CN109711765A - Distribution materials and equipment classification method based on Kraljic buying location model - Google Patents
Distribution materials and equipment classification method based on Kraljic buying location model Download PDFInfo
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Abstract
A kind of distribution materials and equipment classification method based on Kraljic buying location model, comprising the following steps: A, in conjunction with the characteristics of demand of distribution goods and materials determines the corresponding influence factor of two dimensions；B, questionnaire design is carried out, the score of each influence factor is obtained using expert graded, and determine the relative weighting of first order impact factor；C, by design of Scales, the weight of each influence factor is determined using Fuzzy AHP；D, the score of selected distribution goods and materials importance is obtained by expert graded, then obtains the score weighted average of Secondary influence factors, first order impact factor overall situation score is then obtained by the relative weighting of first order impact factor and weights draw value；E, two dimension coordinate diagrams of each distribution goods and materials are drawn；F, the Kraljic goods and materials positional matrix classification results of each distribution goods and materials are determined.The design improves the accuracy of distribution materials and equipment classification, to improve the distribution purchase of goods and materials efficiency and benefit of grid company.
Description
Technical field
The present invention relates to distribution purchase of goods and materials field more particularly to a kind of distributions based on Kraljic buying location model
Materials and equipment classification method is primarily adapted for use in and improves distribution materials and equipment classification accuracy.
Background technique
Power distribution network is the important component of power grid, it is directly facing power consumer, carries 99.9 ﹪ or more clients'
Electric service is the important infrastructure of socio-economic development of improving People's livelihood, ensure.As State Grid Corporation of China's goods and materials are intensive
The propulsion that deepens continuously of management, distribution agreement inventory bid and purchase also go out while improving procurement efficiency, ensureing material supply
Showed some problems, for example, distribution net equipment material because region difference demand characteristics, properties of product, in terms of exist
Very big difference does not consider the factor of differentiation under unified procurement platform, simply uses same procurement strategy and is recruited
Mark buying, will increase the probability for generation of failing to be sold at auction, and will affect power grid construction plan instead, increase extra cost.Past is simple, single
One, the centralized purchasing strategy of extensive style has been unable to satisfy the fast-developing needs of enterprise, it is therefore necessary in intensive management mould
Carry out the procurement strategy research of distribution goods and materials differentiation under formula.
Summary of the invention
The purpose of the present invention is overcome the problems, such as the low defect of distribution materials and equipment classification accuracy existing in the prior art and, mention
For a kind of high distribution materials and equipment classification method based on Kraljic buying location model of distribution materials and equipment classification accuracy.
In order to achieve the above object, the technical solution of the invention is as follows: a kind of matching based on Kraljic buying location model
Net materials and equipment classification method, method includes the following steps:
A, based on two dimensions of Kraljic goods and materials positional matrix, i.e. supply risk and buying importance, in conjunction with distribution object
The characteristics of demand of money determines influence factor corresponding to two dimensions respectively；
B, according to step A it is found that supply risk and buying importance are first order impact factor, supply risk and buying are important
Property corresponding to influence factor be Secondary influence factors, to two-stage influence factor carry out questionnaire design, obtained using expert graded
To the score of each influence factor, and determine the relative weighting of first order impact factor；
C, by design of Scales, the weight of each influence factor is determined using Fuzzy AHP；
D, it first chooses according to the size of nearly purchase of goods and materials amount of money of distribution in 5 years and occupies matching for the total purchase amount of money 80 percent
Net goods and materials carry out the positioning of Kraljic goods and materials positional matrix, then obtain the score of each distribution goods and materials importance by expert graded,
Then obtain the score weighted average of the Secondary influence factors of each distribution goods and materials, then by the first order impact in step B because
The relative weighting of element obtains the first order impact factor overall situation score weighting draw value of each distribution goods and materials；
E, using Drawing Directly method or multidimensional scaling, obtain each distribution goods and materials two dimension coordinate diagrams and each distribution object
The corresponding coordinate value of money；
F, according to the mapping result of step E, the Kraljic goods and materials positional matrix classification results of each distribution goods and materials are obtained.
In step B, supply risk includes three market risk, contract performance risk, supply complexity risk second levels influences
Factor, buying importance include influence of the buying to profit, the importance of procurement activity, buying three second level shadows of value/cost
The factor of sound.
In step B, the relative weighting of the supply risk and buying importance are as follows:
Wherein, [ai, bi] indicate the selected relative weighting of i-th of expert, ai+bi=1, i=1,2 ..., E, E are ginseng
With expert's sum of marking.
The step C specifically includes the following steps:
C1, pass through design of Scales, the score in step B be converted into corresponding Triangular Fuzzy Number are as follows:
| Score | Influence degree | Triangular Fuzzy Number |
| 1 | Do not influence | (1,1,2) |
| 2 | It is extremely low | (1,2,3) |
| 3 | It is very low | (2,3,4) |
| 4 | It is low | (3,4,5) |
| 5 | It is medium relatively low | (4,5,6) |
| 6 | It is medium | (5,6,7) |
| 7 | It is medium higher | (6,7,8) |
| 8 | It is high | (7,8,9) |
| 9 | It is very high | (8,9,10) |
| 10 | It is high | (9,10,10) |
；
It enablesTriangular Fuzzy Number is indicated, wherein 0≤a≤b≤c；
It is rightDe-fuzzy, it may be assumed that
C2, the average value for calculating Triangular Fuzzy Number value corresponding to expert estimation under each influence factor；
It enablesFor e-th of expert Triangular Fuzzy Number value corresponding to the marking of m-th influence factor,For all experts
To the average value of m-th of corresponding Triangular Fuzzy Number value of influence factor marking, then:
Wherein, U=S, I, S and I respectively represent supply risk and buying importance, and E represents expert's sum, and M, which is represented, to be influenced
Factor sum, and M=3；
C3, it is based onBy comparing two-by-two, fuzzy judgment matrix AG is constructedU；
Wherein, Indicate fuzzy judgment matrix AGUFuzzy weighted values element；
Calculate Fog property weight
It is based onIt is rightDe-fuzzy obtains de-fuzzy attribute weightAnd then obtain m
The normalization attribute weight of a influence factor
In step D, enableMarking for e-th of expert to m-th of influence factor of kth kind distribution goods and materials, using step
The Secondary influence factors that the method for rapid C obtains kth kind distribution goods and materials obscure score weighted average
Wherein, M represents influence factor sum, and M=3, K are represented and carried out distribution object using Kraljic goods and materials positional matrix
Provide the distribution goods and materials sum of positioning；
It is based onIt is rightDe-fuzzy obtains It is weighted for the score of Secondary influence factors
Then average value obtains the first order impact factor of kth kind distribution goods and materials by the relative weighting of the first order impact factor in step B
Global score weights draw value
In step E, according to step D's as a result, calculating the Euclidean distance matrix of each distribution goods and materials, Euclidean distance function are as follows:
Wherein, n indicates number of dimensions, and n=2, SijAnd SikRespectively represent the first order impact of jth kind and kth kind distribution goods and materials
Factor overall situation score weights draw value；
The Euclidean distance matrix obtained after calculating is J × J matrix, using each vector of the matrix as multidimensional scaling
Input, be then obtained by calculation each distribution goods and materials two dimension coordinate diagrams and each distribution goods and materials corresponding to coordinate value.
Compared with prior art, the invention has the benefit that
The present invention is based on OK a karaoke club Jack inverse matrices (Kraljic method) and the linear programming based on DEA
Method, in conjunction with the practical use value model of asset life cycle, marginal benefit model, building is fixed based on Kraljic buying
The quantitative model of bit model, to exclude influence of the subjectivity to distribution materials and equipment classification, thus keep distribution materials and equipment classification more accurate,
Therefore the strategy of distribution purchase of goods and materials also becomes more have specific aim.
Detailed description of the invention
Fig. 1 is that the present invention is based on the flow charts of the distribution materials and equipment classification method of Kraljic buying location model.
In figure, KMP indicates Kraljic goods and materials positional matrix.
Specific embodiment
Below in conjunction with Detailed description of the invention and specific embodiment, the present invention is described in further detail.
Referring to Fig. 1, a kind of distribution materials and equipment classification method based on Kraljic buying location model, this method includes following
Step:
A, based on two dimensions of Kraljic goods and materials positional matrix, i.e. supply risk and buying importance, in conjunction with distribution object
The characteristics of demand of money determines influence factor corresponding to two dimensions respectively；
B, according to step A it is found that supply risk and buying importance are first order impact factor, supply risk and buying are important
Property corresponding to influence factor be Secondary influence factors, to two-stage influence factor carry out questionnaire design, obtained using expert graded
To the score of each influence factor, and determine the relative weighting of first order impact factor；
C, by design of Scales, the weight of each influence factor is determined using Fuzzy AHP；
D, it first chooses according to the size of nearly purchase of goods and materials amount of money of distribution in 5 years and occupies matching for the total purchase amount of money 80 percent
Net goods and materials carry out the positioning of Kraljic goods and materials positional matrix, then obtain the score of each distribution goods and materials importance by expert graded,
Then obtain the score weighted average of the Secondary influence factors of each distribution goods and materials, then by the first order impact in step B because
The relative weighting of element obtains the first order impact factor overall situation score weighting draw value of each distribution goods and materials；
E, using Drawing Directly method or multidimensional scaling, obtain each distribution goods and materials two dimension coordinate diagrams and each distribution object
The corresponding coordinate value of money；
F, according to the mapping result of step E, the Kraljic goods and materials positional matrix classification results of each distribution goods and materials are obtained.
In step B, supply risk includes three market risk, contract performance risk, supply complexity risk second levels influences
Factor, buying importance include influence of the buying to profit, the importance of procurement activity, buying three second level shadows of value/cost
The factor of sound.
In step B, the relative weighting of the supply risk and buying importance are as follows:
Wherein, [ai, bi] indicate the selected relative weighting of i-th of expert, ai+bi=1, i=1,2 ..., E, E are ginseng
With expert's sum of marking.
The step C specifically includes the following steps:
C1, pass through design of Scales, the score in step B be converted into corresponding Triangular Fuzzy Number are as follows:
| Score | Influence degree | Triangular Fuzzy Number |
| 1 | Do not influence | (1,1,2) |
| 2 | It is extremely low | (1,2,3) |
| 3 | It is very low | (2,3,4) |
| 4 | It is low | (3,4,5) |
| 5 | It is medium relatively low | (4,5,6) |
| 6 | It is medium | (5,6,7) |
| 7 | It is medium higher | (6,7,8) |
| 8 | It is high | (7,8,9) |
| 9 | It is very high | (8,9,10) |
| 10 | It is high | (9,10,10) |
；
It enablesTriangular Fuzzy Number is indicated, wherein 0≤a≤b≤c；
It is rightDe-fuzzy, it may be assumed that
C2, the average value for calculating Triangular Fuzzy Number value corresponding to expert estimation under each influence factor；
It enablesFor e-th of expert Triangular Fuzzy Number value corresponding to the marking of m-th influence factor,It is all special
Family gives a mark the average value of corresponding Triangular Fuzzy Number value to m-th influence factor, then:
Wherein, U=S, I, S and I respectively represent supply risk and buying importance, and E represents expert's sum, and M, which is represented, to be influenced
Factor sum, and M=3；
C3, it is based onBy comparing two-by-two, fuzzy judgment matrix AG is constructedU；
Wherein, Indicate fuzzy judgment matrix AGUFuzzy weighted values element；
Calculate Fog property weight
It is based onIt is rightDe-fuzzy obtains de-fuzzy attribute weightAnd then obtain m
The normalization attribute weight of a influence factor
In step D, enableMarking for e-th of expert to m-th of influence factor of kth kind distribution goods and materials, using step
The Secondary influence factors that the method for rapid C obtains kth kind distribution goods and materials obscure score weighted average
Wherein, M represents influence factor sum, and M=3, K are represented and carried out distribution object using Kraljic goods and materials positional matrix
Provide the distribution goods and materials sum of positioning；
It is based onIt is rightDe-fuzzy obtains It is weighted for the score of Secondary influence factors
Then average value obtains the first order impact factor of kth kind distribution goods and materials by the relative weighting of the first order impact factor in step B
Global score weights draw value
In step E, according to step D's as a result, calculating the Euclidean distance matrix of each distribution goods and materials, Euclidean distance function are as follows:
Wherein, n indicates number of dimensions, and n=2, sijAnd SikRespectively represent the first order impact of jth kind and kth kind distribution goods and materials
Factor overall situation score weights draw value；
The Euclidean distance matrix obtained after calculating is J × J matrix, using each vector of the matrix as multidimensional scaling
Input, be then obtained by calculation each distribution goods and materials two dimension coordinate diagrams and each distribution goods and materials corresponding to coordinate value.
The principle of the present invention is described as follows:
The quantization for being designed to provide a kind of decision support towards extensive distribution net equipment material purchases of the design point
Method and kit for is analysed, is power grid enterprises in distribution purchase of goods and materials field promotion management level, improves the performance of enterprises.
Linear programming method based on OK a karaoke club Jack inverse matrices (Kraljic method) and based on DEA,
In conjunction with the practical use value model of asset life cycle, marginal benefit model, the quantitative model based on KMP is constructed, with row
Influence except subjectivity to materials and equipment classification, to keep materials and equipment classification more accurate.
Firstly, according to the characteristics of demand of distribution goods and materials, for two dimensions of Kraljic goods and materials positional matrix (KMP), really
The influence factor of fixed each dimension；Secondly, questionnaire design is carried out, it is true using expert graded (Delphi method, Delphi Method)
The score value of fixed different factors；Then, by design of Scales, using Fuzzy AHP (Fuzzy Analytical
Hierarchy Process, FAHP) determine the weights of Secondary influence factors；Next, according to over nearly 5 years procurement value it is big
It is small, choose occupy 80 ﹪ of the total purchase amount of money main goods and materials carry out KMP positioning (according to 20/80 principle, choose procurement value compared with
Greatly, and procurement value summation accounts for the goods and materials of the 80 ﹪ total purchase amount of money and expert is asked to give a mark with regard to its effectiveness (utility)), pass through
Expert graded obtains each expert to the evaluation score of each goods and materials importance, then obtains each goods and materials importance of two dimensions
Weighted average；Finally, Drawing Directly method or MDS method can be used, each goods and materials are determined at " supply risk " and " buying is important
The position of two dimensions of property "；According to mapping result, the KMP positioning of each goods and materials is obtained.It is determining two-stage factor weight and is obtaining object
When providing importance, it is contemplated that the marking of multidigit expert is as a result, therefore, this model is also a group decision model (group
decision making model)。
Marking of the available every expert to each factor influence degree in stepb, is set by the scale in step C
Meter, converts the scores to corresponding Triangular Fuzzy Number (Triangular Fuzzy Number, TFN), comments to eliminate objective
Subjective factor present in valence.
The design can also carry out power distribution network purchase of goods and materials in conjunction with goods and materials needing forecasting method, analyze different classes of distribution
Material requirements characteristic verifies the feasibility of distribution goods and materials project material requirements prediction in conjunction with historical data；Prediction technique is applicable in
Range is furtherd investigate, and carries out data modeling using the method for regression analysis, it is (always golden with annual acceptance of the bid to establish scale of investment
Volume indicates) and all kinds of main goods and materials between the multivariate regression models of procurement value relationship carry out requirement forecasting.
Embodiment:
Referring to Fig. 1, a kind of distribution materials and equipment classification method based on Kraljic buying location model, this method includes following
Step:
A, based on two dimensions of Kraljic goods and materials positional matrix, i.e. supply risk and buying importance, in conjunction with distribution object
The characteristics of demand of money determines influence factor corresponding to two dimensions respectively；
B, according to step A it is found that supply risk and buying importance are first order impact factor, supply risk and buying are important
Property corresponding to influence factor be Secondary influence factors, to two-stage influence factor carry out questionnaire design, obtained using expert graded
To the score of each influence factor, and determine the relative weighting of first order impact factor；
Supply risk includes the market risk, contract performance risk, supply three Secondary influence factors of complexity risk, buying
Importance includes influence of the buying to profit, the importance of procurement activity, buying three Secondary influence factors of value/cost；
The relative weighting of the supply risk and buying importance are as follows:
Wherein, [ai, bi] indicate the selected relative weighting of i-th of expert, ai+bi=1, i=1,2 ..., E, E are ginseng
With expert's sum of marking；
C, by design of Scales, the weight of each influence factor is determined using Fuzzy AHP, specifically includes following step
It is rapid:
C1, pass through design of Scales, the score in step B be converted into corresponding Triangular Fuzzy Number are as follows:
| Score | Influence degree | Triangular Fuzzy Number |
| 1 | Do not influence | (1,1,2) |
| 2 | It is extremely low | (1,2,3) |
| 3 | It is very low | (2,3,4) |
| 4 | It is low | (3,4,5) |
| 5 | It is medium relatively low | (4,5,6) |
| 6 | It is medium | (5,6,7) |
| 7 | It is medium higher | (6,7,8) |
| 8 | It is high | (7,8,9) |
| 9 | It is very high | (8,9,10) |
| 10 | It is high | (9,10,10) |
；
It enablesTriangular Fuzzy Number is indicated, wherein 0≤a≤b≤c；
It is rightDe-fuzzy, it may be assumed that
C2, the average value for calculating Triangular Fuzzy Number value corresponding to expert estimation under each influence factor；
It enablesFor e-th of expert Triangular Fuzzy Number value corresponding to the marking of m-th influence factor,It is all special
Family gives a mark the average value of corresponding Triangular Fuzzy Number value to m-th influence factor, then:
Wherein, U=S, I, S and I respectively represent supply risk and buying importance, and E represents expert's sum, and M, which is represented, to be influenced
Factor sum, and M=3；
C3, it is based onBy comparing two-by-two, fuzzy judgment matrix AG is constructedU；
Wherein, Indicate fuzzy judgment matrix AGUFuzzy weighted values element；
Calculate Fog property weight
It is based onIt is rightDe-fuzzy obtains de-fuzzy attribute weightAnd then obtain m
The normalization attribute weight of a influence factor
D, it first chooses according to the size of nearly purchase of goods and materials amount of money of distribution in 5 years and occupies matching for the total purchase amount of money 80 percent
Net goods and materials carry out the positioning of Kraljic goods and materials positional matrix, then obtain the score of each distribution goods and materials importance by expert graded,
Then obtain the score weighted average of the Secondary influence factors of each distribution goods and materials, then by the first order impact in step B because
The relative weighting of element obtains the first order impact factor overall situation score weighting draw value of each distribution goods and materials；
It enablesMarking for e-th of expert to m-th of influence factor of kth kind distribution goods and materials, using the method for step C
The Secondary influence factors for obtaining kth kind distribution goods and materials obscure score weighted average
Wherein, M represents influence factor sum, and M=3, K are represented and carried out distribution object using Kraljic goods and materials positional matrix
Provide the distribution goods and materials sum of positioning；
It is based onIt is rightDe-fuzzy obtains local score weighted average For second level
Then the score weighted average of influence factor obtains kth kind by the relative weighting of the first order impact factor in step B and matches
The first order impact factor overall situation score of net goods and materials weights draw value
E, using Drawing Directly method or multidimensional scaling, obtain each distribution goods and materials two dimension coordinate diagrams and each distribution object
The corresponding coordinate value of money；
According to step D's as a result, calculating the Euclidean distance matrix of each distribution goods and materials, Euclidean distance function are as follows:
Wherein, n indicates number of dimensions, and n=2, SijAnd SikRespectively represent the first order impact of jth kind and kth kind distribution goods and materials
Factor overall situation score weights draw value；
The Euclidean distance matrix obtained after calculating is J × J matrix, using each vector of the matrix as multidimensional scaling
Input, be then obtained by calculation each distribution goods and materials two dimension coordinate diagrams and each distribution goods and materials corresponding to coordinate value；
F, according to the mapping result of step E, the Kraljic goods and materials positional matrix classification results of each distribution goods and materials are obtained.
Claims (6)
1. a kind of distribution materials and equipment classification method based on Kraljic buying location model, which is characterized in that this method includes following
Step:
A, based on two dimensions of Kraljic goods and materials positional matrix, i.e. supply risk and buying importance, in conjunction with distribution goods and materials
Characteristics of demand determines influence factor corresponding to two dimensions respectively；
B, according to step A it is found that supply risk and buying importance are first order impact factor, supply risk and buying importance institute
Corresponding influence factor is Secondary influence factors, carries out questionnaire design to two-stage influence factor, is obtained respectively using expert graded
The score of influence factor, and determine the relative weighting of first order impact factor；
C, by design of Scales, the weight of each influence factor is determined using Fuzzy AHP；
D, the distribution object for occupying the total purchase amount of money 80 percent is first chosen according to the size of nearly purchase of goods and materials amount of money of distribution in 5 years
Money carries out the positioning of Kraljic goods and materials positional matrix, then obtains the score of each distribution goods and materials importance by expert graded, then
The score weighted average of the Secondary influence factors of each distribution goods and materials is obtained, the first order impact factor in step B is then passed through
Relative weighting obtains the first order impact factor overall situation score weighting draw value of each distribution goods and materials；
E, using Drawing Directly method or multidimensional scaling, obtain each distribution goods and materials two dimension coordinate diagrams and each distribution goods and materials institute
Corresponding coordinate value；
F, according to the mapping result of step E, the Kraljic goods and materials positional matrix classification results of each distribution goods and materials are obtained.
2. a kind of distribution materials and equipment classification method based on Kraljic buying location model according to claim 1, feature
Be: in step B, supply risk include the market risk, three contract performance risk, supply complexity risk second levels influence because
Element, buying importance include that influence of the buying to profit, the importance of procurement activity, buying three second levels of value/cost influence
Factor.
3. a kind of distribution materials and equipment classification method based on Kraljic buying location model according to claim 2, feature
It is: in step B, the relative weighting of the supply risk and buying importance are as follows:
Wherein, [ai, bi] indicate the selected relative weighting of i-th of expert, ai+bi=1, i=1,2 ..., E, E are to participate in marking
Expert sum.
4. a kind of distribution materials and equipment classification method based on Kraljic buying location model according to claim 3, feature
Be: the step C specifically includes the following steps:
C1, pass through design of Scales, the score in step B be converted into corresponding Triangular Fuzzy Number are as follows:
；
It enablesTriangular Fuzzy Number is indicated, wherein 0≤a≤b≤c；
It is rightDe-fuzzy, it may be assumed that
C2, the average value for calculating Triangular Fuzzy Number value corresponding to expert estimation under each influence factor；
It enablesFor e-th of expert Triangular Fuzzy Number value corresponding to the marking of m-th influence factor,For all experts couple
The average value of m-th of corresponding Triangular Fuzzy Number value of influence factor marking, then:
Wherein, U=S, I, S and I respectively represent supply risk and buying importance, and E represents expert's sum, and M represents influence factor
Sum, and M=3；
C3, it is based onBy comparing two-by-two, fuzzy judgment matrix AG is constructedU；
Wherein, Indicate fuzzy judgment matrix AGUFuzzy weighted values element；
Calculate Fog property weight
It is based onIt is rightDe-fuzzy obtains de-fuzzy attribute weightAnd then obtain m-th of shadow
The normalization attribute weight of the factor of sound
5. a kind of distribution materials and equipment classification method based on Kraljic buying location model according to claim 4, feature
It is: in step D, enablesMarking for e-th of expert to m-th of influence factor of kth kind distribution goods and materials, using step C
Method obtain kth kind distribution goods and materials Secondary influence factors obscure score weighted average
Wherein, M represents influence factor sum, and M=3, K are represented and determined using Kraljic goods and materials positional matrix progress distribution goods and materials
The distribution goods and materials sum of position；
It is based onIt is rightDe-fuzzy obtains It is weighted and averaged for the score of Secondary influence factors
Value, the first order impact factor for then obtaining kth kind distribution goods and materials by the relative weighting of the first order impact factor in step B are global
Score weights draw value
6. a kind of distribution materials and equipment classification method based on Kraljic buying location model according to claim 5, feature
It is: in step E, according to step D's as a result, calculating the Euclidean distance matrix of each distribution goods and materials, Euclidean distance function are as follows:
Wherein, n indicates number of dimensions, and n=2, sijAnd sikRespectively represent the first order impact factor of jth kind and kth kind distribution goods and materials
Global score weights draw value；
The Euclidean distance matrix obtained after calculating is J × J matrix, using each vector of the matrix as the defeated of multidimensional scaling
Enter, be then obtained by calculation each distribution goods and materials two dimension coordinate diagrams and each distribution goods and materials corresponding to coordinate value.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111401804A (en) * | 2020-02-28 | 2020-07-10 | 雅砻江流域水电开发有限公司 | Simulation-based engineering material supply chain network planning method and system |
| CN115409318A (en) * | 2022-07-22 | 2022-11-29 | 南方海洋科学与工程广东省实验室(广州) | Natural-based water purification scheme optimization method integrating fuzzy AHP and MDS |
-
2018
- 2018-11-29 CN CN201811445473.7A patent/CN109711765A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111401804A (en) * | 2020-02-28 | 2020-07-10 | 雅砻江流域水电开发有限公司 | Simulation-based engineering material supply chain network planning method and system |
| CN111401804B (en) * | 2020-02-28 | 2023-07-11 | 雅砻江流域水电开发有限公司 | Engineering material supply chain network planning method and system based on simulation |
| CN115409318A (en) * | 2022-07-22 | 2022-11-29 | 南方海洋科学与工程广东省实验室(广州) | Natural-based water purification scheme optimization method integrating fuzzy AHP and MDS |
| CN115409318B (en) * | 2022-07-22 | 2024-03-19 | 南方海洋科学与工程广东省实验室(广州) | Natural-based water purification scheme optimization method integrating fuzzy AHP and MDS |
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