CN114399227A - Production scheduling method and device based on digital twins and computer equipment - Google Patents
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Abstract
The present application relates to a digital twin based production scheduling method, apparatus, computer device, storage medium and computer program product. The method includes the steps that a production line digital twin model of an actual production line is built on a simulation platform according to a business process, the simulation platform is used for carrying out business process simulation according to a current operation rule set based on the production line digital twin model and determining simulation operation indexes, an optimization algorithm is used for carrying out optimization updating on the current operation rule set, the simulation platform is used for carrying out business process simulation according to the operation rule set after optimization updating and determining simulation operation indexes based on the production line digital twin model and selecting the operation rule set with the simulation operation indexes meeting optimization targets as a production scheduling plan; according to the method, by performing digital twin simulation on an actual production line, the problem that the complex business process is difficult to standardize due to mathematical modeling can be avoided, and the obtained production scheduling plan is good in interpretability.
Description
Technical Field
The present application relates to the field of production scheduling technologies, and in particular, to a production scheduling method and apparatus based on digital twin, a computer device, a storage medium, and a computer program product.
Background
Production scheduling is a research hotspot in the field of manufacturing industry, is an important way for improving industrial processing efficiency, and is an effective way for improving the economic benefit and market competitiveness of enterprises. Generally, production scheduling is a process for optimizing the execution efficiency or cost of a production task by determining the processing sequence of workpieces and scheduling the allocation of resources on the premise of meeting the process and resource constraints for a decomposable production task. At present, in a manufacturing enterprise, the basic flow of production scheduling is as follows:
(1) and performing mathematical modeling on the optimization problem needing to be solved according to the business process. Considering the difference of optimization objectives, the mathematical modeling problem can be divided into a single objective optimization problem and a multi-objective optimization problem.
(2) And selecting a feasible optimization algorithm according to the result of the mathematical modeling. For production scheduling, the selection of the optimization algorithm includes: enumeration algorithm, heuristic algorithm and meta-heuristic intelligent search algorithm. For production manufacturing enterprises, enumeration algorithms are not suitable for the complexity of the production scheduling problem, so that in a general scene, an optimization algorithm is generally selected from a heuristic algorithm and a meta-heuristic intelligent search algorithm.
(3) And running the selected optimization algorithm on the corresponding computing resource according to the result of the mathematical modeling, and obtaining a corresponding optimization result.
(4) The optimization result obtained in the optimization process cannot be directly applied to the production business problem, because the corresponding optimization result is different from the matching process of the real business problem under different mathematical modeling processes. Therefore, after obtaining the corresponding optimization result, the researcher needs to translate the corresponding optimization result into a solution required by the business process, and then apply the solution to the real business process.
However, the existing production scheduling method has the following problems: the form of the mathematical modeling is influenced by both the business process and the subsequently adopted optimization algorithm, and generally, the selection of the mathematical modeling and the optimization algorithm needs to be considered globally. However, in the existing method, the mathematical modeling process requires a lot of experience of researchers, so that the standardization is difficult, the mathematical modeling has huge difference according to different business processes, and even for the same business process, the mathematical modeling modes and methods selected by different researchers based on experience are greatly different. In actual operation, under the condition of slight change of the business process, the result of the mathematical modeling may need to be changed greatly, and the process is difficult to standardize. And the results of different mathematical modeling can play a decisive role in the selection of the subsequent optimization algorithm and the execution efficiency of the optimization algorithm. In addition, the results of the existing production scheduling method lack interpretability, researchers cannot explain the properties of convergence and the like, and cannot obtain the results under the conditions of the traceability optimization algorithm, and even the execution results of two continuous optimization algorithms have great difference.
Disclosure of Invention
In view of the above, it is necessary to provide a method, an apparatus, a computer device, a computer readable storage medium and a computer program product for digital twin-based production scheduling.
In a first aspect, the present application provides a method for digital twin-based production scheduling, the method comprising:
a production line digital twin model of an actual production line is set up on a simulation platform according to a business process, the production line digital twin model comprises a plurality of production devices for realizing part processing according to the business process and a device model of logistics devices, the logistics devices are used for transporting parts between the two production devices, and the production devices are used for processing the parts;
performing business process simulation and determining simulation operation indexes according to a current operation rule set by using a simulation platform based on a production line digital twin model, wherein the operation rule set comprises operation rules of each anchor point in the production line digital twin model, and the anchor point is an equipment model interacted with a part in the production line digital twin model;
optimizing and updating the current operation rule set by using an optimization algorithm, performing business process simulation according to the optimized and updated operation rule set by using a simulation platform based on a production line digital twin model, determining simulation operation indexes, and performing iterative operation until the optimization and updating are finished;
and selecting the operation rule group with the simulation operation index meeting the optimization target as a production scheduling plan.
In one embodiment, the optimizing and updating the current operation rule set by using an optimization algorithm and performing the business process simulation includes: optimizing and updating the current operation rule set by using an optimization algorithm to obtain a plurality of different optimized and updated operation rule sets; and performing service flow simulation respectively according to each optimized and updated operation rule group by using a simulation platform based on a production line digital twin model in parallel, and obtaining respective simulation operation indexes.
In one embodiment, the optimizing and updating the current operation rule set by using an optimization algorithm comprises the following steps: and optimizing the operation rules of at least one anchor point in the current operation rule group by using a meta-heuristic search algorithm, and selecting one operation rule from all operation rules contained in a rule base corresponding to each anchor point to optimize the current operation rule.
In one embodiment, the method further comprises: updating parameters of a production line digital twin model according to the real-time running state of an actual production line in the process of executing a production scheduling plan by the actual production line; and performing business process simulation according to the production scheduling plan by using a production line digital twin model after parameter updating by using the simulation platform, determining a simulation operation index, and outputting early warning information when the simulation operation index does not reach an expected target.
In one embodiment, the operation rule of the anchor point indicates a processing sequence of the equipment model to the plurality of parts at the current process stage of the business process, and the operation rule of the anchor point includes at least one of the following: and sequentially processing the parts according to the sequence of the waiting time of the parts in the current working procedure stage from long to short, and sequentially processing the parts according to the sequence of the processing time of the parts from long to short.
In one embodiment, the optimization objective includes at least one of: the total time consumption of all the parts to be machined after the business process is executed is shortest, the average time consumption of each part to be machined after the business process is executed is shortest, the total waiting time of all the parts to be machined in the process of executing the business process is shortest, the average waiting time of each part to be machined after the business process is executed is shortest, the idle waiting time of the equipment model is shortest, and the occupancy rate of the equipment model is highest.
In a second aspect, the present application further provides a digital twin-based production scheduling apparatus, comprising:
the system comprises a model building module, a simulation platform and a control module, wherein the model building module is used for building a production line digital twin model of an actual production line on the simulation platform according to a business process, the production line digital twin model comprises a plurality of production equipment for realizing part processing according to the business process and an equipment model of logistics equipment, the logistics equipment is used for transporting parts between the two production equipment, and the production equipment is used for processing the parts;
the simulation module is used for simulating the business process and determining simulation operation indexes according to a current operation rule set by utilizing a simulation platform based on a production line digital twin model, wherein the operation rule set comprises operation rules of each anchor point in the production line digital twin model, and the anchor point is an equipment model interacted with the part in the production line digital twin model;
the iteration updating module is used for optimizing and updating the current operation rule set by using an optimization algorithm, simulating the service flow by using a simulation platform based on a production line digital twin model according to the optimized and updated operation rule set, determining a simulation operation index, and performing iteration operation until the optimization and updating are finished;
and the plan determination module is used for selecting the operation rule group of which the simulation operation index meets the optimization target as the production scheduling plan.
In a third aspect, the present application also provides a computer device. The computer arrangement comprises a memory in which a computer program is stored and a processor which, when executing the computer program, carries out the steps of the digital twin based production scheduling method as provided by the first aspect.
In a fourth aspect, the present application further provides a computer-readable storage medium. A computer readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the digital twin based production scheduling method provided by the first aspect.
In a fifth aspect, the present application further provides a computer program product. A computer program product comprising a computer program which, when executed by a processor, carries out the steps of the digital twin based production scheduling method provided by the first aspect.
According to the production scheduling method, the production scheduling device, the computer equipment, the storage medium and the computer program product based on the digital twin, the operation of performing mathematical modeling on a complex business process can be avoided by performing the digital twin simulation on an actual production line, the problem that the mathematical modeling is difficult to standardize is avoided, excessive modeling experience is not required, the production scheduling problem can be effectively processed, and the implementation difficulty and the experience requirements on designers are reduced. In addition, the simulation process of the whole production scheduling plan is finally obtained, the operation rule of each anchor point has high interpretability, extra translation operation is not needed, and the source tracing is realized.
Furthermore, the whole production line twin simulation adopts a parallel execution mode, so that the consumption of the solution evaluation process on the calculation time is reduced, the time for obtaining the production scheduling plan is reduced, and the time for the whole production scheduling optimization process is shortened.
In the actual production line operation process, in the production scheduling method based on the digital twin, the early warning can be timely carried out when the production scheduling can not meet the expected target through the operation of the digital twin simulation, and the whole process has strong interpretability.
Drawings
FIG. 1 is a flow diagram of a digital twin based production scheduling method in one embodiment.
FIG. 2 is a schematic flow diagram illustrating a simulation for parallel optimization according to an embodiment.
Fig. 3 is a schematic flow chart of performing early warning in the process of executing the production scheduling method in the actual production line.
FIG. 4 is a block diagram of a digital twin based production scheduling apparatus in one embodiment;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, a digital twin-based production scheduling method is provided, and this embodiment is illustrated by applying the method to a terminal, it is to be understood that the method may also be applied to a server, and may also be applied to a system including a terminal and a server, and is implemented by interaction between the terminal and the server. In this embodiment, the method includes the steps of:
102, building a production line digital twin model of an actual production line on a simulation platform according to a business process, wherein in the application scene of the application, the business process is a process for sequentially executing a plurality of processing procedures on parts to be processed by using the actual production line, and one production task generally comprises the business process for processing a plurality of parts to be processed.
The actual production line comprises a plurality of production devices and a plurality of logistics devices, and actually may further comprise storage devices. The storage device is used for storing parts to be machined or called production materials, and a plurality of parts to be machined of one type or a plurality of types are stored in the storage device. The production equipment is used for processing parts, the processing operation of each production equipment on the parts is generally realized as one processing procedure, and different production equipment can perform different processing operations on the parts. The logistics equipment is used for transporting parts, and comprises the transportation of parts between the production equipment of two adjacent processing procedures, such as a common AGV trolley, and actually also comprises the transportation of parts between the storage equipment and the production equipment of the first processing procedure.
The specific operation of building the production line digital twin model of the actual production line can be realized based on the existing digital twin technology, and is not repeated in the application. The built digital twin model of the production line comprises production equipment in the actual production line and equipment models of logistics equipment, and also possibly comprises equipment models of warehousing equipment, and the structure and the virtual operation process of the digital twin model of the production line are the same as those of the actual production line.
And 104, performing business process simulation according to the current operation rule set by using a simulation platform based on a production line digital twin model and determining a simulation operation index.
The operation rule set comprises operation rules of each anchor point in the production line digital twin model, and the anchor point is an equipment model which interacts with the part in the production line digital twin model, such as production equipment which is responsible for processing the part, or logistics equipment which is responsible for transporting the part.
In practical application, each device needs to process a plurality of parts, and when a plurality of parts wait for the device to process at the same time, the sequence in which the device processes the plurality of parts affects the whole business process. For example, for a manufacturing facility, the order in which parts A, B, C are processed by the manufacturing facility may affect the efficiency or cost of performing the entire manufacturing task, assuming that parts A, B, C are waiting to be processed by the manufacturing facility. The operating rules of the anchor point therefore dictate the order of processing of the equipment model between the multiple parts at the current process stage of the business process.
Specifically, the operation rule of the anchor point includes at least one of the following: and sequentially processing the parts according to the sequence of the waiting time of the parts in the current working procedure stage from long to short, and sequentially processing the parts according to the sequence of the processing time of the parts from long to short. The parts are sequentially processed according to the waiting time of the parts in the current working procedure stage from long to short, namely, the parts which reach the current working procedure stage are processed preferentially. And sequentially processing the parts according to the sequence of the processing time length of each part from long to short, namely preferentially processing the part with the shortest finishing time.
And 106, optimizing and updating the current operation rule set by using an optimization algorithm, simulating the service flow by using a simulation platform based on a production line digital twin model according to the optimized and updated operation rule set, determining simulation operation indexes, and performing iterative operation until the optimization and updating are finished.
And 108, obtaining simulation operation indexes of a plurality of operation rule groups, and selecting the operation rule group of which the simulation operation index meets the optimization target as a production scheduling plan.
Since the production task generally requires higher execution efficiency and/or lower cost, and the cost can be generally considered to be related to the running time of the equipment, the evaluation and the investigation are carried out from the time dimension, so that the simulation operation index is the index of the time dimension. The method specifically comprises at least one of the following steps: the method comprises the steps of total time consumption of all parts to be machined after the business process is executed, average time consumption of each part to be machined after the business process is executed, total waiting time of all parts to be machined in the process of executing the business process, average waiting time of each part to be machined after the business process is executed, idle waiting time of an equipment model and occupancy rate of the equipment model. The total time spent by all parts to be machined after the business process is completed is the time spent by the whole production task from the beginning of the processing of the first part to the completion of the processing of the last part. The waiting time of the part comprises the time for waiting to be processed of the part between any two process stages, and in the process of processing the part according to the business process, the consumed time of the part from the first processing procedure to the last processing procedure includes the consumed time of actually processing the part by each production device and the total waiting time. The idle wait period for the equipment model includes the period of time the equipment model waits between processing any two parts. The occupancy rate of the equipment model is the proportion of the duration of the time actually used for processing the parts in the whole starting operation process of the equipment model.
Correspondingly, the optimization objective includes at least one of: the total time consumption of all the parts to be machined after the business process is executed is shortest, the average time consumption of each part to be machined after the business process is executed is shortest, the total waiting time of all the parts to be machined in the process of executing the business process is shortest, the average waiting time of each part to be machined after the business process is executed is shortest, the idle waiting time of the equipment model is shortest, and the occupancy rate of the equipment model is highest. Therefore, the optimal or suboptimal operation rule set in the simulation operation process can be selected as the production scheduling plan, and the optimization goal is achieved. The obtained production scheduling plan is the operation rule of each device in each actual production line, and has strong interpretability and performability.
In the production scheduling method based on the digital twin, the operation of mathematical modeling on a complex business process can be avoided by performing the digital twin simulation on an actual production line, the problem that the mathematical modeling is difficult to standardize is avoided, the production scheduling problem can be effectively processed without excessive modeling experience, and the implementation difficulty and the experience requirements on designers are reduced. In addition, the simulation process of the whole production scheduling plan is finally obtained, the operation rule of each anchor point has high interpretability, extra translation operation is not needed, and the source tracing is realized.
In step 106, when the optimization algorithm is used to perform optimization updating on the current operation rule set, the meta-heuristic search algorithm is used to perform optimization on the operation rule of at least one anchor point in the current operation rule set. The meta-heuristic search algorithm adopts an iterative optimization method, namely, a corresponding solution is generated according to the existing optimization experience in each round, and the corresponding solution is evaluated, so that more optimization experiences are obtained until the algorithm completes iteration. The optional space for optimizing the operation rule of each anchor point is all the operation rules in the rule base of the anchor point, that is, one operation rule is selected from all the operation rules contained in the rule base corresponding to each anchor point to optimize the current operation rule.
In the present application, please refer to fig. 2, the process of evaluating one operation rule set is to execute a service simulation process of a production line digital twin model, and since the time of single simulation is long, in one embodiment, an optimization algorithm is used to perform optimization updating on the current operation rule set to obtain a plurality of different optimized and updated operation rule sets, and then a simulation platform is used in parallel to perform service flow simulation according to each optimized and updated operation rule set based on the production line digital twin model, and obtain respective simulation operation indexes, fig. 2 takes N sets of parallel simulation as an example. Namely, the twin simulation of the whole production line adopts a parallel execution mode, so that the consumption of the evaluation process of the solution to the calculation time is reduced, the time for obtaining the production scheduling plan is reduced, and the time for the whole production scheduling optimization process is shortened.
After the production scheduling plan is obtained, the actual production line executes the production scheduling plan, that is, each device in the actual production line works according to the operation rule of the corresponding device model in the production scheduling plan. However, in the actual operation process, the actual production line is affected by various factors, and it is difficult to achieve the ideal effect of simulation, for example, the actual production line is damaged in the operation process.
In an embodiment, referring to fig. 3, in the process of executing the production scheduling plan by the actual production line, parameters of the production line digital twin model are updated according to the real-time operating state of the actual production line, and based on the existing digital twin model, various parameters in the actual production line may be synchronized to the production line digital twin model, which is a conventional operation, and details of the embodiment are omitted. The real-time operation state comprises state information of each production device, state information of logistics devices and state information of each part, and the state information at least comprises the current processing operation state.
And performing business process simulation according to the production scheduling plan by using a production line digital twin model after parameter updating by using a simulation platform and determining a simulation operation index, wherein the simulation operation index is the simulation operation index when the simulation platform continues to operate according to the production scheduling plan in the current real-time operation state. And outputting early warning information when the simulation operation index does not reach the expected target, wherein the early warning information is used for indicating that the expected target cannot be successfully achieved. Wherein the expected target corresponds to the data type of the simulation operation index, and comprises at least one of the following: the total time consumption of all the parts to be machined after the business process is executed is smaller than a first threshold, the average time consumption of each part to be machined after the business process is executed is smaller than a second threshold, the total waiting time of all the parts to be machined in the process of executing the business process is smaller than a third threshold, the average waiting time of each part to be machined after the business process is executed is smaller than a fourth threshold, the idle waiting time of the equipment model is smaller than a fifth threshold, and the occupancy rate of the equipment model reaches a sixth threshold.
In the production scheduling method based on the digital twin, early warning can be timely carried out when the production scheduling can not meet the expected target through the operation of the digital twin simulation in the running process of an actual production line, and the whole process has strong interpretability.
Based on the same inventive concept, the embodiment of the present application further provides a digital twin-based production scheduling device for implementing the above-mentioned digital twin-based production scheduling method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the method, so that the specific limitations in one or more embodiments of the digital twin-based production scheduling device provided below can be referred to the limitations on the digital twin-based production scheduling method in the above description, and details are not repeated here.
In one embodiment, as shown in fig. 4, there is provided a digital twin-based production scheduling apparatus, including: a model building module 410, a simulation module 420, an iteration updating module 430, and a plan determination module 440, wherein:
the model building module 410 is used for building a production line digital twin model of an actual production line on the simulation platform according to a business process, the production line digital twin model comprises a plurality of production devices for realizing part processing according to the business process and a device model of logistics devices, and the logistics devices are used for transporting parts between the two production devices, and the production devices are used for processing the parts.
And the simulation module 420 is used for performing business process simulation and determining simulation operation indexes according to the current operation rule set by using the simulation platform based on the production line digital twin model, wherein the operation rule set comprises operation rules of each anchor point in the production line digital twin model, and the anchor point is an equipment model interacted with the part in the production line digital twin model.
And the iteration updating module 430 is configured to perform optimization updating on the current operation rule set by using an optimization algorithm, perform service flow simulation according to the operation rule set after optimization updating by using a simulation platform based on a production line digital twin model, determine a simulation operation index, and perform iteration operation until the optimization updating is finished.
And the plan determining module 440 is configured to select a running rule set with simulation job indexes meeting the optimization target as a production scheduling plan.
The various modules in the above-described digital twin-based production scheduling apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing the production line digital twin model and the rule base of each anchor point. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a digital twin based production scheduling method.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor realizing the steps of the above-described method embodiment of the digital twin based production scheduling method when executing the computer program.
In an embodiment, a computer readable storage medium is provided, having stored thereon a computer program, which when executed by a processor, carries out the steps of the above-mentioned method embodiment of the digital twin based production scheduling method.
In an embodiment, a computer program product is provided, comprising a computer program which, when being executed by a processor, realizes the steps of the above-mentioned method embodiment of the digital twin based production scheduling method.
It should be noted that, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), Magnetic Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (10)
1. A method for digital twin-based production scheduling, the method comprising:
the method comprises the steps that a production line digital twin model of an actual production line is built on a simulation platform according to a business process, the production line digital twin model comprises a plurality of production devices for realizing part processing according to the business process and a device model of logistics devices, and the logistics devices are used for transporting parts between the two production devices, and the production devices are used for processing the parts;
utilizing the simulation platform to perform business process simulation according to a current operation rule set based on the production line digital twin model and determining simulation operation indexes, wherein the operation rule set comprises operation rules of each anchor point in the production line digital twin model, and the anchor point is an equipment model interacted with a part in the production line digital twin model;
optimizing and updating the current operation rule set by using an optimization algorithm, performing business process simulation according to the optimized and updated operation rule set by using the simulation platform based on the production line digital twin model, determining simulation operation indexes, and performing iterative operation until the optimization and updating are finished;
and selecting the operation rule group with the simulation operation index meeting the optimization target as a production scheduling plan.
2. The method of claim 1, wherein performing optimization updating and business process simulation on the current set of operation rules by using an optimization algorithm comprises:
optimizing and updating the current operation rule set by using an optimization algorithm to obtain a plurality of different optimized and updated operation rule sets;
and performing service flow simulation respectively according to each optimized and updated operation rule group by using the simulation platform based on the production line digital twin model in parallel, and obtaining respective simulation operation indexes.
3. The method of claim 1, wherein the optimally updating the current set of operating rules with the optimization algorithm comprises:
and optimizing the operation rules of at least one anchor point in the current operation rule group by using a meta-heuristic search algorithm, and selecting one operation rule from all operation rules contained in a rule base corresponding to each anchor point to optimize the current operation rule.
4. The method of claim 1, further comprising:
updating parameters of the production line digital twin model according to the real-time running state of the actual production line in the process of executing the production scheduling plan by the actual production line;
and performing business process simulation according to the production scheduling plan by using the simulation platform based on the production line digital twin model after parameter updating, determining a simulation operation index, and outputting early warning information when the simulation operation index does not reach an expected target.
5. The method of any of claims 1-4, wherein the anchor operating rules indicate an order of processing of the equipment model between the plurality of parts at a current process stage of the business process, the anchor operating rules including at least one of: and sequentially processing the parts according to the sequence of the waiting time of the parts in the current working procedure stage from long to short, and sequentially processing the parts according to the sequence of the processing time of the parts from long to short.
6. The method of any of claims 1-4, wherein the optimization objective comprises at least one of: the total time consumption of all the parts to be machined after the business process is executed is shortest, the average time consumption of each part to be machined after the business process is executed is shortest, the total waiting time of all the parts to be machined in the process of executing the business process is shortest, the average waiting time of each part to be machined after the business process is executed is shortest, the idle waiting time of the equipment model is shortest, and the occupancy rate of the equipment model is highest.
7. A digital twin-based production scheduling apparatus, the apparatus comprising:
the system comprises a model building module, a simulation platform and a control module, wherein the model building module is used for building a production line digital twin model of an actual production line on the simulation platform according to a business flow, the production line digital twin model comprises a plurality of production devices for realizing part processing according to the business flow and a device model of logistics devices, the logistics devices are used for transporting parts between the two production devices, and the production devices are used for processing the parts;
the simulation module is used for simulating the business process and determining a simulation operation index according to a current operation rule set by utilizing the simulation platform based on the production line digital twin model, wherein the operation rule set comprises operation rules of each anchor point in the production line digital twin model, and the anchor point is an equipment model interacted with a part in the production line digital twin model;
the iteration updating module is used for optimizing and updating the current operation rule set by using an optimization algorithm, simulating the service flow by using the simulation platform based on the production line digital twin model according to the optimized and updated operation rule set, determining a simulation operation index, and performing iteration operation until the optimization and updating are finished;
and the plan determination module is used for selecting the operation rule group of which the simulation operation index meets the optimization target as the production scheduling plan.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 6 when executed by a processor.
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