Cosmetic manufacturing intelligent scheduling and personnel competence matching method and system

Abstract

The application discloses a kind of intelligent production scheduling and personnel competence matching method and system for cosmetic manufacturing.The method comprises: obtaining production task and its standard operation procedure, and each process is provided with the number of workers and the mode of filling position;According to the planned amount and the theoretical capacity, the expected working hours are calculated, and the tasks across shifts are divided into multiple shift segments;For each process of each shift segment, available employee pool is formed by obtaining attendance available employees, candidate employees with corresponding skill records are screened, the top N employees are selected according to the preset scoring model and assigned to the current process;If the number of assigned personnel is insufficient, perform the filling operation according to the filling mode, and if it is still insufficient after filling, generate a gap record;After each personnel selection or filling operation is completed, the occupancy record set is updated synchronously.The application integrates the standard operation procedure, employee skills and attendance status into the production scheduling decision, and realizes precise matching and automatic production scheduling of personnel in the multi-line, multi-process and multi-batch parallel production scene.

Description

Technical Field

[0001] This invention relates to the field of industrial intelligent scheduling technology, specifically to a method and system for production planning and automatic personnel allocation in cosmetics manufacturing. It is particularly suitable for automatic production scheduling and personnel competency matching in parallel production scenarios involving multiple production lines, processes, and batches, such as filling and packaging. Background Technology

[0002] In cosmetic manufacturing companies, production scheduling typically involves multiple production lines (such as filling and packaging lines) operating in parallel. Each production line's tasks include multiple processes (such as bottle loading, filling, capping, and boxing), and each process requires a different number of operators with varying skill requirements. Currently, cosmetic production scheduling generally relies on manual experience. Schedulers need to repeatedly check Excel production plans, paper or electronic standard operating procedures (SOPs), employee skill registers, and daily attendance sheets to manually allocate personnel for each production line and each process.

[0003] This traditional production scheduling method has the following technical drawbacks: First, scheduling is inefficient and prone to errors. Manual scheduling requires handling a large number of heterogeneous data sources (production plans, SOPs, skill sheets, attendance sheets). The data is scattered and has inconsistent formats. Schedulers need to manually compare and transcribe data between multiple data sources, which is not only inefficient, but also prone to problems such as duplicate personnel assignments, cross-production line conflicts, missed or incorrect scheduling, making it impossible to directly implement the scheduling plan.

[0004] Second, there is a lack of precise matching between personnel capabilities and job requirements. Existing production scheduling methods typically only categorize personnel by department or job type at a coarse-grained level, failing to accurately verify whether a specific employee possesses the necessary skills for a particular position on a specific production line. When the skill levels of the same employee vary across different production lines and positions, manual scheduling struggles to accurately identify and differentiate between them, potentially assigning employees lacking the necessary competence to critical processes, thus leading to potential production quality issues.

[0005] Third, attendance constraints are difficult to incorporate into production scheduling decisions in real time. Employee leave and rest days are usually managed by a separate attendance system. When scheduling manually, it is easy to miss or fail to obtain the attendance status of the day in a timely manner, which may lead to the wrong scheduling of employees on leave or rest days into production tasks. It may only be discovered after the production plan arrives at the production site that the personnel are not in place and temporary adjustments are required.

[0006] Fourth, scheduling tasks across shifts is difficult. Cosmetic production often requires continuous operation, and a single production task may span both day and night shifts. When scheduling manually, it is difficult to accurately calculate the allocation of working hours and personnel coordination for tasks across shifts, which can easily lead to gaps or overlaps in personnel arrangements between shifts.

[0007] Fifth, there is a lack of transparent identification when there are insufficient personnel. When there are not enough qualified personnel for a certain position, traditional methods either fail to complete production scheduling or force unskilled personnel to take up the positions without recording it. Managers cannot know the precise location of the gap (which production line, which position, which shift) and the nature of the gap (whether temporary workers are needed, internal training is needed, or external recruitment is needed), resulting in a lack of data support for personnel training and recruitment, and poor interpretability and manageability of production scheduling results.

[0008] Sixth, there is a disconnect between production scheduling and personnel training. Existing production scheduling systems only focus on "who is on duty that day," without recording or analyzing personnel shortage data, let alone feeding back shortage information to the personnel training stage. When a position is chronically understaffed, companies cannot promptly identify this trend from production scheduling data and initiate targeted training or recruitment, resulting in personnel pipeline development lagging behind production needs.

[0009] In recent years, some literature has disclosed technical solutions involving production scheduling or personnel matching. For example, Chinese patent application CN119358969A discloses an intelligent production scheduling method and system, which generates a scheduling plan based on historical production data and intelligent process models, and performs allocation processing based on personnel skill data; Chinese patent application CN118154149A discloses a scheduling method and system based on skill matching, which constructs constraints and solves the scheduling problem through employees' basic work information and job skill information; Chinese patent application CN120725362A discloses an adaptive intelligent scheduling system based on employee ability tags, which performs matching quantification by constructing an employee-task knowledge graph. However, the above solutions still have the following shortcomings: First, they fail to achieve accurate matching and verification between SOP standard data and production line and job basic data, which can easily lead to mismatches due to similar product names or different production lines; second, personnel skill matching is still limited to the level of job name or ability label, and is not accurate to the cross dimension of "production line + job"; third, there is a lack of a classification and processing mechanism for personnel shortage scenarios (such as the distinction between part-time shortages, skill deficiencies, and personnel shortages); and fourth, there is a lack of data closure between production scheduling results and personnel training, making it impossible to use the gaps found in production scheduling to drive the construction of personnel pipelines.

[0010] In view of this, the present invention aims to provide a method and system for intelligent scheduling and personnel competency matching in cosmetic manufacturing that can overcome the above-mentioned defects. Summary of the Invention

[0011] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method and system for intelligent scheduling and matching of personnel competence in cosmetic manufacturing. It aims to solve the technical problems in existing production scheduling methods, such as the disconnect between SOP standards, employee skills and attendance data, the reliance on human experience leading to low efficiency and error, the lack of fine-grained matching between personnel capabilities and job requirements, the difficulty in accurately scheduling tasks across shifts, the lack of transparency in personnel shortages, and the lack of a data loop between scheduling results and personnel training, making it impossible to use the gaps found in scheduling to drive the construction of personnel pipelines.

[0012] To achieve the above objectives, the present invention adopts the following technical solution: a method for intelligent scheduling and personnel competency matching in cosmetic manufacturing, comprising: obtaining at least one production task within the scheduling date, and a standard operating procedure (SOP) corresponding to each production task; the SOP includes at least one process, each process being associated with a job title, number of employees, and a replacement mode; calculating the estimated working hours for each production task based on the planned quantity of each production task and the theoretical capacity of the SOP, and dividing the cross-shift production task into at least two shift segments according to a preset shift time boundary; for each process in each shift segment, performing the following steps: obtaining employees whose attendance status is available on the current day, forming an available employee pool; screening candidate employees from the available employee pool, wherein the candidate employees simultaneously meet the following requirements: not being assigned to other processes within the same production task, not being assigned to other production lines within the same shift, and possessing skill records for the target production line and target position; and assigning each employee to a target position according to a preset scoring model. The candidate employees are calculated with a comprehensive score. The top N employees, ranked from highest to lowest comprehensive score, are assigned to the current process, where N is the number of employees in the process. The scoring model includes at least the job key level factor, skill priority factor, proficiency factor, and fatigue constraint factor. If the number of employees assigned to the current process is less than N, a replacement operation is performed according to the replacement mode of the process. If, after the replacement operation, the number of employees assigned to the current process is still less than N, a gap record is generated according to the replacement mode of the process. The personnel identifier in the gap record is empty, but the process, production line, shift attributes, and gap type identifier are retained. After each personnel selection or replacement operation is completed, the occupancy record set is updated synchronously. The occupancy record set includes at least: a task-level occupancy subset, used to record employees assigned in the current production task; a time-period-level occupancy subset, used to record employees locked by a specific production line in the current shift; and a scenario-level occupancy subset, used to record the distribution of employees in each production line and position in the current shift.

[0013] Based on the above technical solution, the technical solution provided by the present invention has at least the following advantages: (1) By incorporating standard operating procedures, employee skill records and daily attendance status into production scheduling decisions, the automatic matching of production tasks, process requirements and personnel capabilities is realized, eliminating the need for manual verification between multiple data sources, thereby significantly improving production scheduling efficiency and reducing the error rate caused by data transcription and manual judgment.

[0014] (2) By using the employee skill record and job competency screening mechanism with “production line + job” as the smallest unit, the matching of personnel capabilities is refined from the coarse granularity of traditional job name or job type to the fine granularity of specific production line and specific job, which effectively avoids mismatch caused by equipment or process differences in the same job of the same employee on different production lines, and ensures the compliance of personnel capabilities in key processes.

[0015] (3) By constructing a multi-factor dynamic scoring model that includes job key level factor, skill priority factor, proficiency factor and fatigue constraint factor, the allocation of personnel takes into account multiple dimensions such as job key level, skill priority, proficiency and fatigue constraint, and achieves comprehensive optimal personnel selection under multi-objective constraints.

[0016] (4) By updating the three-level occupancy record set at the task level, time period level and scenario level in real time, the assigned personnel are locked in real time during the production scheduling process. This eliminates common problems in manual production scheduling, such as the same employee being repeatedly assigned in the same task and cross-production line conflicts in the same shift, from the algorithm level, and ensures the physical executability of the production scheduling results.

[0017] (5) By generating gap records with gap type identifiers according to the filling mode of the process, the personnel shortage situation is output in the form of structured data, so that managers can accurately know the gap type and gap quantity of each production line, each position and each shift, providing clear data support for temporary employment scheduling and personnel recruitment.

[0018] As an optional embodiment of the present invention, the calculation method of the comprehensive score includes: Comprehensive score = Process criticality level score + Skill priority score + Proficiency score + Reuse bonus. Fatigue deduction; wherein, the process key level score is determined based on the process key level and preset key process weight; the skill priority score is determined based on the candidate employee's skill priority; the proficiency score is determined based on the candidate employee's proficiency level in the target position on the target production line; the reuse bonus is determined based on the candidate employee's presence status in the scenario-level occupied subset; and the fatigue deduction is determined based on the number of times the candidate employee has been assigned that day. This scheme quantifies multiple factors affecting personnel matching into a unified scoring formula, enabling the system to automatically make optimal personnel selection while taking into account skill matching, experience proficiency, reuse efficiency, and fatigue fairness, avoiding the uncertainty brought about by subjective human judgment, and providing a quantitative basis for the review and interpretation of production scheduling results.

[0019] As an optional aspect of this invention, the reuse bonus specifically includes: when the candidate employee already exists in the occupancy records of the current shift, current production line, and current position, the reuse bonus is a first reuse bonus value; when the candidate employee already exists in the occupancy records of the current shift and current production line but not in the occupancy record of the current position, the reuse bonus is a second reuse bonus value; the first reuse bonus value is greater than the second reuse bonus value. This scheme, by differentiating the reuse bonus settings for the same production line and shift, prioritizes employees who have already participated in production on the same production line and in the same shift during personnel allocation, especially those who have already operated in the same position. This reduces the adaptation costs caused by frequent switching between different production lines and positions, thereby improving production efficiency and personnel utilization.

[0020] As an optional embodiment of the present invention, the shift time boundaries include the start time of the day shift, the end time of the day shift, and the start time of the night shift. The step of dividing the production task spanning multiple shifts into at least two shift segments includes: using the end time of the day shift as the dividing point, assigning the portion of the production task from the start time to the end time of the day shift as the day shift segment, and assigning the portion from the end time of the day shift to the end time as the night shift segment. Each shift segment inherits the product information, production line information, and standard operating procedure information of the production task, and allocates the segment's planned workload according to the proportion of the segment's working hours to the estimated working hours. This solution automatically divides long-duration production tasks spanning day and night shifts into independent segments corresponding to the corresponding shifts by using preset shift boundaries as dividing points, and allocates the planned workload according to the working hour ratio. This allows each shift to independently obtain the production task, planned workload, and estimated working hours for that segment, thereby enabling personnel allocation by shift and avoiding the allocation confusion and working hour calculation errors that easily occur when manually handling cross-shift tasks.

[0021] As an optional aspect of this invention, obtaining the standard operating procedure (SOP) corresponding to each production task includes: based on the product code and production line identifier of the production task, searching among the approved SOPs for a SOP whose product code matches the product code of the production task and whose production line identifier matches the production line identifier of the production task; if a match is successful, writing the identifier information of the SOP into the production task; if a match fails, the production task does not enter the scheduling process. This scheme adopts a joint precise matching mechanism of "product code + production line identifier" to ensure that the SOP matched by each production task corresponds to both the correct product and the correct production line, effectively avoiding the problems of mismatch between similar products or between different production lines that may be caused by matching only by product name, and ensuring the accuracy of subsequent scheduling basic data such as processes, working hours, and number of workers.

[0022] As an optional aspect of this invention, the gap type identifier is determined based on the filling mode of the process: when the filling mode is part-time, the gap type identifier is a part-time gap; when the filling mode is internal staff, the gap type identifier is a skills deficiency gap; and when the filling mode is mandatory, the gap type identifier is a personnel gap. This solution automatically distinguishes between three different types—part-time gaps, skills deficiency gaps, and personnel gaps—based on the preset filling mode of the process. This allows managers to accurately determine the nature of personnel gaps for each production line, each position, and each shift, thereby enabling them to formulate appropriate response strategies—arranging temporary workers for part-time gaps, internal training for skills deficiency gaps, and external recruitment for personnel gaps—achieving refined management of personnel shortage scenarios.

[0023] As an optional embodiment of the present invention, the method further includes gap feedback and personnel training steps: The gap records are aggregated and statistically analyzed by production line and job position to generate gap frequency and number of vacancies for each production line and job position; based on the results of the aggregated statistics, seed employees to be trained are identified, and competency assessment tasks for corresponding production lines and positions are created for the seed employees; the assessment results of the competency assessment tasks are obtained, and for employees who pass the assessment, their skill records in the corresponding production line and position are written, allowing them to enter the candidate employee selection range for subsequent production scheduling. This solution achieves a complete closed loop of "gap discovery during production scheduling—identification of training targets—organization of assessment—skills backwriting—participation in the next round of production scheduling" by feeding back gap records generated during the production scheduling process to the personnel development strategy module. This upgrades the production scheduling system from a simple personnel arrangement tool to a decision support system that drives continuous organizational capability building, forming a positive cycle of mutual promotion between production execution and personnel pipeline development.

[0024] Based on the same inventive concept, this invention also provides an intelligent scheduling and personnel competency matching system for cosmetic manufacturing, characterized by comprising: a data acquisition module, used to acquire at least one production task within the scheduling date, the standard operating procedure corresponding to each production task, employee skill records, and daily attendance status; the standard operating procedure includes at least one process, each process being associated with a job title, number of employees, and a replacement mode; a time calculation and splitting module, used to calculate the estimated time based on the planned quantity of each production task and the theoretical capacity of the standard operating procedure, and to split the cross-shift production task into at least two shift segments according to preset shift time boundaries; and a candidate employee screening module, used to screen candidate employees from an available employee pool for each process in each shift segment, wherein the candidate employees simultaneously meet the following criteria: not assigned to other processes within the same production task, not assigned to other production lines within the same shift, and possessing a target production line and target position. The system includes: a skills record module; a scoring and selection module, used to calculate a comprehensive score for each candidate employee according to a preset scoring model, and select the top N employees in the comprehensive score from high to low to be assigned to the current process, where N is the number of employees in the process; the scoring factors of the scoring model include at least a job key level factor, a skill priority factor, a proficiency factor, and a fatigue constraint factor; a replacement execution module, used to perform a replacement operation according to the replacement mode of the process when the number of employees assigned to the current process is less than N; a gap generation module, used to generate a gap record according to the replacement mode of the process when the number of employees assigned to the current process is still less than N after the replacement operation, where the personnel identifier in the gap record is empty, but the process, production line, shift attributes, and gap type identifier are retained; and a conflict locking module, used to synchronously update the occupancy record set after each personnel selection or replacement operation, where the occupancy record set includes at least a task-level occupancy subset, a time-level occupancy subset, and a scenario-level occupancy subset.

[0025] As an optional embodiment of the present invention, the calculation method of the comprehensive score in the scoring and selection module includes: Comprehensive score = Process criticality level score + Skill priority score + Proficiency score + Reuse bonus. Fatigue deduction; wherein, the reuse bonus is determined based on the existence status of the candidate employee in the scenario-level occupancy subset: when the candidate employee already exists in the occupancy record of the current shift, the current production line, and the current position, the reuse bonus is the first reuse bonus value; when the candidate employee already exists in the occupancy record of the current shift and the current production line, but not in the occupancy record of the current position, the reuse bonus is the second reuse bonus value, and the first reuse bonus value is greater than the second reuse bonus value.

[0026] As an optional embodiment of the present invention, it further includes a development strategy module: the development strategy module is used to aggregate and statistically analyze the gap records according to the dimensions of production line and position, and generate the gap frequency and number of gaps for each production line and position; determine the seed employees to be trained based on the results of the aggregated statistics, and create competency assessment tasks for the seed employees corresponding to the production line and corresponding position; obtain the assessment results of the competency assessment tasks, and for employees who pass the assessment, write the skill records of the employees in the corresponding production line and corresponding position. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the automatic scheduling main process in the intelligent scheduling and personnel competency matching method for cosmetic manufacturing in Embodiment 1 of the present invention.

[0028] Figure 2 This is a schematic diagram of the closed-loop feedback process of production gaps flowing back to personnel training in Embodiment 2 of the present invention. Detailed Implementation

[0029] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0030] Example 1 This embodiment provides a method and system for intelligent production scheduling and personnel competency matching in cosmetic manufacturing. Taking a single shift production task on a certain day as an example, the complete process of automatic production scheduling is explained in detail.

[0031] The intelligent scheduling and personnel competency matching system for cosmetic manufacturing used in this embodiment includes a data acquisition module, a time calculation and breakdown module, a candidate employee screening module, a scoring and selection module, a replacement execution module, a gap generation module, a conflict locking module, and a development strategy module. The data acquisition module is used to acquire production tasks, standard operating procedures, employee skill records, and daily attendance status within the production scheduling date. The standard operating procedure includes at least one process, and each process is associated with a job title, number of workers, and a replacement mode. The time calculation and splitting module is used to calculate the estimated time based on the planned quantity and theoretical capacity of the standard operating procedure, and to split the cross-shift production tasks into multiple shift segments according to the preset shift time boundaries. The candidate employee screening module is used to screen candidate employees from the available employee pool. The candidate employees must simultaneously meet the following conditions: not assigned to other processes within the same production task, not assigned to other production lines within the same shift, and possess skill records for the target production line and target position. The scoring and selection module is used to calculate a comprehensive score for candidate employees according to a preset scoring model, and select the top N employees from high to low comprehensive scores to be assigned to the current process, where N is the number of workers in the process. The scoring factors of the scoring model include at least a job key level factor, a skill priority factor, a proficiency factor, and a fatigue constraint factor. The replacement execution module is used to... When the number of employees assigned to the current process is less than N, a replacement operation is performed according to the replacement mode of the process. The gap generation module is used to generate gap records according to the replacement mode of the process when the number of employees assigned to the current process is still less than N after the replacement operation. The personnel identifier in the gap record is empty, but the process, production line, shift attributes and gap type identifier are retained. The conflict locking module is used to synchronously update the occupancy record set after each personnel selection or replacement operation. The occupancy record set includes at least a task-level occupancy subset, a time-level occupancy subset and a scenario-level occupancy subset. The development strategy module is used to aggregate and statistically analyze the gap records by production line and position dimensions to generate the gap frequency and number of gaps for each production line and position. Based on the aggregated statistical results, seed employees to be trained are determined, and competency assessment tasks for the corresponding production line and corresponding position are created for the seed employees. The assessment results of the competency assessment tasks are obtained. For employees who pass the assessment, their skill records in the corresponding production line and corresponding position are written, so that the employees enter the candidate employee selection range for subsequent production scheduling.

[0032] The data parameters used by the system for automatic production scheduling can be divided into seven groups, which are explained below: 1. Production Task Parameters: These include scheduling date, production line number, job type, product code, product name, specifications, batch number, hourly capacity, planned quantity, and shift. Production tasks are typically imported from Excel files. After the system parses the data, it generates scheduling task records and establishes a link between the production line number and the system's master production line data.

[0033] 2. Standard Operating Procedure (SOP) Parameters: These include the SOP document code, product code, product name, specifications, applicable production line, standard number of workers, theoretical hourly capacity, number of processes, and process details. Process details further include process number, job or workstation name, number of workers, fill-in mode or skill restrictions, critical process level, equipment, and critical points.

[0034] 3. Production Line and Job Parameters: This includes factory area, production line grouping, production line number, production line name, production line status, job name, production line to which the job belongs, and job filling mode. Job filling modes include at least three types: part-time fillable, internal employee fillable, and mandatory. This parameter determines how the system generates vacancies or whether it allows incompetent employees to fill in when there is a shortage of personnel.

[0035] 4. Employee Competency Parameters: These include employee ID, name, gender, employment status, department, overall performance evaluation level, and employee skill records. Employee skill records are organized in a "employee-production line-position" unit, further recording proficiency and skill priority. In this embodiment, whether an employee possesses the competence for a specific position is primarily determined by the existence of corresponding production line and position skill records; proficiency and priority are used for scoring and ranking.

[0036] 5. Attendance Status Parameters: These include employee, date, status, leave type, and remarks. Status includes currently employed, on leave, and off-duty. When building the candidate employee pool, the system reads the daily attendance records and excludes unavailable personnel such as those on leave or off-duty.

[0037] 6. Scheduling Rule Parameters: The system maintains scheduling rule weights and switches through rule tables, including fatigue and fairness constraints, maximum daily allocation times, key process weights, proficiency soft bonuses, line or process change costs, bonuses for reusing the same process, bonuses for reusing within the same production line, and scheduling interpretation switches. Scheduling rule parameters can be configured in the system; default values ​​are used when not configured.

[0038] 7. Historical production scheduling feedback parameters: including personnel allocation records, part-time vacancies, skills deficiency indicators, personnel shortages, start and end times, shifts, estimated working hours, and ratings. The development strategy module reads production scheduling records from the most recent period, calculates the frequency and number of vacancies for each production line and position, and assists in formulating personnel training plans.

[0039] The following explains the methods for importing, verifying, and matching standard operating procedures.

[0040] The system supports importing standard operating procedures (SOPs) via a dual-worksheet template. The first sheet details the product's operating hours, recording the SOP file code, product code, product name, product specifications, factory production line, number of standard workers, and theoretical hourly capacity. The second sheet details the process hours, recording the names of each process under the same file code, the number of workers, and the criticality level of each process. The system uses the file code as the primary key to link product-level and process-level SOPs.

[0041] When importing standard operating procedures (SOPs), the system first groups the work processes according to file codes, and then reads the product time details. If the factory production line is stored in numerical form, the system will round it down and convert it to a production line number. If the production line does not exist in the system, the system can automatically create a basic record for the running production line. Subsequently, the system searches for the job in the job database by "work process name + production line identifier". Only if the work process name exists under the corresponding production line will the SOP be successfully imported. This avoids inconsistencies between the work process name in the SOP and the job database, which could lead to mismatches between personnel and subsequent production scheduling.

[0042] After the standard operating procedures are imported, the system writes the replacement modes configured in the job list into the corresponding standard operating procedure processes. If a job is "part-time available", it means that a part-time vacancy can be generated when there is a shortage of personnel in this process; if it is "internal staff available", it means that when there is a shortage of qualified personnel, replacement personnel can be selected from other available employees and marked as lacking skills; if it is "mandatory", it means that only employees with the skill records for this position on this production line can be formally employed, and a significant personnel gap will be generated when there is a shortage of personnel.

[0043] The matching of production tasks and standard operating procedures (SOPs) employs a strict matching method: the system only searches for an SOP in the approved SOPs list if the production task simultaneously possesses a product code and a production line identifier. Upon successful matching, the system writes the SOP identifier and SOP code into the task. This design avoids misuse of SOPs by different production lines, similar product names, or products from the same series due to fuzzy matching based solely on product names.

[0044] The following explains the methods for importing and parsing production tasks.

[0045] The system reads production task data from an Excel file uploaded by the user. This imported file includes at least the following fields: production line, job type, product code, product name, specifications, batch number, output / hour, planned quantity, and shift. The system reads the first worksheet of the Excel file and parses it row by row, starting from the second row.

[0046] For the production line field, the system removes blanks to obtain the production line text number. It then searches the production line master data for records with the same number, name, or imported values ​​prefixed with the production line number, thus obtaining the production line identifier. This method is compatible with different reporting conventions such as "530104", "530104 line", and "530104 production line". The system then associates the production line identifier with the production line master data.

[0047] For production / H and planned quantity, the system supports ordinary numbers as well as quantities in the form of simple formulas (e.g., the planned quantity cell can be "12000" or "6000+6000"). The system retains the numbers and arithmetic operators and calculates them as integers; if it cannot be parsed, it is treated as 0, and rows with a planned quantity of 0 are not included in the production scheduling.

[0048] To avoid duplicate imports, the system uses "Production Line Number + Batch Number + Planned Quantity" as the deduplication key. If duplicate key values ​​appear in the same import batch, only the first valid task is retained. When generating a task, the system records the date, production line number, job type, product code, product name, specifications, batch number, output, planned quantity, shift, and production line identifier, and initializes the standard operating procedure matching status to "Not Matched".

[0049] like Figure 1 As shown, the intelligent scheduling and personnel competency matching method for cosmetic manufacturing in this embodiment is implemented according to the following steps.

[0050] Step 1: Read the production schedule date. The system generates the start and end times for the selected date. The system only processes production tasks for that date whose standard operating procedure status is "Matched" and which are associated with a standard operating procedure. Tasks are sorted by production line number, allowing tasks on the same production line to be arranged sequentially.

[0051] Step 2: Read rule parameters. The system reads the scheduling rules from the rule table; if the rule table is not configured, the default rules are used. In this embodiment, the rule table is not configured, and the system uses the default rules, including: enabling fatigue and fairness constraints, a maximum daily allocation of 3 times, a key process weight of 30, a proficiency level bonus of 12, enabling line / process change cost, a bonus of 200 for reuse of the same process on the same production line and shift, a bonus of 50 for reuse across processes on the same production line and shift, and enabling scheduling interpretation.

[0052] Step 3: Read the task and standard operating procedure (SOP) steps. The system obtains the SOP corresponding to the production task using the aforementioned method, and reads all steps of the SOP in ascending order of step number. Each step provides parameters such as job title, number of workers, fill-in mode, and criticality level.

[0053] Step 4: Construct a pool of available employees. The system queries all current employees and their skill records, and reads their attendance for the day. If an employee has no abnormal attendance records for the day or their status is "on duty," they are added to the pool of available employees; if their status is "on leave," "off duty," etc., they are excluded. For night shifts, the system further excludes personnel types that are unsuitable for night shift assignments.

[0054] Step 5: Calculate task time. For each production task, the system reads the planned task quantity and the theoretical hourly capacity of the standard operating procedure, and calculates the production duration according to "estimated time = planned quantity / theoretical hourly capacity". If the hourly capacity is empty or 0, no valid production segment is generated, and the system continues to the next segment / process / task.

[0055] Step 6: Serial scheduling within the same production line. The system maintains a production line end time mapping, recording the end time of each scheduled task. The first task on a production line starts at 8:00 by default; subsequent tasks on the same production line start at the end time of the previous task, thus ensuring that multiple batches on the same production line do not overlap in time.

[0056] Step 7: Shift Splitting. In this embodiment, the day shift starts at 8:00 AM and ends at 6:00 PM. The night shift begins after 6:00 PM and can continue until 8:00 AM the following day. If a task starts at 8:00 AM and lasts for 16 hours, the system splits it into two segments: a 10-hour day shift and a 6-hour night shift. The planned workload for each segment is calculated using the formula: "Segment Planned Workload = Segment Work Hours / Total Task Work Hours × Task Planned Workload". The start time, end time, shift number, and estimated work hours for each segment are recorded.

[0057] Step 8: Retrieve and break down tasks and processes. For each task segment, the system processes them one by one according to the standard operating procedure process number. If the number of workers required for a certain process is 0, then skip that process (this process is automated and does not require personnel) and continue to the next segment / process / task. Otherwise, the system generates the personnel requirements for that process based on the process job name, job ID, target production line, and fill-in mode.

[0058] Step 9: Generate formal candidates. Candidate employees must simultaneously meet the following conditions: belong to the available employee pool for the current segment; not assigned to other processes within the same production task; not locked to other production lines within the same shift; possess skill records for the target production line and target position. Skill record matching prioritizes exact matching using the position ID; if no position ID exists, matching based on the same position name within the same production line will be used.

[0059] Step 10: Calculate the score. The candidate's overall score is obtained by summing multiple scoring factors. Process Criticality Score = Process Priority × Critical Process Weight (Critical process weight is 30). Skill Priority Score = (20 - Employee's Minimum Skill Priority) × 30. Proficiency Score = (Employee's Corresponding Skill Proficiency - 1) × Proficiency Weight (Each proficiency level adds 12 points). Fatigue Deduction = Number of Assignments by the Employee that Day × 80. If fatigue and fairness constraints are enabled and the employee's number of assignments for the day has reached the maximum number (maximum number of assignments per day is 3), then the employee will no longer be included in the candidate results. If the employee has previously been used in the same production line, shift, and process, a reuse score of 200 points will be added; if the employee has participated in production in other processes of the same production line and shift, a reuse score of 50 points will be added. The formula for calculating the overall score is: Overall Score = Process Criticality Score + Skill Priority Score + Proficiency Score + Reuse Bonus. Fatigue deduction.

[0060] Step 11: Sort and Select Personnel. The system sorts employees from highest to lowest based on their overall scores and selects the top N employees, where N is the number of workers required for this process. A production scheduling record is generated for each selected employee, recording the task ID, employee ID, process number, job title, whether they are part-time, whether they lack skills, score, planned segment quantity, hourly capacity, estimated working hours, start time, end time, and shift.

[0061] Step 12: Conflict Locking. After selecting an employee, the system immediately updates the occupancy record set (i.e., three locking structures): First, the set of employees already assigned to the same task, preventing the same employee from being assigned to multiple processes within the same production task; second, the "shift + employee" corresponding production line mapping, preventing the same employee from crossing production lines within the same shift; and third, the "production line + shift" employee set and the "production line + shift + position" employee set, used for bonus points for reuse within the same production line or process in subsequent tasks.

[0062] Step 13: Internal Replacement. If there are insufficient qualified personnel, and the replacement mode for this position is "internal staff available," the system will continue searching for non-conflicting employees from the pool of available employees for the current segment. These employees are not required to possess the skills required for the position, but points will be deducted from their overall score. The selected replacement personnel will have their official personnel records generated, and their skills will be marked as insufficient. The selection of replacement candidates is also based on a comprehensive score ranking, but replacement personnel do not receive any proficiency points for the current process.

[0063] Step 14: Gap Generation. If there are still insufficient candidates after formal selection and internal filling, the system generates vacancy allocation records based on the number of vacancies. If the filling mode is "part-time available", it is recorded as a part-time vacancy; if the filling mode is "internal staff available", it is recorded as a skills deficiency vacancy; if the filling mode is "must be competent", it is recorded as a personnel vacancy. The employee ID in the vacancy record is empty, and the score is 0, but the process, position, shift, time, planned quantity, and vacancy nature are retained. After the vacancy record for the current process is generated, it is determined whether all tasks, segments, and processes have been completed; if not, it returns to continue processing the next process, the next segment, or the next task; if so, it proceeds to Step 15.

[0064] Step 15: Results Summary. After all tasks, segments, and processes have been processed, the system batch writes the production scheduling and allocation records and returns statistical results, including the total number of tasks, the number of tasks processed, the number of shifts, the total number of allocation records, the number of employees assigned, the number of part-time vacancies, the number of personnel vacancies, the number of skill shortages, and scheduling explanation information. The front end displays the number of tasks, the number of shifts, the number of available personnel, the number of assigned personnel, the number of part-time personnel needed, the personnel vacancies, and the number of personnel waiting for shifts.

[0065] Taking a single shift's production task on a certain day as an example, the above production scheduling process is fully explained: Production line 530104 needs to produce product A, product code A001, with a planned quantity of 12,000 units. The system already has the approved standard operating procedure for production line 530104, with a theoretical hourly capacity of 3,000 units / hour. The standard operating procedure includes four processes: bottle loading (2 people, part-time can fill in), filling (1 person, must be competent in fill in), capping (2 people, must be competent in fill in), and boxing (3 people, internal staff can fill in).

[0066] Execution process: After the scheduler uploads the production task Excel file, the system parses the production line number, product code, batch number, planned quantity, and shift, and associates it with production line 530104. The system matches the approved standard operating procedure with product code A001 and production line 530104. The system calculates the estimated working hours as 12000 / 3000 = 4 hours. The first task for production line 530104 that day starts at 8:00, therefore this task is scheduled until 12:00, all within the day shift.

[0067] The system reads the daily attendance records and finds that Employee A is on leave and Employee B is off duty; therefore, neither of them is added to the available employee pool. The system then filters employees with filling skill records from production line 530104 for the filling position, calculating scores based on process priority, skill priority, proficiency, reuse within the same production line, and assigned number of times. The employee with the highest score is selected. The same logic applies to the capping position, selecting two employees. If there is only one fully qualified employee for the bottle loading position, the remaining employee is added as a part-time vacancy, creating a "part-time vacancy ×1" slot. If there are insufficient qualified personnel for the boxing position but available internal employees, the system selects the available employee to fill the vacancy and marks them as "skills insufficient."

[0068] Output: The system generates a staffing schedule for the 8:00-12:00 day shift, displaying the names of employees for each process, and clearly showing "Part-time vacancy for bottle loading ×1" and "Insufficient packing skills ×1". Managers can use this information to arrange temporary part-time staff or include the packing position vacancy in subsequent staff training plans.

[0069] Example 2 This embodiment uses a cross-shift production task on a certain day as an example to illustrate the complete closed loop of automatic task splitting and gap backflow to personnel training in the production scheduling process. This embodiment follows the system structure, input parameter grouping description, standard operating procedure import verification method, and production task import parsing method described in Embodiment 1, and will not be repeated here.

[0070] like Figure 2 As shown, taking the production task of production line 530109 on a certain day as an example: Product B, product code B009, planned quantity 40,000 units. The system already has the audited standard operating procedure for production line 530109, with a theoretical hourly capacity of 2,500 units / H. The standard operating procedure includes filling (1 person, supplementary mode required), checkweighing (2 people, supplementary mode required), appearance inspection (1 person, supplementary mode required), and boxing (3 people, supplementary mode internal staff can supplement).

[0071] Step A: Upload the production task Excel file. The scheduler uploads the production plan table, which includes fields such as production line number, product code, batch number, planned quantity, and shift, to the system.

[0072] Step B: Parse task fields and associate them with production lines. The system parses the production line number, product code, batch number, planned quantity, and shift, and associates them with production line 530109.

[0073] Step C: Match product code and production line identifier to the approved standard operating procedure. The system matches the product code B009 and the production line identifier 530109 to the approved standard operating procedure.

[0074] Step D: Read the standard operating procedure (SOP) process, number of personnel, capacity, and replacement mode. The system reads all process information for the SOP: filling (1 person, must be competent), checkweighing (2 people, must be competent), visual inspection (1 person, must be competent), boxing (3 people, internal staff can fill in), and theoretical hourly capacity (2500 units / H).

[0075] Step E: Read employee skills, attendance, and scheduling rules. The system reads the skill records and daily attendance status of all employees. All day shift employees are present as scheduled; for the night shift segment, the system further excludes personnel types unsuitable for night shift scheduling.

[0076] Step F: Calculate working hours and arrange them sequentially according to the production line. The system calculates the estimated working hours as 40000 / 2500 = 16 hours. Production line 530109 has no other tasks that day, so this task will be scheduled starting at 8:00 AM.

[0077] Step G: Split the task into day / night shift segments. This task runs from 8:00 AM to 12:00 AM, spanning the 6:00 PM day shift deadline. The system splits it into two segments: a 10-hour day shift (8:00 AM - 6:00 PM) and a 6-hour night shift (6:00 PM - 12:00 AM). The planned quantity for the day shift segment is 25,000 units (10 / 16 × 40,000), and the planned quantity for the night shift segment is 15,000 units (6 / 16 × 40,000). Each shift segment independently inherits product information, production line information, and standard operating procedure information.

[0078] Step H: Screening candidates for job competency by process. During the day shift, the system screens formal candidates with the corresponding job skill records for production line 530109 for each process. During the night shift, only one competent employee was found for the inspection and weighing position, while the standard operating procedure requires two people, and the replacement mode for this position is "must be competent".

[0079] Step 1: Multi-factor scoring and ranking for candidate selection. The system calculates a comprehensive score for formal candidates in each process and ranks them from highest to lowest score. If employees on the same production line during the day shift have already participated in previous processes, subsequent employees on the same production line and in the same process will receive reuse bonus points (200 points for reuse in the same process and 50 points for reuse across processes on the same production line), thereby reducing job change costs.

[0080] Step J: Conflict locking and allocation record generation. The system immediately updates the occupied record set (i.e., the three locking structures) after selecting an employee, generating production scheduling allocation records for each process. All personnel for each process in the day shift segment are fully staffed; the night shift segment's weighing position has one employee, but is still short one person.

[0081] Step K: Determine if the number of employees is sufficient. The system determines whether the number of employees assigned to each process equals the number of workers needed. All processes during the day shift meet the requirement; however, the night shift's weighing and inspection position has only one employee assigned, requiring two more, so the requirement is not met. The packing position's replacement mode is "internal employees can fill in." The system searches for non-conflicting employees from the available employee pool, without requiring them to have packing skills. The replacement employee is recorded as a formal employee and marked "insufficient skills."

[0082] Step L: Output the production scheduling personnel table. The system outputs the production scheduling results, clearly displaying the name of the employee corresponding to each process, and clearly indicating "Inspection and weighing personnel shortage ×1" and "Insufficient packing skills ×1".

[0083] Step M: Internal replacement or generation of gap records. For the weighing position, because the replacement mode is "must be competent," unskilled personnel are not allowed to fill the gap. The system generates personnel gaps based on the number of vacancies. For the packing position, the replacement mode is "internal employees can fill the gap." The system selects unskilled employees from the available employee pool to fill the gap and marks them as lacking skills. If the gap is still insufficient after replacement, a skill deficiency gap is generated.

[0084] After production scheduling is completed, the next steps are gap filling and personnel training: Step N: Gap Repatriation Development Strategy. The development strategy module reads gap data from the production scheduling records and calculates gaps by task, production line, position, shift, and date. The system reads the production scheduling records for the past 60 days, aggregates gaps by production line and position, and records the gap date, minimum gap, maximum gap, and shortage status. Simultaneously, the system reads the skill records of current employees and calculates the number and list of competent personnel for each production line and position. In this embodiment, the system found multiple personnel shortages in the weighing position of production line 530109 in the records of the past 60 days, indicating a shortage for this position.

[0085] Step O: Seed Employee Development and Assessment. After reviewing the competent personnel, shortage numbers, and shortage status for the weighing position on production line 530109 in the development strategy dashboard, managers select two candidates for development, creating seed employee records. The system creates a competency assessment task for the weighing position on production line 530109, linking the production line, position, target level, assessment mentor, assessment method, and deadline. Assessment methods include written exams, written exams plus practical skills assessments, exceptional performance evaluations, and skills transfer. Managers add the two seed employees as assessment participants. For assessment participants, the system verifies the completeness of the score fields according to the assessment method; both employees pass the written exam plus practical skills assessment, and the supervisor approves through electronic signatures and comments.

[0086] Step P: Skills are recorded after passing the assessment. After the assessment task is reviewed and approved, the system checks whether each participating employee has passed. If both employees pass the assessment, the system automatically creates skill records for both employees in the 530109 production line inspection position—recording employee ID, production line identifier, position name, skill level, and proficiency—making them official candidates for subsequent production scheduling; at the same time, the seed employee mark for both employees in the 530109 production line inspection position is deleted.

[0087] At this point, the production scheduling system has formed a complete closed loop: "production scheduling identifies gaps—identifies trainees—organizes assessments—skills review—participation in the next round of production scheduling." The next time production line 530109 is scheduled, these two employees will be able to enter the formal candidate pool in step H, thus reducing the personnel shortage for the weighing position.

[0088] Example 3 This embodiment provides an alternative implementation of standard operating procedure matching, which can replace or supplement the precise matching method described in step 3 of embodiment 1.

[0089] In step 3 of Example 1, the system adopts a precise matching method of "product code + production line identifier" - the matching is successful only when the product code of the production task is completely consistent with the product code of the standard operating procedure and the production line identifier is completely consistent.

[0090] As an alternative, a controlled fuzzy match of "product name + production line identifier" can be added after an exact match fails. Specifically, when the system performs an exact match as described in step 3 of Example 1 and fails to find any standard operating procedure, the system uses the "product name" and "production line identifier" of the production task as matching conditions to find a standard operating procedure with a matching production line identifier and a matching product name.

[0091] After a successful fuzzy match, the system does not directly associate the production task with the standard operating procedure and enter the scheduling process. Instead, it marks the matching result as "pending confirmation" or "partial match" and only allows it to enter automatic scheduling after being reviewed by management personnel, in order to avoid mismatches caused by similar product names.

[0092] Example 4 The difference between this embodiment and Embodiment 1 lies in the admission criteria for formal candidates in step 9.

[0093] In step 9 of Example 1, the system uses "existence of skill records for the same production line and position" as a hard admission condition for formal candidates—whether an employee possesses the competence for a certain position is mainly determined by whether they have skill records for the corresponding production line and position. This is a binary judgment method: if there is a record, they enter the formal candidate list; if there is no record, they do not enter the formal candidate list.

[0094] In this embodiment, when the system generates formal candidates in step 9, a skill level threshold is further introduced as an admission condition. In this embodiment, the employee skill record not only records whether they "possess" the skills for a certain position, but also records the specific skill level (such as Y01, Y02, Y03, Y04, etc., with a higher level number indicating a higher skill level); at the same time, each step of the standard operating procedure can be configured with a corresponding skill level requirement (for example, a certain step requires Y03 or above).

[0095] Specifically, in step 9, when screening candidate employees, the system not only determines whether an employee has a skill record for the corresponding production line and position, but also further determines whether the employee's skill level meets or exceeds the skill level threshold required for that process. Only employees who simultaneously meet the criteria of "having a skill record" and "having a skill level that meets or exceeds the threshold" are allowed to enter the formal candidate pool. For example, if a process requires skill level Y03, then employees with skill levels Y03 or Y04 can enter the formal candidate pool, while employees with skill levels Y01 or Y02 are not.

[0096] For employees whose skill level is below the process requirements (such as Y01, Y02) but who are still available, the system does not exclude them from production scheduling, but instead treats them as internal replacement candidates. In the internal replacement stage of step 13, when there are not enough formal candidates, the system can select replacement personnel from these employees with insufficient skill levels and mark them as "insufficient skills" in the allocation record.

[0097] Compared with the binary judgment method in Example 1, this example retains the quality assurance mechanism of "sufficient skill level is required for formal employment" and provides employees who are below the requirements with the opportunity to participate in production through internal replacement. At the same time, the ability risk is made explicit by marking "insufficient skills", which provides accurate data support for subsequent training and improvement.

[0098] It should be noted that the skill level thresholds described in this embodiment can be flexibly configured according to the criticality of different processes—critical processes can be set with higher level requirements (such as Y03 and above), while non-critical processes can be set with lower level requirements (such as Y01 and above) or no level requirements can be set, in order to adapt to different production management strategies.

[0099] Example 5 The difference between this embodiment and Embodiment 1 lies in the method of determining the time boundary for shift splitting in step 7.

[0100] In step 7 of embodiment 1, the system adopts fixed shift time boundaries—the day shift starts at 8:00 and ends at 18:00. After 18:00, the night shift begins and can continue until 8:00 the next day.

[0101] In this embodiment, the shift time boundaries are not fixed values, but are read directly from the scheduling rules by the system. The scheduling rule parameters are pre-configured with the start time and end time (or end time) of the day shift, as well as the start time and end time of the night shift. When the system performs the shift splitting in step 7, it directly reads these rule parameters as shift boundaries, instead of using the fixed times preset in the code.

[0102] For example, the production scheduling rules can be configured with day shift hours from 08:00 to 20:00 and night shift hours from 20:00 to 08:00 the next day. The system determines the splitting point based on the read rule parameters and splits the cross-shift production tasks according to the read shift boundaries, thereby adapting to different workshops, seasons, or temporary shift adjustments.

[0103] Example 6 The difference between this embodiment and Embodiment 1 lies in the range of candidate sources for internal filling in step 13.

[0104] In step 13 of Example 1, when there are insufficient qualified personnel and the job filling mode is "internal employees can fill," the system searches for non-conflicting employees from the available employee pool of the current segment to fill the position. The filling personnel are not required to have the skills required for the position; points are deducted based on the scoring, and insufficient skills are marked.

[0105] In this embodiment, the system further incorporates production line grouping experience during the internal replacement phase. Production line grouping refers to classifying multiple production lines with similar equipment configurations or similar process requirements into the same production line group. When a certain position on a production line is understaffed and requires internal replacement, the system can include employees with similar position experience within the same production line group into a secondary candidate pool and participate in the scoring with a lower weight.

[0106] After the replacement is completed, the selected replacement personnel (regardless of whether they come from the current production line or other production lines within the same production line group) will have a formal personnel record generated, and their skills will be marked as insufficient. The system will then inform the user that there is a personnel shortage in this process of the production line. The user can view the number of shortages for a specific process of a specific production line in the development strategy module and manually select seed employees accordingly.

[0107] Compared with Example 1, this example expands the range of personnel sources for internal replacement by introducing production line grouping experience. While retaining the principle of "priority for the same production line", it provides more available replacement options when there is a shortage of personnel, thereby improving the completion rate of production scheduling.

[0108] Example 7 The difference between this embodiment and Embodiment 2 lies in the method of determining the seed employees.

[0109] In step O of Example 2, after the manager views the competent personnel, the number of vacancies and the shortage status of a certain production line and a certain position in the development strategy dashboard, the manager manually selects personnel to be trained for the position of that production line and forms a seed employee record.

[0110] In this embodiment, the system can automatically recommend seed employees according to a preset recommendation strategy and provide reasons for the recommendation. Specifically, the system reads the following data: the current list of employees waiting for shifts, the employees' overall performance evaluation level, the number of historical skills the employees already possess (i.e., the number of production line-job skill records they have mastered), and the frequency of job vacancies for each production line and job (derived from historical production scheduling feedback parameters). The system comprehensively evaluates candidate employees based on the above data and automatically selects recommended candidates according to preset rules. For example, the system can prioritize recommending employees with higher overall performance evaluation levels and higher job vacancies frequency. After the recommendation is completed, the system also provides reasons for the recommendation for management personnel to refer to and confirm.

[0111] After reviewing the seed employees recommended by the system, managers can confirm acceptance, adjust, or reject the recommendations. Upon acceptance, the system automatically creates a seed employee record for the corresponding production line and position, and initiates the subsequent performance evaluation task creation process. The reasons for the recommendation can include detailed scoring information and evaluation results across various dimensions, allowing managers to understand the basis for the recommendation.

[0112] Compared to the method in Example 2 where selection was entirely done manually by managers, this example reduces the decision-making cost for managers in screening candidates from a large number of employees by automatically recommending candidates and providing reasons for the recommendations. At the same time, it retains the final review authority of managers, thus balancing the system's decision-making assistance capabilities with the reliability of human judgment.

Claims

1. A method for intelligent scheduling and personnel competency matching in cosmetic manufacturing, characterized in that, include: Obtain at least one production task within the production scheduling date, and a standard operating procedure corresponding to each production task; the standard operating procedure includes at least one process, and each process is associated with a job title, number of workers, and fill-in mode; Based on the planned quantity of each production task and the theoretical capacity of the standard operating procedure, the estimated working hours of each production task are calculated, and the production tasks spanning multiple shifts are divided into at least two shift segments according to the preset shift time boundaries. For each of the procedures in each of the said shift segments, perform the following steps: Obtain employees whose attendance status is available for the day and create a pool of available employees. Candidate employees are selected from the available employee pool, and the candidate employees simultaneously meet the following requirements: they have not been assigned to other processes within the same production task, they have not been assigned to other production lines within the same shift, and they possess the skill records for the target production line and target position. A comprehensive score is calculated for each candidate employee according to a preset scoring model. The top N employees are selected from the highest to the lowest comprehensive scores and assigned to the current process, where N is the number of employees in the process. The scoring factors of the scoring model include at least the job key level factor, skill priority factor, proficiency factor, and fatigue constraint factor. If the number of employees assigned to the current process is less than N, then a replacement operation is performed according to the replacement mode of the process. If, after the aforementioned replacement operation, the number of employees assigned to the current process is still less than N, a gap record is generated according to the replacement mode of the process. The personnel identifier in the gap record is empty, but the process, production line, shift attributes, and gap type identifier are retained. After each personnel selection or replacement operation is completed, the occupancy record set is updated synchronously. The occupancy record set includes at least: a task-level occupancy subset, which records employees who have been assigned in the current production task; a time-period-level occupancy subset, which records employees who have been locked by a specific production line in the current shift; and a scenario-level occupancy subset, which records the distribution of employees in each production line and position in the current shift.

2. The intelligent scheduling and personnel competency matching method for cosmetic manufacturing according to claim 1, characterized in that, The calculation method for the comprehensive score includes: Overall score = Process criticality score + Skill priority score + Proficiency score + Reuse bonus - Fatigue deduction; The process key level score is determined based on the process key level and the preset key process weight; the skill priority score is determined based on the candidate employee's skill priority; the proficiency score is determined based on the candidate employee's proficiency level in the target production line and target position; the reuse bonus is determined based on the candidate employee's presence status in the scenario-level occupancy subset; and the fatigue deduction is determined based on the number of times the candidate employee has been assigned that day.

3. The intelligent scheduling and personnel competency matching method for cosmetic manufacturing according to claim 2, characterized in that, The reuse bonus includes: When the candidate employee already exists in the occupancy record of the current shift, current production line, and current position, the reuse bonus is the first reuse bonus value; When the candidate employee already exists in the current shift and current production line occupancy record, but does not exist in the current position occupancy record, the reuse bonus is the second reuse bonus value; The first reuse bonus is greater than the second reuse bonus.

4. The intelligent scheduling and personnel competency matching method for cosmetic manufacturing according to claim 1, characterized in that, The shift time boundaries include the start time of the day shift, the end time of the day shift, and the start time of the night shift; the step of splitting the production task across shifts into at least two shift segments includes: Using the day shift end time as the dividing point, the part of the production task from the start time to the day shift end time is classified as the day shift segment, and the part from the day shift end time to the end time is classified as the night shift segment. Each shift segment inherits the product information, production line information, and standard operating procedure information of the production task, and allocates the segment's planned workload according to the proportion of the segment's working hours to the estimated working hours.

5. The intelligent scheduling and personnel competency matching method for cosmetic manufacturing according to claim 1, characterized in that, The step of obtaining the standard operating procedure corresponding to each production task includes: Based on the product code and production line identifier of the production task, find the standard operating procedures in the approved standard operating procedures that have the same product code and production line identifier as the production task. After a successful match, the identification information of the standard operating procedure is written into the production task; If a match fails, the production task will not be included in the production scheduling process.

6. The intelligent scheduling and personnel competency matching method for cosmetic manufacturing according to claim 1, characterized in that, The gap type identifier is determined according to the filling mode of the process: When the filling mode is part-time filling, the gap type is identified as part-time gap; When the filling mode is that internal employees can fill the gap, the gap type is identified as a skills deficiency gap; When the filling mode is required, the gap type is identified as a personnel gap.

7. The intelligent scheduling and personnel competency matching method for cosmetic manufacturing according to claim 1, characterized in that, It also includes gap-filling and personnel training steps: The gap records are aggregated and statistically analyzed by production line and job position to generate the gap frequency and number of gaps for each production line and job position. Based on the results of the aggregated statistics, seed employees to be trained are identified, and competency assessment tasks for corresponding production lines and positions are created for the seed employees. Obtain the assessment results of the competency assessment task. For employees who pass the assessment, write their skill records in the corresponding production line and position, so that the employees can be included in the candidate employee selection range for subsequent production scheduling.

8. A smart production scheduling and personnel competency matching system for cosmetic manufacturing, characterized in that, include: The data acquisition module is used to acquire at least one production task within the production scheduling date, the standard operating procedure corresponding to each production task, the employee's skill record, and the attendance status for the day; the standard operating procedure includes at least one process, and each process is associated with a job title, number of employees, and replacement mode; The time calculation and splitting module is used to calculate the estimated time based on the planned quantity of each production task and the theoretical capacity of the standard operating procedure, and to split the production task across shifts into at least two shift segments according to the preset shift time boundaries. The candidate employee screening module is used to screen candidate employees from the available employee pool for each process in each shift segment. The candidate employees simultaneously meet the following requirements: they have not been assigned to other processes in the same production task, they have not been assigned to other production lines in the same shift, and they have the skill records for the target production line and the target position. The scoring and selection module is used to calculate a comprehensive score for each candidate employee according to a preset scoring model, and select the top N employees from the highest to the lowest comprehensive scores to be assigned to the current process, where N is the number of employees in the process. The scoring factors of the scoring model include at least the job key level factor, skill priority factor, proficiency factor, and fatigue constraint factor. The fill-in execution module is used to perform a fill-in operation according to the fill-in mode of the process when the number of employees assigned to the current process is less than N. The gap generation module is used to generate a gap record according to the filling mode of the process when the number of employees assigned to the current process is still less than N after the filling operation. The personnel identifier in the gap record is empty, but the process, production line, shift attributes and gap type identifier are retained. The conflict locking module is used to synchronously update the occupancy record set after each personnel selection or replacement operation is completed. The occupancy record set includes at least a task-level occupancy subset, a time-period-level occupancy subset, and a scene-level occupancy subset.

9. The intelligent scheduling and personnel competency matching system for cosmetic manufacturing according to claim 8, characterized in that, In the scoring and selection module, the calculation method for the comprehensive score includes: Overall score = Process criticality score + Skill priority score + Proficiency score + Reusability bonus Fatigue deduction; The reuse bonus is determined based on the presence status of the candidate employee in the scenario-level occupancy subset: when the candidate employee already exists in the occupancy records of the current shift, current production line, and current position, the reuse bonus is the first reuse bonus value; when the candidate employee already exists in the occupancy records of the current shift and current production line but not in the occupancy records of the current position, the reuse bonus is the second reuse bonus value, and the first reuse bonus value is greater than the second reuse bonus value.

10. The intelligent scheduling and personnel competency matching system for cosmetic manufacturing according to claim 8, characterized in that, It also includes a development strategy module: the development strategy module is used to aggregate and statistically analyze the gap records by production line and position, and generate the gap frequency and number of gaps for each production line and position; determine the seed employees to be trained based on the results of the aggregated statistics, and create competency assessment tasks for the seed employees corresponding to the production line and corresponding position; obtain the assessment results of the competency assessment tasks, and for employees who pass the assessment, write the employee's skill records in the corresponding production line and corresponding position, so that the employee enters the candidate employee selection range for subsequent production scheduling.

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