CN116402313A - Product scheduling method, device, electronic equipment and computer readable storage medium - Google Patents

Product scheduling method, device, electronic equipment and computer readable storage medium Download PDF

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CN116402313A
CN116402313A CN202310626151.7A CN202310626151A CN116402313A CN 116402313 A CN116402313 A CN 116402313A CN 202310626151 A CN202310626151 A CN 202310626151A CN 116402313 A CN116402313 A CN 116402313A
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product
production
scheduling
produced
constraint
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CN116402313B (en
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何永杰
蒋抱阳
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Industrial Fulian Foshan Industrial Demonstration Base Co ltd
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Industrial Fulian Foshan Industrial Demonstration Base Co ltd
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Abstract

The invention discloses a product scheduling method, a device, electronic equipment and a computer readable storage medium, and relates to the technical field of computers, wherein the product scheduling method comprises the following steps: obtaining production limit information of a product to be produced; obtaining a first production constraint condition of the product to be produced based on the production constraint information; acquiring an objective function for guiding the production of the product to be produced; establishing a scheduling planning model for guiding product scheduling based on the objective function and the first production constraint condition; and solving the production model to obtain the production line body and the product yield of the product to be produced. The method and the device can improve reliability of product scheduling and production efficiency.

Description

Product scheduling method, device, electronic equipment and computer readable storage medium
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for product scheduling, an electronic device, and a computer readable storage medium.
Background
In the manufacturing industry, product production scheduling has close correlation with workshop production efficiency. In order to prevent resource waste, workers can carry out production scheduling according to equipment types and annual demand.
However, with the expansion of the production scale of the manufacturing industry, the equipment is increased, the quantity of processing raw materials is huge, the production reliability is insufficient through manual experience, and the production efficiency is affected.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a product scheduling method, a device, electronic equipment and a computer readable storage medium, which can improve the reliability of scheduling and further improve the production efficiency of products.
In order to solve the technical problems, the invention provides a product scheduling method, which comprises the following steps: obtaining production limit information of a product to be produced; obtaining a first production constraint condition of the product to be produced based on the production constraint information; acquiring an objective function for guiding the production of the product to be produced; establishing a scheduling planning model for guiding product scheduling based on the objective function and the first production constraint condition; and solving the production model to obtain the production line body and the product yield of the product to be produced.
By adopting the technical scheme, the scheduling planning model is obtained and solved by acquiring the first production constraint condition and the objective function, so that the production line body corresponding to the product and the product yield are acquired, and compared with manual scheduling, the reliability and the efficiency of product scheduling can be improved, and the production efficiency is further improved.
In some embodiments, the first production constraint includes any one of the following and combinations thereof: inventory constraints, out-of-stock demand constraints, overtime constraints, man-hour constraints, production time constraints, and yield constraints.
In some embodiments, the objective function includes any one of and a combination of the following: minimizing a weighted sum function of overtime and number of modulo changes, minimizing a weighted sum function of overtime and highest number of flushes, and minimizing a stock function.
In some embodiments, the scheduling model is a mixed integer programming model, and the solving the scheduling model to obtain the production line body and the product yield of the product to be produced includes: and solving the mixed integer programming model to obtain the production line body and the product yield of the product to be produced.
In some embodiments, after said solving the production planning model to obtain the production line body and the product yield of the product to be produced, the method further comprises: acquiring production line limit information of the product to be produced; obtaining a second production constraint condition of the product to be produced based on the production line limit information; and inputting the second production constraint condition, the production line body of the product to be produced and the product yield into a preset scheduling planning model to obtain the production sequence of the product to be produced on the production line body and the production time of the product to be produced.
In some embodiments, the scheduling planning model is a constraint planning model.
In some embodiments, the second production constraint includes any one of and a combination of the following: calendar constraint conditions of production line and production sequence constraint of products.
The application also provides a product scheduling device, including: the constraint acquisition module is used for acquiring production limit information of a product to be produced and acquiring a first production constraint condition of the product to be produced based on the production limit information; the function acquisition module is used for acquiring an objective function for guiding the production of the product to be produced; and the model solving module is used for establishing a scheduling planning model for guiding the product scheduling based on the objective function and the first production constraint condition, and solving the scheduling planning model to obtain the production line body and the product yield of the product to be produced.
The application also provides electronic equipment, which comprises a processor and a memory, wherein the memory is used for storing instructions, and the processor is used for calling the instructions in the memory, so that the electronic equipment executes the product scheduling method.
The present application also provides a computer readable storage medium storing computer instructions that, when executed on an electronic device, cause the electronic device to perform the product scheduling method described above.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings which are required in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of steps of a method for product scheduling according to an embodiment of the present application;
FIG. 2 is a flow chart illustrating steps of a method for scheduling products according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a scenario of product scheduling according to an embodiment of the present disclosure;
FIG. 4 is a schematic structural view of a product scheduling device according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an embodiment of the electronic device of the present application.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The product scheduling method can be applied to one or more electronic devices. The electronic device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and its hardware includes, but is not limited to, a processor, a micro-program controller (Microprogrammed Control Unit, MCU), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a programmable gate array (Field-Programmable Gate Array, FPGA), a digital processor (Digital Signal Processor, DSP), an embedded device, and the like. The electronic device may be a portable electronic device (e.g., a cell phone, tablet computer), a personal computer, a server, etc.
FIG. 1 is a flow chart illustrating steps of an embodiment of a method for product scheduling according to the present application. The order of the steps in the flow diagrams may be changed, and some steps may be omitted, according to different needs.
Referring to fig. 1, the product scheduling method may include the following steps.
Step 101, obtaining production limit information of a product to be produced.
The product to be produced may include a product to be produced by a factory and parts thereof, hereinafter, the product to be produced will be referred to simply as a product. The product may be a side wall, an inner plate, an outer plate, or other parts of a vehicle, or may be a product of other equipment, which is not limited in the embodiment of the present application.
The production limit information is constraint information of product production, and the production limit information may be set according to man-hour requirements of actual production of the product, safety stock, product yield, and the like, which is not limited in this embodiment.
In some embodiments, the electronic device may provide a visual interface, where the visual interface displays a safety stock quantity setting column, a maximum overtime setting column, a maximum production man-hour setting column, a product non-producible time period setting column, and the like, and the user may fill specific data in the visual interface to set production limitation information, and the electronic device receives, through the visual interface, the production limitation information of the product to be produced set by the user.
In other embodiments, the production limit information may be pre-stored in a memory and retrieved from a storage device.
Step 102, obtaining a first production constraint condition of a product to be produced based on the production constraint information.
In some embodiments, the first production constraint includes any one of the following and combinations thereof:
1. inventory constraints.
Figure SMS_1
，
wherein ,
Figure SMS_11
is a product->
Figure SMS_3
Is->
Figure SMS_8
For the planning date set->
Figure SMS_5
For planning date set->
Figure SMS_7
Element of (a)>
Figure SMS_14
For the line body set, +.>
Figure SMS_17
For line body set->
Figure SMS_9
Element of (a)>
Figure SMS_13
Is a product->
Figure SMS_2
On-line body->
Figure SMS_6
Date of upper program->
Figure SMS_12
Production lot quantity of>
Figure SMS_15
Is a product->
Figure SMS_16
On planning day +.>
Figure SMS_18
Demand of->
Figure SMS_4
Is a product->
Figure SMS_10
Is a safety stock of the (c).
2. And (5) a constraint condition of the ex-warehouse requirement.
Illustratively, the ex-warehouse demand constraints may include any one or a combination of the following, but are not limited thereto:
Figure SMS_19
；
Figure SMS_20
；
Figure SMS_21
；
wherein ,
Figure SMS_34
is a product->
Figure SMS_24
On the planning date->
Figure SMS_31
Real-time inventory of->
Figure SMS_35
For the line body set, +.>
Figure SMS_37
For line body set->
Figure SMS_38
Element of (a)>
Figure SMS_39
Is a product->
Figure SMS_33
On-line body->
Figure SMS_36
Date of upper program->
Figure SMS_22
Production lot quantity of>
Figure SMS_30
Is a product->
Figure SMS_25
On planning day +.>
Figure SMS_27
Demand of->
Figure SMS_28
Is a product->
Figure SMS_32
On the planning date->
Figure SMS_23
Real-time inventory of->
Figure SMS_26
Is a product->
Figure SMS_29
Is a safety stock of the (c).
3. Overtime constraints.
The overtime constraints may include any one of the following, and combinations thereof, to which the present embodiments are not limited:
Figure SMS_40
；
Figure SMS_41
；
wherein ,
Figure SMS_51
for the product set, < >>
Figure SMS_43
For the product set->
Figure SMS_48
Element of (a)>
Figure SMS_55
For the line body set, +.>
Figure SMS_58
For line body set->
Figure SMS_56
Element of (a)>
Figure SMS_57
Is a product->
Figure SMS_50
On-line body->
Figure SMS_54
Date of upper program->
Figure SMS_42
Production lot quantity of>
Figure SMS_46
Is a product->
Figure SMS_45
Is>
Figure SMS_52
For planning date->
Figure SMS_49
Is added with the time of the overtime of->
Figure SMS_53
Is constant with WH->
Figure SMS_44
For the planned date set->
Figure SMS_47
The maximum overtime period, e.g., can be set to 2, the WH is the total period of the shift set, e.g., 8 hours on the shift, lateThe WH may be set to 16 for 8 hours in class.
4. Man-hour constraints. Man-hour constraints include any one of the following and combinations thereof:
Figure SMS_59
；
Figure SMS_60
；
wherein ,
Figure SMS_62
for the line body set, +.>
Figure SMS_67
For line body set->
Figure SMS_70
Element of (a)>
Figure SMS_64
Is a product->
Figure SMS_66
On-line body->
Figure SMS_68
Date of upper program->
Figure SMS_71
Production lot quantity of>
Figure SMS_61
Is a product->
Figure SMS_65
M is a preset integer, and the value of M can be set according to the requirement,/->
Figure SMS_69
For class collection, add->
Figure SMS_72
For elements in class set GThe element is a polypeptide which is a polypeptide,
Figure SMS_63
，
Figure SMS_73
；
Figure SMS_74
characterization of the product->
Figure SMS_75
On the planning date->
Figure SMS_76
At most, it is produced only once on one wire.
5. Production time constraints. The production constraints may characterize the time that the production line body is not available, the time that the production mold is not available, such as mold repair time, mold change wait time, etc.
For example, the production time constraints may include any one or combination of the following:
Figure SMS_77
；
Figure SMS_78
；
wherein ,
Figure SMS_79
；
Figure SMS_81
for the product set, < >>
Figure SMS_86
For the product set->
Figure SMS_90
Element of (a)>
Figure SMS_82
Is a product->
Figure SMS_83
Unit production time, & gt>
Figure SMS_87
Is a product->
Figure SMS_89
On-line body->
Figure SMS_80
Date of upper program->
Figure SMS_84
Production lot quantity of>
Figure SMS_88
Is a line body->
Figure SMS_91
In planning period->
Figure SMS_85
And M is a preset integer, and the value of M can be set according to the requirement.
6. Yield constraints.
For example, the yield constraints may include any one of and a combination of the following:
Figure SMS_92
；
Figure SMS_93
；
Figure SMS_94
，/>
Figure SMS_95
，/>
Figure SMS_96
；
Figure SMS_97
，/>
Figure SMS_98
，/>
Figure SMS_99
；
Figure SMS_100
；
Figure SMS_101
；
Figure SMS_102
；
wherein ,
Figure SMS_117
is a product->
Figure SMS_121
Minimum lot size, +.>
Figure SMS_122
Is a product->
Figure SMS_104
Maximum batch size, +.>
Figure SMS_108
For the planning date set->
Figure SMS_112
For planning date set->
Figure SMS_115
Element of (a)>
Figure SMS_116
For the line body set, +.>
Figure SMS_119
For line body set->
Figure SMS_123
In the presence of an element of the group,
Figure SMS_124
is a product->
Figure SMS_118
On-line body->
Figure SMS_125
Date of upper program->
Figure SMS_126
Production lot quantity of>
Figure SMS_127
Is a preset positive integer value, +.>
Figure SMS_106
Is a product->
Figure SMS_111
Is>
Figure SMS_113
、/>
Figure SMS_120
、/>
Figure SMS_103
All of which represent specific parts, wherein,
Figure SMS_107
is a side wall>
Figure SMS_110
Is an inner plate>
Figure SMS_114
Is an outer plate>
Figure SMS_105
Is part->
Figure SMS_109
Is a material yield of the (c).
7. Other constraints. Other constraints may include the following and any combination thereof:
if it is
Figure SMS_128
=1, then->
Figure SMS_129
；
If it is
Figure SMS_130
=0, then->
Figure SMS_131
=0；
If it is
Figure SMS_132
Then->
Figure SMS_133
=1；
If it is
Figure SMS_134
=0, then->
Figure SMS_135
=0；
wherein ,
Figure SMS_136
；/>
Figure SMS_137
is a product->
Figure SMS_138
On-line body->
Figure SMS_139
Date of upper program->
Figure SMS_140
Production lot quantity of>
Figure SMS_141
Is a product->
Figure SMS_142
Is a product of the production per hour.
In the first production constraint condition described above,
Figure SMS_143
，/>
Figure SMS_144
is a positive natural number set; />
Figure SMS_145
，/>
Figure SMS_146
Is a positive real number set; />
Figure SMS_147
。
In addition to the above-described constraints, the first production constraint may include constraints of production materials, for example, constraints of production material demand by products, constraints of trolley collection capacity, constraints of trolley demand, and the like, but is not limited thereto.
In step 102, the electronic device may generate production constraints based on the production constraint information if the production constraint information is received from the user interface, e.g., assign the production constraint information to corresponding variables in the production constraints, so as to facilitate subsequent establishment of product scheduling.
Step 103, obtaining an objective function for guiding production of the product to be produced.
In some embodiments, the objective function includes any one of and a combination of the following: minimizing a weighted sum function of overtime and number of modulo changes, minimizing a weighted sum function of overtime and highest number of flushes, and minimizing a stock function.
The number of mold exchanges is the number of times the mold used to produce the product is exchanged.
When the mold is pressed down once, a product can be generated, the stroke frequency refers to the pressing down of the mold, one mold correspondingly generates a product, and the highest stroke frequency refers to the total quantity of products.
Minimizing the stock function refers to a function that makes the stock of the product as small as possible while ensuring that the stock of the product is not less than the safe stock.
Illustratively, the weighted sum function that minimizes the overtime versus the number of modulo changes may be as follows:
Figure SMS_148
，
wherein ,
Figure SMS_151
weight for preset overtime, +.>
Figure SMS_154
For the planning date set->
Figure SMS_156
For planning date set->
Figure SMS_150
Element of (a)>
Figure SMS_159
For planning date->
Figure SMS_161
Is added with the time of the overtime of->
Figure SMS_162
Weight for preset number of mode changes, +.>
Figure SMS_152
For the product set, < >>
Figure SMS_155
For the product set->
Figure SMS_158
Element of (a)>
Figure SMS_160
For the line body set, +.>
Figure SMS_149
For line body set->
Figure SMS_153
Element of (a)>
Figure SMS_157
The values of (2) are as follows:
Figure SMS_163
。
the weighted sum function of the minimum overtime and the maximum number of flushes can be as follows:
Figure SMS_164
，
wherein ,
Figure SMS_172
weight of preset overtime, +.>
Figure SMS_167
For the planning date set->
Figure SMS_170
For planning date set->
Figure SMS_168
In the presence of an element of the group,
Figure SMS_171
for planning date->
Figure SMS_175
Is added with the time of the overtime of->
Figure SMS_180
Weight of the preset highest number of times of the stroke, +.>
Figure SMS_174
For the product set, < >>
Figure SMS_181
For the product set->
Figure SMS_165
Element of (a)>
Figure SMS_169
For the line body set, +.>
Figure SMS_176
For line body set->
Figure SMS_179
Element of (a)>
Figure SMS_177
Is a product->
Figure SMS_178
On-line body->
Figure SMS_166
Date of upper program->
Figure SMS_173
Is a production lot of (a) a lot.
And 104, establishing a scheduling planning model for guiding product scheduling based on the objective function and the first production constraint condition.
The scheduling planning model may be a mixed integer planning model. Compared with a heuristic algorithm, the scheduling method based on the mixed integer programming model is short in time consumption, and therefore the scheduling efficiency of product scheduling can be improved.
A mixed integer programming model (Mixed Integer Programming, MIP) for directing production of a product includes a first production constraint in step 102 and an objective function in step 103.
And 105, solving the scheduling planning model to obtain the production line body and the product yield of the product to be produced.
For example, where the scheduling planning model is a mixed integer planning model, the mixed integer planning model is solved in order to minimize the objective function while making some or all of the variables integers under the first production constraint.
In the scheduling planning of products, decision variable values such as the number of the products are meaningful when the values are integers, so that the products can be more matched with the scheduling requirements of the products by solving through a mixed integer planning model, and the scheduling results of the products are more accurate.
In step 105, the mixed integer programming model may be solved by a solver such as Branch and Bound (Branch and Bound), cutting Planes (Cutting Planes), lagrangian relaxation, etc., to obtain the production results for each product.
The production results of each product include the production line body and the product yield for the product to be produced. Namely, the production scheduling result can represent the corresponding relation among the products, the product yield and the production line body. For example, the product is produced on the line body 1, and the product yield of the product 1 is 100 pieces.
In some embodiments, after obtaining the production line body and the product yield of the product to be produced, referring to fig. 2, the product scheduling method may further include the steps of:
step 201, obtaining production line limitation information of a product to be produced.
The production line limitation information is constraint information of production of the product on the production line, and the production line limitation information can be set according to a production line calendar, production sequences of different products on the production line, and the like, which is not limited in this embodiment.
Step 202, obtaining a second production constraint condition of the product to be produced based on the production line limitation information.
In some embodiments, the second production constraint includes any one of and a combination of the following: calendar constraint conditions of production line and production sequence constraint of products. Product production sequence constraints may include: some products have a specific production sequence, some products cannot be continuously produced on a production line, some products require the last production, but are not limited thereto.
The second production constraint may be as follows:
1. product ordering value field.
Figure SMS_182
，/>
Figure SMS_183
，
wherein ,
Figure SMS_184
for the number of production sequences of product i +.>
Figure SMS_185
For the product set, < >>
Figure SMS_186
For the product set->
Figure SMS_187
In the presence of an element of the group,
Figure SMS_188
characterization of the product set->
Figure SMS_189
The number of elements in the matrix.
2. Production order of product level.
Figure SMS_190
；
Wherein the product is
Figure SMS_192
Product->
Figure SMS_196
Product->
Figure SMS_198
Product->
Figure SMS_193
The production sequence on the line body is +.>
Figure SMS_194
、/>
Figure SMS_197
、/>
Figure SMS_200
and
Figure SMS_191
Label characterizes the product set of the product class, +.>
Figure SMS_195
The product grade is ordered as +.>
Figure SMS_199
That is, the higher the product level, the earlier the ordering of product production, and the smaller the production order number.
3. Product i and product j are produced discontinuously.
Figure SMS_201
And->
Figure SMS_202
；
wherein ,
Figure SMS_203
for the product set, < >>
Figure SMS_204
and />
Figure SMS_205
All are product sets->
Figure SMS_206
Element of (a)>
Figure SMS_207
For the production sequence of product j on line, < >>
Figure SMS_208
Is a product->
Figure SMS_209
Production sequence on the line body.
For example, the inner and outer plates cannot be continuously produced
Figure SMS_210
s，outerBoards/>
Figure SMS_211
。
4. The product is produced at the last.
Figure SMS_212
；
wherein ,
Figure SMS_213
for the product set, < >>
Figure SMS_214
Characterization of the product set->
Figure SMS_215
The number of elements in->
Figure SMS_216
Is a product->
Figure SMS_217
Production sequence on the line body.
5. The product is produced on the Y-th production line.
Figure SMS_218
；
wherein ,
Figure SMS_219
is a product->
Figure SMS_220
Production sequence on production line.
For example, products
Figure SMS_221
For the inner panel widget, the fourth or 5 th production of the inner panel widget on the production line, the second production constraint includes:
Figure SMS_222
；
wherein ,
Figure SMS_223
is a collection of inner plate widgets.
And 203, inputting the second production constraint condition, the production line body of the product to be produced and the product yield into a preset scheduling planning model to obtain the production sequence of the product to be produced on the production line body and the production time of the product to be produced.
And obtaining a feasible solution meeting the second production constraint condition based on the scheduling planning model, thereby obtaining a scheduling result, namely the production sequence of the products to be produced on the production line body and the production time of the products to be produced.
In some embodiments, the scheduling planning model may be a constraint planning model (Constraint programming, CP). The constraint planning model has the advantage of solving the problem of large-scale product scheduling, and can quickly find a feasible solution, thereby improving the scheduling efficiency.
The constraint planning model may employ an advanced planning and scheduling (Advanced Planning and Scheduling, APS) model that may derive scheduling results by constraint propagation, constraint solving, and the like.
The steps 201 to 203 may implement automatic scheduling of the product to be produced.
For example, referring to fig. 3, fig. 3 is a schematic view of a scenario of product scheduling, and in the schematic view of fig. 3, a corresponding model may be automatically established based on product production requirements, so as to automatically perform scheduling and scheduling. For example, determining an objective function and a first constraint condition of the scheduling based on the production demand of the product, thereby obtaining a scheduling planning model; solving a scheduling planning model to obtain a scheduling result, namely a production cycle plan, wherein the scheduling result comprises the corresponding relation of products, product yield and a production line body; then, scheduling is performed based on the production cycle plan. For example, determining a second production constraint based on the process schedule and the associated constraints of the fine schedule; and obtaining a scheduling result based on the scheduling planning model and the second production constraint condition, wherein the scheduling result refers to the production sequence of each product to be produced on the production line body and the planned production time of the product to be produced.
According to the embodiment of the application, the mixed integer model and the scheduling planning model are established, and scheduling planning can be automatically and reasonably performed under the condition of various production limits, so that the reliability of product scheduling is improved, and the production efficiency is further improved.
Based on the same ideas of the product scheduling method in the above embodiments, the present application also provides a product scheduling apparatus that can be used to perform the above product scheduling method. For ease of illustration, only those portions of the product manufacturing apparatus embodiments are shown in the schematic structural drawings that relate to the embodiments of the present application, and those skilled in the art will appreciate that the illustrated structures are not limiting of the apparatus and may include more or fewer components than illustrated, or may combine certain components, or a different arrangement of components.
As shown in fig. 4, the product scheduling apparatus includes a constraint acquisition module 401, a function acquisition module 402, and a model solving module 403. In some embodiments, the modules described above may be programmable software instructions stored in memory and executable by a processor call. It will be appreciated that in other embodiments, the modules may be program instructions or firmware (firmware) that are resident in the processor.
A constraint obtaining module 401, configured to obtain production constraint information of a product to be produced, and obtain a first production constraint condition of the product to be produced based on the production constraint information;
a function obtaining module 402, configured to obtain an objective function for guiding production of the product to be produced;
the model solving module 403 is configured to establish a scheduling planning model for guiding product scheduling based on the objective function and the first production constraint condition, and solve the scheduling planning model to obtain a production line body and a product yield of the product to be produced.
In some embodiments, the first production constraint in constraint acquisition module 401 comprises any one of the following and combinations thereof: inventory constraints, out-of-stock demand constraints, overtime constraints, man-hour constraints, production time constraints, and yield constraints.
In some embodiments, the objective function in the function acquisition module 402 includes any one of and a combination of the following: minimizing a weighted sum function of overtime and number of modulo changes, minimizing a weighted sum function of overtime and highest number of flushes, and minimizing a stock function.
In some embodiments, the scheduling model in the model solving module 403 is a mixed integer programming model, and the solving the scheduling model to obtain the production line body and the product yield of the product to be produced includes: and solving the mixed integer programming model to obtain the production line body and the product yield of the product to be produced.
In some embodiments, the model solving module 403 is further configured to obtain line limit information of the product to be produced after the solving the scheduling planning model to obtain a line body of the product to be produced and a product yield; obtaining a second production constraint condition of the product to be produced based on the production line limit information; and inputting the second production constraint condition, the production line body of the product to be produced and the product yield into a preset scheduling planning model to obtain the production sequence of the product to be produced on the production line body and the production time of the product to be produced.
In some embodiments, the scheduling planning model in model solving module 403 is a constraint planning model.
In some embodiments, the second production constraint in model solving module 403 comprises any one of and a combination of the following: calendar constraint conditions of production line and production sequence constraint of products.
Fig. 5 is a schematic diagram of an embodiment of an electronic device of the present application.
The electronic device 100 comprises a memory 20, a processor 30 and a computer program 40 stored in the memory 20 and executable on the processor 30. The steps of the above-described embodiments of the product scheduling method, such as steps 101 through 105 shown in fig. 1, are implemented by the processor 30 when executing the computer program 40.
By way of example, the computer program 40 may likewise be partitioned into one or more modules/units that are stored in the memory 20 and executed by the processor 30. The one or more modules/units may be a series of computer program instruction segments capable of performing particular functions for describing the execution of the computer program 40 in the electronic device 100. For example, it may be divided into a constraint acquisition module 401, a function acquisition module 402, and a model solving module 403 shown in fig. 4.
It will be appreciated by those skilled in the art that the schematic diagram is merely an example of the electronic device 100 and is not meant to be limiting of the electronic device 100, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the electronic device 100 may also include input-output devices, network access devices, buses, etc.
The processor 30 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor, a single-chip microcomputer or the processor 30 may be any conventional processor or the like.
The memory 20 may be used to store computer programs 40 and/or modules/units, and the processor 30 implements various functions of the electronic device 100 by running or executing the computer programs and/or modules/units stored in the memory 20, as well as invoking data stored in the memory 20. The memory 20 may mainly include a storage program area that may store an operating system, application programs required for at least one function (such as a sound playing function, an image playing function, etc.), and a storage data area; the storage data area may store data (such as audio data) created according to the use of the electronic device 100, and the like. In addition, the memory 20 may include high-speed random access memory, and may also include nonvolatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other nonvolatile solid state storage device.
The modules/units integrated with the electronic device 100 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each method embodiment described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
In addition, the product scheduling method, device, electronic equipment and computer readable storage medium provided by the embodiments of the present invention are described in detail, and specific examples are adopted to illustrate the principles and embodiments of the present invention, and the description of the above embodiments is only used to help understand the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.

Claims (10)

1. A method of product scheduling, the method comprising:
obtaining production limit information of a product to be produced;
obtaining a first production constraint condition of the product to be produced based on the production constraint information;
acquiring an objective function for guiding the production of the product to be produced;
establishing a scheduling planning model for guiding product scheduling based on the objective function and the first production constraint condition;
and solving the scheduling planning model to obtain the production line body and the product yield of the product to be produced.
2. The product scheduling method of claim 1, wherein the first production constraint comprises any one of and a combination of the following: inventory constraints, out-of-stock demand constraints, overtime constraints, man-hour constraints, production time constraints, and yield constraints.
3. The product scheduling method of claim 1, wherein the objective function comprises any one of and a combination of the following:
minimizing a weighted sum function of overtime and number of modulo changes, minimizing a weighted sum function of overtime and highest number of flushes, and minimizing a stock function.
4. The method of product scheduling according to claim 1, wherein the scheduling model is a mixed integer scheduling model, and the solving the scheduling model to obtain the production line body and the product yield of the product to be produced comprises:
and solving the mixed integer programming model to obtain the production line body and the product yield of the product to be produced.
5. The product scheduling method according to any one of claims 1 to 4, further comprising, after said solving said scheduling model to obtain a line body and a product yield of said product to be produced:
acquiring production line limit information of the product to be produced;
obtaining a second production constraint condition of the product to be produced based on the production line limit information;
and inputting the second production constraint condition, the production line body of the product to be produced and the product yield into a preset scheduling planning model to obtain the production sequence of the product to be produced on the production line body and the production time of the product to be produced.
6. The product scheduling method of claim 5, wherein the scheduling model is a constraint planning model.
7. The product scheduling method of claim 5, wherein the second production constraint comprises any one of and a combination of the following:
calendar constraint conditions of production line and production sequence constraint of products.
8. A product scheduling apparatus, the apparatus comprising:
the constraint acquisition module is used for acquiring production limit information of a product to be produced and acquiring a first production constraint condition of the product to be produced based on the production limit information;
the function acquisition module is used for acquiring an objective function for guiding the production of the product to be produced;
and the model solving module is used for establishing a scheduling planning model for guiding the product scheduling based on the objective function and the first production constraint condition, and solving the scheduling planning model to obtain the production line body and the product yield of the product to be produced.
9. An electronic device comprising a processor and a memory, wherein the memory is configured to store instructions, the processor configured to invoke the instructions in the memory, to cause the electronic device to perform the product scheduling method of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions which, when run on an electronic device, cause the electronic device to perform the product scheduling method of any one of claims 1 to 7.
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