CN115983443A - Method and equipment for optimizing storage of new energy automobile - Google Patents
Method and equipment for optimizing storage of new energy automobile Download PDFInfo
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Abstract
The invention aims to provide a method and equipment for optimizing storage of a new energy automobile, which are used for obtaining a storage decision scheme with the optimal total storage cost of each accessory by inputting optimized object output and optimized constraint output into a storage optimization model for calculation based on an optimization target and a convergence limiting condition of the storage optimization model, driving storage optimization based on the optimized object output and the optimized constraint output, and subsequently adjusting a production decision by using the storage decision scheme to realize the optimal total storage cost of each component under the condition of ensuring a production target. The efficiency of accessory storage optimization of new energy automobile is promoted.
Description
Technical Field
The invention relates to a method and equipment for optimizing storage of a new energy automobile.
Background
The new energy automobile manufacturing industry is different from the traditional automobile industry in production process and process, and the storage optimization problem of core management is an important component of the operation cost of enterprises at present.
Disclosure of Invention
The invention aims to provide a method and equipment for optimizing storage of a new energy automobile.
According to one aspect of the invention, a method for optimizing storage of a new energy automobile is provided, and the method comprises the following steps:
the method comprises the steps of obtaining production information of the new energy automobile and warehousing information of accessories of the new energy automobile, and optimizing an object model based on the production information and the warehousing information; obtaining an optimized object output based on the optimized object model, wherein the optimized object output includes: preliminarily determining a purchasing time period for purchasing accessories of the new energy automobile;
acquiring supplier information of accessories of the new energy automobile and logistics environment information influencing the accessories of the new energy automobile, and optimizing a constraint model based on the supplier information and the logistics environment information; based on the optimized constraint model; obtaining an optimized constraint output based on the optimized constraint model, wherein the optimized constraint output includes: actual quantity of the electric new energy automobile in goods arriving within a certain time period;
determining an optimization target and a convergence limiting condition of the warehousing optimization model; and inputting the optimized object output and the optimized constraint output into the warehousing optimization model for calculation based on the optimization target and the convergence limiting condition of the warehousing optimization model to obtain a warehousing decision-making scheme with the optimal total inventory cost of each accessory.
Further, in the method, acquiring production information of the new energy automobile and storage information of accessories of the new energy automobile, and optimizing an object model based on the production information and the storage information includes:
acquiring production information of the new energy automobile and storage information of accessories of the new energy automobile;
obtaining corresponding production input based on the production information of the new energy automobile, wherein the production input comprises: the order delivery period of the new energy automobile, the time required by the production of accessories, the quantity of the accessories and the process working hours;
the storage information based on new energy automobile's accessory obtains corresponding storage input, storage input includes: the existing inventory and in-transit inventory of accessories;
optimizing an object model based on the production input and warehousing input.
Further, in the method, acquiring supplier information of the new energy vehicle and logistics environment information affecting the new energy vehicle, and optimizing a constraint model based on the supplier information and the logistics environment information includes:
acquiring supplier information of accessories of the new energy automobile and logistics environment information influencing the accessories of the new energy automobile;
deriving a corresponding vendor input based on the vendor information, comprising: average delivery time of suppliers, supplier delivery quantity, alternative supplier delivery time, alternative supplier delivery quantity, and parts transit time for the parts;
obtaining a corresponding environment input based on the logistics environment information, including: supplier location, shipping path for the parts, shipping time for the shipping path, alternative shipping path for the parts, and alternative shipping path shipping time;
optimizing a constraint model based on the vendor input and the environmental input.
Further, in the above method, the warehouse optimization model is as follows:
C min ＝∑(A b -X y )，
wherein: c min For total inventory cost optimization of each part, X y X parts, A, required for y at a time b A parts were purchased at time b before time y.
Further, in the above method, the optimization objective of the warehouse optimization model includes: inventory costs are minimized, production order latency quantity is minimized, and total time to produce orders is minimized.
Further, in the above method, the object model is as follows:
X z ＝∑O t-p *N-M 1 +∑M 2 -∑M 3 ，
wherein: x z X number of fittings required in the time period z range; o is t-p In order to deliver the order on time within the time period z, the required number of accessories in a certain time point t-p is O, t is the order delivery period, p is the production lead period, N is the number of accessories, and Sigma O t-p The total number of all applicable accessories at all time points in the time period z; m 1 The number of existing stocks; sigma M 2 The total number of the determined number of trips at each time point in the time period z; sigma M 3 The total number of planned usage quantities for each time point within the time period z.
Further, in the above method, the constraint model is as follows:
wherein: a. The f A parts purchased in the time period f;
for each W of all selected suppliers during time period f t +W d The total number of deliverables at the time point; w is a group of s If the supplier is selected, 1 is selected and 0 is not selected, wherein W is the default supplier s Alternative supply with alternative 1Time of business W s Is selected to be 0; />
For the supplier at W t +W d The number of deliverables at a point in time; w t Time required for default path, W d The increased time required for the alternative path.
According to another aspect of the invention, there is also provided a storage optimization device for a new energy automobile, wherein the storage optimization device comprises:
the system comprises a first device, a second device and a third device, wherein the first device is used for acquiring production information of the new energy automobile and storage information of accessories of the new energy automobile and optimizing an object model based on the production information and the storage information; obtaining an optimized object output based on the optimized object model, wherein the optimized object output includes: preliminarily determining a purchasing time period for purchasing accessories of the new energy automobile;
the second device is used for acquiring supplier information of accessories of the new energy automobile and logistics environment information influencing the accessories of the new energy automobile, and optimizing a constraint model based on the supplier information and the logistics environment information; based on the optimized constraint model; obtaining an optimized constraint output based on the optimized constraint model, wherein the optimized constraint output includes: actual quantity of the electric new energy automobile in goods arriving within a certain time period;
the third device is used for determining an optimization target and a convergence limiting condition of the warehousing optimization model; and inputting the optimized object output and the optimized constraint output into the warehousing optimization model for calculation based on the optimization target and the convergence limiting condition of the warehousing optimization model to obtain a warehousing decision-making scheme with the optimal total inventory cost of each accessory.
According to another aspect of the present invention, there is also provided a computer readable medium having computer readable instructions stored thereon, the computer readable instructions being executable by a processor to perform the method of any of the above.
According to another aspect of the present invention, there is also provided an apparatus for information processing at a network device, the apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein the computer program instructions, when executed by the processor, trigger the apparatus to perform any of the methods described above.
Compared with the prior art, the optimized object output is obtained based on the optimized object model, wherein the optimized object output comprises the following steps: determining whether to purchase the accessories of the new energy automobile or not, and determining the purchasing time if determining to purchase the accessories of the new energy automobile; optimizing a constraint model based on the vendor information and environmental information; based on the optimized constraint model; obtaining an optimized constraint output based on the optimized constraint model, wherein the optimized constraint output includes: the arrival time of the new energy automobile parts; and based on the optimization target and the convergence limiting condition of the warehousing optimization model, inputting the optimized object output and the optimized constraint output into the warehousing optimization model for calculation to obtain a warehousing decision-making scheme with the optimal total inventory cost of each part, driving warehousing optimization based on the optimized object output and the optimized constraint output, and subsequently adjusting a production decision by the warehousing decision-making scheme to realize the optimal total inventory cost of each part under the condition of ensuring the production target. The efficiency of accessory storage optimization of new energy automobile is promoted.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments made with reference to the following drawings:
fig. 1 shows a schematic diagram of a method for optimizing storage of a new energy vehicle according to an embodiment of the invention.
The same or similar reference numbers in the drawings identify the same or similar elements.
Detailed Description
The present invention is described in further detail below with reference to the attached drawing figures.
In a typical configuration of the present application, the terminal, the device serving the network, and the trusted party each include one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory computer readable media (transient media), such as modulated data signals and carrier waves.
As shown in fig. 1, the invention provides a method for optimizing storage of a new energy automobile, which comprises the following steps:
the method comprises the following steps of S1, acquiring production information of a new energy automobile and warehousing information of accessories of the new energy automobile, and optimizing an object model based on the production information and the warehousing information; obtaining an optimized object output based on the optimized object model, wherein the optimized object output includes: preliminarily determining a purchasing time period for purchasing accessories of the new energy automobile;
here, the production information is data related to manufacturing of new energy vehicles required for enterprise production management based on sales orders of new energy vehicles, such as: production orders, BOM information, process information and the like of the new energy automobile;
the warehousing information is inventory-related data of accessories required for enterprise production management based on sales orders of new energy vehicles, such as: the existing inventory of batteries, motors, chips, etc., the number of parts in transit, the projected number, etc. As the new energy automobile enterprises adopt more electric and electronic components than the control systems of the traditional automobile enterprises, the amount of inventory type data to be managed is increased.
Since each accessory can be managed as a warehousing object, one or more warehousing objects need to be selected to participate in optimizing the object model, such as a battery, a monitoring chip, and the like.
The obtained influence factors can be deeply analyzed to obtain a model core data input time range, such as:
● Near three months of production information;
● The last year data of the warehousing information.
S2, acquiring supplier information of accessories of the new energy automobile and logistics environment information influencing the accessories of the new energy automobile, and optimizing a constraint model based on the supplier information and the logistics environment information; based on the optimized constraint model; obtaining an optimized constraint output based on the optimized constraint model, wherein the optimized constraint output includes: actual quantity of the electric new energy automobile in goods arriving within a certain time period;
here, the supplier information is data related to procurement accessories required for enterprise production management based on a sales order of the new energy vehicle, such as: the supplier delivery period of the accessory, the supplier location of the accessory, etc. Because the new energy automobile enterprise is established later than the traditional automobile enterprise, the supply chain system has more fragile links, such as: insufficient productivity of chip parts, etc.
The environmental information is logistics-related data of accessories required for enterprise production management based on sales orders of new energy vehicles, such as: transport route, regional information, etc. Due to the uncertainty of the supply chain environment in recent years, such as: pass delay, etc. The warehousing requirements of new energy automobile enterprises take the new energy automobile enterprises as important factors to manage.
S3, determining an optimization target and a convergence limiting condition of the warehousing optimization model; and inputting the optimized object output and the optimized constraint output into the warehousing optimization model for calculation based on the optimization target and the convergence limiting condition of the warehousing optimization model to obtain a warehousing decision-making scheme with the optimal total inventory cost of each accessory.
Herein, the present invention obtains an optimized object output based on an optimized object model, wherein the optimized object output includes: determining whether to purchase the accessories of the new energy automobile or not, and determining the purchasing time if determining to purchase the accessories of the new energy automobile; optimizing a constraint model based on the vendor information and environmental information; based on the optimized constraint model; obtaining an optimized constraint output based on the optimized constraint model, wherein the optimized constraint output includes: the arrival time of the new energy automobile parts; and based on the optimization target and the convergence limiting condition of the warehousing optimization model, inputting the optimized object output and the optimized constraint output into the warehousing optimization model for calculation to obtain a warehousing decision-making scheme with the optimal total inventory cost of each part, driving warehousing optimization based on the optimized object output and the optimized constraint output, and subsequently adjusting a production decision by the warehousing decision-making scheme to realize the optimal total inventory cost of each part under the condition of ensuring the production target. The efficiency of accessory storage optimization of new energy automobile is promoted.
In an embodiment of the method for optimizing the warehousing of the new energy automobile, in step S1, production information of the new energy automobile and warehousing information of accessories of the new energy automobile are obtained, and an object model is optimized based on the production information and the warehousing information, and the method includes:
s11, acquiring production information of the new energy automobile and storage information of accessories of the new energy automobile;
step S12, obtaining corresponding production input based on the production information of the new energy automobile, wherein the production input comprises the following steps: the order delivery period of the new energy automobile, the time required by the production of accessories, the quantity of the accessories and the process working hours;
here, the production input is to collect and analyze data of production information to form input information, such as: the order delivery period of the new energy automobile, the time and the quantity of accessories required by production, the process working hours and the like;
data filtering can be performed to remove noisy data and invalid data, such as the phenomenon level (data of a great numerical value) of a certain area; performing data completion, and supplementing data of a missing point according to a data trend, such as a phenomenon level (data of a minimum numerical value) of a certain area, so as to obtain corresponding production input;
step S13, obtaining corresponding warehousing input based on warehousing information of accessories of the new energy automobile, wherein the warehousing input comprises: the existing inventory and in-transit inventory of accessories;
here, the warehousing input is to collect and analyze data of warehousing information to form input information, such as: the existing inventory of parts, in-transit inventory, etc.
Data filtering can be performed to remove noisy data and invalid data, such as the magnitude of phenomenon (data of a maximum value) of a certain area; performing data completion, and supplementing data of a missing point according to a data trend, such as a phenomenon level (data of a minimum numerical value) of a certain area, so as to obtain corresponding storage input;
and S14, optimizing an object model based on the production input and the storage input.
Here, the embodiment further analyzes the production information to obtain a corresponding production input, and further analyzes the warehousing information to obtain a corresponding warehousing input, where the production input and the warehousing input are as follows: the time and the number of the accessories required for production, and the existing storage number and period of the accessories; the optimized object output is subsequently used as the input of the storage optimization model solution, so that the efficiency and the accuracy of the subsequent storage optimization model calculation can be improved.
In an embodiment of the method for optimizing storage of a new energy vehicle, in step S2, supplier information of accessories of the new energy vehicle and logistics environment information of accessories affecting the new energy vehicle are obtained, and a constraint model is optimized based on the supplier information and the logistics environment information, including:
s21, acquiring supplier information of accessories of the new energy automobile and logistics environment information influencing the accessories of the new energy automobile;
step S22, obtaining a corresponding supplier input based on the supplier information, including: average delivery time of suppliers, supplier delivery quantity, alternative supplier delivery time, alternative supplier delivery quantity, and parts transit time for the parts;
here, the supplier input is to collect and analyze data of the purchase information to form input information, such as: average delivery time, number of parts and parts transit time of the supplier of the parts, etc.
Data filtering can be performed to remove noisy data and invalid data, such as the magnitude of phenomenon (data of a maximum value) of a certain area; performing data complementation, complementing the data of the missing point according to the data trend, such as the phenomenon level (data of a minimum numerical value) of a certain area, so as to obtain the corresponding supplier input;
step S23, obtaining corresponding environment input based on the logistics environment information, including: supplier location, shipping path for the part, shipping time for the shipping path, alternate shipping path for the part, and alternate shipping path shipping time;
here, the environmental input is to collect and analyze data of the logistics information to form input information, such as: transportation path restrictions of accessories, alternative path transportation time, etc.;
data filtering can be performed to remove noisy data and invalid data, such as the magnitude of phenomenon (data of a maximum value) of a certain area; performing data complementation, complementing data of a missing point according to a data trend, such as a phenomenon level (data of a minimum numerical value) of a certain area, so as to obtain corresponding environment input;
step S24, optimizing a constraint model based on the supplier input and the environment input.
In this embodiment, the corresponding supplier input is obtained based on the supplier information, the corresponding environment input is obtained based on the logistics environment information, and the optimized constraint output is obtained, and the optimized constraint output can be subsequently used as the input for solving the warehousing optimization model, so that the efficiency and accuracy of subsequent warehousing optimization model calculation can be improved.
In an embodiment of the method for optimizing the warehousing of the new energy automobile, the warehousing optimization model comprises the following steps:
C min ＝∑(A b -X y )，
wherein: c min For total inventory cost optimization of each part, X y X parts, A, required for y at a time b A parts were purchased at time b before time y.
In this case, optimization objectives of the warehouse optimization model may be set, such as: the inventory cost is minimum, the delay quantity of production orders is minimum, the total delay time of the production orders is minimum, and the like;
the convergence limit is set in such a way that absolute optimization does not exist in model solution, and only relative optimization exists, so that the model solution limit needs to be set. Such as: the model runs for 60 minutes, 100 iterations have no other better solutions, etc.
In an embodiment of the method for optimizing the storage of the new energy automobile, the object model,
the following were used: x z ＝∑O t-p *N-M 1 +∑M 2 -∑M 3 ，
Wherein: x z X number of fittings required in the time period z range; o is t-p In order to deliver the order on time within the time period z, the number of required accessories in a certain time point t-p is O, t is the order delivery period, p is the production lead period, N is the number of accessories, sigma O t-p The total number of all applicable accessories at all time points in the time period z; m 1 The number of existing stocks; sigma M 2 The total number of the determined number of trips at each time point in the time period z; sigma M 3 The total number of planned usage quantities for each time point within the time period z.
For example, taking the model object as the monitor chip, the object model may be set as: x z ＝∑O t-p *N-M 1 +∑M 2 -∑M 3
Wherein: x z X parts such as monitoring chips are needed in the time period z; o is t-p To be within a time period zThe order can be delivered on time, the number of the needed accessories such as monitoring chips in a certain time point t-p is O, t is the order delivery period, p is the production lead period, and N is the number of the accessories; m 1 The number of existing stocks; sigma M 2 The total number of the determined number of trips at each time point in the time period z; sigma M 3 The total number of planned usage quantities for each time point within the time period z.
Specifically, the neural network input layer of the object model: the input is model data after having been preprocessed. Such as: the optimization model of the monitoring chip comprises production orders, BOM number of the monitoring chip, process time of the monitoring chip, inventory of the monitoring chip, on-the-way and planned number.
Neural network hidden layer of object model: the number of hidden layers is determined. Since a continuous function in a closed interval can be approximated by a hidden network. The invention adopts a basic three-layer network to complete the mapping from any M dimension to N dimension. And selecting the number of hidden layer units. The method determines the number of the hidden units based on the fact that the requirements of the error and the upper limit and the lower limit of the hidden units are met and the minimum number of iteration times is used as an index. Such as: the monitor chip object model M is 4, and N is 1.
A neural network output layer of the object model. The neural network will output a set of parameter data. Such as: the optimization model of the monitoring chip is X y 。
When the model is output and analyzed, the reasonability of the object model can be determined by comparing the expected data with the output result of the object model. Such as: through comparison and analysis of model time point prediction output obtained after data training in the first half year and actual data of the time point, a hidden layer of a neural network model of the object model can be adjusted according to an analysis result, and training data of the object model is reselected to optimize an output result of the object model.
In an embodiment of the method for optimizing the storage of the new energy automobile, the constraint model comprises the following steps:
wherein:A f a parts purchased in time period f;
for each W of all selected suppliers during time period f t +W d The total number of deliverables at the time point; w is a group of s If the supplier is selected, 1 is selected, and 0 is not selected, wherein W is the default supplier s Alternative supplier time W of 1 s Is selected to be 0; />
For the supplier at W t +W d The number of deliverables at a point in time; w t Time required for default path, W d The increased time required for the alternative path.
Here, since each component can be managed as a warehousing object, one or more warehousing objects need to be selected to participate in the optimization constraint model, such as a battery, a monitoring chip, and the like.
The obtained influencing factors can be deeply analyzed to obtain core data options of the model, such as: the weight of the alternative options in the supplier information, the weight of the alternative information in the logistics environment information, and the like.
Taking the model object as the monitor chip as an example, the neural network model of the constraint model may be set as:
wherein: a. The f A parts purchased in the time period f range are purchased;
for each W of all selected suppliers within time period f t +W d Total number of deliverables, W, at a time point s Selecting 1 if the supplier is selected or not, and not selecting 0, wherein the default supplier is 1 and the alternative supplier is 0 under the general condition; />
For the supplier at W t +W d The number of deliverables at a point in time; w is a group of t Time required for default path, W d The alternative path requires increased time.
The neural network input layer of the constraint model: the parameters are the supplier input and the environment after having been pre-processed. Neural network hidden layer of constraint model: and determining the number of hidden layers. Since a continuous function in a closed interval can be approximated by a hidden layer network. The invention can complete the mapping from any M dimension to N dimension by adopting a basic three-layer network. And selecting the number of hidden layer units. The method determines the number of the hidden units based on the fact that the requirements of the error and the upper limit and the lower limit of the hidden units are met and the minimum number of iteration times is used as an index. Such as: the monitor chip constraint model M is the number of all the optional suppliers, and N is 1.
And constraining the neural network output layer of the model. The neural network will output a set of parameter data. Monitor chip restriction model is A f The rationality of the constraint model is determined by comparing expected data with the output of the model. Such as: and comparing and analyzing the prediction output of the model time point obtained after the data training of the first half year with the actual data of the time point. Based on the analysis result, the neural network hidden layer of the constraint model can be adjusted, the information of alternative suppliers and alternative paths of the model is reselected, and the output result of the constraint model is optimized.
According to another aspect of the invention, there is also provided a storage optimization device for a new energy automobile, wherein the storage optimization device comprises:
the system comprises a first device, a second device and a third device, wherein the first device is used for acquiring production information of the new energy automobile and storage information of accessories of the new energy automobile and optimizing an object model based on the production information and the storage information; obtaining an optimized object output based on the optimized object model, wherein the optimized object output includes: preliminarily determining a purchasing time period for purchasing accessories of the new energy automobile;
the second device is used for acquiring supplier information of accessories of the new energy automobile and logistics environment information of the accessories affecting the new energy automobile, and optimizing a constraint model based on the supplier information and the logistics environment information; based on the optimized constraint model; obtaining an optimized constraint output based on the optimized constraint model, wherein the optimized constraint output includes: actual quantity of the electric new energy automobile in goods arriving within a certain time period;
the third device is used for determining an optimization target and a convergence limiting condition of the warehousing optimization model; and inputting the optimized object output and the optimized constraint output into the warehousing optimization model for calculation based on the optimization target and the convergence limiting condition of the warehousing optimization model to obtain a warehousing decision-making scheme with the optimal total inventory cost of each accessory.
According to another aspect of the present invention, there is also provided a computer readable medium having computer readable instructions stored thereon, the computer readable instructions being executable by a processor to perform the method of any of the above.
According to another aspect of the present invention, there is also provided an apparatus for information processing at a network device, the apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein the computer program instructions, when executed by the processor, trigger the apparatus to perform any of the methods described above.
Details of the embodiments of the apparatuses of the present invention may specifically refer to corresponding parts of the embodiments of the methods, and are not described herein again.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
It should be noted that the present invention may be implemented in software and/or in a combination of software and hardware, for example, as an Application Specific Integrated Circuit (ASIC), a general purpose computer or any other similar hardware device. In one embodiment, the software program of the present invention may be executed by a processor to implement the steps or functions described above. Likewise, the software programs (including associated data structures) of the present invention can be stored in a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like. Further, some of the steps or functions of the present invention may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various steps or functions.
In addition, some of the present invention can be applied as a computer program product, such as computer program instructions, which when executed by a computer, can invoke or provide the method and/or technical solution according to the present invention through the operation of the computer. Program instructions which invoke the methods of the present invention may be stored on fixed or removable recording media and/or transmitted via a data stream on a broadcast or other signal bearing medium and/or stored within a working memory of a computer device operating in accordance with the program instructions. An embodiment according to the invention herein comprises an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein the computer program instructions, when executed by the processor, trigger the apparatus to perform a method and/or solution according to embodiments of the invention as described above.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it will be obvious that the term "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the apparatus claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Claims (10)
1. A method for optimizing storage of a new energy automobile is disclosed, wherein the method comprises the following steps:
the method comprises the steps of obtaining production information of the new energy automobile and warehousing information of accessories of the new energy automobile, and optimizing an object model based on the production information and the warehousing information; obtaining an optimized object output based on the optimized object model, wherein the optimized object output includes: preliminarily determining a purchasing time period for purchasing accessories of the new energy automobile;
acquiring supplier information of accessories of the new energy automobile and logistics environment information influencing the accessories of the new energy automobile, and optimizing a constraint model based on the supplier information and the logistics environment information; based on the optimized constraint model; obtaining an optimized constraint output based on the optimized constraint model, wherein the optimized constraint output comprises: actual quantity of the electric new energy automobile in goods arriving within a certain time period;
determining an optimization target and a convergence limiting condition of the warehousing optimization model; and inputting the optimized object output and the optimized constraint output into the warehousing optimization model for calculation based on the optimization target and the convergence limiting condition of the warehousing optimization model to obtain a warehousing decision-making scheme with the optimal total inventory cost of each accessory.
2. The method according to claim 1, wherein acquiring production information of the new energy automobile and warehousing information of accessories of the new energy automobile, and optimizing an object model based on the production information and the warehousing information comprises:
acquiring production information of the new energy automobile and storage information of accessories of the new energy automobile;
obtaining corresponding production input based on the production information of the new energy automobile, wherein the production input comprises: the order delivery period of the new energy automobile, the time required by the production of accessories, the quantity of the accessories and the process time;
storage information based on new energy automobile's accessory obtains corresponding storage input, storage input includes: the existing inventory and in-transit inventory of parts;
optimizing an object model based on the production input and warehousing input.
3. The method according to claim 1, wherein acquiring supplier information of accessories of the new energy automobile and logistics environment information affecting the accessories of the new energy automobile, and optimizing a constraint model based on the supplier information and the logistics environment information comprises:
acquiring supplier information of accessories of the new energy automobile and logistics environment information influencing the accessories of the new energy automobile;
deriving a corresponding vendor input based on the vendor information, comprising: average delivery time of suppliers, supplier delivery quantity, alternative supplier delivery time, alternative supplier delivery quantity, and parts transit time for the parts;
obtaining a corresponding environment input based on the logistics environment information, including: supplier location, shipping path for the parts, shipping time for the shipping path, alternative shipping path for the parts, and alternative shipping path shipping time;
optimizing a constraint model based on the vendor input and the environmental input.
4. The method of claim 1, wherein the warehouse optimization model is as follows:
C min ＝∑(A b -X y )，
wherein: c min For the optimization of the total inventory cost of each part, X y X parts are required for the time point y,
A b a parts were purchased at time b before time y.
5. The method of claim 1, wherein the optimization objectives of the warehouse optimization model comprise: inventory costs are minimized, production order latency quantity is minimized, and total time to produce orders is minimized.
6. The method of claim 1, wherein the object model is as follows:
X z ＝∑O t-p *N-M 1 +∑M 2 -∑M 3 ，
wherein: x z X fittings are needed in the time period z range; o is t-p In order to deliver the order on time within the time period z, the required number of accessories in a certain time point t-p is O, t is the order delivery period, p is the production lead period, N is the number of accessories, and Sigma O t-p The total number of all applicable accessories at all time points in the time period z; m 1 The number of existing stocks; sigma M 2 The total number of the determined number of trips at each time point in the time period z; sigma M 3 The total number of planned usage quantities for each time point within the time period z.
7. The method of claim 1, wherein the constraint model is as follows:
wherein: a. The f A parts are purchased in a time period f;
for each W of all selected suppliers during time period f t +W d Total number of deliverables at a time point; w s If the supplier is selected, 1 is selected, and 0 is not selected, wherein W is the default supplier s Alternative supplier time W of choice 1 s Is selected to be 0; />
For the supplier at W t +W d The number of deliverables at a point in time; w t Time required for default path, W d The increased time required for the alternative path.
8. A be used for new energy automobile storage optimizing equipment, wherein includes:
the system comprises a first device, a second device and a third device, wherein the first device is used for acquiring production information of the new energy automobile and storage information of accessories of the new energy automobile and optimizing an object model based on the production information and the storage information; obtaining an optimized object output based on the optimized object model, wherein the optimized object output includes: preliminarily determining a purchasing time period for purchasing accessories of the new energy automobile;
the second device is used for acquiring supplier information of accessories of the new energy automobile and logistics environment information influencing the accessories of the new energy automobile, and optimizing a constraint model based on the supplier information and the logistics environment information; based on the optimized constraint model; obtaining an optimized constraint output based on the optimized constraint model, wherein the optimized constraint output includes: actual quantity of the electric new energy automobile in goods arriving within a certain time period;
the third device is used for determining an optimization target and a convergence limiting condition of the warehousing optimization model; and inputting the optimized object output and the optimized constraint output into the warehousing optimization model for calculation based on the optimization target and the convergence limiting condition of the warehousing optimization model to obtain a warehousing decision-making scheme with the optimal total inventory cost of each accessory.
9. A computer readable medium having computer readable instructions stored thereon which are executable by a processor to implement the method of any one of claims 1 to 7.
10. An apparatus for information processing at a network device, the apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein the computer program instructions, when executed by the processor, trigger the apparatus to perform the method of any one of claims 1 to 7.
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| CN117350520B (en) * | 2023-12-04 | 2024-02-27 | 浙江大学高端装备研究院 | Automobile production optimization method and system |
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