Workshop production scheduling plan making method, device, equipment, medium and product
By obtaining workshop production data input by users and using expert rule sets and scheduling algorithms to automatically formulate workshop production scheduling plans, the problems of high labor costs and poor applicability in traditional methods are solved, and flexible workshop production scheduling plans are realized, which saves labor costs and improves applicability.
Patent Information
- Application Number
- CN202510667554.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-22
- Publication Date
- 2025-09-16
AI Technical Summary
Traditional workshop production scheduling relies on manual teams, which has high labor costs, is difficult to reuse across projects, and users find it difficult to independently select rules, resulting in poor applicability.
By obtaining workshop production data input by users and utilizing pre-built expert rule sets and scheduling algorithms, data sorting and planning are automatically performed, including multiple sub-scheduling algorithms and data verification, to achieve flexible sorting and planning optimization.
There is no need to rely on manual teams, saving labor costs, achieving cross-project reuse, improving applicability, meeting the diverse needs of users, and flexible and changeable sorting.
Smart Images
Figure CN120654993A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of production scheduling, and in particular to a method, device, equipment, medium and product for formulating a workshop production scheduling plan. Background Art
[0002] Shop floor production scheduling is a crucial component of the manufacturing industry. It involves efficiently and systematically arranging production tasks to meet customer needs while optimizing resource utilization, reducing costs, and improving production efficiency. A sound production schedule ensures a smooth production process, reduces waiting time and waste, and improves overall production efficiency.
[0003] When formulating workshop production scheduling plans under the traditional model, it is usually necessary to carry out complex resource coordination tasks in order to build a multi-disciplinary collaborative project team. During this period, the process team focuses on maintaining process routes and process beat information, the algorithm team develops specific sequencing algorithms in a targeted manner, and the development team is responsible for building a supporting sequencing system. Each link is closely dependent on each other and highly relies on the collaborative work of professionals, resulting in high labor costs. In addition, the project team needs to be re-coordinated to formulate plans for different projects, which is difficult to reuse across projects and has poor applicability. Moreover, in the process of formulating workshop production scheduling plans, developers implement production sorting through hard coding based on the user's indirect description. Users cannot independently select and flexibly configure rules based on on-site conditions. Summary of the Invention
[0004] The present invention provides a workshop production scheduling plan formulation method, device, equipment, medium, and product to address the shortcomings of existing technologies, such as high labor costs, difficulty in cross-project reuse, and difficulty for users to independently select rules. The technical solution of the present invention does not require reliance on a human team, saving labor costs. It can also formulate different target workshop production scheduling plans based on different workshop production data, achieving cross-project reuse and improving applicability. In addition, the sorting of workshop production data is performed according to user-selected rules, which is more flexible and diverse, meeting the diverse needs of users.
[0005] The present invention provides a method for formulating a workshop production scheduling plan, comprising the following steps.
[0006] Obtain workshop production data input by users; sorting the workshop production data based on the rules selected by the user in the expert rule set to obtain target workshop production data; the expert rule set is a pre-built rule set for sorting workshop production data; A target workshop production scheduling plan is formulated based on the scheduling algorithm and the target workshop production data.
[0007] According to a workshop production scheduling plan formulation method provided by the present invention, the scheduling algorithm includes multiple sub-scheduling algorithms; the target workshop production scheduling plan is formulated based on the scheduling algorithm and the target workshop production data, including: For each of the sub-scheduling algorithms, based on the sub-scheduling algorithm and the target workshop production data, formulate a production scheduling plan for the preparatory workshop corresponding to the sub-scheduling algorithm; For each of the preparation workshop production scheduling plans, determining the scheduling efficiency, resource utilization rate and order delivery timeliness rate corresponding to the preparation workshop production scheduling plan based on the preparation workshop production scheduling plan; Determining a comprehensive score corresponding to the production schedule of the preparatory workshop based on the scheduling efficiency, resource utilization, and order delivery timeliness of the production schedule of the preparatory workshop; The production scheduling plan of the preliminary workshop corresponding to the highest comprehensive score is determined as the production scheduling plan of the target workshop.
[0008] According to a workshop production scheduling plan formulation method provided by the present invention, the step of obtaining workshop production data input by a user includes: Obtaining initial workshop production data input by the user through a preset data template; Verifying the initial workshop production data, and if the verification result is correct, converting the initial workshop production data into converted workshop production data in a preset data format; The converted workshop production data is serialized to obtain the serialized workshop production data.
[0009] According to a workshop production scheduling plan formulation method provided by the present invention, the expert rule set includes the following rules: merging of the same batch; order priority sorting; weight sorting; color sorting; order number sorting; cross sorting corresponding to the target field; interval sorting corresponding to the target field; aggregation sorting corresponding to the target field; the target field is the data field corresponding to the workshop production data selected by the user.
[0010] According to a workshop production scheduling plan formulation method provided by the present invention, the step of obtaining workshop production data input by a user further includes: If the scheduling period, planned start time, and planned end time selected by the user are verified to be qualified, determining the pre-execution period according to the scheduling period, planned start time, and planned end time selected by the user; The workshop production data input by the user is acquired based on the pre-execution period.
[0011] According to a workshop production scheduling plan formulation method provided by the present invention, the method further includes: Obtaining actual execution data corresponding to the target workshop production scheduling plan; Determining a plan execution rate corresponding to the target workshop production scheduling plan based on the actual execution data and the target workshop production scheduling plan; When the plan execution rate is lower than a preset threshold, an abnormal situation prompt is fed back.
[0012] The present invention also provides a workshop production scheduling plan formulation device, comprising the following modules: The acquisition module is used to obtain the workshop production data input by the user; a sorting module, configured to sort the workshop production data based on the rules selected by the user in the expert rule set to obtain target workshop production data; the expert rule set is a pre-built rule set for sorting workshop production data; The planning module is used to formulate a production scheduling plan for the target workshop based on the scheduling algorithm and the production data of the target workshop.
[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements any of the above-described workshop production scheduling plan formulation methods.
[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described workshop production scheduling plan formulation methods.
[0015] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-described methods for formulating workshop production scheduling plans.
[0016] The workshop production scheduling plan formulation method, device, equipment, medium and product provided by the present invention obtain the workshop production data input by the user; sort the workshop production data based on the rules selected by the user in the expert rule set to obtain the target workshop production data; the expert rule set is a pre-built rule set for sorting the workshop production data; and the target workshop production scheduling plan is determined based on the scheduling algorithm and the target workshop production data. The technical solution of the present invention does not need to rely on a manual team, saving labor costs, and can formulate different target workshop production scheduling plans based on different workshop production data, realizing cross-project reuse and improving applicability. In addition, the sorting of workshop production data is carried out according to the rules selected by the user, which is more flexible and diverse, meeting the diverse needs of users. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0018] Figure 1 It is a flow chart of the workshop production scheduling plan formulation method provided by the present invention.
[0019] Figure 2 It is a structural schematic diagram of the workshop production scheduling plan formulation device provided by the present invention.
[0020] Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION
[0021] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.
[0022] In response to the above-mentioned problems in the prior art, the present invention provides a method for formulating a workshop production scheduling plan. It should be noted that the execution subject of the present invention can be a scheduling system, which includes a front end for acquiring data and a back end for processing data, or other electronic devices. The embodiments of the present invention are not specifically limited here. Figure 1 It is a flow chart of the workshop production scheduling plan formulation method provided by the present invention, such as Figure 1 As shown, the method includes the following steps 110 to 130.
[0023] Step 110: Acquire workshop production data input by the user.
[0024] Specifically, users can input workshop production data through the display interactive interface, and the execution subject of the present invention can obtain the workshop production data input by the user through the data acquisition port. The workshop production data may include process routes, process takt time, equipment processing and maintenance time, etc.
[0025] In one embodiment, obtaining workshop production data input by a user includes: Obtaining initial workshop production data input by the user through a preset data template; Verifying the initial workshop production data, and if the verification result is correct, converting the initial workshop production data into converted workshop production data in a preset data format; The converted workshop production data is serialized to obtain the serialized workshop production data.
[0026] Specifically, the preset data template is a pre-set template for entering initial workshop production data. The preset data template includes multiple sub-data templates corresponding to the types of workshop production data. For example, the process route, process rhythm, equipment processing and maintenance time correspond to different sub-data templates. Each sub-data template is used to ensure the standardization of the input data and to improve the efficiency of data entry. The execution subject of the present invention can obtain the initial workshop production data input by the user through the preset data template, and can further verify the initial workshop production data, that is, verify the integrity and format compliance of the initial workshop production data. If the verification result is correct, the initial workshop production data can be converted into converted workshop production data in a preset data format. The preset data format can be, for example, JavaScript Object Notation (JSON) format. The converted workshop production data can further be serialized to obtain the serialized workshop production data, which is convenient for subsequent input into the scheduling algorithm.
[0027] In the above embodiment, the integrity and format standardization of the data are ensured by the preset data template and data verification process. In addition, the conversion into the preset data format makes the processing of the workshop production data more convenient. The workshop production data obtained after serialization provides the basis for the sorting in step 120.
[0028] Step 120: Sort the workshop production data based on the rules selected by the user in the expert rule set to obtain target workshop production data; the expert rule set is a pre-built rule set for sorting workshop production data.
[0029] Specifically, an expert rule set can be pre-built based on workshop production experience. It's easy to understand that an expert rule set includes multiple rules. The interactive interface displays the rules in a list format, with each rule accompanied by a brief description and example to help users understand the specific meaning of the rule. Additionally, each rule can be configured with a checkbox to specify the target field. Users can select rules from the expert rule set through the interactive interface, for example, by directly selecting the desired rule in the checkbox.
[0030] In one embodiment, the expert rule set includes the following rules: merging of the same batch; order priority sorting; weight sorting; color sorting; order number sorting; cross sorting corresponding to the target field; interval sorting corresponding to the target field; aggregation sorting corresponding to the target field; the target field is the data field corresponding to the workshop production data selected by the user.
[0031] Specifically, the rules in the expert rule set can be pre-structured and stored. The expert rule set can include the following rules (with a brief description after the rule): (1) merging the same batch, that is, merging the product data of the same batch together; (2) priority sorting, which can be sorted according to the specified fields corresponding to the priority sorting. The specified fields corresponding to the priority sorting can include order amount, remaining delivery time or customer importance level (one or more of them can be selected by the user or pre-set), that is, under the priority sorting rule, product data with higher order amount, shorter remaining delivery time and higher customer importance level can be sorted first; (3) weight sorting, which can be sorted from large to small or from small to large according to the weight of the product; (4) color sorting. Sorting: products can be sorted according to a preset color order; (5) Order number sorting: products can be sorted according to the numerical size of the order number; (6) Cross sorting corresponding to the target field: the user can first select the target field, for example, if the user selects "target field" as "excavator type", the data corresponding to different excavator types will be cross-sorted; (7) Interval sorting corresponding to the target field: for example, if the user selects "target field" as "guardrail", the data corresponding to the products with guardrails and the data corresponding to the next batch of products with guardrails will be separated by a preset row in sequence; (8) Aggregate sorting corresponding to the target field: for example, if the user selects "target field" as "guardrail", the data corresponding to the products with guardrails will be aggregated and sorted, and the data corresponding to the products without guardrails will be aggregated and sorted. In addition, it is easy to understand that the expert rule set can also include other rules in addition to the above rules, and the embodiment of the present invention does not make specific limitations here.
[0032] In the above embodiment, the expert rule set includes a variety of data for users to choose from, so that users can select applicable rules according to the actual situation of the production site, which improves flexibility and better meets user needs.
[0033] Step 130: Formulate a production scheduling plan for the target workshop based on the scheduling algorithm and the target workshop production data.
[0034] Specifically, after obtaining the target workshop production data, a production plan for the target workshop can be formulated based on the target workshop production data using a scheduling algorithm. The scheduling algorithm can be predetermined and packaged.
[0035] In one embodiment, the scheduling algorithm includes a plurality of sub-scheduling algorithms; and formulating a target workshop production scheduling plan based on the scheduling algorithm and the target workshop production data includes: For each of the sub-scheduling algorithms, based on the sub-scheduling algorithm and the target workshop production data, formulate a production scheduling plan for the preparatory workshop corresponding to the sub-scheduling algorithm; For each of the preparation workshop production scheduling plans, determining the scheduling efficiency, resource utilization rate and order delivery timeliness rate corresponding to the preparation workshop production scheduling plan based on the preparation workshop production scheduling plan; Determining a comprehensive score corresponding to the production schedule of the preparatory workshop based on the scheduling efficiency, resource utilization, and order delivery timeliness of the production schedule of the preparatory workshop; The production scheduling plan of the preliminary workshop corresponding to the highest comprehensive score is determined as the production scheduling plan of the target workshop.
[0036] Specifically, the scheduling algorithm includes multiple sub-scheduling algorithms, and the sub-scheduling algorithms may be, for example, large neighborhood algorithm, genetic algorithm, ant colony algorithm, bee colony algorithm, grey wolf algorithm, brute force search algorithm, dynamic programming algorithm, and the like.
[0037] For each sub-scheduling algorithm, a corresponding preliminary workshop production schedule can be developed based on the sub-scheduling algorithm and the target workshop production data. It's easy to understand that each sub-scheduling algorithm can be independently configured with a computing resource pool, ensuring that multiple sub-scheduling algorithms can be calculated in parallel without interfering with each other.
[0038] After obtaining multiple preparatory workshop production schedules, for each preparatory workshop production schedule, the scheduling efficiency, resource utilization rate, and order delivery timeliness rate corresponding to the preparatory workshop production schedule can be determined through simple calculations based on the preparatory workshop production schedule. Furthermore, a bar chart, pie chart, line chart, or other chart corresponding to the preparatory workshop production schedule can be constructed based on the scheduling efficiency, resource utilization rate, and order delivery timeliness rate. A comprehensive score corresponding to the preparatory workshop production schedule can also be determined based on the scheduling efficiency, resource utilization rate, and order delivery timeliness rate corresponding to the preparatory workshop production schedule. Ultimately, the preparatory workshop production schedule with the highest comprehensive score among all preparatory workshop production schedules can be determined as the target workshop production schedule.
[0039] In the above embodiment, multiple preliminary workshop production scheduling plans are obtained through multiple sub-scheduling algorithms, and multiple better workshop production scheduling plans can be obtained. The results of multiple algorithms are further compared to make the final target workshop production scheduling plan the better result among multiple algorithms.
[0040] The workshop production scheduling plan formulation method provided by the present invention obtains workshop production data input by the user; sorts the workshop production data based on the rules selected by the user in the expert rule set to obtain target workshop production data; the expert rule set is a pre-built rule set for sorting workshop production data; and determines the target workshop production scheduling plan based on the scheduling algorithm and the target workshop production data. The technical solution of the present invention does not need to rely on a manual team, saving labor costs, and can formulate different target workshop production scheduling plans based on different workshop production data, realizing cross-project reuse and improving applicability. In addition, the sorting of workshop production data is carried out according to the rules selected by the user, which is more flexible and diverse, meeting the diverse needs of users.
[0041] In one embodiment, obtaining workshop production data input by a user further includes: If the scheduling period, planned start time, and planned end time selected by the user are verified to be qualified, determining the pre-execution period according to the scheduling period, planned start time, and planned end time selected by the user; The workshop production data input by the user is acquired based on the pre-execution period.
[0042] Specifically, users can also select the scheduling cycle, planned start time and planned end time in the interactive interface. The scheduling cycle refers to scheduling by day, week or month, etc. The planned start time is the start time of the target workshop production scheduling plan expected by the user, and the planned end time is the end time of the target workshop production scheduling plan expected by the user.
[0043] Furthermore, the user's selected scheduling period, planned start time, and planned end time can be verified to be qualified, that is, whether the time selection is reasonable. For example, if the scheduling period is selected as monthly, but there are only a few days between the planned start time and the planned end time, this situation is obviously unreasonable. If the user's selected scheduling period, planned start time and planned end time are verified to be qualified, the pre-execution period can be determined based on the user's selected scheduling period, planned start time and planned end time.
[0044] After determining the pre-execution period, the user-entered workshop production data can be filtered based on this period. For example, key information such as order data, equipment processing and maintenance periods, etc. can be extracted. Furthermore, the scheduling algorithm's trigger mechanism can be adjusted based on the pre-execution period, ensuring that the scheduling algorithm's operations are focused on the pre-execution period and accurately outputting the workshop production schedule.
[0045] In the above embodiment, the pre-execution period is determined by the scheduling cycle, planned start time and planned end time selected by the user, thereby controlling the acquisition of workshop production data so that the acquired workshop production data is closer to the planned time.
[0046] In one embodiment, the method further comprises: Obtaining actual execution data corresponding to the target workshop production scheduling plan; Determining a plan execution rate corresponding to the target workshop production scheduling plan based on the actual execution data and the target workshop production scheduling plan; When the plan execution rate is lower than a preset threshold, an abnormal situation prompt is fed back.
[0047] Specifically, after the workshop starts production according to the target workshop production scheduling plan, the actual execution data corresponding to the target workshop production scheduling plan can be obtained. The actual execution data may include core information such as the order online sequence and the equipment start and stop timestamps. It is easy to understand that due to the influence of various factors in the actual production stage, there must be a deviation between the actual execution data and the target workshop production scheduling plan. Therefore, in order to measure the deviation, the plan execution rate corresponding to the target workshop production scheduling plan can be determined based on the actual execution data and the target workshop production scheduling plan. The plan execution rate can be determined by data such as the order online accuracy rate and the equipment on-time startup rate. Further, when the plan execution rate is less than the preset threshold, an abnormal situation prompt can be fed back to the corresponding mobile terminal or work terminal of the business personnel. Among them, the preset threshold can be set as needed, for example, it can be 80%, and the embodiment of the present invention does not make specific limitations here. The abnormal situation prompt may include a description of the difference points, a speculation on the cause of the abnormality, an assessment of the scope of impact, etc., and then after being pushed to the mobile terminal or work terminal of relevant business personnel such as production, process, and scheduling, the business personnel can write optimization information. The execution entity of the present invention can also adjust the parameters of the expert rule set and scheduling algorithm according to the optimization information written by the task personnel, forming a closed-loop management mechanism of continuous improvement to ensure the accurate implementation and dynamic optimization of the scheduling plan.
[0048] In the above embodiment, the deviation between the actual execution data and the target workshop production scheduling plan is reflected by the plan execution rate, and abnormal situation prompts are fed back when the deviation is large, which can form a closed-loop management mechanism of continuous improvement and ensure the accurate implementation and dynamic optimization of the scheduling plan.
[0049] The following describes the workshop production scheduling plan formulation device provided by the present invention. The workshop production scheduling plan formulation device described below and the workshop production scheduling plan formulation method described above can be referenced to each other.
[0050] Figure 2This is a structural diagram of the workshop production scheduling plan formulation device provided by the present invention, such as Figure 2 As shown, the workshop production scheduling plan formulation device 200 includes the following modules: The acquisition module 210 is used to acquire the workshop production data input by the user; A sorting module 220 is configured to sort the workshop production data based on the rules selected by the user in the expert rule set to obtain target workshop production data; the expert rule set is a pre-built rule set for sorting workshop production data; The planning module 230 is used to formulate a production scheduling plan for the target workshop based on the scheduling algorithm and the target workshop production data.
[0051] In one embodiment, the scheduling algorithm includes multiple sub-scheduling algorithms; the planning module 230 is specifically configured to: For each of the sub-scheduling algorithms, based on the sub-scheduling algorithm and the target workshop production data, formulate a production scheduling plan for the preparatory workshop corresponding to the sub-scheduling algorithm; For each of the preparation workshop production scheduling plans, determining the scheduling efficiency, resource utilization rate and order delivery timeliness rate corresponding to the preparation workshop production scheduling plan based on the preparation workshop production scheduling plan; Determining a comprehensive score corresponding to the production schedule of the preparatory workshop based on the scheduling efficiency, resource utilization, and order delivery timeliness of the production schedule of the preparatory workshop; The production scheduling plan of the preliminary workshop corresponding to the highest comprehensive score is determined as the production scheduling plan of the target workshop.
[0052] In one embodiment, the acquisition module 210 is specifically configured to: Obtaining initial workshop production data input by the user through a preset data template; Verifying the initial workshop production data, and if the verification result is correct, converting the initial workshop production data into converted workshop production data in a preset data format; The converted workshop production data is serialized to obtain the serialized workshop production data.
[0053] In one embodiment, the expert rule set includes the following rules: merging of the same batch; order priority sorting; weight sorting; color sorting; order number sorting; cross sorting corresponding to the target field; interval sorting corresponding to the target field; aggregation sorting corresponding to the target field; the target field is the data field corresponding to the workshop production data selected by the user.
[0054] In one embodiment, the workshop production scheduling plan formulation device further includes a verification module, which is specifically used to: If the scheduling period, planned start time, and planned end time selected by the user are verified to be qualified, determining the pre-execution period according to the scheduling period, planned start time, and planned end time selected by the user; The workshop production data input by the user is acquired based on the pre-execution period.
[0055] In one embodiment, the workshop production scheduling plan formulation device further includes a feedback module, which is specifically configured to: Obtaining actual execution data corresponding to the target workshop production scheduling plan; Determining a plan execution rate corresponding to the target workshop production scheduling plan based on the actual execution data and the target workshop production scheduling plan; When the plan execution rate is lower than a preset threshold, an abnormal situation prompt is fed back.
[0056] The workshop production scheduling plan formulation device provided by the present invention obtains workshop production data input by the user; sorts the workshop production data based on the rules selected by the user in the expert rule set to obtain target workshop production data; the expert rule set is a pre-built rule set for sorting workshop production data; and determines the target workshop production scheduling plan based on the scheduling algorithm and the target workshop production data. The technical solution of the present invention does not need to rely on a manual team, saving labor costs, and can formulate different target workshop production scheduling plans based on different workshop production data, realizing cross-project reuse and improving applicability. In addition, the sorting of workshop production data is carried out according to the rules selected by the user, which is more flexible and diverse, meeting the diverse needs of users.
[0057] Figure 3 An example of a physical structure diagram of an electronic device is shown below. Figure 3 As shown, the electronic device may include: a processor 310, a communications interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communications interface 320, and the memory 330 communicate with each other via the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute the workshop production scheduling plan formulation method, which includes: Obtain workshop production data input by users; sorting the workshop production data based on the rules selected by the user in the expert rule set to obtain target workshop production data; the expert rule set is a pre-built rule set for sorting workshop production data; A target workshop production scheduling plan is formulated based on the scheduling algorithm and the target workshop production data.
[0058] Furthermore, the logic instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.
[0059] On the other hand, the present invention further provides a computer program product, comprising a computer program, which may be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the workshop production scheduling plan formulation method provided by the above methods, which includes: Obtain workshop production data input by users; sorting the workshop production data based on the rules selected by the user in the expert rule set to obtain target workshop production data; the expert rule set is a pre-built rule set for sorting workshop production data; A target workshop production scheduling plan is formulated based on the scheduling algorithm and the target workshop production data.
[0060] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for formulating a workshop production schedule provided by the above methods is implemented. The method includes: Obtain workshop production data input by users; sorting the workshop production data based on the rules selected by the user in the expert rule set to obtain target workshop production data; the expert rule set is a pre-built rule set for sorting workshop production data; A target workshop production scheduling plan is formulated based on the scheduling algorithm and the target workshop production data.
[0061] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.
[0062] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.
[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.
Claims
1. A workshop production scheduling plan formulation method, characterized in that: include: Obtain workshop production data input by users; sorting the workshop production data based on the rules selected by the user in the expert rule set to obtain target workshop production data; The expert rule set is a pre-built rule set for sorting workshop production data; A target workshop production scheduling plan is formulated based on the scheduling algorithm and the target workshop production data.
2. The workshop production scheduling plan formulation method according to claim 1, characterized in that: The scheduling algorithm includes a plurality of sub-scheduling algorithms; the production scheduling plan of the target workshop is formulated based on the scheduling algorithm and the production data of the target workshop, including: For each of the sub-scheduling algorithms, based on the sub-scheduling algorithm and the target workshop production data, formulate a production scheduling plan for the preparatory workshop corresponding to the sub-scheduling algorithm; For each of the preparation workshop production scheduling plans, determining the scheduling efficiency, resource utilization rate and order delivery timeliness rate corresponding to the preparation workshop production scheduling plan based on the preparation workshop production scheduling plan; Determining a comprehensive score corresponding to the production schedule of the preparatory workshop based on the scheduling efficiency, resource utilization, and order delivery timeliness of the production schedule of the preparatory workshop; The production scheduling plan of the preliminary workshop corresponding to the highest comprehensive score is determined as the production scheduling plan of the target workshop.
3. The workshop production scheduling plan formulation method according to claim 1, characterized in that: The step of obtaining the workshop production data input by the user includes: Obtaining initial workshop production data input by the user through a preset data template; Verifying the initial workshop production data, and if the verification result is correct, converting the initial workshop production data into converted workshop production data in a preset data format; The converted workshop production data is serialized to obtain the serialized workshop production data.
4. The workshop production scheduling plan formulation method according to claim 1, characterized in that: The expert rule set includes the following rules: merging the same batch; order priority sorting; Sort by weight; Sort by color; Order number sorting; cross sorting corresponding to the target field; interval sorting corresponding to the target field; aggregation sorting corresponding to the target field; the target field is the data field corresponding to the workshop production data selected by the user.
5. The workshop production scheduling plan formulation method according to any one of claims 1 to 4, characterized in that: The step of obtaining workshop production data input by a user further includes: If the scheduling period, planned start time, and planned end time selected by the user are verified to be qualified, determining the pre-execution period according to the scheduling period, planned start time, and planned end time selected by the user; The workshop production data input by the user is acquired based on the pre-execution period.
6. The workshop production scheduling plan formulation method according to any one of claims 1 to 4, characterized in that: The method further comprises: Obtaining actual execution data corresponding to the target workshop production scheduling plan; Determining a plan execution rate corresponding to the target workshop production scheduling plan based on the actual execution data and the target workshop production scheduling plan; When the plan execution rate is lower than a preset threshold, an abnormal situation prompt is fed back.
7. A workshop production scheduling plan making device, characterized in that: include: The acquisition module is used to obtain the workshop production data input by the user; a sorting module, configured to sort the workshop production data based on the rules selected by the user in the expert rule set to obtain target workshop production data; The expert rule set is a pre-built rule set for sorting workshop production data; The planning module is used to formulate a production scheduling plan for the target workshop based on the scheduling algorithm and the production data of the target workshop.
8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the workshop production scheduling plan formulation method as described in any one of claims 1 to 6 is implemented.
9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for formulating a workshop production scheduling plan as described in any one of claims 1 to 6 is implemented.
10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for formulating a workshop production scheduling plan as described in any one of claims 1 to 6 is implemented.