CN113112145A - Data processing method and device, electronic equipment and storage medium - Google Patents

Data processing method and device, electronic equipment and storage medium Download PDF

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CN113112145A
CN113112145A CN202110379913.9A CN202110379913A CN113112145A CN 113112145 A CN113112145 A CN 113112145A CN 202110379913 A CN202110379913 A CN 202110379913A CN 113112145 A CN113112145 A CN 113112145A
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order
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毛青军
张智海
龚海磊
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Tsinghua University
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Abstract

The application provides a data processing method, a data processing device, an electronic device and a storage medium, wherein the method comprises the following steps: obtaining order information of an order to be processed, equipment information, process information and raw material information of equipment for processing the order; inputting the order information, the equipment information, the process information and the raw material information into a pre-constructed decision optimization model to obtain a scheduling scheme; the scheduling scheme is used for adjusting various production decisions of the to-be-processed orders in the execution process. According to the data processing method, the order information of the order to be processed, the equipment information of the equipment for processing the order, the process information and the raw material information are analyzed through the decision optimization model to obtain the scheduling scheme, so that various production decisions of the order to be processed in the execution process can be adjusted subsequently according to the scheduling scheme, the production flow is optimized, the product processing efficiency is improved, and the order execution efficiency is improved.

Description

Data processing method and device, electronic equipment and storage medium
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a data processing method and apparatus, an electronic device, and a storage medium.
Background
Along with the increase of intelligent production equipment and production level, the digitalization degree and the intelligentization degree in the supply chain of the traditional manufacturing enterprise are gradually increased. At present, manufacturing enterprises in China gradually turn to upgrading from automated factories to digital information factories, further perfecting the construction of a digital supply chain and completing the development approach of the integration of the two types of plants. Because of the variety of equipment, many orders and large mobility of operators in the workshops of the manufacturing enterprises, the coordination of the selection of machines, personnel allocation, material supply and other aspects requires production departments to make detailed production plans. The existing production scheduling mode in the workshop of the manufacturing enterprise has the problems of high labor cost and time cost, low product processing efficiency and the like, so the existing production scheduling mode needs to be optimized for reducing the labor cost, reducing the processing and manufacturing time cost and ensuring that orders are delivered in due date.
Disclosure of Invention
The application provides a data processing method, a data processing device, an electronic device and a storage medium, which can adjust various production decisions of an order to be processed in an execution process, thereby optimizing a production flow, improving the product processing efficiency and improving the execution efficiency of the order.
A first aspect of the present application provides a data processing method, including:
obtaining order information of an order to be processed, equipment information, process information and raw material information of equipment for processing the order;
inputting the order information, the equipment information, the process information and the raw material information into a pre-constructed decision optimization model to obtain a scheduling scheme;
the scheduling scheme is used for adjusting various production decisions of the to-be-processed orders in the execution process.
Optionally, the step of inputting the order information, the equipment information, the process information, and the raw material information into a pre-constructed decision optimization model to obtain a scheduling scheme includes:
inputting the order information, the equipment information, the process information and the raw material information into a first-class decision optimization model, wherein the first-class decision optimization model aims at solving the minimum sum of the number of deferred orders and the number of times of order replacement;
obtaining the scheduling scheme according to the output information of the first type decision optimization model; or
Inputting the order information, the equipment information, the process information and the raw material information into a second type decision optimization model, wherein the second type decision optimization model aims at solving the minimum sum of the number of delayed products and the number of times of replacing orders;
and obtaining the scheduling scheme according to the output information of the second type decision optimization model.
Optionally, the step of inputting the order information, the equipment information, the process information, and the raw material information into a pre-constructed decision optimization model to obtain a scheduling scheme includes:
inputting the order information, the equipment information, the process information and the raw material information into the first type decision optimization model to obtain a first scheduling scheme;
inputting the order information, the equipment information, the process information and the raw material information into the second decision optimization model to obtain a second scheduling scheme;
and determining the scheduling scheme from the first scheduling scheme and the second scheduling scheme according to the selection operation of a user.
The first type decision optimization model is constructed by the following steps:
obtaining a first weight value set for the number of the deferred orders and a second weight value set for the number of times of replacing the orders;
and constructing the first-class decision optimization model according to the first weight value, the second weight value, the delay condition of the to-be-processed order and the condition of the equipment replacement order.
Optionally, constructing the first-class decision optimization model according to the first weight value, the second weight value, a status of a pending order postponement, and a status of an equipment replacement order, where the constructing the first-class decision optimization model includes:
constructing a first class of objective function according to the first weight value, the second weight value, the postponed condition of the order to be processed and the condition of the equipment replacement order;
setting constraints for the first class of objective functions, wherein the constraints at least comprise: the constraint conditions based on equipment information, the constraint conditions based on process information, the constraint conditions based on time, the constraint conditions based on the condition of the delay of the order to be processed and the constraint conditions based on the value range of the variable.
Optionally, the decision optimization model of the second type is constructed by the following steps:
obtaining a third weight value set for the number of the postponed products and a fourth weight value set for the number of times of replacing the order;
and constructing the second type of decision optimization model according to the third weight value, the fourth weight value, the product delay condition and the equipment replacement order condition.
Optionally, constructing the second-class decision optimization model according to the third weight value, the fourth weight value, a product postponed condition, and a condition of an equipment replacement order, where the method includes:
constructing a second type of objective function according to the third weight value, the fourth weight value, the product delay condition and the equipment replacement order condition;
setting constraint conditions for the second type target function, wherein the constraint conditions at least comprise: constraints based on equipment information, constraints based on process information, constraints based on time, and constraints based on variable value ranges.
A second aspect of the present application provides a data processing apparatus comprising:
the first obtaining module is used for obtaining order information of an order to be processed, equipment information of equipment for processing the order, process information and raw material information;
the second obtaining module is used for inputting the order information, the equipment information, the process information and the raw material information into a pre-constructed decision optimization model to obtain a scheduling scheme;
the scheduling scheme is used for adjusting various production decisions of the to-be-processed orders in the execution process.
Optionally, the second obtaining module includes:
the first input submodule is used for inputting the order information, the equipment information, the process information and the raw material information into a first-class decision optimization model, and the first-class decision optimization model aims at solving the minimum sum of the number of postponed orders and the number of times of changing the orders;
the first obtaining submodule is used for obtaining the scheduling scheme according to the output information of the first class decision optimization model; or
The second input submodule is used for inputting the order information, the equipment information, the process information and the raw material information into a second decision optimization model, and the second decision optimization model takes the minimum sum of the number of delayed products and the number of times of replacing orders as a target;
and the second obtaining submodule is used for obtaining the scheduling scheme according to the output information of the second type decision optimization model.
Optionally, the second obtaining module includes:
the third input submodule is used for inputting the order information, the equipment information, the process information and the raw material information into the first class decision optimization model to obtain a first scheduling scheme;
the fourth input submodule is used for inputting the order information, the equipment information, the process information and the raw material information into the second decision optimization model to obtain a second scheduling scheme;
a first determining submodule, configured to determine the scheduling scheme from the first scheduling scheme and the second scheduling scheme according to a selection operation of a user.
Optionally, the apparatus further comprises a first construction module, configured to construct the first class of decision-making optimization model; the first building block comprises:
a third obtaining submodule, configured to obtain a first weight value set for the number of deferred orders and a second weight value set for the number of times of order replacement;
and the first construction submodule is used for constructing the first-class decision optimization model according to the first weight value, the second weight value, the delay condition of the order to be processed and the condition of the equipment replacement order.
Optionally, the first building submodule comprises:
the second construction submodule is used for constructing a first type of objective function according to the first weight value, the second weight value, the delay condition of the order to be processed and the condition of the equipment replacement order;
a first setting submodule, configured to set a constraint condition for the first class of objective functions, where the constraint condition at least includes: the constraint conditions based on equipment information, the constraint conditions based on process information, the constraint conditions based on time, the constraint conditions based on the condition of the delay of the order to be processed and the constraint conditions based on the value range of the variable.
Optionally, the apparatus further comprises a second building module, configured to build the decision-making optimization model of the second type; the second building block comprises:
a fourth obtaining submodule, configured to obtain a third weight value set for the deferred product quantity and a fourth weight value set for the number of times of replacing the order;
and the third construction submodule is used for constructing the second-class decision optimization model according to the third weight value, the fourth weight value, the product delay condition and the equipment replacement order condition.
Optionally, the third building submodule comprises:
the fourth construction submodule is used for constructing a second type of objective function according to the third weight value, the fourth weight value, the product delay condition and the equipment replacement order condition;
a second setting submodule, configured to set a constraint condition for the second type target function, where the constraint condition at least includes: constraint conditions based on equipment information, constraint conditions based on process information, constraint conditions based on time and constraint conditions based on variable value range. According to the data processing method, firstly, order information of an order to be processed, equipment information of equipment for processing the order, process information and raw material information are obtained; inputting order information, equipment information, process information and raw material information into a pre-constructed decision optimization model to obtain a scheduling scheme; the scheduling scheme is used for adjusting various production decisions of the order to be processed in the execution process. According to the data processing method, the order information of the order to be processed, the equipment information of the equipment for processing the order, the process information and the raw material information are analyzed through the decision optimization model to obtain the scheduling scheme, so that various production decisions of the order to be processed in the execution process can be adjusted subsequently according to the scheduling scheme, the production flow is optimized, the product processing efficiency is improved, and the order execution efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments of the present application will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.
FIG. 1 is a schematic diagram of a production flow diagram of the warp knitting field shown in an embodiment of the present application;
FIG. 2 is a flow chart illustrating a method of data processing according to an embodiment of the present application;
FIG. 3 is a diagram illustrating a model output result according to an embodiment of the present application;
FIG. 4 is a diagram illustrating an aggregation according to an embodiment of the present application;
FIG. 5 is a diagram illustrating exemplary parameters according to an embodiment of the present application;
FIG. 6 is a decision variable schematic shown in an embodiment of the present application;
fig. 7 is a block diagram illustrating a data processing apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical scheme of the application is applicable to the field of any process production, and the product processing efficiency of equipment can be effectively improved. Because the effect of improving the processing efficiency of the product is particularly obvious when the technical scheme is applied to the warp knitting field, the data processing method is explained in detail later by taking the production flow of the warp knitting field as an example, and the implementation principle when the technical scheme is applied to other process production fields is the same as that of the warp knitting field, and the technical scheme is not repeated in the application.
Fig. 1 is a schematic production flow chart of the warp knitting field according to an embodiment of the present application. In fig. 1, the apparatus comprises a warping machine (with M)1*Is shown, i.e. M in FIG. 111-M1K) And warp knitting machine (using M)2*Is shown, i.e. M in FIG. 121-M2N) Warping machines are used to produce pan heads (pan heads are beams with gauze obtained by winding a gauze around the beam of a textile machine), warp knitting machines are used to produce knitted fabrics from pan heads. The warper machine may be of a plurality of types, each type of warper machine may be multiple, one type of warper machine for processing one type of material and obtaining one type of pan head, and an order typically requires multiple different types of materials. The warp knitting machines can be of various types, the number of each type of warp knitting machine can be multiple, and each order can be produced by using various different types of warp knitting machines respectively. In FIG. 1, the shaded equipment represents equipment currently in operation, and the solid line represents the execution path for order 1 (through warper machines M11, M)13、M14Obtaining the pan head, and passing through a warp knitting machine M21、M22Processing the pan head to obtain the product of order 1), the dotted line represents the execution path of order 2 (by warping machine M)13、M14、M1kObtaining the pan head, and passing through a warp knitting machine M25、M2NThe pan head is processed to obtain the product of order 2).
Fig. 2 is a flowchart illustrating a data processing method according to an embodiment of the present application. Referring to fig. 2, the data processing method of the present application may include:
step S21: order information of an order to be processed, equipment information of equipment used for processing the order, process information and raw material information are obtained.
In this embodiment, J represents an order, the order set to be arranged is J ═ 1, 2., | J | }, J is used as an index, and the order information includes an order number, an order type (a regular order or an urgent order), and a delivery deadline DT of the order JjActual completion time cjProduct quantity of order j
Figure BDA0003012567560000071
And the present embodiment does not limit the specific content of the order information.
The device information includes: the set of types of beaming machines available, S ═ 1, 2., | S | } (indexed by S), the number of beaming machines per type Zs(numbering in order: 1, 2.. Z)sBy zsIndex), the set of types of warp machines available R ═ {1, 2., | R | } (indexed with R), the number of warp machines of each type Br(numbering 1, 2.. B in order)rBy brIndex), warping machine equipment collection platform
Figure BDA0003012567560000072
Set of warp knitting machine devices
Figure BDA0003012567560000073
Per unit processing time p of each type of raw material wiresOrder j is processed in unit processing time of r-th class warp knitting machine
Figure BDA0003012567560000074
And the current status information of the device (including the order being processed, the processing progress, the unprocessed order, etc.), and the specific content of the device information is not limited in this embodiment.
The process information comprises: the warp knitting machine comprises a first procedure warping and a second procedure warp knitting, wherein the first procedure warping is executed by a warping machine, and the second procedure warp knitting is executed by a warp knitting machine.
The raw material information comprises: set of raw wire types for order j that also require processing
Figure BDA0003012567560000075
And amount of each type of raw material filament
Figure BDA0003012567560000076
Raw material stock information, etc., and the specific content of the raw material information is not limited in this embodiment.
In this embodiment, the z-th warper machine of the s-th type can be derived from the above informationsTotal duration of k-th task processing order j on number machine
Figure BDA00030125675600000711
Wherein p issThe time per unit processing time, i.e. the time required to process one product,
Figure BDA00030125675600000712
for decision variables, i.e. for the type s feedstock wire corresponding to order j at zsNumber of products processed by the k-th task on the machine, i.e.
Figure BDA00030125675600000713
Is the amount of processing, i.e. how much product is processed in total. Similarly, the second of the r-th warp knitting machinerTotal duration of kth task process order j on number machine
Figure BDA0003012567560000077
Wherein the content of the first and second substances,
Figure BDA0003012567560000078
in the form of a unit of processing time,
Figure BDA0003012567560000079
as decision variables, i.e. ordersj in the (r) th warp knitting machinerNumber of products processed in the k-th task of the machine, i.e.
Figure BDA00030125675600000710
To the amount of processing, i.e. how much product is processed in total.
In this embodiment, the order information of the to-be-processed order, the equipment information of the equipment for processing the order, the process information, and the raw material information may be obtained in any manner, for example, obtained through information input by a user, or obtained from pre-stored information, which is not limited in this embodiment.
Step S22: and inputting the order information, the equipment information, the process information and the raw material information into a pre-constructed decision optimization model to obtain a scheduling scheme.
The scheduling scheme is used for adjusting various production decisions of the to-be-processed orders in the execution process.
In this embodiment, the scheduling scheme refers to scheduling information of each device, and the scheduling information is information on which each device actually executes an order and includes a production decision that needs to be adjusted. For example, table 1 below is a production decision of the plant 1 after a certain day adjusted according to a scheduling scheme, including: order information to be executed, order execution time, raw materials required for the order, equipment maintenance time, and the like, and the type of the production decision is not particularly limited in this embodiment. Specifically, table 1 is exemplary only and does not represent actual production.
Figure BDA0003012567560000081
TABLE 1
Illustratively, an order set J which needs to be scheduled is {1, 2., | J | }, a type set S of a warping machine is {1, 2., | S | }, a task number K and a raw material yarn type set
Figure BDA0003012567560000082
Number of warping machines of each type Zs(in order)Numbering: 1, 2s) The type set R ═ 1, 2., | R | }, the number of warp knitting machines of each type Br(numbering 1, 2.. B in order)r) After the decision optimization model is input, the decision optimization model can output scheduling information (shown in table 1) of each device, and each device can adjust a production decision according to the scheduling information and then produce according to the adjusted production decision, so that the product processing efficiency can be improved, and the order execution efficiency can be further improved. The production decision may be selected according to the actual requirement of the user, which is not limited in this embodiment.
Fig. 3 is a diagram illustrating a model output result according to an embodiment of the present application. In fig. 3, the darker shaded portion indicates the warping step, and the lighter shaded portion indicates the warp knitting step. The ordinate j _ s _ z _ k represents the kth task of the order j on the z-th machine of the s-th type of warping machine, and the ordinate j _ r _ b _ k represents the kth task of the order j on the b-th machine of the r-th type of warp knitting machine. The abscissa represents a processing time of 0 to 5000 (unit: minute). J0_ s2_ z2_ k0 in the first row represents that order j0 is processed as a k0 task on a z2 warping machine of model s2, and j0_ r2_ b1_ k0 in the first row represents that order j0 is processed as a k0 task on a b1 warping machine of model r 2. According to the data processing method, firstly, order information of an order to be processed, equipment information of equipment for processing the order, process information and raw material information are obtained; inputting order information, equipment information, process information and raw material information into a pre-constructed decision optimization model to obtain a scheduling scheme; the scheduling scheme is used for adjusting various production decisions of the order to be processed in the execution process. According to the data processing method, the order information of the order to be processed, the equipment information of the equipment for processing the order, the process information and the raw material information are analyzed through the decision optimization model to obtain the scheduling scheme, so that various production decisions of the order to be processed in the execution process can be adjusted subsequently according to the scheduling scheme, the production flow is optimized, the product processing efficiency is improved, and the order execution efficiency is improved.
With reference to the foregoing embodiment, in an implementation manner, the present application further provides a method for obtaining a scheduling scheme according to a pre-constructed decision optimization model, and specifically, the foregoing step S22 may include:
inputting the order information, the equipment information, the process information and the raw material information into a first-class decision optimization model, wherein the first-class decision optimization model aims at solving the minimum sum of the number of deferred orders and the number of times of order replacement;
obtaining the scheduling scheme according to the output information of the first type decision optimization model; or
Inputting the order information, the equipment information, the process information and the raw material information into a second type decision optimization model, wherein the second type decision optimization model aims at solving the minimum sum of the number of delayed products and the number of times of replacing orders;
and obtaining the scheduling scheme according to the output information of the second type decision optimization model.
In this embodiment, the types of the decision optimization model may include: a first type of decision optimization model and a second type of decision optimization model. The first type of decision optimization model takes the minimum sum of the number of postponed orders and the number of times of order replacement as a calculation target, and the second type of decision optimization model takes the minimum sum of the number of postponed products and the number of times of order replacement as a calculation target. The order types are divided into regular orders and urgent orders.
Illustratively, the regular orders include order 1 and order 2, the expedited orders include order 3 and order 4, if equipment A switches from processing order 1 to processing order 2, then equipment A changes orders 1, and if equipment A switches from processing order 1 to processing order 2 and then switches to order 3, then equipment 1 changes orders 2.
In specific implementation, the order information, the equipment information, the process information and the raw material information can be input into a first-class decision optimization model, the first-class decision optimization model determines the minimum sum of the delayed order quantity and the order replacement frequency according to the incidence relation among various indexes related in the order information, the equipment information and the process information, and the scheduling information output by the first-class decision optimization model is most beneficial to reducing the delayed order quantity and the order replacement frequency at the minimum sum, so that the scheduling information output by the first-class decision optimization model can be used as a scheduling scheme when the delayed order quantity and the order replacement frequency are at the minimum sum.
Similarly, the order information, the equipment information, the process information and the raw material information can be input into a second decision optimization model, the second decision optimization model determines the minimum sum of the delayed product quantity and the order replacement frequency according to the incidence relation among various indexes related in the order information, the equipment information and the process information, and the scheduling information output by the second decision optimization model is most beneficial to reducing the delayed product quantity and the order replacement frequency at the minimum sum, so that the scheduling information output by the second decision optimization model can be used as a scheduling scheme when the delayed product quantity and the order replacement frequency are at the minimum sum.
The user may optionally select the first type of decision optimization model or the second type of decision optimization model according to the actual production demand, which is not limited in this embodiment.
In this embodiment, the scheduling scheme may be obtained through the scheduling information output by the first-class decision optimization model or the second-class decision optimization model, so that when the device performs production according to the production decision adjusted by the scheduling scheme, the product processing efficiency may be effectively improved, and further the order execution efficiency may be improved.
With reference to the foregoing embodiment, in an implementation manner, the present application further provides another method for obtaining a scheduling scheme according to a pre-constructed decision optimization model, and specifically, the foregoing step S22 may include:
inputting the order information, the equipment information, the process information and the raw material information into the first type decision optimization model to obtain a first scheduling scheme;
inputting the order information, the equipment information, the process information and the raw material information into the second decision optimization model to obtain a second scheduling scheme;
and determining the scheduling scheme from the first scheduling scheme and the second scheduling scheme according to the selection operation of a user.
In this embodiment, the first type decision optimization model and the second type decision optimization model may be used simultaneously, and then the scheduling scheme may be determined according to the selection operation of the user. The first scheduling scheme can be obtained by processing the order information, the equipment information, the process information and the raw material information through the first type decision optimization model, and the second scheduling scheme can be obtained by processing the order information, the equipment information, the process information and the raw material information through the second type decision optimization model.
Since the respective calculation targets of the first-class decision optimization model and the second-class decision optimization model are different, the first scheduling scheme and the second scheduling scheme may be different, and at this time, the user may select one type of the first scheduling scheme and the second scheduling scheme as the scheduling scheme according to actual needs, for example, select the first scheduling scheme as the scheduling scheme, or select the second scheduling scheme as the scheduling scheme.
According to the method, the first-class decision optimization model and the second-class decision optimization model can be used for obtaining two groups of different ideal index values at the same time, the final scheduling scheme is determined according to the selection operation of the user, and the order execution efficiency and the order execution flexibility can be improved.
In an implementation manner, in combination with the above embodiments, the present application further provides a method for constructing a first-class decision optimization model. Specifically, the method may include:
obtaining a first weight value set for the number of the deferred orders and a second weight value set for the number of times of replacing the orders;
and constructing the first-class decision optimization model according to the first weight value, the second weight value, the delay condition of the to-be-processed order and the condition of the equipment replacement order.
In one embodiment, constructing the first-class decision optimization model according to the first weight value, the second weight value, the status of the pending order deferral, and the status of the equipment replacement order may include:
constructing a first class of objective function according to the first weight value, the second weight value, the postponed condition of the order to be processed and the condition of the equipment replacement order;
setting constraints for the first class of objective functions, wherein the constraints at least comprise: the constraint conditions based on equipment information, the constraint conditions based on process information, the constraint conditions based on time, the constraint conditions based on the condition of the delay of the order to be processed and the constraint conditions based on the value range of the variable. In this embodiment, the process of constructing the first-class decision optimization model may include the following steps:
step 1: obtaining a first weight value set for the number of deferred orders and a second weight value set for the number of times of order replacement according to the decision preference of a user, and then constructing a first-class objective function according to the first weight value, the second weight value, the deferred condition of the order to be processed and the condition of the equipment replacement order.
Step 2: constructing a model constraint related to the equipment according to the equipment information, wherein the constraint condition at least comprises the following steps: a piece of equipment can only process one order at a time, the number of equipment used per order cannot exceed the maximum number of equipment, etc.
Step 3: and constructing model constraints related to the process according to the process information, wherein the constraint conditions at least comprise: each order can only be processed on the equipment that can complete the order, the warp knitting process for each order can only be started after the warping process, etc.
Step 4: constructing time-dependent model constraints, wherein the constraint conditions at least comprise: the start time of each order warp knitting process needs to be after the completion time of the warping process, the start time of the next process of each equipment needs to be after the completion time of the previous process, and the like.
Step 5: and constructing a constraint condition for judging whether the order is delayed.
Step 6: and constructing a constraint condition of the value range of the decision variable.
The execution sequence of Step2-Step6 may be arbitrarily adjusted according to actual requirements, which is not limited in this embodiment.
Before describing the process of constructing a decision-making optimization model in detail, a brief description of the production flow in the warp knitting field is provided below.
In actual production, different types of raw material threads are used by different types of beaming machines, so that the beaming machines can be divided into a plurality of subsets according to the types, and each subset can process one type of raw material thread, namely, a plurality of beaming machines can process each type of raw material thread. In addition, one order typically requires the use of multiple different types of feedstock wires.
One warp knitting machine can process multiple types of products, but not all types of products, i.e. for one order, a warp knitting process can be processed by any warp knitting machine in a subset of warp knitting machines.
The unit processing time of the same order on different machines is not necessarily the same, nor is the unit processing time of different orders on the same machine.
Typically, warping machines and warp knitting machines do not actively change orders when processing products. However, in the case of an order with an urgent (the order type is divided into a regular order and an urgent order), the warp knitting machine may be directly changed to process the urgent order when the raw yarn is changed.
The pan heads produced by the warping machine are put in stock in a centralized way, and if no urgent order is made, the pan heads are processed according to the required quantity of the pan heads. If there is an urgent order, the urgent order occupies the pan head generated by the order before the urgent order is occupied. I.e., an expedited order, involves allocating pan head inventory to the expedited order. For example: an urgent order occurs at the time of a certain day when the order is laid out, the stock of the pan heads is known, assuming that the urgent order needs a type A pan heads in total, b stock is available at present (previously produced for previous orders), and if the urgent order uses up the stock, the type A pan heads of the previous orders (including order 1 and order 2) need to be warped again. Let order 1 re-warping amount be b1Order 2. warper amount b2Then b1+b2＝b。b1、b2B in the modelAre parameters and are assigned according to some artificial rules, such as: the pan head amount of 3 days can be fixed, and all the rest pan heads can be used for emergency orders. When the stock quantity b of the disk heads is used, the stock quantity b of the disk heads of the order with the longest delivery date is used first, and then the stock quantity b of the disk heads of the order with the next longest delivery date is used.
When optimizing production decisions using a decision-making optimization model, there are three goals to be addressed: firstly, the number of deferred orders is minimum; secondly, the number of delayed products is minimum; and thirdly, the number of times of changing orders is reduced as much as possible. The first class of decision-making optimization models focuses primarily on goal one and goal three.
The objective function used when constructing the first-class decision optimization model based on the first objective and the third objective in this embodiment is as follows:
minω1∑j∈J Uj+ω2C (1)
in equation (1), the first weight value ω1Is a weight value previously assigned to the object one (minimum number of products postponed), and the second weight value ω is2The weight value is assigned to the third target (the number of replacement orders is as small as possible) in advance. C is an auxiliary variable to the introduction
Figure BDA0003012567560000131
The sum is obtained by summing the sum of the two components,
Figure BDA0003012567560000132
Figure BDA0003012567560000133
if it is
Figure BDA0003012567560000134
Indicating that the order has just started processing at position k, which indicates that the corresponding order is the kth processing on the machine, e.g., a warp knitting machine has processed task A, B, C in sequence, task a is at position 1 on the warp knitting machine, task B is at position 2 on the warp knitting machine, and task C is at position 3 on the warp knitting machine. U shapejIndicating whether order j is deferred.
In the formula (1), UjAnd C, corresponding to the first target, reflecting the delay condition of the order to be processed, and corresponding to the third target, reflecting the condition of the equipment replacement order. C is the sum of the number of orders processed on all the equipment, and the third objective is to make the same order processed on one equipment as much as possible by making C as small as possible. For example A, B, two warp knitting machines, if two warp knitting machines complete task a first and then task B, C is 4, while one warp knitting machine is task a and the other is task B, C is 2, which is more favorable for achieving goal three.
The meaning of each letter involved in the objective function can be specifically referred to tables shown in fig. 4-6, fig. 4 is a set diagram shown in an embodiment of the present application, fig. 5 is a parameter diagram shown in an embodiment of the present application, and fig. 6 is a decision variable diagram shown in an embodiment of the present application.
The set of requirements for the decision optimization model of the present application is listed in fig. 4, which includes: j ═ 1, 2., | J | } (set of orders to be ranked, indexed by J), S ═ 1, 2., | S | } (set of types of warping machines available, indexed by S), and (c) c,
Figure BDA0003012567560000149
(order j-type set of warping machines corresponding to the type of raw yarn to be processed), Zs＝{1，2，...，|ZsL (number of warper types available, in z)sIndex), R ═ {1, 2., | R | } (a set of types of warp machines available, indexed with R) },
Figure BDA00030125675600001410
(type set of warp knitting machines optional for order j), Br＝{1，2，...，|BrL (number of warp knitting machines of each type available, in b)rIndex), K ═ {1, 2,., | K | } (the kth task to be processed on a certain machine).
The parameters that need to be used by the decision optimization model of the present application are listed in FIG. 5, including：DTj(delivery deadline of order j),
Figure BDA0003012567560000141
(the order j also needs to process the amount of the s type raw material wire), ps (the processing time of a single wire barrel of the s type raw material wire),
Figure BDA0003012567560000142
(the number of products to be processed corresponding to order j),
Figure BDA0003012567560000143
(unit processing time of order j in the r-th type warp knitting machine),
Figure BDA0003012567560000144
(a very large number).
The decision variables that need to be used by the decision optimization model of the present application are listed in fig. 6, including:
Figure BDA0003012567560000145
(order j whether or not the s-th type raw material yarn of the first step is selected as the k-th task of the No. zs machine (of the warping machine corresponding to the s-th type raw material yarn) for processing,
Figure BDA0003012567560000146
(order j second step, whether or not the b-th warp knitting machine as the r-type warp knitting machine is selectedrThe k-th task of the machine is to perform the machining,
Figure BDA0003012567560000147
(s type feedstock wire for order j is in zsNumber of processes at kth task on machine) ("k" number of processes at kth task),
Figure BDA0003012567560000148
(order j in the b-th order of the r-th warp knitting machinerNumber of processes at k-th task on machine), c)j(actual completion time of order j), Uj(delivery time, whether order j is deferred, Uj∈{0，1}，If c isj＞UjThen U isj1, otherwise, Uj＝0)。
In combination with the above embodiment, in an implementation manner, the present application further provides a method for constructing a decision optimization model of the second type, and specifically, the method may include:
obtaining a third weight value set for the number of the postponed products and a fourth weight value set for the number of times of replacing the order;
and constructing the second type of decision optimization model according to the third weight value, the fourth weight value, the product delay condition and the equipment replacement order condition.
In one embodiment, constructing the second type of decision optimization model according to the third weight value, the fourth weight value, the status of product postponement, and the status of equipment replacement order may include:
constructing a second type of objective function according to the third weight value, the fourth weight value, the product delay condition and the equipment replacement order condition;
setting constraint conditions for the second type target function, wherein the constraint conditions at least comprise: constraints based on equipment information, constraints based on process information, constraints based on time, and constraints based on variable value ranges.
In this embodiment, the process of constructing the second-class decision optimization model may include the following steps:
step 1': and then, constructing a second type of objective function according to the third weight value, the fourth weight value, the postponed condition of the order to be processed and the condition of the equipment replacement order.
Step 2': constructing a model constraint related to the equipment according to the equipment information, wherein the constraint condition at least comprises the following steps: a piece of equipment can only process one order at a time, the number of equipment used per order cannot exceed the maximum number of equipment, etc.
Step 3': and constructing model constraints related to the process according to the process information, wherein the constraint conditions at least comprise: each order can only be processed on the equipment that can complete the order, the warp knitting process for each order can only be started after the warping process, etc.
Step 4': constructing time-dependent model constraints, wherein the constraint conditions at least comprise: the start time of each order warp knitting process needs to be after the completion time of the warping process, the start time of the next process of each equipment needs to be after the completion time of the previous process, and the like.
Step 5': and constructing a constraint condition of the value range of the decision variable.
The execution sequence of Step2 '-Step 5' may be arbitrarily adjusted according to actual requirements, which is not limited in this embodiment. In this embodiment, the third weight value is a weight value set for goal two (the number of deferred products is the smallest), and the fourth weight value is a weight value set for goal three (the number of replacement orders is the smallest).
And constructing a second decision optimization model based on the second target and the third target, wherein the specifically used target functions are as follows:
Figure BDA0003012567560000161
in the formula (2), [ 2 ]]+Represents: if 2]If the value in (1) is negative, then]+0 if]If the value in (1) is a positive number, then]+Equal to the value itself.
Figure BDA0003012567560000162
Representing the delay time for the task of completing the assigned order j on each warp knitting machine.
Figure BDA0003012567560000163
Representing the delay time multiplied by
Figure BDA0003012567560000164
(rate), i.e., the amount of product that is delayed.
Figure BDA0003012567560000165
Representing only computing assigned tasks: (
Figure BDA00030125675600001614
Time) of product on the machine.
In the formula (2), the first and second groups,
Figure BDA0003012567560000166
the cun corresponds to the target two and is used for reflecting the delay condition of the product, and the C corresponds to the target three and is used for reflecting the condition of the equipment replacement order. The meaning of each letter referred to in equation (2) can be seen in particular in the tables shown in fig. 4-6.
In the present application, the above formula (1) and formula (2) also need to satisfy the following constraints (3) to (23):
Figure BDA0003012567560000167
Figure BDA0003012567560000168
Figure BDA0003012567560000169
Figure BDA00030125675600001610
Figure BDA00030125675600001611
Figure BDA00030125675600001612
Figure BDA00030125675600001613
Figure BDA0003012567560000171
Figure BDA0003012567560000172
Figure BDA0003012567560000173
Figure BDA0003012567560000174
Figure BDA0003012567560000175
Figure BDA0003012567560000176
Figure BDA0003012567560000177
Figure BDA0003012567560000178
Figure BDA0003012567560000179
Figure BDA00030125675600001710
Figure BDA00030125675600001711
Figure BDA00030125675600001712
Figure BDA00030125675600001713
Figure BDA00030125675600001714
constraints (3) for introducing auxiliary variables
Figure BDA00030125675600001715
Constraint (4) is pair
Figure BDA00030125675600001716
And summing up to represent the number of times the order is changed by the warp knitting machine.
Constraint (5) indicates that the number of beaming machines used per type of raw material thread of the first pass of each order is at least 1 and does not exceed a maximum value Zs。
The constraint (6) representing the number of warp knitting machines used for the second pass of each order is at least 1 and does not exceed a maximum value Br。
Constraints (7) - (8) refer to avoiding arranging multiple orders simultaneously at the same location on the same machine of the same type of warping machine.
Constraints (9) - (10) refer to the same machine, and if the machining position in the front of the code is free, the subsequent machining position is not used. Wherein {1} refers to the first position, K is all positions, i.e., positions can take 1, 2, 3, … …, K \ 1} which is the position 2, 3, … … with the first position removed.
Constraint (11) represents s-type raw material wire at zsOn the machine firstTime of k tasks processing order j
Figure BDA0003012567560000181
And in the (b) th warp knitting machine of the (r) th warp knitting machinerTime of k-th task processing order j on machine
Figure BDA0003012567560000182
The constraint (12) means that the total amount of the s-type raw silk required to be warped for the order j is more than or equal to
Figure BDA0003012567560000183
Constraint (13) means that the total warp knitting amount of order j is greater than or equal to
Figure BDA0003012567560000184
The constraints (14-16) indicate the completion time of each order on the warper, and t1 is the completion time of the first pass, i.e., the completion time of the warping process.
Constraints (17-19) represent the completion time of order j on the warp knitting machine, and t2 is the completion time of the second process, i.e. the completion time of the warp knitting process.
Constraint (20) represents the total completion time for order j.
Constraint (21) indicates whether order j is deferred.
Constraints (22-23) are used to define the value ranges of the variables.
Wherein, the first type of decision optimization model needs to satisfy the constraint conditions (3) - (21), and the second type of decision optimization model needs to satisfy the constraint conditions (3) - (20). Specifically, the constraint conditions based on the equipment information correspond to formula (5) to formula (6), and the constraint conditions based on the process information correspond to formula (5) to formula (6) and formula (17). The time-based constraint corresponds to equation (14) -equation (20). The constraint condition based on the status of the pending order postponement corresponds to formula (21). The constraint condition based on the value range of the variable corresponds to the formula (22) and the formula (23). .
According to the data processing method, the order information of the order to be processed, the equipment information of the equipment for processing the order, the process information and the raw material information are analyzed through the decision optimization model to obtain the optimized parameters, so that various production parameters of the order to be processed in the execution process can be adjusted subsequently according to the optimized parameters, the production flow is optimized, the product processing efficiency is improved, and the order execution efficiency is further improved.
When the data processing method of the present application is implemented by a warp-knitting enterprise, some actual conditions may be input into a data processing device (the data processing device is an execution subject of the present application), for example, the actual conditions of the warp-knitting enterprise are shown in table 2 below, and under the actual conditions shown in table 2, the warp-knitting enterprise still has many production schedules (i.e., order execution schedules) to select, and how to select the best production schedule needs to use the decision optimization model provided by the present application. The decision optimization model can optimize production decisions, a plurality of objective functions (an objective function corresponding to the first type of decision optimization model and an objective function corresponding to the second type of decision optimization model) are arranged in the decision optimization model, the decision optimization model can solve an optimal production scheduling scheme according to the objective function set by a user, and the production scheduling scheme provides scheduling information (refer to the table 1 in the foregoing) of each device when the actual conditions in the table 2 are met, so that a warp knitting enterprise can produce according to the scheduling information through a data processing device, the optimization of a production scheduling mode is realized, and the product processing efficiency is improved. The information input in table 2 may be set according to the user's requirement, and is not unique. In table 2, time refers to the time required for the decision optimization model to calculate the output optimal scheduling plan.
Figure BDA0003012567560000191
TABLE 2
As shown in table 2, when the actual conditions (the number of orders is 5, the total number of warping machines is 12, and the total number of warp knitting machines is 15) are satisfied, the time required for obtaining the optimal production scheduling scheme through the objective function corresponding to the first type of decision optimization model is 1 second, and the time required for obtaining the optimal production scheduling scheme through the objective function corresponding to the second type of decision optimization model is 5 seconds. The user can select whether to use the optimal production scheduling scheme obtained through the first type of decision optimization model or the second type of decision optimization model according to the time required for obtaining the optimal production scheduling scheme.
It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the illustrated order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments of the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.
Based on the same inventive concept, the present application further provides a data processing apparatus 700. Fig. 7 is a block diagram illustrating a data processing apparatus according to an embodiment of the present application. Referring to fig. 7, a data processing apparatus 700 of the present application may include:
a first obtaining module 701, configured to obtain order information of an order to be processed, equipment information of equipment used for processing the order, process information, and raw material information;
a second obtaining module 702, configured to input the order information, the device information, the process information, and the raw material information into a pre-constructed decision optimization model to obtain a scheduling scheme;
the scheduling scheme is used for adjusting various production decisions of the to-be-processed orders in the execution process.
Optionally, the second obtaining module 702 includes:
the first input submodule is used for inputting the order information, the equipment information, the process information and the raw material information into a first-class decision optimization model, and the first-class decision optimization model aims at solving the minimum sum of the number of postponed orders and the number of times of changing the orders;
the first obtaining submodule is used for obtaining the scheduling scheme according to the output information of the first class decision optimization model; or
The second input submodule is used for inputting the order information, the equipment information, the process information and the raw material information into a second decision optimization model, and the second decision optimization model takes the minimum sum of the number of delayed products and the number of times of replacing orders as a target;
and the second obtaining submodule is used for obtaining the scheduling scheme according to the output information of the second type decision optimization model.
Optionally, the second obtaining module 702 includes:
the third input submodule is used for inputting the order information, the equipment information, the process information and the raw material information into the first class decision optimization model to obtain a first scheduling scheme;
the fourth input submodule is used for inputting the order information, the equipment information, the process information and the raw material information into the second decision optimization model to obtain a second scheduling scheme;
a first determining submodule, configured to determine the scheduling scheme from the first scheduling scheme and the second scheduling scheme according to a selection operation of a user.
Optionally, the apparatus 700 further comprises a first constructing module, configured to construct the first class of decision-making optimization model; the first building block comprises:
a third obtaining submodule, configured to obtain a first weight value set for the number of deferred orders and a second weight value set for the number of times of order replacement;
and the first construction submodule is used for constructing the first-class decision optimization model according to the first weight value, the second weight value, the delay condition of the order to be processed and the condition of the equipment replacement order.
Optionally, the first building submodule comprises:
the second construction submodule is used for constructing a first type of objective function according to the first weight value, the second weight value, the delay condition of the order to be processed and the condition of the equipment replacement order;
a first setting submodule, configured to set a constraint condition for the first class of objective functions, where the constraint condition at least includes: the constraint conditions based on equipment information, the constraint conditions based on process information, the constraint conditions based on time, the constraint conditions based on the condition of the delay of the order to be processed and the constraint conditions based on the value range of the variable.
Optionally, the apparatus 700 further comprises a second constructing module, configured to construct the decision-making optimization model of the second type; the second building block comprises:
a fourth obtaining submodule, configured to obtain a third weight value set for the deferred product quantity and a fourth weight value set for the number of times of replacing the order;
and the third construction submodule is used for constructing the second-class decision optimization model according to the third weight value, the fourth weight value, the product delay condition and the equipment replacement order condition.
Optionally, the third building submodule comprises:
the fourth construction submodule is used for constructing a second type of objective function according to the third weight value, the fourth weight value, the product delay condition and the equipment replacement order condition;
a second setting submodule, configured to set a constraint condition for the second type target function, where the constraint condition at least includes: constraints based on equipment information, constraints based on process information, constraints based on time, constraints based on conditions of product delays, and constraints based on variable value ranges. Based on the same inventive concept, the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the electronic device implements the steps in the data processing method according to any of the above embodiments of the present application.
Based on the same inventive concept, the present application provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps in the data processing method according to any of the above-mentioned embodiments of the present application.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should be further noted that, in the present application, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The data processing method, the data processing apparatus, the electronic device, and the storage medium according to the present invention are introduced in detail, and a specific example is applied in the present application to explain the principle and the implementation of the present invention, and the description of the above embodiment is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (10)

1. A data processing method, comprising:
obtaining order information of an order to be processed, equipment information, process information and raw material information of equipment for processing the order;
inputting the order information, the equipment information, the process information and the raw material information into a pre-constructed decision optimization model to obtain a scheduling scheme;
the scheduling scheme is used for adjusting various production decisions of the to-be-processed orders in the execution process.
2. The method of claim 1, wherein inputting the order information, equipment information, process information, and raw material information into a pre-constructed decision optimization model to obtain a scheduling scheme comprises:
inputting the order information, the equipment information, the process information and the raw material information into a first-class decision optimization model, wherein the first-class decision optimization model aims at solving the minimum sum of the number of deferred orders and the number of times of order replacement;
obtaining the scheduling scheme according to the output information of the first type decision optimization model; or
Inputting the order information, the equipment information, the process information and the raw material information into a second type decision optimization model, wherein the second type decision optimization model aims at solving the minimum sum of the number of delayed products and the number of times of replacing orders;
and obtaining the scheduling scheme according to the output information of the second type decision optimization model.
3. The method of claim 2, wherein inputting the order information, the equipment information, the process information, and the raw material information into a pre-constructed decision optimization model to obtain a scheduling scheme comprises:
inputting the order information, the equipment information, the process information and the raw material information into the first type decision optimization model to obtain a first scheduling scheme;
inputting the order information, the equipment information, the process information and the raw material information into the second decision optimization model to obtain a second scheduling scheme;
and determining the scheduling scheme from the first scheduling scheme and the second scheduling scheme according to the selection operation of a user.
4. The method of claim 2, wherein the first-class decision optimization model is constructed by:
obtaining a first weight value set for the number of the deferred orders and a second weight value set for the number of times of replacing the orders;
and constructing the first-class decision optimization model according to the first weight value, the second weight value, the delay condition of the to-be-processed order and the condition of the equipment replacement order.
5. The method of claim 4, wherein constructing the first type of decision optimization model according to the first weight value, the second weight value, a status of a pending order postponement, and a status of a device replacement order comprises:
constructing a first class of objective function according to the first weight value, the second weight value, the postponed condition of the order to be processed and the condition of the equipment replacement order;
setting constraints for the first class of objective functions, wherein the constraints at least comprise: the constraint conditions based on equipment information, the constraint conditions based on process information, the constraint conditions based on time, the constraint conditions based on the condition of the delay of the order to be processed and the constraint conditions based on the value range of the variable.
6. The method of claim 2, wherein the decision-making optimization model of the second type is constructed by:
obtaining a third weight value set for the number of the postponed products and a fourth weight value set for the number of times of replacing the order;
and constructing the second type of decision optimization model according to the third weight value, the fourth weight value, the product delay condition and the equipment replacement order condition.
7. The method of claim 6, wherein constructing the second type of decision optimization model according to the third weight value, the fourth weight value, a condition of product postponement, and a condition of equipment replacement order comprises:
constructing a second type of objective function according to the third weight value, the fourth weight value, the product delay condition and the equipment replacement order condition;
setting constraint conditions for the second type target function, wherein the constraint conditions at least comprise: constraints based on equipment information, constraints based on process information, constraints based on time, and constraints based on variable value ranges.
8. A data processing apparatus, comprising:
the first obtaining module is used for obtaining order information of an order to be processed, equipment information of equipment for processing the order, process information and raw material information;
the second obtaining module is used for inputting the order information, the equipment information, the process information and the raw material information into a pre-constructed decision optimization model to obtain a scheduling scheme;
the scheduling scheme is used for adjusting various production decisions of the to-be-processed orders in the execution process.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the data processing method according to any one of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing, carries out the steps in the data processing method according to any of claims 1-7.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023130709A1 (en) * 2022-01-06 2023-07-13 青岛海尔科技有限公司 Production scheduling method and apparatus for household appliance device, device, and storage medium
CN116738239A (en) * 2023-08-11 2023-09-12 浙江菜鸟供应链管理有限公司 Model training method, resource scheduling method, device, system, equipment and medium

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190171776A1 (en) * 2017-12-01 2019-06-06 Industrial Technology Research Institute Methods, devices and non-transitory computer-readable medium for parameter optimization
CN112132546A (en) * 2020-09-25 2020-12-25 杉数科技(北京)有限公司 Method and device for scheduling production
CN112435095A (en) * 2020-11-26 2021-03-02 浙江大学 Carton workshop order production management device

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190171776A1 (en) * 2017-12-01 2019-06-06 Industrial Technology Research Institute Methods, devices and non-transitory computer-readable medium for parameter optimization
CN112132546A (en) * 2020-09-25 2020-12-25 杉数科技(北京)有限公司 Method and device for scheduling production
CN112435095A (en) * 2020-11-26 2021-03-02 浙江大学 Carton workshop order production management device

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023130709A1 (en) * 2022-01-06 2023-07-13 青岛海尔科技有限公司 Production scheduling method and apparatus for household appliance device, device, and storage medium
CN116738239A (en) * 2023-08-11 2023-09-12 浙江菜鸟供应链管理有限公司 Model training method, resource scheduling method, device, system, equipment and medium
CN116738239B (en) * 2023-08-11 2023-11-24 浙江菜鸟供应链管理有限公司 Model training method, resource scheduling method, device, system, equipment and medium

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