CN110287523B - Spare part scheme optimization method and device for multi-batch parts in modularized storage mode - Google Patents
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Abstract
The invention discloses a spare part scheme optimization method and device for parts in multiple batches in a modularized storage mode. The method comprises the following steps: s100, constructing an array Ns, ns representing an initial spare part scheme i 1.ltoreq.i.ltoreq. dyN is the number of spare parts of the unit of item i, dyN is the number of items of the unit in the component; s200, acquiring the standard reaching probability of the current Ns as Pm; s300, entering S400 when Pm is smaller than a minimum threshold probability, otherwise entering S500; s400, updating at least one element item of Ns and proceeding to S200; s500, selecting the current Ns as the optimized spare part scheme. The invention can provide a spare part scheme with standard probability and spare part cost meeting expectations for a plurality of batches of parts in a modularized storage mode.
Description
Technical Field
The invention relates to the field of fault prediction and spare part guarantee, in particular to a spare part scheme optimization method and device for parts in multiple batches in a modularized storage mode.
Background
Some products may be stored for extended periods of time in addition to being in an operational state. The product may fail after a certain period of time in storage; then, in order to grasp the integrity of the product in the storage state, it is necessary to perform inspection of the storage state of the product at an irregular period and repair the product that fails in the storage state, so as to ensure that the number of the intact products can meet the requirement.
Disclosure of Invention
The embodiment of the invention discloses a spare part scheme optimizing method for a plurality of batches of parts in a modularized storage mode; the invention can provide a spare part scheme with standard probability and spare part cost meeting expectations for a plurality of batches of parts in a modularized storage mode.
The method comprises the following steps:
s100, constructing an array Ns= [ Ns ] representing the initial spare part scheme 1 Ns 2 … Ns dyN ]，Ns i 1.ltoreq.i.ltoreq. dyN is the number of spare parts of the unit of item i, dyN is the number of items of the unit in the component;
s200, acquiring the standard reaching probability of the current Ns as Pm;
s300, entering S400 when Pm is smaller than a minimum threshold probability, otherwise entering S500;
s400, updating at least one element item of Ns and proceeding to S200;
s500, selecting the current Ns as the optimized spare part scheme.
In some embodiments of the present disclosure of the invention,
s100 is configured to make Ns i ＝max([0 M+1-N]) I is more than or equal to 1 and less than or equal to dyN, N is the number of sets of components in the warehouse, and M is a natural number.
In some embodiments of the present disclosure of the invention,
s200 is configured to:
s210, acquiring an element dyPr in the matrix dyPx ji ，dyPr ji To have j-1 good probabilities for all ith element at time T1,
s220, acquiring Pm of Ns according to the matrix dyPx;
s400 is configured to update at least one element item of Ns and proceeds to S220.
In some embodiments of the present disclosure of the invention,
s210 is configured to:
s211, making the unit sequence number i=1;
s212, let component number j1=1;
s213, let vector p1= [1-pr pr ], pr be the storage reliability of the ith item unit in the jth 1 component at time T1.
S214, p2=p1 when j1=1, and p2=p1×p2 when j1> 1;
s215, enabling j1 to be in a 1+1 state, entering S213 when j1 is less than or equal to N, otherwise, placing p2 in an ith column in a matrix dyPx, and entering S216;
s216, i=i+1, and if i is equal to or less than dyN, S212 is entered, otherwise S220 is entered.
In some embodiments of the present disclosure of the invention,
s213 is configured to obtain pr from a lifetime type distribution to which the unit is subject.
In some embodiments of the present disclosure of the invention,
s220 is configured to obtain the spare part purchasing cost of Ns as Mnow according to the matrix dyPx;
s400 is configured to generate at least one spare part proposal as Nt based on Ns, obtain the standard reaching probability of Nt as Pt and the spare part purchasing expense Mt according to dyPx, and obtain the marginal cost ratio of Nt as p2m according to Pt, mt, pm and Mnow k Selecting the maximum p2m k And after the corresponding Nt is Ns, the process proceeds to S220.
In some embodiments of the present disclosure of the invention,
s220 is configured to:
s221, let i=1;
s222, let p3=dypx (: i), and let
S223, making i=i+1, if i is less than or equal to dyN, entering S222, otherwise entering S224;
s224, calculating the standard reaching probability of Ns asThe spare part scheme cost is
In some embodiments of the present disclosure of the invention,
s400 is configured to:
s410, let a candidate sequence number k=1;
s420, let nt=ns, and let Nt k ＝Ns k +1；
S430, acquiring Pt and Mt of Nt according to the matrix dyPx;
s440, calculating p2m of Nt k ，
S450, enabling k=k+1, entering S420 when k is less than or equal to dyN, otherwise entering S460;
s460, selecting p2m 1 p2m 2 …p2m K The serial number corresponding to the maximum value is Im, so that Ns in Ns Im ＝Ns Im After +1, the process proceeds to S220.
The embodiment of the invention discloses a spare part scheme optimizing device for parts in a modularized storage mode.
The device comprises:
an initial scheme module configured to construct an array ns= [ Ns ] representing the initial spare part scheme 1 Ns 2 … Ns dyN ]，Ns i 1.ltoreq.i.ltoreq. dyN is the number of spare parts of the unit of item i, dyN is the number of items of the unit in the component;
the probability calculation module is configured to acquire the standard reaching probability of the current Ns as Pm;
the comparison module is configured to enter the scheme updating module when the Pm is smaller than a minimum threshold probability, and enter the scheme selecting module otherwise;
a scheme update module configured to update at least one element item of Ns and enter a probability calculation module;
and the scheme selection module is configured to select the current Ns as the optimized spare part scheme.
In some embodiments of the present disclosure, the apparatus includes a spare part solution evaluation module;
the spare part scheme evaluation module is configured to calculate Pm of the current Ns according to the intact probability of all units in the warehouse before the time T1;
the probability calculation module is configured to invoke the spare part solution evaluation module.
Other features of embodiments of the present invention and advantages thereof will be apparent from the following detailed description of the disclosed exemplary embodiments with reference to the drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method provided in this embodiment;
fig. 2 is a graph of the probability of reaching the standard for the spare part solution provided in this embodiment;
FIG. 3 is a cost effectiveness graph of the spare part solution of the present example provided by the present embodiment;
fig. 4 is a functional block diagram provided in this embodiment.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
As used herein, the terms "if," "if," and the like are optionally interpreted to mean "when … …" ("white" or "upon") or "in response to a determination" or "in response to detection," depending on the context. Similarly, the phrase "if determined … …" or "if detected [ stated condition or event ]" is optionally interpreted to mean "upon determining … …" or "in response to determining … …" or "upon detecting [ stated condition or event ]" or "in response to detecting [ stated condition or event ]" depending on the context.
Additionally, the steps illustrated in the flowcharts of the figures may be performed in a computer device such as a set of computer-executable instructions, and although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order other than that herein.
The components referred to in this example are generally intended to be the products of some intermediate layer structure made up of at least one unit; the units comprised by the components can generally be described in terms of reliable connection relationships. The reliability connection relation among the common units is a series relation, a parallel relation, a series-parallel relation and the like. If all units in a component are to be broken according to the criteria of whether the units are weight-related or not, the component can be reduced to a product of at least one weight-related in series.
For the sake of clarity, the units of the components referred to later in this embodiment are all standard-compliant weight components, and the reliability relationships between the units are all serial products.
Of course, products with reliability in parallel or series-parallel relationship are equally applicable to the present embodiment; again, this is not repeated.
Meanwhile, the storage modes of common products are at least two of an integral storage mode and a modularized storage mode. The integral storage mode means that the product is stored in a whole machine mode. During storage, the individual units that make up the component have been assembled prior to use, i.e., the component is the smallest unit of storage. The modular storage mode means that each unit constituting the component is stored separately and separately. All the units comprised by the component need to be assembled, i.e. the units are the smallest stored units, before the component is activated.
For modular storage products, the repair referred to in this embodiment is essentially a string repair, since there is a splicing process prior to use of the components.
In the embodiment, only the modularized storage parts are used as objects, and a spare part scheme optimization method for multiple batches of parts in a modularized storage mode is formulated; the spare part scheme which meets the probability of reaching standards and has the highest cost performance is optimized by updating the number of each unit in the spare part scheme.
In order to briefly clarify the implementation of the above method, the present embodiment makes the following conventions.
The component consists of dyN units, the distribution law and unit price of the storage life of each unit are known.
N sets of parts which are stored in the warehouse for multiple batches and multiple times are stored in the current warehouse, t j The storage time t of the j-th component j Different from each other, j is more than or equal to 1 and less than or equal to N.
The time when the storage quantity of each unit needs to be supplemented is recorded as the time T1; performing integrity check on all units in the N sets of parts at the moment T1, and performing maintenance work in a mode of replacing fault parts with spare parts; after the maintenance at the time T1 is finished, the probability that the number n of intact parts is larger than M is up to standard probability P (n > M), and the probability P (n > M) is used for evaluating the guarantee effect caused by the number of spare parts in the spare part scheme.
The lower limit of P (n > M) is P1, P1 is the lowest index requirement on the standard reaching probability of the spare part scheme, and only the spare part scheme meeting the requirement that P (n > M) is more than or equal to P1 is a qualified scheme.
In view of the foregoing, the present embodiment provides a method for optimizing a spare part plan for a plurality of batches of parts in a modular storage mode.
The method of the present embodiment has the following steps that can be performed at the terminal device.
S100, constructing an array Ns= [ Ns ] representing the initial spare part scheme 1 Ns 2 … Ns dyN ]Make Ns i ＝max([0 M+1-N]),1≤i≤dyN，Ns i I is more than or equal to 1 and less than or equal to dyN, the number of spare parts of the ith unit, and M is a natural number.
S210, acquiring an element dyPr in the matrix dyPx ji ，dyPr ji J-1 probabilities of being good for all ith element at time T1;
s220, obtaining standard probability Pm of Ns and purchasing cost Mnow of spare parts according to the matrix dyPx.
S300, entering S400 when Pm is smaller than a minimum threshold probability, otherwise entering S500;
s400 is configured to generate at least one spare part proposal as Nt based on Ns, obtain the standard reaching probability of Nt as Pt and the spare part purchasing expense Mt according to dyPx, and obtain the marginal cost ratio of Nt as p2m according to Pt, mt, pm and Mnow k Selecting the maximum p2m k The corresponding Nt is Ns and then enters S220。
S500, selecting the current Ns as the optimized spare part scheme.
The terminal device described above is explained as an example of the present embodiment. The terminal equipment at least comprises a processor and a memory; the memory stores a program for implementing the above steps, and the processor calls and executes the program in the memory after acquiring an external input such as parameters in a contract, for the above steps of the present embodiment.
The processor of the present embodiment implements the following steps when executing the steps S210 and S220.
S211, let element number i=1.
S212, let component number j1=1.
S213, let vector p1= [1-pr pr ], pr be the storage reliability of the ith item unit in the jth 1 component at time T1. Wherein pr is obtained from a life type distribution to which the ith element is subject.
S214, p2=p1 when j1=1, and p2=p1×p2 when j1> 1.
S215, let j1=j1+1, enter S213 when j1+.n, otherwise place p2 in the ith column in matrix dyPx and enter S216.
S216, i=i+1, and if i is equal to or less than dyN, S212 is entered, otherwise S220 is entered.
S220, obtaining standard probability Pm of Ns and purchasing cost Mnow of spare parts according to the matrix dyPx.
S221, let i=1.
S222, let p3=dypx (: i), and let
S223, i=i+1, and if i is less than or equal to dyN, the process proceeds to S222, otherwise, the process proceeds to S224.
S224, calculating the standard reaching probability of Ns asThe spare part scheme cost is
Through the scheme, the probability of j-1 perfect units in all ith units at the moment T1 and the spare part number in the spare part scheme Ns can be obtained, and the current Ns can obtain the standard reaching probability and the spare part purchasing cost according to the spare part number of each unit and are used for evaluating whether the current spare part scheme is qualified or not and the cost performance.
Preferably, the present embodiment determines pr of the unit according to the type of life distribution to which each unit in the component specifically obeys. The type of life distribution to which the unit is subjected is generally one of an exponential distribution, a gamma distribution, a lognormal distribution, a normal distribution, and a weibull distribution.
Exponential distribution, commonly used to describe the lifetime of electronic components; such as printed circuit board packages, electronic components, resistors, capacitors, integrated circuits, etc. The unit with the lifetime T conforming to the exponential distribution is called an exponential unit, and is denoted as T-Exp (mu), then
The gamma distribution is often used for describing faults caused by similar 'impact', if the unit can withstand external impact for a plurality of times, the faults are generated when the times of the impact on the unit are accumulated to a certain time; if there is a surge phenomenon in the power grid, some electronic devices may fail when the number of surge impacts to be received exceeds a certain number. The units whose lifetime T follows the gamma distribution are gamma units, denoted as T-Ga (alpha, b), then
A lognormal distribution, commonly used to describe cells of general reliability; such as insulators, semiconductor components, and workpieces subject to metal fatigue. The cells whose lifetime T follows the lognormal distribution are lognormal cells, denoted as T.about.LN (μ, σ) 2 )，
Normal distribution, often used to describe mechanical part life; such as sinkFlow rings, gearboxes, reducers, etc. The unit with the life T obeying normal distribution is a normal unit and is marked as T-N (mu, sigma) 2 )，
The Weibull distribution is mainly used for describing faults caused by aging and is suitable for electromechanical parts; such as ball bearings, relays, batteries, hydraulic pumps, gears, material fatigue pieces, etc. The units whose lifetime T obeys the Weibull distribution are Weibull units, denoted as T-W (alpha, b),
the processor of the present embodiment realizes the following steps when executing the program as S400.
S410, let a candidate sequence number k=1.
S420, let nt=ns, and let Nt k ＝Ns k +1。
S430, acquiring Pt and Mt of Nt according to the matrix dyPx.
S440, calculating p2m of Nt k ，
S450, let k=k+1, and enter S420 when k is less than or equal to dyN, otherwise enter S460.
S460, selecting p2m 1 p2m 2 …p2m K The serial number corresponding to the maximum value is Im, so that Ns in Ns Im ＝Ns Im After +1, the process proceeds to S220.
With the above-described scheme, the present embodiment can repeatedly generate several candidate schemes as Nt based on the current Ns, and perform the same steps as S220 to obtain Pt and Mt for Nt for calculating p2m k . By searching the maximum p2m k Returning to S220 after the corresponding Nt is the new Ns, and calculating Pm of the new Ns until Pm is greater than or equal to P1.
Preferably, the embodiment discloses that the following simulation model is used for simulating the guarantee effect of the spare part scheme on the ith unit.
S100, yieldGenerating N random numbers simT j ,1≤j≤N，simT j For simulating the life of the ith element in the jth set of parts, simT i Obeys the life distribution rule of the unit.
S200, traversing and updating simT j ＝simT j +t j ,1≤j≤N，t j The storage time is the start time of the j-th set of components.
S300, at random number simT i Searching for simT in N with i being more than or equal to 1 i The number of random numbers satisfying this requirement is denoted as n1.
S400, order dyN1 i ＝n1+Ns i Wherein Ns is i Number of spare parts for the ith item unit dyN1 i I.e. the number of intact units of item i after the end of maintenance.
S500, after simulating the intact number dyN1 of all units i After i is more than or equal to 1 and less than or equal to dyN, taking the minimum number of the components to be the number of the intact components after maintenance is finished, i.e. simN1=min ({ dyN) i |1≤i≤dyN})。
After the simulation model is operated for a plurality of periods, the embodiment can count the frequency of the simN 1> M, namely the simulation result of the up-to-standard probability P (n > M), and is used for judging the result effectiveness of the processor after executing all the steps of the method.
In summary, this embodiment is presented as the following examples to illustrate the implementation of the method.
The components of a modular store in the convention example consist of 5 units, each unit having a shelf life as shown in Table 1. 3 batches of parts are arranged in the warehouse before the time T1, the number of the parts is 3, 4 and 2 sets, and the time T for starting storage of each batch of warehouse j Years 2, 4 and 6, respectively. All parts were serviced at t1=10 years, requiring that the probability P that the number of parts intact n is greater than M (n > M) at the end of service is not lower than the threshold P1.
Given that m=7, p1=0.9, the spare part plan is formulated according to the method described above, and the minimum spare part demand of each unit meeting the requirements is calculated.
TABLE 1
St100, initialization ns= [ 00 00 0].
St210, obtaining each element dyPr in matrix dyPx ji As in table 2.
TABLE 2
St220, pm=0.0157 and mnow=0 of Ns are obtained from the matrix dyPx of table 2.
St300, go to St400 when Pm < P1, otherwise go to St500.
St400, generating several candidates as in table 3, and calculating Pt and Mt for candidate Nt.
TABLE 3 Table 3
St500, the probability of reaching standards when the spare part scheme Ns= [ 43 3 21 ] meets the requirements.
Referring to fig. 2, in the simulation results of the standard reaching probabilities of the candidate solutions in table 3 and the results when the method in this embodiment is executed by the processor, the error is only about 0.01, and the root variance simulation result of the standard reaching probability is only about 0.04, which illustrates that the method in this embodiment can optimize the spare part solutions.
Referring to fig. 3, the optimization result of the method of the present embodiment is an outer envelope curve of the traversal result of the traversal method in the prior art, which illustrates that the spare part solution optimized by the method of the present embodiment has the largest probability of reaching standards under the condition of the same spare part cost.
Referring to fig. 4, the present embodiment discloses a spare part solution optimizing apparatus for components in a modular storage mode. The device comprises an initial scheme module, a probability calculation module, a comparison module, a scheme updating module, a scheme selecting module and a scheme evaluating module.
The spare part solution evaluation module is configured to calculate Pm of the current Ns from the probability of being intact for all units in the warehouse before time T1.
The initial scheme module is used for constructing an array Ns= [ Ns ] representing the initial spare part scheme 1 Ns 2 … Ns dyN ]，Ns i 1.ltoreq.i.ltoreq. dyN is the number of spare parts for the unit of item i, dyN is the number of items for the unit in the component.
And the probability calculation module calls the spare part scheme to acquire the standard reaching probability Pm of the current Ns.
The comparison module is used for entering the scheme updating module when the Pm is smaller than a minimum threshold probability, and entering the scheme selecting module otherwise.
The scheme update module is configured to update at least one element item of Ns and enter the probability calculation module.
The scheme selection module is used for selecting the current Ns as the optimized spare part scheme.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A method for optimizing a spare part solution for multiple batches of parts in a modular storage mode, comprising:
s100, constructing an array representing an initial spare part scheme, wherein Ns= [ Ns1 Ns2 … Nmdyn ], nsi is the number of spare parts of the ith unit, i is more than or equal to 1 and less than or equal to dyN, dyN is the number of units in the part, so that nsi=max ([ 0M+1-N ]), N is the number of sets of parts in the warehouse, and M is a natural number;
s210, obtaining an element dyPrji in a matrix dyPx, wherein dyPrji is the probability that j-1 is good for all ith item units at the moment T1,
s220, acquiring standard probability Pm of the current Ns according to the matrix dyPx, and configuring the standard probability Pm as follows:
s221, let i=1,
s222, let p3=dypx (: i), and let
S223, i=i+1, if i is less than or equal to dyN, then S222 is entered, otherwise S224 is entered,
s224, calculating the standard reaching probability of Ns asSpare part solution cost is->
S300, entering S400 when Pm is smaller than a minimum threshold probability, otherwise entering S500;
s400, updating at least one element item of Ns and proceeding to S220;
s500, selecting the current Ns as an optimized spare part scheme.
2. A method for optimizing a replacement part solution for a plurality of batches of parts in a modular storage mode as claimed in claim 1,
s210 is configured to:
s211, making the unit sequence number i=1;
s212, let component number j1=1;
s213, enabling a vector p1= [1-pr pr ] pr to be the storage reliability of an ith item unit in a jth 1 component at the moment T1;
s214, p2=p1 when j1=1, and p2=p1×p2 when j1> 1;
s215, enabling j1 to be in a 1+1 state, entering S213 when j1 is less than or equal to N, otherwise, placing p2 in an ith column in a matrix dyPx, and entering S216;
s216, i=i+1, and if i is equal to or less than dyN, S212 is entered, otherwise S220 is entered.
3. A method for optimizing a replacement part solution for a plurality of batches of parts in a modular storage mode as claimed in claim 2,
s213 is configured to obtain pr from a lifetime type distribution to which the unit is subject.
4. A method for optimizing a replacement part solution for a plurality of batches of parts in a modular storage mode as claimed in claim 1,
s220 is configured to obtain the spare part purchasing cost of the array Ns as Mnow according to the matrix dyPx;
s400 is configured to generate an array of at least one spare part proposal based on Ns as Nt, obtain the standard reaching probability of Nt according to dyPx as Pt and the spare part purchasing expense Mt, and obtain the marginal cost ratio of Nt according to Pt, mt, pm and Mnow as p2m k Selecting the maximum p2m k And after the corresponding Nt is Ns, the process proceeds to S220.
5. A method for optimizing a replacement part solution for a plurality of batches of parts in a modular storage mode as set forth in claim 4,
s400 is configured to:
s410, let a candidate sequence number k=1;
s420, let nt=ns, and let Nt k ＝Ns k +1；
S430, acquiring Pt and Mt of Nt according to the matrix dyPx;
s440, calculating p2m of Nt k ，
S450, enabling k=k+1, entering S420 when k is less than or equal to dyN, otherwise entering S460;
s460, selecting p2m 1 p2m 2 …p2m K The serial number corresponding to the maximum value is Im, so that Ns in Ns Im ＝Ns Im After +1, the process proceeds to S220.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018053935A1 (en) * | 2016-09-20 | 2018-03-29 | 西南石油大学 | Failure mode occurrence probability based operating status fuzzy evaluation and prediction method for rotating device |
| CN107885926A (en) * | 2017-11-06 | 2018-04-06 | 中国人民解放军海军工程大学 | A kind of spares optimization method of Ship Equipment under no maintenance state |
| CN108304971A (en) * | 2018-02-07 | 2018-07-20 | 中国人民解放军海军工程大学 | A kind of spare parts demand amount computational methods of more complete equipments |
Family Cites Families (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102008008357A1 (en) * | 2007-02-08 | 2009-01-15 | Siemens Aktiengesellschaft | Method and system for determining reliability parameters of a technical installation |
| US7945427B2 (en) * | 2008-04-18 | 2011-05-17 | The Boeing Company | Methods and systems for providing unanticipated demand predictions for maintenance |
| US8655705B2 (en) * | 2010-01-13 | 2014-02-18 | Lockheed Martin Corporation | Systems, methods and apparatus for implementing hybrid meta-heuristic inventory optimization based on production schedule and asset routing |
| JP5677167B2 (en) * | 2011-03-30 | 2015-02-25 | 本田技研工業株式会社 | Optimal control system |
| CN104462755B (en) * | 2014-10-30 | 2017-06-23 | 中国船舶重工集团公司第七二六研究所 | Electronic equipment spare parts configuration computational methods based on reliability model |
| CN105608506A (en) * | 2015-12-23 | 2016-05-25 | 中国人民解放军空军第一航空学院 | Margin effect analysis based equipment inventory scheme optimization method and system |
| CN107451692B (en) * | 2017-08-02 | 2021-07-09 | 中国航空工业集团公司西安飞机设计研究所 | Aviation spare part configuration optimization method based on artificial bee colony algorithm |
| CN108509389B (en) * | 2018-03-25 | 2022-01-14 | 中国人民解放军海军工程大学 | Spare part demand calculation method for Weibull serial parts |
| CN108470250B (en) * | 2018-03-25 | 2021-11-02 | 中国人民解放军海军工程大学 | Spare part demand calculation method for normal series connection component |
| CN108334720B (en) * | 2018-04-09 | 2021-08-31 | 中国人民解放军海军工程大学 | Method for calculating demand of normal type unit spare parts under risk of storage failure |
| CN108536960B (en) * | 2018-04-09 | 2022-04-05 | 中国人民解放军海军工程大学 | Method for calculating demand of exponential type unit spare parts under risk of storage failure |
| CN108876002B (en) * | 2018-05-03 | 2021-08-17 | 浙江运达风电股份有限公司 | Method for optimizing inventory of spare parts of wind generating set |
| CN109523160A (en) * | 2018-11-15 | 2019-03-26 | 青岛航讯网络技术服务有限公司 | The more normal state assembly of elements spare parts demand amounts of the long-term support mission of larger cargo ships determine method |
-
2019
- 2019-05-16 CN CN201910406632.0A patent/CN110287523B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018053935A1 (en) * | 2016-09-20 | 2018-03-29 | 西南石油大学 | Failure mode occurrence probability based operating status fuzzy evaluation and prediction method for rotating device |
| CN107885926A (en) * | 2017-11-06 | 2018-04-06 | 中国人民解放军海军工程大学 | A kind of spares optimization method of Ship Equipment under no maintenance state |
| CN108304971A (en) * | 2018-02-07 | 2018-07-20 | 中国人民解放军海军工程大学 | A kind of spare parts demand amount computational methods of more complete equipments |
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