CN110110935B - CPN (CPN neural network) -based enterprise production scheduling optimization system and implementation method - Google Patents
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Abstract
The invention relates to the technical field of manufacturing industry, and discloses an enterprise production schedule optimization system based on a CPN (CPN neural network), which is characterized in that: the system comprises a scheduling optimization system ERP interface module, a production task parameter input module, a CPN neural network computing unit and a scheduling output interface, wherein the scheduling optimization system ERP interface module is in communication connection with the production task parameter input module, and the production task parameter input module is in communication connection with the CPN neural network computing unit. Continuously adjusting the weight of neurons connected with the competitive layer by the input layer according to the competitive network through the CPN neural network to the input production task parameters; through continuous iteration of the competition network layer, competition elimination is realized; the weight vector of the output layer of the production task is adjusted by the weight of the neuron in the winning competition and the output layer connected with the neuron, and iteration is carried out, so that the aim of optimizing the scheduling is fulfilled.
Description
Technical Field
The invention relates to the technical field of manufacturing industry, in particular to an enterprise production scheduling optimization system based on a CPN (CPN neural network) and an implementation method.
Background
At present, the manufacturing industry is one of important props of national economy in China, under the new situation of the improvement of the manufacturing technology, more manufacturing enterprises pay more attention to the high efficiency, the refinement and the intellectualization of the production process under the condition that a production system is stable and reliable, at present, most of the manufacturing enterprises mainly rely on scheduling staff or scheduling staff to schedule orders according to the information of the emergency degree, the processing number, the processing type and the like of production tasks by means of personal working experience, and along with the promotion of the transformation upgrading process of the manufacturing enterprises, the informatization construction is continuously increased, the scheduling problem of the production process is increasingly outstanding, and the method comprises the following steps of:
when the production process has various processing types and large production scheduling workload, once the processing types are changed, the production scheduling is possibly rearranged, the manual scheduling difficulty is high, the scheduling progress is slow, the enterprise production scheduling plan is influenced, and the task scheduling efficiency is low.
When the scheduling is influenced by various factors such as the emergency degree of production tasks, the processing quantity and the processing types in the production process, the number of production workshop scheduling or production scheduling staff is limited, and it is difficult to establish good production scheduling by means of personal subjective experience of the scheduling or production scheduling staff, especially under the condition that the table occurs simultaneously by various production factors, the scheduling or production scheduling staff is difficult to avoid unreasonable task scheduling under large scheduling pressure, the scheduling effect is poor, and even the production efficiency is influenced.
The manual scheduling and the production task scheduling are mostly required to be scheduled in advance, and corresponding production task scheduling plans are formulated, so that paper reporting is often required to be carried out on related leaders for auditing, and the auditing process is complex and has poor flexibility.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an enterprise production scheduling optimization system and an implementation method based on a CPN neural network, which have the advantage of optimizing scheduling, and solve the problems of high manual scheduling difficulty and poor scheduling approval flexibility under the influence of multi-factor production conditions.
The invention provides the following technical scheme: an enterprise production scheduling optimization system based on a CPN (complex programmable network) neural network comprises an ERP interface module of the scheduling optimization system, a production task parameter input module, a CPN neural network computing unit and a scheduling output interface, wherein the ERP interface module of the scheduling optimization system is in communication connection with the production task parameter input module, the production task parameter input module is in communication connection with the CPN neural network computing unit, and the CPN neural network computing unit is in communication connection with the scheduling output interface module;
the scheduling optimization system ERP interface module takes the data information of the production plan of the ERP system as a bridge, and sends a request information request through the interface ERP;
the production task parameter input module is used for adjusting the data structure, carrying out normalization processing and quantization processing on the same class of data of different products, and finishing production task coding;
the CPN neural network calculation unit adjusts the network weight vector according to the input production task parameters, and automatically completes the scheduling optimization of the production task to the propagation neural network;
and the scheduling output interface module finishes the output-optimized production task scheduling.
A method for realizing enterprise production schedule optimization based on CPN neural network comprises the following steps:
step S1: an enterprise production scheduling optimization system of the CPN neural network sends a production plan data request command to an ERP system;
step S2: the production task parameter input module is used for adjusting the type and the data structure of the data information of the production plan, carrying out normalization processing and quantization processing on the same type of data of different production tasks, wherein the production input parameters are as follows:
P K ＝(P 1 ,P 2 ,…,P N )
the normalization processing of the single group of production planning tasks is as follows:
wherein the number of tasks is N, the input vector is PN,
step S4: determining production task parameters, the number of neural network input layers, the number of competing network layers, the number of neural nodes of the competing network layers, the number of neural network output layers and production scheduling output parameters which are input by a scheduling model, wherein the input parameters are PN, the number of the neural network input layers sin=1, the number of input neurons is N, the number of competing network layers sq=1, the number of the neural network output competing neurons is (n+1), the number of the neural network output layers so=1, and the number of output neurons is N;
step S5: carrying out normalization processing on the weight vector of the neural network layer number of the input layer in the model, and calculating the weight of the competitive layer number in the scheduled neural model;
the input neuron and competing layer neuron weight vectors are normalized as follows:
the sum of the competing layer neuron weight vectors is:
the maximum weight Wm of the input neuron and the competitive layer neuron is as follows:
meanwhile, the output weight of the competing neuron m is 1, and the adjustment weight factor Wm is Wp, and is as follows:
W p (t+1)＝W p (t)+ξ(P P -W p (t))(p＝1,…,N)
wherein W is p (0)＝W m (0) ζ is an iteration factor of the weights of the input neurons and the competing layer neurons;
step S6: adjusting a connection weight vector of neurons of a competing layer and neurons of an output layer in a scheduled neural model, the adjusted weight vector V of neurons of the competing layer and neurons of the output layer jk The method comprises the following steps:
V jk (t+1)＝V jk (t)+σλ m (C k -C j )
wherein C is an output layer neuron, sigma is an iteration factor of the weights of the output neuron and a competitive layer neuron, and lambada m is the weight of the output layer to which the competitive layer winning neuron is connected;
step S7: and outputting the optimized corresponding production task schedule according to the production task codes.
Preferably, the data information to be scheduled in step S1 includes a production task type, a production task workload, a production process number, and a production task urgency.
Preferably, the data quantization in step S2 is divided into two types, one type is a unified expression form of the same parameter for different production tasks; the other is to normalize different input parameters of the production task participated in the scheduling, remove the dimension of the scheduling task and enable the value of a single parameter to fall between 0 and 1.
Preferably, the layer weight vector of the neural network model in step S5 is adjusted, the weight vectors connected in the input layer and the competing layer are normalized, the connection weights of the neurons in the input layer and the competing layer are summed, and the nearest vector of the connection weight vector and the input parameter is calculated.
Preferably, the neurons and records from the input layer to the maximum weight in the competitive layer in step S5 are reserved, the output of the neurons is set to 1, the neuron outputs of the rest competitive layers are set to 0, and the competitive layer weight vector is modified.
Compared with the prior art, the invention has the following beneficial effects:
according to the CPN-based enterprise production scheduling optimization system and the implementation method, the weight of neurons connected with a competitive layer at an input layer is continuously adjusted according to the competitive network through the CPN neural network on the input production task parameters; through continuous iteration of the competition network layer, competition elimination is realized; the weight vector of the output layer of the production task is adjusted by the weight of the neuron in the winning competition and the output layer connected with the neuron, and iteration is carried out, so that the aim of optimizing the scheduling is fulfilled.
Drawings
FIG. 1 is a block diagram of the system architecture of the present invention.
Figure 2 is a system flow diagram of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present disclosure. It will be apparent that the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments, which can be made by one of ordinary skill in the art without the need for inventive faculty, are within the scope of the present disclosure, based on the described embodiments of the present disclosure.
Unless defined otherwise, technical or scientific terms used in this disclosure should be given the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure belongs. The use of the terms "comprising" or "includes" and the like in this disclosure is intended to cover an element or article listed after that term and equivalents thereof without precluding other elements or articles. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may also include electrical connections, whether direct or indirect.
In order to keep the following description of the embodiments of the present disclosure clear and concise, the present disclosure omits detailed description of known functions and known components to avoid unnecessarily obscuring the concepts of the present invention.
Referring to fig. 1-2, an enterprise production scheduling optimization system based on a CPN neural network, the system includes a scheduling optimization system ERP interface module, a production task parameter input module, a CPN neural network computing unit, and a scheduling output interface, where the scheduling optimization system ERP interface module is communicatively connected with the production task parameter input module, the production task parameter input module is communicatively connected with the CPN neural network computing unit, and the CPN neural network computing unit is communicatively connected with the scheduling output interface module;
the scheduling optimization system ERP interface module takes the data information of the production plan of the ERP system as a bridge, and sends a request information request through the interface ERP;
the production task parameter input module is used for adjusting the data structure, carrying out normalization processing and quantization processing on the same class of data of different products, and finishing production task coding;
the CPN neural network calculation unit adjusts the network weight vector according to the input production task parameters, and automatically completes the scheduling optimization of the production task to the propagation neural network;
and the scheduling output interface module finishes the output-optimized production task scheduling.
A method for realizing enterprise production schedule optimization based on CPN neural network comprises the following steps:
step S1: an enterprise production scheduling optimization system of the CPN neural network sends a production plan data request command to an ERP system;
step S2: the production task parameter input module is used for adjusting the type and the data structure of the data information of the production plan, carrying out normalization processing and quantization processing on the same type of data of different production tasks, wherein the production input parameters are as follows:
P K ＝(P 1 ,P 2 ,…,P N )
the normalization processing of the single group of production planning tasks is as follows:
wherein the number of tasks is N, the input vector is PN,
step S4: determining production task parameters, the number of neural network input layers, the number of competing network layers, the number of neural nodes of the competing network layers, the number of neural network output layers and production scheduling output parameters which are input by a scheduling model, wherein the input parameters are PN, the number of the neural network input layers sin=1, the number of input neurons is N, the number of competing network layers sq=1, the number of the neural network output competing neurons is (n+1), the number of the neural network output layers so=1, and the number of output neurons is N;
step S5: carrying out normalization processing on the weight vector of the neural network layer number of the input layer in the model, and calculating the weight of the competitive layer number in the scheduled neural model;
the input neuron and competing layer neuron weight vectors are normalized as follows:
the sum of the competing layer neuron weight vectors is:
the maximum weight Wm of the input neuron and the competitive layer neuron is as follows:
meanwhile, the output weight of the competing neuron m is 1, and the adjustment weight factor Wm is Wp, and is as follows:
W p (t+1)＝W p (t)+ξ(P P -W p (t))(p＝1,…,N)
wherein W is p (0)＝W m (0) ζ is an iteration factor of the weights of the input neurons and the competing layer neurons;
step S6: adjusting connection weight vector of neurons of competitive layer and neurons of output layer in scheduling neural modelAdjusted weight vector V of element jk The method comprises the following steps:
V jk (t+1)＝V jk (t)+σλ m (C k -C j )
wherein C is an output layer neuron, sigma is an iteration factor of the weights of the output neuron and a competitive layer neuron, and lambada m is the weight of the output layer to which the competitive layer winning neuron is connected;
step S7: and outputting the optimized corresponding production task schedule according to the production task codes.
In an alternative embodiment, the data information to be scheduled in step S1 includes a production task type, a production task workload, a production process number, and a production task urgency.
In an alternative embodiment, the data quantization in step S2 is divided into two types, one being the same parameter unified expression form for different production tasks; the other is to normalize different input parameters of the production task participated in the scheduling, remove the dimension of the scheduling task and enable the value of a single parameter to fall between 0 and 1.
In an alternative embodiment, the layer weight vector of the neural network model in step S5 is adjusted, the weight vectors of the connection in the input layer and the competing layer are normalized, the connection weights of the neurons in the input layer and the competing layer are summed, and the nearest vector of the connection weight vector and the input parameter is calculated.
In an alternative embodiment, the neuron and record from the input layer to the maximum weight in the competitive layer in step S5 are reserved, the output of the neuron is set to 1, the neuron outputs of the rest of the competitive layers are set to 0, and the competitive layer weight vector is modified.
According to the invention, the weight of neurons connected with the competitive layer at the input layer is continuously adjusted according to the competitive network by using the CPN neural network to the input production task parameters; through continuous iteration of the competition network layer, competition elimination is realized; the weight vector of the output layer of the production task is adjusted by the weight of the neuron in the winning competition and the output layer connected with the neuron, and iteration is carried out, so that the aim of optimizing the scheduling is fulfilled.
The above embodiments are only exemplary embodiments of the present invention and are not intended to limit the present invention, the scope of which is defined by the claims. Various modifications and equivalent arrangements of this invention will occur to those skilled in the art, and are intended to be within the spirit and scope of the invention.
Claims (6)
1. The method for realizing the optimization of the production schedule of the enterprise based on the CPN neural network is characterized by comprising the following steps:
step S1: an enterprise production scheduling optimization system of the CPN neural network sends a production plan data request command to an ERP system;
step S2: the production task parameter input module is used for adjusting the type and the data structure of the data information of the production plan, carrying out normalization processing and quantization processing on the same type of data of different production tasks, wherein the production input parameters are as follows:
PK＝(P1,P2,…,PN)
the normalization processing of the single group of production planning tasks is as follows:
wherein the number of tasks is N, the input vector is PN,
step S3: determining production task parameters, the number of neural network input layers, the number of competing network layers, the number of neural nodes of the competing network layers, the number of neural network output layers and production scheduling output parameters which are input by a scheduling model, wherein the input parameters are PN, the number of the neural network input layers sin=1, the number of input neurons is N, the number of competing network layers sq=1, the number of the neural network output competing neurons is (n+1), the number of the neural network output layers so=1, and the number of output neurons is N;
step S4: carrying out normalization processing on the weight vector of the neural network layer number of the input layer in the model, and calculating the weight of the competitive layer number in the scheduled neural model;
the input neuron and competing layer neuron weight vectors are normalized as follows:
the sum of the competing layer neuron weight vectors is:
the maximum weight Wm of the input neuron and the competitive layer neuron is as follows:
meanwhile, the output weight of the competing neuron m is 1, and the adjustment weight factor Wm is Wp, and is as follows:
Wp(t+1)＝Wp(t)+ξ(PP-Wp(t))(p＝1,…,N)
wherein Wp (0) =wm (0), ζ is an iteration factor of the weights of the input neurons and the competing layer neurons;
step S5: the connection weight vector of the neurons of the competitive layer and the neurons of the output layer in the scheduling neural model is adjusted, and the adjusted weight vector Vjk of the neurons of the competitive layer and the neurons of the output layer is:
Vjk(t+1)＝Vjk(t)+σλm(Ck-Cj)
wherein C is an output layer neuron, sigma is an iteration factor of the weights of the output neuron and a competitive layer neuron, and lambada m is the weight of the output layer to which the competitive layer winning neuron is connected;
step S6: and outputting the optimized corresponding production task schedule according to the production task codes.
2. The method for optimizing production schedule of enterprise based on CPN neural network according to claim 1, wherein the data information to be scheduled in step S1 includes production task type, production task workload, production process number and production task urgency.
3. The method for optimizing production schedule of enterprise based on CPN neural network according to claim 1, wherein the data quantization in step S2 is divided into two types, one type is the same parameter unified expression form for different production tasks; the other is to normalize different input parameters of the production task participated in the scheduling, remove the dimension of the scheduling task and enable the value of a single parameter to fall between 0 and 1.
4. The method for optimizing production schedule of enterprise based on CPN neural network according to claim 1, wherein the layer weight vector of the neural network model in step S5 is adjusted, the weight vectors of the connection in the input layer and the competing layer are normalized, the connection weights of the neurons in the input layer and the competing layer are summed, and the nearest vector of the connection weight vector and the input parameter is calculated.
5. The method of claim 1, wherein the neurons and records from the input layer to the maximum weight in the competitive layer in step S5 are reserved, the output of the neurons is set to 1, the output of the neurons of the other competitive layers is set to 0, and the competitive layer weight vector is modified.
6. A system for implementing a method for implementing CPN-neural network-based enterprise production schedule optimization as claimed in any one of claims 1-5, wherein: the system comprises a scheduling optimization system ERP interface module, a production task parameter input module, a CPN neural network computing unit and a scheduling output interface, wherein the scheduling optimization system ERP interface module is in communication connection with the production task parameter input module, the production task parameter input module is in communication connection with the CPN neural network computing unit, and the CPN neural network computing unit is in communication connection with the scheduling output interface module;
the scheduling optimization system ERP interface module takes the data information of the production plan of the ERP system as a bridge, and sends a request information request through the interface ERP;
the production task parameter input module is used for adjusting the data structure, carrying out normalization processing and quantization processing on the same class of data of different products, and finishing production task coding;
the CPN neural network calculation unit adjusts the network weight vector according to the input production task parameters, and automatically completes the scheduling optimization of the production task to the propagation neural network;
the schedule output interface completes the output-optimized production task schedule.
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