CN112862312A - Manufacturing service resource dynamic scheduling method and system based on random online algorithm - Google Patents
Manufacturing service resource dynamic scheduling method and system based on random online algorithm Download PDFInfo
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Abstract
The invention discloses a manufacturing service resource dynamic scheduling method and a system based on a random online algorithm, which are applied to a server and store the serial numbers of all manufacturer clients with the distance between the manufacturer clients and the buyer clients within a set range in a container; receiving a production target and a production resource submitted by a buyer client connected with a server; storing the received production target and the production resource in a resource pool; receiving production state data submitted by a plurality of manufacturer clients connected with a server; traversing the production state data submitted by all manufacturer clients; storing the production state data of each manufacturer at each moment in a queue; and according to the production state data of each manufacturer at each moment, randomly selecting one manufacturer capable of realizing the production target, and pushing the production resources in one production period to the corresponding manufacturer client.
Description
Technical Field
The application relates to the technical field of large-scale network collaborative manufacturing, in particular to a manufacturing service resource dynamic scheduling method and system based on a random online algorithm.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
In recent years, emerging industries such as the internet and the like are continuously developed, great convenience is brought to production and life of human beings, and meanwhile, rapid development of national economy is promoted. As a traditional post industry, the manufacturing industry still occupies an important position in national economy at present when high and new technologies are rapidly developed and informatization is highly popularized, and can reflect the scientific and technological development level and comprehensive competitive strength of a country to a certain extent. In a large-scale networked collaborative manufacturing process, a manufacturing service is a manufacturing capability that can be shared, and its resources include hardware devices, production technologies, processing capabilities, and the like. The manufacturing service resource has the characteristics, can be scheduled and used through a network, and adopts a random online algorithm to complete scheduling operation. The random online algorithm is a classic game algorithm for flexible prediction, and a random factor is introduced by designing a reasonable probability density function on the basis of a deterministic online algorithm, so that the reliability of a prediction result is integrally improved. The random online algorithm can efficiently realize diversified prediction modes such as short-term prediction, long-term prediction, variable window prediction and the like by observing and analyzing historical behaviors.
Today, collaborative manufacturing has been adopted by more and more service manufacturers. On one hand, the cooperative manufacturing can strengthen the communication among enterprises, integrate manufacturing service resources, improve the production efficiency, realize the cooperative production and resource sharing of products and improve the competitive strength of the whole industrial cluster; on the other hand, the cooperative manufacturing can effectively expand the dimensionality of the production line and meet various requirements of response market, intelligent production scheduling and the like.
In the large-scale network collaborative manufacturing process, resource scheduling and cost management are more concerned by manufacturers. The resource scheduling can effectively lower the production cost of each manufacturer by decomposing and recombining various manufacturing service resources. On one hand, in the actual production process, resources are diverse, requirements are heterogeneous, and the existing resources and requirements can be fully integrated by selecting a proper resource scheduling method, so that the response speed of the resources is improved, and the reliability of each production link is enhanced. On the other hand, the reasonable resource scheduling method is used, the constraint of a space region on the whole production system can be broken through, so that links such as purchasing, production, sale and the like can be developed efficiently, the market demand can be responded quickly, and the investment risk can be effectively solved.
The inventor finds that at present, some mature resource scheduling algorithms are provided, including an elevator scheduling algorithm, a high-priority scheduling algorithm, a high-response-ratio priority scheduling algorithm, a time slice-based round robin scheduling algorithm and the like, and in the large-scale collaborative manufacturing production process, the algorithms are limited by practical factors such as time, space and the like, cannot realize a personalized resource scheduling strategy according to the practical situation and the characteristics of each manufacturer, and further cannot relieve the system load pressure and reduce the system operation cost.
Disclosure of Invention
In order to overcome the defects of the prior art, the application provides a manufacturing service resource dynamic scheduling method and system based on a random online algorithm; in order to effectively reduce the production cost of each manufacturer in the large-scale network collaborative manufacturing process, the invention introduces a random online algorithm, realizes the dynamic scheduling of manufacturing service resources by monitoring the actual production condition of each manufacturer in real time, and makes reasonable judgment on whether effective contact needs to be established between a buyer and each manufacturer in a time slot system.
In a first aspect, the present application provides a method for dynamically scheduling manufacturing service resources based on a random online algorithm;
a manufacturing service resource dynamic scheduling method based on a random online algorithm is applied to a server and comprises the following steps:
storing the serial numbers of all manufacturer clients with the distance between the manufacturer clients and the buyer clients within a set range in a container;
receiving a production target and a production resource submitted by a buyer client connected with a server;
storing the received production target and the production resource in a resource pool;
receiving production state data submitted by a plurality of manufacturer clients connected with a server;
traversing the production state data submitted by all manufacturer clients; storing the production state data of each manufacturer at each moment in a queue;
and according to the production state data of each manufacturer at each moment, randomly selecting one manufacturer capable of realizing the production target, and pushing the production resources in one production period to the corresponding manufacturer client.
In a second aspect, the present application provides a manufacturing service resource dynamic scheduling system based on a random online algorithm;
the manufacturing service resource dynamic scheduling system based on the random online algorithm comprises:
a storage module configured to: storing the serial numbers of all manufacturer clients with the distance between the manufacturer clients and the buyer clients within a set range in a container;
a first receiving module configured to: receiving a production target and a production resource submitted by a buyer client connected with a server;
a storage module configured to: storing the received production target and the production resource in a resource pool;
a second receiving module configured to: receiving production state data submitted by a plurality of manufacturer clients connected with a server;
a traversal module configured to: traversing the production state data submitted by all manufacturer clients; storing the production state data of each manufacturer at each moment in a queue;
a push module configured to: and according to the production state data of each manufacturer at each moment, randomly selecting one manufacturer capable of realizing the production target, and pushing the production resources in one production period to the corresponding manufacturer client.
In a third aspect, the present application further provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein a processor is connected to the memory, the one or more computer programs are stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the method according to the first aspect.
In a fourth aspect, the present application also provides a computer-readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.
In a fifth aspect, the present application also provides a computer program (product) comprising a computer program for implementing the method of any of the preceding first aspects when run on one or more processors.
Compared with the prior art, the beneficial effects of this application are:
in the running process, the invention can monitor the production state of each manufacturer in real time from the aspects of resource allocation, working load and the like of each manufacturer, can effectively realize reasonable distribution of production tasks and dynamic scheduling of service resources among the manufacturers by introducing a random online algorithm according to the actual production condition of each manufacturer on the premise of ensuring that the production cost of each manufacturer is as low as possible, and can help each manufacturer and a buyer to establish a cooperative relationship for effective prediction.
By setting the resource pool, the production target and the production resource can be prevented from being repeatedly collected from the client of the buyer, the running pressure of the CPU of the client of the buyer can be reduced, and the utilization rate of the CPU and the throughput capacity of the system are improved.
Traversing the production state data submitted by all manufacturer clients; the production state data of each manufacturer at each moment is stored in one queue, so that the complete traversal of all manufacturer client data can be realized, and data omission is avoided.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flow chart of the method of the first embodiment.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example one
The embodiment provides a manufacturing service resource dynamic scheduling method based on a random online algorithm;
as shown in fig. 1, the method for dynamically scheduling manufacturing service resources based on a random online algorithm is applied to a server, and includes:
s101: storing the serial numbers of all manufacturer clients with the distance between the manufacturer clients and the buyer clients within a set range in a container;
s102: receiving a production target and a production resource submitted by a buyer client connected with a server;
s103: storing the received production target and the production resource in a resource pool;
s104: receiving production state data submitted by a plurality of manufacturer clients connected with a server;
s105: traversing the production state data submitted by all manufacturer clients; storing the production state data of each manufacturer at each moment in a queue;
s106: and according to the production state data of each manufacturer at each moment, randomly selecting one manufacturer capable of realizing the production target, and pushing the production resources in one production period to the corresponding manufacturer client.
Further, the method further comprises:
s107: calculating a threshold value based on a random online algorithm and a probability density function;
s108: judging whether the yield of the current manufacturer in the latest production cycle reaches the threshold value, if so, continuing to push the production resources in the next production cycle to the corresponding manufacturer client; meanwhile, establishing a communication relationship between the manufacturer client and the buyer client;
if not, traversing the production state data submitted by all the manufacturer clients again; and according to the production state data of each manufacturer at each moment, randomly selecting one manufacturer capable of realizing the production target, pushing the production resources in one production period to the corresponding manufacturer client, and meanwhile, putting the original manufacturer into a container to wait for the next period of processing.
Further, the production resources in one production cycle are pushed to the corresponding manufacturer client; the method comprises the following specific steps:
adding the production resources in a production period into the transfer list, and updating the manufacturing service resource state of each manufacturer client;
the manufacturing service resource that should be invalidated is added to the delete list, and the manufacturer client number that has been revoked is removed from the container.
Further, the method further comprises: and for the newly submitted and submitted orders of the client side of the buyer, carrying out production task allocation and manufacturing service resource scheduling according to whether the actual production state of each client side of the current manufacturer is saturated or not.
According to the actual production condition of each manufacturer in the past production period, a random online algorithm is introduced to predict whether each manufacturer should undertake the production task issued by a buyer at a certain moment so as to save the production cost; according to the actual production state of each manufacturer and the manufacturing service resources currently owned by each manufacturer, on the premise of ensuring lower cost, the manufacturing service resources are dynamically scheduled; at the current time node, each manufacturer determines whether to establish a cooperative relationship with the buyer according to the production performance of the past production period, outputs the production scheme of each manufacturer in the next production period and outputs the dynamic scheduling scheme of the manufacturing service resources.
Further, randomly selecting a manufacturer capable of realizing the production target; the method comprises the following specific steps:
order to
For the number of products manufacturer k can produce at time i, let diTotal number of demands of the buyer for the product at time i;
when in use
When, it means that manufacturer k can afford the buyer an amount of
The production task of (1);
when in use
Indicating that manufacturer k is saturated in production at time i, i.e., unable to undertake additional production tasks;
further, if the production state data is not reached, the production state data submitted by all the manufacturer clients is traversed again; randomly selecting a manufacturer capable of realizing a production target according to the production state data of each manufacturer at each moment, and pushing production resources in a production period to a corresponding manufacturer client; the method specifically comprises the following steps:
assume that the current time is t0Randomly selecting a manufacturer k with production capacity at the current moment, wherein one production cycle of the manufacturer is T; suppose from t0-T +1 to T0All production tasks within the time period are prioritized to this manufacturer k, which is counted from t0-T +1 to T0Number of products u produced in a time periodkWherein
If the current production state of the manufacturer k is saturated, the occupied manufacturing service resources are released, and the corresponding production task is distributed to other manufacturers k'.
Further, the step S107: calculating a threshold value based on a random online algorithm and a probability density function; the method specifically comprises the following steps:
the threshold z is obtained by the following probability density function designed in a random online algorithm:
where R is the initial cost the manufacturer pays to produce, α is the discount the manufacturer enjoys on production after the production reaches a certain standard, p is the unit price, e is the natural index, δ (·) is the Dike δ function, β is a balance point, which represents the point at t0At the moment, whether the manufacturer k accepts or rejects the production task issued by the buyer, the production cost of the manufacturer k and the production cost of the buyer are the same.
Further, let X be the cost of producing n products with a partnership being established, and let Y be the cost of producing n products without a partnership being established.
Wherein the content of the first and second substances,
X＝p·n
Y＝R+α·p·n
when X is Y, let β be n, the equilibrium point β is found to satisfy the following formula:
further, if the production resource is reached, the production resource in the next production period is continuously pushed to the corresponding manufacturer client; meanwhile, establishing a communication relationship between the manufacturer client and the buyer client; the method specifically comprises the following steps:
when u isk>z, mark manufacturer k as popular, at t0At the moment, the manufacturer should receive the production task issued by the buyer, and take the corresponding manufacturing service resource from the resource pool to carry out production. Meanwhile, the buyer establishes a cooperative relationship with the manufacturer k, so that the manufacturer k can effectively reduce the production cost in the whole production period of the product.
Further, if the production state data is not reached, the production state data submitted by all the manufacturer clients is traversed again; randomly selecting a manufacturer capable of realizing a production target according to the production state data of each manufacturer at each moment, pushing production resources in a production period to a corresponding manufacturer client, and meanwhile, putting an original manufacturer into a container to wait for the next period of processing; the method specifically comprises the following steps:
when u isk< z at t0At the moment, the manufacturer k should not accept the production task issued by the buyer, that is, the buyer is not suggested to establish a cooperative relationship with the manufacturer k, otherwise, a certain cost loss will be caused. At this moment, another manufacturer k' with production capacity is randomly selected again, and u is calculatedk′Wherein
Re-randomly generating the threshold value z using a probability density function, and dividing uk′And comparing the z with the z, and performing corresponding decision through analysis.
Further, the production resources in one production period are added into the transfer list, and the manufacturing service resource state of each manufacturer client is updated; adding the manufacturing service resource which should be invalidated into a deletion list, and removing the manufacturer client number which is cancelled from a container; the method specifically comprises the following steps:
when manufacturer k is at tiAfter receiving the production task issued by the buyer, the system starts to produce and updates the current manufacturing service resource state of each manufacturer to meet the requirement
Wherein liIs at tiThe task has not yet been assigned at that time.
For unallocated production tasks
The traversal will continue to be performed and manufacturing service resources will be dynamically allocated to other manufacturers in an idle state until the production tasks of the buyer are allocated complete locations.
The production tasks and manufacturing service resources which have already been allocated are added to the deletion list for destruction. While it is removed from the container V for the manufacturer that has finished processing. At this point, at t has already been completed0Traversing the production state of each manufacturer at any moment, and enabling t0＝t0+1, start traversing the production state of each manufacturer at the next moment.
A method and system for optimizing dynamic scheduling of manufacturing service resources through a random online algorithm abstract all manufacturers which are located in a mapping range of a buyer and meet production conditions into a series of element objects through early-stage screening, and place the element objects in a container; monitoring the production state of each manufacturer in the container in real time, and traversing the production states of all the manufacturers in a data stream mode; randomly selecting a manufacturer with production capacity for each moment in the past production cycle, reviewing the production state sequence of the manufacturer in the past production cycle, and preferentially arranging all production tasks in a complete production cycle starting from the moment to the manufacturer; introducing a random online algorithm, and searching a threshold value through a reasonably designed probability density function; the manufacturer is marked as popular when its production volume in the past production cycle reaches this threshold. The manufacturer marked as popular should undertake the production task issued by the buyer at the moment, take the corresponding manufacturing service resource from the resource pool to carry out production, and establish effective contact between the buyer and the manufacturer; on the contrary, when the number of the products produced by the manufacturer in the past production cycle does not reach the threshold, in order to save cost, another manufacturer is randomly selected again at the moment to perform corresponding processing, and meanwhile, the original manufacturer is put into the container again to wait for the processing of the next cycle; adding the manufacturing service resources to be transferred into a transfer list, and updating the current states of the manufacturing service resources of each manufacturer; adding the manufacturing service resources that have failed to the delete list and removing the containers from the manufacturers that have processed; by traversing all manufacturers in the container, production tasks and manufacturing service resources are dynamically allocated to each manufacturer, thereby reducing the overall cost of production.
Example two
The embodiment provides a manufacturing service resource dynamic scheduling system based on a random online algorithm;
the manufacturing service resource dynamic scheduling system based on the random online algorithm comprises:
a storage module configured to: storing the serial numbers of all manufacturer clients with the distance between the manufacturer clients and the buyer clients within a set range in a container;
a first receiving module configured to: receiving a production target and a production resource submitted by a buyer client connected with a server;
a storage module configured to: storing the received production target and the production resource in a resource pool;
a second receiving module configured to: receiving production state data submitted by a plurality of manufacturer clients connected with a server;
a traversal module configured to: traversing the production state data submitted by all manufacturer clients; storing the production state data of each manufacturer at each moment in a queue;
a push module configured to: and according to the production state data of each manufacturer at each moment, randomly selecting one manufacturer capable of realizing the production target, and pushing the production resources in one production period to the corresponding manufacturer client.
It should be noted here that the storage module, the first receiving module, the storage module, the second receiving module, the traversing module, and the pushing module correspond to steps S101 to S106 in the first embodiment, and the modules are the same as the corresponding steps in the implementation example and application scenario, but are not limited to the disclosure in the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
In the foregoing embodiments, the descriptions of the embodiments have different emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The proposed system can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the above-described modules is merely a logical division, and in actual implementation, there may be other divisions, for example, multiple modules may be combined or integrated into another system, or some features may be omitted, or not executed.
EXAMPLE III
The present embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, a processor is connected with the memory, the one or more computer programs are stored in the memory, and when the electronic device runs, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the method according to the first embodiment.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.
The method in the first embodiment may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
Example four
The present embodiments also provide a computer-readable storage medium for storing computer instructions, which when executed by a processor, perform the method of the first embodiment.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A manufacturing service resource dynamic scheduling method based on a random online algorithm is applied to a server and is characterized by comprising the following steps:
storing the serial numbers of all manufacturer clients with the distance between the manufacturer clients and the buyer clients within a set range in a container;
receiving a production target and a production resource submitted by a buyer client connected with a server;
storing the received production target and the production resource in a resource pool;
receiving production state data submitted by a plurality of manufacturer clients connected with a server;
traversing the production state data submitted by all manufacturer clients; storing the production state data of each manufacturer at each moment in a queue;
and according to the production state data of each manufacturer at each moment, randomly selecting one manufacturer capable of realizing the production target, and pushing the production resources in one production period to the corresponding manufacturer client.
2. The method for dynamically scheduling manufacturing service resources based on a random online algorithm according to claim 1, wherein the method further comprises:
calculating a threshold value based on a random online algorithm and a probability density function;
judging whether the yield of the current manufacturer in the latest production cycle reaches the threshold value, if so, continuing to push the production resources in the next production cycle to the corresponding manufacturer client; meanwhile, establishing a communication relationship between the manufacturer client and the buyer client;
if not, traversing the production state data submitted by all the manufacturer clients again; and according to the production state data of each manufacturer at each moment, randomly selecting one manufacturer capable of realizing the production target, pushing the production resources in one production period to the corresponding manufacturer client, and meanwhile, putting the original manufacturer into a container to wait for the next period of processing.
3. The method for dynamically scheduling manufacturing service resources based on the random online algorithm according to claim 1, wherein the production resources in one production cycle are pushed to the corresponding manufacturer client; the method comprises the following specific steps:
adding the production resources in a production period into the transfer list, and updating the manufacturing service resource state of each manufacturer client;
the manufacturing service resource that should be invalidated is added to the delete list, and the manufacturer client number that has been revoked is removed from the container.
4. The method for dynamically scheduling manufacturing service resources based on a random online algorithm according to claim 1, wherein the method further comprises: and for the newly submitted and submitted orders of the client side of the buyer, carrying out production task allocation and manufacturing service resource scheduling according to whether the actual production state of each client side of the current manufacturer is saturated or not.
5. The dynamic scheduling method of manufacturing service resources based on random online algorithm as claimed in claim 1 or 2, wherein a manufacturer capable of achieving production goal is randomly selected; the method comprises the following specific steps:
order to
For the number of products manufacturer k can produce at time i, let diTotal number of demands of the buyer for the product at time i;
when in use
When, it means that manufacturer k can afford the buyer an amount of
The production task of (1);
when in use
Indicating that manufacturer k is saturated in production at time i, i.e., unable to assume additional production tasks.
6. The method for dynamically scheduling manufacturing service resources based on random online algorithm according to claim 2, wherein if not, the production state data submitted by all manufacturer clients are traversed again; randomly selecting a manufacturer capable of realizing a production target according to the production state data of each manufacturer at each moment, and pushing production resources in a production period to a corresponding manufacturer client; the method specifically comprises the following steps:
assume that the current time is t0Randomly selecting a manufacturer k with production capacity at the current moment, wherein one production cycle of the manufacturer is T; suppose from t0-T +1 to T0All production tasks within the time period are prioritized to this manufacturer K, which is counted from t0-T +1 to T0Number of products u produced in a time periodkWherein
If the current production state of the manufacturer k is saturated, the occupied manufacturing service resources are released, and the corresponding production task is distributed to other manufacturers k'.
7. The method of claim 2, wherein a threshold is calculated based on the stochastic online algorithm and the probability density function; the method specifically comprises the following steps:
the threshold z is obtained by the following probability density function designed in a random online algorithm:
where R is the initial cost the manufacturer pays to produce, α is the discount the manufacturer enjoys on production after the production reaches a certain standard, p is the unit price, e is the natural index, δ (·) is the Dike δ function, β is a balance point, which represents the point at t0At the moment, whether the manufacturer k accepts or rejects the production task issued by the buyer, the production cost of the manufacturer k and the production cost of the buyer are the same;
if so, continuing to push the production resources in the next production period to the corresponding manufacturer client; meanwhile, establishing a communication relationship between the manufacturer client and the buyer client; the method specifically comprises the following steps:
when u isk>z, mark manufacturer k as popular, at t0At the moment, the manufacturer should receive the production task issued by the buyer, and take the corresponding manufacturing service resource from the resource pool to carry out production; meanwhile, the buyer establishes a cooperative relationship with the manufacturer k, so that the production cost of the manufacturer k can be effectively reduced in the whole production period;
if not, traversing the production state data submitted by all the manufacturer clients again; randomly selecting a manufacturer capable of realizing a production target according to the production state data of each manufacturer at each moment, pushing production resources in a production period to a corresponding manufacturer client, and meanwhile, putting an original manufacturer into a container to wait for the next period of processing; the method specifically comprises the following steps:
when u isk< z at t0At the moment, the manufacturer k should not accept the production task issued by the buyer, namely, the buyer is not suggested to establish a cooperative relationship with the manufacturer k, otherwise, certain cost loss is caused; at this moment, another manufacturer k' with production capacity is randomly selected again, and u is calculatedk′Wherein
Re-randomly generating the threshold value z using a probability density function, and dividing uk′Comparing with z, and making corresponding decision through analysis;
adding the production resources in one production period into a transfer list, and updating the states of the manufacturing service resources of the clients of various manufacturers; adding the manufacturing service resource which should be invalidated into a deletion list, and removing the manufacturer client number which is cancelled from a container; the method specifically comprises the following steps:
when manufacturer k is at tiAfter receiving the production task issued by the buyer, the system starts to produce and updates the current manufacturing service resource state of each manufacturer to meet the requirement
Wherein liIs at tiA task that has not yet been allocated at a time;
for unallocated production tasks
Continuously executing traversal, and dynamically allocating manufacturing service resources to other manufacturers in an idle state until the production tasks of the buyers are allocated to the positions;
adding the production tasks and the manufacturing service resources which are distributed into the deletion list for destruction; at the same time, the manufacturer who has finished processing removes the manufacturer from the container V; at this point, at t has already been completed0Traversing the production state of each manufacturer at any moment, and enabling t0＝t0+1, start traversing the production state of each manufacturer at the next moment.
8. The manufacturing service resource dynamic scheduling system based on the random online algorithm is characterized by comprising the following steps:
a storage module configured to: storing the serial numbers of all manufacturer clients with the distance between the manufacturer clients and the buyer clients within a set range in a container;
a first receiving module configured to: receiving a production target and a production resource submitted by a buyer client connected with a server;
a storage module configured to: storing the received production target and the production resource in a resource pool;
a second receiving module configured to: receiving production state data submitted by a plurality of manufacturer clients connected with a server;
a traversal module configured to: traversing the production state data submitted by all manufacturer clients; storing the production state data of each manufacturer at each moment in a queue;
a push module configured to: and according to the production state data of each manufacturer at each moment, randomly selecting one manufacturer capable of realizing the production target, and pushing the production resources in one production period to the corresponding manufacturer client.
9. An electronic device, comprising: one or more processors, one or more memories, and one or more computer programs; wherein a processor is connected to the memory, the one or more computer programs being stored in the memory, the processor executing the one or more computer programs stored in the memory when the electronic device is running, to cause the electronic device to perform the method of any of the preceding claims 1-7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 7.
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