan after the adjustment. This process fully demonstrates dynamic collaboration across production lines.

[0102] In one possible implementation, such as Figure 2 As shown, the method also includes automatic replenishment steps S401-S405 for inventory materials. These steps S401-S405 run continuously during the production scheduling process to ensure the stability of material supply, thereby supporting the reliable execution of the production scheduling plan. S401: For each material stored in the material library, calculate the supply risk score of the material based on at least two of the following dimensions: basic attributes of the material, frequency of use, number of suppliers, and historical on-time delivery rate of suppliers. Then, classify the supply risk level of the material according to the supply risk score, so as to classify the material into high-risk, medium-risk, or low-risk levels.

[0103] In this step, the basic attributes include main materials, auxiliary materials, and substitute materials. These attributes are used to distinguish the criticality of materials in production. Main materials (such as core components) are assigned higher scores, auxiliary materials (such as packaging materials) are assigned next, and substitute materials are assigned the lowest scores. For example, main materials are assigned 50 points, auxiliary materials are assigned 30 points, and substitute materials are assigned 20 points.

[0104] The frequency of use reflects the turnover speed of materials in the warehouse, and can be divided into high turnover frequency (F material), medium turnover frequency (M material) and low turnover frequency (R material). For example, F material is 60 minutes, M material is 30 minutes and R material is 10 minutes.

[0105] The number of suppliers is used to measure the singularity of supply. Materials supplied by a single supplier are riskier than materials with multiple alternative suppliers. For example, the number of suppliers is: 1 = 50 points, 3 or fewer = 30 points, and more than 3 = 20 points.

[0106] The supplier's historical on-time delivery rate is used to quantify the supplier's reliability in fulfilling its obligations. For example, an on-time delivery rate >95% = 20 points, 85%-95% = 30 points, and <85% = 50 points.

[0107] The server sets scoring criteria and weights for each dimension. For example, the weight of basic attributes is 0.2, the weight of usage frequency is 0.4, the weight of the number of suppliers is 0.2, and the weight of the supplier's historical on-time delivery rate is 0.2.

[0108] The material is quantitatively scored based on its actual performance across various dimensions. Finally, the supply risk score for the material is calculated using the following formula: Supply risk score = basic attributes × 0.2 + usage frequency × 0.4 + number of suppliers × 0.2 + supplier's historical on-time delivery rate × 0.2.

[0109] Subsequently, materials are automatically classified into the corresponding supply risk level according to the preset score range (for example, a supply risk score >38 is a high risk level, 22-37 is a medium risk level, and <22 is a low risk level).

[0110] S402: Real-time monitoring of the current available inventory of each material in the material warehouse.

[0111] In this step, the server connects to the warehouse management system to continuously obtain the real-time total inventory of each material and calculate its current available inventory (i.e., the real-time total inventory minus the quantity already occupied by other production orders).

[0112] S403: For high-risk materials, when the current available inventory of the material drops to the first warning threshold, a purchase warning message for the material is generated; when it drops to the second warning threshold, a purchase order for the material is generated; the first warning threshold is greater than the second warning threshold and the second warning threshold is greater than the safety stock threshold.

[0113] In this step, for high-risk materials, the server employs a two-tiered early warning mechanism. The first early warning threshold is typically set high (e.g., safety stock plus projected consumption for the next 10 days). When inventory falls below this first early warning threshold, the server sends a procurement warning to the production manager or purchasing agent, prompting them to pay attention but temporarily suspending automatic procurement for manual review.

[0114] The second warning threshold is closer to the safety stock (e.g., the safety stock plus the expected consumption for the next 5 days). Once it is reached, the server will no longer wait and will automatically create a purchase order to initiate the purchase directly, in order to minimize the risk of material shortage.

[0115] S404: For materials with a medium risk level, when the current available inventory of the material drops to the third warning threshold, a purchase order for the material is generated; the third warning threshold is greater than the safety stock threshold and less than the second warning threshold.

[0116] In this step, for medium-risk materials, the server sets a third warning threshold as an automatic replenishment point. This third warning threshold is higher than the safety stock level but lower than the second warning threshold for high-risk materials. When the current available inventory drops to this point, the server automatically generates a purchase order and pushes it to the purchasing department for execution, achieving timely replenishment.

[0117] S405: For low-risk materials, when the current available inventory of the material drops to the safety stock threshold, generate a purchase request for the material and submit the purchase request for approval.

[0118] In this step, for low-risk materials, the server only generates a purchase request when the current available inventory reaches the safety stock threshold. This purchase request must be submitted to relevant personnel (such as the production manager) for approval before it can be converted into a formal purchase order. This provides flexibility for manual cost control and procurement timing for non-critical materials.

[0119] This embodiment, through the above steps S401 to S405, realizes a differentiated and automated inventory monitoring and replenishment strategy based on the material supply risk level, effectively balancing supply security and inventory costs, and providing a stable and reliable material supply environment for production line scheduling.

[0120] In one possible implementation, after generating the production schedule, it is further coordinated with the work plan of the quality inspection department to improve overall production and operational efficiency. This is achieved through the following steps: S105: Generate the work plan for the quality inspection department based on the daily planned output of each production process included in the production schedule.

[0121] In this step, after the server completes the production scheduling plan (i.e., determines the daily planned output and planned end time of each process), it automatically triggers the quality inspection plan generation logic. Based on the preset quality inspection rules (e.g., a certain production process requires full inspection or sampling inspection according to a specific ratio), and combined with the daily planned output of that production process, the expected number of products that the quality inspection department needs to inspect each day is calculated. At the same time, based on the planned end time of that production process, the expected delivery time of the corresponding batch of products to the quality inspection department is calculated. Thus, in the quality inspection department's work plan, the corresponding inspection tasks, required personnel and equipment resources are arranged, and the expected inspection start time and completion node are set.

[0122] This collaborative mechanism ensures that the work schedule of the quality inspection department is precisely matched with the production rhythm of the production line. The quality inspection department can know in advance the peak and trough of future workload, thereby rationally allocating human resources and inspection equipment, and avoiding the backlog of quality inspection work or idle resources due to changes in production plans. Especially for processes or orders with early expected completion times, the server can prioritize scheduling their inspection plans for the quality inspection department, thus allowing sufficient time for possible "partial delivery," accelerating finished product turnover and capital recovery. This process realizes proactive collaboration between production and quality inspection, extending the advantages of accurate production scheduling to downstream processes in the factory, and improving the responsiveness and reliability of the overall supply chain.

[0123] Based on the same technical concept, embodiments of this application also provide a dynamic collaborative production scheduling device for production lines, such as... Figure 3 As shown, the device includes: The first determining module 301 is used to determine the material fulfillment constraint time of the production order based on the material requirements of the production order, the real-time inventory of materials, and the material procurement in-transit information after receiving the production order, and to determine the production line available time window of each production process based on the production line occupancy status of each production process of the product involved in the production order. The first generation module 302 is used to perform a flow-line time connection scheduling for each production process based on the material kitting constraint time, the production line available time window, and the employee capacity information of each production process, with the goal of minimizing the production order completion time, and generate a production schedule that includes the planned start time, planned end time, and daily planned output of each production process. The data acquisition module 303 is used to collect the actual output of each production process and the employee attendance information for the next day during the production process according to the production schedule. The adjustment module 304 is used to dynamically adjust the planned end time of each production process and the planned start time of unexecuted production processes in the subsequent production schedule based on the deviation between the actual output of each process and the daily planned output, the employee attendance information for the next day, and the employee capacity information.

[0124] Optionally, the material requirements include the target materials and their required quantities; the first determining module 301, when determining the material fulfillment constraint time for the production order based on the material requirements of the production order, the real-time inventory of materials, and the material procurement in-transit information, is specifically used for: For each of the target materials, calculate the difference between the real-time inventory of the target material and the quantity already occupied by other production orders to obtain the current available inventory of the target material; If the current available inventory meets the quantity required for the target material in the production order, then the preparation time of the target material is determined to be the current time plus the time required for the target material to be issued and pre-processed. If the current available inventory does not meet the quantity required for the target material in the production order, then determine whether the target material is included in the materials being procured based on the material procurement in-transit information. When the target material is included in the procurement of materials in transit, if the sum of the procurement quantity of the target material and the current available inventory quantity meets the quantity of the target material required in the production order, then the preparation time of the target material is based on the estimated arrival time of the target material plus the time required for the outbound and pre-processing of the target material. When the target material is not included in the materials being procured, a procurement plan for the target material is generated based on the current available inventory and required quantity of the target material. The preparation time of the target material is then calculated by adding the promised delivery time of the procurement plan to the time required for the material to be issued and pre-processed. Take the latest value among the preparation times of all target materials as the material kitting constraint time for the production order.

[0125] Optionally, the device further includes: A module is established to create a material substitution relationship database; the material substitution relationship database records the correspondence between materials and substitute materials that can be used to replace them; The first determining module 301, when determining whether the target material is included in the procurement in transit based on the material procurement in transit information if the current available inventory does not meet the quantity required for the target material in the production order, specifically performs the following: If the current available inventory does not meet the quantity required for the target material, then the material substitution relationship database is queried to determine whether there are any substitute materials that can be used to replace the target material; If it exists, obtain the current available inventory of the substitute material, and determine whether the sum of the current available inventory of the substitute material and the current available inventory of the target material meets the quantity required by the target material. If the required quantity of the target material is met, the preparation time of the target material is determined to be the current time plus the target duration; the target duration is the longer of the time required for the replacement material to be released and pre-processed and the time required for the target material to be released and pre-processed. If the required quantity of the target material is not met, then the material procurement in transit information is used to determine whether the target material is included in the procurement in transit.

[0126] Optionally, the device further includes: The calculation module is used to calculate the supply risk score of each material stored in the material library based on at least two of the following dimensions: the material's basic attributes, usage frequency, number of suppliers, and the supplier's historical on-time delivery rate. Based on the supply risk score, the module classifies the material into a supply risk level, such as high risk, medium risk, or low risk. The monitoring module is used to monitor the current available inventory of each material in the material warehouse in real time; The second generation module is used to generate a purchase warning for high-risk materials when the current available inventory of the material drops to the first warning threshold; and to generate a purchase order for the material when it drops to the second warning threshold; the first warning threshold is greater than the second warning threshold and the safety stock threshold. The third generation module is used to generate a purchase order for materials with a medium risk level when the current available inventory of the material drops to a third warning threshold; the third warning threshold is greater than the safety stock threshold and less than the second warning threshold. The fourth generation module is used to generate a purchase request for low-risk materials when the current available inventory of the material drops to the safety stock threshold, and submit the purchase request for approval.

[0127] Optionally, the device further includes a second determining module, which is used to determine employee productivity information in the following manner: For each production process, the number of qualified products produced and the actual production time consumed by the employee in that production process are collected daily within a historical time period. Based on the number of qualified products produced and the actual production time consumed in that production process, the historical baseline capacity of the employee in that production process is calculated. Assign a proficiency coefficient to the employee based on their proficiency in the production process. Assign a complexity coefficient to the production process based on its complexity. Based on the operating conditions of the production equipment required for this production process, configure the equipment operating condition coefficient for this production process; Based on the shift schedule and environment of this production process, assign a shift environment coefficient to this production process; Based on the employee's historical baseline capacity, proficiency coefficient, complexity coefficient, equipment condition coefficient, scheduling environment coefficient, and their respective weights for that production process, predict the employee's capacity information for that production process.

[0128] Optionally, the device further includes a comparison module, which is used to: after the second determining module predicts the employee's productivity information for that production process, to: According to a preset time interval, the actual production capacity information of each employee for each production process is periodically collected, and the deviation rate between the actual production capacity information and the predicted employee production capacity information is compared. If the deviation rate exceeds the preset deviation rate, the weighting ratio of historical benchmark capacity, proficiency coefficient, complexity coefficient, equipment condition coefficient, and scheduling environment coefficient will be dynamically adjusted.

[0129] Optionally, when the adjustment module 304 dynamically adjusts the planned end time of each production process and the planned start time of unexecuted production processes in the subsequent production schedule based on the deviation between the actual output of each process and the daily planned output, employee attendance information for the next day, and employee capacity information, it is specifically used for: When an employee's leave request is detected based on their attendance information for the following day, it is determined whether the production order is allowed to be delivered late: If delayed delivery is permitted, the remaining production quantity for each production process of the production order is calculated based on the deviation between the actual output of each process and the daily planned output. Excluding the production output of employees on leave during their leave period, and combining the production output information of employees on duty, the daily planned output of each production process is recalculated. Based on the recalculated daily planned output of each production process, the planned start time of the affected unexecuted production processes and the planned end time of the affected executed production processes will be postponed. If on-time delivery is required, check the production line employee occupancy plan corresponding to other production orders in the factory that are in a deferred delivery status, and filter out on-duty employees who have the ability to operate the corresponding production process of the employee on leave. The selected on-duty employees will be temporarily reassigned to the production process corresponding to the production order of the employee on leave. Based on the employee capacity information of the reassigned employees and the remaining production quantity of each production process, the daily planned output of each production process is reallocated. Based on the daily planned output of each production process that has been reallocated, the planned start time of the production process that has not been executed for the production order, the planned end time of the affected production process, and the subsequent production schedule of the production line corresponding to the original production order of the reassigned employee are adjusted simultaneously.

[0130] Figure 4A schematic diagram of an electronic device provided in this application embodiment includes: a processor 401, a memory 402, and a bus 403. The memory 402 stores machine-readable instructions executable by the processor 401. When the electronic device runs the above-described information processing method, the processor 401 and the memory 402 communicate through the bus 403. The processor 401 executes the machine-readable instructions to perform the steps of the method described in Embodiment 1.

[0131] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps described in Embodiment 1.

[0132] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, electronic devices, and computer-readable storage media described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0133] In the several embodiments provided in this application, it should be understood that the disclosed methods, apparatuses, electronic devices, and computer-readable storage media can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or modules may be electrical, mechanical, or other forms.

[0134] 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 network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0135] In addition, the functional units in the various embodiments of this application 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.

[0136] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion 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 this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0137] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims.

Claims

1. A dynamic collaborative production scheduling method for production lines, characterized in that, include: Upon receiving a production order, the material availability constraint time for the production order is determined based on the material requirements of the production order, the real-time inventory of materials, and the material procurement in-transit information. The available time window for each production process is determined based on the production line occupancy status of each production process of the products involved in the production order. Based on the material kitting constraint time, the production line available time window, and the employee capacity information of each production process, with the goal of minimizing the production order completion time, a production line-style time connection scheduling is performed on each production process to generate a production schedule that includes the planned start time, planned end time, and daily planned output of each production process. During the production process according to the aforementioned production schedule, the actual output of each production process and the employee attendance information for the next day are collected daily. Based on the deviation between the actual output of each process and the daily planned output, employee attendance information for the next day, and employee capacity information, the planned end time of each production process in the subsequent production scheduling plan, as well as the planned start time of the unexecuted production process, are dynamically adjusted.

2. The method according to claim 1, characterized in that, The material requirements include the target materials and their required quantities; determining the material availability constraint time for the production order based on the material requirements of the production order, real-time material inventory, and material procurement in-transit information includes: For each of the target materials, calculate the difference between the real-time inventory of the target material and the quantity already occupied by other production orders to obtain the current available inventory of the target material; If the current available inventory meets the quantity required for the target material in the production order, then the preparation time of the target material is determined to be the current time plus the time required for the target material to be issued and pre-processed. If the current available inventory does not meet the quantity required for the target material in the production order, then determine whether the target material is included in the materials being procured based on the material procurement in-transit information. When the target material is included in the procurement of materials in transit, if the sum of the procurement quantity of the target material and the current available inventory quantity meets the quantity of the target material required in the production order, then the preparation time of the target material is based on the estimated arrival time of the target material plus the time required for the outbound and pre-processing of the target material. When the target material is not included in the materials being procured, a procurement plan for the target material is generated based on the current available inventory and required quantity of the target material. The preparation time of the target material is then calculated by adding the promised delivery time of the procurement plan to the time required for the material to be issued and pre-processed. Take the latest value among the preparation times of all target materials as the material kitting constraint time for the production order.

3. The method according to claim 2, characterized in that, The method further includes: Establish a material substitution relationship database; the material substitution relationship database records the correspondence between materials and substitute materials that can be used to replace them; If the current available inventory does not meet the quantity required for the target material in the production order, then based on the material procurement in-transit information, it is determined whether the target material is included in the procurement in-transit materials, including: If the current available inventory does not meet the quantity required for the target material, then the material substitution relationship database is queried to determine whether there are any substitute materials that can be used to replace the target material; If it exists, obtain the current available inventory of the substitute material, and determine whether the sum of the current available inventory of the substitute material and the current available inventory of the target material meets the quantity required by the target material. If the required quantity of the target material is met, the preparation time of the target material is determined to be the current time plus the target duration; the target duration is the longer of the time required for the replacement material to be released and pre-processed and the time required for the target material to be released and pre-processed. If the required quantity of the target material is not met, then the material procurement in transit information is used to determine whether the target material is included in the procurement in transit.

4. The method according to claim 2, characterized in that, The method further includes: For each material stored in the material library, calculate the supply risk score based on at least two of the following dimensions: the material's basic attributes, usage frequency, number of suppliers, and the supplier's historical on-time delivery rate. Then, classify the material into a supply risk level, such as high risk, medium risk, or low risk, based on the supply risk score. Real-time monitoring of the current available inventory of each material in the material warehouse; For high-risk materials, when the current available inventory of the material drops to the first warning threshold, a purchase warning message for the material is generated; when it drops to the second warning threshold, a purchase order for the material is generated; the first warning threshold is greater than the second warning threshold and the safety stock threshold. For materials with a medium risk level, when the current available inventory of the material drops to the third warning threshold, a purchase order for the material is generated; the third warning threshold is greater than the safety stock threshold and less than the second warning threshold. For materials with low risk levels, when the current available inventory of the material drops to the safety stock threshold, a purchase request for the material is generated and submitted for approval.

5. The method according to claim 1, characterized in that, The method also includes determining employee productivity information through the following means: For each production process, the number of qualified products produced and the actual production time consumed by the employee in that production process are collected daily within a historical time period. Based on the number of qualified products produced and the actual production time consumed in that production process, the historical baseline capacity of the employee in that production process is calculated. Assign a proficiency coefficient to the employee based on their proficiency in the production process. Assign a complexity coefficient to the production process based on its complexity. Based on the operating conditions of the production equipment required for this production process, configure the equipment operating condition coefficient for this production process; Based on the shift schedule and environment of this production process, assign a shift environment coefficient to this production process; Based on the employee's historical baseline capacity, proficiency coefficient, complexity coefficient, equipment condition coefficient, scheduling environment coefficient, and their respective weights for that production process, predict the employee's capacity information for that production process.

6. The method according to claim 5, characterized in that, After predicting the employee's productivity information for that production process, the method further includes: According to a preset time interval, the actual production capacity information of each employee for each production process is periodically collected, and the deviation rate between the actual production capacity information and the predicted employee production capacity information is compared. If the deviation rate exceeds the preset deviation rate, the weighting ratio of historical benchmark capacity, proficiency coefficient, complexity coefficient, equipment condition coefficient, and scheduling environment coefficient will be dynamically adjusted.

7. The method according to claim 1, characterized in that, The method of dynamically adjusting the planned end time of each production process and the planned start time of unexecuted production processes in subsequent production scheduling based on the deviation between the actual output of each process and the planned output of each day, employee attendance information for the next day, and employee capacity information includes: When an employee's leave request is detected based on their attendance information for the following day, it is determined whether the production order is allowed to be delivered late: If delayed delivery is permitted, the remaining production quantity for each production process of the production order is calculated based on the deviation between the actual output of each process and the daily planned output. Excluding the production output of employees on leave during their leave period, and combining the production output information of employees on duty, the daily planned output of each production process is recalculated. Based on the recalculated daily planned output of each production process, the planned start time of the affected unexecuted production processes and the planned end time of the affected executed production processes will be postponed. If on-time delivery is required, check the production line employee occupancy plan corresponding to other production orders in the factory that are in a deferred delivery status, and filter out on-duty employees who have the ability to operate the corresponding production process of the employee on leave. The selected on-duty employees will be temporarily reassigned to the production process corresponding to the production order of the employee on leave. Based on the employee capacity information of the reassigned employees and the remaining production quantity of each production process, the daily planned output of each production process is reallocated. Based on the daily planned output of each production process that has been reallocated, the planned start time of the production process that has not been executed for the production order, the planned end time of the affected production process, and the subsequent production schedule of the production line corresponding to the original production order of the reassigned employee are adjusted simultaneously.

8. A dynamic collaborative production scheduling device for production lines, characterized in that, include: The first determining module is used to determine the material fulfillment constraint time of the production order after receiving the production order, based on the material requirements of the production order, the real-time inventory of materials, and the material procurement in-transit information, and to determine the production line availability time window of each production process based on the production line occupancy status of each production process of the product involved in the production order. The first generation module is used to perform a flow-line time connection scheduling for each production process based on the material kitting constraint time, the production line available time window, and the employee capacity information of each production process, with the goal of minimizing the production order completion time, and generate a production schedule that includes the planned start time, planned end time, and daily planned output of each production process. The data acquisition module is used to collect the actual output of each production process and the employee attendance information for the next day during the production process according to the production schedule. The adjustment module is used to dynamically adjust the planned end time of each production process and the planned start time of unexecuted production processes in the subsequent production schedule based on the deviation between the actual output of each process and the daily planned output, the employee attendance information for the next day, and the employee capacity information.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is in operation, the processor communicates with the memory via the bus, and the machine-readable instructions, when executed by the processor, perform the steps of the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method as described in any one of claims 1 to 8.

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