Method and device for determining production scheduling plan, electronic equipment and medium

Abstract

The invention relates to the field of data processing, and provides a production scheduling plan determination method and device, electronic equipment and a medium, and the method comprises the steps: obtaining packaging task information which comprises a packaging task set, the total number of stations and the total number of personnel; according to the packaging task information, a scheduling plan optimization model is constructed, the scheduling plan optimization model comprises constraint conditions and an optimization target, the constraint conditions are attribute information corresponding to each packaging task in the packaging task set, and the optimization target is that the total man-hour difference of any two stations is minimum; and solving the production scheduling plan optimization model to obtain a target production scheduling plan corresponding to the packaging task information, the target production scheduling plan being a target personnel number and a target station corresponding to each packaging task in the packaging task set. In the process of making the production scheduling plan, participation of production scheduling plan making personnel is not needed, the problem that the quality of the production scheduling plan is greatly influenced by personnel experience under the condition that the data volume of the packaging unit is large is solved, and the production scheduling plan with the optimal productivity balance effect is formed.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing, and in particular to a method, device, electronic device and medium for determining a production scheduling plan. Background Art

[0002] With the rapid development of vehicle technology, vehicles have become an important means of transportation in people's daily lives. In order to solve the problem of semi-bulk packaging unit production, the overseas logistics department needs to make reasonable arrangements for the available workstations and personnel in the production workshop.

[0003] When making production schedules, the planners need to consider the limited relationship between some packaging units and workstations, the adjacent relationship between the same or similar packaging units, and other issues, making the production schedule formulation process cumbersome and complicated. At the same time, as the amount of packaging unit data increases, the production schedule specification process is greatly affected by the experience of personnel, making it difficult to obtain a stable and good production schedule. Summary of the invention

[0004] In view of this, the purpose of the present disclosure is to propose a method, device, electronic device and medium for determining a production schedule, so as to solve the problem that the current production schedule formulation process has become cumbersome and complicated, and as the amount of packaging unit data increases, the production schedule specification process is greatly affected by personnel experience, making it difficult to obtain a stable and good production schedule.

[0005] Based on the above purpose, a first aspect of the present disclosure provides a method for determining a production schedule, the method comprising:

[0006] Acquire packaging task information, wherein the packaging task information includes a packaging task set, a total number of workstations, and a total number of personnel;

[0007] Constructing a production scheduling optimization model according to the packaging task information, wherein the production scheduling optimization model includes constraints and optimization goals, the constraints are the attribute information corresponding to each packaging task in the packaging task set, and the optimization goal is to minimize the difference in total working hours between any two workstations;

[0008] The production scheduling optimization model is solved to obtain a target production scheduling corresponding to the packaging task information, wherein the target production scheduling is a target number of personnel and a target workstation corresponding to each packaging task in the packaging task set.

[0009] In some embodiments, solving the production scheduling optimization model to obtain a target production scheduling plan corresponding to the packaging task information includes:

[0010] Taking the constraint conditions as constraints, determining the target number of personnel and target workstations corresponding to each packaging task, and obtaining at least one initial production scheduling plan;

[0011] For each initial production schedule: obtain the task working hours corresponding to each packaging task, determine the target packaging task packaged by each workstation in the initial production schedule; determine the total working hours corresponding to each workstation according to the target packaging task, the target number of personnel and the task working hours; perform mean processing on the total working hours corresponding to all workstations to obtain the average working hours; perform variance processing on the total working hours corresponding to each workstation according to the average working hours to obtain the variance sum corresponding to the initial production schedule;

[0012] The sum of variances corresponding to all initial production schedules is calculated, and the initial production schedule with the smallest sum of variances is taken as the target production schedule.

[0013] Through the above scheme, the variance sum corresponding to each initial production schedule is calculated separately, and then the target production schedule is selected. The determination of the target production schedule is more accurate, avoiding the situation where the optimal target production schedule is selected incorrectly due to partial data errors.

[0014] In some embodiments, determining the total working hours corresponding to each workstation according to the target packaging task, the target number of personnel, and the task working hours includes:

[0015] Get the total number of tasks for the target packaging task;

[0016] Multiply the total number of tasks by the task working hours to obtain the total task working hours;

[0017] The total working hours of the task are compared with the target number of personnel to obtain the total working hours corresponding to each workstation.

[0018] Through the above scheme, the total number of tasks, task working hours and target number of personnel of the target packaging task are used to determine the total working hours corresponding to the workstation, so that the total working hours corresponding to each workstation are determined more accurately.

[0019] In some embodiments, the method further comprises:

[0020] Determine a plurality of target packaging tasks corresponding to each workstation according to the target production scheduling plan;

[0021] For each workstation, attribute information corresponding to each target packaging task is determined, and the packaging sequence of the multiple target packaging tasks is optimized according to the attribute information to obtain an optimized target production scheduling plan.

[0022] Through the above scheme, in the process of packaging the packaging task, the actual time required for the packaging task, in addition to the packaging man-hours, also takes into account the time for replacing the mold used for packaging the packaging task. By optimizing the packaging sequence according to the attribute information of the target packaging task, the number of times the mold is replaced is reduced, and the production efficiency of the packaging station is improved.

[0023] In some embodiments, the attribute information includes a task name and configuration information.

[0024] The step of optimizing the packaging sequence of the plurality of target packaging tasks according to the attribute information to obtain an optimized target production scheduling plan includes:

[0025] Obtaining a task name of each target packaging task, and clustering the multiple target packaging tasks according to the task name to obtain at least one packaging task group;

[0026] Determine the configuration information corresponding to each packaging task group and obtain the preset configuration order;

[0027] The at least one packaging task group is sorted according to the configuration information and the configuration sequence to obtain an optimized packaging sequence.

[0028] Through the above scheme, for multiple target packaging tasks required to be packaged at each workstation, in order to reduce the number of times the molds are replaced, the multiple target packaging tasks are sorted. Target packaging tasks with the same configuration information indicate that they use the same molds. Putting target packaging tasks using the same molds in an adjacent order for packaging can effectively reduce the number of times the molds are replaced and shorten the total working hours corresponding to the workstation.

[0029] In some embodiments, clustering the plurality of target packages according to the task names to obtain at least one packaging task group includes:

[0030] Removing symbols from the task name, and / or removing Chinese characters representing directions from the task name, to obtain a processed task name;

[0031] The target packaging tasks with the same processed task name are divided into the same packaging task group.

[0032] Through the above scheme, by processing the task names, the influence of irrelevant words in the task names on clustering is avoided, thereby improving the accuracy of clustering.

[0033] In some embodiments, the sorting process of the at least one packaging task group according to the configuration information and the configuration order includes:

[0034] In response to the existence of multiple packaging task groups with the same configuration information, for the multiple packaging task groups with the same configuration information:

[0035] Get the number of packaging tasks corresponding to each packaging task group;

[0036] The plurality of packaging task groups are sorted in descending order of the number of packaging tasks.

[0037] Through the above scheme, for the packaging task groups with the same configuration information, they can also be further sorted according to the number of packaging tasks to avoid the packaging task groups with the same configuration information having no fixed order, which causes confusion in the production schedule.

[0038] Based on the same inventive concept, the second aspect of the present disclosure proposes a device for determining a production schedule, comprising:

[0039] A data acquisition module is configured to acquire packaging task information, wherein the packaging task information includes a packaging task set, a total number of workstations, and a total number of personnel;

[0040] A model building module is configured to build a production scheduling optimization model according to the packaging task information, wherein the production scheduling optimization model includes constraints and optimization goals, the constraints are attribute information corresponding to each packaging task in the packaging task set, and the optimization goal is to minimize the difference in total working hours between any two workstations;

[0041] The model solving module is configured to solve the production scheduling optimization model to obtain a target production scheduling plan corresponding to the packaging task information, wherein the target production scheduling plan is a target number of personnel and a target workstation corresponding to each packaging task in the packaging task set.

[0042] Based on the same inventive concept, the third aspect of the present disclosure proposes an electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable by the processor, wherein the processor implements the method for determining a production schedule as described above when executing the computer program.

[0043] Based on the same inventive concept, the fourth aspect of the present disclosure proposes a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute the method for determining a production scheduling plan as described above.

[0044] Based on the same inventive concept, the fifth aspect of the present disclosure provides a vehicle, including the production scheduling determination device described in the second aspect or the electronic device described in the third aspect or the storage medium described in the fourth aspect.

[0045] As can be seen from the above, the present disclosure proposes a method, device, electronic device and medium for determining a production schedule, and obtains packaging task information, wherein the packaging task information includes a packaging task set, a total number of workstations and a total number of personnel, so as to determine the corresponding workstation and personnel for each packaging task in the packaging task set according to the number of workstations and the total number of personnel. A production schedule optimization model is constructed based on the packaging task information, wherein the production schedule optimization model includes constraints and optimization targets, and the constraints are the attribute information corresponding to each packaging task in the packaging task set. The production schedule optimization model is constructed based on the packaging task information. When constructing the production schedule optimization model, the optimization target is set to minimize the total working hours difference between any two workstations, so that the production schedule result obtained by solving the production schedule optimization model later satisfies the optimization target, so that the working hours difference of personnel participating in packaging production at different workstations is minimized, avoiding large differences in working hours of personnel at different workstations, causing user dissatisfaction, and meeting the requirements of the best production schedule results. At the same time, the production schedule optimization model is used to determine the target production schedule, which simplifies the production schedule formulation process. The production scheduling optimization model is solved to obtain the target production scheduling corresponding to the packaging task information, wherein the target production scheduling is the target number of personnel and target workstations corresponding to each packaging task in the packaging task set. In the process of formulating the production scheduling, the participation of the production scheduling personnel is not required, which solves the problem that the quality of the production scheduling is greatly affected by the experience of the personnel when the amount of packaging unit data is large. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 A flowchart of a method for determining a production schedule according to an embodiment of the present disclosure;

[0048] Figure 2 A flowchart of a method for determining a production schedule according to another embodiment of the present disclosure;

[0049] Figure 3 A structural block diagram of a device for determining a production schedule according to an embodiment of the present disclosure;

[0050] Figure 4 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0051] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0052] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0053] The terms used in this disclosure are explained as follows:

[0054] KD: Knocked Down (KD), semi-knocked down assembly.

[0055] cplex: CPLEX is a high-performance optimization solver for linear, mixed integer, and quadratic programming problems.

[0056] gurobi: Gurobi is a new generation of large-scale optimizer developed by Gurobi Optimization, an American company.

[0057] ortool: OR-Tools is an optimization solver created by Google in C++ and can be used in Python, Java or C#.

[0058] In response to the production problem of automobile KD packaging units, the overseas logistics department needs to arrange the production of the required packaging units according to the available workstations and personnel in the KD production workshop and the principle of balanced production scheduling, and obtain the personnel arrangement of each workstation and the production plan of the packaging units.

[0059] In this process, planners need to consider the personnel plan of each workstation (the number of workers at the workstation affects the production time of the packaging unit arranged at this workstation), the grouping relationship of the multi-dimensional attributes of the packaging unit, the limited relationship between some packaging units and workstations, the adjacent relationship of the same or similar packaging units, etc., and finally achieve the balance of working hours for each production station. The planning process of planners is complex and cumbersome. As the amount of packaging unit data increases, the balancing process is affected by the experience of personnel, and the production scheduling often obtains feasible results. Therefore, how to automatically obtain the optimal KD production plan has become an urgent problem that the logistics department needs to solve.

[0060] Based on the above description, this embodiment proposes a method for determining a production schedule, such as Figure 1 As shown, the method includes:

[0061] Step 101, obtaining packaging task information, wherein the packaging task information includes a packaging task set, a total number of workstations, and a total number of personnel.

[0062] In a specific implementation, packaging task information is obtained, wherein the packaging task information includes a packaging task set, a total number of workstations, and a total number of personnel, wherein the packaging task set includes at least one packaging task, and the packaging task is a KD packaging task.

[0063] The workstation is a work unit of a packaging task, and each packaging task corresponds to a workstation. The personnel are personnel working at the workstation.

[0064] Step 102: construct a production scheduling optimization model based on the packaging task information, wherein the production scheduling optimization model includes constraints and optimization goals, the constraints are the attribute information corresponding to each packaging task in the packaging task set, and the optimization goal is to minimize the difference in total working hours between any two workstations.

[0065] In specific implementation, a production scheduling optimization model is constructed according to the packaging task information. According to the constructed production scheduling optimization model, it is possible to determine at which workstation each packaging task in the packaging task information is packaged, and the number of personnel corresponding to each workstation can be determined.

[0066] The constraint condition of the production scheduling optimization model is the attribute information corresponding to each packaging task in the packaging task set. The attribute information is the information carried by the packaging task itself, including workshop attribute information, task name and configuration attributes, etc.

[0067] The workshop attribute information refers to the production workshop corresponding to the packaging task, the task name refers to the name corresponding to the packaging task, and the configuration attribute refers to the mold information required for packaging the packaging task. In this embodiment, each packaging task corresponds to unique attribute information.

[0068] In some embodiments, the constraints of the production scheduling optimization model also include production scheduling order constraints between any two packaging tasks. Due to the different types of packaged objects in each packaging task, or due to other external constraints such as production materials required to produce the packaging tasks, a packaging task is restricted to be produced before or after another packaging task.

[0069] In some embodiments, the constraints of the production scheduling optimization model also include constraints on the number of people at workstations, such as constraints on certain workstations requiring the number of people at the workstations to be a certain fixed number.

[0070] In some embodiments, the constraints of the production scheduling optimization model also include designated workstation constraints, such as certain packaging tasks must be packaged at a designated workstation.

[0071] The optimization goal of the production scheduling optimization model is set to minimize the difference in total working hours between any two workstations, that is, the difference in the total working hours between every two workstations is minimized, so that the subsequent production scheduling results obtained by solving the production scheduling optimization model meet the optimization goal, so that the difference in working hours of personnel involved in packaging production at different workstations is minimized, avoiding dissatisfaction among staff between two workstations due to too long working hours at some workstations and short working hours at some workstations.

[0072] For example, there are 3 workstations, namely workstation A, workstation B and workstation C. There are 10 staff members. Production schedule A calculates that the total working hours of workstation A are 8.7 hours, the total working hours of workstation B are 8.5 hours, and the total working hours of workstation C are 8.6 hours. Production schedule B calculates that the total working hours of workstation A are 9.3 hours, the total working hours of workstation B are 8.1 hours, and the total working hours of workstation C are 8.9 hours. Then production schedule A is more in line with the optimization goal of the production schedule optimization model.

[0073] Step 103, solving the production scheduling optimization model to obtain a target production scheduling corresponding to the packaging task information, wherein the target production scheduling is a target number of personnel and a target workstation corresponding to each packaging task in the packaging task set.

[0074] In specific implementation, after the production scheduling optimization model is built, the production scheduling optimization model is solved to obtain the target production scheduling. The target production scheduling includes the target number of personnel and target workstations corresponding to each packaging task in the packaging task set, that is, the production personnel can obtain from the production scheduling which workstation each packaging task is produced at and how many personnel are assigned to each workstation.

[0075] Through the above scheme, the packaging task information is obtained, wherein the packaging task information includes the packaging task set, the total number of workstations and the total number of personnel, so as to determine the corresponding workstations and personnel for each packaging task in the packaging task set according to the number of workstations and the total number of personnel. A production scheduling optimization model is constructed according to the packaging task information, wherein the production scheduling optimization model includes constraints and optimization targets, and the constraints are the attribute information corresponding to each packaging task in the packaging task set. The production scheduling optimization model is constructed based on the packaging task information, and the optimization target of the production scheduling optimization model is to minimize the difference in the total working hours of any two workstations. The target production scheduling plan can be obtained by solving the production scheduling optimization model, so that the total working hours difference between each workstation is minimized, and the best production scheduling result is obtained, which simplifies the production scheduling plan formulation process. The production scheduling optimization model is solved to obtain the target production scheduling plan corresponding to the packaging task information, wherein the target production scheduling plan is the target number of personnel and target workstation corresponding to each packaging task in the packaging task set. In the process of formulating the production plan, there is no need for the participation of the production plan makers, which solves the problem that when the amount of packaging unit data is large, the quality of the production plan is greatly affected by the experience of the personnel, and forms a production plan with the best capacity balancing effect.

[0076] The production scheduling optimization model includes constraints and optimization targets. There is at least one solution result in the process of solving the production scheduling optimization model. For multiple solution results, the solution result closest to the optimization target needs to be selected as the result output by the final production scheduling optimization model. Therefore, in some embodiments, step 103 specifically includes:

[0077] Step 1031, using the constraint conditions as constraints, determine the target number of personnel and target workstations corresponding to each packaging task, and obtain at least one initial production scheduling plan.

[0078] Step 1032, for each initial production schedule: obtain the task working hours corresponding to each packaging task, and determine the target packaging task packaged by each workstation in the initial production schedule; determine the total working hours corresponding to each workstation according to the target packaging task, the target number of personnel and the task working hours; perform mean processing on the total working hours corresponding to all workstations to obtain the average working hours; perform variance processing on the total working hours corresponding to each workstation according to the average working hours to obtain the variance sum corresponding to the initial production schedule.

[0079] Step 1033, the sum of variances corresponding to all initial production schedules is counted, and the initial production schedule with the smallest sum of variances is used as the target production schedule.

[0080] In the specific implementation, the corresponding relationship between each packaging task and the workstation and the number of personnel at the workstation is arranged based on the constraint conditions, and the corresponding relationship between the packaging tasks and the workstation and the number of personnel at the workstation that meets the constraint conditions is determined as the initial production schedule.

[0081] In some embodiments, the constraint conditions may also include a production order constraint, a workstation number constraint, or a designated workstation constraint between any two packaging tasks. Therefore, during the arrangement process, the packaging tasks containing the above three types of constraints may be prioritized to determine the target number of people and target workstations corresponding to the packaging tasks.

[0082] For each initial production schedule obtained:

[0083] The task working hours corresponding to the packaging task are obtained, wherein the task working hours represent the time required for completing the packaging of the packaging task. The target packaging tasks for each workstation to be packaged are determined by summarizing the initial production schedule, wherein the number of the target packaging tasks is at least one.

[0084] The total working hours corresponding to each workstation are determined according to the target packaging task, the target number of personnel and the task working hours, wherein the total working hours corresponding to the workstation represent the time required to complete all the corresponding target packaging tasks according to the target number of personnel.

[0085] The total working hours corresponding to all workstations are processed by average to obtain the average working hours. The total working hours corresponding to each workstation are processed by variance according to the average working hours to obtain the variance sum corresponding to the initial production scheduling plan.

[0086] After calculating the sum of variances corresponding to each initial production schedule, the sum of variances corresponding to all initial production schedules are counted, and the initial production schedule with the smallest sum of variances is taken as the target production schedule.

[0087] Exemplarily, the initial production schedule A is that the tasks required to be packaged for station 1 are task a and task b, the tasks required to be packaged for station 2 are task c, and the tasks required to be packaged for station 3 are task d and task e. And the number of personnel in station 1 is 2, the number of personnel in station 2 is 1, and the number of personnel in station 3 is 1. It is determined that the task working hours corresponding to task a are 15, the task working hours corresponding to task b are 20, the task working hours corresponding to task c are 35, the task working hours corresponding to task d are 20, and the task working hours corresponding to task e are 10. The total working hours corresponding to station 1 are calculated to be 25, the total working hours corresponding to station 2 are 30, and the total working hours corresponding to station 3 are 35. The mean of the total working hours corresponding to all stations is calculated, and the average working hours is 30. According to the average working hours, the total working hours corresponding to each station are subjected to variance processing, and the variances are 12.5, 0 and 12.5 respectively, and the sum of the variances is 25.

[0088] The initial production schedule B is that the task required to be packaged at workstation 1 is task a, the tasks required to be packaged at workstation 2 are task b and task c, and the tasks required to be packaged at workstation 3 are task d and task e. And the number of staff at workstation 1 is 1, the number of staff at workstation 2 is 2, and the number of staff at workstation 3 is 1. Determine that the task working hours corresponding to task a are 15, the task working hours corresponding to task b are 20, the task working hours corresponding to task c are 35, the task working hours corresponding to task d are 20, and the task working hours corresponding to task e are 10. The total working hours corresponding to workstation 1 are calculated to be 30, the total working hours corresponding to workstation 2 are 27.5, and the total working hours corresponding to workstation 3 are 35. Calculate the mean of the total working hours corresponding to all workstations, and the average working hours are 30.84. According to the average working hours, the total working hours corresponding to each workstation are subjected to variance processing, and the variances are 8.65, 5.58, and 8.65 respectively, and the sum of the variances is 22.88.

[0089] Compare the sum of variances corresponding to the initial production schedule A and the sum of variances corresponding to the initial production schedule B, and take the initial production schedule B as the target production schedule.

[0090] Through the above scheme, the variance sum corresponding to each initial production schedule is calculated separately, and then the target production schedule is selected. The determination of the target production schedule is more accurate, avoiding the situation where the optimal target production schedule is selected incorrectly due to partial data errors.

[0091] In the execution process of step 1031 to step 1033, in some embodiments, the step of determining the total working hours corresponding to each workstation according to the target packaging task, the target number of personnel and the task working hours in step 1032 specifically includes:

[0092] Step A, obtaining the total number of tasks of the target packaging task.

[0093] Step B: multiply the total number of tasks by the task working hours to obtain the total task working hours.

[0094] Step C: performing ratio processing on the total working hours of the task and the target number of personnel to obtain the total working hours corresponding to each workstation.

[0095] In specific implementation, first obtain the total number of tasks of the target packaging task, multiply the total number of tasks by the task working hours to obtain the total task working hours. Calculate the ratio between the total task working hours and the target number of personnel, and the ratio is the total working hours corresponding to the workstation.

[0096] Based on the example of step 1031 to step 1033, the total number of tasks corresponding to task a is 2, the total number of tasks corresponding to task b is 1, the total number of tasks corresponding to task c is 1, the total number of tasks corresponding to task d is 1, and the total number of tasks corresponding to task e is 1.

[0097] In the initial production schedule A, the task hours corresponding to workstation 1 are 2*15+1*20, that is, the task hours corresponding to workstation 1 are 50. Similarly, the task hours corresponding to workstation 2 are 35, and the task hours corresponding to workstation 3 are 30.

[0098] The total working hours of the task are ratioed with the target number of personnel to obtain the total working hours corresponding to each workstation. In the initial production schedule A, the total working hours corresponding to workstation 1 are 25, the total working hours corresponding to workstation 2 are 30, and the total working hours corresponding to workstation 3 are 35.

[0099] Through the above scheme, the total number of tasks, task working hours and target number of personnel of the target packaging task are used to determine the total working hours corresponding to the workstation, so that the total working hours corresponding to each workstation are determined more accurately.

[0100] In the process of packaging the packaging task, the actual time required for the packaging task includes not only the packaging time, but also the time for replacing the mold used for packaging the packaging task. Therefore, in order to improve the production efficiency of the packaging station, the number of times the mold is replaced should be reduced. In some embodiments, after obtaining the target production schedule corresponding to the packaging task information in step 103, it also includes:

[0101] Step 104: determine multiple target packaging tasks corresponding to each workstation according to the target production scheduling plan.

[0102] Step 105: for each workstation, determine the attribute information corresponding to each target packaging task, optimize the packaging sequence of the multiple target packaging tasks according to the attribute information, and obtain an optimized target production scheduling plan.

[0103] In specific implementation, the mold required for the packaging task is determined by the type of the packaged object packaged in the packaging task. The same type of packaged objects can be packaged using the same mold. Therefore, the packaging units of packaged objects with similar packaging appearance or other attributes are arranged adjacent to each other in chronological order, which can effectively reduce the number of mold changes.

[0104] Then, multiple target packaging tasks corresponding to each workstation are determined according to the target production schedule, and for each workstation, attribute information corresponding to each target packaging task is determined. The packaging sequence of the multiple target packaging tasks is optimized according to the attribute information to obtain an optimized target production schedule.

[0105] Through the above scheme, in the process of packaging the packaging task, the actual time required for the packaging task, in addition to the packaging man-hours, also takes into account the time for replacing the mold used for packaging the packaging task. By optimizing the packaging sequence according to the attribute information of the target packaging task, the number of times the mold is replaced is reduced, and the production efficiency of the packaging station is improved.

[0106] Based on the execution steps of the aforementioned steps 101 to 104, in some embodiments, the attribute information includes the task name and configuration information, and the packaging sequence of multiple target packaging tasks can be optimized according to the task name and the configuration information. Specifically, step 105 includes:

[0107] Step 1051 , obtaining a task name of each target packaging task, and clustering the multiple target packaging tasks according to the task name to obtain at least one packaging task group.

[0108] Step 1052, determine the configuration information corresponding to each packaging task group, and obtain a preset configuration sequence.

[0109] Step 1053: sort the at least one packaging task group according to the configuration information and the configuration sequence to obtain an optimized packaging sequence.

[0110] In a specific implementation, for multiple target packaging tasks corresponding to each workstation, the task name of each target packaging task is obtained, and the multiple target packaging tasks are clustered according to the task name to obtain at least one packaging task group. That is, the target packaging tasks with the same task name are clustered to obtain at least one packaging task group.

[0111] Determine the configuration information corresponding to each packaging task group. In this embodiment, all packaging tasks in each packaging task group have the same configuration information. Obtain a preset configuration sequence, wherein the configuration sequence is a packaging sequence corresponding to different configuration information preset by a user.

[0112] The at least one packaging task group is sorted according to the configuration information and the configuration sequence to obtain an optimized packaging sequence.

[0113] Exemplarily, there are three packaging task groups, namely packaging task group A, packaging task group B and packaging task group C. It is determined that the configuration information corresponding to packaging task group A is P1, the configuration information corresponding to packaging task group B is P2, and the configuration information corresponding to packaging task group C is P3. The preset configuration sequence is obtained as P1>P2>P3, and the optimized packaging sequence is to first package the packaging task group A, then package the packaging task group B, and finally package the packaging task group C.

[0114] Through the above scheme, for multiple target packaging tasks required to be packaged at each workstation, in order to reduce the number of times the molds are replaced, the multiple target packaging tasks are sorted. Target packaging tasks with the same configuration information indicate that they use the same molds. Putting target packaging tasks using the same molds in an adjacent order for packaging can effectively reduce the number of times the molds are replaced and shorten the total working hours corresponding to the workstation.

[0115] In order to avoid the influence of irrelevant words in the task name on clustering, the plurality of target packages may be clustered according to the task name. The specific clustering process is as described in step 1051. Step 1051 specifically includes:

[0116] Step 10511, remove the symbols in the task name, and / or remove the Chinese characters representing the direction in the task name, to obtain a processed task name.

[0117] Step 10512: group the target packaging tasks with the same processed task name into the same packaging task group.

[0118] In specific implementation, the task name of the packaging task usually includes, in addition to phrases used to characterize the types of packaged objects, functional symbols such as "*", "#" or "-". These symbols are irrelevant to the molds required to produce packaging units. Therefore, before clustering processing, symbols irrelevant to mold selection can be removed to avoid their interference with the clustering processing process.

[0119] At the same time, packaging tasks for packaged objects with mirror-symmetrical or similar packaging appearance can also be produced using the same mold. Packaging tasks with such characteristics usually have Chinese characters indicating directions such as "front", "back", "left", and "right" in their task names. Clustering processing can be performed after removing the Chinese characters indicating directions in the task names.

[0120] The packaging tasks that use the same mold during production are grouped together. The target packaging tasks with the same task name after processing indicate that the objects they package are similar in type and can be produced using the same mold. Therefore, the target packaging tasks with the same task name after processing are grouped together into the same packaging task group.

[0121] Through the above scheme, by processing the task names, the influence of irrelevant words in the task names on clustering is avoided, thereby improving the accuracy of clustering.

[0122] In order to avoid the packaging task groups with the same configuration information having no fixed order, which would cause confusion in the production schedule, the packaging task groups with the same configuration information can also be further sorted according to the number of packaging tasks, that is, step 1053 specifically includes:

[0123] Step 10531, in response to the existence of multiple packaging task groups with the same configuration information, for the multiple packaging task groups with the same configuration information:

[0124] Get the number of packaging tasks corresponding to each packaging task group;

[0125] The plurality of packaging task groups are sorted in descending order of the number of packaging tasks.

[0126] In specific implementation, if there are multiple packaging task groups with the same configuration information, for the multiple packaging task groups with the same configuration information:

[0127] The number of packaging tasks corresponding to each packaging task group is obtained, and the multiple packaging task groups are sorted according to the packaging task numbers. In this embodiment, the packaging task groups are sorted in descending order.

[0128] Exemplarily, the configuration information of packaging task group A and packaging task group B are both P1. At this time, the number of packaging tasks obtained for packaging task group A is 10, and the number of packaging tasks obtained for packaging task group B is 4. Then, packaging task group A is packaged first, and packaging task group B is packaged secondly.

[0129] Through the above scheme, for the packaging task groups with the same configuration information, they can also be further sorted according to the number of packaging tasks to avoid the packaging task groups with the same configuration information having no fixed order, which causes confusion in the production schedule.

[0130] Based on the same inventive concept, another embodiment of the present disclosure proposes a method for determining a production schedule, such as Figure 2 As shown, the method includes:

[0131] Step A, obtain the packaging task set, the total number of workstations and the total number of staff.

[0132] Step B, dividing the packaging tasks in the packaging task set, specifically the division includes:

[0133] According to the task name, it is divided into identical packaging unit groups and similar packaging unit groups, among which identical packaging unit groups are packaging unit groups with the same name, and similar packaging unit groups are packaging unit groups with exactly the same content after removing the Chinese characters that begin with the name of A or B packaging unit, such as "front", "back", "left", "right", "front and back", "left and right".

[0134] Divide according to workshop attributes, workshop attribute group set, divide packaging units according to workshop attributes, and packing units without workshop attributes are grouped separately. Workshop attribute priority, record workshop priority according to workshop attribute sequence.

[0135] Divide into configuration attribute sets according to configuration attributes, divide packaging units according to configuration attributes, and separate packaging units without configuration attributes into a separate group. Configuration attribute priority is to record the configuration attribute sequence priority. Configuration attribute constraints are to record the relevant constraints according to the total number of staff and the total number of workstations of configuration attributes.

[0136] Step C, obtaining the configuration attribute constraint mode, and then constructing a production scheduling optimization model. The model constraints of the production scheduling optimization model specifically include:

[0137] Workstation constraints: The total number of workstations is J, the number of staff at each workstation satisfies the upper and lower bounds, and a single workstation occupies a single workshop attribute or a mixed packaging unit with no workshop attributes.

[0138] Constraint on the total number of staff (i.e. the total number of personnel in the above embodiment): the total number of staff is P.

[0139] Packaging task set constraints: The same packaging task group is arranged at a single workstation, some packaging tasks are limited to production stations, and similar packaging tasks are produced in a centralized manner.

[0140] The total fixed working hours are calculated according to the number of fixed staff at the selected workstation and the number of packaging units.

[0141] Workshop attribute constraints: The attribute sets of each workshop satisfy the sequential relationship.

[0142] Configuration attribute constraints: Configuration attributes in the same workshop are concentrated for production (occupying consecutive workstations). Among the workstations occupied by the configuration attributes, there is only one workstation that can be mixed with other configuration attributes, and it is located at the last or first position in the set of occupied workstations. Configuration attributes cannot be mixed with other attributes in the workstations they occupy. The configuration attributes of each workshop meet the order of precedence, the configuration attributes meet the limit on the number of occupied workstations, the configuration attributes meet the limit on the total number of staff, and the number of staff at the workstations corresponding to the configuration attributes meet the upper and lower bounds.

[0143] The optimization goal of the production scheduling optimization model is specifically to make the total scheduled working hours of the packaging tasks assigned to each workstation as close as possible and to maximize the number of similar packaging unit groups assigned to the same workstation.

[0144] Step D: The process of building the production scheduling optimization model specifically includes:

[0145] Workstation related constraints:

[0146] lb j ≤p j ≤ub j , the workstation personnel meet the upper and lower bound requirements, among which the upper bound of each workstation personnel is lb j ,j=1,…,m, the lower bound of each workstation is ub j ,j=1,…,m.

[0147] y j,p ≤1, the workshop attribute occupied by the workstation is less than or equal to one (a single workstation can only produce tasks with one workshop attribute or only has tasks without attributes), where the integer variable determines the number of workshop attributes contained in the j-th production station j=1,…,m; p=1,…,w.

[0148] Constraints on the total number of staff:

[0149] The total number of staff is P, where p j is an integer variable, and the number of personnel at each workstation is j=1,…,m.

[0150] Packing unit group constraints:

[0151] sagm = 1, sagm is the number of workstations occupied by any group in the same group as sag, and sag is the result of grouping the same production task. i,j = 1, i is the generated task expected to be fixed on the j station, x i,j is a 0 / 1 variable, which determines whether the i-th production task is produced at the j-th station i=1,…,n;j=1,…,m. obj1=∑(sigm-1), sigm is the number of stations occupied by any group in the sigm similar group, sigm is an integer variable, and the number of stations occupied by any similar grouping result.

[0152] Calculation of total working hours. The total scheduled working hours for production tasks are calculated as follows: t,i is a floating point variable, the total man-hours of any generated task, p j is an integer variable, each production station has j=1,…,m, and the production time of each production task is t i ,i=1,…,n.

[0153] Workshop properties: The workstation selected by production task i in workshop W-1 must be before the workstation selected by production task i in workshop W, where i is any generated task in each workshop attribute, w is a production task, and a total of W workshop attributes are included.

[0154] Configuration properties:

[0155] z_max r -z_min r =z r -1, limit continuous occupation of workstations, z r is the number of workstations occupied by each configuration attribute r=1,…,k,z_min r is the minimum index of the workstation occupied by each configuration attribute, z_max rIt is the maximum index of the position occupied by each configuration attribute.

[0156] Here i is a set of attribute tasks in K. When the attribute occupied by the workstation is equal to the attribute possessed by the selected workstation, the effect of not mixing with others can be achieved. K is n production tasks, which contains a total of K configuration attributes.

[0157] Here i is a set of attribute tasks in K. When the attribute occupied by the workstation is equal to the attribute possessed by the selected workstation plus one, the mixed effect of a single workstation can be achieved.

[0158] z_max r ≤z_max r-1 , the configuration attributes of r-1 take precedence over the configuration attributes of r.

[0159] z r =z r,mfix , z r,mfix The number of workstations occupied by user configuration attributes.

[0160] z r,pfix Configure the total number of users for the attribute.

[0161] z_lb j,p,r ≤z j,p,r ≤ z_ub j,p,r , z_lb j,p,r Satisfy the upper and lower bounds configured by the user.

[0162] The optimization objective of the production scheduling optimization model is expressed by the formula:

[0163] obj1=∑(sigm-1); obj2=∑(staion t -staion s ) 2 ;

[0164] Among them, sigm is an integer variable, the number of stations occupied by any similar grouping results, STAION t It is a floating point variable, the total production time of any workstation.

[0165] Step E: using a solver to solve the production scheduling optimization model to obtain a target production scheduling result.

[0166] Step F, updating the packing order of the packing task according to the target production scheduling result, the specific process is to traverse the workstations to update the packing units in order according to the workshops and configuration attributes, and adjust the positions of the same or similar packing units.

[0167] Step G: output the target production scheduling results.

[0168] The following is a specific example:

[0169] The packaging task similarity dimension is the data pre-processing for establishing the scheduling constraints of a single workstation for the same packaging task; the workshop attribute dimension is the pre-processing for establishing the workshop sequence relationship constraints; the configuration attribute dimension is the pre-processing for the configuration attribute related constraints. The main task in the early stage is to divide the data to ensure that the scheduling constraints considered by the user are met.

[0170] For example, under workshop 1, A1 / B / C with attribute P1 and N with attribute P3 are each a group, and the user can configure the positional relationship between P1 and P3, such as P1>P3 or P3>P1. In addition, the user can also configure the internal constraints of a single attribute, specifically, among the workstations occupied by the configuration attribute, there is only one workstation that can be mixed with other configuration attributes, and it is located at the last or first position in the occupied workstation set. The workstations occupied by the configuration attribute cannot be mixed with other attributes. Total working hours = single task working hours * number of packaging tasks / number of people at the workstation.

[0171] Assume the user configuration is as follows:

[0172] Assume that the original data is as shown in Table 1:

[0173] Table 1

[0174] Packaging tasks Workshop Properties Configuration properties Single task working hours Number of packaging tasks A1 Workshop 1 P1 10 1 B Workshop 1 P1 15 2 C Workshop 1 P1 20 1 D P2 11 1 E P2 15 1 F1 Workshop 2 P2 6 2 G Workshop 2 P2 18 2 H Workshop 2 P2 9 3 I 30 3 A2 8 1 J Workshop 3 P4 7 4 K Workshop 3 P4 7 3 L Workshop 3 P4 9 2 F2 7 1 M Workshop 1 6 5 N Workshop 1 P3 7 1 A3 Workshop 1 7 1

[0175] Available resources in the current data scenario: 5 workstations, 16 personnel. It is required that the packaging tasks with the same letters be arranged at a single workstation, that is, A1, A2, A3 must be at the same workstation, and F1 and F2 must be at the same workstation; the workshop attributes are arranged according to the workshop sequence, that is, workshop 1, workshop 2, workshop 3. The configuration attributes in the same workshop are arranged in the order of P1, P2, P3, and P4, and P1, P2, and P3 can be mixed with other attribute or non-attribute packaging units, but P4 cannot be mixed with other attribute or non-attribute packaging units.

[0176] According to the above configuration, the production scheduling optimization model is built to realize the optimization process of complex business scenarios. After the production scheduling optimization model is built, according to the model constraints and optimization goals, the solver is used to solve the problem. The solver can be cplex, gurobi, ortool and other solving tools. The production scheduling results are shown in Table 2:

[0177] Table 2

[0178]

[0179] In this embodiment, by identifying the available workstations in the production system, the packaging tasks of the locked workstations and the workstations with a fixed number of people are prioritized and calculated to obtain the local production arrangement of the locked workstations. The remaining personnel in the production system are arranged for production, and the boundaries of the workstation personnel considered in the personnel arrangement process are limited to complete the personnel deployment of each workstation in the production system. The production tasks are arranged to the production workstations, and the tasks with grouping requirements are processed according to the grouping requirements, and the production hours of each production task under the production workstation and personnel configuration are calculated. The working hours of each workstation are summarized and calculated to obtain the working hours summary results of each workstation under the personnel arrangement, and the variance and minimum of the working hours summary results of each workstation and the average working hours of the workstation are calculated by combining branch and bound with heuristic algorithms to obtain the production system equilibrium solution result, that is, the target production scheduling result. In the process of formulating the production scheduling plan, there is no need for the participation of the production scheduling plan maker, which solves the problem that the quality of the production scheduling plan is greatly affected by the experience of the personnel when the amount of packaging unit data is large, and forms the production scheduling plan with the best production capacity balancing effect.

[0180] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0181] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0182] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a device for determining a production schedule.

[0183] refer to Figure 3 , Figure 3 The production scheduling determination device of the embodiment includes:

[0184] The data acquisition module 201 is configured to acquire packaging task information, wherein the packaging task information includes a packaging task set, a total number of workstations, and a total number of personnel;

[0185] The model building module 202 is configured to build a production scheduling optimization model according to the packaging task information, wherein the production scheduling optimization model includes constraints and optimization goals, the constraints are attribute information corresponding to each packaging task in the packaging task set, and the optimization goal is to minimize the difference in total working hours between any two workstations;

[0186] The model solving module 203 is configured to solve the production scheduling optimization model to obtain a target production scheduling plan corresponding to the packaging task information, wherein the target production scheduling plan is a target number of personnel and a target workstation corresponding to each packaging task in the packaging task set.

[0187] In some embodiments, the model solving module 203 specifically includes:

[0188] An initial production schedule determination unit is configured to determine the target number of personnel and target workstations corresponding to each packaging task based on the constraint conditions, and obtain at least one initial production schedule;

[0189] The variance and determination unit is configured to: for each initial production schedule: obtain the task working hours corresponding to each packaging task, determine the target packaging task packaged by each workstation in the initial production schedule; determine the total working hours corresponding to each workstation according to the target packaging task, the target number of personnel and the task working hours; perform mean processing on the total working hours corresponding to all workstations to obtain average working hours; perform variance processing on the total working hours corresponding to each workstation according to the average working hours to obtain the variance and corresponding to the initial production schedule;

[0190] The target production schedule determination unit is configured to calculate the sum of variances corresponding to all initial production schedules, and take the initial production schedule with the smallest sum of variances as the target production schedule.

[0191] In some embodiments, the variance and determination unit is further configured to:

[0192] Get the total number of tasks for the target packaging task;

[0193] Multiply the total number of tasks by the task working hours to obtain the total task working hours;

[0194] The total working hours of the task are compared with the target number of personnel to obtain the total working hours corresponding to each workstation.

[0195] In some embodiments, the device further includes an optimization module, and the optimization module specifically includes:

[0196] a target determination unit, configured to determine a plurality of target packaging tasks corresponding to each workstation according to the target production scheduling plan;

[0197] The production scheduling optimization unit is configured to determine the attribute information corresponding to each target packaging task for each workstation, optimize the packaging sequence of the multiple target packaging tasks according to the attribute information, and obtain an optimized target production scheduling plan.

[0198] In some embodiments, the attribute information includes a task name and configuration information, and the production scheduling optimization unit is specifically configured as follows:

[0199] Obtaining a task name of each target packaging task, and clustering the multiple target packaging tasks according to the task name to obtain at least one packaging task group;

[0200] Determine the configuration information corresponding to each packaging task group and obtain the preset configuration order;

[0201] The at least one packaging task group is sorted according to the configuration information and the configuration sequence to obtain an optimized packaging sequence.

[0202] In some embodiments, the production scheduling optimization unit is further configured to:

[0203] Removing symbols from the task name, and / or removing Chinese characters representing directions from the task name, to obtain a processed task name;

[0204] The target packaging tasks with the same processed task name are divided into the same packaging task group.

[0205] In some embodiments, the production scheduling optimization unit is further configured to:

[0206] In response to the existence of multiple packaging task groups with the same configuration information, for the multiple packaging task groups with the same configuration information:

[0207] Get the number of packaging tasks corresponding to each packaging task group;

[0208] The plurality of packaging task groups are sorted in descending order of the number of packaging tasks.

[0209] For the convenience of description, the above device is described by dividing it into various modules according to its functions. Of course, when implementing the present disclosure, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0210] The device of the above embodiment is used to implement the corresponding production scheduling plan determination method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0211] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for determining the production schedule described in any of the above embodiments is implemented.

[0212] Figure 4 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0213] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0214] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0215] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0216] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0217] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0218] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0219] The electronic device of the above-mentioned embodiment is used to implement the corresponding production scheduling plan determination method in any of the above-mentioned embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0220] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method for determining the production schedule as described in any of the above embodiments.

[0221] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0222] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the method for determining the production schedule as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0223] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides a vehicle, including the device for determining the production schedule in the above-mentioned embodiments, the electronic device in the above-mentioned embodiments, and the computer-readable storage medium in the above-mentioned embodiments, and the vehicle equipment implements the method for determining the production schedule described in any of the above embodiments.

[0224] The vehicle of the above-mentioned embodiment is used to implement the method for determining the production schedule described in any of the above-mentioned embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0225] It is understandable that before using the technical solutions of each embodiment of the present disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner, and the user's authorization will be obtained.

[0226] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly remind the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can independently choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0227] As an optional but non-limiting implementation, in response to receiving the user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0228] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0229] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure is limited to these examples. Based on the concept of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of simplicity.

[0230] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present disclosure difficult to understand, the known power / ground connections to the integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present disclosure difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure will be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present disclosure, it is apparent to those skilled in the art that the embodiments of the present disclosure can be implemented without these specific details or with changes in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0231] Although the present disclosure has been described in conjunction with specific embodiments of the present disclosure, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0232] The embodiments of the present disclosure are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the present application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A method for determining a production schedule, characterized in that: include: Acquire packaging task information, wherein the packaging task information includes a packaging task set, a total number of workstations, and a total number of personnel; Constructing a production scheduling optimization model according to the packaging task information, wherein the production scheduling optimization model includes constraints and optimization objectives, the constraints are attribute information corresponding to each packaging task in the packaging task set, and the optimization objective is to minimize the difference in total working hours between any two workstations; The production scheduling optimization model is solved to obtain a target production scheduling corresponding to the packaging task information, wherein the target production scheduling is a target number of personnel and a target workstation corresponding to each packaging task in the packaging task set.

2. The method according to claim 1, characterized in that The step of solving the production scheduling optimization model to obtain a target production scheduling plan corresponding to the packaging task information includes: Taking the constraint conditions as constraints, determining the target number of personnel and target workstations corresponding to each packaging task, and obtaining at least one initial production scheduling plan; For each initial production schedule: obtain the task working hours corresponding to each packaging task, determine the target packaging task packaged by each workstation in the initial production schedule; determine the total working hours corresponding to each workstation according to the target packaging task, the target number of personnel and the task working hours; perform mean processing on the total working hours corresponding to all workstations to obtain the average working hours; perform variance processing on the total working hours corresponding to each workstation according to the average working hours to obtain the variance sum corresponding to the initial production schedule; The sum of variances corresponding to all initial production schedules is calculated, and the initial production schedule with the smallest sum of variances is taken as the target production schedule.

3. The method according to claim 2, characterized in that Determining the total working hours corresponding to each workstation according to the target packaging task, the target number of personnel and the task working hours includes: Get the total number of tasks for the target packaging task; Multiply the total number of tasks by the task working hours to obtain the total task working hours; The total working hours of the task are compared with the target number of personnel to obtain the total working hours corresponding to each workstation.

4. The method according to claim 1, characterized in that: Also includes: Determine a plurality of target packaging tasks corresponding to each workstation according to the target production scheduling plan; For each workstation, attribute information corresponding to each target packaging task is determined, and the packaging sequence of the multiple target packaging tasks is optimized according to the attribute information to obtain an optimized target production scheduling plan.

5. The method according to claim 4, characterized in that The attribute information includes task name and configuration information. The step of optimizing the packaging sequence of the plurality of target packaging tasks according to the attribute information to obtain an optimized target production scheduling plan includes: Obtaining a task name of each target packaging task, and clustering the multiple target packaging tasks according to the task name to obtain at least one packaging task group; Determine the configuration information corresponding to each packaging task group and obtain the preset configuration order; The at least one packaging task group is sorted according to the configuration information and the configuration sequence to obtain an optimized packaging sequence.

6. The method according to claim 5, characterized in that The clustering of the plurality of target packages according to the task names to obtain at least one packaging task group includes: Removing symbols from the task name, and / or removing Chinese characters representing directions from the task name, to obtain a processed task name; The target packaging tasks with the same processed task name are divided into the same packaging task group.

7. The method according to claim 5, characterized in that The sorting process of the at least one packaging task group according to the configuration information and the configuration sequence includes: In response to the existence of multiple packaging task groups with the same configuration information, for the multiple packaging task groups with the same configuration information: Get the number of packaging tasks corresponding to each packaging task group; The plurality of packaging task groups are sorted in descending order of the number of packaging tasks.

8. A device for determining a production schedule, characterized in that: include: A data acquisition module is configured to acquire packaging task information, wherein the packaging task information includes a packaging task set, a total number of workstations, and a total number of personnel; A model building module is configured to build a production scheduling optimization model according to the packaging task information, wherein the production scheduling optimization model includes constraints and optimization goals, the constraints are attribute information corresponding to each packaging task in the packaging task set, and the optimization goal is to minimize the difference in total working hours between any two workstations; The model solving module is configured to solve the production scheduling optimization model to obtain a target production scheduling plan corresponding to the packaging task information, wherein the target production scheduling plan is a target number of personnel and a target workstation corresponding to each packaging task in the packaging task set.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the program. 10 . A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the method according to claim 1 .

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