CN103473405A - Customer-demand-oriented product module planning method - Google Patents
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- CN103473405A CN103473405A CN2013103912758A CN201310391275A CN103473405A CN 103473405 A CN103473405 A CN 103473405A CN 2013103912758 A CN2013103912758 A CN 2013103912758A CN 201310391275 A CN201310391275 A CN 201310391275A CN 103473405 A CN103473405 A CN 103473405A
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Abstract
The invention provides a customer-demand-oriented product module planning method. The method includes: establishing a DDM (design dependency matrix) according to correspondence between customer demands and a module; dividing module association into strong one and weak one; establishing a product module DSM (design structure matrix) on the basis of DDM clustering results and inter-module association; clustering the DSM on the premise of guaranteeing customer demand clustering so as to achieve customer-demand-oriented product module planning. Module planning process is simple and susceptible to subjectivity, so that the defects of the prior art are overcome.
Description
Technical field
The present invention relates to product task division field, be specially a kind of product module planing method of customer demand as guiding of take.
Background technology
Modular design method, as strengthening enterprise's quick-reaction capability (QRC), improves the effective means of product complexity, in manufacturing industry, is widely applied.Modular design be take module as basis, by module being combined and revises the new product of formation.Because the complexity of product is more and more higher, the type of modules that forms product is also more and more.Too much product module is not only bad for management, and it is indefinite easily to cause task to be distributed, and reduces design efficiency.At Under the market economy condition, client's demand determines the developing direction of product.In order to keep the ability of modular design method rapid reaction customer demand, reduce the module management cost, need to be planned module, according to the Module Division result, instruct the decomposition of task, improve design efficiency, reduce handling cost.Therefore, product module being planned, is the difficult point of product task division.
A kind of product module planing method is according to the functional association relation of module and the geometric relativity of intermodule, and product module is divided.According to the subfunction of product and mutual relation thereof, take into account the parts of product and dependence is each other carried out product module planning.The function of module and geometric relationship all belong to the product design constraint.Function and geometric relationship based on module are divided module, and energy is function of organization's module effectively, avoid or reduce the geometry associativity of intermodule, realize the decomposition of product task.The limitation of this method is: only consider the product design constraint, ignored the external factor such as customer demand.The module planning result is more suitable for the matured product of enterprise, and when customer demand changes, new demand may be related to a plurality of task department, will increase interdepartmental Cost for Coordination, the cycle stretch-out of developing new product.
A kind of product module planing method is to consider Enterprise Resource, for each factor that affects module planning, adds weights, uses the optimized algorithms such as intelligent algorithm or fuzzy clustering to be divided product module.At first determine each influence factor that Module Division is relevant, as client, supplier, functions of modules, interface conjunctionn etc.According to each factor, the influence degree of Module Division is just determined to weights for each influence factor, all combined factors are considered, set up Module Division target equation (matrix).Use the optimized algorithms such as intelligent algorithm or fuzzy clustering to be solved, obtain the module planning result.The limitation of this method is: the determining mainly based on knowhow of weights, the module planning result easily is subject to subjective impact.
Summary of the invention
The technical matters solved
The objective of the invention is to carry out product module planning for prior art and do not focus on customer demand, and easily be subject to the deficiency of subjective impact, propose a kind of product module planing method of customer demand as guiding of take.
Technical scheme
The present invention, according to the corresponding relation of customer demand and intermodule, sets up design dependency matrix (DDM, Design Dependency Matrix); The module relation relation is divided into to strong association with weak associated; Based on the DDM cluster result, incidence relation between binding modules, set up product module project organization matrix (DSM, Design structure matrix); Guaranteeing, under customer demand cluster prerequisite, DSM to be carried out to cluster, realize considering the product module planning of customer demand.The module planning process is simple, and acceptor's viewing does not ring, thereby efficiently solves the defect existed in prior art.
Technical scheme of the present invention is:
Describedly a kind ofly take customer demand and be to it is characterized in that the product module planing method of guiding: adopt following steps:
Step 1: according to the incidence relation of customer demand and module, set up customer demand-module DDM matrix: the rectangular matrix that wherein the DDM matrix is the capable n row of m, DDM matrix i is capable represents i customer demand r
i, j module c shown in DDM matrix j list
jif, r
iwith c
jthere is incidence relation, matrix element m
ij=1, otherwise m
ij=0, finally set up DDM matrix M=[m
ij], (i=1,2 ..., m; J=1,2 ..., n);
Step 2: remove the null value row in matrix M, generate the matrix M of row-column dependence
r; By matrix M
rdiagonalization, generator matrix M
diag;
Step 3: to M
diagcluster, form cluster result M
c:
Step 3.1: selection matrix M
diagenter step 3.2:
Step 3.2: select to enter highly the highest rectangle white space in the lower triangular matrix in the matrix of this step and enter step 3.3;
Step 3.3: take the summit, the rectangle white space upper right corner that enters this step as intersecting strokes and dots horizontal line h and perpendicular line v, by matrix M
diagbe divided into four quadrants;
Step 3.4: if the right side area of v has the row that blocked by h, select the rectangle white space highly taken second place in lower triangular matrix, enter step 3.3; If there are not the row that blocked by h in the right side area of v, the zone in quadrant 2 and quadrant 4 is divided into to two sub-matroids;
Step 3.5: for the subclass matrix of quadrant 4, circulation execution step 3.2～step 3.4, until h and matrix M
diaglower boundary overlaps; Form cluster result M
c;
Step 4: according to cluster result M
c, set up product module DSM-M
p; M wherein
pin module according to the cluster matrix M
crow sequentially sort, the last row that adds successively the onrelevant module to DSM and rank rear;
Step 5: according to cluster result, by M
pbe divided into several sub-blocks, by M
peach sub-block with B, mean, be followed successively by B from top to bottom along diagonal line
1～B
k;
Step 6: module relationship is classified according to the geometry associativity relation of intermodule: two module position are correlated with and Size dependence is strong association, and two modules only position are correlated with as weak association, and it is onrelevant that there is not incidence relation in two modules; Square formation M according to the geometry associativity result of intermodule to n * n
passignment, if between module i and module j, exist strong associated, matrix element m
pij=2, if between module i and module j, exist weak associated, matrix element m
pij=1, if between module i and module j without connection, matrix element m
pij=0, i ≠ j;
Step 7: to M
pcluster, form cluster result M
p-C:
Step 7.1: traversal onrelevant module column, search strong associated element m
ij;
Step 7.2: if i is capable
module j and sub-block B
kin module exist strong associatedly, module j is incorporated to B
k;
Step 7.3: circulation execution step 7.1～step 7.2, until exist the irrelevant module of strong associated element all to be incorporated in corresponding sub-block;
Step 7.4: to B
kcarry out diagonalization, make B
kin all nonzero elements be gathered near diagonal line;
Step 7.5: if sub-block B
rwith sub-block B
tthe strong relating module of middle existence, adjust B
tposition to B
r+1, r<t;
Step 7.6: by B
kwith the height of other module according to weak associated element, sorted, wherein B
kheight with the highest weak associated element, be as the criterion;
Step 7.7: highly identical weak relating module is divided into to a sub-block, is called weak associated sub-block, thereby obtains the cluster matrix M
p-C;
Step 8: each sub-block is combined according to the strong incidence relation between module number and sub-block, the module planning scheme that formation comprises S module subset, make that module number in the modules subset is poor is no more than 5, and the module in the module subset does not exist with the module in other module subset strong associated.
Beneficial effect
The invention has the beneficial effects as follows, analyzed by the strong and weak incidence relation of the corresponding relation to customer demand and module and intermodule, adopt engineering problem is converted into to the thought that fabric problem solves.Use the module that the DDM analytical approach will be relevant to customer demand to carry out cluster, based on this, strong and weak incidence relation between binding modules, used the module that DSM will be irrelevant with customer demand to incorporate the module cluster result, forms to take the module planning scheme of customer demand as guiding.Carry out product module planning by the method, product module is divided into to some classes according to customer demand, be assigned to each task department.When new customer demand occurs, can locate fast correlation module according to customer demand, change the mode of operation of traditional design process " customer demand---functional requirement---product module ".And the method do not need manually to determine weights, can avoid the subjectivity impact in the module planning process.
The accompanying drawing explanation
Fig. 1 is tank type half-trailer customer demand-module DDM.
Fig. 2 is the matrix M of removing after null value is listed as
r.
Fig. 3 is to matrix M
rm as a result after diagonalization
diag.
Fig. 4 is to matrix M
diagm as a result after cluster
c.
Fig. 5 is according to M
cdSM-M with the strong and weak incidence relation foundation of product module
p.
Fig. 6 is by M
psub-block carry out the result of cluster.
Fig. 7 is the product module programme according to DSM cluster result combination producing.
Embodiment
Below in conjunction with drawings and Examples, the inventive method is described further:
Embodiment: certain type tank type half-trailer comprises 35 functional modules.For this kind of tank type half-trailer, client's requirement item has 14.
Because the intermodule geometry associativity is tight, can't carry out by geometric relativity the division of module.Traditional division methods is by Module Division, to be three classes according to main functional modules and related accessories thereof: tank body and annex, vehicle frame and annex, pipeline and annex.Use this kind of programme, when customer demand is both relevant to tank body also relevant with vehicle frame, just need two interagency coordinations, jointly adjusted inefficiency.
The technical scheme that the present invention solves its technical matters is: the described a kind of product module planing method of customer demand as guiding of take, at first set up the DDM matrix according to the corresponding relation of customer demand and module, by DDM being removed to the computings such as null value row, diagonalization, cluster, product module is carried out to cluster according to the corresponding relation with customer demand.Then setting up the DSM matrix according to the geometry associativity relation of DDM cluster result and intermodule, by the further cluster of DSM, is some subclasses by Module Division.The DSM cluster result of finally take is combined to form the product module programme as basis.It comprises the steps:
Step 1: set up according to the incidence relation of customer demand and module the rectangular matrix that customer demand-module DDM matrix: DDM is 14 row 35 row, matrix M, as shown in Figure 1, DDM matrix i is capable represents i customer demand r
i, j module c shown in DDM matrix j list
jif, r
iwith c
jthere is incidence relation, matrix element m
ij=1, otherwise m
ij=0.
Step 2: remove the null value row in matrix M, generate the matrix M of row-column dependence
r, as shown in Figure 2; By matrix M
rdiagonalization, generator matrix M
diag, as shown in Figure 3; Diagonalization adopts Simon Li at article Amatrix-based clustering approach for the decomposition of design problems, Research in Engineering Design, 2011.22 (4): the method proposed in 263-278.
Step 3: to M
diagcluster forms cluster result MC:
Step 3.1: selection matrix M
diagenter step 3.2:
Step 3.2: select to enter highly the highest rectangle white space in the lower triangular matrix in the matrix of this step and enter step 3.3;
Step 3.3: take the summit, the rectangle white space upper right corner that enters this step as intersecting strokes and dots horizontal line h and perpendicular line v, by matrix M
diagbe divided into four quadrants; Wherein the upper right side, point of crossing is first quartile, successively counterclockwise order be second and third, four-quadrant;
Step 3.4: if the right side area of v has the row that blocked by h, select the rectangle white space highly taken second place in lower triangular matrix, enter step 3.3; If there are not the row that blocked by h in the right side area of v, the zone in quadrant 2 and quadrant 4 is divided into to two sub-matroids;
Step 3.5: for the subclass matrix of quadrant 4, circulation execution step 3.2～step 3.4, until h and matrix M
diaglower boundary overlaps; Form cluster result M
c, as shown in Figure 4;
Step 4: according to cluster result M
c, set up product module DSM-M
p; M wherein
pin module according to the cluster matrix M
crow sequentially sort, the last row that adds successively the onrelevant module to DSM and rank rear;
Step 5: according to cluster result, by M
pbe divided into several sub-blocks, because the onrelevant module does not participate in the DDM cluster, each onrelevant module is an independent sub-block.By M
peach sub-block with B, mean, be followed successively by B from top to bottom along diagonal line
1～B
k;
Step 6: module relationship is classified according to the geometry associativity relation of intermodule: two module position are correlated with and Size dependence is strong association, and two modules only position are correlated with as weak association, and it is onrelevant that there is not incidence relation in two modules; Square formation M according to the geometry associativity result of intermodule to n * n
passignment, if between module i and module j, exist strong associated, matrix element m
pij=2, if between module i and module j, exist weak associated, matrix element m
pij=1, if between module i and module j without connection, matrix element m
pij=0, i ≠ j;
M
pbeing the square formation of 35 * 35, according to the geometry associativity of intermodule, is M
passignment.If between module i and module j, exist strong associated, matrix element m
pij=2(i ≠ j), use symbol ● mean.If between module i and module j, exist weak associated, matrix element m
pij=1(i ≠ j), use symbol zero to mean.If nothing connection, matrix element m between module i and module j
pij=0(i ≠ j), be blank in the drawings, (the shadow region signal is identical in 7 for customer demand corresponding to region representation corresponding sub block shown in the first row shade, Fig. 6) as shown in Figure 5.
Step 7: to M
pcluster, form cluster result M
p-C:
Step 7.1: traversal onrelevant module column, search strong associated element m
ij;
Step 7.2: if i is capable
module j(is as module 7) and sub-block B
kin module exist strong associated, by module j(c
j, r
j) be incorporated to BK;
Step 7.3: circulation execution step 7.1～step 7.2, until exist the irrelevant module of strong associated element all to be incorporated in corresponding sub-block;
Step 7.4: to B
kcarry out diagonalization, make B
kin all nonzero elements be gathered near diagonal line;
Step 7.5: if sub-block B
rwith sub-block B
tthe strong relating module of middle existence, adjust B
tposition to B
r+1, r<t;
Step 7.6: by B
kwith the height of other module according to weak associated element, sorted, wherein B
kheight with the highest weak associated element, be as the criterion;
Step 7.7: highly identical weak relating module is divided into to a sub-block, is called weak associated sub-block, thereby obtains the cluster matrix M
p-C; The present embodiment will be divided weak associated sub-block 5,8,9,10 by highly identical weak relating module.Generate the cluster matrix M
p-C, as shown in Figure 6.
Step 8: each sub-block is combined according to the strong incidence relation between module number and sub-block, the module planning scheme that formation comprises S module subset, make that module number in the modules subset is poor is no more than 5, and the module in the module subset does not exist with the module in other module subset strong associated.
The present embodiment is combined each sub-block according to the strong incidence relation between module number and sub-block, forms the module planning scheme { B that comprises 3 module subsets
1, B
2,, { B
3, B
4, B
5, { B
6, B
7, B
8, B
9, B
10, the module number of subset 1～3 is respectively 12,9,14, and the module in the module subset and module in other module subset do not exist strong associated, as shown in Figure 7.
Claims (1)
1. take customer demand and be to it is characterized in that the product module planing method of guiding: adopt following steps for one kind:
Step 1: according to the incidence relation of customer demand and module, set up customer demand-module DDM matrix: the rectangular matrix that wherein the DDM matrix is the capable n row of m, DDM matrix i is capable represents i customer demand r
i, j module c shown in DDM matrix j list
jif, r
iwith c
jthere is incidence relation, matrix element m
ij=1, otherwise m
ij=0, finally set up DDM matrix M=[mi
j], (i=1,2 ..., m; J=1,2 ..., n);
Step 2: remove the null value row in matrix M, generate the matrix M of row-column dependence
r; By matrix M
rdiagonalization, generator matrix M
diag;
Step 3: to M
diagcluster, form cluster result M
c:
Step 3.1: selection matrix M
diagenter step 3.2:
Step 3.2: select to enter highly the highest rectangle white space in the lower triangular matrix in the matrix of this step and enter step 3.3;
Step 3.3: take the summit, the rectangle white space upper right corner that enters this step as intersecting strokes and dots horizontal line h and perpendicular line v, by matrix M
diagbe divided into four quadrants;
Step 3.4: if the right side area of v has the row that blocked by h, select the rectangle white space highly taken second place in lower triangular matrix, enter step 3.3; If there are not the row that blocked by h in the right side area of v, the zone in quadrant 2 and quadrant 4 is divided into to two sub-matroids;
Step 3.5: for the subclass matrix of quadrant 4, circulation execution step 3.2～step 3.4, until h and matrix M
diaglower boundary overlaps; Form cluster result M
c;
Step 4: according to cluster result M
c, set up product module DSM-M
p; M wherein
pin module according to the cluster matrix M
crow sequentially sort, the last row that adds successively the onrelevant module to DSM and rank rear;
Step 5: according to cluster result, by M
pbe divided into several sub-blocks, by M
peach sub-block with B, mean, be followed successively by B from top to bottom along diagonal line
1～B
k;
Step 6: module relationship is classified according to the geometry associativity relation of intermodule: two module position are correlated with and Size dependence is strong association, and two modules only position are correlated with as weak association, and it is onrelevant that there is not incidence relation in two modules; Square formation M according to the geometry associativity result of intermodule to n * n
passignment, if between module i and module j, exist strong associated, matrix element m
pij=2, if between module i and module j, exist weak associated, matrix element m
pij=1, if between module i and module j without connection, matrix element m
pij=0, i ≠ j;
Step 7: to M
pcluster, form cluster result M
p-C:
Step 7.1: traversal onrelevant module column, search strong associated element m
ij;
Step 7.2: if i is capable
module j and sub-block B
kin module exist strong associatedly, module j is incorporated to B
k;
Step 7.3: circulation execution step 7.1～step 7.2, until exist the irrelevant module of strong associated element all to be incorporated in corresponding sub-block;
Step 7.4: to B
kcarry out diagonalization, make B
kin all nonzero elements be gathered near diagonal line;
Step 7.5: if sub-block B
rwith sub-block B
tthe strong relating module of middle existence, adjust B
tposition to B
r+1, r<t;
Step 7.6: by B
kwith the height of other module according to weak associated element, sorted, wherein B
kheight with the highest weak associated element, be as the criterion;
Step 7.7: highly identical weak relating module is divided into to a sub-block, is called weak associated sub-block, thereby obtains the cluster matrix M
p-C;
Step 8: each sub-block is combined according to the strong incidence relation between module number and sub-block, the module planning scheme that formation comprises S module subset, make that module number in the modules subset is poor is no more than 5, and the module in the module subset does not exist with the module in other module subset strong associated.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104572590A (en) * | 2015-01-27 | 2015-04-29 | 大连大学 | Product structure design structure matrix partitioning method |
| CN105787113A (en) * | 2016-03-24 | 2016-07-20 | 武汉大学 | Mining algorithm for DPIPP (distributed parameterized intelligent product platform) process information on basis of PLM (product lifecycle management) database |
| CN107590332A (en) * | 2017-09-06 | 2018-01-16 | 厦门理工学院 | A kind of modularization redesign method of public transport chassis |
| CN110866685A (en) * | 2019-11-06 | 2020-03-06 | 重庆大学 | Task adjusting method, device, equipment and storage medium |
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| CN102609854A (en) * | 2011-01-25 | 2012-07-25 | 青岛理工大学 | Client partitioning method and device based on unified similarity calculation |
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2013
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| CN102609854A (en) * | 2011-01-25 | 2012-07-25 | 青岛理工大学 | Client partitioning method and device based on unified similarity calculation |
Non-Patent Citations (2)
| Title |
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| SIMON LI: "A matrix-based clustering approach for the decomposition of design problems", 《RESEARCH IN ENGINEERING DESIGN》, 31 December 2011 (2011-12-31) * |
| 程贤福 等: "基于设计关联矩阵与可拓聚类的产品模块划分方法", 《机械设计》, vol. 29, no. 1, 31 January 2012 (2012-01-31) * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104572590A (en) * | 2015-01-27 | 2015-04-29 | 大连大学 | Product structure design structure matrix partitioning method |
| CN105787113A (en) * | 2016-03-24 | 2016-07-20 | 武汉大学 | Mining algorithm for DPIPP (distributed parameterized intelligent product platform) process information on basis of PLM (product lifecycle management) database |
| CN105787113B (en) * | 2016-03-24 | 2019-03-19 | 武汉大学 | A kind of mining algorithm based on PLM database towards DPIPP technique information |
| CN107590332A (en) * | 2017-09-06 | 2018-01-16 | 厦门理工学院 | A kind of modularization redesign method of public transport chassis |
| CN107590332B (en) * | 2017-09-06 | 2023-06-16 | 厦门理工学院 | Modularized redesign method of bus chassis |
| CN110866685A (en) * | 2019-11-06 | 2020-03-06 | 重庆大学 | Task adjusting method, device, equipment and storage medium |
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