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CN113657655A - A product production method with optimal allocation of service-oriented resources - Google Patents

A product production method with optimal allocation of service-oriented resources Download PDF

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CN113657655A
CN113657655A CN202110901409.0A CN202110901409A CN113657655A CN 113657655 A CN113657655 A CN 113657655A CN 202110901409 A CN202110901409 A CN 202110901409A CN 113657655 A CN113657655 A CN 113657655A
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陈勇
陈镜
柴兴华
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CETC 54 Research Institute
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Abstract

The invention discloses a production method of a product with optimal configuration of service resources, and belongs to the technical field of intelligent cloud manufacturing and business models. The invention uses the principle that the tenderer and the bidder negotiate to reach the transaction in the tendering and bidding process for reference, introduces a negotiation mechanism into the bidirectional selection process of production tasks (tenderers) and equipment resources (bidders), and utilizes an algorithm to quantitatively describe the tendering and bidding process between equipment, thereby effectively realizing the optimal configuration of the resources and being used for cooperative production and manufacture under the economic globalization background.

Description

Product production method for optimal allocation of service resources
Technical Field
The invention relates to the technical field of intelligent cloud manufacturing and business models, in particular to a production method for a product with optimal configuration of service resources.
Background
In the context of economic globalization, the production and manufacturing of a product involves multiple devices of multiple enterprises, and these devices are usually transformed into information-based services, and have the capability of interacting with information such as device status, production status, and operation parameters, which means that the production device can participate in the production process of the product wherever it is.
The problem that the distributed production line is formed by selecting the optimal equipment resources according to information such as product types, delivery places, quality requirements, the capacity and the state of each production equipment and the like exists because more than one equipment with the same production capacity is arranged in the global range.
Disclosure of Invention
In order to effectively solve the problem of optimal allocation of the servitized production resources under the condition of many-to-many production tasks and equipment resources, the invention provides a product production method for optimal allocation of the servitized resources. The method can serve product integrators and part manufacturers, find the optimal equipment and the optimal service object, and realize the optimal balanced configuration of the supply and demand parties under the cloud manufacturing condition.
In order to achieve the purpose, the invention adopts the technical scheme that:
a production method of a product with optimal configuration of service resources is realized based on an Internet platform and comprises the following steps:
(1) the product integrator generates production subtasks according to production links related to product production, and sorts the production subtasks according to the production flow sequence; inquiring a part manufacturer capable of accepting the production subtasks and sending production task requirement information to the part manufacturer for each production subtask;
(2) the part producer bids corresponding product integrators according to the production conditions of the part producer and the production task demand information of the product integrators;
(3) the product integrator performs comprehensive quantitative evaluation on the part manufacturers of the bidders of each production subtask, selects the part manufacturer with the highest score to cooperate, and sends a bid-winning notice;
(4) after receiving the bid-winning notice, the part producer adds the task into the contract task set and replies confirmation information, and the contracts of the part producer and the part producer are established;
(5) after receiving the confirmation information, the product integrator marks the task as a contract task; and after the product integrator receives the confirmation information of the bidders in each production link, driving each bidder to carry out production according to the production flow sequence.
Further, in the step (3), the comprehensive quantitative evaluation mode is as follows:
score＝w1×vproduction quotes+w2×vQuality of the product+w3×vProduct delivery capability+w4×vLength of cooperative relationship+w5×vAfter-sale service
Where score is the evaluation score, w is the weighting factor of the corresponding factor, and v is the evaluation index of the corresponding factor.
Further, in the step (2), if the part manufacturer receives the production task requirement information of a plurality of product integrators and the production conditions cannot meet the production requirements of all the product integrators, screening the product integrators with the prior scores through an evaluation formula to bid; the evaluation formula is as follows:
score＝w1×vbid by production unit+w2×vProduction task issuing unit payment credit
Where score is the evaluation score, w is the weighting factor of the corresponding factor, and v is the evaluation index of the corresponding factor.
Further, the evaluation index v is normalized by a min-max normalization method, wherein the normalization method is as follows:
Figure BDA0003199915880000021
in the formula, z is a normalized value, x is an original value of the normalized evaluation index, and maxValue and minValue are respectively a maximum value and a minimum value of the normalized evaluation index.
Further, the weight coefficient is calculated in the following manner:
a) constructing a priority relation judgment matrix A (a) by means of expert scoringij)n×nN is index number, matrix element aijValue of (b) represents an index viAnd an index vjImportance of the comparison, aijThe value is 0-1, the diagonal elements of the matrix are 0.5, and one index is equal to the importance of the matrix;
b) let matrix a be (a)ij)n×nSumming by rows, the sum of the ith row is:
Figure BDA0003199915880000022
Figure BDA0003199915880000024
constructing fuzzy consistent judgment matrix R ═ (R)ij)n×nWherein
Figure BDA0003199915880000023
c) And optimizing the weight coefficient by adopting a sum-row normalization method:
changing the matrix A into a reciprocal judgment matrix E, E ═ Eij)n×n，
Figure BDA0003199915880000031
Computing an initial weight vector A(0)：
Figure BDA0003199915880000032
With A(0)For iterative initial value V0The weight vector is optimized by a characteristic value method, and V is used0＝(v01,v02,…,v0n)TFor iterative initial values, use is made of the iterative formula Vk+1＝EVkV is obtainedk+1And calculating its infinite norm Vk+1||∞；
If V | |k+1||-||VkIf < epsilon, epsilon is the threshold, then Vk+1||∞The maximum characteristic value is obtained, and the iteration is finished; will Vk+1Carrying out normalization processing to obtain a vector Vk+1 (norm)As optimized weight vectors, i.e.
Figure BDA0003199915880000033
Otherwise, with
Figure BDA0003199915880000034
As a new iteration value, performing iteration optimization calculation again;
and the elements in the optimized weight vector are weight coefficients.
The invention has the beneficial effects that:
1. the invention introduces a negotiation mechanism into the bidirectional selection process of production tasks and equipment resources, and quantitatively describes the bidding process among equipment by using an algorithm, thereby effectively realizing the optimal configuration of the resources.
2. Under the background of cloud manufacturing, the information and networking characteristics of the part production equipment matched with the product are used as shared service resources, so that a product integrator can select the optimal part production equipment to produce the parts on the basis of comprehensively considering factors such as price, performance, after-sale service and the like, and support is provided for the own complete machine manufacturing. Alternatively, the parts production may select which product integrator to provide the parts based on a combination of bidding and payment credit considerations.
3. The invention can realize the optimal configuration and benefit maximization of the part production equipment and solve the problem of service resource configuration of both the supply and demand parties.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
A method for producing a product with optimal allocation of services resources, comprising the steps of:
(1) bidding production tasks;
a producer forms a bidding task set according to production links related to product production, sorts the bidding tasks according to a production flow sequence, inquires information of collaborative production equipment capable of accepting the production tasks aiming at each production link, forms a collaborative production equipment set capable of completing the bidding tasks, and sends production task demand information including the content of the production product to be bid, the product specification, the quality requirement, the delivery time and the production price to the collaborative production equipment in the set.
(2) Bidding a production task;
for a production equipment provider, after receiving related task information, the cooperative production equipment with production capacity performs comprehensive quantitative evaluation according to the working state, the task state and the production task condition of the cooperative production equipment and the payment credit of a production task issuing unit, and determines whether to bid or not and which production task to bid specifically according to the evaluation result.
(3) Evaluating production tasks;
for a production task bidder, a production link may have a plurality of bidders, comprehensive quantitative evaluation needs to be performed according to the bidding price, product quality, delivery capacity, cooperation relationship length and after-sales service condition of the bidders, and an enterprise with the highest score is selected according to the evaluation result for cooperation.
For a production task, if all production links find suitable bidders, go to step (4), otherwise, go to step (2).
(4) Executing a production task;
after receiving the bid-winning notice, the bidder who wins the bid formally adds the task into the contract task set and replies confirmation information, so that the contracts of the bidder and the bidder are established. And after receiving the confirmation information, the tenderer marks the task as a contract task, and drives each bidder to carry out production according to the production flow sequence after receiving the confirmation information of all bidders in each production link.
And (3) normalizing by adopting a standardized method to realize the measurement unification among different dimension evaluation indexes of the working state, the task state, the production task condition and the payment credit, the bid price, the product quality, the delivery capacity, the cooperative relationship length and the after-sale service condition.
For example, the formula for normalization using the min-max normalization method is as follows:
Figure BDA0003199915880000051
if A, B, C the bid prices of the three co-production facilities are 10, 20 and 30 ten thousand yuan, the normalized values are 0, 0.5 and 1, respectively.
If the production task bids of three producers, namely Company-1, Company-2 and Company-3, are 15, 25 and 40 ten thousand dollars respectively, the normalized values are 0, 0.4 and 1 respectively.
For the evaluation items such as product quality, delivery capacity and after-sales service condition, a numerical evaluation value can be given by adopting a percentage system scoring mode, and then normalization is carried out by utilizing a standardization method.
And (3) adopting a method for carrying out weighted average quantitative analysis on the normalized evaluation value to realize the preferred ordering of candidate production equipment and candidate producers.
The weighting quantitative analysis formula for supporting the evaluation of each bidder by the producer is as follows:
score＝w1×vbid price+w2×vQuality of the product+w3×vDelivery capability+w4×vAfter-sale service
The weighting quantitative analysis formula supporting the bidder to select whether to bid and to which producer to bid is as follows:
score＝w1×vproducing product content+w2×vProduct specification+w3×vQuality requirement+w4×vDelivery time+w5×vProduction price+w4×vDelivery time++w5×vWorking state+w6×vTask state+w7×vProduction task situation+w8×vProduction task issuing unit payment credit
And (3) calculating the weight value of each evaluation index by adopting a sum-row normalization method to realize the consistency of the weight values.
The weight analysis is the important content of the quantitative evaluation analysis method, and the weight is the importance degree of each evaluation index in the quantitative analysis process. The weighted value consistency calculation comprises three main steps: constructing a priority relation judgment matrix, constructing a fuzzy consistent judgment matrix and calculating a weight set.
a) Constructing a priority relationship decision matrix
The priority relation judgment matrix reflects the priority relation between two index factors related to the previous layer and the current layer aiming at a certain element of the previous layer, and the element B of the previous layer and the element a of the next layer are assumediAnd ajThere is a connection. By rijRepresenting element aiAnd ajWhen compared against element B, there is a fuzzy relationship such as "… is much more important than …". To determine the element aiRelative to ajThe importance of (a) is to establish a fuzzy judgment scale represented by the numbers 0.1-0.9, as shown in table 1.
TABLE 1 Scale of relative importance
Figure BDA0003199915880000061
The judgment matrix composed of expert judgment values is expressed as
Figure BDA0003199915880000062
In the formula aijIs an index factor UeiRelative to UejOf importance of ajiIs an index factor UejRelative to UeiWherein i ═ 1, 2, 3, …, n; e is 1, 2, 3 …, m. The value on the main diagonal represents the importance of each factor itself being self-comparing and therefore, it takes on a value of 0.5.
b) Constructing fuzzy consistent judgment matrix
Due to the complexity of the problem to be researched and the difference of different experts in the understanding of the importance of the same index factor, the finally formed judgment matrix is often inconsistent. Therefore, it is necessary to modify the fuzzy complementary matrix obtained as described above into a fuzzy consistent matrix. The fuzzy consistent matrix is constructed by the following steps:
the first step is as follows: let matrix a be (a)ij)n×nBy row, i.e. summing
Figure BDA0003199915880000071
The second step is that: the following mathematical transformations are made:
Figure BDA0003199915880000072
through the two steps, the fuzzy consistent matrix R ═ (R) can be obtainedij)n×n。
c) Computing a set of weights
Calculating the weight value of the level of each factor by adopting a sum-row normalization method, wherein the calculation steps are as follows:
the first step is as follows: changing the complementary consistency judgment matrix A into a reciprocal judgment matrix E, E ═ Eij)n×n。
Figure BDA0003199915880000073
The second step is that: computing an initial weight vector A(0)
Figure BDA0003199915880000074
The third step: with A(0)For iterative initial value V0The weight vector is optimized by a characteristic value method, and V is used0＝(v01,v02,…,v0n)TFor iterative initial values, use is made of the iterative formula Vk+1＝EVkV is obtainedk+1And calculating its infinite norm Vk+1||∞。
If V | |k+1||-||VkIf | is less than epsilon, then | Vk+1||∞The maximum eigenvalue is obtained, and the iteration is finished. Will Vk+1Vector V obtained by normalization processingk+1 (norm)As an optimized weight vector A, i.e.
Figure BDA0003199915880000075
Otherwise, with
Figure BDA0003199915880000076
And performing iterative optimization calculation again as a new iterative initial value.
The weighted value distribution of 5 evaluation indexes, namely bid price, product quality, delivery capacity, cooperative relationship length and after-sales service condition, involved in the production task evaluation process is taken as an example to explain the weighted value consistency calculation method.
The expert was used to score the importance of the five factors, since it was a pairwise comparison between the five indices, thus forming a 5 × 5 matrix, where the elements in the matrix are defined as shown in table 2.
TABLE 2PADetermining matrix meaning
Figure BDA0003199915880000081
PAThe specific value conditions are as follows:
Figure BDA0003199915880000082
summing the above matrices by rows and by
Figure BDA0003199915880000083
After conversion, the following fuzzy consistency judgment matrix is formed.
Figure BDA0003199915880000084
Finally, the weight values of 5 evaluation indexes corresponding to the bidding price, the product quality, the delivery capacity, the cooperative relationship length and the after-sale service condition are respectively as follows: 0.25, 0.18, 0.21, 0.16, 0.20.
Under the background of cloud manufacturing, a plurality of product matching part production devices have informatization and networking characteristics and become shared service resources, and product integrators can select the optimal part production device to produce parts on the basis of comprehensively considering factors such as price, performance, after-sales service and the like, so that support is provided for the self complete machine manufacturing. Alternatively, the parts production may select which product integrator to provide the parts based on a combination of bidding and payment credit considerations. In this context, in order to achieve optimal configuration and maximum benefit of the component production equipment, it is necessary to solve the problem of resource allocation for servicing both the supply and demand parties.
In order to effectively solve the problem of optimal allocation of the servitization production resources under the condition of many-to-many product integrators and part manufacturers, the invention provides a product production method for optimal allocation of the servitization resources, which adopts a multi-factor weighted evaluation method to carry out quantitative analysis on various resource allocation schemes, and simultaneously adopts a rule-mixing method to scientifically determine a series of weight coefficients in a weighted evaluation formula, so that the method can be used for solving the problem of bidirectional optimal allocation and selection between production tasks and equipment resources under the background of economic globalization.
In a word, the invention uses the principle that the tenderer and the bidder negotiate to reach the transaction in the tendering and bidding process for reference, introduces a negotiation mechanism into the bidirectional selection process of a production task (tenderer) and equipment resources (bidders), and utilizes an algorithm to quantitatively describe the bidding process between equipment, thereby effectively realizing the optimal configuration of the resources and being used for cooperative production and manufacture under the economic globalization background.

Claims (5)

1.一种服务化资源最优配置的产品生产方法，其特征在于，基于互联网平台实现，包括以下步骤：1. a product production method for optimal configuration of service-oriented resources, is characterized in that, based on Internet platform realization, comprises the following steps: (1)产品集成商根据产品生产涉及的生产环节生成生产子任务，并按照生产流程次序对生产子任务进行排序；针对每一个生产子任务，查询可受理该任务的零部件生产商，并向零部件生产商发送生产任务需求信息；(1) The product integrator generates production sub-tasks according to the production links involved in product production, and sorts the production sub-tasks according to the order of the production process; for each production sub-task, query the parts manufacturers that can accept the task, and send the Parts manufacturers send production task demand information; (2)零部件生产商根据自身的生产条件以及产品集成商的生产任务需求信息，对相应的产品集成商进行投标；(2) Parts manufacturers bid on corresponding product integrators according to their own production conditions and the production task demand information of product integrators; (3)产品集成商对每一生产子任务的投标方零部件生产商进行综合量化评估，选择得分最高的零部件生产商进行合作，并发送中标通知；(3) The product integrator conducts a comprehensive quantitative evaluation of the bidder's component manufacturers for each production sub-task, selects the component manufacturer with the highest score for cooperation, and sends a bid winning notice; (4)零部件生产商收到中标通知后，将任务加入到其合同任务集中，并回复确认信息，双方合同成立；(4) After receiving the notification of winning the bid, the component manufacturer will add the task to its contract task set, and reply to the confirmation message, and the contract between the two parties is established; (5)产品集成商收到确认信息后，将任务标记为合同任务；当产品集成商收到各个生产环节的投标方的确认信息后，按照生产流程顺序，驱动各个投标方开展生产。(5) After the product integrator receives the confirmation information, it marks the task as a contract task; when the product integrator receives the confirmation information from the bidders in each production link, it drives each bidder to carry out production according to the sequence of the production process. 2.根据权利要求1所述的一种服务化资源最优配置的产品生产方法，其特征在于，步骤(3)中，综合量化评估的方式为：2. the product production method of a kind of service-oriented resource optimal allocation according to claim 1, is characterized in that, in step (3), the mode of comprehensive quantitative evaluation is: score＝w1×v生产报价+w2×v产品质量+w3×v产品交付能力+w4×v合作关系长短+w5×v售后服务 score=w 1 ×v production quotation +w 2 ×v product quality +w 3 ×v product delivery capability +w 4 ×v length of partnership +w 5 ×v after- sales service 式中，score为评估得分，w为相应因素的权重系数，v为相应因素的评估指标。In the formula, score is the evaluation score, w is the weight coefficient of the corresponding factor, and v is the evaluation index of the corresponding factor. 3.根据权利要求1所述的一种服务化资源最优配置的产品生产方法，其特征在于，步骤(2)中，若零部件生产商收到多家产品集成商的生产任务需求信息，且生产条件无法满足所有产品集成商的生产需求，则通过评估公式筛选出得分居前的产品集成商进行投标；评估公式如下：3. the product production method of a kind of service-oriented resource optimal configuration according to claim 1, is characterized in that, in step (2), if parts manufacturer receives the production task demand information of multiple product integrators, And if the production conditions cannot meet the production requirements of all product integrators, the product integrators with the highest scores will be screened out for bidding through the evaluation formula; the evaluation formula is as follows: score＝w1×v生产单位出价+w2×v生产任务发布单位支付信用 score=w 1 ×v production unit bid +w 2 ×v production task release unit payment credit 式中，score为评估得分，w为相应因素的权重系数，v为相应因素的评估指标。In the formula, score is the evaluation score, w is the weight coefficient of the corresponding factor, and v is the evaluation index of the corresponding factor. 4.根据权利要求2或3所述的一种服务化资源最优配置的产品生产方法，其特征在于，评估指标v采用min-max标准化方法进行归一化，归一化方式为：4. the product production method of a kind of service-oriented resource optimal allocation according to claim 2 or 3, is characterized in that, evaluation index v adopts min-max normalization method to carry out normalization, and normalization method is:
Figure FDA0003199915870000021
Figure FDA0003199915870000021
 式中，z为归一化值，x为被归一化评估指标的原始值，maxValue和minValue分别为被归一化评估指标的最大取值和最小取值。In the formula, z is the normalized value, x is the original value of the normalized evaluation index, maxValue and minValue are the maximum and minimum values of the normalized evaluation index, respectively. 5.根据权利要求2或3所述的一种服务化资源最优配置的产品生产方法，其特征在于，权重系数的计算方式为：5. The product production method of a kind of service-oriented resource optimal allocation according to claim 2 or 3, is characterized in that, the calculation method of weight coefficient is: a)通过专家打分的方式构造优先关系判断矩阵A＝(aij)n×n，n为指标个数，矩阵元素aij的值表示指标vi与指标vj相比的重要程度，aij取值为0～1，矩阵的对角元素取值均为0.5，表示一个指标与自身相比同等重要；a) Construct priority relationship judgment matrix A=(a ij ) n×n by expert scoring, where n is the number of indicators, the value of matrix element a ij represents the importance of index v i compared with index v j , a ij The value is 0 to 1, and the diagonal elements of the matrix are all 0.5, indicating that an indicator is equally important as itself; b)将矩阵A＝(aij)n×n按行求和，第i行的和为：
Figure FDA0003199915870000022
n；构造模糊一致判断矩阵R＝(rij)n×n，其中
Figure FDA0003199915870000023
b) Sum the matrix A=(a ij ) n×n by row, and the sum of the i-th row is:
Figure FDA0003199915870000022
n; construct fuzzy consistent judgment matrix R=(r ij ) n×n , where
Figure FDA0003199915870000023
 c)采用和行归一法对权重系数进行优化：c) Use the sum-row normalization method to optimize the weight coefficient: 将矩阵A变为互反型判断矩阵E，E＝(eij)n×n，
Figure FDA0003199915870000024
Change the matrix A into a reciprocal judgment matrix E, E=(e ij ) n×n ,
Figure FDA0003199915870000024
 求初始权重向量A(0)：Find the initial weight vector A (0) :
Figure FDA0003199915870000025
Figure FDA0003199915870000025
 以A(0)为迭代初值V0，采用特征值法对权重向量进行优化，以V0＝(v01,v02,…,v0n)T为迭代初值，利用迭代式Vk+1＝EVk求Vk+1，并求其无穷范数||Vk+1||∞；Taking A (0) as the initial value of iteration V 0 , using the eigenvalue method to optimize the weight vector, taking V 0 =(v 01 ,v 02 ,...,v 0n ) T as the initial value of iteration, using the iterative formula V k+ 1 = EV k to find V k+1 , and find its infinite norm ||V k+1 || ∞ ; 若||Vk+1||-||Vk||＜ε，ε为阈值，则||Vk+1||∞即为最大特征值，迭代结束；将Vk+1进行归一化处理，得到的向量Vk+1 (norm)作为优化后的权重向量，即If ||V k+1 ||-||V k ||<ε, and ε is the threshold, then ||V k+1 || ∞ is the maximum eigenvalue, and the iteration ends; normalize V k+1 After processing, the obtained vector V k+1 (norm) is used as the optimized weight vector, namely
Figure FDA0003199915870000031
Figure FDA0003199915870000031
 否则，以
Figure FDA0003199915870000032
作为新的迭代值，再次进行迭代优化计算；Otherwise, with
Figure FDA0003199915870000032
As a new iterative value, perform the iterative optimization calculation again; 优化后权重向量中的元素即为权重系数。The elements in the weight vector after optimization are the weight coefficients.
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