Production scheduling result generation method, device, electronic device and readable storage medium

Abstract

The present application discloses a method, device, electronic device and readable storage medium for generating production scheduling results, which are applied to the field of production management technology. The method includes obtaining production scheduling rule data and processing parts to be scheduled by parsing the production tasks to be scheduled; generating production scheduling processing costs based on the production scheduling rule data and each processing part to be scheduled based on the pre-set production rules; based on the production scheduling processing costs and the production tasks to be scheduled, calling the pre-built production scheduling model for calculation, and finally generating the production scheduling results, thereby realizing the low-cost, efficient and accurate generation of production scheduling results, and meeting the actual production scheduling needs of users.

Description

Technical Field

[0001] The present application relates to the technical field of production management, and in particular to a method, device, electronic device and readable storage medium for generating production scheduling results. Background Art

[0002] Faced with the diversification and personalized development of market demand, the traditional mass production model has been unable to adapt to market changes, and the multi-variety production model has emerged. Today, companies not only need to provide diversified and customized products to meet customer needs, but also need to reduce costs and increase efficiency by shortening production cycles, efficiently utilizing production resources, and improving output and quality.

[0003] Related technologies usually use manual scheduling, and scheduling of multi-variety production models often needs to face problems such as multiple product categories, high frequency of model changes, and frequent changes in production plans. In addition, for example, in the steel industry, the process "transition" requirements when switching steel grades must also be considered. Relying solely on manual scheduling is inefficient, difficult, and the quality of scheduling results depends on manual experience, which is highly accidental and cannot meet users' real-world scheduling needs for low-cost, efficient, and accurate scheduling. Summary of the invention

[0004] The present application provides a method, device, electronic device and readable storage medium for generating production scheduling results, which realizes low-cost, efficient and accurate generation of production scheduling results to meet the actual production scheduling needs of users.

[0005] To solve the above technical problems, the embodiments of the present invention provide the following technical solutions:

[0006] An embodiment of the present invention provides a method for generating a production scheduling result, including:

[0007] By analyzing the tasks to be scheduled, the scheduling rule data and the parts to be scheduled are obtained;

[0008] Based on the preset production rules, the production scheduling cost is generated according to the production scheduling rule data and each processing part to be scheduled;

[0009] Based on the production scheduling processing cost and the tasks to be scheduled, a pre-built production scheduling model is called to perform calculations to generate a production scheduling result.

[0010] Optionally, the construction process of the production scheduling model includes:

[0011] Responding to the production scheduling target function establishment instruction, generating the production scheduling target function;

[0012] Respond to constraint establishment instructions and generate production scheduling constraint conditions based on production constraint information and business needs;

[0013] Generate a production scheduling calculation model according to the production scheduling constraint conditions and the production scheduling objective function;

[0014] In response to the algorithm construction instruction, an optimization algorithm for calculating the production scheduling calculation model is generated;

[0015] The optimization goal of the production scheduling objective function is to minimize the total processing cost and minimize the number of workpieces to be scheduled that are not included in the production scheduling plan while satisfying the production scheduling constraints.

[0016] Optionally, the production scheduling calculation model is:

[0017]

[0018] Where Z is the production scheduling objective function, c ij (i, j∈{1, 2, ..., n}) is the processing cost from the scheduled processing part i to the scheduled processing part j, loss is the preset scheduling loss value of a single scheduled processing part that is not included in the scheduling plan, n is the total number of scheduled processing parts, g i is the corresponding value of the scheduled processing part i under the constraints of the production restriction information and the business requirements, y is For the scheduled workpiece i to be produced in the sth production schedule, x ijs It is 0 or 1, Q1 is the minimum boundary limit, and Q2 is the maximum boundary limit.

[0019] Optionally, obtaining the production scheduling rule data and the workpieces to be scheduled by parsing the tasks to be scheduled includes:

[0020] Respond to the production schedule collection instruction and obtain the production tasks to be scheduled based on the production schedule demand information and product parameter information;

[0021] Performing data preprocessing on the tasks to be scheduled;

[0022] According to the received production scheduling rule information and data preprocessing results, the production scheduling rule data and the workpieces to be scheduled are determined.

[0023] Optionally, the performing data preprocessing on the to-be-scheduled tasks includes:

[0024] In response to the verification instruction, the data fields corresponding to the basic contract information and workpiece processing information of the production task to be scheduled are respectively verified for completeness and accuracy;

[0025] Respond to the identification instruction and set a unique number for each task to be scheduled;

[0026] Calculate the processing time of each workpiece to be scheduled in each unit through the average processing speed of each unit and each product category;

[0027] In response to the product classification instruction, the parts to be scheduled with the same or similar processing technology in each production task to be scheduled are classified.

[0028] Optionally, the generating of the production scheduling cost based on the preset production rules and the production scheduling rule data and each to-be-scheduled processing part includes:

[0029] Constructing a rule operator library in advance according to processing requirements, processing priorities and user-defined instructions; the rule operator library includes a plurality of first-category operators corresponding to production rules and a second-category operator corresponding to the user-defined instructions;

[0030] Matching each parameter field of the production scheduling rule data with the rule operator library to generate multiple production scheduling rules;

[0031] Call the processing cost calculation formula to calculate the production scheduling cost according to each scheduling rule and each processing part to be scheduled.

[0032] Optionally, calling the processing cost calculation formula to calculate the production scheduling processing cost according to each production scheduling rule and each processing part to be scheduled includes:

[0033] Call the processing cost calculation formula to calculate the processing cost corresponding to each production scheduling rule;

[0034] The weighted calculation formula is called to perform weighted summation on the processing costs corresponding to each production scheduling rule to obtain the production scheduling processing cost;

[0035] Among them, the processing cost calculation formula is:

[0036]

[0037] The weighted calculation relationship is:

[0038] Among them, c k is the processing cost of scheduling rule k, c ij (i, j∈{1, 2, ..., n}) is the penalty value for arranging the production of scheduled processing part j after scheduled processing part i, and n is the total number of scheduled processing parts; C 总 is the production scheduling and processing cost, a k is the weight coefficient of scheduling rule k, m is the total number of scheduling rules, and D is a diagonal matrix whose diagonals of the same order are all infinite.

[0039] Another aspect of the present invention provides a production scheduling result generating device, including:

[0040] The production scheduling data acquisition module is used to obtain the production scheduling rule data and the workpieces to be scheduled by analyzing the tasks to be scheduled;

[0041] A production scheduling cost calculation module, which is used to generate a production scheduling cost based on a preset production rule, the production scheduling rule data and each processing part to be scheduled;

[0042] The production scheduling result acquisition module is used to call a pre-built production scheduling model to perform calculations based on the production scheduling processing cost and the tasks to be scheduled, so as to generate a production scheduling result.

[0043] An embodiment of the present invention further provides an electronic device, comprising a processor, wherein the processor is configured to implement the steps of the method for generating a production scheduling result as described in any of the preceding items when executing a computer program stored in a memory.

[0044] Finally, an embodiment of the present invention further provides a readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for generating production scheduling results as described in any of the preceding items are implemented.

[0045] The advantage of the technical solution provided by the present application is that, for the workpieces that need to be scheduled, the scheduling cost is determined based on the scheduling rules and production rules, and then the scheduling model is called to obtain the final scheduling result, thereby solving the problems of difficult and time-consuming manual scheduling, low on-time delivery rate, and poor scheduling results under the multi-variety production mode. The purpose of reducing costs and increasing efficiency can be achieved, and scheduling results can be generated at low cost, efficiently, and accurately to meet the actual scheduling needs of users.

[0046] In addition, the embodiment of the present invention also provides a corresponding implementation device, electronic device and readable storage medium for the production scheduling result generation method, which further makes the method more practical, and the device, electronic device and readable storage medium have corresponding advantages.

[0047] It is to be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 A flowchart of a method for generating production scheduling results provided by an embodiment of the present invention;

[0050] Figure 2 A flowchart of another method for generating production scheduling results provided by an embodiment of the present invention;

[0051] Figure 3 A structural diagram of a specific implementation of the production scheduling result generating device provided in an embodiment of the present invention;

[0052] Figure 4 A structural diagram of a specific implementation of an electronic device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] The terms "first", "second", "third", "fourth", etc. in the specification and claims of this application and the above drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may include steps or units that are not listed.

[0055] After introducing the technical solutions of the embodiments of the present invention, various non-limiting implementation methods of the present application are described in detail below.

[0056] See first Figure 1 , Figure 1 A flowchart of a method for generating a production scheduling result provided by an embodiment of the present invention is provided. The embodiment of the present invention can be applied to any intelligent production scheduling system for automatic production scheduling, such as an intelligent production scheduling system for a large plate factory. The embodiment of the present invention may include the following contents:

[0057] S101: Obtaining production scheduling rule data and workpieces to be scheduled by parsing the tasks to be scheduled.

[0058] In this embodiment, it is necessary to collect the production schedule first, and the production schedule collection instruction can be responded to, and the production tasks to be scheduled can be obtained according to the production schedule demand information and product parameter information; the production schedule demand information includes but is not limited to the delivery time, the planning status, and the customer importance, and the product parameter information includes but is not limited to the product category, specification size, and processing technology, that is, the production schedule to be scheduled can be screened and collected according to the delivery time, planning status, customer importance, product category, specification size, and processing technology. After obtaining the production schedule to be scheduled, in order to more accurately obtain the production schedule rule data and the workpieces to be scheduled, the data preprocessing can be performed on the production tasks to be scheduled. The data preprocessing process may include: responding to the verification instruction, respectively verifying the integrity and accuracy of the data fields corresponding to the basic contract information and workpiece processing information of the production tasks to be scheduled; responding to the identification instruction, setting a unique label for each production task to be scheduled; calculating the processing hours of each workpiece to be scheduled in each unit through the average processing speed of each unit and each product category; responding to the product classification instruction, classifying the workpieces to be scheduled with the same or similar processing technology in each production task to be scheduled. For example, you can first check the completeness and accuracy of the contract basic information, specification dimensions, process parameter requirements, and processing priority data fields, and then uniquely label each task to be scheduled; by counting the historical processing time of different product categories, calculate the average processing speed of each unit and each product category, and convert the processing hours of each workpiece in each unit based on the average processing speed of each unit and each product category; classify products with the same or similar processing technology. According to the received scheduling rule information and data preprocessing results, determine the scheduling rule data and the workpieces to be scheduled. The scheduling rule information can be the scheduling rules that each workpiece to be scheduled needs to comply with, which is input by the user, or it can be automatically read from a fixed path. The scheduling rule data is determined based on these scheduling rule information, and the final workpiece to be scheduled is determined based on the classified products.

[0059] S102: Based on the preset production rules, the production scheduling rule data and each part to be scheduled for processing are used to generate the production scheduling cost.

[0060] In this embodiment, the production rules are production rules related to the processing technology and processing priority during the generation process. Different production rules correspond to different processing orders of the workpieces to be scheduled, and the corresponding processing costs are also different. The production scheduling rule data is determined according to the current actual application scenario, the conditions of the workpieces to be processed, and the equipment parameters of the processing equipment.

[0061] S103: Based on the production scheduling processing cost and the tasks to be scheduled, a pre-built production scheduling model is called to perform calculations to generate a production scheduling result.

[0062] The scheduling model is used to determine the optimal scheduling result based on the scheduling cost and the tasks to be scheduled. It is a model with an input end and an output end. The scheduling processing cost and the tasks to be scheduled are input into the scheduling model as input information. After a series of data processing by the scheduling model, the corresponding optimal scheduling result is finally output. The scheduling model has a built-in optimization algorithm, which can be a heuristic algorithm, for example. The scheduling result includes the basic information of the workpiece to be scheduled, the plan number, the planned processing order, the processing machine, the planned production time, the processing time, and the processing mold information.

[0063] In the technical solution provided in the embodiment of the present invention, for the workpieces that need to be scheduled, the scheduling cost is determined based on the scheduling rules and production rules, and then the scheduling model is called to obtain the final scheduling result, thereby solving the problems of difficult and time-consuming manual scheduling, low on-time delivery rate, and poor scheduling results in a multi-variety production mode. The purpose of reducing costs and increasing efficiency can be achieved, and scheduling results can be generated at low cost, efficiently, and accurately to meet the actual scheduling needs of users.

[0064] In the above embodiment, there is no limitation on how to construct the production scheduling model. In this embodiment, a method for constructing the production scheduling model is provided, which may include the following steps:

[0065] Responding to the production scheduling target function establishment instruction, generating the production scheduling target function;

[0066] Respond to constraint establishment instructions and generate production scheduling constraint conditions based on production constraint information and business needs;

[0067] Generate a production scheduling calculation model based on production scheduling constraints and production scheduling objective functions;

[0068] In response to the algorithm building instruction, an optimization algorithm for calculating the production scheduling calculation model is generated;

[0069] The optimization goal of the production scheduling objective function is to minimize the total processing cost and minimize the number of parts to be scheduled that are not scheduled in the production scheduling plan while satisfying the production scheduling constraint conditions. Production restriction information includes but is not limited to equipment production capacity and capacity restriction constraints, and business requirements include but are not limited to single plan minimum and maximum quantity restrictions, single plan minimum and maximum weight restrictions, and single plan minimum and maximum processing hours. As an optional implementation, the production scheduling calculation model can be expressed as:

[0070]

[0071] Among them, Z is the production scheduling objective function, c ij(i, j∈{1, 2, ..., n}) is the processing cost from the scheduled workpiece i to the scheduled workpiece j, loss is the preset scheduling loss value of a single workpiece that is not scheduled in the scheduling plan, or the penalty value of a single workpiece that is not scheduled, that is, the "abandonment" penalty. The larger the loss value is, the less acceptable it is for the workpiece not to be scheduled. n is the total number of workpieces to be scheduled. Formula (2) is based on business needs and is generally a single plan maximum or minimum quantity or weight or capacity constraint. g i is the corresponding value of the scheduled processing part i under the constraints of production restriction information and business requirements. When it is the maximum quantity limit, g i =1. is c is the production of the scheduled workpiece i in the sth production schedule; ij From the matrix C 总 It is obtained that x represents the processing cost from workpiece i to workpiece j; ijs is 0 or 1. If the sth production schedule is from scheduled processing part i to scheduled processing part j, then x ijs is 1, if the s-th production schedule does not go from scheduled processing part i to scheduled processing part j, then x ijs =0, Q1 is the minimum boundary limit, and Q2 is the maximum boundary limit. For n workpieces to be scheduled, formula (1) is the scheduling objective function. Under the condition of satisfying the scheduling constraints, the total processing cost is the lowest and the number of workpieces to be scheduled that are not scheduled in the scheduling plan is the least. Formula (3) means that a workpiece can only be in one plan at most; formulas (4) and (5) ensure that the previous workpiece and the next workpiece are in the same plan; formula (6) limits the value of the variable.

[0072] After determining the above production scheduling model, heuristic algorithms can be used for optimization and solution. An algorithm library equipped with heuristic algorithms for selection can be pre-built. Of course, the algorithm library also supports the access of self-developed solution algorithms. A heuristic algorithm refers to an algorithm based on intuitive or empirical construction, which can give an approximate optimal solution to an instance of the optimization problem within an acceptable computing cost such as computing time, occupied space, etc. The degree of deviation of the approximate solution from the true optimal solution may not be predictable in advance. The heuristic algorithms equipped in the solution algorithm library refer to the genetic algorithm, ant colony algorithm, particle swarm algorithm, simulated annealing method, and neural network, which are currently based on natural body algorithms.

[0073] Taking into account the process complexity and equipment limitations of multi-variety production, under the concept of controlling production costs, it may happen that some workpieces cannot be included in the current calculation. The workpiece may be included in the next calculation. The production scheduling model constructed in this embodiment adds a "discard" penalty to avoid the situation where all workpieces are included in the plan, which leads to increased production costs or no solution to the model. This is more in line with production reality and effectively improves the accuracy of production scheduling.

[0074] The above embodiment does not impose any limitation on the calculation method of the production scheduling cost. Based on the above embodiment, this embodiment provides an optional implementation method of generating the production scheduling cost based on the preset production rules, according to the production scheduling rule data and each to-be-scheduled processing part, which may include:

[0075] A rule operator library is constructed in advance according to processing requirements, processing priorities and user-defined instructions; the rule operator library includes a plurality of first-class operators corresponding to production rules and a second-class operator corresponding to user-defined instructions;

[0076] Match each parameter field of the production scheduling rule data with the rule operator library to generate multiple production scheduling rules;

[0077] Call the processing cost calculation formula to calculate the production scheduling cost according to each scheduling rule and each processing part to be scheduled.

[0078] In this embodiment, a rule operator library can be established by decomposing the production rules related to the processing technology requirements and processing priorities in the production process. The rule operator library may include preset operators, i.e., first-class operators, and customizable extension operators, i.e., second-class operators. Customizable extension operators are operators generated based on user-defined instructions issued by users according to actual application scenarios or actual needs. Preset operators include but are not limited to transition operators, avoidance operators, preferred operators, and secondary operators. Transition operators refer to the production situation in which the same production line cannot produce product B immediately after producing product A, but must produce product B after a period of time after producing product C. This type of operator that cannot be produced directly and needs to be established with a processing mode that specifies product transition is collectively referred to as a transition operator. Common transition operators are related to width transition rules, thickness transition rules, processing temperature transition rules, and variety transition rules. Avoidance operators refer to the production situation in which the same production line can only produce product A after replacing the mold or performing certain operations. If other products are to be produced, the mold needs to be replaced again or certain operations need to be performed. Common avoidance operators are related to mold flexible processing rules, process flexible processing rules, and machine equipment flexible processing rules. The preferred operator refers to the production situation where product A needs to be produced first when the production line can produce both product A and product B. Common preferred operators are related to delivery time, customer importance, product variety, and product value. The second-choice operator refers to the operator established for the response strategy adopted to avoid production interruption when there is a shortage of production resources on the production line. These response strategies generally need to face high production costs and multiple quality issues, but the losses are less than the shutdown of the entire production line, so the second-choice operator is generally not used or is used less. According to business needs, the parameter fields corresponding to each scheduling rule can be pre-bound to the corresponding operators in the above rule operator library, and these rule combinations can be customized to realize dynamic configuration of rule combinations. By establishing rule operators and configurable rule combinations, the scheduling rules are made more flexible and can adapt to a variety of production scenarios, with stronger practicality and better universality.

[0079] The process of calling the processing cost calculation formula and calculating the production scheduling processing cost according to each production scheduling rule and each processing part to be scheduled may include:

[0080] Call the processing cost calculation formula to calculate the processing cost corresponding to each production scheduling rule;

[0081] The weighted calculation formula is called to perform weighted summation on the processing costs corresponding to each production scheduling rule to obtain the production scheduling processing cost;

[0082] Among them, the processing cost calculation formula is:

[0083]

[0084] The weighted calculation relationship is:

[0085] Among them, c k is the processing cost of scheduling rule k, and the processing cost calculation formula c k The element c in ij (i, j∈{1, 2, ..., n}) is the penalty value for arranging the production of scheduled processing part j after scheduled processing part i. The penalty value range is a non-negative integer in [0, +∞). The larger the penalty value, the greater the production cost. If it is a transition operator, then c AB tends to positive infinity, but makes c AC +c CB is very small or zero; if it is an avoidance operator, the penalty value between products that need to be avoided tends to positive infinity, but the penalty value between products that do not need to be avoided is very small or zero; if it is a preferred operator, the penalty value satisfies c iA <c iB (i∈{1,2,…,n}); if it is a secondary selection operator, a high penalty value will be assigned to products that meet the response strategy. C 总 is the production scheduling and processing cost, a k is the weight coefficient of scheduling rule k, m is the total number of scheduling rules, and D is a diagonal matrix whose diagonals of the same order are all infinite.

[0086] This embodiment aims at the NP-hard (non-deterministic polynomial) problem of production scheduling. Most of the rules are constructed into a penalty matrix in the form of penalty coefficients, instead of directly adding these rules one by one to the modeling constraints. This greatly simplifies the spatial search of the NP problem, significantly improves the calculation speed, and effectively improves the production scheduling efficiency.

[0087] In order to make the technical personnel in the relevant field more clearly understand the technical solution of the present application, the present application is also combined with Figure 2 To give an illustrative example, the following may be included:

[0088] A1: Screen and collect tasks to be scheduled according to delivery time, planning status, customer importance, product category, specification size, and processing technology;

[0089] A2: For the collected production tasks to be scheduled, check the completeness and accuracy of the basic contract information, specifications and dimensions, process parameter requirements, and processing priority data fields; uniquely label each production task to be scheduled; calculate the average processing speed of each unit and each product category by statistically analyzing the historical processing time of different product categories, and convert the processing hours of each workpiece in each unit; classify products with the same or similar processing technology.

[0090] A3: Decompose the production rules related to processing technology requirements and processing priorities in the production process, and establish a rule operator library. According to business needs, pre-bind the parameter fields corresponding to each scheduling rule with the corresponding operators in the operator library to generate multiple scheduling rules. According to the scheduling rule combination configured by the user, call the above rule operator library accordingly, and generate the corresponding single rule penalty matrix C for each scheduling rule that needs to be considered. k (k∈{1,2,…,m}), m is the number of rules. That is, for n workpieces to be scheduled, the n numbered workpieces are generated into an n-order square matrix, that is, a single rule penalty matrix, which can be expressed as:

[0091]

[0092] A4: Based on The C obtained in A3 k (k∈{1,2,…,m}) to obtain a comprehensive weighted penalty matrix. k is the weight coefficient of the production scheduling rule. The larger the weight, the larger the penalty value and the higher the production scheduling rule cost. D is a diagonal matrix with infinite diagonals of the same order.

[0093] A5: Based on A4, the production scheduling model is obtained by modeling according to the equipment production capacity, capacity limit constraints, single plan minimum and maximum quantity limits, single plan minimum and maximum weight limits, and single plan minimum and maximum processing time limits as scheduling constraints. The scheduling model can be expressed as:

[0094]

[0095] A6: Use heuristic algorithms to optimize and solve the production scheduling model.

[0096] A7: By decoding the solution result of A6, the output includes the basic information of the workpiece, the plan number, the processing sequence within the plan, the processing machine, the planned production time, the processing time, and the processing mold information as the production schedule.

[0097] It can be seen from the above that this embodiment can solve the problems of difficult and time-consuming manual production scheduling, low on-time delivery rate, and poor scheduling results in a multi-variety production mode, thereby achieving the goal of reducing costs and increasing efficiency.

[0098] It should be noted that there is no strict order of execution between the steps in this application. As long as they comply with the logical order, these steps can be executed simultaneously or in a certain preset order. Figure 1-Figure 2 This is just a schematic and does not mean that this is the only execution order.

[0099] The embodiment of the present invention also provides a corresponding device for the method for generating production scheduling results, which further makes the method more practical. The device can be described from the perspective of functional modules and hardware. The production scheduling result generation device provided by the embodiment of the present invention is introduced below. The production scheduling result generation device described below and the production scheduling result generation method described above can be referred to each other.

[0100] From the perspective of functional modules, see Figure 3 , Figure 3 A structural diagram of a production scheduling result generating device provided in an embodiment of the present invention in a specific implementation manner, the device may include:

[0101] The production scheduling data acquisition module 301 is used to obtain the production scheduling rule data and the workpieces to be scheduled by analyzing the tasks to be scheduled;

[0102] The production scheduling cost calculation module 302 is used to generate the production scheduling cost based on the preset production rules, the production scheduling rule data and each processing part to be scheduled;

[0103] The production scheduling result acquisition module 303 is used to call a pre-built production scheduling model to perform calculations based on the production scheduling processing cost and the tasks to be scheduled, so as to generate a production scheduling result.

[0104] Optionally, in some implementations of the present embodiment, the above-mentioned device may also include a model building module, which is used to respond to a production scheduling objective function establishment instruction to generate a production scheduling objective function; respond to a constraint establishment instruction to generate production scheduling constraints based on production restriction information and business needs; generate a production scheduling calculation model according to the production scheduling constraints and the production scheduling objective function; respond to an algorithm construction instruction to generate an optimization algorithm for calculating the production scheduling calculation model; wherein the optimization goal of the production scheduling objective function is to minimize the total processing cost and minimize the number of parts to be scheduled that are not included in the production scheduling plan while satisfying the production scheduling constraints.

[0105] As an optional implementation, the above-mentioned model building module may be a module for building the following production scheduling model, and the production scheduling calculation model is:

[0106]

[0107] Among them, Z is the production scheduling objective function, c ij (i, j∈{1, 2, ..., n}) is the processing cost from the scheduled processing part i to the scheduled processing part j, loss is the preset scheduling loss value of a single scheduled processing part that is not included in the scheduling plan, n is the total number of scheduled processing parts, g i is the corresponding value of the scheduled processing part i under the constraints of production restriction information and business requirements, y isFor the scheduled workpiece i to be produced in the sth production schedule, x ijs It is 0 or 1, Q1 is the minimum boundary limit, and Q2 is the maximum boundary limit.

[0108] Optionally, in some other implementations of this embodiment, the above-mentioned production scheduling data acquisition module 301 can be further used to: respond to production scheduling plan collection instructions, obtain tasks to be scheduled based on production scheduling demand information and product parameter information; perform data preprocessing on tasks to be scheduled; determine production scheduling rule data and workpieces to be scheduled based on the received production scheduling rule information and data preprocessing results.

[0109] As an optional implementation of the above embodiment, the above-mentioned production scheduling data acquisition module 301 may include a data preprocessing unit, which is used to respond to verification instructions to verify the integrity and accuracy of the data fields corresponding to the basic contract information and workpiece processing information of the production tasks to be scheduled; respond to identification instructions to set a unique label for each production task to be scheduled; calculate the processing hours of each production workpiece to be scheduled in each unit through the average processing speed of each unit and each product category; respond to product classification instructions to classify the production workpieces to be scheduled with the same or similar processing technology in each production task to be scheduled.

[0110] Optionally, in some other implementations of the present embodiment, the above-mentioned production scheduling cost calculation module 302 can also be used to: construct a rule operator library in advance according to processing technology requirements, processing priorities and user-defined instructions; the rule operator library includes multiple first-class operators corresponding to production rules and second-class operators corresponding to user-defined instructions; match each parameter field of the production scheduling rule data with the rule operator library to generate multiple production scheduling rules; call the processing cost calculation relationship to calculate the production scheduling processing cost according to each production scheduling rule and each processing part to be scheduled.

[0111] As an optional implementation of the above embodiment, the above production scheduling cost calculation module 302 can be further used to: call the processing cost calculation formula to calculate the processing cost corresponding to each production scheduling rule; call the weighted calculation formula to perform weighted summation on the processing costs corresponding to each production scheduling rule to obtain the production scheduling processing cost; wherein the processing cost calculation formula is:

[0112]

[0113] The weighted calculation relationship is:

[0114] Among them, c k is the processing cost of scheduling rule k, c ij (i, j∈{1, 2, ..., n}) is the penalty value for arranging the production of scheduled processing part j after scheduled processing part i, and n is the total number of scheduled processing parts; C 总is the production scheduling and processing cost, a k is the weight coefficient of scheduling rule k, m is the total number of scheduling rules, and D is a diagonal matrix whose diagonals of the same order are all infinite.

[0115] The functions of the functional modules of the production scheduling result generating device described in the embodiment of the present invention can be specifically implemented according to the method in the above method embodiment. The specific implementation process can refer to the relevant description of the above method embodiment, which will not be repeated here.

[0116] It can be seen from the above that the embodiments of the present invention achieve low-cost, efficient and accurate generation of production scheduling results, meeting the actual production scheduling needs of users.

[0117] The production scheduling result generating device mentioned above is described from the perspective of functional modules. Furthermore, the present application also provides an electronic device, which is described from the perspective of hardware. Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application in one implementation manner. Figure 4 As shown, the electronic device includes a memory 40 for storing a computer program; and a processor 41 for implementing the steps of the method for generating a production scheduling result as mentioned in any of the above embodiments when executing the computer program.

[0118] Among them, the processor 41 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the processor 41 may also be a controller, a microcontroller, a microprocessor or other data processing chip. The processor 41 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 41 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 41 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 41 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0119] The memory 40 may include one or more computer-readable storage media, which may be non-transitory. The memory 40 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash storage devices. The memory 40 may be an internal storage unit of an electronic device in some embodiments, such as a hard disk of a server. The memory 40 may also be an external storage device of an electronic device in other embodiments, such as a plug-in hard disk equipped on a server, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. Further, the memory 40 may also include both an internal storage unit of an electronic device and an external storage device. The memory 40 may not only be used to store application software and various types of data installed in the electronic device, such as: the code of the program for executing the vulnerability processing method, etc., but may also be used to temporarily store data that has been output or is to be output. In this embodiment, the memory 40 is at least used to store the following computer program 401, wherein, after the computer program is loaded and executed by the processor 41, the relevant steps of the method for generating the scheduling result disclosed in any of the aforementioned embodiments can be implemented. In addition, the resources stored in the memory 40 may also include an operating system 402 and data 403, and the storage method may be temporary storage or permanent storage. The operating system 402 may include Windows, Unix, Linux, etc. The data 403 may include but is not limited to data corresponding to the scheduling result generation result.

[0120] In some embodiments, the electronic device may further include a display screen 42, an input / output interface 43, a communication interface 44 or a network interface, a power supply 45 and a communication bus 46. Among them, the display screen 42 and the input / output interface 43 such as a keyboard belong to the user interface, and the optional user interface may also include a standard wired interface, a wireless interface, etc. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device, etc. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface. The communication interface 44 may optionally include a wired interface and / or a wireless interface, such as a WI-FI interface, a Bluetooth interface, etc., which is usually used to establish a communication connection between the electronic device and other electronic devices. The communication bus 46 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0121] Those skilled in the art will understand that Figure 4 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than those shown in the figure, for example, it may also include a sensor 47 for realizing various functions.

[0122] The functions of the functional modules of the electronic device described in the embodiment of the present invention can be specifically implemented according to the method in the above method embodiment. The specific implementation process can refer to the relevant description of the above method embodiment, which will not be repeated here.

[0123] It can be seen from the above that the embodiments of the present invention achieve low-cost, efficient and accurate generation of production scheduling results, meeting the actual production scheduling needs of users.

[0124] It is understandable that if the production scheduling result generation method in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium to execute all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), electrically erasable programmable ROM, register, hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, removable disk, CD-ROM, magnetic disk or optical disk and other media that can store program code.

[0125] Based on this, an embodiment of the present invention further provides a readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the method for generating a production scheduling result as described in any of the above embodiments are performed.

[0126] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the hardware disclosed in the embodiments, including devices and electronic devices, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part.

[0127] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0128] The above is a detailed introduction to a production scheduling result generation method, device, electronic device and readable storage medium provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A method for generating production scheduling results, characterized in that: include: Screen and collect production tasks to be scheduled according to delivery time, planning status, customer importance, product category, specification size, and processing technology; By parsing the tasks to be scheduled, the scheduling rule data and the workpieces to be scheduled are obtained; the scheduling rule data and the workpieces to be scheduled are obtained by parsing the tasks to be scheduled: for the tasks to be scheduled, the completeness and accuracy of the basic contract information, specification size, process parameter requirements, and processing priority data fields are checked; each task to be scheduled is uniquely numbered; by counting the historical processing time of different product categories, the average processing speed of each unit and each product category is calculated, and the processing hours of each workpiece in each unit are converted; products with the same or similar processing technology are classified; Based on the preset production rules, the production scheduling cost is generated according to the production scheduling rule data and each processing part to be scheduled; Based on the production scheduling processing cost and the tasks to be scheduled, a pre-built production scheduling model is called to perform calculations to generate a production scheduling result; The method of generating the production scheduling cost based on the preset production rules and the production scheduling rule data and each workpiece to be scheduled for production includes: Constructing a rule operator library in advance according to processing requirements, processing priorities and user-defined instructions; the rule operator library includes a plurality of first-category operators corresponding to production rules and a second-category operator corresponding to the user-defined instructions; Matching each parameter field of the production scheduling rule data with the rule operator library to generate multiple production scheduling rules; Call the processing cost calculation formula to calculate the production scheduling cost according to each scheduling rule and each processing part to be scheduled: Call the processing cost calculation formula to calculate the processing cost corresponding to each production scheduling rule; The weighted calculation formula is called to perform weighted summation on the processing costs corresponding to each production scheduling rule to obtain the production scheduling processing cost; Among them, the processing cost calculation formula is: The weighted calculation relationship is: Among them, C k is the processing cost of scheduling rule k, c ij (i, j∈{1, 2, ..., n}) is the penalty value for arranging the production of scheduled processing part j after scheduled processing part i, and n is the total number of scheduled processing parts; C 总 is the production scheduling and processing cost, a k is the weight coefficient of scheduling rule k, m is the total number of scheduling rules, and D is a diagonal matrix with infinite diagonals of the same order; The construction process of the production scheduling model includes: Responding to the production scheduling target function establishment instruction, generating the production scheduling target function; Respond to constraint establishment instructions and generate production scheduling constraint conditions based on production constraint information and business needs; Generate a production scheduling calculation model according to the production scheduling constraint conditions and the production scheduling objective function; In response to the algorithm building instruction, an optimization algorithm for calculating the production scheduling calculation model is generated; The optimization goal of the production scheduling objective function is to minimize the total processing cost and minimize the number of workpieces to be scheduled that are not scheduled in the production scheduling plan while satisfying the production scheduling constraint conditions; the production scheduling calculation model is: Where Z is the production scheduling objective function, c ij (i, j∈{1, 2, ..., n}) is the processing cost from the scheduled processing part i to the scheduled processing part j, loss is the preset scheduling loss value of a single scheduled processing part that is not included in the scheduling plan, n is the total number of scheduled processing parts, g i is the corresponding value of the scheduled processing part i under the constraints of the production restriction information and the business requirements, y is For the scheduled workpiece i to be produced in the sth production schedule, x ijs It is 0 or 1, Q1 is the minimum boundary limit, and Q2 is the maximum boundary limit.

2. The method for generating production scheduling results according to claim 1, characterized in that: The method of obtaining the production scheduling rule data and the workpieces to be scheduled by parsing the tasks to be scheduled includes: Respond to the production schedule collection instruction and obtain the production tasks to be scheduled based on the production schedule demand information and product parameter information; Performing data preprocessing on the tasks to be scheduled; According to the received production scheduling rule information and data preprocessing results, the production scheduling rule data and the workpieces to be scheduled are determined.

3. The method for generating production scheduling results according to claim 2, characterized in that: The data preprocessing of the to-be-scheduled tasks includes: In response to the verification instruction, the data fields corresponding to the basic contract information and workpiece processing information of the production task to be scheduled are respectively verified for completeness and accuracy; Respond to the identification instruction and set a unique number for each task to be scheduled; Calculate the processing time of each workpiece to be scheduled in each unit through the average processing speed of each unit and each product category; In response to the product classification instruction, the parts to be scheduled with the same or similar processing technology in each production task to be scheduled are classified.

4. A production scheduling result generating device, characterized in that: include: The production scheduling data acquisition module is used to screen and collect tasks to be scheduled according to delivery time, planning status, customer importance, product category, specification size, and processing technology; By parsing the tasks to be scheduled, the scheduling rule data and the workpieces to be scheduled are obtained; the scheduling rule data and the workpieces to be scheduled are obtained by parsing the tasks to be scheduled: for the tasks to be scheduled, the completeness and accuracy of the basic contract information, specification size, process parameter requirements, and processing priority data fields are checked; each task to be scheduled is uniquely numbered; by counting the historical processing time of different product categories, the average processing speed of each unit and each product category is calculated, and the processing hours of each workpiece in each unit are converted; products with the same or similar processing technology are classified; A production scheduling cost calculation module, used to generate a production scheduling cost based on a preset production rule, according to the production scheduling rule data and each processing part to be scheduled; A production scheduling result acquisition module is used to call a pre-built production scheduling model to perform calculation based on the production scheduling processing cost and the task to be scheduled, so as to generate a production scheduling result; A model building module is used to respond to a production scheduling objective function establishment instruction to generate a production scheduling objective function; respond to a constraint establishment instruction to generate production scheduling constraint conditions based on production constraint information and business requirements; generate a production scheduling calculation model based on the production scheduling constraint conditions and the production scheduling objective function; respond to an algorithm construction instruction to generate an optimization algorithm for calculating the production scheduling calculation model; The optimization goal of the production scheduling objective function is to minimize the total processing cost and minimize the number of parts to be scheduled that are not scheduled in the production scheduling plan while satisfying the production scheduling constraint conditions; Wherein, the production scheduling cost calculation module is further used for: Constructing a rule operator library in advance according to processing requirements, processing priorities and user-defined instructions; the rule operator library includes a plurality of first-category operators corresponding to production rules and a second-category operator corresponding to the user-defined instructions; Matching each parameter field of the production scheduling rule data with the rule operator library to generate multiple production scheduling rules; Call the processing cost calculation formula to calculate the production scheduling cost according to each scheduling rule and each processing part to be scheduled: Call the processing cost calculation formula to calculate the processing cost corresponding to each production scheduling rule; The weighted calculation formula is called to perform weighted summation on the processing costs corresponding to each production scheduling rule to obtain the production scheduling processing cost; Among them, the processing cost calculation formula is: The weighted calculation relationship is: Among them, C k is the processing cost of scheduling rule k, c ij (i, j∈{1, 2, ..., n}) is the penalty value for arranging the production of scheduled processing part j after scheduled processing part i, and n is the total number of scheduled processing parts; C 总 is the production scheduling and processing cost, a k is the weight coefficient of scheduling rule k, m is the total number of scheduling rules, and D is a diagonal matrix with infinite diagonals of the same order; The production scheduling calculation model is: Where Z is the production scheduling objective function, c ij (i, j∈{1, 2, ..., n}) is the processing cost from the scheduled processing part i to the scheduled processing part j, loss is the preset scheduling loss value of a single scheduled processing part that is not included in the scheduling plan, n is the total number of scheduled processing parts, g i is the corresponding value of the scheduled processing part i under the constraints of the production restriction information and the business requirements, y is For the scheduled workpiece i to be produced in the sth production schedule, x ijs It is 0 or 1, Q1 is the minimum boundary limit, and Q2 is the maximum boundary limit.

5. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to implement the steps of the production scheduling result generating method as claimed in any one of claims 1 to 3 when executing the computer program stored in the memory.

6. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for generating a production scheduling result according to any one of claims 1 to 3 are implemented.

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