CN107330608A - A kind of production scheduling method based on Techniques of Neural Network - Google Patents
A kind of production scheduling method based on Techniques of Neural Network Download PDFInfo
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Abstract
The invention discloses a kind of production scheduling method based on Techniques of Neural Network, methods described comprises the following steps：Extracted by critical data and resource is abstracted into neuron node by mathematical modeling in a computer；Judge whether each neuron node is in idle condition；When judged result is that neuron node is idle, the maximum scheduling plan of lower probability that imposes a condition is calculated using neural network algorithm.Present invention can apply to manufacturing business, production efficiency and utilization rate of equipment and installations can be improved, potential production production capacity is fully excavated.
Description
Technical field
The present invention relates to the field of manufacturing, more particularly to a kind of production scheduling method based on Techniques of Neural Network.
Background technology
Manufacturing in industry enterprise, often with multiple resources, multiple product tasks, each product task often has again
Multiple process tasks, higher efficiency and Geng Gao production capacities are reached, it is necessary to rationally scientifically arrange the life of each product in order that must produce
Production task.Therefore, for manufacturing industry enterprise, efficient production scheduling is most important.Current most enterprises are all adopted
With the manual scheduling mode based on paper list, but with order volume increase and product category increase, traditional manual method
Deficiency is dealt with, especially when running into the customization order of multiple types small lot, and manual scheduling easily causes production scene chaotic,
It can not more ensure that product hands over the phase.To solve manual scheduling problem, present Some Enterprises, which have begun to use, carries automatic arrangement program
The information system of function, such as ERP, MES, but this type of information system can only be true according to being provided in live automation equipment or factory
Determine creation data（Such as standard machining period, number of workers）Automatic arrangement program could be carried out, the production in factory is not accounted for
There is dynamic changing process, and the probability change that different production schedulings caused can process production capacity in activity, in the case, should
Production production capacity is frequently can lead to after category information online implementing and declines the problems such as can not implementing with production planning and sequencing.It can see in summary
Go out, manufacture industry enterprise be badly in need of it is a kind of can really improve production production capacity, dynamic can be adjusted according to factory's uncertain data
Whole automatic production scheduling system.
The content of the invention
In view of this, it is an object of the invention to provide a kind of production scheduling method based on Techniques of Neural Network, carry
High enterprises production efficiency and utilization rate of equipment and installations, fully excavate potential production production capacity.Its specific method is as follows：
A kind of production scheduling method based on Techniques of Neural Network, comprises the following steps：
Step one, extracted by critical data and resource is abstracted into neuron node by mathematical modeling in a computer；
Step 2, judges whether each neuron node is in idle condition；
Step 3, when judged result is that neuron node is idle, calculated using neural network algorithm impose a condition it is lower general
The maximum scheduling plan of rate.
Further, in step one, resource can be people, equipment etc., and the existing forms of resource are not limited.
Further, in step one, neuron node can be a model, a program or one section in computer
Code etc., the existing forms of neuron node in a computer are not limited.
Further, in step one, set up between neuron node and real resources by communication equipment and communication network
Correspondence, the behavior that resource truly occurs will be recorded in the data space of neuron node one by one.
Further, in step one, neuron node can calculate various rows by continuous education resource historical data
For the probability of generation.
Further, in step 2, when neuron node does not have task or the number of tasks to be less than setting value, then neuron section
Point is in idle condition.
Further, in step 2, if judged result, which is neuron node, is not located in idle condition, without rear
Continuous scheduling processing.
Further, in step 3, neural network algorithm first calculates a number of scheduling, neuron node pair
Each scheduling feedback probability, the joint that the probability calculation that neural network algorithm feeds back according to neuron node goes out each scheduling is general
Rate, is picked out in the maximum scheduling of lower probability that imposes a condition.
Further, in step 3, including but not limited to production capacity maximization, quality maximization, efficiency are imposed a condition most
Bigization etc..
Brief description of the drawings
Accompanying drawing described herein is used for providing a further understanding of the present invention, constitutes the part of the application, of the invention
Schematic description and description is used to explain the present invention, does not constitute inappropriate limitation of the present invention.
Fig. 1 is a kind of step flow chart of the production scheduling method based on Techniques of Neural Network of the present invention.
Fig. 2 is a kind of neuron node figure using the present invention.
Embodiment
Illustrate embodiments of the present invention below by particular specific embodiment, the present invention is furture elucidated, Ying Li
These embodiments are solved to be only illustrative of the invention and is not intended to limit the scope of the invention, after the present invention has been read, ability
The technical staff in domain is to the modification of the various equivalent form of values of the present invention in appended claims limited range of the present invention.
Fig. 1 is a kind of step flow chart of the production scheduling method based on Techniques of Neural Network of the present invention.Such as Fig. 1 institutes
Show that a kind of production scheduling method based on Techniques of Neural Network of the present invention comprises the following steps：
Step 101, extracted by critical data and resource is abstracted into neuron node by mathematical modeling in a computer.Here institute
The resource stated is the people for task of realizing, neuron node is a class object in computer program.Resource described here is led to
Cross smart machine and the network equipment to communicate with the neuron node foundation in computer, resource occurs real behavior and passes through intelligence
Energy equipment and wireless network record are in the data space of neuron node.Neuron node can pass through continuous education resource history
Data, calculate the probability of various actions generation.
Step 102, judge whether each neuron node is in idle condition.If current time resource does not have task or appointed
Number be engaged in less than setting value, then the corresponding neuron node of resource is in idle condition.In present pre-ferred embodiments, number of tasks
Arranges value can be set according to the experience of user.
Step 103, when judged result is that neuron node is idle, calculated and imposed a condition using neural network algorithm
The maximum scheduling plan of lower probability.
The present invention will be further illustrated by a specific embodiment below：
Fig. 2 is a kind of neuron node figure using the present invention.As shown in Fig. 2 employee in enterprise is abstracted into nerve by the present invention
First node, and each neuron node is connected according to the tissue frame of enterprise.
Assuming that having product task A, B, each product task currently only includes 1 process task, and phase between product task
It is mutually independent, for example have：A{a1}；B{b1}；Assuming that having two resource workshop group leaders 1 and workshop group leader 2；Each resource can only be processed
One process.
Assuming that current inter-vehicular group leader 1 and workshop group leader 2 are all in idle condition, neural network algorithm is directed to workshop group leader
1 and the permutation and combination of workshop group leader 2 go out two kinds of scheduling plan P1 { a1, b1 } and P2 { b1, a1 }, it is assumed that impose a condition to be processed into
Work(, it is 60% that the node of workshop group leader 1 provides the processing probability of success for scheduling P1, and the node of workshop group leader 2 is provided for scheduling P1 to be added
The work probability of success is 80%, and it is 70%, the node pair of workshop group leader 2 that the node of workshop group leader 1 provides the processing probability of success for scheduling P2
It is 70% to provide the processing probability of success in scheduling P2.The joint that neural network algorithm calculates scheduling P1 and P2 processes successfully general
Rate, and judge that P1 probability is more than P2, select scheduling P1 to be used as final scheduling for this.
Claims (9)
1. a kind of production scheduling method based on Techniques of Neural Network, it is characterised in that methods described comprises the following steps：
Step one, extracted by critical data and resource is abstracted into neuron node by mathematical modeling in a computer；
Step 2, judges whether each neuron node is in idle condition；
Step 3, when judged result is that neuron node is idle, calculated using neural network algorithm impose a condition it is lower general
The maximum scheduling plan of rate.
2. the production scheduling method according to claim 1 based on Techniques of Neural Network, it is characterised in that in step one
In, resource can be people, equipment etc., and the existing forms of resource are not limited.
3. the production scheduling method according to claim 1 based on Techniques of Neural Network, it is characterised in that in step one
In, neuron node can be a model, a program or one section of code etc. in computer, and neuron node is not limited and is existed
Existing forms in computer.
4. the production scheduling method according to claim 1 based on Techniques of Neural Network, it is characterised in that in step one
In, correspondence, the row that resource truly occurs are set up by communication equipment and communication network between neuron node and real resources
For that will record one by one in the data space of neuron node.
5. the production scheduling method according to claim 1 based on Techniques of Neural Network, it is characterised in that in step one
In, neuron node by continuous education resource historical data, can calculate the probability of various actions generation.
6. the production scheduling method according to claim 1 based on Techniques of Neural Network, it is characterised in that in step 2
In, when neuron node does not have task or number of tasks less than setting value, then neuron node is in idle condition.
7. the production scheduling method according to claim 1 based on Techniques of Neural Network, it is characterised in that in step 2
In, if judged result, which is neuron node, is not located in idle condition, handled without follow-up scheduling.
8. the production scheduling method according to claim 1 based on Techniques of Neural Network, it is characterised in that in step 3
In, neural network algorithm first calculates a number of scheduling, and neuron node is to each scheduling feedback probability, neuron net
The probability calculation that network algorithm feeds back according to neuron node goes out the joint probability of each scheduling, picks out and is imposing a condition lower probability most
Big scheduling.
9. the production scheduling method according to claim 1 based on Techniques of Neural Network, it is characterised in that in step 3
In, the including but not limited to production capacity that imposes a condition is maximized, quality is maximized, efficiency is maximized etc..
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111506552A (en) * | 2019-01-30 | 2020-08-07 | 宁波创元信息科技有限公司 | Dynamic database design method and system of tree structure |
| CN111932217A (en) * | 2020-10-10 | 2020-11-13 | 宁波创元信息科技有限公司 | Neural-MOS neuron network intelligent production operating system and operation method thereof |
| CN113297314A (en) * | 2021-07-28 | 2021-08-24 | 深圳市永达电子信息股份有限公司 | Data visualization method and device and storage medium |
| CN114139879A (en) * | 2021-11-08 | 2022-03-04 | 杭州科强信息技术有限公司 | Intelligent scheduling method under pipeline mode |
| CN114240200A (en) * | 2021-12-21 | 2022-03-25 | 贵州卓讯软件股份有限公司 | Cross-enterprise production management system and method |
| CN114240195A (en) * | 2021-12-21 | 2022-03-25 | 贵州卓讯软件股份有限公司 | Cross-enterprise production scheduling system and method |
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