CN109118122A - Outsourcing supplier's evaluation method based on mixing PSO-Adam neural network - Google Patents

Outsourcing supplier's evaluation method based on mixing PSO-Adam neural network Download PDF

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CN109118122A
CN109118122A CN201811083184.7A CN201811083184A CN109118122A CN 109118122 A CN109118122 A CN 109118122A CN 201811083184 A CN201811083184 A CN 201811083184A CN 109118122 A CN109118122 A CN 109118122A
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李益兵
宋东林
王磊
陈志鹏
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Wuhan University of Technology WUT
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Abstract

The invention discloses a kind of outsourcing supplier's evaluation methods based on mixing PSO-Adam neural network, comprising the following steps: establishes outsourcing supplier's evaluation data set；Data set is divided into the similar exclusive subsets of k size using the method that reserves；Use the union of k-1 subset as training set every time, remaining subset obtains k group training set and test set as test set；Neural network is established, the model structure of neural network includes input layer, hidden layer, output layer；Neural metwork training, the neural network model after being trained；After completing to the training of neural network, by the input layer of outsourcing supplier's data set input neural network, the output of neural network output layer is the evaluation score of outsourcing supplier.The method of the present invention objective can efficiently carry out outsourcing supplier and evaluate decision, reduce the dependence to personal experience, reduce supplier evaluation difficulty, reduce overhead cost for supply chain.

Description

Outsourcing supplier's evaluation method based on mixing PSO-Adam neural network
Technical field
The present invention relates to assessment techniques more particularly to a kind of outsourcing supplier based on mixing PSO-Adam neural network to comment Valence method.
Background technique
As the emerging technologies such as artificial intelligence, big data, Internet of Things and cloud computing deepen continuously manufacturing, cause The new round Industrial Revolution of developing direction, the manufacture ring of enterprise are turned to globalization, informationization, intelligence, wisdom and green The change of essence also has occurred in border.More and more enterprise's selections carry out the Collaborative Manufacturing of networking with other enterprises, and will Some non-core process requirements give more professional outsourcing supplier, to be absorbed in the raising of itself core competitiveness.When The external coordination environment of preceding complexity provides more diversified cooperation processing resource to manufacturing enterprise, however the dispersion of cooperation processing resource Property, also to the evaluation of the outsourcing supplier of manufacturing enterprise, more stringent requirements are proposed for diversity, dynamic, associativity, this makes Traditional method is difficult to meet the needs of outsourcing supplier evaluates under current environment.Therefore, it is necessary to develop a kind of new external coordination Supplier's decision-making technique is further reduced dependence of the conventional method to personal experience, reduces the difficulty of supplier evaluation, reduces and supplies Chain management cost is answered, to adapt to the external coordination environment of current complexity.
Summary of the invention
The technical problem to be solved in the present invention is that for the defects in the prior art, providing a kind of based on mixing PSO- The outsourcing supplier of Adam neural network evaluates decision-making technique, makes up the too strong defect of Multiobjective Decision Making Method subjectivity, thus It is objective efficiently to carry out outsourcing supplier's evaluation decision, the dependence to personal experience is reduced, supplier evaluation difficulty is reduced, is reduced Overhead cost for supply chain.
The technical solution adopted by the present invention to solve the technical problems is: a kind of based on mixing PSO-Adam neural network Outsourcing supplier's evaluation method, comprising the following steps:
1) outsourcing supplier's evaluation data set is established, includes: from qualification rate, the matter for examining equipment to acquire in the data set Measure stability and rate of breakdown data；The production and processing efficiency acquired from process equipment；The information acquired from MES system passes Pass timeliness and accuracy of information communication data；The average cross ambiguity segmentation being calculated according to existing assessment indicator system is poor Volume, the rate that delivers goods on schedule deliver accuracy rate, manufacture processing staff's quality, sophisticated equipment ratio, processing request change completion rate, work Phase changes completion rate, processing quotation difference and transportation cost difference data；According to expert opinion obtain technology standardization degree, The level of IT application, service level data；
2) use reserves method and data set D is divided into the similar exclusive subsets of k size, i.e. D=D1∪D2∪…∪Dk,
3) use the union of k-1 subset as training set every time, remaining subset obtains k group training set as test set With test set, the normalization of sample data is finally carried out using min-max standardization；
4) neural network is established, the model structure of neural network includes input layer, hidden layer, output layer；Input layer and defeated The neuron number of layer is determined by actual sample out, the network of the model selection list hidden layer of this neural network；
5) neural metwork training, the neural network model after being trained；
6) after completing to the training of neural network, by the input layer of outsourcing supplier's data set input neural network, nerve The output of network output layer is the evaluation score of outsourcing supplier.
According to the above scheme, neural metwork training in the step 5), the neural network model after being trained, using PSO- Adam hybrid optimization algorithm completes the training to neural network,
It is specific as follows:
5.1) PSO algorithm parameter and coding structure: particle length, population scale and the number of iterations of PSO are determined；
5.2) PSO algorithm initialization: the Position And Velocity of each particle in initialization particle populations；
5.3) global optimization: carrying out particle iteration, and global search is carried out in solution space, finds globally optimal solution, every time After the completion of iteration, according to formula (10) (11) more new particle optimal location PiWith group optimal location PG；
5.4) neural network local optimum is carried out using Adam.
According to the above scheme, when the step 5.3) carries out particle iteration, in the latter stage of PSO iteration, it will accumulate in same point Extra particle carry out randomization resetting.
The beneficial effect comprise that: outsourcing supplier's evaluation method energy based on mixing PSO-Adam neural network It is enough for current complicated external coordination environment, it is objective and efficiently solve outsourcing supplier's evaluation problem, be further reduced to individual The dependence of experience reduces supplier evaluation difficulty, reduces overhead cost for supply chain.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples, in attached drawing:
Fig. 1 is the algorithm flow chart of the embodiment of the present invention；
Fig. 2 is that the outsourcing supplier of the embodiment of the present invention evaluates network；
Fig. 3 is the PSO algorithm fitness curve of the embodiment of the present invention；
Fig. 4 is the Adam neural metwork training collection goodness of fit of the embodiment of the present invention；
Fig. 5 is the Adam neural network test set goodness of fit of the embodiment of the present invention.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to embodiments, to the present invention It is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, is not used to limit The fixed present invention.
As shown in Figure 1, outsourcing supplier's evaluation method based on mixing PSO-Adam neural network, this method are divided into data Acquisition and processing, the global optimization of PSO algorithm, Adam neural network local optimum three phases, detailed step are as follows:
(1) data acquisition and procession
Step 1 data processing
The characteristics of present invention combination outsourcing supplier and actual conditions are established based on destination layer, rule layer, indicator layer Three-level assessment indicator system.Destination layer is the comprehensive ability evaluation of outsourcing supplier；Rule layer is divided into eight classes, respectively quality Level, delivery capability, manufacture working ability, information transfer capacity, exception processes ability, cost level, enterprise's internal and external environment, clothes Business is horizontal；And with these eighth types of 22 indicator layers to be unfolded.Outsourcing supplier's assessment indicator system is as shown in Table 1.
1 outsourcing supplier's assessment indicator system of table
According to outsourcing supplier's assessment indicator system, data set is by being divided into acquisition, quantifying, qualitative three kinds of data.Acquisition Index examines the data of equipment, process equipment and MES system to obtain by acquisition, and quantitative data is according to related data according to existing Assessment indicator system is calculated, and for qualitative index, by expert system or establishes expert opinion group and score It arrives.
Data set D is divided into the similar exclusive subsets of k size, i.e. D=D using the method that reserves (hold-out)1∪D2 ∪…∪Dk,Each subset DiThe consistency of data distribution, i.e. D are all kept as far as possibleiFrom D It is obtained by stratified sampling.Then, use the union of k-1 subset as training set every time, remaining subset is obtained as test set To k group training set and test set, sample data finally is realized using min-max standardization (Min-Max Normalization) Normalization, formula is such as shown in (1).
Wherein, X is not normalized sample data, X*It is a sample data after normalization, XminIt is sample number According to minimum value, XmaxIt is the maximum value of sample data.
The design of step 2) Artificial Neural Network Structures
The model structure of neural network includes input layer, hidden layer, output layer.The neuron number of input layer and output layer It is to be determined by the sample of realistic model.The present invention selects the network of single hidden layer, and the neural network of single hidden layer is not limiting mind While through number of network node, arbitrary nonlinear mapping may be implemented, and the training time is relatively short, precision, which can reach, to be wanted It asks.The selection of node in hidden layer k is according to following four kinds of empirical equations:
(1)N is input layer number in formula, and k is node in hidden layer, and N is sample number, as i > k Provide number of combinations
(2)N is input layer number in formula, and k is node in hidden layer, and m is output node layer Number, constant of the t between [1,10].
(3) k=log2N, n is input layer number in formula, and k is node in hidden layer.
(4)N is input layer number in formula, and k is node in hidden layer, and m is output layer number of nodes.
The evaluation problem of outsourcing supplier is substantially a kind of Generalized Multivariate regression problem, therefore by the target letter of neural network Number is set as MSE, and formula is such as shown in (2):
Wherein, m refers to the number of output node, and N is training sample number,It is network desired output,It is network reality Border output valve.
In order to allow network to obtain powerful nonlinear fitting ability, the present invention is added in the active coating and output layer of network Tanh activation primitive maps the output of neuron, and tanh formula is such as shown in (3).
Step 3) neural metwork training
3.1) PSO algorithm parameter and coding structure are determined
PSO in the present invention in global search space for finding optimal neural network initial weight threshold value, it may be assumed that net Connection weight w of the network input layer to hidden layerji, the threshold value b of hidden layer1j, the connection weight w of hidden layer to output layerkj, output Layer threshold value b2k.In standard three-layer neural network structure, network input layer number of nodes is m, node in hidden layer n, output layer Number of nodes is q, then the connection weight w of input layer to hidden layerjiNumber be n × m.The threshold value b of hidden layer1jNumber be n, Connection weight w of the hidden layer to output layerkjNumber be q × n.Output layer threshold value b2kNumber be q.The particle length of PSO is Parameter summation to be optimized, calculation formula is such as shown in (4).
N=m × n+n × q+n+q (4)
3.2) PSO algorithm global optimization
PSO algorithm initialization
Before PSO starting, the Position And Velocity of each particle in random initializtion particle populations is needed.Each particle Massless is considered without the point in the D dimension space of volume, wherein D is the number of parameter.Assuming that the population of a N number of particle, The original position of its population can indicate as follows:
w0=[w1,0,w2,0…wn,0] (5)
v0=[v1,0,v2,0…vn,0] (6)
Wherein, W0And V0Respectively refer to the initial position collection and initial velocity collection of particle.W is the D n dimensional vector n of particle coordinate.And V It is the D n dimensional vector n of particle rapidity.
PSO algorithm iteration
In the iteration searching process of PSO, the coordinate of particle is a parameter set, represents the possibility of optimization problem Solution.The purpose of particle is to be moved to a more excellent position from current location in search space.Particle movement speed is worked as by particle Preceding speed, personal best particle, population optimal location determine that position is determined by current location and movement speed after particle updates. Particle rapidity of the invention and position iterative formula are as follows:
vi,t+1=x [vi,t+c1r1(Pi-wi,t)+c2r2(PG-wi,t)] (7)
wi,t+1=wi,t+vi,t+1 (8)
Wherein, vI, t+1It is the pace of change vector of particle i, vI, tIt is the current velocity vector of particle i；wI, t+1It is particle i Updated coordinate, wI, tIt is the changing coordinates of particle i；c1, c2It is acceleration constant；r1, r2It is two groups to be uniformly distributed at random Number；Pi, PGRespectively refer to the personal best particle of particle i and group's optimal location of particle populations.Wherein x is for stable particle The compressibility factor that movement speed is added, is defined as follows:
Wherein, φ=c1+c2>4.Particle optimal location PiWith group optimal location PGIt is updated according to following formula:
ifξ(Pi) > ξ (wi)then Pi=wi,t,1≤i≤N (10)
ifξ(PG) > ξ (wi)then PG=wi,t,1≤i≤N (11)
Wherein, ξ is fitness function.
Particle TSP question
The particle inside population reaches unanimity too early in order to prevent, so that the search space of algorithm reduces, herein original PSO on the basis of, joined TSP question and particle reset mechanism, prevent network over-fitting, guarantee the generalization ability of network. In PSO iteration mid-term, with some value in random chance resetting random particles coordinate vector.In the latter stage of PSO iteration, will assemble Randomization resetting is carried out in the extra particle of same point.
3.3) Adam neural network local optimum
Step 4Adam neural network iteration
The present invention carries out the Feedback error of neural network, the method ratio SGD of this autoadapted learning rate using Adam Convergence rate is faster, it is easier to converge to global extremum.Adam needs to calculate the band weight average m of gradienttThere is partial variance with cum rights vt, calculation formula is as follows:
mt=β1mt-1+(1-β1)gt (12)
Due to mtAnd vtInitialization is 0 vector, therefore is easy to be biased to 0 vector, so needing to mtAnd vtCarry out deviation school Just, the gradient zone weight average after correction isGradient cum rights after correction has the partial variance to beUpdating formula is as follows:
Therefore, Adam final more new formula is as follows:
Wherein β1Take default value 0.9, β2Default value be 0.999, ε value be 10-8。
Step 5) outsourcing supplier evaluation
After PSO-Adam hybrid optimization algorithm is completed to the training of neural network, the evaluation of outsourcing supplier can be carried out. By the input layer of outsourcing supplier's data set input neural network, the output of neural network output layer is commenting for outsourcing supplier Valence score.
One specific embodiment:
(1) data acquisition and procession
Step 1 data processing
The method of reserving is used to extract 24 groups of outsourcing supplier's data of certain equipment for building materiaIs manufacturing company as sample data, specific number According to as shown in table 2, wherein S1To S20To have flag data collection, S21To S24For data set to be evaluated, finally marked using min-max The normalization of standardization (Min-Max Normalization) realization sample data.
2 outsourcing supplier of table evaluates sample data table (1)
2 outsourcing supplier of table evaluates sample data table (2)
Step 2 Neural Network Structure Design
The data of analytical table 2 can determine that neural network input layer number of nodes is 22 according to this method, output layer number of nodes It is 1, the hidden layer number of plies is 1, number of nodes 12, and it is as shown in Figure 2 to obtain outsourcing supplier's evaluation network.
Step 3 determines PSO algorithm parameter and coding structure
Particle length press according to formula (4) calculate 289.Particle inertia weight enables algorithm from 0.9 linear decrease to 0.4 There is stronger ability of searching optimum at the initial stage of operation, and being capable of fast convergence in the later period of operation.Minimum change is set Step-length and minimum change fitness are 1e-6, and the result that algorithm obtains when less than threshold value tends towards stability, and algorithm is enabled to terminate, to reduce Calculation amount.The population scale of PSO algorithm is usually determined by experiment with the number of iterations, and it is as shown in table 3 to obtain PSO major parameter.
3 PSO major parameter table of table
PSO algorithm global optimization
Step 4 PSO algorithm initialization
The PSO particle length known to PSO coding structure is 289, therefore initializes the position of each particle in particle populations It is as follows with speed:
w0=[w1,0,w2,0…w289,0]
v0=[v1,0,v2,0…v289,0]
Step 5 PSO algorithm iteration
Particle iteration is carried out according to formula (7) (8), global search is carried out in solution space, finds globally optimal solution, every time After the completion of iteration, according to formula (10) (11) more new particle optimal location PiWith group optimal location PG。
Step 6 particle TSP question
The particle inside population reaches unanimity too early in order to prevent, so that the search space of algorithm reduces, in PSO iteration Phase, with some value in random chance resetting random particles coordinate vector.In the latter stage of PSO iteration, same point will accumulate in Extra particle carries out randomization resetting.
Fitness curve is obtained by PSO algorithm initialization and iteration as shown in figure 3, PSO is by 15 generations as seen from the figure The overall situation simultaneously optimizes, and the MSE of neural network is down to 0.1289 from 0.7653, error significantly reduces.
Adam neural network local optimum
Step 7 neural network iteration
Local optimum, the mean square error and fitting of neural network are carried out using Adam neural network described in this method respectively Goodness is as shown in table 4, and training set, the goodness of fit figure difference of test set are as shown in Figure 4, Figure 5.
4 algorithm performance table of table
It can be seen that very high precision and the goodness of fit can be obtained using method of the invention, and the method was using In journey can the parameters such as adaptive polo placement learning rate, influence evaluation result by initial parameters such as learning rates, reduce method Enforcement difficulty, enable supplier evaluation result more objective credible.
Step 8 outsourcing supplier evaluation
4 groups of data samples to be evaluated are evaluated using the neural network of PSO-Adam algorithm optimization, evaluation knot Fruit is as shown in table 5.
5 sample to be tested evaluation result of table
It is found that outsourcing supplier S21Evaluation score highest, S22Score is lower；S21And S23It can be used as outstanding outsourcing supplier It is included in resources bank；S24It can be used as the outsourcing supplier commonly cooperated, S22Cooperation Risk is higher, need to cooperate with caution.Evaluation result is handed over It is examined by panel of expert of company, has obtained the generally approval of associate.Application example proves, based on mixing PSO-Adam nerve Outsourcing supplier's evaluation method of network can be objective and efficiently solve outsourcing supplier for current complicated external coordination environment Evaluation problem is further reduced the dependence to personal experience, reduces supplier evaluation difficulty, reduces overhead cost for supply chain.
It should be understood that for those of ordinary skills, it can be modified or changed according to the above description, And all these modifications and variations should all belong to the protection domain of appended claims of the present invention.

Claims (3)

1. a kind of outsourcing supplier's evaluation method based on mixing PSO-Adam neural network, which is characterized in that including following step It is rapid:
1) outsourcing supplier's evaluation data set is established, includes: steady from the qualification rate, quality for examining equipment to acquire in the data set Qualitative and rate of breakdown data；The production and processing efficiency acquired from process equipment；From MES system acquire information transmitting and When property and accuracy of information communication data；The average cross ambiguity segmentation difference that is calculated according to existing assessment indicator system, by Phase delivery ratio delivers accuracy rate, manufacture processing staff's quality, sophisticated equipment ratio, processing request change completion rate, duration change Completion rate, processing quotation difference and transportation cost difference data；According to the technology standardization degree of expert opinion acquisition, informationization Horizontal, service level data；
2) use reserves method and data set D is divided into the similar exclusive subsets of k size, i.e. D=D1∪D2∪…∪Dk,
3) use the union of k-1 subset as training set every time, remaining subset obtains k group training set and survey as test set Examination collection finally carries out the normalization of sample data using min-max standardization；
4) neural network is established, the model structure of neural network includes input layer, hidden layer, output layer；Input layer and output layer Neuron number determined by actual sample, the network of the model selection list hidden layer of this neural network；
5) neural metwork training, the neural network model after being trained；
6) after completing to the training of neural network, by the input layer of outsourcing supplier's data set input neural network, neural network The output of output layer is the evaluation score of outsourcing supplier.
2. outsourcing supplier's evaluation method according to claim 1 based on mixing PSO-Adam neural network, feature It is, neural metwork training in the step 5), the neural network model after being trained, is calculated using PSO-Adam hybrid optimization Method completes the training to neural network,
It is specific as follows:
5.1) PSO algorithm parameter and coding structure: particle length, population scale and the number of iterations of PSO are determined；
5.2) PSO algorithm initialization: the Position And Velocity of each particle in initialization particle populations；
5.3) global optimization: carrying out particle iteration, and global search is carried out in solution space, finds globally optimal solution, each iteration After the completion, according to formula (10) (11) more new particle optimal location PiWith group optimal location PG；
5.4) neural network local optimum is carried out using Adam.
3. outsourcing supplier's evaluation method according to claim 2 based on mixing PSO-Adam neural network, feature It is, when the step 5.3) carries out particle iteration, in the latter stage of PSO iteration, the extra particle that will accumulate in same point is carried out Randomization resetting.
CN201811083184.7A 2018-09-17 2018-09-17 Outsourcing supplier's evaluation method based on mixing PSO-Adam neural network Pending CN109118122A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111105127A (en) * 2019-11-06 2020-05-05 武汉理工大学 Modular product design evaluation method based on data driving
CN112417759A (en) * 2020-11-19 2021-02-26 天津大学 Heat conduction inverse problem solving method based on dynamic neural network
CN112508378A (en) * 2020-11-30 2021-03-16 国网北京市电力公司 Processing method and device for screening power equipment production manufacturers

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111105127A (en) * 2019-11-06 2020-05-05 武汉理工大学 Modular product design evaluation method based on data driving
CN111105127B (en) * 2019-11-06 2023-04-07 武汉理工大学 Modular product design evaluation method based on data driving
CN112417759A (en) * 2020-11-19 2021-02-26 天津大学 Heat conduction inverse problem solving method based on dynamic neural network
CN112417759B (en) * 2020-11-19 2022-09-23 天津大学 Heat conduction inverse problem solving method based on dynamic neural network
CN112508378A (en) * 2020-11-30 2021-03-16 国网北京市电力公司 Processing method and device for screening power equipment production manufacturers

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Application publication date: 20190101