Production scheduling method, electronic device and storage medium

Abstract

The present disclosure provides a production scheduling method, an electronic device and a storage medium, which solve the problems that during the existing production scheduling model solving, the difference between production scheduling plans and the actual processing condition of products is great, and the productivity utilization rate of production lines is low. The production scheduling method of the present disclosure comprises: on the basis of a first objective function of a first time cycle, constructing a first long-cycle model; on the basis of a second objective function of a second time cycle, constructing a first short-cycle model, wherein the first objective function is generated on the basis of an optimization objective of the first time cycle and product processing costs of the first time cycle, and the second objective function is generated on the basis of an optimization objective of the second time cycle and product processing costs of the second time cycle; dividing a production scheduling cycle to obtain m first time cycles, each first time cycle comprising a plurality of the second time cycles; and inputting data related to product production into the first long-cycle model and the first short-cycle model, so as to obtain a production scheduling result of each second time cycle in the m first time cycles.

Description

Production scheduling method, electronic device and storage medium Technical Field

[0001] The present disclosure relates to the field of industrial manufacturing technology, and in particular to a production scheduling method, electronic equipment, and storage medium. Background Art

[0002] Most common discrete batch production planning and scheduling models are single-line problems, with little consideration given to product switching during production and capacity ramp-up after product switching. Furthermore, in the model solving process, variables at distant times are usually ignored or simplified into long-cycle models, with little consideration given to the coordination of long- and short-cycle models. As a result, the solved production schedule differs significantly from the actual product processing situation, resulting in low utilization of the production line and reduced on-time delivery rates for orders.

[0003] Summary of the Invention

[0004] The present disclosure aims to solve at least one of the technical problems existing in the prior art, and proposes a production scheduling method, electronic equipment and storage medium. Through the solution of the present disclosure, the production line's capacity utilization rate can be improved, the order delay amount can be reduced, and the inventory level can be lowered.

[0005] One aspect of an embodiment of the present disclosure provides a production scheduling method, specifically comprising the following steps:

[0006] A first long-cycle model is constructed based on a first objective function of a first time period; a first short-cycle model is constructed based on a second objective function of a second time period; wherein the first objective function is generated based on the optimization target of the first time period and the product processing cost of the first time period; and the second objective function is generated based on the optimization target of the second time period and the product processing cost of the second time period;

[0007] Dividing the production scheduling cycle to obtain m first time periods, where the first time period includes a plurality of second time periods; wherein m≥1;

[0008] Product data is input into the first long-cycle model and the first short-cycle model to obtain a production scheduling result for each of the second time periods within m first time periods, wherein the product data is data related to product production.

[0009] In some embodiments, the step of inputting product data into the first long-cycle model and the first short-cycle model to obtain a production scheduling result for each of the second time periods within m first time periods includes: solving the first long-cycle model and the first short-cycle model m times, and for the kth solution process among the m solution processes, the method includes:

[0010] Constructing a second short-cycle model for the first k first time periods based on the product-production line matching results for the first k first time periods and the first short-cycle model; k∈2,…,m;

[0011] Constructing a second long-cycle model based on the first time periods and their number that have not been used to construct the second short-cycle model and the first long-cycle model; wherein the number of first time periods that have not been used to construct the second short-cycle model is equal to mk; and constructing a first coupling constraint between the second short-cycle model and the second long-cycle model based on the second short-cycle model and the second long-cycle model;

[0012] According to the product data, the second short cycle model, the second long cycle model and the coupling constraints of the first k first time periods are solved to obtain the production scheduling results within the first k first time periods and the product and production line matching results of mk first time periods.

[0013] In some embodiments, the step of solving the second short-cycle model, the second long-cycle model, and the coupling constraint for the first k first time periods based on the product data to obtain the production scheduling results for the first k first time periods and the product-production line matching results for mk first time periods includes:

[0014] The second short cycle model of the first k first time periods, the second long cycle model and the coupling constraints are solved according to the product data, and in the solution process, the values ​​of the key variables obtained by solving the second short cycle model in the k-1th solution process are fixed, so as to obtain the production scheduling results in the first k first time periods and the product and production line matching results of mk first time periods, wherein the key variables include: any one or more of variables related to production status, variables related to tangents, variables related to ramping, and variables related to mass production.

[0015] In some embodiments, in the last solving process, the step of solving the first long-cycle model and the first short-cycle model according to the product data to obtain the production scheduling result includes:

[0016] Constructing a second short-cycle model for the first m first time periods based on the product-production line matching results of the first m first time periods and the first short-cycle model;

[0017] The second short cycle model of the first m first time periods is solved according to the product data to obtain the production scheduling results for all second time periods within the first m first time periods.

[0018] In some embodiments, before solving the first long-period model and the first short-period model for the first time, the method further includes:

[0019] A third long cycle model is constructed based on the number of first time periods and the first long cycle model, and the third long cycle model is solved according to product data to obtain a product and production line matching result for each first time period.

[0020] In some embodiments, after obtaining mk product-to-production line matching results for the first time period, the method further includes:

[0021] Extracting the first product and production line matching result of the first time period from the mk product and production line matching results of the first time period;

[0022] When k=1, the first k first time period product and production line matching results include: the first first time period product and production line matching result among all first time period product and production line matching results obtained by solving the third long cycle model;

[0023] When 1<k≤m, the product and production line matching results of the first k first time periods include: the product and production line matching result of the first first time period among the mk product and production line matching results of the first time periods extracted in each solution process.

[0024] In some embodiments, the product processing cost of the first time period includes one or more of: a tangential cost of the first time period, a production cost of the first time period, an inventory cost of the first time period, and a postponement cost of the first time period.

[0025] In some embodiments, the first objective function comprises:

[0026] The optimization target of the first time period = the first coefficient × the tangent cost of the first time period + the second coefficient × the production cost of the first time period + the third coefficient × the inventory cost of the first time period + the fourth coefficient × the extension cost of the first time period, wherein the first coefficient, the second coefficient, the third coefficient and the fourth coefficient are respectively used to adjust their corresponding costs.

[0027] In some embodiments, the second time period product processing cost includes one or more of a second time period tangent cost, a second time period production cost, a second time period inventory cost, and a second time period extension cost.

[0028] In some embodiments, the second objective function is: fifth coefficient × second time period tangent cost + sixth coefficient × second time period production cost + seventh coefficient × second time period inventory cost + eighth coefficient × second time period extension cost, wherein the fifth coefficient, the sixth coefficient, the seventh coefficient and the eighth coefficient are respectively used to adjust their corresponding costs.

[0029] In some embodiments, the step of constructing a first long-term model based on the first target function of the first time period includes:

[0030] Constructing a first long-cycle model based on a first objective function and a first constraint for a first time period, wherein the first constraint comprises any one or more of a first production inventory constraint, a capacity constraint, a first tangent constraint, a first ramp-up yield constraint, and a first mass production yield constraint for the first time period;

[0031] The step of constructing a first short-cycle model based on the second objective function of the second time period includes: constructing a first short-cycle model based on the second objective function and second constraints of the second time period; wherein the second constraints include any one or more of a second production inventory constraint, a production line state switching constraint, a second tangent constraint, a mold constraint, a second ramp-up output constraint, and a second mass production output constraint regarding the second time period.

[0032] In some embodiments, the first production inventory constraint is used to Product inventory for a specific time period, The amount of product extensions in the time period, Mass production output or ramp-up output in a certain time period, Product demand in a time period, constraints Product inventory for the time period and The amount of product extensions for the time period, where 2, 3, ..., m;

[0033] The capacity constraint is used to constrain the product during processing on the production line according to the maximum capacity of the product on the production line. Tangent consumption output and / or volume production output within the time period;

[0034] The first tangent constraint is used to constrain the maximum number of switching times of the same type of products within a unit time period;

[0035] The first ramp-up yield constraint is used to constrain the ramp-up yield of the production line. After the tangent occurs, the products after the tangent are on the production line. Output during the time period;

[0036] The first mass production output constraint is used to constrain the number of products on the production line according to the maximum mass production output of the production line in a single time period of the unit second time period. The mass production output in the time period.

[0037] In some implementations, the first production inventory constraint includes a first expression, which is:

[0038] Among them, i represents the product, represents the first time period, l represents the production line, L represents the production line set, Indicates that product i in the first time period Inventory, Indicates that product i in the first time period The amount of delay, Indicates the first time period of product i in production line l The mass production output, Indicates the first time period of product i in production line l The climbing output, Indicates that product i in the first time period Demand;

[0039] The capacity constraint includes a second expression, which is:

[0040] Where F represents the set of products, Indicates the first time period of product i in production line l A tangent occurs, g indicates how many days of production capacity a single tangent consumes. Indicates the first time period of product i in production line l Mass production took place, Indicates that production line 1 is in the first time period production capacity;

[0041] The first tangent constraint includes a third expression, which is:

[0042] Where f represents the product type, and k represents the maximum number of line cuts allowed within a production line time period;

[0043] The first ramp-up yield constraint includes a fourth expression, which is:

[0044] Among them, p il is the ramp-up output of product i on production line l;

[0045] The first mass production yield constraint includes a fifth expression, which is:

[0046] Among them, q il Indicates the maximum production output in a single time period per second time period.

[0047] In some embodiments, the second production inventory constraint includes: determining the product inventory in time period t and the product deferral amount in time period t based on the product inventory in time period t-1, the product deferral amount in time period t-1, the mass production output or ramp-up output in time period t, and the product demand in time period t, where t∈1,2,3,...n, where n is the number of the second time periods included in the first time period;

[0048] The production line state switching constraint is used to determine the production line state based on whether the production line continuously produces the same type of product from time period t-1 to time period t, and whether the production line switches the type of product it produces in time period t; or to determine the production line state based on whether the production line continuously produces the same type of product from time period t-1 to time period t, and the number of days after the production line is tangent in time period t;

[0049] The second tangent constraint is used to constrain the occupancy of the tangent capacity when a production line is tangent, based on the tangent capacity of the production line group;

[0050] The mold constraint is used to constrain the number of production lines producing the same type of products based on the number of sets of molds for the same type of products;

[0051] The second ramp-up output constraint is used to constrain the output of the product after the tangent occurs in the time period t on the production line according to the total ramp-up output of the production line;

[0052] The second mass production output constraint includes: constraining the mass production output of the product on the production line in a time period t according to the maximum mass production output of the production line in a single time period.

[0053] In some implementations, the second production inventory constraint includes a sixth expression, which is:

[0054] Production inventory constraints describe the inventory status of products. For each product, each time period has:

[0055] Where i represents the product, t represents the second time period, L represents the production line set, l represents the production line, I it represents the inventory of product i in the second time period t, B it represents the extension amount of product i in the second time period t, x ilt represents the mass production output of product i in the second time period t of production line l, z iltrepresents the ramp-up output of product i in the second time period t of production line l, d it represents the demand for product i in the second time period t;

[0056] The production line state switching constraint includes a seventh expression and an eighth expression, and the seventh expression is:

[0057] Among them, y il(t-1) represents the production status of product i in the second time period t-1 of production line l, S (ik)lt Indicates that production line l switches from producing product i to producing product k in the second time period t. ilt Indicates whether production line l continuously produces product i from the second time period t-1 to the second time period t;

[0058] The eighth expression is:

[0059] Among them, y ilt represents the production status of product i in the second time period t of production line l, g ki It represents the length of the tangent time when switching from producing product k to producing product i. When the tangent occurs, k and i do not belong to the same product type;

[0060] The second tangent constraint includes a ninth expression, which is:

[0061] Among them, L g Indicates g production line group, C g is the tangent capacity of the g production line group, w l Indicates the occupancy of the tangent capacity when the production line l is tangent;

[0062] The mold constraint includes a tenth expression, which is:

[0063] Among them, mold f represents the number of model sets of type f;

[0064] The second ramp-up yield constraint includes an eleventh expression, which is:

[0065] Among them, p il is the ramp-up output of product i on production line l;

[0066] The second mass production yield constraint includes a twelfth expression, which is: ilt ≤y ilt *q il

[0067] Among them, qil Indicates the maximum mass production output of product i on production line l in a single time period.

[0068] In some embodiments, the first coupling constraint includes:

[0069] For the second short cycle model and the second long cycle model of the first k first time periods, the inventory and extension amount at the end of the last second time period of the second short cycle model of the first k first time periods are equal to the inventory and extension amount at the beginning of the first first time period of the second long cycle model.

[0070] In some implementations, the step of dividing the production scheduling cycle to obtain m first time periods includes:

[0071] The production scheduling cycle is evenly divided to obtain m first time periods.

[0072] In some implementations, the step of dividing the production scheduling cycle to obtain m first time periods includes:

[0073] The production scheduling cycle is divided to obtain m first time periods, and the length of the first time period is determined according to the number of orders within the production scheduling cycle.

[0074] Another aspect of the embodiments of the present disclosure further provides an electronic device, comprising: at least one processor; and a memory, wherein the memory stores a computer program that can be run on the processor, and when the computer program is executed by the processor, the steps of the above method are implemented.

[0075] In another aspect of the embodiments of the present disclosure, a storage medium is provided, which stores a computer program that implements the above method steps when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other embodiments can be obtained based on these drawings without paying any creative work.

[0077] FIG1 is a flow chart of a production scheduling method provided by an embodiment of the present disclosure;

[0078] FIG2 is a flow chart of the production scheduling method provided by an embodiment of the present disclosure when solving the long-short cycle model for the first time;

[0079] FIG3 is a flow chart of the production scheduling method provided by an embodiment of the present disclosure when solving the long-short cycle model for the kth time;

[0080] FIG4 is a flow chart of the production scheduling method provided by an embodiment of the present disclosure when solving the long-short cycle model for the last time;

[0081] FIG5 is a flow chart of a production scheduling method provided by an embodiment of the present disclosure in the model solving stage. DETAILED DESCRIPTION

[0082] In order to make the purposes, technical solutions and advantages of the present disclosure more clear, the embodiments of the present disclosure are further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. However, it should be understood that the disclosed embodiments are merely examples, and other embodiments may take various alternative forms. The drawings are not necessarily drawn to scale; certain features may be exaggerated or minimized to show the details of specific components. Therefore, the specific structural and functional details disclosed herein should not be interpreted as restrictive, but merely as a representative basis for teaching those skilled in the art to use the present application in various ways. As will be understood by those of ordinary skill in the art, the various features shown and described with reference to any one of the figures can be combined with the features shown in one or more other figures to produce embodiments that are not explicitly shown or described. The combination of features shown provides representative embodiments for typical applications. However, various combinations and modifications of features consistent with the teachings of the present disclosure may be desirable for certain specific applications or implementations.

[0083] Unless otherwise defined, the technical terms or scientific terms used in this disclosure should have the usual meanings understood by people with ordinary skills in the field to which this disclosure belongs. The "first", "second" and similar words used in this disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, similar words such as "one", "an" or "the" do not indicate a quantity limit, but rather indicate the presence of at least one. Similar words such as "include" or "comprising" mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. The term "and / or" when used to list two or more items means that any one of the listed items can be used by itself, or any combination of two or more of the listed items can be used.

[0084] The first aspect of the disclosed embodiment proposes a production scheduling method. In the production scheduling scheme of the disclosed embodiment, the different time scales of the production plan are comprehensively considered, and a long- and short-cycle model with a mixed time scale is constructed. Factors such as production line capacity, order delivery time, order inventory, mold switching, and capacity ramp-up are comprehensively considered. In the model solution process, based on the mixed time scale model, a rolling solution strategy that coordinates long and short time scales is designed to realize the automatic generation of production plans, improve the production line's capacity utilization, reduce order delays, and lower inventory levels.

[0085] FIG1 is a flow chart of a production scheduling method provided by an embodiment of the present disclosure. In the embodiment shown in FIG1 , the method includes the following steps:

[0086] S10. Construct a first long-cycle model based on the first objective function of the first time period; construct a first short-cycle model based on the second objective function of the second time period; wherein the first objective function is generated based on the optimization target of the first time period and the product processing cost of the first time period; and the second objective function is generated based on the optimization target of the second time period and the product processing cost of the second time period.

[0087] Specifically, the first long-cycle model is used to indicate that multiple products can be produced within a planning period (a planning period in the long-cycle model is equivalent to a first time period), regardless of the production order of the products, and that production capacity within the period is limited. The first short-cycle model is used to indicate that at most one product can be produced within a planning period (a planning period in the short-cycle model is equivalent to a second time period), and that either production occurs or nothing is produced, and the impact of the production order of different products needs to be considered. A planning period in the long-cycle model includes several planning periods in the short-cycle model.

[0088] It should be noted that a first time period includes several second time periods. Assuming the first time period is measured in months and the second time period is measured in days, a first time period can include 30 second time periods. Assuming the first time period is measured in weeks and the second time period is measured in days, a first time period can include 7 second time periods. Assuming the first time period is measured in days and the second time period can also be measured in days, a first time period can include 10, 20, or 25 days, etc.; assuming the first time period is measured in days and the second time period can also be measured in hours, a first time period can include 24 hours. Both the first and second time periods can be measured in seconds, minutes, hours, shifts, days, weeks, months, years, and other units, but are not limited to these. Different units correspond to different time period types.

[0089] The product processing costs for the first time period include one or more of: a tangent cost for the first time period, a production cost for the first time period, an inventory cost for the first time period, and a delay cost for the first time period. In some examples, the tangent cost for the first time period is related to the number of tangents; the production cost for the first time period is related to the production status of the product on the production line; the inventory cost for the first time period is related to the inventory volume of the product produced on the production line; and the delay cost for the first time period is related to the delay in production of the product on the production line and the importance of the customer.

[0090] The product processing cost of the second time period includes one or more of: a tangent cost of the second time period, a production cost of the second time period, an inventory cost of the second time period, and an extension cost of the second time period.

[0091] In some examples, the first objective function is: optimization target of the first time period = first coefficient × tangent cost of the first time period + second coefficient × production cost of the first time period + third coefficient × inventory cost of the first time period + fourth coefficient × extension cost of the first time period; wherein the first coefficient, the second coefficient, the third coefficient and the fourth coefficient are respectively used to adjust the corresponding costs, and the larger the numerical value corresponding to each coefficient, the greater the impact of the first time period cost corresponding to the coefficient on the optimization target of the first time period, and the higher the importance among all costs. For example, the first coefficient can be 0, 0.2, 0.3, 0.5, 0.7, 1, 1.5, 2, 2.4, 3, 3.6, 4, 4.8, 5, ..., the second coefficient can be 0, 0.2, 0.3, 0.5, 0.7, 1, 1.5, 2, 2.4, 3, 3.6, 4, 4.8, 5, ..., the third coefficient can be 0, 0.2, 0.3, 0.5, 0.7, 1, 1.5, 2, 2.4, 3, 3.6, 4, 4.8, 5, ..., and the fourth coefficient can be 0, 0.2, 0.3, 0.5, 0.7, 1, 1.5, 2, 2.4, 3, 3.6, 4, 4.8, 5, .... When the value of the coefficient is 0, it means that the corresponding cost has a negligible impact on the optimization objective of the first time period. The coefficient type can be single precision or double precision, etc., but is not limited thereto.

[0092] The second objective function is: fifth coefficient × second time period tangent cost + sixth coefficient × second time period production cost + seventh coefficient × second time period inventory cost + eighth coefficient × second time period extension cost. The fifth, sixth, seventh, and eighth coefficients are used to adjust their corresponding costs. The larger the value of each coefficient, the greater the impact of the corresponding second time period cost on the optimization objective of the second time period, and the higher its importance among all costs. For example, the fifth coefficient may be 0, 0.2, 0.3, 0.5, 0.7, 1, 1.5, 2, 2.4, 3, 3.6, 4, 4.8, 5, ..., the sixth coefficient may be 0, 0.2, 0.3, 0.5, 0.7, 1, 1.5, 2, 2.4, 3, 3.6, 4, 4.8, 5, ..., the seventh coefficient may be 0, 0.2, 0.3, 0.5, 0.7, 1, 1.5, 2, 2.4, 3, 3.6, 4, 4.8, 5, ..., and the eighth coefficient may be 0, 0.2, 0.3, 0.5, 0.7, 1, 1.5, 2, 2.4, 3, 3.6, 4, 4.8, 5, .... When the value of a coefficient is 0, it means that the corresponding cost has a negligible effect on the optimization objective of the second time period.

[0093] In some examples, to improve the computational accuracy of the first long-cycle model and the second short-cycle model, a first constraint and a second constraint are determined; the first long-cycle model is constructed based on the first objective function and the first constraint for the first time period; and the first short-cycle model is constructed based on the second objective function and the second constraint for the second time period. The first constraint may include any one or more of a first production inventory constraint, a production capacity constraint, a first tangent constraint, a first ramp-up yield constraint, and a first mass production yield constraint for the first time period, but is not limited thereto. The first constraint may also include other constraints, such as raw material constraints. The second constraint may include any one or more of a second production inventory constraint, a production line state switching constraint, a second tangent constraint, a mold constraint, a second ramp-up yield constraint, and a second mass production yield constraint for the second time period, but is not limited thereto. The second constraint may also include other constraints, such as raw material constraints.

[0094] In some examples, the first production inventory constraint is used to Product inventory for a specific time period, The amount of product extensions in the time period, Mass production output or ramp-up output in a certain time period, Product demand in a time period, constraints Product inventory for the time period and The amount of product extensions for the time period, where In some examples, the first production inventory constraint is used to describe the inventory status of the product and can be expressed by a first expression, which is:

[0095] Among them, i represents the product, represents the first time period, l represents the production line, L represents the production line set, Indicates that product i in the first time period of inventory, Indicates that product i in the first time period The amount of delay, Indicates the first time period of product i in production line l The mass production output, Indicates the first time period of product i in production line l The climbing output, Indicates that product i in the first time period demand.

[0096] The first production inventory constraint is described below by taking an example. It should be understood that the embodiments described herein are only used to illustrate and explain the present disclosure, and are not used to limit the present disclosure.

[0097] Specifically, the first period The unit can be seconds, minutes, hours, shifts, days, weeks, months, years, etc., but is not limited thereto. is the set of the first time period. For example, The unit is day. Assuming that a production plan of 5 months is required, Indicates the first month, Indicates the second month, Indicates the third month, Indicates the 4th month, Indicates the 5th month.

[0098] F represents the set of products. Assuming that the factory can currently produce 5 types of products, then F = {1, 2, 3, 4, 5}, and i can be any one of 1, 2, 3, 4, and 5.

[0099] L represents the set of production lines. Assuming that the factory currently has 5 production lines, then L = {1, 2, 3, 4, 5}, and l can be any one of 1, 2, 3, 4, and 5.

[0100] Inventory refers to the number of products that have been produced. For example, the inventory of product 2 in the second month is 200.

[0101] The delay quantity indicates the number of products that cannot be produced according to the production schedule. For example, if the delay quantity of product 2 in the second month is 30, then

[0102] When switching between different types of products, the mold needs to be replaced. After the mold is replaced, the product needs to go through a period of ramp-up before it can reach mass production output. For example, product 2 is produced on production line 1. In the second month of production, the output of the first day of ramp-up is 0, the output of the second day of ramp-up is 2000, the output of the third day of ramp-up is 3000, and the output of the fourth day of ramp-up is 4000. The ramp-up output

[0103] Production output refers to the amount of product produced per unit time after a product reaches mass production on a production line. For example, the output of product 1 on production line 1 in one day is 4,000 units.

[0104] Product i in the first time period The demand can be associated with the user's order. For example, the demand for the product within 5 months can be pre-defined based on the order data.

[0105] In some examples, the first ramp-up yield constraint describes the cumulative yield constraint during the ramp-up period after the tangent occurs. Specifically, according to the ramp-up yield of the production line, after the tangent occurs, the product after the tangent is placed on the production line. In some examples, the first ramp-up production constraint can be expressed by a fourth expression, which is:

[0106] in, Indicates the first time period of product i in production line l Whether a tangent occurs, p il is the ramp-up output of product i on production line l.

[0107] The first ramp-up yield constraint is described below by taking an example. It should be understood that the embodiments described herein are only used to illustrate and explain the present disclosure, and are not used to limit the present disclosure.

[0108] Predefined, when When tangent occurs, When , it means that no tangent occurs. The ramp output of product i on production line l is [0, 2000, 3000, 4000], then p il =9000. Assume that product 2 is produced on production line 1. On the 10th day of production, the inventory of product 2 reaches the demand for product 2 in the third month. In the third month, product 2 undergoes a tangent, and production line 1 switches from producing product 2 to producing product 3. Therefore, the ramp-up output of product 3 on line 1 is

[0109] In some examples, the first mass production yield constraint describes the mass production constraint of product i on production line 1. Specifically, the maximum mass production yield of the product i on the production line can be constrained according to the maximum mass production yield of the production line in a single time period of the second time period. In some examples, the first mass production yield constraint can be expressed by a fifth expression, which is:

[0110] in, Indicates the first time period of product i in production line l The number of days to mass production, q il Indicates the maximum production output in a single time period per second time period.

[0111] The first mass production output constraint is described below by taking examples. It should be understood that the embodiments described herein are only used to illustrate and explain the present disclosure, and are not used to limit the present disclosure.

[0112] Assumptions Indicates that product i is on production line l in the first time period The number of days for mass production is 28 days, and the maximum mass production output of product i on production line l in one day is q il is 4000, then product i is in the first time period of production line l Mass production output

[0113] In some examples, the first tangent constraint is used to constrain the maximum number of switching times of the same type of product within a unit time period. In some examples, the first tangent constraint includes a third expression, which is:

[0114] Where f represents the product type, and k represents the maximum number of line cuts allowed within a production line time period;

[0115] Specifically, the maximum number of line tangents k can be predefined based on the number of people responsible for line tangents within the factory, for example, k = 3. Assume that the number of people responsible for line tangents within the factory is 1, and the number of people required to tangent production line 1 is 0.3, the number of people required to tangent production line 2 is 0.4, the number of people required to tangent production line 3 is 0.2, and the number of people required to tangent production line 4 is 0.9. If this person is only responsible for tangenting production line 1, then k = 3; if this person is only responsible for tangenting production line 2, then k = 2; if this person is only responsible for tangenting production line 3, then k = 5; and if this person is only responsible for tangenting production line 4, then k = 1.

[0116] In some examples, the capacity constraint describes the maximum capacity of a time period. Specifically, the capacity constraint is used to constrain the processing of products on the production line based on the maximum capacity of the product on the production line. The consumption output of the tangent and / or the output of the mass production within the time period. In some examples, the capacity constraint can be expressed as a second expression, which is:

[0117] Where F represents the set of products, Indicates the first time period of product i in production line l Whether a tangent occurs, g shows how many days of production capacity a single tangent consumes, Indicates the first time period of product i in production line l Whether the number of days of mass production has occurred, Indicates that production line 1 is in the first time period production capacity.

[0118] The capacity constraint is described below by taking examples. It should be understood that the embodiments described herein are only used to illustrate and explain the present disclosure and are not used to limit the present disclosure.

[0119] Predefined, when When tangent occurs, , indicating that no tangent occurs.

[0120] Assume a first time period It contains 30 second time periods. A single line cut consumes 3 days of production capacity, i.e. g = 3. Product i is in the first time period of production line l. 1 tangent occurred but To preset defined values ​​in the product data.

[0121] In some examples, the second production inventory constraint describes the inventory status of the product. Specifically, the second production inventory constraint can determine the product inventory in time period t and the product deferral amount in time period t based on the product inventory in time period t-1, the product deferral amount in time period t-1, the mass production output or ramp-up output in time period t, and the product demand in time period t, where t∈1,2,3,...n, where n is the number of second time periods included in the first time period. In some examples, the second production inventory constraint includes a sixth expression, which is:

[0122] Where i represents the product, t represents the second time period, L represents the production line set, l represents the production line, I it represents the inventory of product i in the second time period t, B it represents the extension amount of product i in the second time period t, x iltrepresents the mass production output of product i in the second time period t of production line l, z ilt represents the ramp-up output of product i in the second time period t of production line l, d it represents the demand for product i in the second time period t.

[0123] Specifically, the unit of the second time period t can be seconds, minutes, hours, shifts, days, weeks, months, years, etc., but is not limited thereto. The unit of the second time period t can be smaller than that of the first time period. The unit of t∈T is the set of the second time period. For example, the unit of t is day.

[0124] The explanations of the variables in the second production inventory constraint are basically the same as those in the first production inventory constraint, so they will not be repeated here.

[0125] In some examples, the production line state switching constraint is used to describe the two states of mass production to tangent and from tangent to mass production. Specifically, the production line state tangent constraint can be used to determine the production line state based on whether the production line continuously produces the same type of product from the time period t-1 to the time period t, and whether the production line switches the type of product it produces in the time period t; or determine the production line state based on whether the production line continuously produces the same type of product from the time period t-1 to the time period t, and the number of tangent days after the production line is tangent in the time period t. In some examples, the production line state switching constraint includes the seventh expression and the eighth expression, and the seventh expression is:

[0126] Among them, y il(t-1) represents the production status of product i in the second time period t-1 of production line l, S (ik)lt Indicates that production line l switches from producing product i to producing product k in the second time period t. ilt Indicates whether production line 1 continuously produces product i from the second time period t-1 to the second time period t.

[0127] The eighth expression is:

[0128] Among them, y ilt represents the production status of product i in the second time period t of production line l, g ki It represents the length of the tangent time when switching from producing product k to producing product i. When the tangent occurs, k and i do not belong to the same product type. S (ki)l(t-gki) Indicates that production line 1 is in the second time period tg ki , switch from producing product k to producing product i.

[0129] The following describes the production line state switching constraints by taking examples. It should be understood that the embodiments described herein are only used to illustrate and explain the present disclosure and are not used to limit the present disclosure.

[0130] The seventh expression is used to describe the state of production line 1 from mass production to tangent production.

[0131] It is predefined that when production line l continuously produces product i from the second time period t-1 to the second time period t, o ilt =1, when production line l does not continuously produce product i from the second time period t-1 to the second time period t, o ilt =0,

[0132] Assume that on the second day of a first time period, production line l is producing product i. On the third day, production line l is cut off and switches from producing product i to producing product k. Then o ilt =0, S (ik)l3 =1,y il2 =1.

[0133] The eighth expression is used to describe the state of production line 1 from tangent to mass production.

[0134] It is predefined that when production line l continuously produces product i from the second time period t-1 to the second time period t, o ilt =1, when production line l does not continuously produce product i from the second time period t-1 to the second time period t, o ilt =0; The tangent time length for switching from product k to product i is 5.

[0135] Assume that production line l is producing product k. On the 10th day, production line l is cut off and switches from producing product k to producing product i. Then o il10 =0, S (ki)l(10-5) =1,y il10 =1.

[0136] In some examples, the second tangent constraint describes the occupation of the tangent capacity by the production line tangent. Specifically, the second tangent constraint is used to constrain the occupation of the tangent capacity when the production line tangent occurs based on the tangent capacity of the production line group. In some examples, the second tangent constraint includes a ninth expression, which is:

[0137] Among them, L g Indicates g production line group, C g is the tangent capacity of the g production line group, w l Indicates the occupancy of the tangent capacity when the production line l is tangent.

[0138] The second tangent constraint is described below by taking examples. It should be understood that the embodiments described herein are only used to illustrate and explain the present disclosure, and are not used to limit the present disclosure.

[0139] The production lines are divided into different groups, and different manpower is responsible for different production line groups. The manpower consumed when tangenting different production lines can be the same or different. For example, there are 10 production lines, production lines 1-4 are production line group 1, and production lines 5-10 are production line group 2. The total manpower consumed when tangenting production line group 1 is 1, and the manpower required for tangenting each production line 1-4 is [0.3, 0.4, 0.5, 0.8]. Then, production line 1 and production line 2 in production line group 1 can be tangented at the same time (0.3+0.4=0.7<1). If production line 4 in production line group 1 is tangent, the other production lines in production line group 1 cannot be tangent.

[0140] In some examples, mold constraints are used to constrain the number of production lines that produce the same type of product based on the number of molds for the same type of product. The mold constraint includes a tenth expression, which is:

[0141] Among them, mold f Indicates the number of model sets of type f.

[0142] The same type of products share a set of molds. The mold data includes the number of molds and the mold maintenance date. The number of molds determines how many production lines can produce the same type of products at the same time. The mold cannot be used during maintenance.

[0143] The mold constraint is described below by taking examples. It should be understood that the embodiments described herein are only used to illustrate and explain the present disclosure, and are not used to limit the present disclosure.

[0144] Suppose there are six production lines: Line 1 is producing product 1, Line 2 is producing product 2, Line 3 is producing product 2, Line 4 is producing product 3, Line 5 is producing product 3, and Line 6 is producing product 1. Products 1, 2, and 3 are different types of products, with Product 1 having three molds, Product 2 having three molds, and Product 3 having three molds. On the sixth day of producing Product 1, Lines 1 and 6 simultaneously experience a line truncation. Line 1 switches from producing Product 1 to producing Product 3, and Line 2 switches from producing Product 1 to producing Product 3. Currently, Lines 1, 2, 4, and 5 are producing Product 3, requiring four molds. However, Line 3 only has three molds. Therefore, the current production schedule does not meet this mold constraint.

[0145] Suppose there are six production lines: Line 1 is producing product 1, Line 2 is producing product 2, Line 3 is producing product 2, Line 4 is producing product 3, Line 5 is producing product 3, and Line 6 is producing product 1. Products 1, 2, and 3 are different types of products, with Product 1 having four molds, Product 2 having four molds, and Product 3 having four molds. On the sixth day of producing Product 1, Lines 1 and 6 simultaneously experience a line truncation. Line 1 switches from producing Product 1 to producing Product 3, and Line 2 switches from producing Product 1 to producing Product 3. Currently, Lines 1, 2, 4, and 5 are producing Product 3, requiring four molds. Line 3 has four molds, and the current production schedule meets this mold constraint.

[0146] In some examples, the second ramp-up yield constraint is used to constrain the output of the product on the production line for a period of time t after the tangent occurs, based on the ramp-up yield of the product on the production line. The second ramp-up yield constraint includes an eleventh expression, which is:

[0147] Among them, p il is the ramp-up output of product i on production line l.

[0148] The second ramp-up production constraint is described below by taking examples. It should be understood that the embodiments described herein are only used to illustrate and explain the present disclosure, and are not used to limit the present disclosure.

[0149] Assume that production line 1 is producing product k. On the 15th day of producing product k, production line 1 switches from producing product k to producing product i. The tangent length of production line 1 switching from producing product k to producing product i is 4. The ramp-up output of product i on production line 1 is [0, 1000, 2000, 3000]. Then the ramp-up output of product i on production line 1 on the 15th day is z. il15 ≤6000.

[0150] In some examples, the second mass production yield constraint describes the yield of product i on production line l. Specifically, the second mass production yield constraint is used to constrain the mass production yield of the product on the production line in time period t based on the maximum mass production yield of the production line in a single time period. The second mass production yield constraint includes a twelfth expression, which is: ilt ≤y ilt* q il

[0151] Among them, q il Indicates the maximum mass production output of product i on production line l in a single time period.

[0152] The second mass production output constraint is described below by taking examples. It should be understood that the embodiments described herein are only used to illustrate and explain the present disclosure, and are not used to limit the present disclosure.

[0153] Assume that when the product is mass-produced, y ilt =1, when the product has not yet been mass-produced, y ilt = 0. Production line 1 is producing product i. On the 5th day of producing product i, production line 1 starts mass production of product i. The maximum daily mass production output of product i on production line 1 is 3000. Then the total mass production output of product i on production line 1 during time period t is x il15 ≤1*3000.

[0154] In some examples, a second coupling constraint may be constructed between the first long-cycle model and the first short-cycle model. Specifically, the inventory and rollover amount at the end of the last second time period of the first short-cycle model within the first m first time periods are constrained for the inventory and rollover amount at the beginning of the first first time period of the first long-cycle model in the (m+1)th first time period. In some examples, this second coupling constraint may be expressed using a thirteenth expression, which is:

[0155] S20. Divide the production scheduling cycle to obtain m first time periods, where the first time period includes a plurality of second time periods; wherein m≥1.

[0156] The production scheduling cycle can be divided flexibly in a variety of ways according to actual needs.

[0157] In some examples, the production scheduling cycle can be evenly divided to obtain m first time periods. For example, if the production scheduling cycle is 150 days, the 150 days can be evenly divided into three first time periods, each of which is 50 days long, i.e., 50 second time periods. Alternatively, the 150 days can be evenly divided into four first time periods. Then, 150 / 4=37 with a remainder of 2. Therefore, except for the last first time period, each first time period is 37 days. The last first time period can be 2 days or 39 days.

[0158] In some examples, evenly dividing the production scheduling cycle into m first time periods may result in an uneven number of orders to be produced in each first time period, and an uneven number of products to be produced in each first time period, which cannot maximize the utilization of the production line. To further improve the utilization of the production line, the length of the first time period can be determined based on the number of orders in the production scheduling cycle. Assume that the production scheduling cycle is 150 days, and the preliminary plan is divided into 5 first time periods, each first time period has 30 days, and the number of orders within 150 days is 300. Based on the number of first time periods, the average number of orders is 60, and the number of orders contained in each first time period is 60. The length of the first time period is determined based on this number of orders. For example, the orders are sorted by their expiration time, and the first 60 orders are divided into the first first time period, and the orders from 61 to 120 are divided into the second first time period, and so on, so that the number of orders contained in each long period is equal.

[0159] S30. Input product data into the first long-cycle model and the first short-cycle model to obtain a production scheduling result for each of the second time periods within m first time periods, wherein the product data is data related to product production.

[0160] Specifically, data related to product production includes, but is not limited to, production line data, order data, mold data, production line group data, and factory calendar data.

[0161] The production line data may include the available production scheduling time periods for each production line, the initial state of the production line (eg, the type of product being produced by the production line), and the production line's capacity ramp-up data (eg, [0, 2000, 3000, 4000]).

[0162] Order data can include the product ID, product category (i.e., product type), order priority, order delivery date, initial inventory, available production lines and their capacity per unit time, and the raw materials required to produce the product and the quantity of raw materials per unit. An order can have multiple delivery dates, and the delivery quantity for each delivery date must be specified. Products typically have a default or more suitable production line, which can be specified by priority within the available production lines.

[0163] Mold data may include the molds required for product processing. Products of the same type share a set of molds during processing. Mold data includes the number of molds and the maintenance date of the molds. The number of molds determines how many production lines the same type of product can be produced on at the same time. The mold cannot be used during maintenance.

[0164] Production line group data can include the number of production line groups and the production lines within each group. Specifically, based on the factory's actual operations, production lines with similar production settings can be grouped together. Each production line group is managed by different personnel, and the manpower required to trim different production lines varies.

[0165] Factory calendar data includes the factory's production calendar. This calendar is relative, not absolute, and is formatted as a number. It can define normal working hours, weekends, holidays, and factory maintenance time. For example, [0, 0, 1, 1, 0, 0, 2, 2, 3, 3] defines a 10-day working calendar, where 0 represents normal working hours, 1 represents weekends, 2 represents holidays, and 3 represents maintenance time. Numeric values ​​are used only for differentiation and are not limited to numerical values ​​or strings. Furthermore, units are not limited to days; they can also be expressed in seconds, minutes, hours, shifts, days, weeks, months, and years. Different units correspond to different time periods.

[0166] In some examples, product data may also include raw material data, which can include key raw materials required for product production, initial inventory levels, and replenishment schedules and quantities. Raw material lead times may also be included; after the lead time, the raw materials are considered to be in sufficient supply. The lead time refers to the timeframe for raw materials to be considered. For example, a lead time of 30 indicates that the availability of raw materials for the previous 30 days is considered.

[0167] The following describes a process for solving the first long-cycle model and the first short-cycle model based on product data.

[0168] Figure 2 is a flowchart of the production scheduling method provided by an embodiment of the present disclosure when solving the long-short cycle model for the first time; Figure 3 is a flowchart of the production scheduling method provided by an embodiment of the present disclosure when solving the long-short cycle model for the kth time; Figure 4 is a flowchart of the production scheduling method provided by an embodiment of the present disclosure when solving the long-short cycle model for the last time.

[0169] As shown in Figure 2, during the first solution, the following steps are performed:

[0170] S101: Construct a third long cycle model based on the number of first time periods and the first long cycle model, and solve the third long cycle model according to product data to obtain a product and production line matching result for each first time period.

[0171] Specifically, the maximum value of the first time period in the first long cycle model is determined according to the number of the first time period, and the third long cycle model is constructed accordingly. For example, if the number of the first time period m=5, then the maximum value of the first time period in the first long cycle model is The maximum value of is 5, in the second long cycle model It can be equal to 1, 2, 3, 4, 5. The third long cycle model is solved according to the product data to obtain the product and production line matching results for each first time period. The product and production line matching results include the corresponding relationship between the product and the production line. According to the above solution, the optimal production line for the product can be obtained among the production lines of the product in each first time period. For example, in the first first time period, production line 1 produces products 1 and 2, production line 2 produces production line 3, and production line 3 produces products 4 and 5. In the second first time period, production line 1 produces product 6, production line 2 produces production lines 3 and 5, and production line 3 produces product 5.

[0172] It should be noted that the difference between the third long-cycle model and the first long-cycle model is that the first long-cycle model is a mathematical model in which only variables are defined, and none of the variables have definite values. The third long-cycle model, based on the first long-cycle model, has certain variables with definite values ​​to facilitate subsequent solutions.

[0173] S102. Obtain the product and production line matching result of the first first time period from all the obtained product and production line matching results of the first time period, and construct a second short cycle model based on the product and production line matching result of the first first time period and the first short cycle model.

[0174] Assuming a production schedule of 120 days, step S101 obtains the matching results of products and production lines for four first time periods, each of which contains 30 second time periods, and each second time period is 1 day. This is explained as an example. From the matching results of products and production lines for the four first time periods, the matching results of products and production lines for the first first time period are obtained. For example, production line 1 produces products 1 and 2, production line 2 produces production line 3, and production line 3 produces products 4 and 5. The maximum value of the second time period t in the first short-cycle model is determined based on the number of second time periods included in the first first time period. Based on the matching results of products and production lines for the first first time period, the maximum value of the second time period t in the first short-cycle model, and the first short-cycle model, a second short-cycle model is constructed. The difference between the second short-cycle model and the first short-cycle model is that the first short-cycle model is a mathematical model in which only variables are defined, and none of the variables have fixed values. The second short-cycle model, based on the first short-cycle model, has fixed values ​​for some variables, facilitating subsequent solutions.

[0175] S103. Construct a second long-cycle model based on the number of first time periods that have not been used to construct the second short-cycle model and the first long-cycle model; construct a first coupling constraint of the second short-cycle model and the second long-cycle model based on the second short-cycle model and the second long-cycle model; wherein the number of first time periods that have not been used to construct the second short-cycle model is equal to m-1.

[0176] The second long-cycle model is constructed based on the number of first time periods not yet used to construct the second short-cycle model and the first long-cycle model. In this solution, the number of first time periods not yet used to construct the second short-cycle model is m-1. Based on the number of first time periods not yet used to construct the second short-cycle model, the maximum value of the first time period in the first long-cycle model is determined to be m-1, and the second long-cycle model is constructed based on this. The difference between the second long-cycle model and the third long-cycle model lies in the different maximum values ​​of the first time period. For example, if the maximum value of the first time period in the third long-cycle model is 1, 2, 3, or 4, then the maximum value of the first time period in the second long-cycle model is 1, 2, or 3, and the first first time period in the second long-cycle model corresponds to the second first time period in the third long-cycle model.

[0177] The first coupling constraint of the second short cycle model and the second long cycle model is constructed according to the second short cycle model, the second long cycle model and the second coupling constraint, so that the inventory and extension amount in the last second time period of the second short cycle model are equal to the inventory and extension amount in the first first time period of the second long cycle model.

[0178] It should be noted that the difference between the second coupling constraint and the first coupling constraint is that the second coupling constraint is a mathematical model in which only variables are defined, and none of the variables have definite values. The first coupling constraint has definite values ​​for some variables based on the second coupling constraint.

[0179] S104. Solve the second short-cycle model, the second long-cycle model and the first coupling constraint according to the product data to obtain the production scheduling results within the first first time period and the product and production line matching results of m-1 first time periods, and extract the product and production line matching results of the first first time period from the m-1 first time period product and production line matching results.

[0180] The second short cycle model, the second long cycle model and the first coupling constraint are solved according to the production line data, order data, mold data, production line group data and factory calendar data to obtain the scheduling results in the first first time period and the product and production line matching results of m-1 first time periods. The scheduling results in the first first time period are the product and production line matching results in each second time period. Taking the scheduling cycle as 40 days, the matching results of products and production lines in 4 first time periods are obtained in step S101. Each first time period contains 10 second time periods. The unit of each second time period is 1 day. The factory has production line 1 and production line 2. The products that the factory needs to produce in the scheduling cycle are products 1-4. For example, the product and production line matching results in each second time period are explained. The product and production line matching results of each second time period in the first first time period are shown in Table 1.

[0181] Table 1

[0182] As shown in Figure 3, during the k-th solution process, the following steps are performed:

[0183] S201. Construct a second short-cycle model for the first k first time periods based on the product-production line matching results and the first short-cycle model for the first k first time periods; k∈2, ..., m-1.

[0184] Specifically, the first k product-to-production line matching results for the first time period include the first product-to-production line matching result for the first time period among the mk product-to-production line matching results for the first time period extracted during each solution process. The process for constructing the second short-term model during the k-th solution process is essentially the same as that during the first solution process, differing only in the corresponding first time period and the number of first time periods in the second short-term model. Therefore, this is not detailed here.

[0185] S202. Construct a second long-cycle model based on the first time period and the number of first time periods that have not been used to construct the second short-cycle model and the first long-cycle model; wherein the number of first time periods that have not been used to construct the second short-cycle model is equal to mk; and construct a first coupling constraint of the second short-cycle model and the second long-cycle model based on the second short-cycle model and the second long-cycle model.

[0186] During the k-th solution process, the construction process of the second long-period model and the first coupling constraint is basically the same as the construction process of the third long-period model and the first coupling constraint during the first solution process. The only difference is that the corresponding first time period and the number of first time periods in the second long-period model are different, so they are not repeated here.

[0187] S203. Solve the second short cycle model, the second long cycle model and the coupling constraints of the first k first time periods according to the product data to obtain the production scheduling results within the first k first time periods and the product and production line matching results of mk first time periods.

[0188] Specifically, in the kth solution process, the solution process of the second short-period model, the second long-period model and the coupling constraint is basically the same as the solution process of the second short-period model, the second long-period model and the first coupling constraint in the first solution process, so it will not be repeated here.

[0189] In some examples, after obtaining the production scheduling results within the first k first time periods and the product and production line matching results of mk first time periods, the product and production line matching results of the first first time period among the mk first time periods can also be extracted to be used for constructing a second short cycle model in the k+1 solution process.

[0190] In some examples, in the process of solving the second short-cycle model, the second long-cycle model and the coupling constraints of the first k first time periods according to the product data, in order to improve the calculation speed, the values ​​of the key variables obtained by solving the second short-cycle model in the first k-1 solution process can be fixed, so as to obtain the production scheduling results in the first k first time periods and the product and production line matching results of mk first time periods. Among them, the key variables include: any one or more of variables related to production status, variables related to tangents, variables related to ramping, and variables related to mass production. For example, the key variables include but are not limited to 0 / 1 in the long-cycle model and the short-cycle model (such as whether the production variable y ilt , whether the tangent variable S (ij)lt etc.) variables.

[0191] As shown in Figure 4, during the last solution, the following steps are performed:

[0192] S301: Construct a second short-cycle model for the first m first time periods based on the product-production line matching results and the first short-cycle model for the first m first time periods.

[0193] S302: Solve the second short cycle model of the first m first time periods according to the product data to obtain the production scheduling results for all second time periods within the first m first time periods.

[0194] In some examples, after obtaining the production scheduling results for all second time periods within all first time periods, the method further includes processing and outputting the production scheduling results.

[0195] The model solution is not the final production scheduling result. The model results need to be corrected and adjusted according to actual needs to maximize the production line capacity utilization, increase output, and improve order delivery rate.

[0196] Specifically, you can restore variables that were solved as a whole during the model solution according to preset rules. These preset rules are set based on actual needs. For example, if the ramp output is solved as a whole during the model solution, the model result will be [0, 0, 0, 9000], and you need to restore it to [0, 2000, 3000, 4000].

[0197] The disclosed example can also manually adjust the production scheduling results, including but not limited to the following adjustments:

[0198] 1) Adjust the production volume on the mass production date to the rated production volume;

[0199] 2) Adjust the production date of the product to be as early as possible to reduce idle time on the production line;

[0200] 3) Use heuristics to try to schedule the unscheduled demands one production line at a time, from back to front. If all demands cannot be scheduled, schedule the largest part of them.

[0201] Figure 5 is a flow chart of a production scheduling method provided by an embodiment of the present disclosure in the model solving stage. The solving process of the long-cycle model and the short-cycle model will be described below with reference to Figure 5 .

[0202] In this example, the production scheduling cycle is 120 days and is divided into four first time periods: m1, m2, m3, and m4. Each first time period contains 30 second time periods, that is, 30 days. m1 contains n1 to n30, m2 contains n31 to n60, m3 contains n61 to n90, and m4 contains n91 to n120. Therefore, the long-cycle model and the short-cycle model need to be solved four times. The specific solution process is as follows:

[0203] In the first solving process, first determine the maximum value of the first time period in the first long cycle model according to the number of the first time periods, and construct the third long cycle model. The third long cycle model includes 4 first time periods, namely m1~m4; then solve the third long cycle model according to the product data to obtain the product and production line matching results of the 4 first time periods; obtain the product and production line matching results of the first first time period from the matching results of all the products and production lines of the first time periods, and construct the second short cycle model based on the product and production line matching results of the first first time period and the first short cycle model. The second short cycle model corresponds to the first first time period and includes the first short cycle model. Two time periods n1~n30; construct a second long cycle model based on the first time period that has not been used to construct the second short cycle model, the number of the first time periods and the first long cycle model, and the second long cycle model includes three first time periods, namely m2~m4; construct the first coupling constraint of the second short cycle model and the second long cycle model based on the second short cycle model and the second long cycle model, so that the inventory and extension amount of the last second time period of the second short cycle model are equal to the inventory and extension amount of the first first time period of the second long cycle model, that is, the inventory and extension amount of the last time period n30 in the second short cycle model are equal to the initial inventory and extension amount of the second long cycle model m2. The second short-cycle model, the second long-cycle model, and the first coupling constraint are solved based on the product data. This yields the scheduling results for the first first time period (i.e., the product-line matching results for each second time period n1 through n30 within m1) and the product-line matching results for the next three first time periods m2 through m4. The first first time period's product-line matching results are then extracted, corresponding to first time period m2. Product data includes production line data, order data, mold data, production line group data, and factory calendar data.

[0204] During the second solution process, a second short-cycle model for the first two first time periods is constructed based on the product-line matching results and the first short-cycle model. The second short-cycle model corresponds to the first two first time periods and includes second time periods n1 to n60. A second long-cycle model is constructed based on the first time periods not yet used to construct the second short-cycle model, their number, and the first long-cycle model. The second long-cycle model includes two first time periods, m3 to m4. Based on the second short-cycle model and the second long-cycle model, a first coupling constraint is constructed for the second short-cycle model and the second long-cycle model, ensuring that the inventory and extension amounts in the last second time period of the second short-cycle model are equal to the inventory and extension amounts in the first first time period of the second long-cycle model. That is, the inventory and extension amounts in the last time period n60 of the second short-cycle model are equal to the initial inventory and extension amounts in the second long-cycle model m3. The second short-cycle model, the second long-cycle model, and the coupling constraints of the first two first time periods are solved based on the product data to obtain the production scheduling results in the first two first time periods (i.e., the product and production line matching results of each second time period n1 to n60 in m1 and m2) and the product and production line matching results of the last two first time periods m3 and m4. During the solution process, since the production scheduling results of each second time period corresponding to the first first time period in the second short-cycle model have been solved in the first solution process, the values ​​of the key variables obtained by solving the third short-cycle model in the first solution process can be fixed, and the values ​​of the key variables can be directly assigned to the corresponding key variables in the second short-cycle model. Key variables include but are not limited to 0 / 1 variables in the long-cycle model and the short-cycle model.

[0205] During the third solution process, a second short-cycle model for the first three first time periods is constructed based on the product-line matching results and the first short-cycle model. During the third solution process, the second short-cycle model corresponds to the first three first time periods and includes second time periods n1 to n90. A second long-cycle model is constructed based on the first time periods not yet used to construct the second short-cycle model, their number, and the first long-cycle model. During the third solution process, the second long-cycle model includes one first time period, m4. Based on the second short-cycle model and the second long-cycle model, a first coupling constraint is constructed for the second short-cycle model and the second long-cycle model, ensuring that the inventory and rollover amount in the last second time period of the second short-cycle model equal the inventory and rollover amount in the first first time period of the second long-cycle model. That is, the inventory and rollover amount in the last time period n90 of the second short-cycle model equal the initial inventory and rollover amount of the second long-cycle model m4. The second short-cycle model, second long-cycle model, and coupling constraints for the first three first time periods are solved based on the product data to obtain the production scheduling results for the first three first time periods (i.e., the product and production line matching results for each second time period n1 to n90 in m1, m2, and m3) and the product and production line matching results for the last first time period m4. During the solution process, since the production scheduling results for each second time period corresponding to the first two first time periods in the second short-cycle model have already been solved in the second solution process, the values ​​of the key variables obtained by solving the third short-cycle model in the first solution process and the values ​​of the key variables obtained by solving the second short-cycle model in the second solution process can be fixed, and the fixed key variable values ​​can be directly assigned to the corresponding key variables in the second short-cycle model. Key variables include but are not limited to 0 / 1 variables in the long-cycle model and the short-cycle model.

[0206] In the fourth solution, a short-cycle model for all cycles is constructed based on the product-line matching results of the first m cycles. The key variables of the first m-1 cycles are fixed, and then the solution is obtained. The overall solution process is shown in the following diagram:

[0207] During the fourth solution, based on the product-line matching results and the first short-cycle model for the first four first time periods, a second short-cycle model for the first four first time periods is constructed. During the fourth solution, the second short-cycle model corresponds to the first four first time periods, i.e., all second time periods n1 to n120. Therefore, there is no need to continue constructing a new second long-cycle model and new coupling constraints. The second short-cycle model is directly solved based on the product data to obtain the scheduling results for all first time periods (i.e., the scheduling results for each second time period n1 to n120 in m1, m2, m3, and m4, including but not limited to the product-line matching results). During the solution, the values ​​of the key variables from the first three solution processes can also be fixed and directly assigned to the corresponding key variables in the new second short-cycle model. Key variables include but are not limited to 0 / 1 variables in the long-cycle model and the short-cycle model.

[0208] Obtain the production scheduling results of all second time periods within the production scheduling cycle, adjust the results that need to be optimized in the production scheduling results, and obtain the final production scheduling results.

[0209] Based on the same inventive concept, the second aspect of the embodiment of the present disclosure proposes an electronic device, comprising: at least one processor; and a memory, wherein the memory stores a computer program that can be run on the processor, and the computer program implements the steps of the above method when executed by the processor. The memory, as a non-volatile storage medium, can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as the program instructions / modules corresponding to the production scheduling method in the embodiment of the present application. The processor executes various functional applications and data processing of the system by running the non-volatile software programs, instructions and modules stored in the memory, that is, implementing the production scheduling method of the above method embodiment.

[0210] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the system, etc. In addition, the memory may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include a memory remotely located relative to the processor, and these remote memories may be connected to the local module via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0211] Based on the same inventive concept, a third aspect of the embodiments of the present disclosure provides a storage medium storing a computer program that implements the above method steps when executed by a processor.

[0212] The above are exemplary embodiments of the present disclosure, but it should be noted that various changes and modifications can be made without departing from the scope of the present disclosure as defined in the claims. The functions, steps and / or actions of the method claims according to the disclosed embodiments described herein do not need to be performed in any particular order. The serial numbers of the embodiments disclosed in the above embodiments of the present disclosure are for description only and do not represent the advantages and disadvantages of the embodiments. In addition, although the elements disclosed in the embodiments of the present disclosure may be described or required in individual form, they may also be understood as multiple unless expressly limited to the singular.

[0213] The discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the disclosure (including the claims) of the embodiments of the present disclosure is limited to these examples. Based on the concept of the embodiments of the present disclosure, the technical features of the above embodiments or different embodiments may also be combined, and there are many other variations of different aspects of the embodiments of the present disclosure, which are not provided in detail for the sake of simplicity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure shall be included in the scope of protection of the embodiments of the present disclosure.

Claims

1. A scheduling method, characterized in that, it includes: Constructing a first long-term model based on a first objective function of a first time period; Constructing a first short-term model based on a second objective function of a second time period; wherein, the first objective function is generated based on an optimization objective of the first time period and the product processing cost of the first time period; the second objective function is generated based on an optimization objective of the second time period and the product processing cost of the second time period; Dividing the scheduling period to obtain m first time periods, and the first time period includes several second time periods; wherein, m≥1; Inputting product data into the first long-term model and the first short-term model to obtain scheduling results of each second time period within the m first time periods, wherein the product data is data related to product production.

2. The method according to claim 1, characterized in that, wherein, the step of inputting the product data into the first long-term model and the first short-term model to obtain scheduling results of each second time period within the m first time periods includes: solving the first long-term model and the first short-term model m times. For the k-th solving process among the m solving processes, the method includes: Constructing a second short-term model of the first k first time periods according to the product-line matching results of the products in the first k first time periods and the first short-term model; k∈1,……,m-1; Constructing a second long-term model according to the first time periods that have not been used to construct the second short-term model and their quantity and the first long-term model; wherein, the quantity of the first time periods that have not been used to construct the second short-term model is equal to m-k; constructing a first coupling constraint between the second short-term model and the second long-term model according to the second short-term model and the second long-term model; Solving the second short-term model of the first k first time periods, the second long-term model and the coupling constraint according to the product data to obtain scheduling results within the first k first time periods and product-line matching results of the m-k first time periods.

3. The method according to claim 2, characterized in that, the step of solving the second short-term model of the first k first time periods, the second long-term model and the coupling constraint according to the product data to obtain scheduling results within the first k first time periods and product-line matching results of the m-k first time periods includes: Solve the second shortest period model, the second longest period model, and the coupling constraints for the first k first time periods according to the product data. During the solving process, fix the values of the key variables obtained by solving the second shortest period model in the (k - 1)-th solving process, so as to obtain the production scheduling results for the first k first time periods and the product-line matching results for the m - k first time periods. Among them, the key variables include any one or more of variables related to production status, variables related to tangent, variables related to ramp-up, and variables related to mass production.

4. The method according to claim 2, wherein, before the first time of solving the first longest period model and the first shortest period model, the method further includes: Construct a third longest period model based on the number of first time periods and the first longest period model, and solve the third longest period model according to the product data to obtain the product-line matching results for each first time period.

5. [Corrected according to Rule 26 on 05.01.2024] The method according to claim 4, wherein, after obtaining the product-line matching results for the m - k first time periods, the method further includes: Extract the product-line matching result of the first first time period from the product-line matching results of the m - k first time periods; when k = 1, the product-line matching results of the first k first time periods include: the product-line matching result of the first first time period among the matching results of all first time periods of products and production lines obtained by solving the third longest period model; when 1 < k ≤ m, the product-line matching results of the first k first time periods include: the product-line matching result of the first first time period extracted in each solving process from the product-line matching results of the m - k first time periods.

6. [Corrected according to Rule 26 on 05.01.2024] The method according to claim 1, wherein, the product processing cost of the first time period includes one or more of the tangent cost of the first time period, the production cost of the first time period, the inventory cost of the first time period, and the delay cost of the first time period.

7. The method according to claim 6, wherein, the first objective function includes: The optimization objective of the first time period = the first coefficient × the tangent cost of the first time period + the second coefficient × the production cost of the first time period + the third coefficient × the inventory cost of the first time period + the fourth coefficient × the delay cost of the first time period, where the first coefficient, the second coefficient, the third coefficient, and the fourth coefficient are respectively used to adjust the corresponding costs.

8. The method according to claim 1, wherein, the product processing cost of the second time period includes one or more of the tangent cost of the second time period, the production cost of the second time period, the inventory cost of the second time period, and the delay cost of the second time period.

9. The method according to claim 8, wherein, the second objective function is: the fifth coefficient × the second time period tangent cost + the sixth coefficient × the second time period production cost + the seventh coefficient × the second time period inventory cost + the eighth coefficient × the second time period delay cost, wherein the fifth coefficient, the sixth coefficient, the seventh coefficient, and the eighth coefficient are respectively used to adjust the corresponding costs.

10. The method according to claim 1, wherein, the step of constructing the first long - term model based on the first objective function of the first time period includes: constructing the first long - term model based on the first objective function of the first time period and the first constraints; wherein the first constraints include any one or more of the first production inventory constraint, production capacity constraint, first tangent constraint, first ramp - up production constraint, and first mass production constraint regarding the first time period; the step of constructing the first short - term model based on the second objective function of the second time period includes: constructing the first short - term model based on the second objective function of the second time period and the second constraints; wherein the second constraints include any one or more of the second production inventory constraint, production line state switching constraint, second tangent constraint, mold constraint, second ramp - up production constraint, and second mass production constraint regarding the second time period.

11. The method according to claim 10, wherein, The first production inventory constraint is used to according to Product inventory for a time period, Product delay volume for the time period, Production volume or ramp-up volume during a time period, Product demand during a time period, constraint Product inventory for the time period and The product delay volume for a time period, where The production capacity constraint is used to constrain the product when it is processed on the production line according to the maximum production capacity of the product on the production line, the consumption output of the tangent and / or the output of mass production within a time period; the first tangent constraint is used to constrain the maximum number of switching times of the same type of product within a unit time period; The first ramp-up production constraint is used to constrain, according to the ramp-up production of the production line, the products after the tangent line on the production line after the tangent line occurs the output within a time period; The first mass production output constraint is used to constrain products on the production line according to the maximum mass production output of the production line in a single time period of a unit second time period the mass production output within a time period.

12. The method according to claim 11, wherein, The first production inventory constraint includes a first expression, and the first expression is: where i represents the product, Indicates the first time period, l represents the production line, and L represents the set of production lines. Indicates product i in the first time period inventory, Indicates product i in the first time period The amount of delay, Indicates the first time period of product i on production line l The mass production output, Denote the first time period of product i on production line l Ramping production volume, Indicates product i in the first time period the demand; The production capacity constraint includes a second expression, and the second expression is: Among them, F represents the set of products, Denote the first time period of product i on production line l Has a tangent occurred? g represents the production capacity consumed by a single tangent in terms of the number of days. Indicates the first time period of product i on production line l The number of days of mass production Indicates production line l in the first time period the production capacity; The first tangent constraint includes a third expression, and the third expression is: wherein, f represents the product type, and k represents the maximum number of tangents allowed for a production line within a time period; The first ramp-up production constraint includes a fourth expression, and the fourth expression is: where p il is the ramp-up production volume of product i on production line l; The first mass production output constraint includes a fifth expression, and the fifth expression is: where q il represents the maximum mass production output per single time period in a unit second time period.

13. The method according to claim 10, wherein, the second production inventory constraint includes: determining the product inventory and product delay quantity at time t according to the product inventory at time t - 1, the product delay quantity at time t - 1, the mass production output or ramp - up production at time t, and the product demand at time t, where t ∈ 1, 2, 3, …… n, and n is the number of the second time periods included in the first time period; the production line state switching constraint is used to determine the production line state according to whether the production line continuously produces the same type of product from time t - 1 to time t and whether the production line switches the type of product it produces at time t; or determine the production line state according to whether the production line continuously produces the same type of product from time t - 1 to time t and the number of tangent days after the production line makes a tangent at time t; the second tangent constraint is used to constrain the occupancy of the tangent capacity when the production line makes a tangent according to the tangent capacity of the production line group; the mold constraint is used to constrain the number of production lines producing the same type of product according to the number of sets of molds for the same type of product; The second ramp-up production constraint is used to constrain the production volume of the products after the tangent within a t time period on the production line according to the ramp-up production volume of the products on the production line. The second mass production constraint includes: constraining the mass production volume of the products within a t time period on the production line according to the maximum mass production volume of the production line in a single time period.

14. The method according to claim 13, wherein, the second production inventory constraint includes a sixth expression, and the sixth expression is: The production inventory constraint describes the inventory status of the product. For each product, there is for each time period: Among them, i represents the product, t represents the second time period, L represents the set of production lines, l represents the production line, and I it represents the inventory of product i in the second time period t, B it represents the backlog of product i in the second time period t, x ilt represents the mass production output of product i on production line l in the second time period t, z ilt represents the ramp-up output of product i on production line l in the second time period t, d it represents the demand for product i in the second time period t; The production line state transition constraint includes a seventh expression and an eighth expression. The seventh expression is as follows: where y il(t-1) represents the production status of product i on production line l in the second time period t - 1, S (ik)lt represents that production line l switches from producing product i to producing product k in the second time period t, o ilt represents whether production line l continuously produces product i from the second time period t - 1 to the second time period t; The eighth expression is as follows: where y ilt represents the production status of product i in the second time period t of production line l, and g ki represents the tangent time length when switching from producing product k to producing product i, where k and i do not belong to the same product type when the tangent occurs; The second tangent constraint includes a ninth expression, and the ninth expression is as follows: Among them, L g represents the g production line group, and C g is the tangent capacity of the g production line group, and w l represents the occupancy of the tangent capacity when a tangent occurs on production line l; The die constraint includes a tenth expression, and the tenth expression is: Among them, mold f represents the number of model sets of type f; The second climbing production constraint includes an eleventh expression, and the eleventh expression is: where p il is the ramp-up production volume of product i on production line l; The second mass production output constraint includes a twelfth expression, and the twelfth expression is: x ilt ≤y ilt *q il Among them, q il represents the maximum mass production output of production line l of product i in a single time period.

15. The method according to claim 2, wherein, the first coupling constraint includes: For the second short-cycle model and the second long-cycle model of the first k first time periods, the inventory and backlog at the end of the last second time period of the second short-cycle model of the first k first time periods are equal to the inventory and backlog at the start of the first first time period of the second long-cycle model.

16. The method according to claim 2, wherein, for the k-th solution process among m solution processes, the method includes: constructing a second short-cycle model of the first k first time periods according to the product-line matching results of the first k first time periods and the first short-cycle model, where k = m; solving the second short-cycle model of the first k first time periods according to the product data to obtain the production scheduling results for all second time periods within the first k first time periods.

17. The method according to claim 1, wherein, the step of dividing the production scheduling period to obtain m first time periods includes: averagely dividing the production scheduling period to obtain m first time periods.

18. The method according to claim 17, wherein, the step of dividing the production scheduling period to obtain m first time periods includes: dividing the production scheduling period to obtain m first time periods, and determining the length of the first time period according to the number of orders within the production scheduling period.

19. An electronic device, comprising: at least one processor; and a memory storing a computer program that can run on the processor, wherein when the processor executes the program, it executes the steps of the method according to any one of claims 1 to 18.

20. A storage medium storing a computer program, wherein, when the computer program is executed by a processor, it executes the steps of the method according to any one of claims 1 to 18.

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