CN114265380A - High-end equipment manufacturing robustness scheduling method and device based on VNS - Google Patents

High-end equipment manufacturing robustness scheduling method and device based on VNS Download PDF

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CN114265380A
CN114265380A CN202210195558.4A CN202210195558A CN114265380A CN 114265380 A CN114265380 A CN 114265380A CN 202210195558 A CN202210195558 A CN 202210195558A CN 114265380 A CN114265380 A CN 114265380A
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workpieces
workpiece
batch
processing
workshop
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CN114265380B (en
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陆少军
马崇轺
刘心报
郑锐
江涛
赵婷
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Hefei University of Technology
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Abstract

The invention provides a VNS-based high-end equipment manufacturing robustness scheduling method and device, and relates to the technical field of high-end equipment manufacturing. The invention designs an intelligent optimization algorithm for improving variable neighborhood search aiming at the problem of dual-target hierarchical scheduling of complex products related to deterioration effect and uncertain processing time in the manufacture of high-end equipment.

Description

High-end equipment manufacturing robustness scheduling method and device based on VNS
Technical Field
The invention relates to the technical field of high-end equipment manufacturing, in particular to a VNS-based high-end equipment manufacturing robustness scheduling method and device.
Background
In a high-end device manufacturing process, a large number of intermediate products are produced at a certain stage, and it is difficult for one factory to meet these requirements, so that there are a plurality of factories to produce the desired intermediate products. Meanwhile, a manufacturer often considers a plurality of targets in the production process of a product, and then needs to design a production distribution plan to ensure that each factory can complete the plan within a specified time and each target is as optimal as possible.
The Variable Neighborhood Search algorithm (VNS) is a widely used meta-heuristic algorithm for solving such problems, and was first proposed by Mladenovi ć, n., & Hansen, P. (1997), which diversifies the Search direction by changing the Neighborhood structure, thereby enhancing the Search capability and optimizing the computational performance. The basic idea of the variable neighborhood search algorithm is a process of systematically changing a neighborhood structure set to expand a search range in a search process to obtain a local optimal solution, and then systematically changing the neighborhood structure set to expand the search range to find another local optimal solution based on the local optimal solution.
However, conventional VNS can only solve the single-target optimization problem, and the conventional VNS neighborhood structure is mainly random switching. Therefore, the search efficiency of the conventional VNS is relatively low, and an effective solution cannot be quickly provided for the considered multi-objective optimization problem.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a VNS-based high-end equipment manufacturing robustness scheduling method and device, and solves the problem that the existing scheduling method is poor in effect in practical application.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
in a first aspect, a VNS-based high-end equipment manufacturing robustness scheduling method is provided, where the method includes:
s1, acquiring workpiece information, workshop information, algorithm parameters and a neighborhood structure;
s2, randomly generating an initial distribution result containing the distribution relation between each workpiece and each workshop;
s3, determining the processing sequence and the batch of the workpieces of each workshop in the initial distribution result through a processing sequence and batch generation algorithm to obtain an initial dispatching scheme; wherein, the scheduling scheme comprises: the order and lot of work pieces for each plant;
s4, updating the initial scheduling scheme by a variable neighborhood search algorithm with the maximum and minimum completion probability as a target to obtain the current optimal scheduling scheme;
and S5, optimizing the current optimal dispatching scheme by using the processing sequence and batch improvement algorithm with the aim of minimizing the sum of the inventory costs of all workshops to obtain the optimal dispatching scheme.
Further, the workpiece information includes: number of workpieces, completion due time, basic machining time for each workpiece;
the workshop information includes: number of plants, batch capacity, degradation factor of processing, degradation factor of loading.
Further, the steps of the process sequence and lot generation algorithm include:
s31, distributing the data to the workshop
Figure 100002_DEST_PATH_IMAGE002
The workpieces are arranged in a non-decreasing sequence according to the average value of the basic processing time to obtain the processing sequence of the workpieces;
s32, if the distribution is to the workshop
Figure 20140DEST_PATH_IMAGE002
Number of workpieces
Figure 100002_DEST_PATH_IMAGE004
Less than batch capacitycThen all workpieces are placed in a first batch; otherwise, it will
Figure 100002_DEST_PATH_IMAGE006
Placing a workpiece in a first batch, and placing the remaining workpieces in a batch sizecSequentially distributing the raw materials in a plurality of batches to obtain a workshop
Figure 205919DEST_PATH_IMAGE002
The order and batch of processing of the workpieces;
and S33, repeatedly executing S31-S32 until the processing sequence and the batches corresponding to all workshops are obtained.
Further, the obtaining formula of the minimum completion probability of the scheduling scheme is as follows:
Figure 100002_DEST_PATH_IMAGE008
Figure 100002_DEST_PATH_IMAGE010
Figure 100002_DEST_PATH_IMAGE012
Figure 100002_DEST_PATH_IMAGE014
wherein the content of the first and second substances,
Figure 100002_DEST_PATH_IMAGE016
indicating the result of the dispensing of the workpiece, whereinjResult of distribution of individual workpiecesv j The value of (d) indicates the assigned plant;
Figure 100002_DEST_PATH_IMAGE018
representing a minimum completion probability of the scheduling scheme;
Figure 100002_DEST_PATH_IMAGE020
representing a plant
Figure 176018DEST_PATH_IMAGE002
At the expiration date
Figure 100002_DEST_PATH_IMAGE022
A previous completion probability;
Figure 100002_DEST_PATH_IMAGE024
representing a standard normal distribution probability function;
Figure 100002_DEST_PATH_IMAGE026
and
Figure 100002_DEST_PATH_IMAGE028
respectively representing workshops during processing of workpieces in non-decreasing sequence of mean values of basic processing times
Figure 124775DEST_PATH_IMAGE002
Mean and variance of completion times;
Figure 100002_DEST_PATH_IMAGE030
presentation to plant
Figure 894148DEST_PATH_IMAGE002
To (1) arAn average value of basic processing times of the respective processed workpieces, which are subject to normal distribution;
Figure 100002_DEST_PATH_IMAGE032
a variance representing a base processing time that follows a normal distribution;
Figure 100002_DEST_PATH_IMAGE034
a deterioration coefficient indicating the processing of the workpiece;
Figure 100002_DEST_PATH_IMAGE036
a deterioration coefficient indicating a loading lot;
cindicating the batch capacity.
Further, the variable neighborhood searching algorithm includes:
s41, making the current iteration numbert=0, current neighborhood structurek=1；
S42, ordert=t+1, and initializing the current number of searchestime=0；
S43, ordertime=time+1 in the second placekSearching a new scheduling scheme by the variable neighborhood, and acquiring the minimum completion probability of the new scheduling scheme;
s44, if the minimum completion probability of the new scheduling scheme is smaller than the initial scheduling scheme, executing S45; otherwise, it ordersk=1, and the new scheduling scheme is taken as an initial scheduling scheme, and S46 is performed;
s45, orderk=k+1, if
Figure 100002_DEST_PATH_IMAGE038
If not, executing S46; otherwise, it ordersk=1, then perform S46;
s46, iftimeIf the number of times of searching is larger than that of each iteration, S47 is executed; otherwise, go to S43;
s47, iftIf the number of iterations is larger than the maximum number of iterations, taking the initial scheduling scheme as the current optimal scheduling scheme, and acquiring the minimum completion probability of the initial scheduling scheme; otherwise, S42 is executed.
Further, the neighborhood structure includes:
the first neighborhood structure: randomly selecting a workpiece from the workshops with the lowest completion probability and randomly distributing the workpiece to other workshops;
the second neighborhood structure: exchanging a workpiece randomly selected from the workshop with the lowest finishing probability with a workpiece with a smaller average basic processing time randomly selected from other workshops;
the third neighborhood structure: randomly reverses a portion of the scheme encoding now.
Further, the processing order and batch improvement algorithm comprises:
s51, obtaining the maximum improved searching timesitimesInter-vehicle in current optimal scheduling scheme
Figure 241822DEST_PATH_IMAGE002
Is completed with probability of completion
Figure 133555DEST_PATH_IMAGE020
And inventory cost
Figure 100002_DEST_PATH_IMAGE040
Make the current number of improved searchesitime=0, acquisition plant
Figure 458357DEST_PATH_IMAGE002
The difference between the average values of the basic processing times of the respective adjacent workpieces in the processing order of (1) is obtained as a setD(ii) a Order tox=2，y=1, placing the first workpiece in the first virtual group;
Figure 100002_DEST_PATH_IMAGE042
Figure 100002_DEST_PATH_IMAGE044
wherein the content of the first and second substances,
Figure 215441DEST_PATH_IMAGE030
presentation to plant
Figure 761829DEST_PATH_IMAGE002
To (1) arAn average value of basic processing times of the respective processed workpieces, which are subject to normal distribution;
s52, if
Figure 100002_DEST_PATH_IMAGE046
Then will bexA workpiece is put inyA virtual group; otherwise, it will bexA workpiece is put iny+1 virtual groups, ordery=y+1；
Wherein:
Figure 100002_DEST_PATH_IMAGE048
Figure 100002_DEST_PATH_IMAGE050
Figure 886780DEST_PATH_IMAGE026
and
Figure 761195DEST_PATH_IMAGE028
respectively representing workshops during processing of workpieces in non-decreasing sequence of mean values of basic processing times
Figure 590480DEST_PATH_IMAGE002
Mean and variance of completion times;
Figure 866740DEST_PATH_IMAGE034
a deterioration coefficient indicating the processing of the workpiece;
Figure 241221DEST_PATH_IMAGE036
a deterioration coefficient indicating a loading lot;
s53, ifxSmaller than the workshop
Figure 602932DEST_PATH_IMAGE002
Number of workpieces
Figure 845695DEST_PATH_IMAGE004
Then, thenx=x+1 and return to S52; otherwise, jumping to S54;
s54, ifySmaller than the workshop
Figure 104025DEST_PATH_IMAGE002
Number of workpieces
Figure 774041DEST_PATH_IMAGE004
Randomly reversing the partial machining pass of the workpiece in each virtual setThen forming a new workshop workpiece processing sequence according to the sequence of the virtual groups,itime=itime+1, go to S55; otherwise, jumping to S58;
s55, if the distribution is to the workshop
Figure 623048DEST_PATH_IMAGE002
Number of workpieces
Figure 279289DEST_PATH_IMAGE004
Less than batch capacitycThen all workpieces are placed in a first batch; otherwise, it will
Figure 795721DEST_PATH_IMAGE006
Placing a workpiece in a first batch, and placing the remaining workpieces in a batch sizecSequentially distributing the raw materials in a plurality of batches to obtain an updated workshop
Figure 495692DEST_PATH_IMAGE002
The order and batch of processing of the workpieces;
s56 plant based on updating
Figure 97575DEST_PATH_IMAGE002
The processing order and the batch of the workpieces are obtained and updated
Figure 557506DEST_PATH_IMAGE002
Is/are as follows
Figure 100002_DEST_PATH_IMAGE052
、
Figure 100002_DEST_PATH_IMAGE054
Completion probability
Figure 100002_DEST_PATH_IMAGE056
And inventory cost
Figure 100002_DEST_PATH_IMAGE058
；
S57, if
Figure 100002_DEST_PATH_IMAGE060
And is
Figure 100002_DEST_PATH_IMAGE062
Then the updated workshop
Figure 505608DEST_PATH_IMAGE002
The processing sequence and the batch of the workpieces are used as the current optimal scheduling scheme; otherwise, jumping to S58;
s58, ifitime＜itimesThen return to S54; otherwise, outputting the current optimal scheduling scheme.
Further, the plant
Figure 392793DEST_PATH_IMAGE002
Inventory cost of
Figure 950813DEST_PATH_IMAGE040
The calculation formula of (2) is as follows:
Figure 100002_DEST_PATH_IMAGE064
Figure 100002_DEST_PATH_IMAGE066
Figure 100002_DEST_PATH_IMAGE068
wherein the content of the first and second substances,
Figure 100002_DEST_PATH_IMAGE070
indicating allocation in a plant
Figure 788670DEST_PATH_IMAGE002
First, therProcessing the workpiece;
Figure 100002_DEST_PATH_IMAGE072
representing workpieces
Figure 669907DEST_PATH_IMAGE070
(ii) inventory cost;
Figure 100002_DEST_PATH_IMAGE074
representing the inventory cost per unit time;
Figure 259151DEST_PATH_IMAGE026
indicating that the work is being machined in non-decreasing order of mean value of basic machining time
Figure 835626DEST_PATH_IMAGE002
An average of completion times;
Figure 100002_DEST_PATH_IMAGE076
representing workpieces
Figure 152207DEST_PATH_IMAGE070
Average of the actual completion times of (a);
Figure 100002_DEST_PATH_IMAGE078
representing workpieces
Figure 638683DEST_PATH_IMAGE070
Actual completion time of (d);
Figure 100002_DEST_PATH_IMAGE080
=1 denotes the secondiA workshopgA processed workpiece andg+1 workpieces not in the same batch, otherwise
Figure 916605DEST_PATH_IMAGE080
=0。
Further, the randomly reversing the partial processing order of the workpieces in each virtual group comprises:
step 1: initializationq=1；
Step 2: if it is firstqIf the number of the workpieces of each virtual group is 1, jumping to the step 4; otherwise, jumping to the step 3;
and step 3: randomly selecting a plurality of workpieces which are continuously machined from the virtual group, and reversing the machining sequence of the workpieces;
and 4, step 4: output the firstqOrder of processing of each virtual groupq=q+1；
And 5: and repeating the step 2 to the step 5 until all the virtual groups are traversed.
In a second aspect, a VNS-based high-end equipment manufacturing robustness scheduling apparatus is provided, the apparatus comprising:
the parameter acquisition module is used for acquiring workpiece information, workshop information, algorithm parameters and a neighborhood structure;
the initial distribution result generation module is used for randomly generating an initial distribution result containing the distribution relation between each workpiece and the workshop;
the initial dispatching scheme generation module is used for determining the processing sequence and the batch of the workpieces of each workshop in the initial distribution result through a processing sequence and batch generation algorithm to obtain an initial dispatching scheme; wherein, the scheduling scheme comprises: the order and lot of work pieces for each plant;
the current optimal scheduling scheme generating module is used for updating the initial scheduling scheme by using the maximized minimum completion probability as a target through a variable neighborhood search algorithm to obtain a current optimal scheduling scheme;
and the processing sequence and batch improvement module is used for optimizing the current optimal dispatching scheme by using a processing sequence and batch improvement algorithm with the aim of minimizing the sum of the inventory costs of all workshops as a target to obtain the optimal dispatching scheme.
In a third aspect, a computer readable storage medium storing a computer program for high-end equipment manufacturing scheduling, wherein the computer program causes a computer to perform the VNS-based high-end equipment manufacturing robust scheduling method described above.
In a fourth aspect, an electronic device is provided, comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing the VNS-based high-end equipment manufacturing robustness scheduling method described above.
(III) advantageous effects
The invention provides a VNS-based high-end equipment manufacturing robustness scheduling method and device. Compared with the prior art, the method has the following beneficial effects:
1) the invention designs an intelligent optimization algorithm for improving variable neighborhood search aiming at the problem of dual-target hierarchical scheduling of complex products related to deterioration effect and uncertain processing time in the manufacture of high-end equipment.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a schematic illustration of the allocation of workpieces and workshops, and workpiece processing sequencing and lot determination in accordance with an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. 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 invention.
The embodiment of the application solves the problem that the existing scheduling method is poor in effect in practical application by providing the VNS-based high-end equipment manufacturing robustness scheduling method and device.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
in the high-end equipment manufacturing process, since uncertainty is an inevitable factor in the actual high-end equipment production planning scheduling environment: the pre-established production plan may face multiple disturbances during the implementation process, and the system parameters may change in real time, so that an accurate value of the job processing time may not be obtained, and the job processing time may be randomly distributed within an interval. There is also a deteriorating effect in the manufacturing process, and the actual machining time of the workpiece is prolonged due to aging or temperature rise of machine parts.
In terms of research problems, the traditional scheduling algorithm is based on a specific production situation, and the existence of deterioration effect in production and processing is not always considered; meanwhile, due to the limitation and restriction of various conditions, in actual production, the processing time of the operation cannot obtain a determined value, but obeys a certain probability distribution, so that the completion time of the production plan is also an uncertain value, and for a supply chain manager, the probability of completing production within a certain period is considered to be more practical than the probability of considering the completion time in the traditional scheduling algorithm; in actual production, some decisions of an enterprise are often not determined by one factor, and a traditional scheduling algorithm only considering a single target has large limitation, and a scheme which is not practical is likely to be generated in practical application. Meanwhile, in the actual manufacturing process of high-end equipment, the worsening effect of processing, uncertainty of processing time and comprehensive decision of a plurality of targets are often involved, and a traditional production scheduling method based on a certain situation may not be suitable or a scheme deviating from the actual requirement may be obtained because proper parameter values cannot be obtained.
In research methods, the NP-hard problem is generally solved by adopting a heuristic algorithm, wherein the variable neighborhood search algorithm is a meta-heuristic algorithm which is widely applied to solving the production scheduling problem. The variable neighborhood search is characterized in that a neighborhood structure formed by different actions is used for alternative search, and the concentration and the dispersion are well balanced. Meanwhile, the performance of the variable neighborhood search algorithm is also influenced by neighborhood definition and search strategies, more factors can be considered in a complex production situation, the neighborhood definition or the search strategies in the previous research are not applicable, or the algorithm needs to consume more time and cannot obtain a satisfactory solution in a limited time. Therefore, for the production scheduling of complex products, it is necessary to design a good neighborhood definition and search strategy.
Therefore, the invention aims to consider the problem of dual-target hierarchical scheduling of complex products of deterioration effect (the deterioration effect considered here is based on the starting time of a job, namely the actual processing time of the job is a function related to the starting time of the job) and uncertain processing time, and provides an intelligent algorithm combining a variable neighborhood search algorithm and a heuristic algorithm according to the mode characteristics of the production and processing of high-end equipment, so that a proper neighborhood is defined and three neighborhood structures are designed in a targeted manner, the algorithm can make a reasonable and satisfactory scheduling decision for each manufacturer in a short time or provide decision support for decision makers of enterprises, thereby improving the production efficiency of high-end equipment manufacturing systems, reducing the inventory cost and further improving the market competitive advantage.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
One use scenario of the embodiment of the present invention is as follows:
in a product manufacturing process, is finishedHas an expiration time of
Figure 590163DEST_PATH_IMAGE022
Need forNA different workpiece havingMA parallel workshop, in which each workpiece can be produced in one workshop in a continuous batch mode and the capacity of each batch isc(i.e., the maximum number of workpieces that can be processed in a batch), a loading time is required before beginning processing of each batch of workpieces, which results in a reduction in the number of workpieces that can be processed in a batchNThe processing time of each workpiece is uncertain (following a normal distribution).
In this embodiment:
Figure 54642DEST_PATH_IMAGE022
indicating a time-out due;
Mindicating the number of plants;
Figure 989100DEST_PATH_IMAGE004
is distributed to a workshop
Figure 903835DEST_PATH_IMAGE002
The number of workpieces;
Figure DEST_PATH_IMAGE082
to express the productjA workpiece; and products are in commonNA workpiece;
Figure DEST_PATH_IMAGE084
to represent
Figure 127006DEST_PATH_IMAGE082
The basic processing time of (a);
Figure 519810DEST_PATH_IMAGE070
indicating allocation in a plant
Figure 308775DEST_PATH_IMAGE002
First, therProcessing the workpiece;
Figure DEST_PATH_IMAGE086
representing workpieces
Figure 676302DEST_PATH_IMAGE070
A base machining time (referring to an actual machining time of the workpiece without considering other factors);
Figure 308141DEST_PATH_IMAGE030
presentation to plant
Figure 114423DEST_PATH_IMAGE002
To (1) arThe mean value of the basic machining time of each machined workpiece,
Figure 289052DEST_PATH_IMAGE032
the variance is represented as a constant, invariant.
Figure DEST_PATH_IMAGE088
Representing workpieces
Figure 561902DEST_PATH_IMAGE070
The actual processing time of (a);
Figure 678107DEST_PATH_IMAGE034
a deterioration coefficient indicating the processing of the workpiece;
Figure DEST_PATH_IMAGE090
representing workpieces
Figure 694604DEST_PATH_IMAGE070
The start processing time of (2);
Figure DEST_PATH_IMAGE092
representing workpieces
Figure 379532DEST_PATH_IMAGE070
The actual completion time of.
Figure DEST_PATH_IMAGE094
Is shown in the workshop
Figure 823283DEST_PATH_IMAGE002
To (1) akCarrying out batch processing;
Figure DEST_PATH_IMAGE096
representing batches
Figure 226451DEST_PATH_IMAGE094
Actual loading time of (a);
Figure 374536DEST_PATH_IMAGE036
a deterioration coefficient indicating a loading lot;
Figure DEST_PATH_IMAGE098
representing batches
Figure 664703DEST_PATH_IMAGE094
The start time of (c);
Figure DEST_PATH_IMAGE100
representing a plant
Figure 794202DEST_PATH_IMAGE002
The actual processing time of (a);
Figure DEST_PATH_IMAGE102
and
Figure DEST_PATH_IMAGE104
respectively representing workshops during processing of workpieces in non-decreasing sequence of mean values of basic processing times
Figure 700978DEST_PATH_IMAGE002
Mean and variance of completion time.
Figure DEST_PATH_IMAGE012A
Figure DEST_PATH_IMAGE014A
Figure DEST_PATH_IMAGE106
=1 denotes the secondjThe workpieces are distributed atiWorkshop the firstrProcessing otherwise
Figure 842634DEST_PATH_IMAGE106
=0；
Figure 580783DEST_PATH_IMAGE080
=1 denotes the secondiA workshopgA processed workpiece andg+1 workpieces not in the same batch, otherwise
Figure 84445DEST_PATH_IMAGE080
=0；
Figure DEST_PATH_IMAGE108
Figure DEST_PATH_IMAGE110
Figure DEST_PATH_IMAGE112
Figure DEST_PATH_IMAGE114
Figure 482714DEST_PATH_IMAGE020
Representing a plant
Figure 972601DEST_PATH_IMAGE002
At the expiration date
Figure 955469DEST_PATH_IMAGE022
A previous completion probability;
Figure DEST_PATH_IMAGE010A
Figure DEST_PATH_IMAGE116
represents the total inventory cost;
Figure DEST_PATH_IMAGE118
representing workpieces
Figure 895612DEST_PATH_IMAGE070
(ii) inventory cost;
Figure 573718DEST_PATH_IMAGE074
representing the cost of inventory per unit time.
Example 1:
as shown in fig. 1, the present invention provides a VNS-based high-end equipment manufacturing robustness scheduling method, which is executed by a computer, and includes:
s1, acquiring workpiece information, workshop information, algorithm parameters and a neighborhood structure;
s2, randomly generating an initial distribution result containing the distribution relation between each workpiece and each workshop;
s3, determining the processing sequence and the batch of the workpieces of each workshop in the initial distribution result through a processing sequence and batch generation algorithm to obtain an initial dispatching scheme; wherein, the scheduling scheme comprises: the order and lot of work pieces for each plant;
s4, updating the initial scheduling scheme by a variable neighborhood search algorithm with the maximum and minimum completion probability as a target to obtain the current optimal scheduling scheme;
and S5, optimizing the current optimal dispatching scheme by using the processing sequence and batch improvement algorithm with the aim of minimizing the sum of the inventory costs of all workshops to obtain the optimal dispatching scheme.
The beneficial effect of this embodiment does:
1) the embodiment of the invention considers the deterioration effect of workpiece production and processing, uncertainty of workpiece processing time, double-target decision and other factors based on the actual production condition, can be suitable for the proposed complex scheduling problem, and has more practical significance and wider application range. Particularly in the case of manufacturing large-scale complex products, the scheme obtained by the invention has strong robustness, and can provide a reliable decision scheme for a decision maker in a short time.
The following describes the implementation process of the embodiment of the present invention in detail:
and S1, acquiring preset parameters such as workpiece information, workshop information, algorithm parameters and neighborhood structures.
In particular, the method comprises the following steps of,
the acquired workpiece information includes: number of workpiecesNTime to completion
Figure 742663DEST_PATH_IMAGE022
The base machining time (following a normal distribution) for each workpiece is recorded
Figure 455404DEST_PATH_IMAGE084
；
The acquired workshop information comprises: number of plantsMCapacity of each batchcCoefficient of deterioration in working
Figure 300869DEST_PATH_IMAGE034
Coefficient of deterioration of loading
Figure 731850DEST_PATH_IMAGE036
；
The algorithm parameters include: relevant parameters of the variable neighborhood searching algorithm, such as maximum iteration times, neighborhood structure number and maximum searching times of each iteration; and parameters associated with the process sequence and lot improvement algorithm, e.g. maximum improvement search times。
And S2, randomly generating an initial distribution result containing the distribution relation between each workpiece and the workshop.
Specifically, all workpieces are numbered, all workpieces are distributed to corresponding workshops in a coding mode, and an initial distribution result is randomly generated.
As shown in FIG. 2, assuming that the embodiment has 10 workpieces in total, the initial allocation result can be recorded as
Figure 563540DEST_PATH_IMAGE016
Here, theN=10, and
Figure DEST_PATH_IMAGE120
a value of (1) ~MRandom integer of (1) representsjThe respective work pieces are distributed to the corresponding workshops.
Thus, the workshop allocated to each workpiece can be obtained.
S3, determining the processing sequence and the batch of the workpieces of each workshop in the initial distribution result through a processing sequence and batch generation algorithm to obtain an initial dispatching scheme; wherein, the scheduling scheme comprises: the order and lot of work pieces for each plant.
Optionally, the processing order and batch generation algorithm of this embodiment may adopt the following steps:
s31, distributing the data to the workshop
Figure 537312DEST_PATH_IMAGE002
According to the average value of basic processing timeReducing the sequence and arranging to obtain the processing sequence of the workpieces;
s32, if the distribution is to the workshop
Figure 822188DEST_PATH_IMAGE002
Number of workpieces
Figure 474886DEST_PATH_IMAGE004
Less than batch capacitycThen all the workpieces are put in the first batch, namely the workshop has only one batch at the moment; otherwise, the workpiece needs to be further divided into batches, and therefore, the workpiece needs to be divided into batches
Figure 985633DEST_PATH_IMAGE006
Placing a workpiece in a first batch, and placing the remaining workpieces in a batch sizecSequentially distributing the raw materials in a plurality of batches to obtain a workshop
Figure 672966DEST_PATH_IMAGE002
The order and batch of processing of the workpieces;
and S33, repeatedly executing S31-S32 until the processing sequence and the batches corresponding to all workshops are obtained.
As shown in fig. 2, in a workshop
Figure DEST_PATH_IMAGE122
And batch capacitycFor example, =3, the workpieces assigned to the workshop are No. 3, 5, 6, and 10, and 4 workpieces in total are 6, 3, 5, and 10 after being arranged in non-decreasing order according to the average value of the basic processing time, so that No. 6 workpieces need to be placed in the first lot, and No. 3, 5, and 10 workpieces need to be placed in the second lot, and thus the workshop is configured to have No. 6 workpieces placed therein
Figure DEST_PATH_IMAGE124
The number of lots of workpieces of (1) is 2, and the processing is performed in the order of generating the lots, and the processing order of the workpieces in each lot is processed in descending order of the average value of the basic processing time, that is, in No. 6, 3, 5, 10.
Compared with other processing sequences and batch generation algorithms, the steps have the advantages that: the average value of the workshop completion time processed according to the scheme is the smallest of all the sequencing and batch sequencing, so that the workshop processed according to the processing scheme can maximize the completion probability after determining that the workpiece is distributed to the workshop before the completion due date.
S4, updating the initial scheduling scheme by a variable neighborhood search algorithm with the maximum and minimum completion probability as a target to obtain the current optimal scheduling scheme.
Optionally, the variable neighborhood search algorithm of this embodiment may adopt the following steps:
s41, making the current iteration numbert=0, current neighborhood structurek=1；
S42, ordert=t+1, and initializing the current number of searchestime=0；
S43, ordertime=time+1 in the second placekSearching a new scheduling scheme by the variable neighborhood, and acquiring the minimum completion probability of the new scheduling scheme;
s44, if the minimum completion probability of the new scheduling scheme is smaller than the initial scheduling scheme, executing S45; otherwise, it ordersk=1, and the new scheduling scheme is taken as an initial scheduling scheme, and S46 is performed;
s45, orderk=k+1, if
Figure DEST_PATH_IMAGE125
If not, executing S46; otherwise, it ordersk=1, then perform S46;
s46, iftimeIf the number of times of searching is larger than that of each iteration, S47 is executed; otherwise, go to S43;
s47, iftIf the number of iterations is larger than the maximum number of iterations, taking the initial scheduling scheme as the current optimal scheduling scheme, and acquiring the minimum completion probability of the initial scheduling scheme; otherwise, S42 is executed.
Optionally, the number of the neighborhood structures designed in this embodiment is three, specifically as follows:
the first neighborhood structure: randomly selecting a workpiece from the workshops with the lowest completion probability and randomly distributing the workpiece to other workshops;
the second neighborhood structure: exchanging a workpiece randomly selected from the workshop with the lowest finishing probability with a workpiece with a smaller average basic processing time randomly selected from other workshops;
the third neighborhood structure: randomly encoding the current schemeVIs reversed.
In this embodiment, the calculation formula of the minimum completion probability of the scheduling scheme is as follows:
Figure DEST_PATH_IMAGE008A
Figure DEST_PATH_IMAGE126
representing a plant
Figure DEST_PATH_IMAGE127
At the expiration date
Figure DEST_PATH_IMAGE128
The previous completion probability and the calculation formula is as follows:
Figure DEST_PATH_IMAGE010AA
Figure DEST_PATH_IMAGE129
and
Figure DEST_PATH_IMAGE130
respectively representing workshops during processing of workpieces in non-decreasing sequence of mean values of basic processing times
Figure 434468DEST_PATH_IMAGE127
The mean and variance of the completion time, and the calculation formula is:
Figure DEST_PATH_IMAGE012AA
Figure DEST_PATH_IMAGE014AA
therefore, the minimum completion probability of the scheduling scheme is maximized by the variable neighborhood search algorithm, and the processing task is ensured to be completed on time as far as possible.
And S5, optimizing the current optimal dispatching scheme by using the processing sequence and batch improvement algorithm with the aim of minimizing the sum of the inventory costs of all workshops to obtain the optimal dispatching scheme.
Optionally, in order to reduce the inventory cost, the processing sequence and lot improvement algorithm of the present embodiment may employ the following steps:
s51, obtaining the maximum improved searching timesitimesInter-vehicle in current optimal scheduling scheme
Figure 964675DEST_PATH_IMAGE127
Is completed with probability of completion
Figure 793960DEST_PATH_IMAGE126
And inventory cost
Figure DEST_PATH_IMAGE131
Make the current number of improved searchesitime=0, acquisition plant
Figure 742324DEST_PATH_IMAGE127
The difference between the average values of the basic processing times of the respective adjacent workpieces in the processing order of (1) is obtained as a setD(ii) a Order tox=2，y=1, placing the first workpiece in the first virtual group;
Figure DEST_PATH_IMAGE042A
Figure DEST_PATH_IMAGE044A
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE132
representing a plant
Figure 24794DEST_PATH_IMAGE127
To (1) arAn average value of basic processing times of the respective processed workpieces;
s52, if
Figure DEST_PATH_IMAGE133
Then will bexA workpiece is put inyA virtual group; otherwise, it will bexA workpiece is put iny+1 virtual groups, ordery=y+1；
Wherein:
Figure DEST_PATH_IMAGE048A
Figure DEST_PATH_IMAGE050A
Figure 183243DEST_PATH_IMAGE129
and
Figure 160426DEST_PATH_IMAGE130
respectively representing workshops during processing of workpieces in non-decreasing sequence of mean values of basic processing times
Figure 681406DEST_PATH_IMAGE127
Mean and variance of completion times;
Figure DEST_PATH_IMAGE134
a deterioration coefficient indicating the processing of the workpiece;
Figure DEST_PATH_IMAGE135
a deterioration coefficient indicating a loading lot;
s53, ifxSmaller than the workshop
Figure 554685DEST_PATH_IMAGE127
Number of workpieces
Figure DEST_PATH_IMAGE136
Then, thenx=x+1 and return to S52; otherwise, jumping to S54;
s54, ifySmaller than the workshop
Figure 59484DEST_PATH_IMAGE127
Number of workpieces
Figure 715724DEST_PATH_IMAGE136
Randomly reversing the partial processing sequence of the workpieces in each virtual group, and then forming a new workshop workpiece processing sequence according to the sequence of the virtual groups,itime=itime+1, go to S55; otherwise, jumping to S58;
s55, if the distribution is to the workshop
Figure 966577DEST_PATH_IMAGE127
Number of workpieces
Figure 807494DEST_PATH_IMAGE136
Less than batch capacitycThen all workpieces are placed in a first batch; otherwise, it will
Figure DEST_PATH_IMAGE137
Placing a workpiece in a first batch, and placing the remaining workpieces in a batch sizecSequentially distributing the raw materials in a plurality of batches to obtain an updated workshop
Figure 62239DEST_PATH_IMAGE127
The order and batch of processing of the workpieces;
s56 plant based on updating
Figure 522171DEST_PATH_IMAGE127
The processing order and the batch of the workpieces are obtained and updated
Figure 627530DEST_PATH_IMAGE127
Is/are as follows
Figure DEST_PATH_IMAGE138
、
Figure DEST_PATH_IMAGE139
Completion probability
Figure DEST_PATH_IMAGE140
And inventory cost
Figure DEST_PATH_IMAGE141
；
S57, if
Figure DEST_PATH_IMAGE142
And is
Figure DEST_PATH_IMAGE143
Then the updated workshop
Figure 357458DEST_PATH_IMAGE127
The processing sequence and the batch of the workpieces are used as the current optimal scheduling scheme; otherwise, jumping to S58;
s58, ifitime＜itimesThen return to S54; otherwise, outputting the current optimal scheduling scheme.
Wherein the content of the first and second substances,
Figure 915478DEST_PATH_IMAGE131
the calculation formula of (2) is as follows:
Figure DEST_PATH_IMAGE064A
Figure DEST_PATH_IMAGE066A
therefore, the workshops are parallel and do not interfere with each other, so that the inventory cost of each workshop is reduced, and the total inventory cost can be further reduced on the premise of maximizing the minimum completion probability.
Further, optionally, randomly reversing the partial machining order of the workpieces in each virtual group comprises:
step 1: initializationq=1；
Step 2: if it is firstqIf the number of the workpieces of each virtual group is 1, jumping to the step 4; otherwise, jumping to the step 3;
and step 3: randomly selecting a plurality of workpieces which are continuously machined from the virtual group, and reversing the machining sequence of the workpieces; (similar to the third neighborhood architecture implementation)
And 4, step 4: output the firstqOrder of processing of each virtual groupq=q+1；
And 5: and repeating the step 2 to the step 5 until all the virtual groups are traversed.
In addition, further optionally, in this embodiment, the implementation steps of the three neighborhood structures are:
the first neighborhood structure is realized by the following steps:
step 1: calculating the completion probability of all workshops to obtain the sequence number of the workshop with the minimum completion probability
Figure DEST_PATH_IMAGE145
Let us order
Figure DEST_PATH_IMAGE147
；
Step 2: randomly generating 1 in the range of 1 to
Figure DEST_PATH_IMAGE149
Random integer
Figure DEST_PATH_IMAGE151
，
And step 3: if it is not
Figure DEST_PATH_IMAGE153
Randomly generating 1 in the range of 1 to
Figure DEST_PATH_IMAGE155
Random integer
Figure DEST_PATH_IMAGE157
(ii) a Otherwise, returning to the step 2;
and 4, step 4: if it is not
Figure DEST_PATH_IMAGE159
Then, then
Figure DEST_PATH_IMAGE161
Obtaining a new solution and ending; otherwise, go back to step 3.
The second neighborhood structure is realized by the following steps:
step 1: calculating the completion probability of all workshops to obtain the sequence number of the workshop with the minimum completion probability
Figure 8461DEST_PATH_IMAGE145
；
Step 2: randomly generating 1 in the range of 1 to
Figure 358540DEST_PATH_IMAGE149
Random integer
Figure DEST_PATH_IMAGE163
；
And step 3: if it is not
Figure DEST_PATH_IMAGE165
Then randomly generating 1 in the range of 1 to
Figure 478943DEST_PATH_IMAGE149
Random integer
Figure DEST_PATH_IMAGE167
(ii) a Otherwise, jumping to the step 2;
and 4, step 4: if it is not
Figure DEST_PATH_IMAGE169
And is
Figure DEST_PATH_IMAGE171
Then, then
Figure DEST_PATH_IMAGE173
，
Figure DEST_PATH_IMAGE175
Obtaining a new solution and ending; otherwise, jumping to the step 3;
the third neighborhood structure is realized by the following steps:
step 1: randomly generating two from 1 to
Figure 98493DEST_PATH_IMAGE149
Random different integers
Figure DEST_PATH_IMAGE177
And
Figure DEST_PATH_IMAGE179
；
step 2: if it is not
Figure DEST_PATH_IMAGE181
Then, then
Figure DEST_PATH_IMAGE183
，
Figure DEST_PATH_IMAGE185
，
Figure DEST_PATH_IMAGE187
；
And step 3: order to
Figure DEST_PATH_IMAGE189
0；
Figure DEST_PATH_IMAGE191
And 4, step 4: order to
Figure DEST_PATH_IMAGE193
，
Figure DEST_PATH_IMAGE195
，
Figure DEST_PATH_IMAGE197
，
Figure DEST_PATH_IMAGE199
；
And 5: if it is not
Figure DEST_PATH_IMAGE201
Returning to the step 4; otherwise, obtaining a new solution and ending.
Example 2:
a VNS-based high-end equipment manufacturing robustness scheduling apparatus, the apparatus comprising:
the parameter acquisition module is used for acquiring workpiece information, workshop information, algorithm parameters and a neighborhood structure;
the initial distribution result generation module is used for randomly generating an initial distribution result containing the distribution relation between each workpiece and the workshop;
the initial dispatching scheme generation module is used for determining the processing sequence and the batch of the workpieces of each workshop in the initial distribution result through a processing sequence and batch generation algorithm to obtain an initial dispatching scheme; wherein, the scheduling scheme comprises: the order and lot of work pieces for each plant;
the current optimal scheduling scheme generating module is used for updating the initial scheduling scheme by using the maximized minimum completion probability as a target through a variable neighborhood search algorithm to obtain a current optimal scheduling scheme;
and the processing sequence and batch improvement module is used for optimizing the current optimal dispatching scheme by using a processing sequence and batch improvement algorithm with the aim of minimizing the sum of the inventory costs of all workshops as a target to obtain the optimal dispatching scheme.
Example 3:
a computer-readable storage medium storing a computer program for high-end equipment manufacturing scheduling, wherein the computer program causes a computer to perform the steps of:
s1, acquiring workpiece information, workshop information, algorithm parameters and a neighborhood structure;
s2, randomly generating an initial distribution result containing the distribution relation between each workpiece and each workshop;
s3, determining the processing sequence and the batch of the workpieces of each workshop in the initial distribution result through a processing sequence and batch generation algorithm to obtain an initial dispatching scheme; wherein, the scheduling scheme comprises: the order and lot of work pieces for each plant;
s4, updating the initial scheduling scheme by a variable neighborhood search algorithm with the maximum and minimum completion probability as a target to obtain the current optimal scheduling scheme;
and S5, optimizing the current optimal dispatching scheme by using the processing sequence and batch improvement algorithm with the aim of minimizing the sum of the inventory costs of all workshops to obtain the optimal dispatching scheme.
Example 4:
an electronic device, comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing the steps of:
s1, acquiring workpiece information, workshop information, algorithm parameters and a neighborhood structure;
s2, randomly generating an initial distribution result containing the distribution relation between each workpiece and each workshop;
s3, determining the processing sequence and the batch of the workpieces of each workshop in the initial distribution result through a processing sequence and batch generation algorithm to obtain an initial dispatching scheme; wherein, the scheduling scheme comprises: the order and lot of work pieces for each plant;
s4, updating the initial scheduling scheme by a variable neighborhood search algorithm with the maximum and minimum completion probability as a target to obtain the current optimal scheduling scheme;
and S5, optimizing the current optimal dispatching scheme by using the processing sequence and batch improvement algorithm with the aim of minimizing the sum of the inventory costs of all workshops to obtain the optimal dispatching scheme.
It can be understood that the VNS-based high-end equipment manufacturing robustness scheduling apparatus, the storage medium, and the electronic device provided in the embodiment of the present invention correspond to the VNS-based high-end equipment manufacturing robustness scheduling method, and for explanation, examples, and beneficial effects of relevant contents, parts may refer to corresponding contents in the VNS-based high-end equipment manufacturing robustness scheduling method, and details are not described here.
In summary, compared with the prior art, the invention has the following beneficial effects:
(1) the invention designs an intelligent optimization algorithm for improving variable neighborhood search aiming at the problem of dual-target hierarchical scheduling of complex products related to deterioration effect and uncertain processing time in the manufacture of high-end equipment.
(2) In the variable neighborhood algorithm, three simple and feasible neighborhood structures are designed, so that the algorithm can achieve good balance between the concentration and the dispersion during operation, the convergence rate of the algorithm is improved, and the local optimal solution can be skipped in the iteration process.
(3) The method considers the deterioration effect of workpiece production and processing, the uncertainty of workpiece processing time, double-target decision and other factors based on the actual production condition. A simpler code is defined and may be adapted to other conventional production schedules. By combining the processing sequence and batch generation algorithm and the processing sequence and batch improvement algorithm, the method can be suitable for the proposed complex scheduling problem, and has more practical significance and wider application range. Particularly in the case of manufacturing large-scale complex products, the scheme obtained by the invention has strong robustness, and can provide a reliable decision scheme for a decision maker in a short time.
It should be noted that, through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments. In this document, relational terms such as first and second, and the like may be 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 apparatus 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 apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (12)

1. A VNS-based high-end equipment manufacturing robustness scheduling method is characterized by comprising the following steps:
s1, acquiring workpiece information, workshop information, algorithm parameters and a neighborhood structure;
s2, randomly generating an initial distribution result containing the distribution relation between each workpiece and each workshop;
s3, determining the processing sequence and the batch of the workpieces of each workshop in the initial distribution result through a processing sequence and batch generation algorithm to obtain an initial dispatching scheme; wherein, the scheduling scheme comprises: the order and lot of work pieces for each plant;
s4, updating the initial scheduling scheme by a variable neighborhood search algorithm with the maximum and minimum completion probability as a target to obtain the current optimal scheduling scheme;
and S5, optimizing the current optimal dispatching scheme by using the processing sequence and batch improvement algorithm with the aim of minimizing the sum of the inventory costs of all workshops to obtain the optimal dispatching scheme.
2. The VNS-based robust scheduling method for high-end equipment manufacturing of claim 1, wherein the artifact information comprises: number of workpieces, completion due time, basic machining time for each workpiece;
the workshop information includes: number of plants, batch capacity, degradation factor of processing, degradation factor of loading.
3. The VNS-based high-end equipment manufacturing robust scheduling method of claim 1, wherein the process sequence and lot generation algorithm step comprises:
s31, distributing the data to the workshop
Figure DEST_PATH_IMAGE002
The workpieces are arranged according to the average value of the basic processing time in a non-decreasing order to obtainThe sequence of workpiece processing;
s32, if the distribution is to the workshop
Figure 307522DEST_PATH_IMAGE002
Number of workpieces
Figure DEST_PATH_IMAGE004
Less than batch capacitycThen all workpieces are placed in a first batch; otherwise, it will
Figure DEST_PATH_IMAGE006
Placing a workpiece in a first batch, and placing the remaining workpieces in a batch sizecSequentially distributing the raw materials in a plurality of batches to obtain a workshop
Figure 349296DEST_PATH_IMAGE002
The order and batch of processing of the workpieces;
and S33, repeatedly executing S31-S32 until the processing sequence and the batches corresponding to all workshops are obtained.
4. The VNS-based high-end equipment manufacturing robustness scheduling method of claim 1, wherein the minimum completion probability of the scheduling scheme is obtained by the formula:
Figure DEST_PATH_IMAGE008
Figure DEST_PATH_IMAGE010
Figure DEST_PATH_IMAGE012
Figure DEST_PATH_IMAGE014
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE016
indicating the result of the dispensing of the workpiece, whereinjResult of distribution of individual workpiecesv j The value of (d) indicates the assigned plant;
Figure DEST_PATH_IMAGE018
representing a minimum completion probability of the scheduling scheme;
Figure DEST_PATH_IMAGE020
representing a plant
Figure 974706DEST_PATH_IMAGE002
At the expiration date
Figure DEST_PATH_IMAGE022
A previous completion probability;
Figure DEST_PATH_IMAGE024
representing a standard normal distribution probability function;
Figure DEST_PATH_IMAGE026
and
Figure DEST_PATH_IMAGE028
respectively representing workshops during processing of workpieces in non-decreasing sequence of mean values of basic processing times
Figure 108532DEST_PATH_IMAGE002
Mean and variance of completion times;
Figure DEST_PATH_IMAGE030
presentation to plant
Figure 985221DEST_PATH_IMAGE002
To (1) arAn average value of basic processing times of the respective processed workpieces, which are subject to normal distribution;
Figure DEST_PATH_IMAGE032
a variance representing a base processing time that follows a normal distribution;
Figure DEST_PATH_IMAGE034
a deterioration coefficient indicating the processing of the workpiece;
Figure DEST_PATH_IMAGE036
a deterioration coefficient indicating a loading lot;
cindicating the batch capacity.
5. The VNS-based robust scheduling method for high-end equipment manufacturing of claim 1, wherein the variable neighborhood search algorithm comprises:
s41, making the current iteration numbert=0, current neighborhood structurek=1；
S42, ordert=t+1, and initializing the current number of searchestime=0；
S43, ordertime=time+1 in the second placekSearching a new scheduling scheme by the variable neighborhood, and acquiring the minimum completion probability of the new scheduling scheme;
s44, if the minimum completion probability of the new scheduling scheme is smaller than the initial scheduling scheme, executing S45; otherwise, it ordersk=1, and the new scheduling scheme is taken as an initial scheduling scheme, and S46 is performed;
s45, orderk=k+1, if
Figure DEST_PATH_IMAGE038
If not, executing S46; otherwise, it ordersk=1, then perform S46;
s46, iftimeIf the number of times of searching is larger than that of each iteration, S47 is executed; otherwise, go to S43;
s47, iftIf the number of iterations is larger than the maximum number of iterations, taking the initial scheduling scheme as the current optimal scheduling scheme, and acquiring the minimum completion probability of the initial scheduling scheme; otherwise, S42 is executed.
6. The VNS-based high-end equipment manufacturing robust scheduling method of claim 1, wherein the neighborhood structure comprises:
the first neighborhood structure: randomly selecting a workpiece from the workshops with the lowest completion probability and randomly distributing the workpiece to other workshops;
the second neighborhood structure: exchanging a workpiece randomly selected from the workshop with the lowest finishing probability with a workpiece with a smaller average basic processing time randomly selected from other workshops;
the third neighborhood structure: randomly reverses a portion of the scheme encoding now.
7. The VNS-based robust scheduling method for high-end equipment manufacturing of claim 1, wherein the process sequence and lot improvement algorithm comprises:
s51, obtaining the maximum improved searching timesitimesInter-vehicle in current optimal scheduling scheme
Figure 401159DEST_PATH_IMAGE002
Is completed with probability of completion
Figure 261668DEST_PATH_IMAGE020
And inventory cost
Figure DEST_PATH_IMAGE040
Make the current number of improved searchesitime=0, get carWorkshop
Figure 663830DEST_PATH_IMAGE002
The difference between the average values of the basic processing times of the respective adjacent workpieces in the processing order of (1) is obtained as a setD(ii) a Order tox=2，y=1, placing the first workpiece in the first virtual group;
Figure DEST_PATH_IMAGE042
Figure DEST_PATH_IMAGE044
wherein the content of the first and second substances,
Figure 316397DEST_PATH_IMAGE030
presentation to plant
Figure 778603DEST_PATH_IMAGE002
To (1) arAn average value of basic processing times of the respective processed workpieces, which are subject to normal distribution;
s52, if
Figure DEST_PATH_IMAGE046
Then will bexA workpiece is put inyA virtual group; otherwise, it will bexA workpiece is put iny+1 virtual groups, ordery=y+1；
Wherein:
Figure DEST_PATH_IMAGE048
Figure DEST_PATH_IMAGE050
Figure 722812DEST_PATH_IMAGE026
and
Figure 194245DEST_PATH_IMAGE028
respectively representing workshops during processing of workpieces in non-decreasing sequence of mean values of basic processing times
Figure 311106DEST_PATH_IMAGE002
Mean and variance of completion times;
Figure 475371DEST_PATH_IMAGE034
a deterioration coefficient indicating the processing of the workpiece;
Figure 310472DEST_PATH_IMAGE036
a deterioration coefficient indicating a loading lot;
s53, ifxSmaller than the workshop
Figure 320016DEST_PATH_IMAGE002
Number of workpieces
Figure 697908DEST_PATH_IMAGE004
Then, thenx=x+1 and return to S52; otherwise, jumping to S54;
s54, ifySmaller than the workshop
Figure 626549DEST_PATH_IMAGE002
Number of workpieces
Figure 824313DEST_PATH_IMAGE004
Randomly reversing the partial processing sequence of the workpieces in each virtual group, and then forming a new workshop workpiece processing sequence according to the sequence of the virtual groups,itime=itime+1, go to S55; otherwise, jumping to S58;
s55, if the distribution is to the workshop
Figure 637548DEST_PATH_IMAGE002
Number of workpieces
Figure 729001DEST_PATH_IMAGE004
Less than batch capacitycThen all workpieces are placed in a first batch; otherwise, it will
Figure 235068DEST_PATH_IMAGE006
Placing a workpiece in a first batch, and placing the remaining workpieces in a batch sizecSequentially distributing the raw materials in a plurality of batches to obtain an updated workshop
Figure 185707DEST_PATH_IMAGE002
The order and batch of processing of the workpieces;
s56 plant based on updating
Figure 68212DEST_PATH_IMAGE002
The processing order and the batch of the workpieces are obtained and updated
Figure 483013DEST_PATH_IMAGE002
Is/are as follows
Figure DEST_PATH_IMAGE052
、
Figure DEST_PATH_IMAGE054
Completion probability
Figure DEST_PATH_IMAGE056
And inventory cost
Figure DEST_PATH_IMAGE058
；
S57, if
Figure DEST_PATH_IMAGE060
And is
Figure DEST_PATH_IMAGE062
Then the updated workshop
Figure 937478DEST_PATH_IMAGE002
The processing sequence and the batch of the workpieces are used as the current optimal scheduling scheme; otherwise, jumping to S58;
s58, ifitime＜itimesThen return to S54; otherwise, outputting the current optimal scheduling scheme.
8. The VNS-based robust scheduling of high-end equipment manufacturing of claim 1, wherein the plant floor
Figure 844254DEST_PATH_IMAGE002
Inventory cost of
Figure 123926DEST_PATH_IMAGE040
The calculation formula of (2) is as follows:
Figure DEST_PATH_IMAGE064
Figure DEST_PATH_IMAGE066
Figure DEST_PATH_IMAGE068
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE070
indicating allocation in a plant
Figure 924392DEST_PATH_IMAGE002
First, therProcessing the workpiece;
Figure DEST_PATH_IMAGE072
representing workpieces
Figure 100158DEST_PATH_IMAGE070
(ii) inventory cost;
Figure DEST_PATH_IMAGE074
representing the inventory cost per unit time;
Figure 684111DEST_PATH_IMAGE026
indicating that the work is being machined in non-decreasing order of mean value of basic machining time
Figure 377260DEST_PATH_IMAGE002
An average of completion times;
Figure DEST_PATH_IMAGE076
representing workpieces
Figure 501074DEST_PATH_IMAGE070
Average of the actual completion times of (a);
Figure DEST_PATH_IMAGE078
representing workpieces
Figure 113321DEST_PATH_IMAGE070
Actual completion time of (d);
Figure DEST_PATH_IMAGE080
=1 denotes the secondiA workshopgA processed workpiece andfirst, theg+1 workpieces not in the same batch, otherwise
Figure 588165DEST_PATH_IMAGE080
=0。
9. The VNS-based robust scheduling method for high-end equipment manufacturing of claim 1, wherein the randomly reversing the partial machining order of the workpieces in each virtual group comprises:
step 1: initializationq=1；
Step 2: if it is firstqIf the number of the workpieces of each virtual group is 1, jumping to the step 4; otherwise, jumping to the step 3;
and step 3: randomly selecting a plurality of workpieces which are continuously machined from the virtual group, and reversing the machining sequence of the workpieces;
and 4, step 4: output the firstqOrder of processing of each virtual groupq=q+1；
And 5: and repeating the step 2 to the step 5 until all the virtual groups are traversed.
10. A VNS-based high-end equipment manufacturing robustness scheduling apparatus, comprising:
the parameter acquisition module is used for acquiring workpiece information, workshop information, algorithm parameters and a neighborhood structure;
the initial distribution result generation module is used for randomly generating an initial distribution result containing the distribution relation between each workpiece and the workshop;
the initial dispatching scheme generation module is used for determining the processing sequence and the batch of the workpieces of each workshop in the initial distribution result through a processing sequence and batch generation algorithm to obtain an initial dispatching scheme; wherein, the scheduling scheme comprises: the order and lot of work pieces for each plant;
the current optimal scheduling scheme generating module is used for updating the initial scheduling scheme by using the maximized minimum completion probability as a target through a variable neighborhood search algorithm to obtain a current optimal scheduling scheme;
and the processing sequence and batch improvement module is used for optimizing the current optimal dispatching scheme by using a processing sequence and batch improvement algorithm with the aim of minimizing the sum of the inventory costs of all workshops as a target to obtain the optimal dispatching scheme.
11. A computer-readable storage medium storing a computer program for high-end equipment manufacturing scheduling, wherein the computer program causes a computer to perform the VNS-based high-end equipment manufacturing robust scheduling method of any one of claims 1-9.
12. An electronic device, comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing the VNS-based high-end equipment manufacturing robust scheduling method of any one of claims 1-9.
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090150209A1 (en) * 2000-09-06 2009-06-11 Masterlink Corporation System and method for managing mobile workers
CN106959675A (en) * 2017-03-21 2017-07-18 山东大学 A kind of multi-objective scheduling optimization method towards Flow Shop
CN107578178A (en) * 2017-09-11 2018-01-12 合肥工业大学 Based on the dispatching method and system for becoming neighborhood search and gravitation search hybrid algorithm
DE102018107120A1 (en) * 2017-05-22 2018-11-22 Biotronik Se & Co. Kg Selective activation of parasympathetic nerve in a vagus nerve stimulation device
CN112286149A (en) * 2020-10-15 2021-01-29 山东师范大学 Flexible workshop scheduling optimization method and system considering crane transportation process
CN113177781A (en) * 2021-05-17 2021-07-27 合肥工业大学 Production assembly cooperative scheduling method and system based on variable neighborhood and genetic operator

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090150209A1 (en) * 2000-09-06 2009-06-11 Masterlink Corporation System and method for managing mobile workers
CN106959675A (en) * 2017-03-21 2017-07-18 山东大学 A kind of multi-objective scheduling optimization method towards Flow Shop
DE102018107120A1 (en) * 2017-05-22 2018-11-22 Biotronik Se & Co. Kg Selective activation of parasympathetic nerve in a vagus nerve stimulation device
CN107578178A (en) * 2017-09-11 2018-01-12 合肥工业大学 Based on the dispatching method and system for becoming neighborhood search and gravitation search hybrid algorithm
CN112286149A (en) * 2020-10-15 2021-01-29 山东师范大学 Flexible workshop scheduling optimization method and system considering crane transportation process
CN113177781A (en) * 2021-05-17 2021-07-27 合肥工业大学 Production assembly cooperative scheduling method and system based on variable neighborhood and genetic operator

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
郭盈: "基于流水线调度问题的模型与局部搜索算法", 《中国科学技术大学学报》 *
陆少军: "考虑恶化和学习效应的多机制造系统智能优化方法", 《系统科学与数学》 *

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