CN110138687B - Cloud manufacturing resource matching method considering trust degree - Google Patents

Cloud manufacturing resource matching method considering trust degree Download PDF

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CN110138687B
CN110138687B CN201910396439.3A CN201910396439A CN110138687B CN 110138687 B CN110138687 B CN 110138687B CN 201910396439 A CN201910396439 A CN 201910396439A CN 110138687 B CN110138687 B CN 110138687B
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CN110138687A (en
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何建民
张红
刘业政
贾艳
张苗苗
吴琦超
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Hefei University of Technology
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    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract

The invention discloses a cloud manufacturing resource matching method considering trust degree, which is applied to the resource matching problem of a cloud manufacturing platform and comprises the following steps: 1, acquiring historical transaction information of resource supply and demand parties and calculating real-time trust between the supply and demand parties; 2, acquiring demand information and actual level information of both supply and demand parties of the resource and calculating the matching degree of each attribute between the supply and demand parties; 3, calculating the matching degree between the two resource supply and demand parties; and 4, establishing a multi-target matching model of the cloud manufacturing resources to obtain an optimized matching scheme of the cloud manufacturing resources. The invention provides a new matching method for the resource matching problem of the cloud manufacturing platform, which can realize the optimal configuration of cloud manufacturing resources and improve the configuration efficiency of the cloud manufacturing resources.

Description

Cloud manufacturing resource matching method considering trust degree
Technical Field
The invention relates to the field of cloud manufacturing, in particular to a cloud manufacturing resource matching method considering trust degree.
Background
The manufacturing industry in China is in the key period of transformation and upgrading, and the integration and reasonable configuration of manufacturing resources are one of the key targets to be realized by manufacturing enterprises in the transformation and upgrading process. Cloud manufacturing is based on the idea of cloud computing, and technologies such as big data and the Internet of things are utilized, so that idle manufacturing resources are reasonably utilized on one hand, and investment of manufacturing resources such as processing equipment and parts in small and medium-sized manufacturing enterprises is reduced on the other hand. Therefore, the cloud manufacturing platform has become one of the important approaches for manufacturing resource trading.
Existing bilateral matching studies are mainly directed to one-time transactions in which the degree of confidence between matching subjects is not considered. The cloud manufacturing platform has the advantages of efficient collaboration, information sharing and the like, and each member tends to establish a long-term cooperative relationship with other members and repeatedly participates in the matching process for many times. In order to reduce the risk of resource transaction, each member on the platform evaluates the possible future behaviors of other members according to the historical transaction situation. The existing cloud manufacturing platform lacks a set of trust evaluation mechanism based on historical transaction, and does not consider the real-time trust degree between members in the resource matching process, so that the obtained matching scheme does not meet the actual requirements of manufacturing resource supply and demand parties.
Disclosure of Invention
The invention provides a cloud manufacturing resource matching method considering the trust degree in order to solve the problem that the matching scheme of a cloud manufacturing platform cannot meet the actual requirements of both manufacturing resource supply and demand parties, so that the optimal configuration of cloud manufacturing resources can be realized and the configuration efficiency of the cloud manufacturing resources is improved.
The invention adopts the following technical scheme for solving the technical problems:
the cloud manufacturing resource matching method considering the trust degree is characterized by comprising the following steps of:
step 1: acquiring historical transaction information of both resource supply and demand parties and calculating the real-time trust degree between the supply and demand parties;
step 1.1: obtaining the time t from the end to the current matchnowHistorical transaction information related to both resource supply and demand parties is stored in the cloud manufacturing platform;
step 1.2: calculating the time t from the current matching by adopting the formula (1) according to the historical transaction informationnowThe ith demand side DiTo the jth supplier SjAt the kth transactionTime attenuation factor thetaijk：
Figure BDA0002058295960000011
In the formula (1), t0Starting time, t, calculated for confidenceijkIs the ith demand side DiTo the jth supplier SjAt the time of the kth transaction, nijAt a starting time t0To the current matching time tnowThe ith demander D in the time periodiTo the jth supplier SjK is 1,2, …, nij；
Calculating the time t from the cutoff to the current matching by adopting the formula (2)nowJ th supplier SjAnd the ith demander DiTime decay factor θ ' at k ' th transaction 'jik′：
Figure BDA0002058295960000021
In formula (2), t'jik′For the jth supplier SjAnd the ith demander DiTime of k 'th transaction, n'jiFor calculating the starting time t at the confidence level0To the current matching time tnowOf the jth supplier SjAnd the ith demander DiK 'is 1,2, …, n'ji；
Step 1.3: calculating the time t from the cutoff to the current matching by adopting the formula (3)nowThe ith demand side DiTo the jth supplier SjTime decay factor theta of transactionij：
Figure BDA0002058295960000022
Calculating the time t from the cutoff to the current matching by adopting the formula (4)nowJ th supplier SjAnd the ith demander DiTime decay factor of transaction θ'ji：
Figure BDA0002058295960000023
Step 1.4: calculating the ith demand side D by adopting the formula (5)iTo the jth supplier SjConfidence level c of rating obtained in k-th transactionijk：
Figure BDA0002058295960000024
In the formula (5), eijkFor the ith demander and the jth supplier SjThe evaluation value obtained in the k transaction;
calculation of the jth supplier S using equation (6)jAnd the ith demander DiConfidence level c 'of evaluation obtained in k-th transaction'jik′：
Figure BDA0002058295960000031
In formula (6), e'jik′For the jth supplier SjAnd the ith demander DiThe rating value obtained at the k' th transaction;
step 1.5: the current matching time t is calculated by adopting the formula (7)nowWhen the number of transactions between the supplier and the demander is not 0, the ith demander DiRelative to the jth supplier SjReal-time trust of
Figure BDA0002058295960000032
Figure BDA0002058295960000033
In the formula (7), σ is the valid time window, rijkIs the ith demand side DiTo the jth supplier SjThe amount of money in the kth transaction;
the current matching time t is calculated by adopting the formula (8)nowWhen the number of transactions between the suppliers and the demanders is not 0, the jth supplier SjWith respect to the ith demander DiReal-time trust of
Figure BDA0002058295960000034
Figure BDA0002058295960000035
R 'in the formula (8)'jik′For the jth supplier and ith demander DiThe amount of the transaction at the k' th time;
step 1.6: the current matching time t is calculated by adopting the formula (9)nowThe ith demand side DiRelative to the jth supplier SjReal-time trust of
Figure BDA0002058295960000036
Figure BDA0002058295960000037
The current matching time t is calculated by the formula (10)nowJ th supplier SjWith respect to the ith demander DiReal-time trust of
Figure BDA0002058295960000038
Figure BDA0002058295960000039
Step 2: acquiring demand information of both supply and demand parties of resources and calculating the matching degree of each attribute between the supply and demand parties;
step 2.1: acquiring an expected value and an actual value of actual level information of demand information submitted by both resource supply and demand parties in current matching, wherein the expected value and the actual value are stored in a cloud manufacturing platform; and the expected value and the actual value are both expressed by triangular fuzzy numbers;
step 2.2: the ith demand side D is calculated by adopting the formula (11)iRelative to the jth supplier SjDegree of matching xi at p-th attributeijp：
Figure BDA0002058295960000041
In the formula (11), uip(x)、ujp(x) Are respectively the ith demand side DiAnd the jth supplier SjFuzzy membership functions describing the p-th attribute, cijpz、cijpuAre respectively the ith demand side DiTo the jth supplier SjUpper and lower limits of common interval in p-th attribute, dipz、dikuAre respectively the ith demand side DiAn interval upper limit and an interval lower limit describing the p-th attribute in the range;
calculation of the jth supplier S by equation (12)jWith respect to the ith demander DiDegree of matching xi 'at the q-th attribute'jiq：
Figure BDA0002058295960000042
In the formula (12), uiq(x)、ujq(x) Are respectively the ith demand side DiAnd the jth supplier SjFuzzy membership function, c 'describing the q-th attribute'ijqz、c′ijquAre respectively the ith demand side DiTo the jth supplier SjUpper and lower limits of the common interval, s, in the qth attributejqz、sjquRespectively the j supply side SjAn interval upper limit and an interval lower limit describing the qth attribute in the range;
and step 3: calculating the matching degree between the two resource supply and demand parties;
the current matching time t is calculated by equation (13)nowThe ith demand side DiRelative to the jth supplier SjDegree of matching at p-th attribute
Figure BDA0002058295960000043
Figure BDA0002058295960000044
The current matching time t is calculated by equation (14)nowLower jth supplier SjWith respect to the ith demander DiDegree of matching at qth attribute
Figure BDA0002058295960000045
Figure BDA0002058295960000051
And 4, step 4: establishing a multi-target bilateral matching model of the cloud manufacturing resources to obtain an optimized matching scheme of the cloud manufacturing resources;
step 4.1: constructing a target function of cloud manufacturing resource bilateral matching:
determining an objective function f of the degree of matching of all consumers with respect to all suppliers using equation (15)1：
Figure BDA0002058295960000052
In formula (15), ksFor all suppliers S ═ S1,S2,…,Sj,…,SnSet of attributes to consider, wjpFor the jth supplier SjWeight to p-th attribute, xijIs the ith demand side DiTo the jth supplier SjA decision variable of (c);
determining an objective function f of the degree of matching of all suppliers to all demanders using equation (16)2：
Figure BDA0002058295960000053
In formula (16), kdFor all consumers D ═ D1,D2,…,Di,…,DmSet of attributes to consider, wiqIs the ith demand side DiA weight for the qth attribute;
determining an objective function f for the number of matches using equation (17)3：
Figure BDA0002058295960000054
Step 4.2: determining constraint conditions for bilateral matching of cloud manufacturing resources:
determining the ith demand D using equations (18) and (19)iAnd the jth supplier SjConstraint of the number of matches:
Figure BDA0002058295960000055
Figure BDA0002058295960000056
in formulae (18) and (19), HiIs the ith demand side DiUpper limit of the number of matches, HjFor the jth supplier SjThe upper limit of the number of matches of (c),
determining the ith demand side D by using the formula (20) and the formula (21)iAnd the jth supplier SjMatching satisfaction degree constraint conditions of (1):
Figure BDA0002058295960000057
Figure BDA0002058295960000058
in the formulae (20) and (21), LiIs the ith demand side DiLower limit of degree of matching, LjFor the jth supplier SjLower limit of matching degree of;
order the ith demand side DiTo the jth supplier SjDecision variable xijWith a constraint of xij∈N0；
Step 4.3: solving a cloud manufacturing resource bilateral matching model to obtain an optimized matching scheme:
converting the resource bilateral matching multi-objective function into a single objective function by adopting an equation (22):
f＝αf1+βf2+λf3 (22)
in formula (22), α + β + λ is 1 and α, β, λ ∈ [0,1 ];
and solving the multi-target bilateral matching model by using the target function f as a target and adopting a branch-and-bound method to obtain an optimized matching scheme.
Compared with the prior art, the invention has the beneficial effects that:
1. based on the actual bilateral matching process of the cloud manufacturing resources, the influence of the real-time trust between the cloud manufacturing resource supply and demand parties on the matching degree is considered in the matching process, the real-time trust between the resource supply and demand parties is combined with the matching degree between the attributes, and the cloud manufacturing resource matching method considering the trust degree is created, so that the matching scheme obtained by adopting the method can meet the actual requirements of the cloud manufacturing resource supply and demand parties, and the configuration efficiency of the cloud manufacturing resources is further improved;
2. when the real-time trust between the supply and demand parties of the cloud manufacturing resource is calculated, the three factors of transaction time, transaction amount and transaction evaluation are considered, the evaluation trust is also considered, the influence of individual malicious evaluation behaviors on the real-time trust can be effectively avoided, and the real-time trust between the supply and demand parties in the cloud manufacturing platform can be more accurately evaluated;
3. when the matching degree between the attributes of the supplier and the demander of the cloud manufacturing resources is calculated, the expected value and the actual value of each attribute are described by adopting the fuzzy membership function in consideration of the situation that the expected value of the supplier and the demander to the attribute is fuzzy number in practice. When the public range is calculated, fuzzy membership functions of a demand party and a supply party are considered at the same time, and not only the fuzzy membership function of one party is considered, so that the defect that the distance between attribute values cannot be reflected due to the fact that the matching degree of each attribute between the supply and demand parties of cloud manufacturing resources is calculated directly by adopting fuzzy information axiom is overcome.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2a is a graph of membership functions corresponding to benefit type triangular fuzzy numbers (a, b, c);
FIG. 2b is a graph of membership functions corresponding to the cost-based triangular fuzzy numbers (a, b, c);
FIG. 2c is a graph of membership functions corresponding to the target triangular fuzzy numbers (a, b, c).
Detailed Description
With reference to fig. 1, in this embodiment, a cloud manufacturing resource matching method considering the trust level is performed according to the following steps:
step 1: acquiring historical transaction information of both resource supply and demand parties and calculating the real-time trust degree between the supply and demand parties;
step 1.1: obtaining the time t from the end to the current matchnowHistorical transaction information related to both resource supply and demand parties stored in the cloud manufacturing platform specifically comprises transaction time, transaction amount and transaction evaluation;
step 1.2: calculating the time t from the current matching by adopting the formula (1) according to the historical transaction informationnowThe ith demand side DiTo the jth supplier SjTime decay factor theta at kth transactionijk：
Figure BDA0002058295960000071
In the formula (1), t0Starting time, t, calculated for confidenceijkIs the ith demand side DiTo the jth supplier SjAt the time of the kth transaction, nijAt a starting time t0To the current matching time tnowThe ith demander D in the time periodiTo the jth supplier SjK is 1,2, …, nij；
Calculating the time t from the cutoff to the current matching by adopting the formula (2)nowJ th supplier SjAnd the ith demander DiTime decay factor θ ' at k ' th transaction 'jik′：
Figure BDA0002058295960000072
In formula (2), t'jik′For the jth supplier SjAnd the ith demander DiTime of k 'th transaction, n'jiFor calculating the starting time t at the confidence level0To the current matching time tnowOf the jth supplier SjAnd the ith demander DiK 'is 1,2, …, n'ji；
Step 1.3: calculating the time t from the cutoff to the current matching by adopting the formula (3)nowThe ith demand side DiTo the jth supplier SjTime decay factor theta of transactionij：
Figure BDA0002058295960000073
Calculating the time t from the cutoff to the current matching by adopting the formula (4)nowJ th supplier SjAnd the ith demander DiTime decay factor of transaction θ'ji：
Figure BDA0002058295960000074
Step 1.4: calculating the ith demand side D by adopting the formula (5)iTo the jth supplier SjConfidence level c of rating obtained in k-th transactionijk：
Figure BDA0002058295960000081
In the formula (5), eijkFor the ith demander and the jth supplier SjIf a plurality of evaluation values are submitted after the transaction is ended, taking the average evaluation value as the evaluation value of the transaction;
calculation of the jth supplier S using equation (6)jAnd the ith demander DiConfidence level c 'of evaluation obtained in k-th transaction'jik′：
Figure BDA0002058295960000082
In formula (6), e'jik′For the jth supplier SjAnd the ith demander DiThe rating value obtained at the k' th transaction;
step 1.5: the current matching time t is calculated by adopting the formula (7)nowWhen the number of transactions between the supplier and the demander is not 0, the ith demander DiRelative to the jth supplier SjReal-time trust of
Figure BDA0002058295960000083
Figure BDA0002058295960000084
In the formula (7), σ is the valid time window, rijkIs the ith demand side DiTo the jth supplier SjThe amount of money in the kth transaction;
the current matching time t is calculated by adopting the formula (8)nowWhen the number of transactions between the suppliers and the demanders is not 0, the jth supplier SjWith respect to the ith demander DiReal-time trust of
Figure BDA0002058295960000085
Figure BDA0002058295960000086
R 'in the formula (8)'jik′For the jth supplier and ith demander DiThe amount of the transaction at the k' th time;
step 1.6: the current matching time t is calculated by adopting the formula (9)nowThe ith demand side DiRelative to the jth supplier SjReal-time trust of
Figure BDA0002058295960000087
Figure BDA0002058295960000091
The current matching time t is calculated by the formula (10)nowJ th supplier SjWith respect to the ith demander DiReal-time trust of
Figure BDA0002058295960000092
Figure BDA0002058295960000093
Step 2: acquiring demand information of both supply and demand parties of resources and calculating the matching degree of each attribute between the supply and demand parties;
step 2.1: acquiring an expected value and an actual value of actual level information of demand information submitted by both resource supply and demand parties in current matching, wherein the expected value and the actual value are stored in a cloud manufacturing platform; the expected value and the actual value are both expressed by triangular fuzzy numbers; membership functions corresponding to benefit-type, cost-type and target-type triangular fuzzy numbers (a, b, c) are shown in fig. 2a, fig. 2b and fig. 2c, respectively;
step 2.2: based on the improved fuzzy information axiom, the ith demand side D is calculated by adopting the formula (11)iRelative to the jth supplier SjDegree of matching xi at p-th attributeijp：
Figure BDA0002058295960000094
In the formula (11), uip(x)、ujp(x) Are respectively the ith demand side DiAnd the jth supplier SjFuzzy membership functions describing the p-th attribute, cijpz、cijpuAre respectively the ith demand side DiTo the jth supplier SjUpper and lower limits of common interval in p-th attribute, dipz、dikuAre respectively the ith demand side DiAn interval upper limit and an interval lower limit describing the p-th attribute in the range;
calculation of the jth supplier S by equation (12)jWith respect to the ith demander DiDegree of matching xi 'at the q-th attribute'jiq：
Figure BDA0002058295960000095
In the formula (12), uiq(x)、ujq(x) Are respectively the ith demand side DiAnd the jth supplier SjFuzzy membership function, c 'describing the q-th attribute'ijqz、c′ijquAre respectively the ith demand side DiTo the jth supplier SjUpper and lower limits of the common interval, s, in the qth attributejqz、sjquRespectively the j supply side SjAn interval upper limit and an interval lower limit describing the qth attribute in the range;
and step 3: calculating the matching degree between the two resource supply and demand parties;
the current matching time t is calculated by equation (13)nowThe ith demand side DiRelative to the jth supplier SjDegree of matching at p-th attribute
Figure BDA0002058295960000101
Figure BDA0002058295960000102
The current matching time t is calculated by equation (14)nowLower jth supplier SjWith respect to the ith demander DiDegree of matching at qth attribute
Figure BDA0002058295960000103
Figure BDA0002058295960000104
And 4, step 4: establishing a multi-target bilateral matching model of the cloud manufacturing resources to obtain an optimized matching scheme of the cloud manufacturing resources;
step 4.1: constructing a target function of cloud manufacturing resource bilateral matching:
determining an objective function f of the degree of matching of all consumers with respect to all suppliers using equation (15)1：
Figure BDA0002058295960000105
In formula (15), ksFor all suppliers S ═ S1,S2,…,Sj,…,SnSet of attributes to consider, wjpFor the jth supplier SjWeight to p-th attribute, xijIs the ith demand side DiTo the jth supplier SjA decision variable of (c);
determining an objective function f of the degree of matching of all suppliers to all demanders using equation (16)2：
Figure BDA0002058295960000106
In formula (16), kdFor all consumers D ═ D1,D2,…,Di,…,DmSet of attributes to consider, wiqIs the ith demand side DiA weight for the qth attribute;
determining an objective function f for the number of matches using equation (17)3：
Figure BDA0002058295960000107
Step 4.2: determining constraint conditions for bilateral matching of cloud manufacturing resources:
determining the ith demand D using equations (18) and (19)iAnd the jth supplier SjConstraint of the number of matches:
Figure BDA0002058295960000108
Figure BDA0002058295960000109
in formulae (18) and (19), HiIs the ith demand side DiUpper limit of the number of matches, HjFor the jth supplier SjThe upper limit of the number of matches of (c),
determining the ith demand side D by using the formula (20) and the formula (21)iAnd the jth supplier SjMatching satisfaction degree constraint conditions of (1):
Figure BDA0002058295960000111
Figure BDA0002058295960000112
in the formulae (20) and (21), LiIs the ith demand side DiLower limit of degree of matching, LjFor the jth supplier SjLower limit of matching degree of;
ith demand side DiTo the jth supplier SjDecision variable xijWith a constraint of xij∈N0；
Step 4.3: solving a cloud manufacturing resource bilateral matching model to obtain an optimized matching scheme:
converting the resource bilateral matching multi-objective function into a single objective function by adopting an equation (22):
f＝αf1+βf2+λf3 (22)
in formula (22), α + β + λ is 1, α, β, λ ∈ [0,1], and the sum of α, β, and λ is 1 by numerical normalization;
and solving the multi-target bilateral matching model by using the target function f as a target and adopting a branch-and-bound method to obtain an optimized matching scheme.

Claims (1)

1. A cloud manufacturing resource matching method considering trust degree is characterized by comprising the following steps:
step 1: acquiring historical transaction information of both resource supply and demand parties and calculating the real-time trust degree between the supply and demand parties;
step 1.1: obtaining the time t from the end to the current matchnowHistorical transaction information related to both resource supply and demand parties is stored in the cloud manufacturing platform;
step 1.2: calculating the time t from the current matching by adopting the formula (1) according to the historical transaction informationnowThe ith demand side DiTo the jth supplier SjTime decay factor theta at kth transactionijk：
Figure FDA0002058295950000011
In the formula (1), t0Starting time, t, calculated for confidenceijkIs the ith demand side DiTo the jth supplier SjAt the time of the kth transaction, nijAt a starting time t0To the current matching time tnowThe ith demander D in the time periodiTo the jth supplier SjK is 1,2, …, nij；
Calculating the time t from the cutoff to the current matching by adopting the formula (2)nowJ th supplier SjAnd the ith demander DiTime decay factor θ ' at k ' th transaction 'jik′：
Figure FDA0002058295950000012
In formula (2), t'jik′For the jth supplier SjAnd the ith demander DiTime of k 'th transaction, n'jiFor calculating the starting time t at the confidence level0To the current matching time tnowOf the jth supplier SjAnd the ith demander DiK 'is 1,2, …, n'ji；
Step 1.3: calculating the time t from the cutoff to the current matching by adopting the formula (3)nowThe ith demand side DiTo the jth supplier SjTime decay factor theta of transactionij：
Figure FDA0002058295950000013
Calculating the time t from the cutoff to the current matching by adopting the formula (4)nowJ th supplier SjAnd the ith demander DiTime decay factor of transaction θ'ji：
Figure FDA0002058295950000014
Step 1.4: calculating the ith demand side D by adopting the formula (5)iTo the jth supplier SjConfidence level c of rating obtained in k-th transactionijk：
Figure FDA0002058295950000021
In the formula (5), eijkIs the ith demand side and the jthSupplier SjThe evaluation value obtained in the k transaction;
calculation of the jth supplier S using equation (6)jAnd the ith demander DiConfidence level c 'of evaluation obtained in k-th transaction'jik′：
Figure FDA0002058295950000022
In formula (6), e'jik′For the jth supplier SjAnd the ith demander DiThe rating value obtained at the k' th transaction;
step 1.5: the current matching time t is calculated by adopting the formula (7)nowWhen the number of transactions between the supplier and the demander is not 0, the ith demander DiRelative to the jth supplier SjReal-time trust of
Figure FDA0002058295950000023
Figure FDA0002058295950000024
In the formula (7), σ is the valid time window, rijkIs the ith demand side DiTo the jth supplier SjThe amount of money in the kth transaction;
the current matching time t is calculated by adopting the formula (8)nowWhen the number of transactions between the suppliers and the demanders is not 0, the jth supplier SjWith respect to the ith demander DiReal-time trust of
Figure FDA0002058295950000025
Figure FDA0002058295950000026
R 'in the formula (8)'jik′As the jth supplierAnd the ith demander DiThe amount of the transaction at the k' th time;
step 1.6: the current matching time t is calculated by adopting the formula (9)nowThe ith demand side DiRelative to the jth supplier SjReal-time trust of
Figure FDA0002058295950000027
Figure FDA0002058295950000031
The current matching time t is calculated by the formula (10)nowJ th supplier SjWith respect to the ith demander DiReal-time trust of
Figure FDA0002058295950000032
Figure FDA0002058295950000033
Step 2: acquiring demand information of both supply and demand parties of resources and calculating the matching degree of each attribute between the supply and demand parties;
step 2.1: acquiring an expected value and an actual value of actual level information of demand information submitted by both resource supply and demand parties in current matching, wherein the expected value and the actual value are stored in a cloud manufacturing platform; and the expected value and the actual value are both expressed by triangular fuzzy numbers;
step 2.2: the ith demand side D is calculated by adopting the formula (11)iRelative to the jth supplier SjDegree of matching xi at p-th attributeijp：
Figure FDA0002058295950000034
In the formula (11), uip(x)、ujp(x) Are respectively the ith demand side DiAnd the jth supplier SjFuzzy membership functions describing the p-th attribute, cijpz、cijpuAre respectively the ith demand side DiTo the jth supplier SjUpper and lower limits of common interval in p-th attribute, dipz、dikuAre respectively the ith demand side DiAn interval upper limit and an interval lower limit describing the p-th attribute in the range;
calculation of the jth supplier S by equation (12)jWith respect to the ith demander DiDegree of matching xi 'at the q-th attribute'jiq：
Figure FDA0002058295950000035
In the formula (12), uiq(x)、ujq(x) Are respectively the ith demand side DiAnd the jth supplier SjFuzzy membership function, c 'describing the q-th attribute'ijqz、c′ijquAre respectively the ith demand side DiTo the jth supplier SjUpper and lower limits of the common interval, s, in the qth attributejqz、sjquRespectively the j supply side SjAn interval upper limit and an interval lower limit describing the qth attribute in the range;
and step 3: calculating the matching degree between the two resource supply and demand parties;
the current matching time t is calculated by equation (13)nowThe ith demand side DiRelative to the jth supplier SjDegree of matching at p-th attribute
Figure FDA0002058295950000041
Figure FDA0002058295950000042
The current matching time t is calculated by equation (14)nowLower jth supplier SjWith respect to the ith demander DiDegree of matching at qth attribute
Figure FDA0002058295950000043
Figure FDA0002058295950000044
And 4, step 4: establishing a multi-target bilateral matching model of the cloud manufacturing resources to obtain an optimized matching scheme of the cloud manufacturing resources;
step 4.1: constructing a target function of cloud manufacturing resource bilateral matching:
determining an objective function f of the degree of matching of all consumers with respect to all suppliers using equation (15)1：
Figure FDA0002058295950000045
In formula (15), ksFor all suppliers S ═ S1,S2,…,Sj,…,SnSet of attributes to consider, wjpFor the jth supplier SjWeight to p-th attribute, xijIs the ith demand side DiTo the jth supplier SjA decision variable of (c);
determining an objective function f of the degree of matching of all suppliers to all demanders using equation (16)2：
Figure FDA0002058295950000046
In formula (16), kdFor all consumers D ═ D1,D2,…,Di,…,DmSet of attributes to consider, wiqIs the ith demand side DiA weight for the qth attribute;
determining an objective function f for the number of matches using equation (17)3：
Figure FDA0002058295950000047
Step 4.2: determining constraint conditions for bilateral matching of cloud manufacturing resources:
determining the ith demand D using equations (18) and (19)iAnd the jth supplier SjConstraint of the number of matches:
Figure FDA0002058295950000048
Figure FDA0002058295950000049
in formulae (18) and (19), HiIs the ith demand side DiUpper limit of the number of matches, HjFor the jth supplier SjThe upper limit of the number of matches of (c),
determining the ith demand side D by using the formula (20) and the formula (21)iAnd the jth supplier SjMatching satisfaction degree constraint conditions of (1):
Figure FDA0002058295950000051
Figure FDA0002058295950000052
in the formulae (20) and (21), LiIs the ith demand side DiLower limit of degree of matching, LjFor the jth supplier SjLower limit of matching degree of;
order the ith demand side DiTo the jth supplier SjDecision variable xijWith a constraint of xij∈N0；
Step 4.3: solving a cloud manufacturing resource bilateral matching model to obtain an optimized matching scheme:
converting the resource bilateral matching multi-objective function into a single objective function by adopting an equation (22):
f＝αf1+βf2+λf3 (22)
in formula (22), α + β + λ is 1 and α, β, λ ∈ [0,1 ];
and solving the multi-target bilateral matching model by using the target function f as a target and adopting a branch-and-bound method to obtain an optimized matching scheme.
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