CN112183808A - Optimization method and system for multi-automobile product combination research and development resource allocation - Google Patents

Optimization method and system for multi-automobile product combination research and development resource allocation Download PDF

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CN112183808A
CN112183808A CN202010625455.8A CN202010625455A CN112183808A CN 112183808 A CN112183808 A CN 112183808A CN 202010625455 A CN202010625455 A CN 202010625455A CN 112183808 A CN112183808 A CN 112183808A
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熊晓琴
成艾国
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Abstract

The invention discloses an optimization method and system for multi-automobile product combined research and development resource allocation, which are characterized in that optimization of multi-project research and development resource allocation is converted into an optimization target of 3 pieces of economic value data, technical value data and consumed resource data, a plurality of automobile products are selected for combined research and development, a multi-automobile product combination problem is further converted into an automobile product selection problem based on a technical theme, a multi-objective optimization model is constructed, a Genetic Algorithm (GA) is used for solving, an optimal automobile product research and development combination scheme is obtained for an automobile enterprise to refer to, so that the research and development resource allocation is effectively optimized, and the combination value maximization of the multi-automobile products is realized.

Description

Optimization method and system for multi-automobile product combination research and development resource allocation
Technical Field
The invention relates to the technical field of resource allocation, in particular to an optimization method and system for resource allocation in combination research and development of multiple automobile products.
Background
With the rapid development of the automobile industry, automobile and part enterprises face shorter life cycle of products (i.e. automobile parts) and more intense market competition, customers put forward higher requirements on delivery time, cost and quality of new products and projects, and enterprises need to continuously research and develop new automobile models and new products (possibly relating to each part of an automobile) so as to adapt to the continuously changing and improving requirements of the market and the customers. Therefore, a multi-project research and development mode has become a focus of attention of automobile and parts and accessories enterprises thereof. However, the operation of each development project involves the allocation of development resources, such as development costs. If the supply of research and development resources is insufficient, the situation of insufficient allocation or interference of the research and development resources inevitably occurs between the research and development projects, and particularly the allocation of the research and development resources among multiple projects is troubled, which will affect the success or failure of the projects and the benefits.
Although, at present, there are only a few studies on resource allocation in the development of automobile parts, such as "multi-project human resource allocation research for automobile parts development" (xu late, 2015, shanghai university master paper) and "multi-project management research in the development of special-purpose vehicles" (linshifu, 2011, the master paper of Nanjing university). The research on the configuration of the multi-project human resources for automobile part research aims at evaluating the importance of each project by establishing a set of project importance degree evaluation model on the premise of limited human resources, and then allocating the human resources according to the importance of the multi-project. In the multi-project management research in the special vehicle development process, the priority of projects is systematically evaluated by analyzing various causes causing resource conflict among the projects, and reasonable resource planning and resource allocation measures are provided based on the evaluation. In all of these studies, a plurality of projects are prioritized, then resource allocation is performed based on the prioritized projects, project development plans are made, and resource allocation is performed from the perspective of human resources.
On the other hand, the optimization mode in the research is adopted, so that the enterprise cannot timely cope with the change of the market and the technology: for an automobile enterprise having a plurality of research and development experiences, research and development plans of each project are usually already established in advance, and research and development resources (such as human resources, research and development equipment, research and development expenses, and the like) are also already allocated, so that only the research and development project plan needs to be continuously executed. If a predetermined research and development plan and resource allocation are adjusted, research and development personnel/research and development supervisors are needed, even a plurality of company management high-level layers carry out manual evaluation and manual analysis, but the defects of the manual evaluation and the manual analysis are obvious:
(1) the automobile parts are various, the industrial chain is complex, competitors are numerous, and it is difficult for research and development personnel and company management layers to comprehensively consider all the conditions and factors, for example, the research and development personnel usually perform analysis and evaluation from the technical point of view, and the management layers usually perform analysis and evaluation from the enterprise operation point of view;
(2) the automobile parts are various, the industrial chain is complex, competitors are numerous, the analysis process and the evaluation process are complex, if manual evaluation and manual analysis are adopted, the workload is huge, and long time is consumed.
(3) The resource allocation change of one development project affects the resources available for other product development projects, and often leads the whole body to move. Therefore, in actual work, few automobile enterprises can efficiently adjust research and development plans and reconfigure research and development resources at low risk.
However, with the speed of technical innovation increasing, the situations that the states of various product development projects change are increasing and difficult to avoid. For example, some research and development projects originally consume lower resources, so that fewer research and development resources are allocated to the research and development projects, but the obtained automobile products unexpectedly receive a very good effect in practical application or market, so that very good economic benefits (such as product sales, technical transfer, technical approval and the like) are brought to the automobile enterprises or high potential exists, and accordingly, the automobile enterprises need to adjust research and development plans to strive for investing more research and development resources into the projects. On the contrary, some research and development projects originally invest much research and development resources, but with the updating iteration of technology (for example, some mechanical parts are influenced by intelligent technology, and the requirement for optimizing and modifying the mechanical parts is reduced), the technical value of the part product in the industry may gradually decline, so that the economic benefit brought to the automobile enterprise is also reduced, and accordingly, the automobile enterprise needs to reduce the research and development resources invested in the project, even stop the research and development project. In addition, some research and development projects may not bring great economic benefits to automobile enterprises, but have great research and development effects on corresponding automobile products of competitors (for example, some parts related to standardized interfaces or platform technologies may not sell at high prices, but attract or force upstream and downstream manufacturers to deeply cooperate with themselves, so that the research and development directions of competitors lose significance and status in the industry chain), and accordingly, the automobile enterprises need to reasonably adjust research and development resources invested in the projects. Therefore, on the premise that the total research and development resources are certainly and even reduced, how to rapidly perform dynamic allocation and timely optimization on the allocation of the research and development resources of multiple projects to achieve the maximum research and development value of a multi-product combination (rather than a single product) is a technical problem to be solved urgently at present.
Based on the above, the invention provides an optimization method and system for resource allocation in multi-automobile product combined research and development, which can help enterprises to comprehensively consider various types of data and various factors, and solve the problems that in the prior art, the evaluation is time-consuming and labor-consuming depending on manual analysis of research and development personnel/management layers, the accuracy is not high, and the multiple targets of multiple product research and development projects are difficult to be comprehensively considered, so that the technical effect of enabling the enterprises to manage multi-product research and development in a combined manner, and optimizing and allocating resource allocation in time is achieved.
Disclosure of Invention
Aiming at the technical problems, the invention provides an optimization method for resource allocation in combination research and development of multiple automobile products.
In order to solve the technical problems, the invention adopts the technical scheme that:
an optimization method for resource allocation of multi-automobile product combined research and development comprises the following steps:
respectively acquiring the consumed resource data, the economic value data and the technical value data of each automobile product under each technical theme from a pre-stored automobile product classification library, and respectively constructing the consumed resource objective function F of all automobile products according to the consumed resource data, the economic value data and the technical value data of all automobile products1Economic value objective function F2And a technical value objective function F3；
According to the said consumption resource objective function F1And an economic value objective function F2And a technical value objective function F3Combining a weighting method to construct a multi-automobile product combination research and development optimization total objective function F;
and taking the selection vector of each technical theme as a gene segment, calling a genetic algorithm to solve to obtain an optimal automobile product combination research and development scheme, and then distributing research and development resources according to the optimal multi-automobile product combination research and development scheme.
Further, before the step of obtaining the consumption resource data, the economic value data and the technical value data of the automobile product, the method further comprises the following steps:
respectively acquiring economic value data and technical value data of each automobile product from the automobile product classification library;
calculating average economic value data and average technical value data of all automobile products;
judging whether the economic value data of the current automobile product is larger than the average economic value data of all automobile products or not and whether the technical value data of the automobile product is larger than the average technical value data of all automobile products or not;
and if the economic value data and the technical value data of the current automobile product are respectively greater than the average economic value data and the average technical value data, marking the current automobile product as a basic automobile product.
Further, in the process of calling the genetic algorithm to solve, the population is generated based on the constraint conditions, wherein the constraint conditions include:
constraint one:
Figure BDA0002566182220000041
constraint two:
Figure BDA0002566182220000042
wherein C is individual consumption resource data, CRAn upper limit threshold of consumed resources which can be borne by the automobile enterprise; xi,j,f'Is a selection variable of a basic automobile product, the value is 0 or 1, and Xi,j,fAnd taking the value of the selected variable of the f-th automobile product under the j technical subject under the ith technical level as 0 or 1.
Further, during the process of calling the genetic algorithm to solve, when mutation operation is performed, individuals marked as basic automobile products are selected.
Further, before performing mutation operation, the method further comprises the following steps:
checking whether the individual has only one basic automobile product selected from the same technical theme, and if so, listing the checked basic automobile product in a forbidden list;
and judging whether the randomly selected point location belongs to the forbidden list, if so, judging that the variation violates the second constraint condition, and reselecting one point location until the selected point location does not belong to the forbidden list.
Wherein the consumed resource objective functionF1Economic value objective function F2And a technical value objective function F3Respectively as follows:
Figure BDA0002566182220000051
Figure BDA0002566182220000052
Figure BDA0002566182220000053
wherein, Xi,j,fSelecting a variable of the f-th automobile product under the j technical subject in the ith technical level, wherein the value of the variable is 0 or 1;
Ci,j,fthe consumption resources required by the f-th automobile product under the j technical subject in the i technical level, and
Figure BDA0002566182220000054
wherein eta isfIs the unit technical content of the f-th automobile product; i isfDeveloping the required consumable resources for the unit technology content of the f-th automotive product; t is t1The corresponding production year for the f-th automobile product; q is annual consumed resources (namely consumed resources required by one year) required by acquiring corresponding intellectual property rights for the f-th automobile product; theta is the annual average consumed resource required by the automobile enterprise to implement the f-th automobile product; alpha is the production conversion coefficient of the f-th automobile product;
Ei,j,fgenerating economic value data for the f-th automobile product under the j technical subject in the ith technical level; and is
Figure BDA0002566182220000061
Wherein r is the f-th automotive product discount rate, T1Corresponding expected production for the f-th automotive productMaximum age limit; t is2Maintaining the maximum expected age for the intellectual property corresponding to the f-th automobile product; rt1For the f-th automobile product at t1Expected yield of year, R1Average transfer revenue, R, for the f-th automobile product that the automobile enterprise acquired in the first predetermined acquisition mode2And R3The transfer revenue, R, obtained according to the second preset obtaining mode or the third preset obtaining mode for the f-th automobile product respectively4Average license revenue, R, for the f-th automobile product that the automobile company has acquired in the fourth predetermined acquisition mode5And R6Obtaining the license revenue of the automobile product according to a fifth preset obtaining mode or a sixth preset obtaining mode;
Si,j,fthe technical value data of the f-th automobile product under the j technical subject in the ith technical level are obtained; and is
Figure BDA0002566182220000062
Wherein p isvIs a technical value data index, w, of the f-th automobile productvA weight for the technical value data indicator;
Υjis a combination coefficient of the jth technical subject, and
Figure BDA0002566182220000063
wherein the content of the first and second substances,
Figure BDA0002566182220000064
the total number of the selected automobile products under the jth technical theme is sigma, and the standard deviation of the number of the different automobile products under the jth technical theme is sigma;
wjis the weighting coefficient of the jth technical subject.
Wherein the consumed resource objective function F1Economic value objective function F2And a technical value objective function F3Respectively as follows:
Figure BDA0002566182220000065
Figure BDA0002566182220000066
Figure BDA0002566182220000071
wherein, Xi,j,fSelecting a variable of the f-th automobile product under the j technical subject in the ith technical level, wherein the value of the variable is 0 or 1;
Ci,j,fthe consumed resources required by the f-th automobile product under the j technical subject in the ith technical level; and is
Figure BDA0002566182220000072
Wherein eta isfIs the unit technical content of the f-th automobile product; i isfDevelopment cost for the unit technical content of the f-th automobile product; t is t1The corresponding production year for the f-th automobile product; q is annual consumed resources required by acquiring corresponding intellectual property rights for the f-th automobile product; theta is the annual average consumed resource required by the automobile enterprise to implement the f-th automobile product; alpha is the production conversion coefficient of the f-th automobile product;
Ei,j,fgenerating economic value data for the f-th automobile product under the j technical subject in the ith technical level; and is
Figure BDA0002566182220000073
Wherein r is the f-th automotive product discount rate, T1The maximum value of the expected production life corresponding to the f-th automobile product; t is2Maintaining the maximum expected age for the intellectual property corresponding to the f-th automobile product; rt1For the f-th automobile product at t1Expected yield of year, R1Average transfer revenue, R, for the f-th automobile product that the automobile enterprise acquired in the first predetermined acquisition mode2And R3Respectively f-th automobile productAverage transfer revenue, R, obtained according to the second preset acquisition mode or the third preset acquisition mode4Average license revenue, R, for the f-th automobile product that the automobile company has acquired in the fourth predetermined acquisition mode5And R6Respectively obtaining average license earnings of the automobile products according to a fifth preset obtaining mode and a sixth preset obtaining mode;
Si,j,fthe technical value data of the f-th automobile product under the j technical subject in the ith technical level are obtained; and Si,j,f＝Ai,j,f+Bi,j,f+Pi,j,f+Di,j,fWherein A isi,j,fCompetitive technical value data for the f-th automotive product; b isi,j,fCommercial technical value data for the f-th automotive product; pi,j,fDefensive technical value data of the f-th automobile product; di,j,fNon-essential technical value data for the f-th automotive product;
Υjis a combination coefficient of the jth technical subject, and
Figure BDA0002566182220000081
wherein the content of the first and second substances,
Figure BDA0002566182220000082
and sigma is the standard deviation of the quantities of the selected automobile products under the jth technical subject.
Further, the overall objective function F of the multi-automobile product combination research and development optimization is as follows:
F＝Min(a1F1-a2F2-a3F3+a4) Wherein a is1To index a weight for the consumed resource data, a2Is an economic value data index weight, a3Is a technical value data index weight, a4Is a constant.
Further, the overall objective function F of the multi-automobile product combination research and development optimization is as follows:
F＝Min(a1F1-a3F3-a2F2)，wherein, a1To index a weight for the consumed resource data, a2Is an economic value data index weight, a3And (4) indicating the weight for the technical value data.
Based on the optimization method for the resource allocation of the multi-automobile product combined research and development, the invention also provides an optimization system for the resource allocation of the multi-automobile product combined research and development, which comprises the following steps:
the data storage module is used for storing a plurality of automobile products obtained by classifying technical subjects in advance and pre-recorded consumption resource data, economic value data and technical value data corresponding to each automobile product;
the data acquisition module is used for respectively acquiring the consumed resource data, the economic value data and the technical value data of each automobile product under each technical theme from the data storage module;
the model construction module is used for respectively constructing a resource target function F consumed by all the automobile products according to the consumed resource data, the economic value data and the technical value data of each automobile product acquired by the data acquisition module1Economic value objective function F2And a technical value objective function F3Combining a weighting method to construct a multi-automobile product combination research and development optimization total objective function F;
and the resource optimization module is used for taking the selection vector of each technical theme as a gene segment, calling a genetic algorithm to solve to obtain an optimal multi-automobile product combination research and development scheme, and then distributing research and development resources according to the optimal multi-automobile product combination research and development scheme.
Further, the optimization system further comprises:
the basic product marking module is used for respectively acquiring the economic value data and the technical value data of each automobile product in advance, calculating the average economic value data and the average technical value data of all the automobile products, judging whether the economic value data of each automobile product is larger than the average economic value data or not and whether the technical value data of each automobile product is larger than the average technical value data or not, and if the economic value data of each automobile product is larger than the average economic value data and the technical value data of each automobile product is larger than the average technical value data, marking the automobile products as basic automobile products.
The resource optimization module calls a genetic algorithm to solve, and a population is generated based on constraint conditions, wherein the constraint conditions comprise:
constraint one:
Figure BDA0002566182220000091
constraint two:
Figure BDA0002566182220000092
wherein C is individual consumption resource data, CRAn upper limit threshold of consumed resources which can be borne by the automobile enterprise; xi,j,f'Is a selection variable of a basic automobile product, the value is 0 or 1, and Xi,j,fAnd taking the value of the selected variable of the f-th automobile product under the j technical subject under the ith technical level as 0 or 1.
Further, in the process of calling the genetic algorithm to solve, the resource optimization module selects individuals marked as basic automobile products when performing mutation operation.
Further, in the process of solving by calling a genetic algorithm, before mutation operation, the resource optimization module firstly checks whether an individual has only one basic automobile product selected from the same technical subject, and if so, lists the checked basic automobile products in a forbidden list; and then, judging whether the randomly selected point location belongs to the forbidden list, if so, judging that the variation violates the second constraint condition, and reselecting one point location until the selected point location does not belong to the forbidden list.
The invention has the advantages that:
the optimization method provided by the invention has the advantages that the optimization of multi-project research and development resource allocation is converted into the optimization target of 3 economic value data, technical value data and consumption resource data, a plurality of automobile products are selected for combined research and development, the multi-automobile product combined research and development problem is further converted into the automobile product selection problem based on the technical theme, a multi-target optimization function based on the automobile technical theme is constructed, the solution is carried out by utilizing a Genetic Algorithm (GA), the optimal automobile product combined research and development scheme is obtained for the automobile enterprises to refer to, the research and development resource allocation is effectively optimized, and the value maximization of the automobile product combined research and development is realized. Automobile enterprises only need to use the optimal solution obtained by the model operation to guide research and development activities (such as allocation of research and development resources and combination of technical schemes of all projects) of the automobile enterprises, research and development personnel and research and development managers do not need to be relied on to analyze economic value data, technical value data and consumption resource data manually, and the limitation of manual analysis is broken through a genetic algorithm (for example, all combination possibilities cannot be analyzed manually and comprehensively), so that the problems that time and labor are consumed and evaluated depending on manual analysis of the research and development personnel/management layers, the accuracy is not high, and multiple targets of multiple product research and development projects are difficult to consider comprehensively in the prior art are solved, and the technical effects that the enterprises can manage multiple product research and development in a combined mode, and resource allocation is optimized and allocated timely are achieved.
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FIG. 1 is a schematic illustration of a technical hierarchy division in the automotive industry;
FIG. 2 is a flowchart illustrating an embodiment of a method for optimizing the allocation of research resources for a combination of multiple automotive products according to the present invention;
FIG. 3 is a flowchart of an embodiment of step S33 in FIG. 2;
FIGS. 4a and 4b are schematic diagrams of the classification of technical subject matter reflecting the existing 74 automobile products of an automobile enterprise;
FIG. 5 is a schematic representation of the crossover operation performed in reaction FIG. 3;
FIG. 6 is a distribution diagram of the selection ratio of each of 74 automobile products in an automobile enterprise calculated by a genetic algorithm;
fig. 7 is a functional module diagram of an embodiment of an optimization system for multi-vehicle product combination research and development resource allocation according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The term is defined as:
automotive products: in general, most automobile enterprises do not need to develop all parts related to the automobile, but only develop some parts of the parts, such as a brake system and a steering system in chassis technology, and propose corresponding design schemes. Therefore, the automobile product herein actually refers to a design scheme developed by an automobile enterprise in a research project, for example, a plurality of design schemes may be provided for a certain component, and accordingly, a plurality of automobile products are naturally obtained.
Consumption of resource data: the consumption resource data herein includes development resources (e.g., development costs) invested for the development of the automobile product, and conversion resources (e.g., implementation costs) consumed for converting the automobile product into a physical product, and even acquisition resources consumed for acquiring intangible assets such as intellectual property (e.g., patents, soft works, copyrights, etc.) for protecting the automobile product, such as application fees, annual fees, and payroll of corresponding staff, etc. Typically, the consumable resource data for each automotive product is stored in a financial system within the enterprise. Herein, the consumption resource data of the automobile product is denoted by C, and C is used respectivelyr、Cp、ClRepresenting resource development, resource acquisition and resource conversion, the consumption resource data C of each automobile product is Cr+Cp+Cl。
Economic value data: economic value data in this context refers to expected revenue, i.e., revenue that the automobile product can bring to the automobile enterprise, including conversion revenue (i.e., self-implementation revenue) from implementation to entity product, transfer (e.g., transfer receipts such as technology transfer or intellectual property transfer, etc.)Benefits) or licensing (e.g., licensing revenue such as patent licensing). Herein, E is used to represent economic value data, and E is used to represent economic value datai、Et、ElRepresenting the self-implementation income, the transfer income and the license income, and the economic value data E of each automobile product is Ei+Et+El。
Technical value data: the technical value data in this context refers to the advancement/influence, irreplaceability (e.g., related to core components), etc. of the automobile product in the automobile industry, which enables enterprises to utilize the automobile product under the driving of strategic motivations to bring benefits, such as constructing technical barriers, hindering the development of opponents, increasing negotiation chips, attracting exotic investments, etc., denoted as S, and
Figure BDA0002566182220000121
wherein p isvTechnical value data indexes for each automobile product, including technical attack ability (competitive) A, technical defense ability (defensive) P and technical influence, wvA weight for each technical value data indicator for the automotive product. However, if the technical influence is subdivided, it includes the commercial property of the automobile product, i.e. the automobile product can not only support the technical strategic positioning of the enterprise product, but also is very important for the business of the enterprise at the product level, and the non-necessity, i.e. the automobile product, although it cannot protect the competitive position of the enterprise to other enterprises, and it is not important at the technical level, and will not increase the value of the related products of the enterprise, it keeps a certain importance in the aspect of enterprise image. Therefore, the technical value data of each automobile product can also be expressed as S ═ a + B + P + D, where B is commercial technical value data and D is non-essential technical value data. Generally, the economic value data and the technical value data are generally obtained by comprehensive evaluation of personnel in intellectual property rights, research and development departments and market departments in the enterprises and stored in the internal systems of the enterprises.
Basic automotive products: the basic automobile product in the text actually refers to a core design scheme developed by the automobile enterprise or a main design scheme publicized externally, and the basic automobile product has higher economic value data and technical value data.
The technical level is as follows: the technical hierarchy in this document refers to the technical hierarchy to which the automobile is technically decomposed according to the technical composition of the automobile to obtain the parts in the whole automobile, as shown in fig. 1, all automobile products related to automobile enterprises are classified into different technical subjects according to the principle and structure of the automobile products, and the automobile is firstly divided into three technical hierarchies: the first technical level is the category of automobile products, such as traditional automobiles or new energy automobiles; the second technical level is an automobile technical module, such as a chassis, a power battery and the like; the third level of technology is subdivided technical themes such as suspension devices, battery materials and the like, and the design scheme corresponding to each technical theme is an automobile product. For example, an enterprise mainly develops an automobile chassis technology, and there are 74 existing design schemes related to the chassis technology, so that classification is performed according to an industry technology level division principle, and the 74 existing design schemes of the enterprise belong to 12 technical subjects, such as a braking system, a driving system, a steering system, and the like, respectively, see fig. 4, and each technical subject corresponds to multiple design schemes, wherein each technical subject corresponds to multiple automobile products, and therefore, the enterprise has 74 automobile products. Of course, when different automobile enterprises research and develop the combination of automobile parts, the automobile parts can be hierarchically divided according to the existing automobile products, and then different automobile products are divided into different technical subjects according to the technical characteristics of the automobile products.
The core idea of the invention is as follows: as is well known, in the process of optimizing resource allocation for research and development of multiple projects, not only the cost consumed by the research and development project needs to be considered, but also the economic benefit brought to the enterprise by the automobile product (i.e., the design scheme of a certain part of an automobile) corresponding to the research and development project and the technical value data of the research and development project in the industry need to be considered, for the automobile enterprise, the higher the technical value data and the economic benefit is, the better the resource consumption data is, that is, the optimization of resource allocation for research and development of multiple projects can be actually understood as the economic value data, the technical value data and the resource consumption data 3And selecting a plurality of automobile products for combined research and development as an optimization target so as to realize the maximization of the combined value of the automobile products. In order to enable managers and decision makers of automobile enterprises to synthesize various aspects of data, information and intelligence, and rapidly, dynamically and comprehensively allocate limited research and development resources of the managers and decision makers so as to optimize the research and development of product combinations of the managers and decision makers, the invention provides a technical scheme based on a genetic algorithm, solves many limitations of manual analysis and evaluation (for example, because of more automobile parts, the possibility of product combination is high, and manual work cannot be considered comprehensively), and helps the automobile enterprises to change from a passive mode of 'research and development for research and development, research and development before combination, and research and development plan not changing up' into an active mode of 'purposeful planning, and real-time adjustment and optimization', and has important significance for improving the utilization efficiency of automobile products and the overall quality and value of the automobile product combinations. Specifically, technical subjects of automobile products corresponding to existing research and development projects of an automobile enterprise are classified in advance by technicians or experts in the enterprise based on technical levels in the automobile industry, a design scheme (namely an automobile product) set corresponding to each technical subject is obtained and stored; meanwhile, inputting the consumed resource data, the economic value data and the technical value data of each automobile product in advance according to data obtained by actual research; therefore, the consumed resource objective function F corresponding to all automobile products can be respectively constructed according to the consumed resource data, the economic value data and the technical value data of each automobile product1Economic value objective function F2And a technical value objective function F3Then, a multi-automobile product combination research and development resource allocation optimization total objective function F is constructed by combining a weighting method, and then, an optimal automobile product combination research and development scheme, namely an optimal research and development resource allocation scheme, is obtained by combining a genetic algorithm for solving. The present invention will be described in detail with reference to the following embodiments and the accompanying drawings.
Example one
Referring to fig. 2, a flowchart of an embodiment of an optimization method for developing resource allocation for automobile product combinations based on genetic algorithm according to the present invention is shown, specifically, the optimization method of the embodiment includes the steps of:
and S31, respectively acquiring the consumed resource data, the economic value data and the technical value data of each automobile product under each technical theme from the pre-stored automobile product classification library.
In this embodiment, the automobile product classification library is a library in which automobile products related to an automobile enterprise are classified into different technical subject levels for classification and storage by enterprise technicians (or experts in the automobile field) according to the principle and structure of an automobile in advance; meanwhile, the economic value data, the technical value data, the consumed resource data and the like of each automobile product which is researched by the enterprise and is obtained by investigation and calculation in advance are recorded. Namely, each automobile product in the automobile product classification library is associated or marked with corresponding economic value data, technical value data, consumed resource data and the like.
The technical theme is the last technical level obtained after the automobile is divided into a plurality of technical levels by adopting a technical decomposition mode adopted by the automobile industry, so that after automobile products corresponding to a plurality of existing research and development projects are classified by technical themes, a design scheme (namely an automobile product) set under each technical theme is obtained. As shown in fig. 4, in an embodiment, the automobile is divided into three technical levels (of course, when different automobile enterprises combine automobile products, the automobile technologies can be divided into three technical levels according to the existing automobile products, and the different automobile products can be divided into different technical topics according to the technical features of the automobile products themselves), where the third technical level is a different technical topic, and then the existing 74 research and development projects of a certain enterprise are subject-classified to obtain the automobile products of the enterprise, which relate to the 12 lowest technical levels of the chassis technology, i.e. 12 technical topics, and each of the technical topics corresponds to a different number of automobile products (i.e. design solutions).
As is well known, the automobile product with higher economic value data and technical value data is the basic automobile product, and the proportion of the basic automobile product usually accounts for 20-30% of the total number of the automobile products of the automobile enterprise, which is very important, therefore, the basic automobile product needs to be continuously developed, i.e. in a multi-automobile product combination development scheme, the basic automobile product needs to be selected, and therefore, before or after the classification storage, the screening and marking of the basic automobile product needs to be performed, specifically, the method includes the following steps:
respectively acquiring economic value data and technical value data of each automobile product which are classified and stored in advance;
calculating average economic value data and average technical value data of all automobile products;
judging whether the economic value data of the current automobile product is larger than the average economic value data of all automobile products or not, judging whether the technical value data of the current automobile product is larger than the average technical value data of all automobile products or not, and if the economic value data of the current automobile product is larger than the average economic value data and the technical value data of the current automobile product is also larger than the average technical value data, marking the current automobile product as a basic automobile product.
In one embodiment, if Y isi,j,fThe method is characterized in that the method is an f-th automobile product under the jth technical subject under the ith technical level; y isi,j,f'Is an automobile product Yi,j,fThe economic value data of (a) is,
Figure RE-GDA0002597628330000151
is the average economic value data of all automobile products of an automobile enterprise, Si,j,f'Is an automobile product Yi,j,fThe technical value data of (a) is,
Figure RE-GDA0002597628330000152
for the average technical value data of all automobile products of an automobile enterprise, the automobile product is marked as a basic automobile product only when the following conditions are met, namely:
Figure BDA0002566182220000153
s32, according toRespectively constructing a consumed resource objective function F of all automobile products by using the consumed resource data, the economic value data and the technical value data of each automobile product1Economic value objective function F generated by automobile products2And a technical value objective function F of the automotive product3(ii) a And a multi-automobile product combination research and development optimization target model F is constructed by combining a weighting method.
In this embodiment, the resource consumption objective function F of all the automobile products1Comprises the following steps:
Figure BDA0002566182220000161
wherein, Xi,j,fSelecting variables of the f-th automobile product in the k automobile products under the ith technical level in the m technical levels and the jth technical theme in the n technical themes, wherein the values of the selecting variables are 0 or 1;
Ci,j,fthe consumed resources required by the f-th automobile product in the k-th automobile products under the j technical subject in the ith technical level; and is
Figure RE-GDA0002597628330000162
Wherein eta isfIs the unit technical content of the f-th automobile product; i isfDeveloping the required consumable resources for the unit technology content of the f-th automotive product; t is t1The corresponding production year for the f-th automobile product; q is annual consumed resources required by acquiring corresponding intellectual property rights for the f-th automobile product; theta is the annual average consumed resource required by the automobile enterprise to implement the f-th automobile product; alpha is the production conversion coefficient (usually constant) of the f-th automobile product, reflects the difference degree between the production mode applicable to the innovative technology and the existing production elements and production structures of enterprises, and the larger alpha indicates that the difficulty is higher when the automobile product is applied to actual production, the higher the self-implementation cost is, the smaller alpha indicates that the difficulty is lower when the automobile product is applied to actual production, and the higher the self-implementation cost is; that is, the value of α is (0-1), and the more the value approaches 1, the more difficult the implementation becomes, and the more the value approaches 0, the more difficult the implementation becomesIs small.
In this embodiment, the economic value objective function F of the automobile product2Comprises the following steps:
Figure BDA0002566182220000163
wherein, γjThe greater the number of automobile products under the same technical theme, the more beneficial the automobile enterprise to form scale effect and consolidate the competitive position in the industry chain, so the overall profit is greater than the direct summation of the automobile products, therefore, in the embodiment, the combination coefficient gamma is usedjTo represent the economic value data amplification of the jth technical subject, the calculation method is as follows:
Figure BDA0002566182220000171
wherein the content of the first and second substances,
Figure BDA0002566182220000172
for the total number of the selected automobile products under the jth technical subject, sigma is the standard deviation of the number of the selected different automobile products under the jth technical subject, and the larger sigma is, the greater the combination coefficient gamma isjThe flatter the curve as a function of the number of automotive products, whereas the smaller sigma, the thinner and taller the curve.
Wherein, the economic value data E of the f th automobile product under the jth technical subject under the ith technical leveli,j,fFor the sum of the self-implementation income, the transfer income and the license income of the automobile product, i.e.
Figure BDA0002566182220000173
Wherein r is the f-th automotive product discount rate, T1The maximum value of the corresponding expected production life of the f-th automobile product; t is2Maintaining the maximum expected age for the intellectual property corresponding to the f-th automobile product; rt1For the f-th automobile product at t1Expected yield of year, R1Average transfer of f-th automobile product acquired by automobile enterprise in first preset acquisition modeProfit, R2And R3The transfer revenue, R, obtained according to the second preset obtaining mode or the third preset obtaining mode for the f-th automobile product respectively4Average license revenue, R, for the f-th automobile product that the automobile company has acquired in the fourth predetermined acquisition mode5And R6And obtaining the license revenue of the automobile product according to a fifth preset obtaining mode or a sixth preset obtaining mode respectively.
In a specific embodiment, the first preset acquisition mode is to collect the corresponding transfer income at one time in the form of a transfer contract; the second preset acquisition mode is to fixedly collect a certain transfer income according to the appointed number of the periods and the time; the third preset acquisition mode is to change the acquired transfer income according to the mode of the profit improvement of the other party; the fourth preset acquisition mode is to collect corresponding license income in one time in the form of a license contract; the fifth preset acquisition mode is to fixedly receive a certain permission gain according to the appointed number of the periods and the time; the third preset acquisition mode is to change the received license revenue according to the mode of the profit improvement of the other party.
In this embodiment, the technical value objective function F of all automobile products2Comprises the following steps:
Figure BDA0002566182220000181
wherein, wjThe weighting coefficient of the technical value data of the jth technical subject usually shows different preferences for different technical subjects, for example, some technical subjects have a great effect on hindering the patent of competitors although the economic value data is not outstanding, and therefore, in this embodiment, the weighting coefficient w is usedjTo weight the technical value data of different technical subjects.
Wherein S isi,j,fIs the technical value data of the f-th automobile product under the j technical subject of the ith technical level, and
Figure BDA0002566182220000182
pvis as followsf technical value data indexes of automobile products, including technical attack ability, technical defense ability and technical influence, wvAnd the weight of the technical value data index of the f-th automobile product.
In this embodiment, according to the actual situation, an enterprise may design a value mode of technical attack ability, defense ability and influence ability (see the following table, "technical value data value example"). In one embodiment, the technical attack capability of a product can be evaluated by investigating and calculating the number of effective bids for the product on the market: if the automobile product (such as an automobile carbon brush) only has few competitive products in the market, the demand of manufacturers at the upstream and downstream of the industry chain and even competitors for purchasing the product is high, and if the product is not available, the competitors are difficult to develop and grow in the automobile industry, and the product has strong aggressivity to the competitors. Conversely, if the number of competitive products in the market is large, the attacking force generated by the enterprise developing the product is small.
In some embodiments, the technical defense of a product can be obtained by investigating and calculating the probability of the product becoming a competitive product substitute (e.g., can 100% substitute: if a competitor monopolizes a core component and rejects the supply, but the enterprise can replace the monopolized core component by developing the product, the product has a higher defense. Conversely, if the product is unable to replace the monopolized/locked core components well, the product has a low technical defense.
In some embodiments, the technical impact of a product can be evaluated by investigating and calculating the number of other component products that are connected to, and matched with, the product: if many parts of the vehicle are connected to the component product, or if the function and operation of the component product affects many other component products, the technical impact of the product is high. In some embodiments, the technical impact of a product may also be assessed by the technical field. For example, as a future development trend, the intelligent networking technology will affect the development ecology of the entire automobile industry, impact the old technology, and derive a new technology hotspot, so that a product having a closer relationship with the intelligent networking is considered to have a greater influence. Conversely, if the technology is in an older, gradually obsolete technology domain, the influence is considered to be small. The automotive enterprises may be ranked by impact according to the classification criteria in the automotive industry report and given a corresponding score.
Table one technical value data value example
Figure BDA0002566182220000191
In this embodiment, for an automobile enterprise, it is always desirable that the higher the economic value data and the strategic value is, the better the economic value data and the strategic value are, and the lower the cost is, the better the cost is, therefore, after a resource consumption objective function, an economic value objective function, and a technical value objective function are respectively constructed, three optimization objectives are further processed by a weighting method, and are converted into a single-objective optimization problem, then the final optimization objective of the multi-automobile product combination research and development in this embodiment is:
F＝Min(a1F1-a2F2-a3F3+a4)，
wherein, a1To index a weight for the consumed resource data, a2Is an economic value data index weight, a3Is a technical value data index weight, a4Is a constant.
And S33, taking the selection vector of each technical theme as a gene segment, and calling a genetic algorithm to solve to obtain an optimal automobile product combination development scheme.
In one embodiment, referring to fig. 3, the step S33 of calling the genetic algorithm to solve includes:
and S331, generating an initial population P based on the constraint conditions.
Generally, enterprises develop the most constrained research and development resources, and therefore, consider the current technological update rates of the automotive industryIn addition, in combination with the actual situation of the operation of the automobile enterprise, a certain cost, i.e., a constraint condition of consumed resources (or research and development resources), needs to be set to ensure that the number of the automobile product combinations selected and developed by the automobile enterprise does not exceed the range that the enterprise can bear, i.e., the sum of the consumed resources required by the automobile products selected and developed by the automobile enterprise cannot exceed the upper limit C of the consumed resources that the enterprise can bearRNamely, the constraint condition one is:
Figure BDA0002566182220000201
on the other hand, most technical themes have corresponding core technologies, namely basic automobile products, and under the same technical theme, at least one basic automobile product is included; if there is no basic automobile product under the technical theme, the automobile product under the technical theme can be arbitrarily selected and combined, so that the second constraint condition in this embodiment is:
Figure BDA0002566182220000202
wherein, Xi,j,f'Means that the basic automobile product Y is marked under the jth technical subject in the ith technical leveli,j,f'The selection variable of the f-th automobile product is 0 or 1, and only satisfies
Figure BDA0002566182220000203
Product Y ofi,j,fWill be marked as a basic automotive product Yi,j,f'Wherein, in the step (A),
Figure BDA0002566182220000204
the average economic value data of k automobile products under the jth technical subject in the ith technical level, namely, only the automobile products of which the economic value data is larger than the average economic value data of all the automobile products under the same technical subject can be regarded as basic automobile products.
S332, individual selection is carried out according to a roulette wheel method and an elite strategy, and then crossing and mutation are carried out to obtain a new population.
In a specific embodiment, all individuals are combined into a circular wheel disc according to the size of the fitness, the circular wheel disc with the larger fitness occupies a larger sector area, then the wheel disc is rotated for Z times (Z ═ X-Q, Q is the number of the reserved elite individuals) to perform individual selection, and Z individuals are selected, specifically, 95% of the total population is selected to perform the crossover operation, see fig. 5.
Because the initial population is generated based on the constraint conditions, the gene segments corresponding to the technical subject of each individual are in accordance with the constraint conditions, and therefore, no individual violating the constraint conditions is generated even if the crossing operation is performed between any two parents, but if the crossing operation is performed on the values between the individuals, no new gene segment is generated, so that the calculation result is early-maturing and converging, in the embodiment, a new gene segment is introduced through the mutation operation, so as to ensure the diversity of the population, namely, the new gene segment is introduced through randomly selecting a point location in the individual to perform the mutation, specifically, the point location is 0, otherwise, the point location is 0 if the point location is originally 1, but if the randomly selected point location is the only one basic automobile product in the technical subject, and the point location which is changed from 1 to 0 originally generates an individual which is not in accordance with the constraint conditions, therefore, before the mutation operator is executed (i.e. before performing mutation operation), the method further comprises the following steps:
checking whether the individual has a certain technical theme and only selects one basic automobile product, and if so, listing the checked basic automobile product into a preset forbidden list Bj＝[B1,B2,B3,...Bl]；
Judging whether the randomly selected point location belongs to a forbidden list BjIf yes, judging that the variation violates the constraint condition, reselecting a point location, and judging whether the point location belongs to the forbidden list B againjIf yes, reselecting a point location again until the selected point location does not belong to the forbidden list BjUntil now.
In one embodiment, the crossover rate and the variance rate are 0.8 and 0.3, respectively.
In another embodiment, when performing the mutation operation, the individuals marked as basic automobile products are selected, and the mutation operation still adopts the mutation operation.
S333, judging whether the new population meets the constraint condition, if so, executing the step S334, otherwise, executing the step S335.
In this embodiment, after a new population is generated, it is further necessary to determine whether resource data consumed by an automobile product combination scheme corresponding to the new population is lower than an upper cost limit that can be borne by an enterprise, and for a technical theme of a basic automobile product, whether at least one of the basic automobile products is selected, if both of the basic automobile products and the basic automobile products are satisfied, it is determined that the new population satisfies a constraint condition, otherwise, it is determined that the new population does not satisfy the constraint condition, and a new population satisfying the constraint condition needs to be obtained through a mutation operation again.
S334, the elite individuals of the parent are inserted to generate a new population, and step S336 is executed.
In this embodiment, combining the top 5% of the parent population (i.e., the part of the parent population that is not subject to the crossover and mutation operations and that is reserved in the elite strategy) with the child population that is subject to the crossover and mutation operations results in a new population, and since the top individuals of the parent population (i.e., 5% that is reserved based on the elite strategy) are reserved, even if there are no better individuals than the parent population in the child population that is subject to the crossover and mutation operations, no good individuals are lost. Therefore, as the number of iterations increases, only the best-shown individuals in the new population will get better and better.
S335, another point is selected for mutation, and step S333 is executed.
In this embodiment, before selecting a point location for mutation, it is also necessary to determine whether the point location belongs to the forbidden list, as before performing mutation, and if so, reselect the point location for mutation.
And S336, judging whether the current individual meets the termination condition, if so, executing the step S337, and if not, returning to the step S332 to perform the next iteration processing based on the child population generated by the individual.
In this embodiment, the termination condition refers to the number of iterations of the genetic algorithm, and in one embodiment, the number of iterations is 500.
S337, decoding to obtain an optimal solution, and optimizing research and development resources according to the optimal solution.
Referring to fig. 6, in a specific embodiment, in the optimal solution, the ratio of the selected basic car products is 100%, and then the car products with high ratio are selected, which can be regarded as cost-effective car products, and then the specific steps of optimizing the development resource allocation are as follows: under the constraint condition that the cost does not exceed the upper limit cost of an enterprise, selecting from high to low according to the selected ratio to obtain an optimal automobile product combination scheme, and reasonably adjusting research and development resources according to the selected automobile product, for example, the research and development resources of the automobile product which is not selected are not put into the research and development resources, but the research and development resources are put into the research and development of the selected automobile product.
Example two
The invention also provides another optimization method for multi-automobile product combination research and development resource allocation, which comprises the steps in the first embodiment, except that the technical value objective function F constructed in the step S32 in the first embodiment3Comprises the following steps:
Figure BDA0002566182220000231
wherein S isi,j,f＝Ai,j,f+Bi,j,f+Pi,j,f+Di,j,f，Ai,j,fThe method plays a role in competitive technical value data of the automobile product, namely the automobile product plays a role in positioning leading technical strategy of company business and competitive position of enterprises in the technical field; b isi,j,fThe method is very important for business technical value data, namely technical strategic positioning of the automobile product supporting enterprise products, and the business of the enterprise at the product level; pi,j,fFor defensive technical value data, i.e. theAutomotive products (corresponding intellectual property rights) can be used to limit, rule out competitor's solutions or constitute a further obstacle and difficulty for possible new creations, although it is less important to the business and technical strategic positions of the enterprise; di,j,fThe method is unnecessary technical value data and has certain importance in the aspect of maintaining and improving enterprise images.
Accordingly, the resource consumption objective function F based on the automobile product in the embodiment1Economic value objective function F2And a technical value objective function F3The established multi-automobile product combination research and development optimization total objective function F is as follows:
F＝Min(a1F1-a3F3-a2F2)。
EXAMPLE III
Corresponding to the optimization method for the multi-automobile product combined research and development resource allocation, the invention also provides an optimization system for the multi-automobile product combined research and development resource allocation, and the detailed description is given below by combining specific embodiments and accompanying drawings.
Referring to fig. 7, a functional block diagram of an embodiment of an optimization system for developing resource allocation for multiple automobile product combinations according to the present invention is shown, specifically, the optimization system of the embodiment includes:
the storage module 10 is used for storing the economic value data and the technical value data of each automobile product which are recorded in advance; wherein each automobile product is subject to technical subject classification by experts in advance;
the data acquisition module 11 is used for respectively acquiring the consumed resource data, the economic value data and the technical value data of each automobile product;
a model building module 12, configured to respectively build a resource consumption objective function F of all the automobile products according to the resource consumption data, the economic value data, and the technical value data of each automobile product obtained by the data obtaining module 111Economic value objective function F2And a technical value objective function F3(ii) a A weighting method is combined to construct a multi-automobile product combination research and development optimization total objective function F; in particular toThe specific formulas and construction modes of the resource consumption objective function, the economic value objective function and the technical value objective function are the same as those in the above embodiment, and are not described again here;
and the resource optimization module 13 is used for regarding the selection vector of each technical theme as a gene segment and calling a genetic algorithm to solve to obtain an optimal automobile product combination research and development scheme.
Further, the optimization system of the present embodiment may further include: and the basic product marking module 14 is configured to obtain the economic value data and the technical value data of each automobile product, calculate average economic value data and average technical value data of all the automobile products, determine whether the respective economic value data of each automobile product is greater than the average economic value data, and determine whether the technical value data of each automobile product is greater than the average technical value data, and mark the current automobile product as a basic automobile product if it is determined that the economic value data of the current automobile product is greater than the average economic value data, and the technical value data of the current automobile product is greater than the average technical value data. Of course, screening and labeling of basic automotive products can also be performed by experts.
In a specific embodiment, in the process of calling the genetic algorithm to solve, the resource optimization module generates the population based on constraint conditions, where the constraint conditions include:
constraint one:
Figure BDA0002566182220000241
constraint two:
Figure BDA0002566182220000251
wherein C is individual consumption resource data, CRThe cost upper limit threshold value which can be borne by the automobile enterprise; xi,j,f'Is a selection variable of a basic automobile product, the value is 0 or 1, and Xi,j,fAnd taking the value of the selection variable of the f-th automobile product under the j technical subject in the ith technical level as 0 or 1.
Further, in the process of calling the genetic algorithm to solve, the resource optimization module 13 selects individuals including the marked basic automobile products when performing mutation operations.
Furthermore, in the process of calling the genetic algorithm to solve, before performing mutation operation, the resource optimization module 13 first checks whether the individuals have only one basic automobile product selected from the same technical theme, and if so, lists the checked basic automobile products in a preset forbidden list; and then, judging whether the randomly selected point location belongs to the forbidden list, if so, judging that the variation violates the second constraint condition, and reselecting one point location until the selected point location does not belong to the forbidden list.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (10)

1. An optimization system for resource allocation in combination research and development of multiple automobile products, comprising:
the data storage module is used for storing a plurality of automobile products obtained by classifying technical subjects in advance and pre-recorded consumption resource data, economic value data and technical value data corresponding to each automobile product;
the data acquisition module is used for respectively acquiring the consumed resource data, the economic value data and the technical value data of each automobile product under each technical theme from the data storage module;
the model construction module is used for respectively constructing a resource target function F consumed by all the automobile products according to the consumed resource data, the economic value data and the technical value data of each automobile product acquired by the data acquisition module1Economic value objective function F2And a technical value objective function F3Combining a weighting method to construct a multi-automobile product combination research and development optimization total objective function F;
and the resource optimization module is used for taking the selection vector of each technical theme as a gene segment, calling a genetic algorithm to solve to obtain an optimal multi-automobile product combination research and development scheme, and then distributing research and development resources according to the optimal multi-automobile product combination research and development scheme.
2. The optimization system of claim 1, further comprising:
the basic product marking module is used for respectively acquiring the economic value data and the technical value data of each automobile product in advance, calculating the average economic value data and the average technical value data of all the automobile products, judging whether the economic value data of each automobile product is larger than the average economic value data or not and whether the technical value data of each automobile product is larger than the average technical value data or not, and if the economic value data of each automobile product is larger than the average economic value data and the technical value data of each automobile product is larger than the average technical value data, marking the automobile products as basic automobile products.
3. The optimization system of claim 2, wherein the resource optimization module, in invoking the genetic algorithm to solve, generates the population based on constraints, the constraints comprising:
constraint one:
Figure FDA0002566182210000011
constraint two:
Figure FDA0002566182210000021
wherein C is individual consumption resource data, CRAn upper limit threshold of consumed resources which can be borne by the automobile enterprise; xi,j,f'Is a selection variable of a basic automobile product, the value is 0 or 1, and Xi,j,fFor the f-th automobile under the j technical subject under the ith technical levelThe selection variable of the product is 0 or 1.
4. The optimization system of claim 3, wherein the resource optimization module selects individuals comprising the identified basic automotive product when performing mutation operations during the process of invoking the genetic algorithm to solve.
5. The optimization system of claim 4, wherein during the process of the resource optimization module invoking the genetic algorithm to perform the solution, before performing the mutation operation, it is first checked whether only one basic automobile product is selected from the same technical subject in the individual, and if so, the checked basic automobile product is listed in a preset forbidden list; and then, judging whether the randomly selected point location belongs to the forbidden list, if so, judging that the variation violates the second constraint condition, and reselecting one point location until the selected point location does not belong to the forbidden list.
6. A method for optimizing the resource allocation of multi-automobile product combined research and development is characterized by comprising the following steps:
respectively acquiring the consumed resource data, the economic value data and the technical value data of each automobile product under each technical theme from a pre-stored automobile product classification library, and respectively constructing the consumed resource data objective function F of all automobile products according to the consumed resource data, the economic value data and the technical value data of all automobile products1Economic value objective function F2And a technical value objective function F3；
According to the said consumption resource data objective function F1And an economic value objective function F2And a technical value objective function F3Combining a weighting method to construct a multi-automobile product combination research and development optimization total objective function F;
and taking the selection vector of each technical theme as a gene segment, calling a genetic algorithm to solve to obtain an optimal multi-automobile product combined research and development scheme, and then distributing research and development resources according to the optimal multi-automobile product combined research and development scheme.
7. The optimization method according to claim 6, wherein the step of obtaining the consumed resource data, the economic value data and the technical value data of the automobile product is preceded by the step of:
respectively acquiring economic value data and technical value data of each automobile product from the automobile product classification library;
calculating average economic value data and average technical value data of all automobile products;
judging whether the economic value data of the current automobile product is larger than the average economic value data of all automobile products or not and whether the technical value data of the automobile product is larger than the average technical value data of all automobile products or not;
and if the economic value data and the technical value data of the current automobile product are respectively greater than the average economic value data and the average technical value data, marking the current automobile product as a basic automobile product.
8. The optimization method of claim 7, wherein invoking the genetic algorithm to solve is based on constraints to generate the population, the constraints comprising:
constraint one:
Figure FDA0002566182210000031
constraint two:
Figure FDA0002566182210000032
wherein C is individual consumption resource data, CRAn upper limit threshold of consumed resources which can be borne by the automobile enterprise; xi,j,f'Is a selection variable of a basic automobile product, the value is 0 or 1, and Xi,j,fSelecting variables for the f-th automobile product under the j technical subject under the ith technical levelIs 0 or 1.
9. The optimization method of claim 8, wherein in invoking the genetic algorithm to solve, when performing the mutation operation, selecting individuals comprising the identified basic automotive product.
10. The optimization method of claim 9, further comprising, before performing the mutation operation, the steps of:
checking whether the individual has only one basic automobile product selected from the same technical theme, and if so, listing the checked basic automobile product in a preset forbidden list;
and judging whether the randomly selected point location belongs to the forbidden list, if so, judging that the variation violates the second constraint condition, and reselecting one point location until the selected point location does not belong to the forbidden list.
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