CN115081754B - Production and maintenance scheduling method based on mixed whale-variable neighborhood search - Google Patents

Production and maintenance scheduling method based on mixed whale-variable neighborhood search Download PDF

Info

Publication number
CN115081754B
CN115081754B CN202210995671.0A CN202210995671A CN115081754B CN 115081754 B CN115081754 B CN 115081754B CN 202210995671 A CN202210995671 A CN 202210995671A CN 115081754 B CN115081754 B CN 115081754B
Authority
CN
China
Prior art keywords
whale
maintenance
machine
workpiece
time
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202210995671.0A
Other languages
Chinese (zh)
Other versions
CN115081754A (en
Inventor
刘心报
钱晓飞
胡朝明
颜彬肖
郑锐
陆少军
程浩
胡俊迎
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hefei University of Technology
Original Assignee
Hefei University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hefei University of Technology filed Critical Hefei University of Technology
Priority to CN202210995671.0A priority Critical patent/CN115081754B/en
Publication of CN115081754A publication Critical patent/CN115081754A/en
Application granted granted Critical
Publication of CN115081754B publication Critical patent/CN115081754B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06316—Sequencing of tasks or work
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Theoretical Computer Science (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Physics & Mathematics (AREA)
  • Marketing (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Development Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Educational Administration (AREA)
  • General Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • General Factory Administration (AREA)
  • Multi-Process Working Machines And Systems (AREA)

Abstract

The invention provides a production and maintenance scheduling method based on a hybrid whale-variable neighborhood search algorithm, and relates to the technical field of production and maintenance scheduling. In the scheduling process, input parameters comprise the degradation coefficient of maintenance activities and the degradation coefficient of machine processing capacity, and the production and maintenance are cooperatively scheduled by considering the performance degradation of the machine, so that accurate cooperative scheduling is realized. Meanwhile, the optimal solution output by the whale algorithm is further improved through variable neighborhood search, the search capability of a solution space is expanded, a better solution is obtained, more accurate collaborative scheduling is further realized, the reliability of machine operation is improved, and the operation cost of enterprises is reduced.

Description

Production and maintenance scheduling method based on mixed whale-variable neighborhood search
Technical Field
The invention relates to the technical field of production and maintenance scheduling, in particular to a production and maintenance scheduling method based on hybrid whale-variable neighborhood search.
Background
The problem of production and maintenance coordinated scheduling is paid more and more attention in recent years, is a typical combined optimization problem, and is widely applied to various industries in the manufacturing field, such as smelting industry, chip industry, high-end equipment manufacturing industry and the like. Different from the traditional scheduling mode of taking a maintenance plan as production constraint, the production and maintenance cooperative scheduling takes maintenance activities as part of decision and schedules the maintenance activities together with production tasks.
Currently, although there is a certain research on the cooperative decision problem of production and maintenance, there is almost no scheduling method considering that the performance degradation of the machine will affect the production and maintenance. Especially, it is a common phenomenon in the manufacturing industry that a plurality of influence factors such as processing sequence, maintenance opportunity, and performance degradation of a machine of different workpieces need to be considered, for example, as the machine is continuously aged along with operation of the machine, actual processing time of the workpiece is lengthened, so that the processing sequence of the workpiece affects the total processing time, meanwhile, scheduling of maintenance activities also affects the total processing time, and an existing scheduling model cannot be well solved.
As can be seen from the above description, the prior art does not consider that the performance degradation of the machine has an influence on production and maintenance, resulting in low scheduling accuracy.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a production and maintenance scheduling method based on mixed whale-variable neighborhood search, and solves the technical problem that the machine performance degradation is not considered in the prior art to affect production and maintenance.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
in a first aspect, the invention provides a production and maintenance scheduling method based on hybrid whale-variation neighborhood search, which comprises the following steps:
s1, initializing input parameters of an algorithm and setting execution parameters of mixed whale-variant neighborhood search, wherein the input parameters comprise the number of workpiecesNNumber of machines m, total number of maintenance operationsk max The coefficient of degradation of maintenance activities, the coefficient of degradation of machine processing capacity;
s2, randomly generating according to input parameterspopHas a length ofN+k max -a one-dimensional vector of m as an initial solution for a hybrid whale-variant neighborhood search;
s3, inputting the initial solution into a mixed whale-variable neighborhood search to solve a global optimal solution of a production and maintenance cooperative scheduling problem considering machine performance degradation, wherein the global optimal solution corresponds to a workpiece processing sequence and distribution of maintenance activities, and the mixed whale-variable neighborhood search means: and performing variable neighborhood search operation on the optimal solution output by the whale algorithm.
Preferably, the S3 includes:
s301, inputting an initial solution, and determining the distribution result of the workpieces and the maintenance times distributed to each machine according to a decoding rule; applying heuristic algorithm of single machines to obtain total completion time of each single machine, calculating the fitness value of each whale in the initial population, and recording the one-dimensional vector corresponding to the individual with the best fitness value in the whales asX * (ii) a And make an ordert=1；
S302, judgmentt<t max If yes, go to step S303; otherwise, go to step S310;
s303, for all individualsX k (t) = (x 1 , x 2 , ..., x N , x N+1 , x N+2 , .., x N+kmax-m )Randomly generating a probabilityp k And updating its iteration parametersa,A,C,l,pWherein, in the step (A),a=2*(1-t/t max ) Iteration parameterA=2ar-a，C=2r，lIs [ -1,1 [ ]]A random number in between, and a random number,ris [0,1 ]]A random number in between;
s304, judgingp k <05, if yes, go to step S305, otherwise go to step S306;
s305, judging | A<1, if yes, updating whale individuals according to the following formulaX k ：
Figure 100002_DEST_PATH_IMAGE001
If not, a search agent is randomly selectedX rand (t) And updating whale individuals according to the following formulaX k (t)：
Figure 100002_DEST_PATH_IMAGE002
S306, updating whale individuals according to the following formulaX k (t)：
Figure 100002_DEST_PATH_IMAGE004
Wherein the content of the first and second substances,bis a constant, which defines the shape of the spiral,Lis a random number in (-1, 1),
Figure 100002_DEST_PATH_IMAGE006
is shown astBest solution and the second in generationkVector differences for individuals;
s307, calculating the fitness value of each whale individual in the updated population, and assigning the one-dimensional vector corresponding to the whale individual with the optimal fitness value to the whale individualValue toX * ；
S308, solving the optimal solutionX * Performing neighborhood-varying search operation and updating optimal solutionX * ；
S309, ordert=t+1, and return to S302;
s310, finishing algorithm execution and outputting a global optimal solutionX * And its fitness, and the corresponding production and maintenance scheduling scheme.
Preferably, the inputting the initial solution in S301 determines the assignment result of the workpieces and the number of times of maintenance assigned to each machine according to a decoding rule, and specifically includes:
s301a, inputting a solution vector
Figure DEST_PATH_IMAGE007
；
S301b, reading the decision variables according to the following decoding rules:x i is [0,1 ]]Random number in between, frontNThe number represents the distribution result of each workpiece; if it is notx i Belong to
Figure 100002_DEST_PATH_IMAGE008
Then represents the firstiThe individual workpieces will be assigned toM k A machine, afterk max -m numbers represent the distribution results of the respective maintenance activities; if it is notx i Belong to
Figure DEST_PATH_IMAGE009
Indicates that this maintenance activity is to be assigned to the secondM k A machine for the preparation of a coating on a substrate,k=1,2…M(ii) a If it belongs to
Figure 100002_DEST_PATH_IMAGE010
Then the maintenance activity is not assigned.
Preferably, the heuristic algorithm in S301 specifically includes:
step one, input distribution to machineM l For machining individual workpiecesTime and number of repairsk l ；
Step two, sorting all workpieces according to conventional processing time without enhancement, e.g.
Figure 100002_DEST_PATH_IMAGE012
Whereinn l Presentation machine
Figure 100002_DEST_PATH_IMAGE014
The number of workpieces on the workpiece;
step three, for the firstxA workpiece, an
Figure 100002_DEST_PATH_IMAGE016
，
Figure 100002_DEST_PATH_IMAGE018
(ii) a If it is used
Figure 100002_DEST_PATH_IMAGE020
To form a work
Figure 100002_DEST_PATH_IMAGE022
Is put in
Figure 100002_DEST_PATH_IMAGE024
Group of
Figure 100002_DEST_PATH_IMAGE026
A location; otherwise, the workpiece is put into
Figure 926418DEST_PATH_IMAGE022
Put into
Figure 100002_DEST_PATH_IMAGE028
Group (iv);
step four, repeating the step three until all the workpieces are distributed;
and step five, maintaining after each group of workpieces are processed.
Preferably, the method for calculating the fitness value in S301 specifically includes:
the method comprises the following steps: computing machineM l Expected finishing time of the workpiece at each position on:
Figure 100002_DEST_PATH_IMAGE030
wherein the content of the first and second substances,
Figure 100002_DEST_PATH_IMAGE032
is the firstjThe standard processing time of each workpiece is set,
Figure 100002_DEST_PATH_IMAGE034
is the actual processing time of the workpiece,
Figure 100002_DEST_PATH_IMAGE036
is the coefficient of the fading away,ris the position of the workpiece in a certain set of machining activities;
step two: computing machineM l ToiTime of group processing activity
Figure 100002_DEST_PATH_IMAGE038
：
Figure DEST_PATH_IMAGE039
Wherein the content of the first and second substances,G il showing a machineM l ToiA set of workpieces of a group;
step three: the maximum completion time on each machine is calculated as follows:
Figure DEST_PATH_IMAGE041
Figure DEST_PATH_IMAGE043
Figure DEST_PATH_IMAGE045
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE047
is shown aslMaximum time-out on individual machine;
Figure DEST_PATH_IMAGE049
is shown aslOn the machineiMaintenance time for a secondary maintenance activity;αis the standard time for a maintenance activity;βis the degradation factor of the maintenance activity and,α+βP il is shown to be passing through
Figure 233772DEST_PATH_IMAGE038
After the machining time of (a), the time required for the maintenance activity;
Figure 279088DEST_PATH_IMAGE028
is shown inlDeveloped on a single machine
Figure 939877DEST_PATH_IMAGE028
A secondary maintenance activity;
step four, the maximum completion time in all the machines is the objective functionC max ：
C max =max{C max (M l }
Wherein, the first and the second end of the pipe are connected with each other,
Figure 754249DEST_PATH_IMAGE047
is shown aslMaximum completion time on the individual processing machines.
Preferably, the variable neighborhood searching operation in S308 includes:
s308a, inputting an optimal solution
Figure DEST_PATH_IMAGE051
And let E =1;
s308b, judgment E
Figure DEST_PATH_IMAGE053
If yes, go to step S308c; otherwise, entering step S308h;
s308c, randomly generating integers
Figure DEST_PATH_IMAGE055
S308d, judging
Figure DEST_PATH_IMAGE057
If yes, go to step S308e; otherwise, entering S308f;
s308e, order
Figure DEST_PATH_IMAGE059
Randomly generating integers
Figure DEST_PATH_IMAGE061
(ii) a From
Figure DEST_PATH_IMAGE063
To
Figure DEST_PATH_IMAGE065
Respectively order
Figure DEST_PATH_IMAGE067
Wherein
Figure DEST_PATH_IMAGE069
To represent
Figure DEST_PATH_IMAGE071
To
Figure DEST_PATH_IMAGE073
An element;
s308f, order
Figure 888296DEST_PATH_IMAGE059
Then, againOrder to
Figure DEST_PATH_IMAGE075
；
S308g, judgment
Figure DEST_PATH_IMAGE077
Whether it is true, if so, make
Figure DEST_PATH_IMAGE079
Let E =1, and return to S308b; if not, let E = E +1, and return to S308b;
s308h, outputting the optimal solution
Figure 93012DEST_PATH_IMAGE051
。
In a second aspect, the invention provides a production and maintenance scheduling system based on an improved hybrid whale-variant neighborhood search, the system comprising:
at least one memory cell;
at least one processing unit;
wherein the at least one memory unit has stored therein at least one instruction that is loaded and executed by the at least one processing unit to perform the steps of:
s1, initializing input parameters of an algorithm and setting execution parameters of mixed whale-variant neighborhood search, wherein the input parameters comprise the number of workpiecesNNumber of machines m, total number of maintenance operationsk max The degradation coefficient of the maintenance activity and the degradation coefficient of the machine processing capacity;
s2, randomly generating according to input parameterspopEach length isN+k max -a one-dimensional vector of m as an initial solution for a hybrid whale-variant neighborhood search;
s3, inputting the initial solution into mixed whale-variable neighborhood search to solve a global optimal solution of a production and maintenance cooperative scheduling problem considering machine performance degradation, wherein the global optimal solution corresponds to a workpiece processing sequence and distribution of maintenance activities, and the mixed whale-variable neighborhood search refers to the following steps: and performing variable neighborhood search operation on the optimal solution output by the whale algorithm.
Preferably, the S3 includes:
s301, inputting an initial solution, and determining the distribution result of the workpieces and the maintenance times distributed to each machine according to a decoding rule; obtaining total completion time of each stand-alone machine by applying heuristic algorithm of the stand-alone machines, calculating the fitness value of each whale in the initial population, and recording the one-dimensional vector corresponding to the individual with the best fitness in the whales asX * (ii) a And order
Figure DEST_PATH_IMAGE081
；
S302, judging when
Figure DEST_PATH_IMAGE083
If yes, go to step S303; otherwise, go to step S310;
s303, for all individuals
Figure DEST_PATH_IMAGE085
Randomly generating a probability
Figure DEST_PATH_IMAGE087
And updating its iteration parametersa,A,C,l,pWherein, in the step (A),
Figure DEST_PATH_IMAGE089
iteration parameterA=2ar-a，C=2r，lIs [ -1,1 [ ]]A random number in between, and a random number,ris [0,1 ]]A random number in between;
s304, judging
Figure DEST_PATH_IMAGE091
05, if yes, go to step S305, otherwise go to step S306;
s305, determining
Figure DEST_PATH_IMAGE093
If yes, the following formula is followedIndividual of renewing whale
Figure DEST_PATH_IMAGE095
：
Figure DEST_PATH_IMAGE097
Figure DEST_PATH_IMAGE099
If not, a search agent is randomly selected
Figure DEST_PATH_IMAGE101
And updating the whale individuals according to the following formula
Figure DEST_PATH_IMAGE103
：
Figure DEST_PATH_IMAGE105
Figure DEST_PATH_IMAGE107
S306, updating whale individuals according to the following formula
Figure 462552DEST_PATH_IMAGE103
：
Figure 100002_DEST_PATH_IMAGE004A
Wherein the content of the first and second substances,bis a constant that defines the shape of the spiral,Lis a random number in (-1, 1),
Figure 803534DEST_PATH_IMAGE006
is shown astBest solution and the second in generationkVector differences for individual individuals;
s307, calculating the fitness value of each whale individual in the updated population, and assigning the one-dimensional vector corresponding to the whale individual with the lowest fitness value to the whale individualX * ；
S308, solving the optimal solutionX * Performing neighborhood-varying search operation and updating optimal solutionX * ；
S309, ordert=t+1, and return to S302;
s310, finishing algorithm execution and outputting a global optimal solutionX * Its fitness and its corresponding production and maintenance scheduling scheme.
Preferably, the inputting the initial solution in S301 determines the assignment result of the workpieces and the number of times of maintenance assigned to each machine according to a decoding rule, and specifically includes:
s301a, inputting a solution vector
Figure 62477DEST_PATH_IMAGE007
；
S301b, reading the decision variables according to the following decoding rules:x i is [0,1 ]]Random number in between, frontNThe number represents the distribution result of each workpiece; if it is notx i Belong to
Figure 816807DEST_PATH_IMAGE008
Then represents the firstiThe individual workpieces will be assigned toM k A machine, afterk max -m numbers represent the distribution results of the respective maintenance activities; if it is notx i Belong to
Figure 819398DEST_PATH_IMAGE009
Indicates that this maintenance activity is to be assigned to the secondM k A machine for the preparation of a coating on a substrate,k=1,2…M(ii) a If it belongs to
Figure 608362DEST_PATH_IMAGE010
It means that the maintenance activity is not allocated.
Preferably, the heuristic algorithm in S301 specifically includes:
step one, input distribution to machineM l Time of processing and maintenance of each workpiecek l ；
Step two, sorting all the workpieces according to conventional processing time without enhancement, e.g.
Figure 428419DEST_PATH_IMAGE012
Whereinn l Indicating machine
Figure 935624DEST_PATH_IMAGE014
Number of workpieces;
step three, for the first stepxA workpiece, an
Figure 476327DEST_PATH_IMAGE016
，
Figure 119798DEST_PATH_IMAGE018
(ii) a If it is not
Figure 720544DEST_PATH_IMAGE020
To form a work
Figure 590411DEST_PATH_IMAGE022
Is put in
Figure 934804DEST_PATH_IMAGE024
Group of
Figure 698361DEST_PATH_IMAGE026
A location; otherwise, the workpiece is put into
Figure 470008DEST_PATH_IMAGE022
Put into
Figure 686225DEST_PATH_IMAGE028
Group (d);
step four, repeating the step three until all the workpieces are distributed;
and step five, maintaining each group of workpieces after the processing is finished.
(III) advantageous effects
The invention provides a production and maintenance scheduling method based on mixed whale-variable neighborhood search. Compared with the prior art, the method has the following beneficial effects:
the method comprises the steps of firstly initializing input parameters of an algorithm, setting execution parameters of mixed whale-variable neighborhood search, wherein the input parameters comprise decay coefficients of maintenance activities and machine processing capacity, and randomly generating the input parameters according to the input parameterspopTaking the one-dimensional vector as an initial solution of mixed whale-variable neighborhood search; inputting the initial solution into a mixed whale-variable neighborhood search to solve a global optimal solution of a production and maintenance cooperative scheduling problem considering machine performance degradation, wherein the global optimal solution corresponds to a workpiece processing sequence and distribution of maintenance activities, and the mixed whale-variable neighborhood search refers to the following steps: and performing variable neighborhood search operation on the optimal solution output by the whale algorithm. In the scheduling process, input parameters comprise the degradation coefficient of maintenance activities and the degradation coefficient of the machining capacity of the machine, and the production and maintenance are cooperatively scheduled by considering the degradation of the performance of the machine, so that accurate cooperative scheduling is realized. Meanwhile, the optimal solution output by the whale algorithm is further improved through variable neighborhood search, the search capability of a solution space is expanded, a better solution is obtained, more accurate collaborative scheduling is further realized, the reliability of machine operation is improved, and the operation cost of enterprises is reduced.
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 block diagram of a production and maintenance scheduling method based on a hybrid whale-variant neighborhood search in an embodiment of the invention;
FIG. 2 is a schematic view of a machine according to an embodiment of the present inventionM l Assigned to 10 workpieces and 3 service assignments.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete description of the technical solutions in the embodiments of the present invention, it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
The embodiment of the application provides a generation and maintenance coordination scheduling method and system based on improved hybrid whale-variable neighborhood search, solves the technical problem that in the prior art, the influence of machine performance degradation on production and maintenance is not considered, and achieves efficient and accurate coordinated scheduling.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
in the scheduling process, input parameters comprise the degradation coefficient of maintenance activities and the degradation coefficient of the machining capacity of the machine, and the production and maintenance are cooperatively scheduled by considering the degradation of the performance of the machine, so that accurate cooperative scheduling is realized.
In order to better understand the technical scheme, the technical scheme is described in detail in the following with reference to the attached drawings of the specification and specific embodiments.
The embodiment of the invention considers the technical problem that the performance decline of the machine can influence the production and the maintenance, and also considers the production and the maintenance joint scheduling problem of the position-based workpiece processing time and a plurality of times of maintenance activities, and aims to select the proper workpiece processing sequence and maintenance opportunity scheme by making a decision on the processing sequence of the workpieces with different processing times on different machines and making a decision on the times of the machine maintenance activities and the number of inserted maintenance activities so as to minimize the total time of the production and the maintenance of the equipment. Suppose thatnA workpiece is arranged atmThe standard processing time of the workpiece needs to be considered when processing on the bench machinep j And the actual processing time of the workpiece
Figure 958944DEST_PATH_IMAGE034
Standard time required for a maintenance activityαAnd the actual timema il And the number of maintenance activities, and the like, and then selects the most appropriate workpiece processing sequence and maintenance timing scheme by minimizing production and equipment maintenance time.
A Whale Optimization Algorithm (WOA) is a group intelligent Optimization Algorithm, is an optimized search Algorithm developed by inspiring Whale prey activities, has strong convergence performance, and has the characteristics of few parameters, easiness in implementation and the like. The general steps of the whale optimization algorithm include: (1) initializing whale populations; (2) Constructing a whale population structure, and recording the solution corresponding to the individual with the best fitness in the whale asx * (ii) a (3) The whales in the whale colony randomly surround the prey according to probability or expel the prey by using a bubble net (updating the position); (4) Repeating the steps (2) and (3), and searching an optimal solution in the whole space; (5) And after the number of times of reaching the termination condition, outputting the position of the whale closest to the prey in the whale population, namely outputting a solution with the highest fitness.
Example 1:
the embodiment of the invention provides a production and maintenance scheduling method based on improved mixed whale-variable neighborhood search, which comprises the following steps of:
s1, initializing input parameters of an algorithm and setting execution parameters of mixed whale-variant neighborhood search, wherein the input parameters comprise the number of workpiecesNNumber of machines m, total number of maintenance operationsk max The coefficient of degradation of maintenance activities, the coefficient of degradation of machine processing capacity;
s2, randomly generating according to input parameterspopHas a length ofN+k max -a one-dimensional vector of m as an initial solution for a hybrid whale-variant neighborhood search;
s3, inputting the initial solution into mixed whale-variable neighborhood search to solve a global optimal solution of a production and maintenance cooperative scheduling problem considering machine performance degradation, wherein the global optimal solution corresponds to a workpiece processing sequence and distribution of maintenance activities, and the mixed whale-variable neighborhood search refers to the following steps: and performing variable neighborhood search operation on the optimal solution output by the whale algorithm.
In the scheduling process, input parameters comprise the degradation coefficient of maintenance activities and the degradation coefficient of the machining capacity of the machine, and the production and maintenance are cooperatively scheduled by considering the degradation of the performance of the machine, so that accurate cooperative scheduling is realized. Meanwhile, the optimal solution output by the whale algorithm is further improved through variable neighborhood search, the search capability of a solution space is expanded, a better solution is obtained, more accurate collaborative scheduling is further realized, the reliability of machine operation is improved, and the operation cost of enterprises is reduced.
The following describes each step in detail:
in step S1, initializing input parameters of the algorithm and setting execution parameters of mixed whale-variant neighborhood search, wherein the input parameters comprise the number of workpiecesNNumber of machines m, total number of maintenance operationsk max Standard machining time per workpiecep j Standard time required for a maintenance activityαCoefficient of decay of maintenance activitiesβCoefficient of degradation of machine processing abilityθ. The specific implementation process is as follows:
input parameters for the initialization algorithm, including the number of workpiecesNNumber of machines m, total number of maintenance operationsk max Standard machining time per workpiecep j Standard time required for a maintenance activityαCoefficient of decay of maintenance activitiesβCoefficient of degradation of machine processing abilityθ。
Setting execution parameters of mixed whale-variable neighborhood search, wherein the execution parameters comprise the former iteration times t =1 and the maximum iteration timest max Iteration parameterpAnd a linearly decreasing iteration parametera，Among them, in the embodiment of the inventionp=0.5，
Figure 577007DEST_PATH_IMAGE089
。
In step S2, random generation is performed according to the input parameterspopHas a length ofN+k max The one-dimensional vector of-m serves as the initial solution for the mixed whale-variant neighborhood search. The specific implementation process is as follows:
random generationpopOne-dimensional vector as the initial solution of the algorithm, wherein the length of the vector isN+k max -mThe value range of each element is [0,1 ]]. Each one-dimensional vector represents the position of a whale and is recordediThe position of the whale head is defined as
Figure 100002_DEST_PATH_IMAGE108
The workpieces are arranged in descending order of processing time, wherein,x i is [0,1 ]]Random number in between, frontNThe number represents the distribution result of each workpiece; if it is notx i Belong to
Figure 785134DEST_PATH_IMAGE008
Then represents the firstiThe individual workpieces will be assigned toM k A machine, afterk max -m numbers represent the distribution result of the respective maintenance activities; if it is notx i Belong to
Figure 488648DEST_PATH_IMAGE009
Indicates that this maintenance activity is to be assigned to the secondM k The machine is characterized in that the machine comprises a machine body,k=1,2…M(ii) a If it belongs to
Figure 50211DEST_PATH_IMAGE010
Then the maintenance activity is not assigned.
In step S3, the initial solution is input into a mixed whale-variation neighborhood search to solve a global optimal solution of a production and maintenance cooperative scheduling problem considering machine performance degradation, and the global optimal solution corresponds to the distribution of a workpiece processing sequence and maintenance activities. The specific implementation process is as follows:
s301, inputting an initial solution, determining the distribution result of the workpieces and the maintenance times distributed by each machine according to a decoding rule, obtaining the total completion time on each single machine by applying a heuristic algorithm of the single machine, further calculating the fitness value of each whale in the initial population, and recording a one-dimensional vector corresponding to an individual with the best fitness in the whales as the one-dimensional vectorX * (ii) a And order
Figure 788360DEST_PATH_IMAGE081
. The method specifically comprises the following steps:
s301a, inputting a solution vector
Figure 167388DEST_PATH_IMAGE007
；
S301b, reading the decision variables according to the following decoding rules:x i is [0,1 ]]Random number in between, frontNThe number represents the distribution result of each workpiece; if it is notx i Belong to
Figure 92619DEST_PATH_IMAGE008
Then represents the firstiThe individual workpieces will be assigned toM k A machine, afterk max -m numbers represent the distribution result of the respective maintenance activities; if it is usedx i Belong to
Figure 582506DEST_PATH_IMAGE009
Indicates that this maintenance activity is to be assigned to the secondM k The machine is characterized in that the machine comprises a machine body,k=1,2…M(ii) a If it belongs to
Figure 34216DEST_PATH_IMAGE010
Then the maintenance activity is not assigned.
S301c, applying a heuristic algorithm of the single machines to obtain the total completion time of each single machine. The method specifically comprises the following steps:
c1, input distribution to machines
Figure 584146DEST_PATH_IMAGE014
Time of processing and maintenance of each workpiece
Figure 262252DEST_PATH_IMAGE028
；
c2, sorting all workpieces according to conventional processing time without enhancement, e.g.
Figure 290251DEST_PATH_IMAGE012
Whereinn l Presentation machine
Figure 737413DEST_PATH_IMAGE014
The number of workpieces on the workpiece;
c3 for the firstxA workpiece, such as
Figure 68031DEST_PATH_IMAGE016
(the remainder is taken out),
Figure 233433DEST_PATH_IMAGE018
(round up); if it is not
Figure 65123DEST_PATH_IMAGE020
To form a work
Figure 366791DEST_PATH_IMAGE022
Put into
Figure 992945DEST_PATH_IMAGE024
Group of
Figure 770277DEST_PATH_IMAGE026
A location; otherwise, the workpiece is put
Figure 405658DEST_PATH_IMAGE022
Is put in
Figure 561832DEST_PATH_IMAGE028
Group (d);
c4, repeating the step c3 until all workpieces are distributed;
and c5, maintaining after each group of workpieces are processed.
Examples are: machine with a rotatable shaft
Figure 358887DEST_PATH_IMAGE014
Assigned to 10 workpieces and 3 repairs, the workpieces are first sorted in the machining order without an increase to obtain
Figure DEST_PATH_IMAGE110
In which satisfy
Figure DEST_PATH_IMAGE112
And then workpiece dispensing is performed. Such as the 7 th workpiece, for example,
Figure DEST_PATH_IMAGE114
，
Figure DEST_PATH_IMAGE116
then the 7 th workpiece is assigned to the 3 rd position of group 1. As shown in fig. 2.
S301d, calculating the fitness value of each whale in the initial population, and recording the one-dimensional vector corresponding to the individual with the best fitness in the whales asX * (ii) a And order
Figure 572917DEST_PATH_IMAGE081
. The method specifically comprises the following steps:
d1, computing machine
Figure 11989DEST_PATH_IMAGE014
Expected finishing time of the workpiece at each position on:
Figure DEST_PATH_IMAGE030A
wherein, the first and the second end of the pipe are connected with each other,
Figure 694774DEST_PATH_IMAGE032
is the firstjThe standard processing time of each workpiece is set,
Figure 928309DEST_PATH_IMAGE034
is the actual processing time of the workpiece,
Figure 24441DEST_PATH_IMAGE036
is the coefficient of the fading of the signal,ris the position of the workpiece in a certain set of machining activities;
d2, computing machine
Figure 1624DEST_PATH_IMAGE014
ToiTime of group processing activity
Figure 132391DEST_PATH_IMAGE038
：
Figure 661462DEST_PATH_IMAGE039
Wherein the content of the first and second substances,G il showing a machineM l ToiA set of workpieces of a group;
d3, calculating the maximum completion time on each machine according to the following formula:
Figure DEST_PATH_IMAGE041A
Figure DEST_PATH_IMAGE043A
Figure DEST_PATH_IMAGE045A
wherein, the first and the second end of the pipe are connected with each other,
Figure 369524DEST_PATH_IMAGE047
is shown aslMaximum time-out on individual machine;
Figure 884819DEST_PATH_IMAGE049
is shown aslA machineTo go toiMaintenance time for a secondary maintenance activity;αis the standard time for a maintenance activity;βis the coefficient of decay of the maintenance activity,α+βP il show by passing
Figure 870092DEST_PATH_IMAGE038
After the machining time of (a), the time required for the maintenance activity;
Figure 445430DEST_PATH_IMAGE028
is shown inlDeveloped on a machine
Figure 781733DEST_PATH_IMAGE028
A secondary maintenance activity;
d4, the maximum completion time of all the machines is the objective functionC max ：
C max =max{C max (M l }
Wherein, the first and the second end of the pipe are connected with each other,
Figure 976085DEST_PATH_IMAGE047
denotes the firstlMaximum completion time on a single processing machine.
d5, calculating the fitness value of each whale in the initial solution through an objective function, and recording a one-dimensional vector corresponding to an individual with the lowest fitness value in the whales as a one-dimensional vector
Figure 815865DEST_PATH_IMAGE051
(ii) a And order
Figure 562105DEST_PATH_IMAGE081
。
S302, judging when
Figure 120125DEST_PATH_IMAGE083
If yes, go to step S303; otherwise, go to step S310;
s303, for all individuals
Figure DEST_PATH_IMAGE118
Randomly generating a probability
Figure 633015DEST_PATH_IMAGE087
And updating its iteration parametersa,A,C,l,pWherein, in the step (A),a=2*(1-t/t max ) Iteration parameterA=2ar-a，C=2r，lIs [ -1,1 [ ]]A random number in between, and a random number,ris [0,1 ]]A random number in between.
S304, judging
Figure 592880DEST_PATH_IMAGE091
0And 5, if yes, the step S305 is entered, otherwise, the step S306 is entered.
S305, judgment
Figure 510021DEST_PATH_IMAGE093
If the whale individual is found to be true, updating the whale individual according to the following formula
Figure 555337DEST_PATH_IMAGE095
：
Figure DEST_PATH_IMAGE097A
Figure DEST_PATH_IMAGE099A
If not, a search agent is randomly selected
Figure 153809DEST_PATH_IMAGE101
And updating whale individuals according to the following formula
Figure 92815DEST_PATH_IMAGE103
：
Figure DEST_PATH_IMAGE105A
Figure DEST_PATH_IMAGE107A
S306, updating whale individuals according to the following formula
Figure 384119DEST_PATH_IMAGE103
：
Figure DEST_PATH_IMAGE004AA
Wherein, the first and the second end of the pipe are connected with each other,bis a constant, which defines the shape of the spiral,Lis a random number in (-1, 1),
Figure 572524DEST_PATH_IMAGE006
denotes the firsttBest solution and the second in generationkVector differences for individuals;
s307, calculating the fitness value of each whale individual in the updated population, and assigning the one-dimensional vector corresponding to the whale individual with the optimal fitness value to the whale individualX * ；
S308, solving the optimal solutionX * Performing neighborhood-varying search operation and updating optimal solutionX * . The method specifically comprises the following steps:
s308a, inputting an optimal solution
Figure 37003DEST_PATH_IMAGE051
And let E =1;
s308b, judgment E
Figure 440303DEST_PATH_IMAGE053
If yes, go to step S308c; otherwise, entering step S308h;
s308c, randomly generating integers
Figure 964825DEST_PATH_IMAGE055
；
S308d, judging
Figure 984733DEST_PATH_IMAGE057
If yes, go to step S308e; otherwise, entering S308f;
s308e, order
Figure 862691DEST_PATH_IMAGE059
Randomly generating integers
Figure 651655DEST_PATH_IMAGE061
(ii) a From
Figure 81499DEST_PATH_IMAGE063
To
Figure 323125DEST_PATH_IMAGE065
Respectively order
Figure 129407DEST_PATH_IMAGE067
Wherein, in the process,
Figure 163091DEST_PATH_IMAGE069
to represent
Figure 763837DEST_PATH_IMAGE071
To (1)
Figure 758337DEST_PATH_IMAGE073
An element;
s308f, order
Figure 837152DEST_PATH_IMAGE059
Then order again
Figure DEST_PATH_IMAGE120
；
S308g, judgment
Figure 741654DEST_PATH_IMAGE077
If true, then order
Figure 513301DEST_PATH_IMAGE079
Let E =1, and return to S308b; if not, let E = E +1, and return to S308b, where,
Figure DEST_PATH_IMAGE122
represent
Figure 260677DEST_PATH_IMAGE071
The value of the fitness of (a) is,
Figure DEST_PATH_IMAGE124
representX * The calculation mode of the fitness value is consistent with the step S301d, and the fitness value of each whale in the population is calculated through an objective function, which is not described again;
s308h, outputting the optimal solution
Figure 798975DEST_PATH_IMAGE051
。
S309, ordert=t+1, and returns to S302.
S310, finishing algorithm execution and outputting a global optimal solutionX * Its fitness and its corresponding production and maintenance scheduling scheme.
Example 2:
the embodiment of the invention provides a production and maintenance scheduling system based on improved hybrid whale-variable neighborhood search, which comprises:
at least one memory cell;
at least one processing unit;
wherein the at least one memory unit has stored therein at least one instruction that is loaded and executed by the at least one processing unit to perform the steps of:
s1, initializing input parameters of an algorithm and setting execution parameters of mixed whale-variant neighborhood search, wherein the input parameters comprise the number of workpiecesNNumber of machines m, total number of maintenance operationsk max Coefficient of degradation of maintenance activities, coefficient of degradation of machine processing capacity；
S2, randomly generating according to input parameterspopHas a length ofN+k max -a one-dimensional vector of m as an initial solution for a hybrid whale-variant neighborhood search;
s3, inputting the initial solution into a mixed whale-variable neighborhood search to solve a global optimal solution of a production and maintenance cooperative scheduling problem considering machine performance degradation, wherein the global optimal solution corresponds to a workpiece processing sequence and distribution of maintenance activities, and the mixed whale-variable neighborhood search means: and performing variable neighborhood search operation on the optimal solution output by the whale algorithm.
It can be understood that the production and maintenance scheduling system based on the improved mixed whale-variable neighborhood search provided by the embodiment of the present invention corresponds to the production and maintenance scheduling method based on the improved mixed whale-variable neighborhood search, and the explanation, examples, and beneficial effects of the relevant contents thereof may refer to the corresponding contents in the production and maintenance scheduling method based on the improved mixed whale-variable neighborhood search, and are not repeated here.
In summary, compared with the prior art, the method has the following beneficial effects:
1. in the scheduling process, input parameters comprise the degradation coefficient of maintenance activities and the degradation coefficient of the machining capacity of the machine, and the production and maintenance are cooperatively scheduled by considering the degradation of the performance of the machine, so that accurate cooperative scheduling is realized.
2. According to the embodiment of the invention, the optimal solution output by the whale algorithm is further improved through variable neighborhood search, the search capability of the solution space is expanded to obtain a better solution, more accurate cooperative scheduling is further realized, the reliability of machine operation is improved, and the operation cost of enterprises is reduced.
It is noted that, herein, 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 phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like 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, and 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 (7)

1. A production and maintenance scheduling method based on hybrid whale-variable neighborhood search is characterized by comprising the following steps:
s1, initializing input parameters of an algorithm and setting execution parameters of mixed whale-variant neighborhood search, wherein the input parameters comprise the number of workpiecesNNumber of machines m, total number of maintenance operationsk max The degradation coefficient of the maintenance activity and the degradation coefficient of the machine processing capacity;
s2, randomly generating according to input parameterspopHas a length ofN+k max -a one-dimensional vector of m as an initial solution to a hybrid whale-variant neighborhood search;
s3, inputting the initial solution into a mixed whale-variable neighborhood search to solve a global optimal solution of a production and maintenance cooperative scheduling problem considering machine performance degradation, wherein the global optimal solution corresponds to a workpiece processing sequence and distribution of maintenance activities, and the mixed whale-variable neighborhood search means: performing variable neighborhood search operation on the optimal solution output by the whale algorithm, wherein the variable neighborhood search operation comprises the following steps:
s301, inputtingDetermining the distribution result of the workpieces and the maintenance times distributed by each machine according to a decoding rule by using an initial solution; obtaining total completion time of each stand-alone machine by applying heuristic algorithm of the stand-alone machines, calculating the fitness value of each whale in the initial population, and recording the one-dimensional vector corresponding to the individual with the best fitness value in the whales as the fitness valueX * (ii) a And make an ordert=1；
S302, judgmentt< t max If yes, go to step S303; otherwise, go to step S310;
s303, for all individualsX k (t) = (x 1 , x 2 , ..., x N , x N+1 , x N+2 , .., x N+kmax-m )Randomly generating a probabilityp k And updating its iteration parametersa,A,C,l,pWherein, in the process,a=2*(1-t/t max ) Iteration parameterA=2ar-a，C=2r，lIs [ -1,1 [ ]]A random number in between, and a random number,ris [0,1 ]]A random number in between;
s304, judgingp k <05, if yes, go to step S305, otherwise go to step S306;
s305, judging | A<1, if yes, updating whale individuals according to the following formulaX k ：
Figure DEST_PATH_IMAGE001
If not, a search agent is randomly selectedX rand (t) And updating whale individuals according to the following formulaX k (t)：
Figure DEST_PATH_IMAGE002
S306, updating whale individuals according to the following formulaX k (t)：
Figure DEST_PATH_IMAGE004
Wherein the content of the first and second substances,bis a constant, which defines the shape of the spiral,Lis a random number in (-1, 1),
Figure DEST_PATH_IMAGE006
is shown astBest solution and the second in generationkVector differences for individual individuals;
s307, calculating the fitness value of each whale individual in the updated population, and assigning the one-dimensional vector corresponding to the whale individual with the optimal fitness value to the whale individualX * ；
S308, solving the optimal solutionX * Performing variable neighborhood search operation and updating optimal solutionX * ；
S309, ordert=t+1, and return to S302;
s310, finishing algorithm execution and outputting a global optimal solutionX * The fitness value of the system and a corresponding production and maintenance scheduling scheme;
the method for calculating the fitness value specifically comprises the following steps:
the method comprises the following steps: computing machineM l Expected finishing time of the workpiece at each position:
Figure DEST_PATH_IMAGE008
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE010
is the firstjThe standard processing time of each workpiece is set,
Figure DEST_PATH_IMAGE012
is the actual processing time of the workpiece,
Figure DEST_PATH_IMAGE014
is the coefficient of the fading of the signal,ris the position of the workpiece in a certain set of machining activities;
step two: computing machineM l ToiTime of group processing activity
Figure DEST_PATH_IMAGE016
：
Figure DEST_PATH_IMAGE018
Wherein the content of the first and second substances,G il showing a machineM l ToiA set of workpieces of a group;
step three: the maximum completion time on each machine is calculated as follows:
Figure DEST_PATH_IMAGE020
Figure DEST_PATH_IMAGE022
Figure DEST_PATH_IMAGE024
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE026
is shown aslMaximum time-to-completion on individual machines;
Figure DEST_PATH_IMAGE028
is shown aslOn the machineiMaintenance time for the secondary maintenance activity;αis a mark for one-time maintenanceQuasi-time;βis the degradation factor of the maintenance activity and,α+βP il is shown to be passing through
Figure 280270DEST_PATH_IMAGE016
After the machining time of (c), the time required for the maintenance activity;
Figure DEST_PATH_IMAGE030
is shown inlDeveloped on a single machine
Figure 869514DEST_PATH_IMAGE030
A secondary maintenance activity;
step four, the maximum completion time in all the machines is the objective functionC max Calculating the fitness value of each whale in the population through an objective function:
C max =max{C max (M l )}
wherein the content of the first and second substances,
Figure 118093DEST_PATH_IMAGE026
denotes the firstlMaximum completion time on the individual processing machines.
2. The method for scheduling production and maintenance based on hybrid whale-variant neighborhood search as claimed in claim 1, wherein the step S301 of inputting an initial solution determines the assignment result of the workpieces and the number of times of maintenance assigned to each machine according to a decoding rule, and specifically comprises:
s301a, inputting a solution vector
Figure DEST_PATH_IMAGE031
；
S301b, reading the decision variables according to the following decoding rules:x i is [0,1 ]]Random number in between, frontNThe number represents the distribution result of each workpiece; if it is notx i Belong to
Figure DEST_PATH_IMAGE032
Then represents the firstiThe individual workpieces will be assigned toM k A machine, afterk max -m numbers represent the distribution result of the respective maintenance activities; if it is notx i Belong to
Figure DEST_PATH_IMAGE033
Indicates that this maintenance activity is to be assigned to the secondM k A machine for the preparation of a coating on a substrate,k=1,2 …M(ii) a If it belongs to
Figure DEST_PATH_IMAGE034
Then the maintenance activity is not assigned.
3. The method for hybrid whale-variant neighborhood search based production and maintenance scheduling as claimed in claim 1, wherein the heuristic algorithm in S301 specifically comprises:
step one, input distribution to machineM l Time of processing and maintenance of each workpiecek l ；
Step two, sorting all the workpieces according to conventional processing time without enhancement, e.g.
Figure DEST_PATH_IMAGE036
Whereinn l Presentation machineM l The number of workpieces on the workpiece;
step three, for the first stepxA workpiece, an
Figure DEST_PATH_IMAGE038
，
Figure DEST_PATH_IMAGE040
(ii) a If it is not
Figure DEST_PATH_IMAGE042
A workpiece is put in
Figure DEST_PATH_IMAGE044
Is put in
Figure DEST_PATH_IMAGE046
Group number one
Figure DEST_PATH_IMAGE048
A location; otherwise, the workpiece is put
Figure 732876DEST_PATH_IMAGE044
Is put in
Figure 812828DEST_PATH_IMAGE030
Group (d);
step four, repeating the step three until all the workpieces are distributed;
and step five, maintaining after each group of workpieces are processed.
4. The hybrid whale-variable neighborhood search based production and maintenance scheduling method as claimed in claim 1, wherein the variable neighborhood search operation in the step S308 comprises:
s308a, inputting an optimal solution
Figure DEST_PATH_IMAGE050
And let E =1;
s308b, judgment E
Figure DEST_PATH_IMAGE052
If yes, go to step S308c; otherwise, entering step S308h;
s308c, randomly generating integers
Figure DEST_PATH_IMAGE054
S308d, judging
Figure DEST_PATH_IMAGE056
If yes, go to step S308e; otherwise, entering S308f;
s308e, order
Figure DEST_PATH_IMAGE058
Randomly generating integers
Figure DEST_PATH_IMAGE060
(ii) a From
Figure DEST_PATH_IMAGE062
To
Figure DEST_PATH_IMAGE064
Respectively order
Figure DEST_PATH_IMAGE066
Wherein
Figure DEST_PATH_IMAGE068
Represent
Figure DEST_PATH_IMAGE070
To
Figure DEST_PATH_IMAGE072
An element;
s308f, order
Figure 323706DEST_PATH_IMAGE058
Then order again
Figure DEST_PATH_IMAGE074
；
S308g, judgment
Figure DEST_PATH_IMAGE076
Whether it is true, if so, make
Figure DEST_PATH_IMAGE078
Let E =1, and return to S308b; if not, making E = E +1, and returning to S308b;
s308h, outputting the optimal solution
Figure 997263DEST_PATH_IMAGE050
。
5. A hybrid whale-variable neighborhood search based production and maintenance scheduling system, the system comprising:
at least one memory cell;
at least one processing unit;
wherein the at least one memory unit has stored therein at least one instruction that is loaded and executed by the at least one processing unit to perform the steps of:
s1, initializing input parameters of an algorithm and setting execution parameters of mixed whale-variant neighborhood search, wherein the input parameters comprise the number of workpiecesNNumber of machines m, total number of maintenance operationsk max The degradation coefficient of the maintenance activity and the degradation coefficient of the machine processing capacity;
s2, randomly generating according to input parameterspopHas a length ofN+k max -a one-dimensional vector of m as an initial solution for a hybrid whale-variant neighborhood search;
s3, inputting the initial solution into a mixed whale-variable neighborhood search to solve a global optimal solution of a production and maintenance cooperative scheduling problem considering machine performance degradation, wherein the global optimal solution corresponds to a workpiece processing sequence and distribution of maintenance activities, and the mixed whale-variable neighborhood search means: the variable neighborhood searching operation of the optimal solution output by the whale algorithm comprises the following steps:
s301, inputting an initial solution, and determining the distribution result of the workpieces and the maintenance times distributed by each machine according to a decoding rule; obtaining total completion time of each stand-alone machine by applying heuristic algorithm of stand-alone machines, calculating fitness value of each whale in initial population, and optimizing the fitness value of each whale in the whaleOne-dimensional vector corresponding to one individual of (1) is recorded asX * (ii) a And make an order
Figure DEST_PATH_IMAGE080
(ii) a S302, judging when
Figure DEST_PATH_IMAGE082
If yes, go to step S303; otherwise, go to step S310;
s303, for all individuals
Figure DEST_PATH_IMAGE084
Randomly generating a probability
Figure DEST_PATH_IMAGE086
And updating its iteration parametersa,A,C,l,pWherein, in the step (A),
Figure DEST_PATH_IMAGE088
iteration parameterA=2ar-a，C=2r，lIs [ -1,1]A random number in between, and a random number,ris [0,1 ]]A random number in between;
s304, judging
Figure DEST_PATH_IMAGE090
05, if yes, go to step S305, otherwise go to step S306;
s305, determining
Figure DEST_PATH_IMAGE092
If the whale individual is found to be true, updating the whale individual according to the following formula
Figure DEST_PATH_IMAGE094
：
Figure DEST_PATH_IMAGE096
Figure DEST_PATH_IMAGE098
If not, a search agent is randomly selected
Figure DEST_PATH_IMAGE100
And updating whale individuals according to the following formula
Figure DEST_PATH_IMAGE102
：
Figure DEST_PATH_IMAGE104
Figure DEST_PATH_IMAGE106
S306, updating whale individuals according to the following formula
Figure 759945DEST_PATH_IMAGE102
：
Figure DEST_PATH_IMAGE004A
Wherein, the first and the second end of the pipe are connected with each other,bis a constant, which defines the shape of the spiral,Lis a random number in (-1, 1),
Figure 366507DEST_PATH_IMAGE006
denotes the firsttBest solution and the second in generationkVector differences for individual individuals;
s307, calculating the fitness value of each whale individual in the updated population, and assigning the one-dimensional vector corresponding to the whale individual with the lowest fitness value to the whale individualX * ；
S308, solving the optimal solutionX * Performing variable neighborhood search operation and updating optimal solutionX * ；
S309, ordert=t+1, and return to S302;
s310, finishing algorithm execution and outputting a global optimal solutionX * The fitness value of the system and a corresponding production and maintenance scheduling scheme;
the method for calculating the fitness value specifically comprises the following steps:
the method comprises the following steps: computing machineM l Expected finishing time of the workpiece at each position:
Figure DEST_PATH_IMAGE008A
wherein the content of the first and second substances,
Figure 500816DEST_PATH_IMAGE010
is the firstjThe standard processing time of each workpiece is set,
Figure 723987DEST_PATH_IMAGE012
is the actual processing time of the workpiece,
Figure 726578DEST_PATH_IMAGE014
is the coefficient of the fading of the signal,ris the position of the workpiece in a certain set of machining activities;
step two: computing machineM l ToiTime of group processing activity
Figure 453226DEST_PATH_IMAGE016
：
Figure 351912DEST_PATH_IMAGE018
Wherein the content of the first and second substances,G il showing a machineM l ToiA set of workpieces of a group;
step three: the maximum completion time on each machine is calculated as follows:
Figure DEST_PATH_IMAGE020A
Figure DEST_PATH_IMAGE022A
Figure DEST_PATH_IMAGE024A
wherein, the first and the second end of the pipe are connected with each other,
Figure 875428DEST_PATH_IMAGE026
is shown aslMaximum time-out on individual machine;
Figure 416131DEST_PATH_IMAGE028
is shown aslOn the machineiMaintenance time for a secondary maintenance activity;αis the standard time for a maintenance activity;βis the degradation factor of the maintenance activity and,α+βP il is shown to be passing through
Figure 528444DEST_PATH_IMAGE016
After the machining time of (a), the time required for the maintenance activity;
Figure 66872DEST_PATH_IMAGE030
is shown inlDeveloped on a machine
Figure 264635DEST_PATH_IMAGE030
A secondary maintenance activity;
step four, the maximum completion time in all the machines is the objective functionC max Calculating the fitness value of each whale in the population through an objective function:
C max =max{C max (M l )}
wherein the content of the first and second substances,
Figure 609029DEST_PATH_IMAGE026
is shown aslMaximum completion time on the individual processing machines.
6. The system for scheduling production and maintenance based on hybrid whale-variant neighborhood search as claimed in claim 5, wherein the input initial solution in S301 determines the assignment result of the workpieces and the number of times of maintenance assigned to each machine according to the decoding rule, and specifically comprises:
s301a, inputting a solution vector
Figure 575848DEST_PATH_IMAGE031
；
S301b, reading the decision variables according to the following decoding rules:x i is [0,1 ]]Random number in between, frontNThe number represents the distribution result of each workpiece; if it is usedx i Belong to
Figure 562476DEST_PATH_IMAGE032
Then represents the firstiThe workpieces will be assigned toM k A machine, afterk max -m numbers represent the distribution result of the respective maintenance activities; if it is usedx i Belong to
Figure 981956DEST_PATH_IMAGE033
Indicates that this maintenance activity is to be assigned to the secondM k The machine is characterized in that the machine comprises a machine body,k=1,2 …M(ii) a If it belongs to
Figure 130041DEST_PATH_IMAGE034
Then the maintenance activity is not assigned.
7. The hybrid whale-variation neighborhood search based production and maintenance scheduling system as claimed in claim 5, wherein the heuristic algorithm in S301 specifically comprises:
step one, input distribution to machineM l Time of processing and maintenance of each workpiecek l ；
Step two, sorting all workpieces according to conventional processing time without enhancement, e.g.
Figure 951366DEST_PATH_IMAGE036
Whereinn l Presentation machine
Figure DEST_PATH_IMAGE108
Number of workpieces;
step three, for the first stepxA workpiece, an
Figure 566018DEST_PATH_IMAGE038
，
Figure 269532DEST_PATH_IMAGE040
(ii) a If it is not
Figure 158991DEST_PATH_IMAGE042
A workpiece is put in
Figure 834823DEST_PATH_IMAGE044
Is put in
Figure 417114DEST_PATH_IMAGE046
Group of
Figure 607924DEST_PATH_IMAGE048
A location; otherwise, the workpiece is put into
Figure 301073DEST_PATH_IMAGE044
Is put in
Figure 565832DEST_PATH_IMAGE030
Group (d);
step four, repeating the step three until all the workpieces are distributed;
and step five, maintaining each group of workpieces after the processing is finished.
CN202210995671.0A 2022-08-19 2022-08-19 Production and maintenance scheduling method based on mixed whale-variable neighborhood search Active CN115081754B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210995671.0A CN115081754B (en) 2022-08-19 2022-08-19 Production and maintenance scheduling method based on mixed whale-variable neighborhood search

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210995671.0A CN115081754B (en) 2022-08-19 2022-08-19 Production and maintenance scheduling method based on mixed whale-variable neighborhood search

Publications (2)

Publication Number Publication Date
CN115081754A CN115081754A (en) 2022-09-20
CN115081754B true CN115081754B (en) 2022-11-15

Family

ID=83244151

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210995671.0A Active CN115081754B (en) 2022-08-19 2022-08-19 Production and maintenance scheduling method based on mixed whale-variable neighborhood search

Country Status (1)

Country Link
CN (1) CN115081754B (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5566092A (en) * 1993-12-30 1996-10-15 Caterpillar Inc. Machine fault diagnostics system and method
CN107450498A (en) * 2017-09-11 2017-12-08 合肥工业大学 Based on the production scheduling method and system for improving artificial bee colony algorithm
CN111428318A (en) * 2020-04-30 2020-07-17 上海工程技术大学 Rolling bearing degradation trend prediction method based on whale algorithm optimization
WO2021105246A1 (en) * 2019-11-26 2021-06-03 Basf Se Forecasting industrial aging processes with machine learning methods
CN113011612A (en) * 2021-03-23 2021-06-22 合肥工业大学 Production and maintenance scheduling method and system based on improved wolf algorithm
CN114510874A (en) * 2022-01-13 2022-05-17 北京大学 Production scheduling and machine maintenance optimization method based on joint optimization model

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109886588B (en) * 2019-02-28 2024-01-02 长安大学 Method for solving flexible job shop scheduling based on improved whale algorithm
CN109886589B (en) * 2019-02-28 2024-01-05 长安大学 Method for solving low-carbon workshop scheduling based on improved whale optimization algorithm

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5566092A (en) * 1993-12-30 1996-10-15 Caterpillar Inc. Machine fault diagnostics system and method
CN107450498A (en) * 2017-09-11 2017-12-08 合肥工业大学 Based on the production scheduling method and system for improving artificial bee colony algorithm
WO2021105246A1 (en) * 2019-11-26 2021-06-03 Basf Se Forecasting industrial aging processes with machine learning methods
CN111428318A (en) * 2020-04-30 2020-07-17 上海工程技术大学 Rolling bearing degradation trend prediction method based on whale algorithm optimization
CN113011612A (en) * 2021-03-23 2021-06-22 合肥工业大学 Production and maintenance scheduling method and system based on improved wolf algorithm
CN114510874A (en) * 2022-01-13 2022-05-17 北京大学 Production scheduling and machine maintenance optimization method based on joint optimization model

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
COMBINED PRODUCTION AND MAINTENANCE SCHEDULING FOR A MULTIPLE-PRODUCT, SINGLE-MACHINE PRODUCTION SYSTEM;THOMAS W. SLOAN;《PRODUCTION AND OPERATIONS MANAGEMENT》;20001231;全文 *
Production-driven opportunistic maintenance for batch production;Tangbin Xia 等;《European Journal of Operational Research》;20141231;全文 *
具有时间与位置相关及维修限制的单机排序问题;苟燕等;《运筹学学报》;20160915(第03期);全文 *
带恶化工件的PFS调度的混合遗传算法;轩华等;《工业工程与管理》;20170610(第03期);全文 *
面向高端装备制造协同优化的人工智能方法;陆少军 等;《计算机集成制造系统》;20220730;全文 *

Also Published As

Publication number Publication date
CN115081754A (en) 2022-09-20

Similar Documents

Publication Publication Date Title
CN107590603B (en) Based on the dispatching method and system for improving change neighborhood search and differential evolution algorithm
CN104268722B (en) Dynamic flexible job-shop scheduling method based on multi-objective Evolutionary Algorithm
US20180356803A1 (en) Method and system for batch scheduling uniform parallel machines with different capacities based on improved genetic algorithm
CN113610233B (en) Flexible job shop scheduling method based on improved genetic algorithm
CN110909787A (en) Method and system for multi-objective batch scheduling optimization based on clustering evolutionary algorithm
CN110738365B (en) Flexible job shop production scheduling method based on particle swarm algorithm
CN101901425A (en) Flexible job shop scheduling method based on multi-species coevolution
CN113011612B (en) Production and maintenance scheduling method and system based on improved wolf algorithm
CN114266509A (en) Flexible job shop scheduling method for solving by random greedy initial population genetic algorithm
CN116700176A (en) Distributed blocking flow shop scheduling optimization system based on reinforcement learning
CN112926837B (en) Method for solving job shop scheduling problem based on data-driven improved genetic algorithm
CN112348323A (en) Multi-target energy supply and operation flexible scheduling method
CN109214695B (en) High-end equipment research, development and manufacturing cooperative scheduling method and system based on improved EDA
CN115081754B (en) Production and maintenance scheduling method based on mixed whale-variable neighborhood search
CN114021934A (en) Method for solving workshop energy-saving scheduling problem based on improved SPEA2
JP2000040106A (en) Process arrangement method for production system, process arrangement device for production system and storage medium stored with process arrangement program of production system
CN113568385B (en) Production scheduling method based on multi-variety mixed flow assembly mode
WO2023087418A1 (en) Computer second-type assembly line balance optimization method based on migration genetic algorithm
CN113723695B (en) Remanufacturing scheduling optimization method based on scene
CN115238995A (en) Intelligent scheduling optimization method for complex process
CN109409774A (en) Dispatching method, system and storage medium for intelligence manufacture digitlization workshop
CN114237166A (en) Method for solving multi-rotating-speed energy-saving scheduling problem based on improved SPEA2 algorithm
CN114676987A (en) Intelligent flexible job shop active scheduling method based on hyper-heuristic algorithm
CN114528094A (en) Distributed system resource optimization allocation method based on LSTM and genetic algorithm
CN112784023A (en) Automatic evolution paper-making method and system based on question bank

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant