CN103295117A - Method and system for matching tractor and trailer - Google Patents

Abstract

The invention discloses a method for matching a tractor and a trailer. The method includes steps of determining an index system for matching the tractor and the trailer; defining semantic fuzziness words; determining index weight; building a decision matrix; building a weighting standardization decision matrix; determining a positive ideal solution and a negative ideal solution; respectively calculating distances between each candidate item and the positive ideal solution and between each candidate item and the negative ideal solution; calculating closeness and sequencing to obtain the positive ideal solution. The invention further discloses a system for matching the tractor and the trailer. The method and the system for matching the tractor and the trailer have the advantages of scientificity, efficiency and humanization and can well assist in formulating distribution decisions in highway freight transportation enterprises.

Description

The method and system of coupling tractor and trailer

Technical field

The present invention relates to vehicle management, especially mate the method for tractor and trailer.

Background technology

Getting rid of and hanging transportation is that the advanced person who generally acknowledges both at home and abroad transports organizational form, and its application is to the energy-saving and emission-reduction that realize freight transportation and improve freight transportation efficient significant meaning is arranged.But facts have proved, get rid of and hang transportation and in the application process of reality, exist a series of problems, especially get rid of the problem that haulage vehicle just mates between tractor and the trailer of hanging.

Summary of the invention

Goal of the invention: the present invention will provide a kind of method and system of mating tractor and trailer, gets rid of the extension conevying efficiency with raising.

Technical scheme: a kind of method of mating tractor and trailer may further comprise the steps:

Step 1, Information Monitoring, and determine coupling index between trailer and the tractor according to described information, set up index system;

Step 2, structure linguistic variable and corresponding Triangular Fuzzy Number thereof;

Corresponding relation between step 3, foundation coupling index and the linguistic variable generates a plurality of candidate schemes;

Step 4, set up decision matrix and weighting standardization;

Step 5, calculate positive ideal solution and negative ideal solution, and each candidate scheme and the Hamming distance between ideal solution and the negative ideal solution just from;

Step 6, calculate the exchange premium degree between each candidate scheme and positive ideal solution and the negative ideal solution, and descending sort.

Described decision matrix is:

$\tilde{D}=\left[\begin{array}{cccc}{\tilde{x}}_{11}& {\tilde{x}}_{12}& \·\·\·& {\tilde{x}}_{1n}\\ {\tilde{x}}_{21}& {\tilde{x}}_{22}& \·\·\·& {\tilde{x}}_{2n}\\ \·& \·& & \·\\ \·& \·& \·\·\·& \·\\ \·& \·& & \·\\ {\tilde{x}}_{m1}& {\tilde{x}}_{m2}& \·\·\·& {\tilde{x}}_{\mathrm{mn}}\end{array}\right],$

$\tilde{W}=[{\tilde{w}}_1,{\tilde{w}}_2,\·\·\·,{\tilde{w}}_n],$ Wherein, ${\tilde{x}}_{\mathrm{ij}}=\frac{1}{K}\⊗({\tilde{x}}_{\mathrm{ij}}^1\⊕{\tilde{x}}_{\mathrm{ij}}^2\⊕\·\·\·\⊕{\tilde{x}}_{\mathrm{ij}}^K),$ ${\tilde{w}}_j=\frac{1}{K}\⊗({\tilde{w}}_j^1\⊕{\tilde{w}}_j^2\⊕\·\·\·\⊕{\tilde{w}}_j^K),$

Triangular Fuzzy Number

(i=1,2 ..., m; J=1,2 ..., n), K=1,2 ..., a _Ij ^k, b _Ij ^k, c _Ij ^k, w _J1 ^k, w _J2 ^k, w _J3 ^kNumerical value for Triangular Fuzzy Number.

The method of described weighting standardization is:

When fuzzy number is The time, the value after the standardization is:

The smallest end point value of fuzzy number in the benefit type criterion ${{\tilde{r}}_{\mathrm{ij}}}^1=(\frac{a_{\mathrm{ij}}}{c_j^{+}},\frac{b_{\mathrm{ij}}}{c_j^{+}},\frac{c_{\mathrm{ij}}}{c_j^{+}}),c_j^{+}=m\underset{i}{a}x{c}_{\mathrm{ij}},$

The smallest end point value of fuzzy number in the cost type criterion ${{\tilde{r}}_{\mathrm{ij}}}^2=(\frac{a_j^{-}}{c_{\mathrm{ij}}},\frac{a_j^{-}}{b_{\mathrm{ij}}},\frac{a_j^{-}}{a_{\mathrm{ij}}});a_j^{-}=m\underset{i}{i}n{a}_{\mathrm{ij}};$

The weighting standard matrix:

${{\tilde{v}}_{\mathrm{ij}}}^1={{\tilde{r}}_{\mathrm{ij}}}^1\⊗{\tilde{w}}_j,$ ${{\tilde{v}}_{\mathrm{ij}}}^2={{\tilde{r}}_{\mathrm{ij}}}^2\⊗{\tilde{w}}_j.$

Described step 5 comprises:

A, definite fuzzy positive ideal solution

With fuzzy negative ideal solution

${\tilde{P}}^{+}=({{\tilde{v}}_1}^{+},{{\tilde{v}}_2}^{+},\·\·\·{{\tilde{v}}_n}^{+}),{{\tilde{v}}_j}^{+}=m\underset{i}{a}x{\tilde{v}}_{\mathrm{ij}},\∀j$

${\tilde{P}}^{-}=({{\tilde{v}}_1}^{-},{{\tilde{v}}_2}^{-},\·\·\·{{\tilde{v}}_n}^{-}),{{\tilde{v}}_j}^{-}=m\underset{i}{i}n{\tilde{v}}_{\mathrm{ij}},\∀j$

Adopt center of gravity ambiguity solution method to judge the fuzzy number size, obtain fuzzy number Thereby size sort to determine fuzzy positive ideal solution and fuzzy negative ideal solution,

$v_{\mathrm{ij}}=\frac{v_{\mathrm{ij}1}+v_{\mathrm{ij}2}+v_{\mathrm{ij}3}}{3},\∀i,j;$

B, calculate between each candidate scheme and the fuzzy positive and negative ideal solution Hamming distance from

$D_i^{+}=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}d({\tilde{v}}_{\mathrm{ij}},{\tilde{v}}_j^{+}),i=\mathrm{1,2},\·\·\·,m,$

$D_i^{-}=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}d({\tilde{v}}_{\mathrm{ij}},{\tilde{v}}_j^{-}),i=\mathrm{1,2},\·\·\·,m,$

Wherein

With

Can be tried to achieve by the Euclidean distance formula of two fuzzy numbers:

$d({\tilde{v}}_{\mathrm{ij}},v_j^{+})=\sqrt{\frac{1}{3}[{({\tilde{v}}_{\mathrm{ij}1}-v_{j1}^{+})}^2+{({\tilde{v}}_{\mathrm{ij}2}-v_{j2}^{+})}^2+{({\tilde{v}}_{\mathrm{ij}3}-v_{j3}^{+})}^2]},$

$d({\tilde{v}}_{\mathrm{ij}},v_j^{-})=\sqrt{\frac{1}{3}[{({\tilde{v}}_{\mathrm{ij}1}-v_{j1}^{-})}^2+{({\tilde{v}}_{\mathrm{ij}2}-v_{j2}^{-})}^2+{({\tilde{v}}_{\mathrm{ij}3}-v_{j3}^{-})}^2]}.$

Described step 6 is to calculate the exchange premium degree according to following formula:

${\mathrm{CC}}_i=\frac{D_i^{-}}{D_i^{+}+D_i^{-}},i=\mathrm{1,2},\·\·\·,m.$

CC _iExpression candidate scheme and the exchange premium degree that blurs positive ideal solution.

The present invention also provides a kind of system of mating tractor and trailer, comprising:

Information acquisition module is used for Information Monitoring and determines coupling index between trailer and the tractor according to described information, sets up index system;

Function makes up module, is used for making up linguistic variable and corresponding Triangular Fuzzy Number thereof;

The adaptation function module is used for setting up the corresponding relation between coupling index and the linguistic variable, generates a plurality of candidate schemes;

Decision-making module is used for setting up decision matrix and weighting standardization;

Computing module be used for to calculate positive ideal solution and negative ideal solution, and each candidate scheme and the Hamming distance between ideal solution and the negative ideal solution just from;

Exchange premium degree analysis module calculates the exchange premium degree between each candidate scheme and positive ideal solution and the negative ideal solution, and descending sort.

Described decision-making module comprises the decision matrix unit, and described decision matrix unit is used for pressing following formula and generates decision matrix:

$\tilde{D}=\left[\begin{array}{cccc}{\tilde{x}}_{11}& {\tilde{x}}_{12}& \·\·\·& {\tilde{x}}_{1n}\\ {\tilde{x}}_{21}& {\tilde{x}}_{22}& \·\·\·& {\tilde{x}}_{2n}\\ \·& \·& & \·\\ \·& \·& \·\·\·& \·\\ \·& \·& & \·\\ {\tilde{x}}_{m1}& {\tilde{x}}_{m2}& \·\·\·& {\tilde{x}}_{\mathrm{mn}}\end{array}\right],$

$\tilde{W}=[{\tilde{w}}_1,{\tilde{w}}_2,\·\·\·,{\tilde{w}}_n],$ Wherein, ${\tilde{x}}_{\mathrm{ij}}=\frac{1}{K}\⊗({\tilde{x}}_{\mathrm{ij}}^1\⊕{\tilde{x}}_{\mathrm{ij}}^2\⊕\·\·\·\⊕{\tilde{x}}_{\mathrm{ij}}^K),$ ${\tilde{w}}_j=\frac{1}{K}\⊗({\tilde{w}}_j^1\⊕{\tilde{w}}_j^2\⊕\·\·\·\⊕{\tilde{w}}_j^K),$

Triangular Fuzzy Number

(i=1,2 ..., m; J=1,2 ..., n), K=1,2 ..., a _Ij ^k, b _Ij ^k, c _Ij ^k, w _J1 ^k, w _J2 ^k, w _J3 ^kNumerical value for Triangular Fuzzy Number.

Described decision-making module comprises the weighting standard unit, and described weighting standard unit is used for:

When fuzzy number is

The time, the value after the standardization is:

The smallest end point value of fuzzy number in the benefit type criterion ${{\tilde{r}}_{\mathrm{ij}}}^1=(\frac{a_{\mathrm{ij}}}{c_j^{+}},\frac{b_{\mathrm{ij}}}{c_j^{+}},\frac{c_{\mathrm{ij}}}{c_j^{+}}),c_j^{+}=m\underset{i}{a}x{c}_{\mathrm{ij}},$

The smallest end point value of fuzzy number in the cost type criterion ${{\tilde{r}}_{\mathrm{ij}}}^2=(\frac{a_j^{-}}{c_{\mathrm{ij}}},\frac{a_j^{-}}{b_{\mathrm{ij}}},\frac{a_j^{-}}{a_{\mathrm{ij}}});a_j^{-}=m\underset{i}{i}n{a}_{\mathrm{ij}};$

The weighting standard matrix:

${{\tilde{v}}_{\mathrm{ij}}}^1={{\tilde{r}}_{\mathrm{ij}}}^1\⊗{\tilde{w}}_j,$ ${{\tilde{v}}_{\mathrm{ij}}}^2={{\tilde{r}}_{\mathrm{ij}}}^2\⊗{\tilde{w}}_j.$

Described computing module comprises ideal solution computing unit and Hamming distance from computing unit, and they are respectively applied to:

A, definite fuzzy positive ideal solution

With fuzzy negative ideal solution

${\tilde{P}}^{+}=({{\tilde{v}}_1}^{+},{{\tilde{v}}_2}^{+},\·\·\·{{\tilde{v}}_n}^{+}),{{\tilde{v}}_j}^{+}=m\underset{i}{a}x{\tilde{v}}_{\mathrm{ij}},\∀j$

${\tilde{P}}^{-}=({{\tilde{v}}_1}^{-},{{\tilde{v}}_2}^{-},\·\·\·{{\tilde{v}}_n}^{-}),{{\tilde{v}}_j}^{-}=m\underset{i}{i}n{\tilde{v}}_{\mathrm{ij}},\∀j$

Adopt center of gravity ambiguity solution method to judge the fuzzy number size, obtain fuzzy number

Thereby size sort to determine fuzzy positive ideal solution and fuzzy negative ideal solution:

$v_{\mathrm{ij}}=\frac{v_{\mathrm{ij}1}+v_{\mathrm{ij}2}+v_{\mathrm{ij}3}}{3},\∀i,j;$

B, calculate between each candidate scheme and the fuzzy positive and negative ideal solution Hamming distance from

$D_i^{+}=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}d({\tilde{v}}_{\mathrm{ij}},{\tilde{v}}_j^{+}),i=\mathrm{1,2},\·\·\·,m,$

$D_i^{-}=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}d({\tilde{v}}_{\mathrm{ij}},{\tilde{v}}_j^{-}),i=\mathrm{1,2},\·\·\·,m,$

Wherein With

Can be tried to achieve by the Euclidean distance formula of two fuzzy numbers:

$d({\tilde{v}}_{\mathrm{ij}},v_j^{+})=\sqrt{\frac{1}{3}[{({\tilde{v}}_{\mathrm{ij}1}-v_{j1}^{+})}^2+{({\tilde{v}}_{\mathrm{ij}2}-v_{j2}^{+})}^2+{({\tilde{v}}_{\mathrm{ij}3}-v_{j3}^{+})}^2]},$

$d({\tilde{v}}_{\mathrm{ij}},v_j^{-})=\sqrt{\frac{1}{3}[{({\tilde{v}}_{\mathrm{ij}1}-v_{j1}^{-})}^2+{({\tilde{v}}_{\mathrm{ij}2}-v_{j2}^{-})}^2+{({\tilde{v}}_{\mathrm{ij}3}-v_{j3}^{-})}^2]}.$

Described exchange premium degree analysis module is used for calculating the exchange premium degree according to following formula:

${\mathrm{CC}}_i=\frac{D_i^{-}}{D_i^{+}+D_i^{-}},i=\mathrm{1,2},\·\·\·,m.,$

CC _iExpression candidate scheme and the exchange premium degree that blurs positive ideal solution.

Beneficial effect: method and system of the present invention has greatly improved the work efficiency of freight yard.

Embodiment

Implementation process of the present invention is as follows:

One, Information Monitoring, acquisition method comprise artificial collection, intelligent acquisition, computing machine according to index of correlation calculate assessment or said method combination (acquisition method such as but not limited to, ID, the IC of scanning storage relevant information or, two-dimension code or label etc., perhaps receive the relevant information of related personnel's input or customer demand, evaluation information.), set up to get rid of and hang tractor and trailer coupling index system in the transportation, as shown in the table:

Table 1

Two, based on this index system set up tractor and trailer matching process,

Step 1: make up linguistic variable and corresponding Triangular Fuzzy Number thereof

Characterize the decision-making member to the vague description of evaluation index weight and desired value with the fuzzy semantics word, and defined two groups of fuzzy semantics words, be respectively very inessential, inessential, medium important, important, extremely important and non-constant, poor, general, good, very good.Construction method is estimated important level such as but not limited to basis canned data, smart allocation numerical value, or receive the definition data that are used for input, the perhaps combination of said method.These fuzzy semantics words all have its corresponding Triangular Fuzzy Number as shown in table 2.

Table 2

Step 2: make up decision matrix

Be provided with K decision package and carry out group decision-making, I selection scheme arranged, J evaluate alternatives index arranged, then

Expression decision package E _kTo scheme P _iIndex I _jFuzzy evaluation, be expressed as the form of Triangular Fuzzy Number Simultaneously,

Expression decision package E _kTo selection scheme evaluation index I _jThe fuzzy evaluation of weight is expressed as the form of Triangular Fuzzy Number

The evaluation of comprehensive all decision packages, through type (1) and formula (2) calculate index I _jFuzzy weight

With at index I _jTo scheme P _iThe fuzzy evaluation value.

${\tilde{x}}_{\mathrm{ij}}=\frac{1}{K}\⊗({\tilde{x}}_{\mathrm{ij}}^1\⊕{\tilde{x}}_{\mathrm{ij}}^2\⊕\·\·\·\⊕{\tilde{x}}_{\mathrm{ij}}^K)---\left(1\right)$

${\tilde{w}}_j=\frac{1}{K}\⊗({\tilde{w}}_j^1\⊕{\tilde{w}}_j^2\⊕\·\·\·\⊕{\tilde{w}}_j^K)---\left(2\right)$

Then decision matrix is

$\tilde{D}=\left[\begin{array}{cccc}{\tilde{x}}_{11}& {\tilde{x}}_{12}& \·\·\·& {\tilde{x}}_{1n}\\ {\tilde{x}}_{21}& {\tilde{x}}_{22}& \·\·\·& {\tilde{x}}_{2n}\\ \·& \·& & \·\\ \·& \·& \·\·\·& \·\\ \·& \·& & \·\\ {\tilde{x}}_{m1}& {\tilde{x}}_{m2}& \·\·\·& {\tilde{x}}_{\mathrm{mn}}\end{array}\right],$

$\tilde{W}=[{\tilde{w}}_1,{\tilde{w}}_2,\·\·\·,{\tilde{w}}_n],$

Here

With

(i=1,2,, m; J=1,2,, n) all described by the fuzzy semantics word, and all can be represented by Triangular Fuzzy Number, be ${\tilde{x}}_{\mathrm{ij}}=(a_{\mathrm{ij}},b_{\mathrm{ij}},c_{\mathrm{ij}})$ With ${\tilde{w}}_j=({\tilde{w}}_{j1},{\tilde{w}}_{j2},{\tilde{w}}_{j3}).$

Step 3: standardization decision matrix

If

Be fuzzy number, then the value after the standardization is:

${\tilde{r}}_{\mathrm{ij}}=(\frac{a_{\mathrm{ij}}}{c_j^{+}},\frac{b_{\mathrm{ij}}}{c_j^{+}},\frac{c_{\mathrm{ij}}}{c_j^{+}}),c_j^{+}=m\underset{i}{a}x{c}_{\mathrm{ij}}---\left(3\right)$

The smallest end point value of fuzzy number in the expression benefit type criterion

${\tilde{r}}_{\mathrm{ij}}=(\frac{a_j^{-}}{c_{\mathrm{ij}}},\frac{a_j^{-}}{b_{\mathrm{ij}}},\frac{a_j^{-}}{a_{\mathrm{ij}}});a_j^{-}=m\underset{i}{i}n{a}_{\mathrm{ij}}---\left(4\right)$

The smallest end point value of fuzzy number in the expression cost type criterion

Step 4: calculate the weighting standard matrix

${\tilde{v}}_{\mathrm{ij}}={\tilde{r}}_{\mathrm{ij}}\⊗{\tilde{w}}_j---\left(5\right)$

Step 5: determine fuzzy positive ideal solution

With fuzzy negative ideal solution

${\tilde{P}}^{+}=({{\tilde{v}}_1}^{+},{{\tilde{v}}_2}^{+},\·\·\·{{\tilde{v}}_n}^{+}),{{\tilde{v}}_j}^{+}=m\underset{i}{a}x{\tilde{v}}_{\mathrm{ij}},\∀j---\left(6\right)$

${\tilde{P}}^{-}=({{\tilde{v}}_1}^{-},{{\tilde{v}}_2}^{-},\·\·\·{{\tilde{v}}_n}^{-}),{{\tilde{v}}_j}^{-}=m\underset{i}{i}n{\tilde{v}}_{\mathrm{ij}},\∀j---\left(7\right)$

Adopt center of gravity ambiguity solution method to judge the fuzzy number size, obtain fuzzy number according to formula (8)

Thereby size sort to determine fuzzy positive ideal solution and fuzzy negative ideal solution.

$v_{\mathrm{ij}}=\frac{v_{\mathrm{ij}1}+v_{\mathrm{ij}2}+v_{\mathrm{ij}3}}{3},\∀i,j;\left(8\right)$

Step 6: calculate between each candidate scheme and the fuzzy positive and negative ideal solution Hamming distance from

$D_i^{+}=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}d({\tilde{v}}_{\mathrm{ij}},{\tilde{v}}_j^{+}),i=\mathrm{1,2},\·\·\·,m,\left(9\right)$

$D_i^{-}=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}d({\tilde{v}}_{\mathrm{ij}},{\tilde{v}}_j^{-}),i=\mathrm{1,2},\·\·\·,m,\left(10\right)$

Wherein

With

Can be tried to achieve by the Euclidean distance formula of two fuzzy numbers, suc as formula (11), (12).

$d({\tilde{v}}_{\mathrm{ij}},v_j^{+})=\sqrt{\frac{1}{3}[{({\tilde{v}}_{\mathrm{ij}1}-v_{j1}^{+})}^2+{({\tilde{v}}_{\mathrm{ij}2}-v_{j2}^{+})}^2+{({\tilde{v}}_{\mathrm{ij}3}-v_{j3}^{+})}^2]}---\left(11\right)$

$d({\tilde{v}}_{\mathrm{ij}},v_j^{-})=\sqrt{\frac{1}{3}[{({\tilde{v}}_{\mathrm{ij}1}-v_{j1}^{-})}^2+{({\tilde{v}}_{\mathrm{ij}2}-v_{j2}^{-})}^2+{({\tilde{v}}_{\mathrm{ij}3}-v_{j3}^{-})}^2]}---\left(12\right)$

Step 7: calculate and the relative exchange premium degree that blurs positive ideal solution, and ordering

${\mathrm{CC}}_i=\frac{D_i^{-}}{D_i^{+}+D_i^{-}},i=\mathrm{1,2},\·\·\·,m---\left(13\right)$

CC _iExpression candidate scheme and the relative exchange premium degree that blurs positive ideal solution, CC _iBe worth the more approaching fuzzy positive ideal solution of more big expression scheme i, namely the scheme degree of priority is more high.

Embodiment

Be that 1 tractor and 3 trailers that example is randomly drawed are example with Nanjing shipping company, this working of an invention mode is described.

(1) determines evaluation group and decision-making desired value

1 tractor randomly drawing with Nanjing shipping company and 3 extensions are set up the decision package group as object.This paper characterizes decision package to the vague description of evaluation index weight and desired value with the fuzzy semantics word, and defined two groups of fuzzy semantics words, be respectively very inessential, inessential, medium important, important, extremely important and non-constant, poor, general, good, very good.These fuzzy semantics words all have its corresponding Triangular Fuzzy Number as shown in table 3.

Table 3

It is as shown in table 4 and be to give a mark according to table 5 at the good and bad degree of efficient, cost, machinery, customer satisfaction, security and other factors of 3 trailers in the basis with the tractor that decision package blurs scoring to each index, marking result such as table 5.

Table 4

Table 5

(2) determine the index weight

Use Fuzzy Calculation by table three and draw the index weight, be respectively weight=(0.63,0.88 of efficient, 1.00), the weight of cost=(0.69,0.94,1.00), weight=(0.63,0.88 of machinery, 1.00), the weight of customer satisfaction=(0.69,0.94,1.00), the weight of security=(0.56,0.81,1.00) and weight=(0.31,0.56,081) of other factors.

(3) calculate the normalization decision matrix

Utilize (3), (4), (5) formula to obtain right normalizing standardization matrix as shown in table 6 with the decision matrix normalizing is standardized.

Index/traction protocols	S1	S2	S3
				Efficient	(0.31,0.66,1)	(0.20,0.49,0.81)	(0.08,0.33,0.63)
Cost	(0.21,0.33,0.5)	(0.26,0.52,1)	(0.25,0.47,0.83)
				Machinery	(0.43,0.82,1)	(0.35,0.71,1)	(0.35,0.71,1)
Customer satisfaction	(0.09,0.35,0.63)	(0.47,0.88,1)	(0.43,0.82,1)
				Security	(0.14,0.41,0.75)	(0.18,0.46,0.81)	(0.32,0.66,1)
Other factors	(0.12,0.35,0.71)	(0.18,0.46,0.81)	(0.04,0.21,0.51)

Table 6

(4) fuzzy plus-minus ideal solutions

In the normalization decision matrix (table 6) that is obtained by step (3), (4), (5), can utilize formula (6), (7), (8) to obtain fuzzy plus-minus ideal solutions:

A ⁺={(0.31,0.66,1),(0.21,0.33,0.5),(0.43,0.82,1),(0.47,0.88,1),(0.32,0.66,1),(0.18,0.46,0.81)}；

A ^-={(0.08,0.33,0.63),(0.26,0.52,1),(0.35,0.71,1),(0.09,0.35,0.63),(0.14,0.41,0.75),(0.4,0.21,0.51)}

(5) exchange premium degree result of calculation

After determining positive ideal solution and negative ideal solution, can obtain each alternatives, be the distance between each distribution project and the negative ideal solution of positive ideal solution, and utilize and approach the exchange premium degree that coefficient obtains each alternatives, thereby the matching degree of trailer and tractor is sorted.The value of the exchange premium degree that finally draws is CC1=0.530674, CC2=0.549543, CC3=0.452034.Can find out that second distribution project optimized the most means that second trailer and tractor mate the most.

In a word, the present invention uses group decision-making and fuzzy algorithm, set up a kind of based on this index system, be applicable to tractor and trailer matching process and system under the fuzzy uncertain environment.The present invention has the following advantages: less to getting rid of the research of hanging the vehicle coupling at present, there is not comprehensively to mate every influence factor of tractor and trailer, do not form the appraisement system of sound comprehensive vehicle coupling, so the present invention has filled up above-mentioned blank.The present invention is conducive to enterprise-like corporation for buying and the management of tractor and trailer, formulates the shipping decision-making to enterprise foundation is provided.The present invention's innovation has been quoted fuzzy algorithm and has been estimated for getting rid of the matching degree of hanging vehicle, and the fuzzy theory that adopts is suitable for solving this type of and has uncertainty or subjective cognitive problem, enables standardization, intellectuality and procedure operation.

Claims (10)

1. a method of mating tractor and trailer is characterized in that, may further comprise the steps:

Step 1, Information Monitoring, and determine coupling index between trailer and the tractor according to described information, set up index system;

Step 2, structure linguistic variable and corresponding Triangular Fuzzy Number thereof;

Corresponding relation between step 3, foundation coupling index and the linguistic variable generates a plurality of candidate schemes;

Step 4, set up decision matrix and weighting standardization;

Step 5, calculate positive ideal solution and negative ideal solution, and each candidate scheme and the Hamming distance between ideal solution and the negative ideal solution just from;

Step 6, calculate the exchange premium degree between each candidate scheme and positive ideal solution and the negative ideal solution, and descending sort.

2. the method for coupling tractor as claimed in claim 1 and trailer is characterized in that, described decision matrix is:

$\tilde{D}=\left[\begin{array}{cccc}{\tilde{x}}_{11}& {\tilde{x}}_{12}& \·\·\·& {\tilde{x}}_{1n}\\ {\tilde{x}}_{21}& {\tilde{x}}_{22}& \·\·\·& {\tilde{x}}_{2n}\\ \·& \·& & \·\\ \·& \·& \·\·\·& \·\\ \·& \·& & \·\\ {\tilde{x}}_{m1}& {\tilde{x}}_{m2}& \·\·\·& {\tilde{x}}_{\mathrm{mn}}\end{array}\right],$

$\tilde{W}=[{\tilde{w}}_1,{\tilde{w}}_2,\·\·\·,{\tilde{w}}_n],$ Wherein, ${\tilde{x}}_{\mathrm{ij}}=\frac{1}{K}\⊗({\tilde{x}}_{\mathrm{ij}}^1\⊕{\tilde{x}}_{\mathrm{ij}}^2\⊕\·\·\·\⊕{\tilde{x}}_{\mathrm{ij}}^K),$ ${\tilde{w}}_j=\frac{1}{K}\⊗({\tilde{w}}_j^1\⊕{\tilde{w}}_j^2\⊕\·\·\·\⊕{\tilde{w}}_j^K),$

Triangular Fuzzy Number

(i=1,2 ..., m; J=1,2 ..., n), K=1,2 ..., a _Ij ^k, b _Ij ^k, c _Ij ^k, w _J1 ^k, w _J2 ^k, w _J3 ^kNumerical value for Triangular Fuzzy Number.

3. the method for coupling tractor as claimed in claim 2 and trailer is characterized in that, the method for described weighting standardization is:

When fuzzy number is

The time, the value after the standardization is:

The smallest end point value of fuzzy number in the benefit type criterion ${{\tilde{r}}_{\mathrm{ij}}}^1=(\frac{a_{\mathrm{ij}}}{c_j^{+}},\frac{b_{\mathrm{ij}}}{c_j^{+}},\frac{c_{\mathrm{ij}}}{c_j^{+}}),c_j^{+}=m\underset{i}{a}x{c}_{\mathrm{ij}},$

The smallest end point value of fuzzy number in the cost type criterion ${{\tilde{r}}_{\mathrm{ij}}}^2=(\frac{a_j^{-}}{c_{\mathrm{ij}}},\frac{a_j^{-}}{b_{\mathrm{ij}}},\frac{a_j^{-}}{a_{\mathrm{ij}}});a_j^{-}=m\underset{i}{i}n{a}_{\mathrm{ij}};$

The weighting standard matrix:

${{\tilde{v}}_{\mathrm{ij}}}^1={{\tilde{r}}_{\mathrm{ij}}}^1\⊗{\tilde{w}}_j,$ ${{\tilde{v}}_{\mathrm{ij}}}^2={{\tilde{r}}_{\mathrm{ij}}}^2\⊗{\tilde{w}}_j.$

4. the method for coupling tractor as claimed in claim 3 and trailer is characterized in that, described step 5 comprises:

A, definite fuzzy positive ideal solution

With fuzzy negative ideal solution

${\tilde{P}}^{+}=({{\tilde{v}}_1}^{+},{{\tilde{v}}_2}^{+},\·\·\·{{\tilde{v}}_n}^{+}),{{\tilde{v}}_j}^{+}=m\underset{i}{a}x{\tilde{v}}_{\mathrm{ij}},\∀j$

${\tilde{P}}^{-}=({{\tilde{v}}_1}^{-},{{\tilde{v}}_2}^{-},\·\·\·{{\tilde{v}}_n}^{-}),{{\tilde{v}}_j}^{-}=m\underset{i}{i}n{\tilde{v}}_{\mathrm{ij}},\∀j$

Adopt center of gravity ambiguity solution method to judge the fuzzy number size, obtain fuzzy number

Thereby size sort to determine fuzzy positive ideal solution and fuzzy negative ideal solution,

$v_{\mathrm{ij}}=\frac{v_{\mathrm{ij}1}+v_{\mathrm{ij}2}+v_{\mathrm{ij}3}}{3},\∀i,j;$

B, calculate between each candidate scheme and the fuzzy positive and negative ideal solution Hamming distance from

$D_i^{+}=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}d({\tilde{v}}_{\mathrm{ij}},{\tilde{v}}_j^{+}),i=\mathrm{1,2},\·\·\·,m,$

$D_i^{-}=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}d({\tilde{v}}_{\mathrm{ij}},{\tilde{v}}_j^{-}),i=\mathrm{1,2},\·\·\·,m,$

Wherein

With

Can be tried to achieve by the Euclidean distance formula of two fuzzy numbers:

$d({\tilde{v}}_{\mathrm{ij}},v_j^{+})=\sqrt{\frac{1}{3}[{({\tilde{v}}_{\mathrm{ij}1}-v_{j1}^{+})}^2+{({\tilde{v}}_{\mathrm{ij}2}-v_{j2}^{+})}^2+{({\tilde{v}}_{\mathrm{ij}3}-v_{j3}^{+})}^2]},$

$d({\tilde{v}}_{\mathrm{ij}},v_j^{-})=\sqrt{\frac{1}{3}[{({\tilde{v}}_{\mathrm{ij}1}-v_{j1}^{-})}^2+{({\tilde{v}}_{\mathrm{ij}2}-v_{j2}^{-})}^2+{({\tilde{v}}_{\mathrm{ij}3}-v_{j3}^{-})}^2]}.$

5. the method for coupling tractor as claimed in claim 4 and trailer is characterized in that, described step 6 is: calculate the exchange premium degree according to following formula,

${\mathrm{CC}}_i=\frac{D_i^{-}}{D_i^{+}+D_i^{-}},i=\mathrm{1,2},\·\·\·,m,$

CC _iExpression candidate scheme and the exchange premium degree that blurs positive ideal solution.

6. a system of mating tractor and trailer is characterized in that, comprising:

Information acquisition module is used for Information Monitoring and determines coupling index between trailer and the tractor according to described information, sets up index system;

Function makes up module, is used for making up linguistic variable and corresponding Triangular Fuzzy Number thereof;

The adaptation function module is used for setting up the corresponding relation between coupling index and the linguistic variable, generates a plurality of candidate schemes;

Decision-making module is used for setting up decision matrix and weighting standardization;

Computing module be used for to calculate positive ideal solution and negative ideal solution, and each candidate scheme and the Hamming distance between ideal solution and the negative ideal solution just from;

Exchange premium degree analysis module calculates the exchange premium degree between each candidate scheme and positive ideal solution and the negative ideal solution, and descending sort.

7. the method for coupling tractor as claimed in claim 6 and trailer is characterized in that, described decision-making module comprises the decision matrix unit, and described decision matrix unit is used for pressing following formula and generates decision matrix:

$\tilde{D}=\left[\begin{array}{cccc}{\tilde{x}}_{11}& {\tilde{x}}_{12}& \·\·\·& {\tilde{x}}_{1n}\\ {\tilde{x}}_{21}& {\tilde{x}}_{22}& \·\·\·& {\tilde{x}}_{2n}\\ \·& \·& & \·\\ \·& \·& \·\·\·& \·\\ \·& \·& & \·\\ {\tilde{x}}_{m1}& {\tilde{x}}_{m2}& \·\·\·& {\tilde{x}}_{\mathrm{mn}}\end{array}\right],$

$\tilde{W}=[{\tilde{w}}_1,{\tilde{w}}_2,\·\·\·,{\tilde{w}}_n],$ Wherein, ${\tilde{x}}_{\mathrm{ij}}=\frac{1}{K}\⊗({\tilde{x}}_{\mathrm{ij}}^1\⊕{\tilde{x}}_{\mathrm{ij}}^2\⊕\·\·\·\⊕{\tilde{x}}_{\mathrm{ij}}^K)$ ${\tilde{w}}_j=\frac{1}{K}\⊗({\tilde{w}}_j^1\⊕{\tilde{w}}_j^2\⊕\·\·\·\⊕{\tilde{w}}_j^K),$

Triangular Fuzzy Number

(i=1,2 ..., m; J=1,2 ..., n), K=1,2 ..., a _Ij ^k, b _Ij ^k, c _Ij ^k, w _J1 ^k, w _J2 ^k, w _J3 ^kNumerical value for Triangular Fuzzy Number.

8. the method for coupling tractor as claimed in claim 7 and trailer is characterized in that, described decision-making module comprises the weighting standard unit, and described weighting standard unit is used for:

When fuzzy number is

The time, the value after the standardization is:

The smallest end point value of fuzzy number in the benefit type criterion ${{\tilde{r}}_{\mathrm{ij}}}^1=(\frac{a_{\mathrm{ij}}}{c_j^{+}},\frac{b_{\mathrm{ij}}}{c_j^{+}},\frac{c_{\mathrm{ij}}}{c_j^{+}}),c_j^{+}=m\underset{i}{a}x{c}_{\mathrm{ij}},$

The smallest end point value of fuzzy number in the cost type criterion ${{\tilde{r}}_{\mathrm{ij}}}^2=(\frac{a_j^{-}}{c_{\mathrm{ij}}},\frac{a_j^{-}}{b_{\mathrm{ij}}},\frac{a_j^{-}}{a_{\mathrm{ij}}});a_j^{-}=m\underset{i}{i}n{a}_{\mathrm{ij}};$

The weighting standard matrix:

${{\tilde{v}}_{\mathrm{ij}}}^1={{\tilde{r}}_{\mathrm{ij}}}^1\⊗{\tilde{w}}_j,$ ${{\tilde{v}}_{\mathrm{ij}}}^2={{\tilde{r}}_{\mathrm{ij}}}^2\⊗{\tilde{w}}_j.$

9. the method for coupling tractor as claimed in claim 8 and trailer is characterized in that, described computing module comprises ideal solution computing unit and Hamming distance from computing unit, and they are respectively applied to:

A, definite fuzzy positive ideal solution

With fuzzy negative ideal solution

${\tilde{P}}^{+}=({{\tilde{v}}_1}^{+},{{\tilde{v}}_2}^{+},\·\·\·{{\tilde{v}}_n}^{+}),{{\tilde{v}}_j}^{+}=m\underset{i}{a}x{\tilde{v}}_{\mathrm{ij}},\∀j$

${\tilde{P}}^{-}=({{\tilde{v}}_1}^{-},{{\tilde{v}}_2}^{-},\·\·\·{{\tilde{v}}_n}^{-}),{{\tilde{v}}_j}^{-}=m\underset{i}{i}n{\tilde{v}}_{\mathrm{ij}},\∀j$

Adopt center of gravity ambiguity solution method to judge the fuzzy number size, obtain fuzzy number

Thereby size sort to determine fuzzy positive ideal solution and fuzzy negative ideal solution:

$v_{\mathrm{ij}}=\frac{v_{\mathrm{ij}1}+v_{\mathrm{ij}2}+v_{\mathrm{ij}3}}{3},\∀i,j;$

B, calculate between each candidate scheme and the fuzzy positive and negative ideal solution Hamming distance from

$D_i^{+}=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}d({\tilde{v}}_{\mathrm{ij}},{\tilde{v}}_j^{+}),i=\mathrm{1,2},\·\·\·,m,$

$D_i^{-}=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}d({\tilde{v}}_{\mathrm{ij}},{\tilde{v}}_j^{-}),i=\mathrm{1,2},\·\·\·,m,$

Wherein

With Can be tried to achieve by the Euclidean distance formula of two fuzzy numbers:

$d({\tilde{v}}_{\mathrm{ij}},v_j^{+})=\sqrt{\frac{1}{3}[{({\tilde{v}}_{\mathrm{ij}1}-v_{j1}^{+})}^2+{({\tilde{v}}_{\mathrm{ij}2}-v_{j2}^{+})}^2+{({\tilde{v}}_{\mathrm{ij}3}-v_{j3}^{+})}^2]},$

$d({\tilde{v}}_{\mathrm{ij}},v_j^{-})=\sqrt{\frac{1}{3}[{({\tilde{v}}_{\mathrm{ij}1}-v_{j1}^{-})}^2+{({\tilde{v}}_{\mathrm{ij}2}-v_{j2}^{-})}^2+{({\tilde{v}}_{\mathrm{ij}3}-v_{j3}^{-})}^2]}.$

10. the method for coupling tractor as claimed in claim 9 and trailer is characterized in that, described exchange premium degree analysis module is used for calculating the exchange premium degree according to following formula:

${\mathrm{CC}}_i=\frac{D_i^{-}}{D_i^{+}+D_i^{-}},i=\mathrm{1,2},\·\·\·,m.,$

CC _iExpression candidate scheme and the exchange premium degree that blurs positive ideal solution.