Production scheduling method, device, computer equipment and storage medium

Abstract

The application relates to the technical field of industrial scheduling, and discloses a production scheduling method, device, equipment and storage medium.The method comprises the following steps: acquiring historical scheduling data of a factory; constructing a target function and a constraint condition based on the historical scheduling data of the factory; acquiring order data of products and equipment data of the factory according to the scheduling model constructed according to the target function and the constraint condition; inputting the order data of the products and the equipment data of the factory into the scheduling model to obtain scheduling corresponding to the order data.The application realizes balancing of production resources, i.e., balancing of machine load, so that the machines maintain good performance, the service life of the production machines is reduced, and the daily scheduling production capacity is smoothed.

Description

Technical Field

[0001] This application relates to the field of industrial scheduling technology, and in particular to a production scheduling method, apparatus, computer equipment and storage medium. Background Technology

[0002] Currently, contract manufacturing industries such as semiconductor manufacturing fall under the category of discrete manufacturing. The performance of their production equipment is significantly affected by overload and load variations. Furthermore, order delivery times in the semiconductor industry severely impact customer ratings of suppliers, with low ratings damaging industry reputation. Current production scheduling is typically based on rules such as the Shortest Processing Time (SPT) rule, Weighted Shortest Processing Time (WSPT) rule, Earliest Delivery Date (EDD) rule, and Hodgson's rule. These rules are largely based on process times and order delivery times, without fully considering the balance of equipment load over time. Therefore, how to conduct production scheduling while fully considering equipment load has become a pressing issue. Summary of the Invention

[0003] This application provides a production scheduling method, apparatus, computer equipment, and storage medium to solve the problem that production scheduling in the prior art does not take into account the load of the equipment, resulting in poor availability of the scheduling data.

[0004] To address the above problems, this application provides a production scheduling method, comprising:

[0005] Obtain historical scheduling data from the factory;

[0006] Based on the factory's historical scheduling data, construct the objective function and constraints;

[0007] Construct a scheduling model based on the objective function and constraints;

[0008] Obtain product order data and factory equipment data;

[0009] The product order data and factory equipment data are input into the scheduling model to obtain the schedule corresponding to the order data.

[0010] Furthermore, the objective function includes a first sub-objective function and a second sub-objective function; the construction of the objective function based on the factory's historical scheduling data includes:

[0011] Based on the historical scheduling data, the daily production quantity, demand quantity, time duration, and daily production quantity of each product on a preset machine, a first sub-objective function is constructed, wherein the expression of the first sub-objective function is as follows:

[0012]

[0013] Where, L = Max{ET i -ST i}

[0014]

[0015] Where M represents the total number of process types or machine types, L represents the time length, and ET i Indicates the delivery time of order i, ST i Let m represent the start time of order i, m represent the quantity of orders requested, n represent the number of product types, and x represent the number of product types requested. ijt v represents the quantity of product j in order i produced on day t, which is an integer. jk This represents the quantity of product j produced per machine k, O ij This represents the required quantity of product j in order i;

[0016] A second sub-objective function is constructed based on the daily production quantity, demand quantity, and order start and delivery time data for each product in the historical scheduling data. The expression for the second sub-objective function is as follows:

[0017] Furthermore, the constraints include delivery date constraints, capacity constraints, and legacy constraints. The construction of constraints based on the factory's historical scheduling data includes:

[0018] The delivery constraint is determined according to the following formula:

[0019]

[0020] The capacity constraint is determined according to the following formula:

[0021]

[0022] Among them, Mnum k This represents the maximum load on machine k;

[0023] The legacy constraints are determined according to the following formula:

[0024] x ijt ∈Z

[0025] x ijt ≥0.

[0026] Furthermore, the step of constructing the scheduling model based on the objective function and constraints includes:

[0027] Based on the objective function and constraints, an initial model is constructed.

[0028] The initial model is optimized using a linearization algorithm to obtain the scheduling model.

[0029] Furthermore, the initial model constructed based on the objective function and constraints includes:

[0030] Based on the objective function and constraints, the initial model is constructed using the following formula:

[0031]

[0032] Where α represents the tradeoff coefficient.

[0033] Furthermore, the step of optimizing the initial model using a linearization algorithm to obtain the scheduling model includes:

[0034] Obtain the first nonnegative variable a kt The second nonnegative variable b kt The third nonnegative variable f ijt and the fourth nonnegative variable g ijt ;

[0035] Based on the first nonnegative variable a kt Second nonnegative variable b kt The first sub-objective function is then linearly transformed.

[0036] Based on the third nonnegative variable f ijt and the fourth nonnegative variable g ijt The second sub-objective function is then linearly transformed.

[0037] Based on the first and second objective sub-functions after linear transformation, and the constraints, the scheduling model is constructed, wherein the expression of the scheduling model is as follows:

[0038]

[0039] in

[0040] To address the aforementioned problems, this application also provides a production scheduling device, the device comprising:

[0041] The first acquisition module is used to acquire historical scheduling data of the factory;

[0042] The first construction module is used to construct the objective function and constraints based on the factory's historical scheduling data;

[0043] The second construction module is used to construct a scheduling model based on the objective function and constraints.

[0044] The second acquisition module is used to acquire product order data and factory equipment data;

[0045] The calculation module is used to input the order data of the product and the equipment data of the factory into the scheduling model to obtain the schedule corresponding to the order data.

[0046] Furthermore, the second building module includes:

[0047] The initial model construction submodule is used to construct an initial model based on the objective function and constraints.

[0048] The linearization processing submodule is used to optimize the initial model according to the linearization algorithm to obtain the scheduling model.

[0049] To address the aforementioned problems, this application also provides a computer device, comprising:

[0050] At least one processor; and,

[0051] A memory communicatively connected to the at least one processor; wherein,

[0052] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the production scheduling method as described above.

[0053] To address the aforementioned issues, this application also provides a non-volatile computer-readable storage medium storing computer-readable instructions that, when executed by a processor, implement the production scheduling method described above.

[0054] The production scheduling method, apparatus, computer equipment, and storage medium provided according to the embodiments of this application have at least the following advantages compared with the prior art:

[0055] By acquiring historical scheduling data from the factory; constructing an objective function and constraints based on the historical scheduling data; constructing a scheduling model according to the objective function and constraints; acquiring product order data and factory equipment data; inputting the product order data and factory equipment data into the scheduling model to obtain the schedule corresponding to the order data; this application, by constructing a scheduling model, can balance production resources, i.e., machine load, so that machines can maintain good performance, reduce wear and tear on production machines, and smooth daily production volume, while the schedule obtained by this application has strong usability. Attached Figure Description

[0056] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0057] Figure 1 This is an exemplary system architecture diagram to which this application can be applied;

[0058] Figure 2 A schematic flowchart of a production scheduling method provided in an embodiment of this application;

[0059] Figure 3 A schematic diagram of a production scheduling device provided in an embodiment of this application;

[0060] Figure 4 This is a schematic diagram of the structure of a computer device according to an embodiment of this application. Detailed Implementation

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0062] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily indicate the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly or implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0063] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0064] This application provides a production scheduling method that can be applied to, for example... Figure 1In the system architecture 100 shown, the system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. The network 104 is used as a medium to provide a communication link between the terminal devices 101, 102, and 103 and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0065] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0066] Terminal devices 101, 102, and 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc.

[0067] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal devices 101, 102, and 103.

[0068] It should be noted that the production scheduling method provided in this application embodiment is generally executed by a server / terminal device, and correspondingly, the production scheduling device is generally set in the server / terminal device.

[0069] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0070] This application provides a production scheduling method. (Refer to...) Figure 2 As shown, Figure 2 This is a flowchart illustrating a production scheduling method provided in an embodiment of this application.

[0071] In this embodiment, the production scheduling method includes:

[0072] S1. Obtain historical scheduling data from the factory;

[0073] Specifically, historical scheduling data of the factory is obtained from the database, such as the daily production quantity of each product, the demand quantity of each product, the duration of the production, the daily production quantity of each product on the preset machines, and the start and delivery time of orders.

[0074] When retrieving historical scheduling data from the database, a call request must be sent to the database, carrying a signature verification token; the signature verification result returned by the database must be received, and if the signature verification result is successful, the historical scheduling data in the database must be retrieved.

[0075] Data security is ensured by encrypting the database and requiring signature verification when retrieving historical scheduling data from the database.

[0076] S2. Based on the historical scheduling data of the factory, construct the objective function and constraints;

[0077] Specifically, a first sub-objective function can be constructed based on the daily production quantity, demand quantity, time length, and daily production quantity of each product on a preset machine in the historical scheduling data; a second sub-objective function can be constructed based on the daily production quantity, demand quantity, and order start and delivery time data of each product in the historical scheduling data; and delivery date constraints, capacity constraints, and legacy constraints can be constructed based on the historical scheduling data.

[0078] Furthermore, the objective function includes a first sub-objective function and a second sub-objective function; the construction of the objective function based on the factory's historical scheduling data includes:

[0079] Based on the historical scheduling data, the daily production quantity, demand quantity, time duration, and daily production quantity of each product on a preset machine, a first sub-objective function is constructed, wherein the expression of the first sub-objective function is as follows:

[0080]

[0081] Furthermore, L = Max{ET} i -ST i}

[0082]

[0083] Where M represents the total number of process types or machine types, L represents the time length, and ET i Indicates the delivery time of order i, ST i Let m represent the start time of order i, m represent the quantity of orders requested, n represent the number of product types, and x represent the number of product types requested. ijt v represents the quantity of product j in order i produced on day t, which is an integer.jk This represents the quantity of product j produced per machine k, O ij This represents the required quantity of product j in order i;

[0084] A second sub-objective function is constructed based on the daily production quantity, demand quantity, and order start and delivery time data for each product in the historical scheduling data. The expression for the second sub-objective function is as follows:

[0085] Specifically, the first sub-objective function is constructed to maintain machine performance, reduce machine wear and tear, extend machine lifespan, and balance machine load as much as possible. It is constructed by using historical scheduling data on the daily production quantity, demand quantity, time duration, and daily production quantity of each product on the preset machine.

[0086] Meanwhile, in order to increase risk management of orders, the daily allocation of the same product in an order should be as balanced as possible. Each machine model in the factory can be used for the processing technology of a product. Therefore, a second sub-objective function is constructed by using the daily production quantity of each product, the demand quantity of the product, and the start and delivery time data of the order from historical scheduling data.

[0087] In other embodiments of this application, a third sub-objective function is also provided. That is, in the scheduling model constructed solely based on the first and second sub-objective functions, the scheduling model is used to calculate the product order data and factory equipment data to obtain scheduling data. When the scheduling data has no feasible solution, the machine load can be appropriately set to full load (c%). This is achieved by ensuring the scheduling data has a feasible solution; specifically, c% can be added to the capacity constraint.

[0088] The first and second sub-objective functions are constructed based on historical scheduling data, which facilitates the subsequent generation of the scheduling model, so as to make the machine load as balanced as possible and smooth the daily production volume.

[0089] Furthermore, the constraints include delivery date constraints, capacity constraints, and legacy constraints. The construction of constraints based on the factory's historical scheduling data includes:

[0090] The delivery constraint is determined according to the following formula:

[0091]

[0092] The capacity constraint is determined according to the following formula:

[0093]

[0094] Among them, Mnumk This represents the maximum load on machine k;

[0095] The legacy constraints are determined according to the following formula:

[0096] x ijt ∈Z

[0097] x ijt ≥0.

[0098] Specifically, the delivery date constraint means that each order should be completed within the time from the start time to the delivery time. The capacity constraint means that the daily machine capacity should not exceed the load; however, when the daily machine capacity is limited to not exceeding the load, the scheduling model calculates based on the product order data and factory equipment data. If no feasible operation is found, the machine can be operated at a suitable full load of c%, that is, the capacity constraint will become:

[0099]

[0100] The calculation is performed again based on the revised capacity constraints. The full load percentage (c%) cannot be increased indefinitely, and the specific value of c can be adjusted according to the needs.

[0101] Furthermore, regarding the legacy constraints, all processes corresponding to the product must be completed on the same day and cannot be carried over to the next day; that is, the daily production quantity of the product must be an integer.

[0102] Based on the factory's historical scheduling data, delivery date constraints, capacity constraints, and legacy constraints are obtained so that the scheduling model can obtain the optimal solution under these constraints.

[0103] S3. Construct a scheduling model based on the objective function and constraints;

[0104] Specifically, an initial model is constructed based on the objective function and constraints; the initial model is then optimized using a linearization algorithm to obtain the scheduling model.

[0105] Furthermore, the step of constructing the scheduling model based on the objective function and constraints includes:

[0106] Based on the objective function and constraints, an initial model is constructed.

[0107] The initial model is optimized using a linearization algorithm to obtain the scheduling model.

[0108] Based on the objective function and constraints described above, the initial model is constructed. Since the initial model contains powers, it is not convenient to solve quickly. Therefore, in order to improve the solution speed, the initial model is linearized, specifically the objective function is linearized, thereby obtaining the scheduling model.

[0109] By linearizing the initial model, a scheduling model is obtained, which facilitates rapid solution when using the scheduling model in the future.

[0110] Furthermore, the initial model constructed based on the objective function and constraints includes:

[0111] Based on the objective function and constraints, the initial model is constructed using the following formula:

[0112]

[0113] Where α represents the tradeoff coefficient.

[0114] The trade-off coefficient refers to the weight of the two sub-objective functions. When the factory wants to reduce machine wear and tear and increase machine lifespan, α can be increased; when the factory wants to smooth out daily output, α can be decreased. In this application, α is 0.5, which is an equal distribution, taking both into account. The specific value can be set as needed.

[0115] The initial model is constructed based on the objective function and constraints to facilitate the subsequent obtaining of a scheduling model that is easier to solve.

[0116] Furthermore, the step of optimizing the initial model using a linearization algorithm to obtain the scheduling model includes:

[0117] Obtain the first nonnegative variable a kt The second nonnegative variable b kt The third nonnegative variable f ijt and the fourth nonnegative variable g ijt ;

[0118] Based on the first nonnegative variable a kt Second nonnegative variable b kt The first sub-objective function is then linearly transformed.

[0119] Based on the third nonnegative variable f ijt and the fourth nonnegative variable g ijt The second sub-objective function is then linearly transformed.

[0120] Based on the first and second objective sub-functions after linear transformation, and the constraints, the scheduling model is constructed, wherein the expression of the scheduling model is as follows:

[0121]

[0122] in

[0123] By introducing nonnegative variables to linearize the sub-objective function, a scheduling model is obtained. This model can then be easily solved using a conventional solver to obtain scheduling data, i.e., the production scheduling plan. The constraints are as follows:

[0124]

[0125] a kt ≥0, b kt ≥0, f ijt ≥0, g ijt ≥0, t=1,2,...,L,k=1,2,...,M,i=1,2,...,m,j=1,2,...,n.

[0126] By introducing nonnegative variables, the sub-objective function is linearized to obtain a scheduling model, which facilitates rapid solution when using the scheduling model in the future.

[0127] S4. Obtain product order data and factory equipment data;

[0128] Specifically, the system acquires the target data sent by the user, which includes the order number, quantity requirements for each product, process requirements, time requirements, and other data. Order data is obtained by preprocessing the target data sent by the user. This order data includes the product quantity and its process data, time data, and pre-stored factory equipment data, such as machine data corresponding to the process, and the machine's operating data, such as processing speed and number of machines. The order data and the pre-stored equipment data are then obtained by organizing the target data.

[0129] S5. Input the product order data and the factory equipment data into the scheduling model to obtain the schedule corresponding to the order data.

[0130] Specifically, the order data of the product and the equipment data of the factory are input into the scheduling model to obtain the schedule corresponding to the order data, i.e., the production scheduling plan, which includes the order number, the product type and quantity under the order number, and the daily production quantity arrangement, etc.

[0131] When no feasible solution is found after inputting the product order data and factory equipment data into the scheduling model, the machines can be operated at full capacity (c%). That is, the capacity constraint is changed to:

[0132]

[0133] Keeping the other aspects of the scheduling model unchanged, we recalculate to obtain a feasible solution.

[0134] By acquiring historical scheduling data from the factory; constructing an objective function and constraints based on the historical scheduling data; constructing a scheduling model according to the objective function and constraints; acquiring product order data and factory equipment data; and inputting the product order data and factory equipment data into the scheduling model to obtain the schedule corresponding to the order data, this application, by constructing a scheduling model, can balance production resources, i.e., machine load, so as to maintain good machine performance, reduce wear and tear on production machines, and smooth daily production volume while scheduling each order on time.

[0135] This embodiment also provides a production scheduling device, such as... Figure 3 The diagram shown is a functional block diagram of the production scheduling device of this application.

[0136] The production scheduling device 100 described in this application can be installed in an electronic device. Depending on the functions implemented, the production scheduling device 100 may include a first acquisition module 101, a first construction module 102, a second construction module 103, a second acquisition module 104, and a calculation module 105. The module described in this application can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and which are stored in the memory of the electronic device.

[0137] In this embodiment, the functions of each module / unit are as follows:

[0138] The first acquisition module 101 is used to acquire historical scheduling data of the factory;

[0139] The first construction module 102 is used to construct an objective function and constraints based on the historical scheduling data of the factory.

[0140] Furthermore, the objective function includes a first sub-objective function and a second sub-objective function; the first construction module 102 includes a first objective construction sub-module and a second objective construction sub-module;

[0141] The first objective construction submodule is used to construct a first sub-objective function based on the daily production quantity, demand quantity, time length, and daily production quantity of each product on a preset machine in the historical scheduling data. The expression of the first sub-objective function is as follows:

[0142]

[0143] Furthermore, L = Max{ET} i -STi}

[0144]

[0145] Where M represents the total number of process types or machine types, L represents the time length, and ET i Indicates the delivery time of order i, ST i Let m represent the start time of order i, m represent the quantity of orders requested, n represent the number of product types, and x represent the number of product types requested. ijt v represents the quantity of product j in order i produced on day t, which is an integer. jk This represents the quantity of product j produced per machine k, O ij This represents the required quantity of product j in order i;

[0146] The second objective construction submodule is used to construct a second sub-objective function based on the daily production quantity, product demand quantity, and order start and delivery time data of each product in the historical scheduling data. The expression of the second sub-objective function is as follows:

[0147] By combining the first objective construction submodule and the second objective construction submodule, the first sub-objective function and the second sub-objective function are constructed based on historical scheduling data, which facilitates the subsequent acquisition of the scheduling model, so as to make the machine load as balanced as possible and smooth the daily production volume.

[0148] Furthermore, the constraints include delivery date constraints, capacity constraints, and legacy constraints. The first construction module 102 includes a delivery date constraint construction submodule, a capacity constraint construction submodule, and a legacy constraint construction submodule.

[0149] The delivery date constraint construction submodule is used to determine the delivery date constraint according to the following formula:

[0150]

[0151] The capacity constraint construction submodule is used to determine the capacity constraint conditions according to the following formula:

[0152]

[0153] Among them, Mnum k This represents the maximum load on machine k;

[0154] The legacy constraint construction submodule is used to determine the legacy constraint conditions according to the following formula:

[0155] x ijt ∈Z

[0156] x ijt≥0.

[0157] By combining the delivery date constraint construction submodule, the capacity constraint construction submodule, and the legacy constraint construction submodule, the delivery date constraint conditions, capacity constraint conditions, and legacy constraint conditions are obtained based on the factory's historical scheduling data, so that the scheduling model can obtain the optimal solution under the above constraints.

[0158] The second construction module 103 is used to construct a scheduling model based on the objective function and constraints.

[0159] Furthermore, the second building module 103 includes an initial building submodule and a linearization submodule;

[0160] The initial construction submodule is used to construct an initial model based on the objective function and constraints.

[0161] The linearization submodule is used to optimize the initial model according to the linearization algorithm to obtain the scheduling model.

[0162] By combining the initial construction submodule and the linearization submodule, the initial model is linearized to obtain the scheduling model, which facilitates rapid solution when using the scheduling model later.

[0163] Furthermore, the initial construction submodule includes specific construction units;

[0164] The specific construction unit is used to construct the initial model based on the objective function and constraints using the following formula:

[0165]

[0166] Where α represents the tradeoff coefficient.

[0167] The initial model is constructed using specific building units based on the objective function and constraints, so as to obtain a scheduling model that is easy to solve later.

[0168] Furthermore, the linearization submodule includes a variable acquisition unit, a first linear transformation unit, a second linear transformation unit, and a final construction unit;

[0169] The variable acquisition unit is used to acquire the first non-negative variable a. kt The second nonnegative variable b kt The third nonnegative variable f ijt and the fourth nonnegative variable g ijt ;

[0170] The first linear transformation unit is used to transform the first nonnegative variable a. kt Second nonnegative variable b ktThe first sub-objective function is then linearly transformed.

[0171] The second linear transformation unit is used to transform the third nonnegative variable f. ijt and the fourth nonnegative variable g ijt The second sub-objective function is then linearly transformed.

[0172] The final construction unit is used to construct the scheduling model based on the first and second objective sub-functions after linear transformation, and the constraints, wherein the expression of the scheduling model is as follows:

[0173]

[0174] in

[0175] By combining the variable acquisition unit, the first linear transformation unit, the second linear transformation unit, and the final construction unit, and by introducing non-negative variables, the sub-objective function is linearized to obtain the scheduling model, which facilitates rapid solution when using the scheduling model in the future.

[0176] The second acquisition module 104 is used to acquire product order data and factory equipment data;

[0177] The calculation module 105 is used to input the order data of the product and the equipment data of the factory into the scheduling model to obtain the schedule corresponding to the order data.

[0178] By employing the above-described apparatus, the production scheduling device 100, through the coordinated use of the first acquisition module 101, the first construction module 102, the second construction module 103, the second acquisition module 104, and the calculation module 105, acquires historical scheduling data of the factory; constructs an objective function and constraints based on the historical scheduling data of the factory; constructs a scheduling model according to the objective function and constraints; acquires product order data and factory equipment data; and inputs the product order data and factory equipment data into the scheduling model to obtain the schedule corresponding to the order data. This application, by constructing a scheduling model, can balance production resources, i.e., machine load, while scheduling each order on time, so as to maintain good machine performance, reduce wear and tear on production machines, and smooth daily production volume.

[0179] This application also provides a computer device. Please refer to the following for details. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0180] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected via a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0181] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0182] The memory 41 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 4. Of course, the memory 41 may also include both the internal storage unit and its external storage device of the computer device 4. In this embodiment, the memory 41 is typically used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions for production scheduling methods. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or will be output.

[0183] In some embodiments, the processor 42 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to execute computer-readable instructions stored in the memory 41 or to process data, for example, to execute computer-readable instructions for the production scheduling method.

[0184] The network interface 43 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 4 and other electronic devices.

[0185] This embodiment implements the production scheduling method described in the above embodiment by having the processor execute computer-readable instructions stored in the memory. This includes: acquiring historical scheduling data from the factory; constructing an objective function and constraints based on the historical scheduling data; constructing a scheduling model based on the objective function and constraints; acquiring product order data and factory equipment data; and inputting the product order data and factory equipment data into the scheduling model to obtain the schedule corresponding to the order data. By constructing a scheduling model, this application can balance production resources, i.e., machine load, to maintain good machine performance, reduce wear and tear on production machines, and smooth daily production volume while scheduling orders on time.

[0186] This application also provides a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to perform the steps of the production scheduling method described above. These steps include: acquiring historical scheduling data of the factory; constructing an objective function and constraints based on the historical scheduling data; constructing a scheduling model based on the objective function and constraints; acquiring product order data and factory equipment data; and inputting the product order data and factory equipment data into the scheduling model to obtain the schedule corresponding to the order data. By constructing a scheduling model, this application can balance production resources, i.e., machine load, to maintain good machine performance, reduce wear and tear on production machines, and smooth daily production volume while scheduling orders on time.

[0187] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0188] The production scheduling apparatus, computer equipment, and computer-readable storage medium of the above embodiments of this application have the same technical effects as the production scheduling method of the above embodiments, and will not be elaborated here.

[0189] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A production scheduling method, characterized in that, The method includes: Obtain historical scheduling data from the factory; Based on the historical scheduling data of the factory, an objective function and constraints are constructed. The objective function includes a first sub-objective function and a second sub-objective function. The constraints include delivery date constraints, capacity constraints, and legacy constraints. Constructing a scheduling model based on the objective function and constraints specifically includes: obtaining a first non-negative variable, a second non-negative variable, a third non-negative variable, and a fourth non-negative variable; performing a linear transformation on the first sub-objective function based on the first and second non-negative variables; performing a linear transformation on the second sub-objective function based on the third and fourth non-negative variables; and constructing the scheduling model based on the linearly transformed first and second sub-objective functions and the constraints. Obtain product order data and factory equipment data; Input the product order data and the factory equipment data into the scheduling model to obtain the schedule corresponding to the order data; The objective function and constraints are constructed based on the factory's historical scheduling data, including: A first sub-objective function is constructed based on the daily production quantity, demand quantity, time length, and daily production quantity of each product on a preset machine in the historical scheduling data; a second sub-objective function is constructed based on the daily production quantity, demand quantity, and order start and delivery time data of each product in the historical scheduling data. The delivery date constraint is that each order must be generated within the time from the start time to the delivery time. The capacity constraint is that the daily machine capacity must not exceed the load. When the daily machine capacity is limited to not exceeding the load, the scheduling model calculates the product's order data and equipment data. If there is no feasible solution, the machine will run at full load c%. The legacy constraint is that all corresponding processes for the product must be generated on the same day.

2. The production scheduling method according to claim 1, characterized in that, The first sub-objective function expression and the second sub-objective function expression include: in, in, This indicates the total number of process types or machine types. Indicates the length of time. Indicates order Delivery time, Indicates order The start time, Indicates the quantity of demanded orders. Indicates the quantity of product types. Indicates order Products in exist The quantity produced per day is an integer. Indicates product In each machine The number produced per unit Indicates order medium-sized products The quantity required; 3. The production scheduling method according to claim 2, characterized in that, The constraints constructed based on the factory's historical scheduling data include: The delivery constraint is determined according to the following formula: The capacity constraint is determined according to the following formula: in, Indicates machine Maximum load; The legacy constraints are determined according to the following formula: 。 4. The production scheduling method according to claim 3, characterized in that, The process of constructing the scheduling model based on the objective function and constraints includes: Based on the objective function and constraints, an initial model is constructed. The initial model is optimized using a linearization algorithm to obtain the scheduling model.

5. The production scheduling method according to claim 4, characterized in that, The initial model constructed based on the objective function and constraints includes: Based on the objective function and constraints, the initial model is constructed using the following formula: in, This represents the tradeoff coefficient.

6. The production scheduling method according to claim 4, characterized in that, The scheduling model includes: The expression for the scheduling model is as follows: in in, As the first nonnegative variable, As the second nonnegative variable, As the third nonnegative variable, It is the fourth non-negative variable.

7. A production scheduling device, characterized in that, The device includes: The first acquisition module is used to acquire historical scheduling data of the factory; The first construction module is used to construct an objective function and constraints based on the historical scheduling data of the factory. The objective function includes a first sub-objective function and a second sub-objective function, and the constraints include delivery date constraints, capacity constraints, and legacy constraints. The first construction module is further configured to construct a first sub-objective function based on the daily production quantity, demand quantity, time length, and daily production quantity of each product on a preset machine in the historical scheduling data; and to construct a second sub-objective function based on the daily production quantity, demand quantity, and order start and delivery time data of each product in the historical scheduling data; the delivery date constraint is that each order is generated within the time from start to delivery; the capacity constraint is that the daily machine capacity does not exceed the load; when the daily machine capacity is limited to not exceeding the load, the scheduling model calculates the product order data and equipment data; if there is no feasible solution, the machine will run at full load c%; and the legacy constraint is that all corresponding processes of the product are generated on the same day. The second construction module is used to construct a scheduling model based on the objective function and constraints, specifically including: obtaining a first non-negative variable, a second non-negative variable, a third non-negative variable, and a fourth non-negative variable; performing a linear transformation on the first sub-objective function based on the first and second non-negative variables; performing a linear transformation on the second sub-objective function based on the third and fourth non-negative variables; and constructing the scheduling model based on the linearly transformed first and second sub-objective functions and the constraints. The second acquisition module is used to acquire product order data and factory equipment data; The calculation module is used to input the order data of the product and the equipment data of the factory into the scheduling model to obtain the schedule corresponding to the order data.

8. The production scheduling device according to claim 7, characterized in that, The second building module includes: The initial model construction submodule is used to construct an initial model based on the objective function and constraints. The linearization processing submodule is used to optimize the initial model according to the linearization algorithm to obtain the scheduling model.

9. A computer device, characterized in that, The computer device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores computer-readable instructions, and the processor, when executing the computer-readable instructions, implements the production scheduling method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions that, when executed by a processor, implement the production scheduling method as described in any one of claims 1 to 6.

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