CN114004136A - Spare part carrying capacity determining method and device - Google Patents

Spare part carrying capacity determining method and device Download PDF

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Publication number
CN114004136A
CN114004136A CN202111097609.1A CN202111097609A CN114004136A CN 114004136 A CN114004136 A CN 114004136A CN 202111097609 A CN202111097609 A CN 202111097609A CN 114004136 A CN114004136 A CN 114004136A
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spare part
spare parts
carrying capacity
important
spare
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李萌婕
曹凯
王哲
陈微
陶艳玲
陈锡禹
王靖尧
冯雅晴
高建
臧驰
白庆坤
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Beijing Institute of Radio Metrology and Measurement
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
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    • 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
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    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
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    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/04—Ageing analysis or optimisation against ageing

Abstract

The application discloses a spare part carrying capacity determining method, which comprises the following steps: determining the upper limit of the carrying capacity of each important spare part according to the portable capacity; determining the carrying capacity lower limit of each important spare part according to the set guarantee probability; in the carrying capacity upper limit and the carrying capacity lower limit, an improved genetic algorithm is adopted to traverse all important spare part carrying capacities in the range, the task completion degree of each important spare part carrying capacity scheme is obtained through a binomial probability calculation method, and the spare part carrying capacity optimal scheme corresponding to the highest task completion degree is obtained. The invention also comprises a device for realizing the method. The method and the device solve the problem that the task completion degree is poor due to the fact that the current method for carrying the spare parts is not comprehensively researched.

Description

Spare part carrying capacity determining method and device
Technical Field
The application relates to the technical field of comprehensive guarantee, in particular to a method and a device for determining carrying capacity of spare parts.
Background
Spare parts are indispensable resources in maintenance and guarantee, and are important factors for determining whether a maintenance task can be smoothly completed. Scientific and efficient spare part guarantee is an important guarantee for improving the maintenance success rate. Therefore, how to determine the type and number of spare parts required for a task is a key issue. Spare parts are not scientifically configured, on one hand, spare part shortage is easily caused, and guarantee probability is reduced; on the other hand, some spare parts may be over-configured, resulting in resource waste. Therefore, the study on the carrying capacity of spare parts is particularly important.
There are many studies on spare part carrying capacity, but most studies are limited to exponentially distributed spare parts; for comprehensive guarantee, the types and the number of the spare parts are important, most of researches are to separately research the types and the number of the spare parts to be used as necessities for executing tasks, and the type selection of the spare parts and the number decision of the spare parts belong to the same flow; replacement of spare parts is the primary means of maintenance. However, since the spare parts storage space is limited and the cost is high, a reasonable spare part carrying capacity must be determined to achieve satisfactory task completion. Therefore, how to comprehensively consider the types and the quantity to determine the carrying capacity of the spare parts is an important guarantee for effectively executing the maintenance task.
Disclosure of Invention
The embodiment of the application provides a spare part carrying capacity determining method and device, aims to solve the problem that the task completion degree is poor due to the fact that the current spare part carrying capacity method is not comprehensively researched, provides a spare part carrying capacity calculation model based on importance and the task completion degree, and obtains the final spare part carrying capacity through the model under the limitation of a certain storage space.
The method for determining the carrying capacity of the spare part provided by the embodiment of the application comprises the following steps:
determining the upper limit of the carrying capacity of each important spare part according to the portable capacity; determining the carrying capacity lower limit of each important spare part according to the set guarantee probability;
in the carrying capacity upper limit and the carrying capacity lower limit, traversing all important spare part carrying capacities in the carrying capacity upper limit and the carrying capacity lower limit by adopting an improved genetic algorithm, obtaining the task completion degree of each important spare part carrying capacity scheme by a binomial probability calculation method, and obtaining a spare part carrying capacity optimal scheme corresponding to the highest task completion degree;
for an important spare part, the task completion degree refers to the probability that at least k spare parts can normally work in n + k important spare parts within the working time T, k is the single machine installation number of the important spare part, and n is the carrying number of the important spare part; when the important spare part carrying capacity scheme comprises a plurality of important spare parts, the task completion degree of the important spare part carrying capacity scheme refers to the product of the task completion degrees of various important spare parts.
Preferably, m indexes for evaluating the importance of the spare parts are set, an evaluation matrix of n spare parts is constructed, and an importance score of each spare part is obtained through a comprehensive evaluation analysis method; and the spare parts with the scores exceeding the set threshold are taken as the important spare parts.
Preferably, the portable capacity is the sum of a plurality of carriers, the plurality of carriers being different in volume; the method for determining the upper limit of the carrying capacity of each important spare part according to the portable capacity specifically comprises the following steps:
sorting the spare parts according to the total number of each kind of spare parts which can be placed on the carrier, and configuring the carrier with the largest volume for the spare parts with the smallest number which can be placed;
and/or
And carrying out weighted distribution on the carriers according to the importance scores of the spare parts, wherein the number of the carriers configured by the spare parts with high importance scores is large.
Preferably, the carrying capacity lower limit is a spare part demand calculated under a set guarantee probability condition according to a spare part service life distribution model; the service life distribution model represents the relation between the demand quantity of the spare parts and the guarantee probability, and comprises at least one of exponential service life distribution, Weibull service life distribution and normal service life distribution.
Preferably, the improved genetic algorithm comprises in particular the following steps:
constructing an initial population according to the upper limit and the lower limit of the carrying capacity of the important spare parts;
calculating the fitness of the initial population, wherein the fitness is the task completion degree corresponding to the carrying capacity of the important spare parts;
sequencing according to the individual utility degree, copying the individual with the highest fitness as the next generation, and crossing and mutating other individuals;
and calculating the fitness of the next generation population until an optimal important spare part carrying capacity scheme is obtained.
Preferably, when the spare parts comprise electronic spare parts, mechanical spare parts and electromechanical spare parts, respectively calculating task completion degrees of important spare part carrying capacity schemes of the electronic spare parts, the electromechanical spare parts and the mechanical spare parts, and respectively obtaining a first task completion degree, a second task completion degree and a third task completion degree; the task completion degree of the total important spare part carrying capacity scheme is the product of the first task completion degree, the second task completion degree and the third task completion degree.
The application also provides a device for determining the carrying capacity of the spare part, which is used for realizing the method in any embodiment of the application and comprises an upper limit module, a lower limit module and an optimization module;
the upper limit module is used for determining the carrying capacity upper limit of each important spare part according to the portable capacity;
the lower limit module is used for determining the lower limit of the carrying capacity of each important spare part according to the set guarantee probability;
and the optimization module is used for traversing all important spare part carrying quantities in the carrying quantity upper limit range and the carrying quantity lower limit range by adopting an improved genetic algorithm, obtaining the task completion degree of each important spare part carrying quantity scheme by a binomial probability calculation method, and obtaining a spare part carrying quantity optimal scheme corresponding to the highest task completion degree.
Preferably, the system further comprises an evaluation module and a selection module;
the evaluation module is used for setting m indexes for evaluating the importance of the spare parts, constructing evaluation matrixes of n spare parts and obtaining the importance score of each spare part by a comprehensive evaluation analysis method;
and the selection module is used for selecting the spare parts with the scores exceeding a set threshold value as the important spare parts.
The present application also proposes a computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method according to any one of the embodiments of the present application.
The present application further proposes an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any of the embodiments of the present application
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:
the invention takes the task completion degree as a target and takes the upper limit and the lower limit of important spare parts as initial conditions to obtain an optimal spare part carrying scheme. Meanwhile, the packing problem of the spare parts is considered in the model, the regular customization is carried out on the packing condition according to the size of the spare part box, the spare parts are packed according to the rules, and the final spare part carrying scheme is obtained. The packing problem of the spare parts is solved while the spare part guarantee probability is guaranteed.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flow chart of an embodiment of the method of the present application;
FIG. 2 is a flow chart of another embodiment of a method of the present application;
FIG. 3 is a flowchart of an embodiment of the steps for selecting important spare parts in the present application;
FIG. 4 is a flow chart of an embodiment of steps of the present application using an improved genetic algorithm;
FIG. 5 is a diagram illustrating algorithm convergence according to an embodiment of the present application;
fig. 6 shows an embodiment of the device of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and 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 application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a flow chart of an embodiment of the method of the present application.
The method for determining the carrying capacity of the spare part provided by the embodiment of the application comprises the following steps:
step 101, determining the carrying capacity upper limit of each important spare part according to the carrying capacity;
preferably, the portable capacity is the sum of a plurality of carriers, the plurality of carriers being different in volume; the method for determining the upper limit of the carrying capacity of each important spare part according to the portable capacity specifically comprises the following steps:
the method comprises the steps that firstly, according to the total number of all spare parts which can be placed on a carrier, the spare parts are sequenced, and the carrier with the largest volume is configured for the spare parts with the smallest number which can be placed; and/or: and a second rule, carrying out weighted distribution on the carriers according to the importance scores of the spare parts, wherein the number of the carriers configured by the spare parts with high importance scores is large.
In this step, the "carrier" is used to load or store a spare part container, such as a spare part box or a spare part compartment for dividing the spare part box. For example, a spare part boxing rule is formulated, a box is spatially divided according to the packing parameters of the spare parts, the spare part boxing rule is formulated according to the size of the box, the spare parts are boxed according to the rule, and the upper limit of the carrying capacity of the important spare parts is obtained. And according to the rule I and/or the rule II, calculating the total number of the single-type spare parts which can be placed according to the number of the spare part boxes aiming at the selected important spare parts, placing the spare parts according to the rule, and finally obtaining the maximum number of each type of spare parts which can be placed in the spare part cabinet, namely obtaining the upper limit of the carrying capacity of the important spare parts.
In a specific embodiment of the application, the spare part box is divided by taking the half length of the spare part box as a side, taking the height of the spare part box as a height and taking 80mm as an interval, and the space of each spare part box is divided into n spare part grids;
sorting the spare parts in an ascending order according to the total number of each kind of spare parts which can be placed in the spare part boxes, and preferentially placing the spare parts which can be placed in the least number in the spare part boxes with the largest volume;
and distributing the number of the spare parts boxes according to the importance of the spare parts, and preferentially using the spare parts box with the largest volume for placing the minimum number of the spare parts.
It should be noted that, if the number of the selected spare part categories is greater than the number of the spare part boxes, the number of the spare part grids in the spare part boxes is divided according to the rule of step 101 in the present application.
102, determining a carrying capacity lower limit of each important spare part according to a set guarantee probability;
preferably, the carrying capacity lower limit is a spare part demand calculated under a set guarantee probability condition according to a service life distribution model of the spare part. The service life distribution model represents the relation between the spare part demand and the guarantee probability. Typically, the life distribution model includes at least one of an exponential life distribution, a weibull life distribution, and a normal life distribution, as applicable to different spare part types.
It should be noted that, the relationship between the number of spare parts and the task completion probability is calculated by using the service life distribution models of different types of spare parts, so as to obtain the optimal spare part carrying scheme, which is specifically as follows:
according to the obtained important spare part types, the spare parts are divided into three types of exponential life distribution, Weibull life distribution and normal life distribution. Because the demand of the spare parts, the guarantee probability and the service life distribution model of the spare parts have quantitative relation, the demand of the spare parts can be predicted according to the service life distribution model of the spare parts under the set guarantee probability, and therefore the minimum carrying capacity of the spare parts under the guarantee probability is obtained.
Index distribution: n is a radical ofiNumber of stand-alone installations for ith spare part, TiFor working time, λiTo failure rate, PiGuarantee probability for spare parts, siIs the demand for such spare parts. N is a radical ofiNumber of stand-alone installations for ith spare part, TiFor working time, λiTo failure rate, PiGuarantee probability for spare parts, siIs the demand for such spare parts.
Figure BDA0003269630610000061
Weibull distribution: number of stand-alone installations N of spare part llThe shape parameter of the spare part is betalWith a scale parameter of thetalPosition parameter is 0 and working time is TlThen, the spare part demand is:
Figure BDA0003269630610000062
normal distribution: sheet of spare part jNumber of machine installations NjAverage life of the spare part is EjVariance is σjWith a working time of TjThen, the spare part demand is:
Figure BDA0003269630610000063
in the above-described model, the model,
Figure BDA0003269630610000064
to a corresponding probability of PiThe normal quantile value (or percentile).
103, traversing all important spare part carrying quantities in the carrying quantity upper limit and lower limit ranges by adopting an improved genetic algorithm, obtaining the task completion degree of each important spare part carrying quantity scheme by a binomial probability calculation method, and obtaining a spare part carrying quantity optimal scheme corresponding to the highest task completion degree;
the binomial distribution is a statistical distribution with two possible outcomes, for example, n trials, and the outcome of each trial can be either "success" or "failure". The probability of success is denoted by p, and the failure is (1-p), then the probability of success i times is:
Figure BDA0003269630610000065
the probability of success at least i times is
Figure BDA0003269630610000071
In the calculation of the carrying capacity of the important spare parts, p is expressed by the reliability of the spare parts (the capability of the spare parts to execute the specified function without failure in a certain time and under certain conditions), and the reliability formula of the electronic spare parts is as follows: e.g. of the type-λT(ii) a The reliability formula of the mechanical part is as follows:
Figure BDA0003269630610000072
the reliability formula of the electromechanical part is as follows:
Figure BDA0003269630610000073
the probability (task completion degree) that at least k spare parts work normally to ensure the completion of the task can be obtained by substituting the reliability formula of the spare parts into a binomial distribution formula.
For the ith important spare part, the task completion degree refers to the probability P that not less than k spare parts can normally work in n + k important spare parts within the working time TiK is the number of single machine installation of the ith important spare part, and n is the number of carrying the ith important spare part. When the important spare part carrying capacity scheme comprises a plurality of important spare parts, the task completion degree of the important spare part carrying capacity scheme refers to the product of the task completion degrees of various important spare parts.
Preferably, when the spare parts comprise electronic spare parts, electromechanical spare parts and mechanical spare parts, respectively calculating task completion degrees of important spare part carrying capacity schemes of the electronic spare parts, the mechanical spare parts and the electromechanical spare parts, and respectively obtaining a first task completion degree, a second task completion degree and a third task completion degree;
when the type of the spare parts is electronic spare parts, calculating the task completion degree n of the ith spare part in all the electronic spare parts in the important spare parts1iIs the carrying capacity, k, of the important electronic spare parts1iFor the number of stand-alone installations, lambda, of the ith important electronic spare partiIf there are q electronic spare parts in the important spare parts, the task completion degree formula of the ith electronic spare part is as follows:
Figure BDA0003269630610000074
then, the first task completion is:
Figure BDA0003269630610000075
when the type of the spare parts is mechanical spare parts, calculating the task completion degree of the jth spare part in all the mechanical spare parts in the important spare parts, n2jIs the carrying capacity of the important mechanical spare parts, k2jIs the sheet of the jth important mechanical spare partNumber of machine installations, mujMean life of jth mechanical spare part, σjIs the life variance of the jth mechanical spare part. If there are s mechanical parts in the important parts of the equipment, the task completion degree formula of the jth mechanical part is as follows:
Figure BDA0003269630610000081
then, the second task completion degree is:
Figure BDA0003269630610000082
when the type of the spare parts is electromechanical spare parts, calculating the task completion degree n of the mth spare part in all electromechanical spare parts in the important spare parts3mFor carrying the important electromechanical spare parts, k3mNumber of stand-alone installations, beta, for the mth important electromechanical spare partmIs the shape parameter of the mth electromechanical device, thetamIs the dimension parameter of the mth electromechanical spare part. If there are y pieces of electromechanical equipment in the important equipment, the task completion degree formula of the mth piece of electromechanical equipment is:
Figure BDA0003269630610000083
the third task completion is:
Figure BDA0003269630610000084
the final total task completion degree of the scheme is obtained
P＝P1×P2×P3
And calculating the fitness corresponding to each spare part carrying scheme according to a task completion degree calculation method, taking out the individual with the highest fitness for copying, and carrying out variation, intersection and selection on the other individuals to generate the next generation. The invention adopts single-point crossing, the mutation probability is 0.9, and the selection operator is selected for roulette. The population of each generation is calculated until the maximum number of iterations is reached. And finally obtaining the optimal spare part carrying scheme.
FIG. 2 is a flow chart of another embodiment of the method of the present application. Another embodiment of the method comprises steps 201-204, wherein:
step 201, setting m indexes for evaluating the importance of spare parts, constructing evaluation matrixes of n spare parts, and obtaining an importance score of each spare part through a comprehensive evaluation analysis method; the spare parts with scores exceeding the set threshold are taken as the important spare parts (note that m and n in step 201 have different meanings from m and n in other steps, see the embodiment of fig. 3 below).
The comprehensive evaluation analysis (FCE) described herein refers to a method of converting a plurality of indexes into one index that can reflect a comprehensive situation for analysis, and is described below.
Step 202, synchronizing step 101;
step 203, synchronizing step 102;
step 204, synchronizing step 103.
FIG. 3 is a flowchart of an embodiment of a step 201 of selecting important spare parts according to the present application.
The information covered in each index of the spare parts is selected as an evaluation index influencing the determination of the types of the spare parts, the grading standard of the index is set, and the types of the important spare parts are selected while the index information is kept. And establishing a spare part evaluation index system by adopting five indexes of reliability, weight-related parts, vulnerability, economy and single-machine installation number of the spare parts, wherein the scoring standard is shown in table 1.
TABLE 1 spare parts evaluation index System
Figure BDA0003269630610000091
Set of equipment parts X ═ X1,x2,x3,...,xnAnd the evaluation index is E ═ E1,e2,e3,...,emH, the evaluation vector is xi＝{xi1,xi2,xi3,...,xim}, initial evaluationThe matrix is:
Figure BDA0003269630610000092
after a comprehensive evaluation index system and an initial evaluation matrix are determined, the scores of the indexes need to be collected, and since the evaluation results of the indexes with different dimensions do not have actual significance and value after the indexes are comprehensive, data needs to be standardized during index synthesis, and the indexes are converted from actual values into evaluation values through standardization. The method for standardizing the forward indexes comprises the following steps:
Figure BDA0003269630610000101
the standardization processing method of the reverse index and the dimensionless index comprises the following steps:
Figure BDA0003269630610000102
after processing, a normalized matrix I' is obtained.
Figure BDA0003269630610000103
Then obtaining the characteristic value lambda of the matrix by calculating the correlation coefficient matrix RtAnd a feature vector et(t ═ 1,2,3, …, m), using the eigenvalues λtThe contribution rate C of each index can be calculatedtAnd a weight wt。
Figure BDA0003269630610000104
In the formula: r isijIs the correlation coefficient of index i and index j.
Figure BDA0003269630610000105
The eigenvalue lambda of the matrix can be found by using a matrix calculation formulatThe contribution rate C of each indextAnd a weight wtComprises the following steps:
Figure BDA0003269630610000111
Figure BDA0003269630610000112
eigenvectors e using a matrix of correlation coefficientstAnd normalizing matrix I' may result in a scoring matrix S:
S＝I'*[e1 e2 … em]
the final score S can be obtained according to the score matrix and the weight of each indexZ：
SZ＝S×W
In the formula: w is the weight WtAnd (5) vector quantity.
And sorting according to the score, wherein the spare parts with the score of more than 0.5 are used as important spare parts, the spare parts with the score of 0.1-0.5 are used as general spare parts, and the spare parts with the score of less than 0.1 are used as unimportant spare parts.
FIG. 4 is a flow chart of an embodiment of step 103 or step 204 of the present application using the improved genetic algorithm.
Preferably, the improved genetic algorithm comprises in particular the following steps:
step 401, constructing an initial population according to the upper limit and the lower limit of the carrying capacity of the important spare parts;
step 402, calculating the fitness of the initial population, wherein the fitness is the task completion degree corresponding to the carrying capacity of the important spare parts;
step 403, sorting according to the individual utility degree, copying the individual with the highest fitness as the next generation, and crossing and mutating other individuals;
step 404, calculating the fitness of the next generation population,
and 405, continuously iterating until an optimal important spare part carrying capacity scheme is obtained.
Fig. 5 shows algorithm convergence according to an embodiment of the present application.
Examples
Knowing that a certain task needs to be completed by the equipment 1, the equipment 2 and the equipment 3 together, the existing 10 spare part boxes A, 7 spare part boxes B, 1 spare part box C, 1 spare part box D and 1 spare part box E are available, and the size of the spare part box A is 560mm in length, 180mm in width and 60mm in height; the size of the spare part box B is 560mm in length, 260mm in width and 90mm in height; the size of the spare part box C is 650mm in length, 540mm in width and 200mm in height; d, the size of the spare part box is 650mm in length, 540mm in width and 240mm in height; e spare part case size is 650mm for length, and wide 540mm, high 320mm, and operating duration T is 1000 hours, and the guarantee probability of setting is 0.95. The spare part list is shown in table 2.
Table 2 spare parts list
Figure BDA0003269630610000121
Figure BDA0003269630610000131
And (5) constructing an initial matrix according to the evaluation index system in the step 201, and normalizing and standardizing the initial matrix to further obtain a final score.
Obtaining an initial matrix I by evaluating an index system:
Figure BDA0003269630610000141
normalizing the initial matrix to obtain a normalized matrix I', and calculating to obtain a relation matrix R and a characteristic value lambda of the normalized matrix ItAnd a feature vector et. Finally, finally obtaining the comprehensive score S of the spare part according to the stepsZAs shown in table 3.
TABLE 3 comprehensive score of spare parts
Figure BDA0003269630610000142
Figure BDA0003269630610000151
The spare parts with the scores of 0.5 or more are sorted according to the scores, and the finally obtained important spare part type P is { spare part 4, spare part 5, spare part 14, spare part 15, spare part 23, spare part 24 }.
Dividing the spare part box space:
TABLE 4 spare parts case number
Figure BDA0003269630610000152
And distributing and packing the spare part boxes according to the rule I and the rule II.
TABLE 5 Upper limit of spare parts carrying capacity
Figure BDA0003269630610000153
Figure BDA0003269630610000161
And substituting the spare part parameters in the table 2 into a spare part carrying capacity lower limit calculation formula according to the spare part guarantee probability task working time T to obtain the spare part carrying capacity lower limit.
TABLE 6 Upper and lower limits of spare parts carrying capacity
Figure BDA0003269630610000162
Selecting 600 groups of random data as a population in the range of the upper limit and the lower limit of the carrying capacity of the important spare parts, selecting 20 groups of data from the 600 groups of data as an initial population, wherein the iteration times are 100 times, calculating the fitness of each group of data in the initial population according to a task completion degree calculation method, taking out the individual with the highest fitness for copying, and carrying out variation, intersection and selection on the other individuals to generate the next generation. The population of each generation is calculated until the maximum number of iterations is reached. The improved convergence of the genetic algorithm is shown in fig. 5, and finally, an optimal spare part carrying scheme is obtained.
The task completion degree of the optimal spare part carrying scheme is as follows: p is 90.4%, and the spare part carrying scheme is
TABLE 7 optimal spare part carrying capacity
Figure BDA0003269630610000163
Fig. 6 shows an embodiment of the device of the present application.
The application also provides a device for determining the carrying capacity of the spare part, which is used for realizing the method in any embodiment of the application and comprises an upper limit module 601, a lower limit module 602 and an optimization module 603;
the upper limit module is used for determining the carrying capacity upper limit of each important spare part according to the portable capacity; for example, the boxes are spatially divided according to the packing parameters of the spare parts, the spare part packing rule is formulated according to the sizes of the boxes, the spare parts are packed according to the rule, and the upper limit of the carrying capacity of the important spare parts is obtained. As shown in step 101, the method is characterized in that the spare part packing rule is established according to the number of the spare parts in the spare part box and the importance degree of the spare parts, and the importance degree of the spare parts is also considered while large-volume spare parts are considered.
The lower limit module is used for determining the lower limit of the carrying capacity of each important spare part according to the set guarantee probability; for example, as described in step 102 of the present application, according to the set guarantee probability, the lower limit of the carrying capacity of the important spare parts can be calculated according to the relationship formula between the life distribution of each type of spare parts and the number of spare parts.
And the optimization module is used for traversing all important spare part carrying quantities in the carrying quantity upper limit range and the carrying quantity lower limit range by adopting an improved genetic algorithm, obtaining the task completion degree of each important spare part carrying quantity scheme by a binomial probability calculation method, and obtaining a spare part carrying quantity optimal scheme corresponding to the highest task completion degree. As described in step 103 of the present application, the task completion degree is calculated by using probability calculation, and the spare part solution is optimized by using the task completion degree as a fitness function.
Preferably, the system further comprises an evaluation module 604 and a selection module 605;
the evaluation module is used for setting m indexes for evaluating the importance of the spare parts, constructing evaluation matrixes of n spare parts and obtaining the importance score of each spare part by a comprehensive evaluation analysis method; for example, as described in step 201 of the present application, a spare part index system is established: the information contained in each index of the spare parts is selected as an evaluation index influencing the determination of the types of the spare parts, the grading criterion of the index is set, the index information can be reserved according to the grading criterion, and then the types of the important spare parts are selected. Furthermore, the final score of each spare part is obtained by using a comprehensive evaluation analysis method and a spare part index system, the score is recorded as the importance of the spare part,
and the selection module is used for selecting the spare parts with the scores exceeding a set threshold value as the important spare parts. For example, according to the descending order of the importance, the spare parts with the importance degree greater than 0.5 are obtained as important spare parts.
The present application also proposes a computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method according to any one of the embodiments of the present application.
The present application further proposes an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any of the embodiments of the present application
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that 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 like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (10)

1. A spare part carrying capacity determining method is characterized by comprising the following steps:
determining the upper limit of the carrying capacity of each important spare part according to the portable capacity; determining the carrying capacity lower limit of each important spare part according to the set guarantee probability;
in the carrying capacity upper limit and the carrying capacity lower limit, traversing all important spare part carrying capacities in the carrying capacity upper limit and the carrying capacity lower limit by adopting an improved genetic algorithm, obtaining the task completion degree of each important spare part carrying capacity scheme by a binomial probability calculation method, and obtaining a spare part carrying capacity optimal scheme corresponding to the highest task completion degree;
for an important spare part, the task completion degree refers to the probability that at least k spare parts can normally work in n + k important spare parts within the working time T, k is the single machine installation number of the important spare part, and n is the carrying number of the important spare part; when the important spare part carrying capacity scheme comprises a plurality of important spare parts, the task completion degree of the important spare part carrying capacity scheme refers to the product of the task completion degrees of various important spare parts.
2. The method of claim 1, wherein m indexes for evaluating the importance of the spare parts are set, an evaluation matrix of n spare parts is constructed, and an importance score of each spare part is obtained through a comprehensive evaluation analysis method; and the spare parts with the scores exceeding the set threshold are taken as the important spare parts.
3. The method of claim 1,
the portable capacity is the sum of a plurality of carriers, and the volumes of the plurality of carriers are different;
the method for determining the upper limit of the carrying capacity of each important spare part according to the portable capacity specifically comprises the following steps:
sorting the spare parts according to the total number of each kind of spare parts which can be placed on the carrier, and configuring the carrier with the largest volume for the spare parts with the smallest number which can be placed;
and/or
And carrying out weighted distribution on the carriers according to the importance scores of the spare parts, wherein the number of the carriers configured by the spare parts with high importance scores is large.
4. The method as claimed in claim 1, wherein the lower carrying capacity limit is a spare part demand calculated under a set guarantee probability condition according to a service life distribution model of the spare part; the service life distribution model represents the relation between the demand quantity of the spare parts and the guarantee probability, and comprises at least one of exponential service life distribution, Weibull service life distribution and normal service life distribution.
5. The method according to claim 1, wherein the improved genetic algorithm comprises the steps of:
constructing an initial population according to the upper limit and the lower limit of the carrying capacity of the important spare parts;
calculating the fitness of the initial population, wherein the fitness is the task completion degree corresponding to the carrying capacity of the important spare parts;
sequencing according to the individual utility degree, copying the individual with the highest fitness as the next generation, and crossing and mutating other individuals;
and calculating the fitness of the next generation population until an optimal important spare part carrying capacity scheme is obtained.
6. The method of claim 1,
when the spare parts comprise electronic spare parts, electromechanical spare parts and mechanical spare parts, respectively calculating task completion degrees of important spare part carrying capacity schemes of the electronic spare parts, the mechanical spare parts and the electromechanical spare parts to respectively obtain a first task completion degree, a second task completion degree and a third task completion degree;
the task completion degree of the total important spare part carrying capacity scheme is the product of the first task completion degree, the second task completion degree and the third task completion degree.
7. A spare part carrying capacity determining device is used for realizing the method of any one of claims 1 to 6, and is characterized by comprising an upper limit module, a lower limit module and an optimization module;
the upper limit module is used for determining the carrying capacity upper limit of each important spare part according to the portable capacity;
the lower limit module is used for determining the lower limit of the carrying capacity of each important spare part according to the set guarantee probability;
and the optimization module is used for traversing all important spare part carrying quantities in the carrying quantity upper limit range and the carrying quantity lower limit range by adopting an improved genetic algorithm, obtaining the task completion degree of each important spare part carrying quantity scheme by a binomial probability calculation method, and obtaining a spare part carrying quantity optimal scheme corresponding to the highest task completion degree.
8. The apparatus of claim 7, further comprising an evaluation module, a selection module;
the evaluation module is used for setting m indexes for evaluating the importance of the spare parts, constructing evaluation matrixes of n spare parts and obtaining the importance score of each spare part by a comprehensive evaluation analysis method;
and the selection module is used for selecting the spare parts with the scores exceeding a set threshold value as the important spare parts.
9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method of any one of claims 1 to 6.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method according to any of claims 1 to 6 when executing the computer program.
CN202111097609.1A 2021-09-18 2021-09-18 Spare part carrying capacity determining method and device Pending CN114004136A (en)

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