CN114755982B - Gene expression programming intelligent scheduling method and device based on layered neighborhood structure - Google Patents
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Abstract
The invention discloses a gene expression programming intelligent scheduling method based on a layered neighborhood structure, which comprises the following steps: s1, setting algorithm constraint conditions, combining the existing scheduling conditions of manufacturing enterprises, setting the constraint conditions as basic information of scheduling, wherein the constraint conditions comprise: line quantity constraint, product-line correspondence, line capacity, process route constraint, whether line calendar is on duty or not, and on-duty duration constraint; s2, setting an agent; s3, setting a task target; s4, preparing before delivery; s5, intelligent scheduling calculation, wherein for each task target, each task adopts different scheduling reference determining strategies according to different targets, resource application is carried out, and the strategies are calculated by an intelligent scheduling algorithm; the invention mainly increases the stability of production scheduling and production scheduling to a certain extent through an intelligent production scheduling algorithm, and enhances the scattered manufacturing resources and technology of enterprises to improve the interactive capability.
Description
Technical Field
The invention belongs to the technical field of artificial intelligence, and particularly relates to a gene expression programming intelligent scheduling method and device based on a layered neighborhood structure.
Background
Order scheduling and workshop scheduling of manufacturing enterprises are key to guaranteeing production quality, shortening production cycle and controlling production cost. The traditional enterprise production scheduling and workshop scheduling system belongs to a centralized control type static scheduling system, and is characterized by centralized manufacturing resources, huge production scale, long time span and the like, and is mainly oriented to product manufacturing with single type and simple process structure.
However, in the prior art, the production environment where the enterprise is located is full of various dynamic uncertainties, such as emergency orders, order withdrawal, raw material shortage, equipment failure, personnel departure and other abnormal factors, which seriously interfere with the scheduling of production and production of the enterprise; in addition, in order to expand the production scale, enterprises tend to build processing plants in different places, and the comprehensive utilization of the scattered manufacturing resources and technology of the enterprises has weak interaction capability.
Therefore, the intelligent scheduling method and the intelligent scheduling device for gene expression programming based on the hierarchical neighborhood structure are provided to solve the problems in the prior art, so that for a large-scale complex system, the functions of each Agent in the system are planned, the communication interaction among the agents is coordinated, and the complex problem can be solved rapidly and flexibly.
Disclosure of Invention
The invention aims to provide a gene expression programming intelligent scheduling method and device based on a layered neighborhood structure, which are used for solving the problems that in the prior art, the production environment where an enterprise is located is filled with various dynamic uncertainties, such as emergency orders, order withdrawal, raw material shortage, equipment faults, personnel departure and other abnormal factors, so that the scheduling of the enterprise is seriously disturbed; in addition, in order to expand the production scale, enterprises tend to build processing factories in different places, and the problem of weak interactive capability is solved by comprehensively utilizing scattered manufacturing resources and technology of the enterprises.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the intelligent production scheduling method based on the gene expression programming of the hierarchical neighborhood structure comprises the following steps:
s1, setting algorithm constraint conditions, combining the existing scheduling conditions of manufacturing enterprises, setting the constraint conditions as basic information of scheduling, wherein the constraint conditions comprise: line quantity constraint, product-line correspondence, line capacity, process route constraint, whether line calendar is on duty or not, and on-duty duration constraint;
s2, setting agents, defining the attributes of each agent, and setting action rules of a task agent and a resource agent;
s3, setting task targets, namely setting scheduling targets which are three targets of scheduling and dynamic scheduling support;
s4, preparing before delivery, and performing data processing on each agent meeting constraint conditions according to set contract rules;
s5, intelligent scheduling calculation, wherein for each task target, each task adopts different scheduling reference determining strategies according to different targets, resource application is carried out, and the strategies are calculated by an intelligent scheduling algorithm.
Preferably, the line number constraint in step 1 refers to the number of workshops and the line number of the manufacturing enterprise, for example, the manufacturing enterprise has N workshops and M lines; the line-to-line correspondence is described by a moment Chen Fangshi; the production line capacity is defined at standard working hours of the production line basic information according to the corresponding product tasks of the production line.
Preferably, in the step 2, the Agent is expressed by an Agent, and when the Agent is set, only a task Agent and a resource Agent are set for a simple task; the resource Agent can receive the occupation application of the working procedure of the task Agent to one or more time periods, and after receiving the application, if a certain time period can meet the time requirement of the working procedure, the resource Agent is judged and matched according to the occupation condition of the resource Agent; the task Agent is used for storing process information corresponding to the product type required by the order; the resource Agent stores information of all production test equipment.
Preferably, in the step 2, when the Agent is set, more agents need to be designed for intelligent scheduling of complex tasks, including a management Agent, a resource Agent, an algorithm Agent, a process Agent and a monitoring Agent, wherein the management Agent is a main Agent and is responsible for processing order tasks and coordinating communication and mutual coordination among different agents; the resource Agent comprises a workshop Agent, a production line Agent and a device Agent, and the corresponding relation among the workshop Agent, the production line Agent and the device Agent is described in a matrix mode; the process Agent is responsible for managing the production process of the product and providing inquiry service for other agents; the process Agent prescribes whether the product can be produced, the production process flow, equipment required for production, corresponding raw materials, quality standards which can be achieved by production and the like; the algorithm Agent is used for encapsulating a scheduling and dynamic scheduling algorithm, sending an order task sent by a management Agent to a resource Agent, and calculating resources and process configuration of the order to be completed by combining the resource Agent with a process Agent to call the algorithm Agent; the monitoring Agent is used for detecting the states of all resources in the system, and when each resource, such as equipment, fails or a certain production line cannot normally run because personnel are not enough, the monitoring Agent notifies the resource Agent, and the states of the corresponding resources are updated.
Preferably, the main functions of the management Agent are two, one is used as an external interface, and the order input of a customer is accepted; the other block is in-pair coordination, and each Agent needs to have a unique identifier at the management Agent, so that the coordination management is facilitated; and for the received customer order, automatically calculating an optimal scheduling plan by calling the resource Agent and the algorithm Agent.
Preferably, the three major objectives in step 3 are minimum production time, minimum inventory of product, and maximum resource balance.
Preferably, in the step 5, the intelligent production algorithm is calculated by adopting a gene expression programming algorithm, the gene expression programming algorithm is called GEP for short, and in order that the algorithm can jump out a suboptimal local optimal solution, the neighborhood structure of the gene expression can be changed; the GEP algorithm can continuously search for a better local optimal solution by constructing different neighborhood structures near the current local optimal solution.
Preferably, the neighborhood structure comprises a non-variable element neighborhood structure and a variable element neighborhood structure, the non-variable element neighborhood structure comprises a shift neighborhood structure, a switching neighborhood structure and a variable order neighborhood structure, the variable element neighborhood structure comprises a single-point replacement neighborhood structure, a two-point replacement neighborhood structure and a multi-element replacement neighborhood structure, in order to ensure that the optimal local optimal solution is obtained, the neighborhood structure of all the non-variable elements can be searched, the neighborhood structure of all the variable elements can be searched, or a hierarchical variable neighborhood structure searching method is adopted, the neighborhood structure of the variable elements is selected and searched at the upper layer, the neighborhood structure of all the non-variable elements is searched at the lower layer, and the two are combined, so that the local optimal solution of a larger variable neighborhood range can be obtained.
The device for the intelligent scheduling method of gene expression programming based on the hierarchical neighborhood structure comprises a control module, a data processing module and an execution module, wherein the control module is electrically connected with the data processing module, the data processing module is electrically connected with the execution module, the control module comprises an editing component and an execution component, and the editing component is electrically connected with the execution component.
Preferably, the editing component is used for setting algorithm constraint conditions, agent setting and task target setting; the execution component is used for executing tasks and transmitting data to the data processing module; the data processing module is used for preparing before delivery and calculating intelligent delivery, and the execution module is used for applying for resources.
Compared with the prior art, the intelligent production method and device for gene expression programming based on the hierarchical neighborhood structure have the following advantages:
the invention mainly uses the edit assembly to set constraint conditions, the agent to set and the task target to set, uses the execution assembly to execute the task, transmits the data to the data processing module, uses the data processing module to prepare the production before the production and calculate the intelligent production, finally uses the execution module to apply for the resources, uses the intelligent production algorithm to increase the stability of the production and the production scheduling to a certain extent, and enhances the decentralized manufacturing resources and the interactive capability of the enterprise.
Drawings
FIG. 1 is a flow chart of a hierarchical neighborhood structure-based genetic expression programming intelligent scheduling method of the present invention;
FIG. 2 is a block diagram of an apparatus for a hierarchical neighborhood structure based genetic expression programming intelligent scheduling method of the present invention;
FIG. 3 is a schematic view of a non-changing pixel neighborhood structure according to the present invention;
FIG. 4 is a schematic diagram of an metamaterials neighborhood structure according to the present invention;
FIG. 5 is a flow chart of a hierarchical varying neighborhood structure search method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. The specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides an intelligent production method for gene expression programming based on a hierarchical neighborhood structure, which comprises the following steps:
s1, setting algorithm constraint conditions, combining the existing scheduling conditions of manufacturing enterprises, setting the constraint conditions as basic information of scheduling, wherein the constraint conditions comprise: line quantity constraint, product-line correspondence, line capacity, process route constraint, whether line calendar is on duty or not, and on-duty duration constraint;
s2, setting agents, defining the attributes of each agent, and setting action rules of a task agent and a resource agent;
s3, setting task targets, namely setting scheduling targets which are three targets of scheduling and dynamic scheduling support;
s4, preparing before delivery, and performing data processing on each agent meeting constraint conditions according to set contract rules;
s5, intelligent scheduling calculation, wherein for each task target, each task adopts different scheduling reference determining strategies according to different targets, resource application is carried out, and the strategies are calculated by an intelligent scheduling algorithm;
the production line number constraint in the step 1 refers to the number of workshops and the number of production lines of a manufacturing enterprise, for example, the manufacturing enterprise has N workshops and M production lines; the line-to-line correspondence is described by a moment Chen Fangshi; the production line capacity is defined at standard working hours of the production line basic information according to the corresponding product tasks of the production line;
in the step 2, the Agent is expressed by an Agent, and when the Agent is set, only a task Agent and a resource Agent are set for a simple task; the resource Agent can receive the occupation application of the working procedure of the task Agent to one or more time periods, and after receiving the application, if a certain time period can meet the time requirement of the working procedure, the resource Agent is judged and matched according to the occupation condition of the resource Agent; the task Agent is used for storing process information corresponding to the product type required by the order; the resource Agent is used for storing information of all production test equipment;
in the step 2, when the Agent is set, more agents need to be designed for intelligent scheduling of complex tasks, including a management Agent, a resource Agent, an algorithm Agent, a process Agent and a monitoring Agent, wherein the management Agent is a main Agent and is responsible for processing order tasks and coordinating communication and mutual coordination among different agents; the resource Agent comprises a workshop Agent, a production line Agent and a device Agent, and the corresponding relation among the workshop Agent, the production line Agent and the device Agent is described in a matrix mode; the process Agent is responsible for managing the production process of the product and providing inquiry service for other agents; the process Agent prescribes whether the product can be produced, the production process flow, equipment required for production, corresponding raw materials, quality standards which can be achieved by production and the like; the algorithm Agent is used for encapsulating a scheduling and dynamic scheduling algorithm, sending an order task sent by a management Agent to a resource Agent, and calculating resources and process configuration of the order to be completed by combining the resource Agent with a process Agent to call the algorithm Agent; the monitoring Agent is used for detecting the states of all resources in the system, and when each resource, such as equipment, fails or a certain production line cannot normally run because personnel are not enough, the monitoring Agent notifies the resource Agent, and the states of the corresponding resources are updated;
the management Agent has two main functions, one is used as an external interface, and receives the order input of a customer; the other block is in-pair coordination, and each Agent needs to have a unique identifier at the management Agent, so that the coordination management is facilitated; for the received customer order, automatically calculating an optimal scheduling plan by calling a resource Agent and an algorithm Agent;
the three main targets in the step 3 are the shortest production time, the minimum inventory of products and the maximum resource balance;
the intelligent production scheduling algorithm in the step 5 is calculated by adopting a gene expression programming algorithm, namely GEP for short, and the neighborhood structure of the gene expression can be changed in order that the algorithm can jump out a suboptimal local optimal solution; the GEP algorithm can continuously search for a better local optimal solution by constructing different neighborhood structures near the current local optimal solution;
the neighborhood structure comprises a non-variable element neighborhood structure and a variable element neighborhood structure, the non-variable element neighborhood structure comprises a shift neighborhood structure, a switching neighborhood structure and a variable order neighborhood structure, and the design rule of the non-variable element neighborhood structure can follow the following rule: the tail of the new gene is the same as the tail of the original gene, the element sets are the same, the lengths are the same, and only when the elements are the same as the lengths, the search range can be physically made to be in the vicinity of the current local optimal solution, as shown in fig. 3; the variable element neighborhood structure comprises a single-point replacement neighborhood structure, a two-point replacement neighborhood structure and a multi-element replacement neighborhood structure, in order to ensure that the optimal local optimal solution is obtained, the neighborhood structure of all the constant elements can be searched, the neighborhood structure of all the variable elements can be searched, or a layered variable neighborhood structure searching method is adopted, the neighborhood structure of the variable elements is selected and searched at the upper layer, the neighborhood structure of all the constant elements is searched at the lower layer, and the two are combined, so that the local optimal solution with a larger variable neighborhood range can be obtained, as shown in fig. 4;
the hierarchical variable neighborhood structure searching method comprises the following steps:
A. and (3) each iteration of the GEP algorithm, calculating fitness values of all chromosome individuals in the population, selecting individuals with low fitness, and modifying the variable neighborhood. The set of all the individuals selected to be subjected to the variable neighborhood structure search processing is set as VS= { C1, C2, …, CL }, wherein the fitness values corresponding to the individuals are f1, f2, …, and fL respectively;
B. performing variable neighborhood search operation on all individual Cl in the VS one by one, and ending if the variable neighborhood search operation is traversed;
C. for each Cl, a corresponding set of neighborhood structures ns_s of invariant elements, and a set of domain structures ns_d of variant elements are determined, wherein ns_d belongs to a subset of the terminal set TS. Let ns_s= { S1, S2, …, SN }, ns_d= { D1, D2, …, DM }. In general, ns_s varies from Cl to Cl, and ns_d may be set identically for all Cl, where ns_s is Neighborhood Structure with Same elements and ns_d is Neighborhood Structure with Different elements;
D. as an outer loop, checking whether the variable element in the NS_D is traversed, if so, ending the variable neighborhood search on the individual Cl; returning to the step B; if not, continuing the inner loop unchanged element variable neighborhood search of the step E;
physical meaning of binary pair (Sn, dm): the variable element neighborhood structure and the non-variable element neighborhood structure are combined to form a layered variable neighborhood search design, and a variable neighborhood chromosome with a higher fitness value is searched. The neighborhood structure of the element is unchanged, and only the sequence of the tail of the gene is changed, so that the distance between the gene and the original gene is relatively close; the neighborhood structure of the variable element changes the element and sequence of the tail part of the gene at the same time, so that the variable element is relatively far away from the original gene; the design of the layered variable neighborhood structure can lead the search of the neighborhood range to be from near to far, but all belong to the neighborhood range
E. Solving the fitness value fl (Sn, dm) of the individual after the variable neighborhood aiming at the variable neighborhood structure of the hierarchical binary pair Sn, dm;
F. comparing fl (Sn, dm) with old fitness value fl, if fl (Sn, dm) > fl, cl is replaced with Cl (Sn, dm), fl is replaced with fl (Sn, dm). Until all elements in vs= { C1, C2, …, CL } complete the variational neighborhood search traversal;
G. n=n+1, back to the inner loop step E, as in fig. 5.
The device for the intelligent scheduling method of gene expression programming based on the hierarchical neighborhood structure comprises a control module, a data processing module and an execution module, wherein the control module is electrically connected with the data processing module, the data processing module is electrically connected with the execution module, the control module comprises an editing component and an execution component, and the editing component is electrically connected with the execution component;
the editing component is used for setting algorithm constraint conditions, agent setting and task target setting; the execution component is used for executing the task and transmitting the data to the data processing module; the data processing module is used for preparing before delivery and calculating intelligent delivery, and the execution module is used for applying for resources.
Working principle: in combination with the existing scheduling conditions of manufacturing enterprises, constraint conditions are set as basic information of scheduling, and the constraint conditions comprise: the method comprises the steps of limiting the quantity of production lines, corresponding relation between products and production lines, productivity of production lines, process route limitation, whether a production line calendar is on duty or not and limiting the time length of duty on duty, defining the attribute of each agent, setting action rules of task agents and resource agents, setting scheduling targets, carrying out data processing on each agent meeting constraint conditions according to the set rule, finally determining a strategy for each task target according to different production standard of each task, carrying out resource application, wherein the strategy is calculated by an intelligent production algorithm, the intelligent production algorithm is calculated by a gene expression programming algorithm, the gene expression programming algorithm is GEP for short, and a suboptimal local optimal solution can be jumped out for the algorithm by changing a neighborhood structure of a gene expression; the GEP algorithm can be near the current local optimal solution, the neighborhood structure comprises a non-variable element neighborhood structure and a variable element neighborhood structure, the non-variable element neighborhood structure comprises a shift neighborhood structure, a switching neighborhood structure and a variable sequence neighborhood structure, the variable element neighborhood structure comprises a single-point replacement neighborhood structure, a two-point replacement neighborhood structure and a multi-element replacement neighborhood structure, the local optimal solution of a larger variable neighborhood range can be obtained, the stability of production and production scheduling is improved to a certain extent through the intelligent scheduling algorithm, and the scattered manufacturing resources and the interactive capability of enterprises are enhanced.
Finally, it should be noted that: the foregoing description is only illustrative of the preferred embodiments of the present invention, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements or changes may be made without departing from the spirit and principles of the present invention.
Claims (8)
1. The intelligent production method for gene expression programming based on the layered neighborhood structure is characterized by comprising the following steps of: the method comprises the following steps: s1, setting algorithm constraint conditions, combining the existing scheduling conditions of manufacturing enterprises, setting the constraint conditions as basic information of scheduling, wherein the constraint conditions comprise: line quantity constraint, product-line correspondence, line capacity, process route constraint, whether line calendar is on duty or not, and on-duty duration constraint; s2, setting agents, defining the attributes of each agent, and setting action rules of a task agent and a resource agent; s3, setting task targets, namely setting scheduling targets which are three targets of scheduling and dynamic scheduling support; s4, preparing before delivery, and performing data processing on each agent meeting constraint conditions according to set contract rules; s5, intelligent scheduling calculation, wherein for each task target, each task adopts different scheduling reference determining strategies according to different targets to apply for resources, the strategies are calculated by an intelligent scheduling algorithm, the intelligent scheduling algorithm in the step 5 adopts a gene expression programming algorithm for computing, the gene expression programming algorithm is called GEP for short, and in order that the algorithm can jump out suboptimal local optimal solutions, the neighborhood structure of the gene expression is changed; the GEP algorithm can continuously search for a better local optimal solution near the current local optimal solution by constructing different neighborhood structures, wherein the neighborhood structures comprise a non-variable element neighborhood structure and a variable element neighborhood structure, the non-variable element neighborhood structure comprises a shift neighborhood structure, a switching neighborhood structure and a variable order neighborhood structure, the variable element neighborhood structure comprises a single-point replacement neighborhood structure, a two-point replacement neighborhood structure and a multi-element replacement neighborhood structure, in order to ensure that the optimal local optimal solution is obtained, the neighborhood structures of all the constant elements are searched, or the neighborhood structures of all the variable elements are searched, or a hierarchical variable neighborhood structure searching method is adopted, the neighborhood structures of the variable elements are selected and searched for at the upper layer, and the neighborhood structures of all the constant elements are searched for at the lower layer, so that the local optimal solution of a larger variable neighborhood range can be obtained by combining the two.
2. The hierarchical neighborhood structure-based genetic expression programming intelligent scheduling method of claim 1, wherein: the production line quantity constraint in the step 1 refers to the workshop quantity and production line quantity of a manufacturing enterprise, wherein the manufacturing enterprise has N workshops and M production lines; the line-to-line correspondence is described by a moment Chen Fangshi; the production line capacity is defined at standard working hours of the production line basic information according to the corresponding product tasks of the production line.
3. The hierarchical neighborhood structure-based genetic expression programming intelligent scheduling method of claim 1, wherein: in the step 2, the Agent is expressed by using an Agent, and when the Agent is set, only a task Agent and a resource Agent are set for a simple task; the resource Agent can receive the occupation application of the working procedure of the task Agent to one or more time periods, and after receiving the application, if a certain time period can meet the time requirement of the working procedure, the resource Agent is judged and matched according to the occupation condition of the resource Agent; the task Agent is used for storing process information corresponding to the product type required by the order; the resource Agent stores information of all production test equipment.
4. The hierarchical neighborhood structure-based genetic expression programming intelligent scheduling method of claim 3, wherein: in the step 2, when the Agent is set, more agents are required to be designed for intelligent scheduling of complex tasks, including a management Agent, a resource Agent, an algorithm Agent, a process Agent and a monitoring Agent, wherein the management Agent is a main Agent and is responsible for processing order tasks and coordinating communication and mutual coordination among different agents; the resource Agent comprises a workshop Agent, a production line Agent and a device Agent, and the corresponding relation among the workshop Agent, the production line Agent and the device Agent is described in a matrix mode; the process Agent is responsible for managing the production process of the product and providing inquiry service for other agents; the process Agent prescribes whether the product can be produced, the production process flow, equipment required by production, corresponding raw materials and quality standards which can be achieved by production; the algorithm Agent is used for encapsulating a scheduling and dynamic scheduling algorithm, sending an order task sent by a management Agent to a resource Agent, and calculating resources and process configuration of the order to be completed by combining the resource Agent with a process Agent to call the algorithm Agent; the monitoring Agent is used for detecting the states of all resources in the system, and when each resource and equipment fail or a certain production line cannot normally run because personnel are not enough, the monitoring Agent notifies the resource Agent and updates the state of the corresponding resource.
5. The hierarchical neighborhood structure-based genetic expression programming intelligent scheduling method of claim 4, wherein: the main functions of the management Agent are two, one is used as an external interface, and the management Agent receives the order input of a customer; the other block is in-pair coordination, and each Agent needs to have a unique identifier at the management Agent, so that the coordination management is facilitated; and for the received customer order, automatically calculating an optimal scheduling plan by calling the resource Agent and the algorithm Agent.
6. The hierarchical neighborhood structure-based genetic expression programming intelligent scheduling method of claim 5, wherein: the three major objectives in step 3 are the shortest production time, the minimum inventory of products and the maximum resource balance.
7. An apparatus having the hierarchical neighborhood structure-based genetic expression programming intelligent scheduling method of any one of claims 1-6, comprising a control module, a data processing module, and an execution module, wherein: the control module is electrically connected with the data processing module, the data processing module is electrically connected with the execution module, the control module comprises an editing component and an execution component, and the editing component is electrically connected with the execution component.
8. The apparatus for hierarchical neighborhood structure based genetic expression programming intelligent scheduling method of claim 7, wherein: the editing component is used for setting algorithm constraint conditions, agent setting and task target setting; the execution component is used for executing tasks and transmitting data to the data processing module; the data processing module is used for preparing before delivery and calculating intelligent delivery, and the execution module is used for applying for resources.
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