Simulation-based closed-loop aps scheduling optimization method and system, and storage medium

Abstract

An emulation-based closed-loop APS scheduling optimization method and system, and a storage medium. The method comprises: determining an APS rule selected for scheduling (101); determining a weight configuration set for the selected APS rule (102); generating a scheduling Gantt chart according to the weight configuration of the APS rule (103); deriving an order sequence corresponding to the scheduling Gantt chart (104); loading the order sequence and obtained pre-set simulation model configuration data into a basic simulation model of a factory design simulation software to obtain a scheduling simulation model (105); running the scheduling simulation model and evaluating to obtain key performance indicator data (106); exporting the key performance indicator data as a simulation result (107); determining whether the simulation result meets the needs (108), if yes, outputting a corresponding scheduling Gantt chart (109); otherwise, returning to the step of selecting an APS rule for scheduling. The method can provide a more feasible factory production scheduling scheme.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of digitization, and in particular to a simulation-based closed-loop advanced planning and scheduling (APS) scheduling optimization method, system and storage medium. BACKGROUND

[0002] Currently, industrial enterprises are exploring more intelligent and efficient production planning and scheduling methods. An optimal production plan requires meeting production requirements and constraint conditions at the lowest cost. Opcenter APS (formerly known as "Preactor APS") is developed as an advanced planning and scheduling software solution to meet this requirement, for generating a producible production plan by balancing demand and capacity. Opcenter APS provides users with a professional production data management method and simple and efficient production scheduling rules. SUMMARY

[0003] Therefore, in the embodiments of the present application, on the one hand, a simulation-based closed-loop APS scheduling optimization method is provided, and on the other hand, a simulation-based closed-loop APS scheduling optimization system and a computer readable storage medium are provided, so as to provide a more feasible scheduling scheme.

[0004] The simulation-based closed-loop APS scheduling optimization method provided in the embodiments of the present application comprises: determining an APS rule selected for scheduling; determining a weight configuration set for the selected APS rule; generating a scheduling Gantt chart according to the weight configuration of the APS rule; deriving an order sequence corresponding to the scheduling Gantt chart; loading the order sequence and the obtained pre-set simulation model configuration data into a basic simulation model of a factory design simulation software to obtain a scheduling simulation model; running the scheduling simulation model and evaluating to obtain key performance indicator data; exporting the key performance indicator data as a simulation result; judging whether the simulation result meets the needs, if yes, outputting a corresponding scheduling Gantt chart; otherwise, returning to the step of selecting an APS rule for scheduling.

[0005] In one embodiment, the determining of the weight configuration set for the selected APS rule comprises: adopting a comprehensive particle swarm optimization algorithm to set the weight configuration for the selected APS rule; the comprehensive particle swarm optimization algorithm is improved on the basis of a basic particle swarm optimization algorithm, in which a fitness value is calculated according to the key performance indicator data in the iterative optimization process to determine the optimal position experienced by the particle itself and the optimal position experienced by the particle swarm, a simulated annealing algorithm is adopted to evaluate and process the optimal position of the particle itself obtained each time, a mutation operation of a genetic algorithm is adopted to evaluate and update the optimal position of the particle swarm, the inertia weight value is dynamically adjusted, and finally the next generation of particle population is updated.

[0006] In one embodiment, the step of setting weight configuration for the selected APS rules by using the comprehensive particle swarm optimization algorithm comprises: assigning initial weights to the selected APS rules, and performing particle coding on the initial weights; initializing the weights after the particle coding to obtain a current particle swarm; decoding the current particle swarm to obtain a current weight configuration of the APS rules, and determining the current weight configuration as the weight configuration set for the selected APS rules; after the step of running the scheduling simulation model and evaluating to obtain the key performance indicator data, further comprising: obtaining the key performance indicator data, and calculating fitness values according to the key performance indicator data to determine the optimal position experienced by the particle itself and the optimal position experienced by the particle swarm; determining whether the maximum number of iterations is reached; when the maximum number of iterations is not reached, performing evaluation processing on the optimal position experienced by the particle itself by using the simulated annealing algorithm, and performing evaluation updating on the optimal position experienced by the particle swarm by using the mutation operation of the genetic algorithm; generating a new generation of current particle swarm, and returning to perform the operation of decoding the current particle swarm to obtain the current weight configuration of the APS rules; when the maximum number of iterations is reached, taking the particle swarm corresponding to the optimal position experienced by the particle swarm as the current example swarm, and returning to perform the operation of decoding the current particle swarm to obtain the current weight configuration of the APS rules.

[0007] In one embodiment, the step of determining the APS rules selected for scheduling comprises: selecting the rules required for the current scheduling based on the existing rules and / or the added custom rules of the advanced planning and scheduling software.

[0008] The simulation-based closed-loop APS scheduling optimization system provided in the embodiments of the present application comprises at least one memory and at least one processor, wherein: the at least one memory is used to store a computer program; the at least one processor is used to call the computer program stored in the at least one memory to make the device perform corresponding operations, and the operations comprise: determining the APS rules selected for scheduling; determining the weight configuration set for the selected APS rules; generating a scheduling Gantt chart according to the weight configuration of the APS rules; deriving an order sequence corresponding to the scheduling Gantt chart; loading the order sequence and the pre-set simulation model configuration data obtained into a basic simulation model of a factory design simulation software to obtain a scheduling simulation model; running the scheduling simulation model and evaluating to obtain key performance indicator data; exporting the key performance indicator data as a simulation result; determining whether the simulation result meets the needs, and if yes, outputting the corresponding scheduling Gantt chart; otherwise, returning to perform the step of selecting the APS rules for scheduling.

[0009] In one embodiment, the determining the weight configuration for the selected APS rule setting comprises employing a comprehensive particle swarm optimization algorithm to configure the weight for the selected APS rule setting, wherein the comprehensive particle swarm optimization algorithm is improved based on a basic particle swarm optimization algorithm, and in the iterative optimization process, fitness values are calculated according to the key performance indicator data to determine the optimal position experienced by the particle itself and the optimal position experienced by the particle swarm, and a simulated annealing algorithm is employed to evaluate the optimal position of the particle itself obtained each time, and a mutation operation of a genetic algorithm is employed to evaluate and update the optimal position of the particle swarm, and the inertia weight value is dynamically adjusted, and finally the next generation of particle swarm is updated.

[0010] In one embodiment, the employing the comprehensive particle swarm optimization algorithm to configure the weight for the selected APS rule setting comprises assigning an initial weight to the selected APS rule, and performing particle coding on the initial weight; initializing the weight after the particle coding to obtain a current particle swarm; decoding the current particle swarm to obtain a current weight configuration of the APS rule, and determining the current weight configuration as the weight configuration for the selected APS rule setting; after the step of executing the scheduling simulation model and obtaining the key performance indicator data, further comprising: obtaining the key performance indicator data, and calculating fitness values according to the key performance indicator data to determine the optimal position experienced by the particle itself and the optimal position experienced by the particle swarm; determining whether the maximum number of iterations is reached; when the maximum number of iterations is not reached, employing a simulated annealing algorithm to evaluate the optimal position experienced by the particle itself, and employing a mutation operation of a genetic algorithm to evaluate and update the optimal position experienced by the particle swarm; generating a new generation of current particle swarm, and returning to perform the operation of decoding the current particle swarm to obtain the current weight configuration of the APS rule; when the maximum number of iterations is reached, taking the particle swarm corresponding to the optimal position experienced by the particle swarm as a current example swarm, and returning to perform the operation of decoding the current particle swarm to obtain the current weight configuration of the APS rule.

[0011] In one embodiment, the determining the APS rule selected for scheduling comprises selecting the rule required for the current scheduling based on the existing rules and / or added custom rules of the advanced planning and scheduling software.

[0012] The simulation-based closed-loop APS scheduling optimization system provided in the embodiments of the present application comprises: an advanced planning and scheduling software module; a plant design simulation software module; and a simulation-based scheduling module configured as a COM component, integrated into the advanced planning and scheduling software module through a COM interface, connected to the plant design simulation software module through a COM interface of the plant design simulation software module, and performing the following operations: determining APS rules selected for scheduling; determining a weight configuration of the selected APS rules; generating a scheduling Gantt chart according to the weight configuration of the APS rules; exporting an order sequence corresponding to the scheduling Gantt chart; loading the order sequence and acquired pre-set simulation model configuration data into a basic simulation model of the plant design simulation software module to obtain a scheduling simulation model; running the scheduling simulation model and evaluating to obtain key performance indicator data; exporting the key performance indicator data as a simulation result; determining whether the simulation result meets the needs, and if yes, outputting a corresponding scheduling Gantt chart; otherwise, returning to perform the step of selecting APS rules for scheduling.

[0013] In one embodiment, the determining of the weight configuration of the selected APS rules comprises: setting a weight configuration for the selected APS rules by using a comprehensive particle swarm optimization algorithm; the comprehensive particle swarm optimization algorithm is improved on the basis of a basic particle swarm optimization algorithm, and in the iterative optimization process, a fitness value is calculated according to the key performance indicator data to determine an optimal position experienced by a particle itself and an optimal position experienced by a particle swarm, a simulated annealing algorithm is used to evaluate and process the optimal position of the particle itself obtained each time, a mutation operation of a genetic algorithm is used to evaluate and update the optimal position of the particle swarm, an inertia weight value is dynamically adjusted, and finally a next generation of particle population is updated.

[0014] In one embodiment, the simulation-based scheduling module comprises a scheduling submodule and a simulation submodule; wherein the scheduling submodule is configured to determine APS rules selected for scheduling, and determine weight configurations of the selected APS rules; generate a scheduling Gantt chart according to the weight configurations of the APS rules; export order sequences corresponding to the scheduling Gantt chart into a database; and obtain simulation results based on the order sequences from the database; determine whether the simulation results meet the requirements, and if yes, output the corresponding scheduling Gantt chart; otherwise, return to perform the operation of selecting APS rules for scheduling; the simulation submodule is configured to load the order sequences and pre-set simulation model configuration data from the database, and load the order sequences and the simulation model configuration data into a basic simulation model of a factory design simulation software to obtain a scheduling simulation model; run the scheduling simulation model, and evaluate to obtain key performance indicator data; and store the key performance indicator data as simulation results into the database.

[0015] In one embodiment, the simulation-based scheduling module further comprises an optimization algorithm submodule; and is configured to set weight configurations for the selected APS rules by using a comprehensive particle swarm optimization algorithm, and provide the weight configurations to the scheduling submodule; the comprehensive particle swarm optimization algorithm is improved on the basis of a basic particle swarm optimization algorithm, and in the iterative optimization process, calculates fitness values according to key performance indicator data to determine the optimal position experienced by a particle itself and the optimal position experienced by a particle swarm, and uses a simulated annealing algorithm to evaluate and process the optimal position of the particle itself obtained each time, uses a mutation operation of a genetic algorithm to evaluate and update the optimal position of the particle swarm, dynamically adjusts an inertia weight value, and finally updates to generate a next generation of particle population.

[0016] In one embodiment, the optimization algorithm submodule assigns initial weights to the selected APS rules and performs particle coding on the initial weights; the weights after particle coding are initialized to obtain a current particle swarm; the current particle swarm is decoded to obtain a current weight configuration of the APS rules, and the current weight configuration is provided to the scheduling submodule; key performance indicator data evaluated by the simulation submodule is obtained, and a fitness value is calculated according to the key performance indicator data to determine an optimal position experienced by a particle itself and an optimal position experienced by a particle swarm; it is determined whether the maximum number of iterations is reached; when the maximum number of iterations is not reached, the optimal position experienced by the particle itself is evaluated by using a simulated annealing algorithm, and the optimal position experienced by the particle swarm is evaluated and updated by using a mutation operation of a genetic algorithm; a new generation of current particle swarm is generated, and the operation of decoding the current particle swarm to obtain the current weight configuration of the APS rules is performed; when the maximum number of iterations is reached, the particle swarm corresponding to the optimal position experienced by the particle swarm is taken as a current example swarm, and the operation of decoding the current particle swarm to obtain the current weight configuration of the APS rules is performed.

[0017] In one embodiment, the simulation-based scheduling module further comprises an APS rule configuration module configured to determine rules required for current scheduling based on existing rules of advanced planning and scheduling software and / or added custom rules.

[0018] The computer readable storage medium in the embodiment of the present application has a computer program stored thereon; the computer program can be executed by a processor and implement the simulation-based closed-loop APS scheduling optimization method as described above.

[0019] As can be seen from the above scheme, since the advanced planning and scheduling software and the plant design simulation software are integrated together in the embodiment of the present application, the scheduling and dispatching scheme generated based on the advanced planning and scheduling software can create a corresponding scheduling simulation model through the plant design simulation software, the KPI data of the simulation result can be obtained by running the scheduling simulation model, and whether the corresponding scheduling and dispatching scheme meets the requirements can be determined based on the KPI data, so that a more feasible scheduling scheme can be provided.

[0020] Further, by using the comprehensive particle swarm optimization algorithm to automatically assign weights to the APS rules selected for scheduling, production planning personnel who do not know how to select the APS rule weights when facing new production scenarios or changing production requirements can accurately and efficiently complete scheduling. BRIEF DESCRIPTION OF DRAWINGS

[0021] The above and other features and advantages of the present application will become more apparent by reference to the following detailed description taken in conjunction with the accompanying drawings wherein:

[0022] Figure 1 An exemplary flow chart of a simulation-based closed-loop APS scheduling optimization method according to an embodiment of the present application.

[0023] Figure 2 An exemplary block diagram of a simulation-based closed-loop APS scheduling optimization system according to an embodiment of the present application.

[0024] Figure 3 An exemplary flow chart of a simulation-based closed-loop APS scheduling optimization method according to an embodiment of the present application. Figure 2

[0025] Figure 4 An exemplary dialog box of SSM according to an embodiment of the present application.

[0026] Figure 5 An exemplary configuration of APS rule base according to an embodiment of the present application.

[0027] Figure 6 An exemplary process of creating simulation model according to an embodiment of the present application.

[0028] Figure 7 An exemplary process of APS rule weight distribution automatic optimization according to an embodiment of the present application.

[0029] Figure 8 An exemplary block diagram of a simulation-based closed-loop APS scheduling optimization system according to an embodiment of the present application.

[0030] In the drawings, like reference numerals refer to like elements throughout.

[0031]

[0032]

[0033] DETAILED DESCRIPTION

[0034] ​In the embodiments of the present application, so far, advanced planning and scheduling software such as Opcenter APS, like other industrial APS software, does not have an optimization algorithm for scheduling, but provides a set of APS rules to generate a scheduling Gantt chart. The planner needs to manually adjust and optimize the results according to personal experience, which makes Opcenter APS still have some deficiencies, especially in some complex production scenarios, there are two problems as follows: 1, it is difficult to verify and evaluate the planning and scheduling results given by Opcenter APS; 2, there is no mature Opcenter APS algorithm to optimize the rule-based scheduling scheme at present.

[0035] The first problem above makes the scheduling results of Opcenter APS not very feasible or poor in execution effect in the actual production process, and re-scheduling always pays extra cost. The second problem comes from the rule-based scheduling algorithm provided by Opcenter APS. The APS rules realize very fast scheduling, but they are not user demand-oriented. Therefore, users usually cannot find a good way to evaluate the quality, see if the scheduling meets the production requirements, and also do not know if a better solution can be found. Therefore, Opcenter APS needs a feasible method to optimize the APS rules and verify the scheduling results for different user target requirements.

[0036] For the first problem, in some current research, a calculator is considered to be provided for generating key performance indicator (KPI) statistical information displayed through various charts or reports based on mathematical models according to the current scheduling results in the Gantt chart. Among them, the charts can include "utilization" charts representing production resource utilization, and the reports can include order KPIs related to time and cost. Using KPI statistical data, users can quickly evaluate the scheduling results and make adjustments.

[0037] For the second problem, in some current academic research, various heuristic algorithms such as genetic algorithm (GA), particle swarm optimization algorithm (PSO), ant colony algorithm (AG) and simulated annealing algorithm (SA) are considered to solve the best order scheduling sequence. However, all these intelligent algorithms require a large amount of computing resources and time. In some complex production scenarios, such intelligent algorithms cannot find the global optimal solution, but only the local optimal solution. On the other hand, optimization algorithms must be developed one by one. Therefore, it is difficult to apply different scenarios in actual production.

[0038] To this end, in the embodiments of the present application, a simulation-based closed-loop APS scheduling optimization solution is provided. In the solution, a model in a plant design simulation software such as Plant Simulation is used to replace the data model to simulate the planned production and calculate the KPI statistical data; and an optimization algorithm is developed in the Opcenter APS to optimize the APS rules.

[0039] Specifically, a simulation-based scheduling module (SSM) can be provided for integrating an advanced planning and scheduling software such as Opcenter APS and a plant design simulation software such as Plant Simulation to implement a simulation-based closed-loop APS scheduling optimization scheme. The scheduling scheme formulated based on the advanced planning and scheduling software such as Opcenter APS is provided to the simulation model generated based on the plant design simulation software such as Plant Simulation for verification, and the corresponding KPI data is obtained according to the simulation result, based on which it is determined whether the scheduling scheme meets the requirements. For example, as shown in the simulation-based closed-loop APS scheduling optimization method, the method comprises the following steps: Figure 1

[0040] Step S101, determining the APS rules selected for scheduling.

[0041] Step S102, determining the weight configuration set for the selected APS rules.

[0042] Step S103, generating a scheduling Gantt chart according to the weight configuration of the APS rules.

[0043] Step S104, exporting an order sequence corresponding to the scheduling Gantt chart.

[0044] Step S105, loading the order sequence and the obtained pre-set simulation model configuration data into a basic simulation model of a plant design simulation software to obtain a scheduling simulation model.

[0045] Step S106, running the scheduling simulation model and evaluating to obtain key performance indicator data.

[0046] Step S107, exporting the key performance indicator data as a simulation result.

[0047] Step S108, determining whether the simulation result meets the requirements, if yes, executing step S109; otherwise, returning to execute step S101.

[0048] Step S109, outputting the corresponding scheduling Gantt chart. ​

[0049] Further, the optimal weight of each APS rule in the scheduling can be found by using an optimization iterative algorithm when the scheduling scheme is made based on the advanced planning and scheduling software, and the KPI data obtained from the simulation can be used for optimization calculation when the optimal weight is found. For example, a comprehensive particle swarm optimization algorithm can be provided to set the weight configuration of the selected APS rules. The comprehensive particle swarm optimization algorithm is improved based on the basic particle swarm optimization algorithm, which calculates the fitness value according to the key performance indicator data in the iterative optimization process to determine the optimal position experienced by the particle itself and the optimal position experienced by the particle swarm, and uses the simulated annealing algorithm to evaluate the optimal position of the particle itself obtained each time, uses the mutation operation of the genetic algorithm to evaluate and update the optimal position of the particle swarm, dynamically adjusts the inertia weight value, and finally updates to generate the next generation of particle population.

[0050] To make the objectives, technical solutions and advantages of the present application clearer, the following embodiments further illustrate the present application in detail.

[0051] Figure 2 The framework structure of an embodiment of the simulation-based closed-loop APS scheduling optimization system of the present application is shown in the figure. In this embodiment, the advanced planning and scheduling software is Opcenter APS, and the plant design simulation software is Plant Simulation. As shown in the figure, the framework structure is divided into three layers: application layer 1, service layer 2 and data layer 3. Figure 2

[0052] The application layer 1 represents the integration of Opcenter APS 10 and Plant Simulation 20. Specifically, in the embodiment of the present application, an SSM 30 is provided, which is constructed as a Component Object Model (COM) component, integrated into Opcenter APS 10 through a COM interface, and connected to Plant Simulation 20 through the COM interface of Plant Simulation 20, thereby obtaining the simulation-based closed-loop APS scheduling optimization system in the embodiment of the present application.

[0053] The service layer 2 includes three parts: a scheduling submodule 31 formed by the SSM 30 using the APS framework and the application program interface (API), a simulation submodule 32 including a high-precision simulation model 321 formed by the SSM 30 using the framework and API of Plant Simulation 20, and an optimization algorithm submodule 33 provided by the SSM 30.

[0054] ​The APS rule base 311 includes various APS rules, which include not only the built-in rules of Opcenter APS, but also the user-defined rules. The scheduler 312 is a scheduling engine that performs the scheduling process.

[0055] At the service layer 2, the optimization algorithm submodule 33 is used to schedule the corresponding rules currently selected in the APS rule base 311, and assign initial weights to each rule, encode the assigned initial weights 331, and generate an initial weight distribution particle population for iteration. In order to verify and evaluate each weight distribution, the weight distribution particles are decoded 332 and provided to the scheduling submodule 31 for simulation verification by the simulation submodule 32, and the KPI data 322 reported by the simulation submodule 32 is received, and each weight distribution is iteratively optimized according to the KPI data. After the iteration is completed, the final weight distribution is decoded 332, and the decoded weight distribution is provided to the scheduling submodule 31 to generate an optimal scheduling scheme. The data required for the operation in this process comes from the data layer 3.

[0056] The data layer 3 includes the data sources of all modules of the service layer 2 described above. Basically, it is a SQL Server database 40 serving the Opcenter APS 10, which contains resource data 41, product data 42, inventory data 43, order data 44, etc., which are necessary for scheduling and simulation. On this basis, the database also stores scheduling result data (such as order sequence, etc.) 45, simulation model configuration data 46, which are required for simulation.

[0057] In this embodiment, the scheduling input / output data and the data required for simulation are stored in the same database, and standard data table templates can be used. From order scheduling to simulation production, the data layer 3 can also be applied to possible scenarios such as MRP (Material Requirement Planning), MPS (Master Production Schedule), SCM (Supply Chain Management), etc.

[0058] Figure 3 For the simulation-based closed-loop APS scheduling optimization method based on the framework shown in the embodiment of the present application Figure 2 As shown in the flowchart, the method mainly involves the interaction between the scheduling submodule 31, the simulation submodule 32, and the optimization algorithm submodule 33. Figure 3 As shown in the flowchart, the method mainly involves the interaction between the scheduling submodule 31, the simulation submodule 32, and the optimization algorithm submodule 33.

[0059] Specifically, the scheduling submodule 31 can include the following operations:

[0060] Step S401, import scheduling data.

[0061] In this step, the scheduling data is the data input required for scheduling, such as orders, resources, processes and other related data.

[0062] Step S402: setting the optimization target.

[0063] In this step, the optimization goal is also the scheduling goal, for example, whether all orders are delivered or the finished product inventory is minimized, and / or the inventory of work-in-progress is minimized, and / or some orders are processed in advance, and / or the inventory is within a range, and / or the cost is minimized, etc.

[0064] Step S403: Determine the APS rule selected for scheduling.

[0065] In this step, the user can select the required APS rules, or select the required APS rules according to the pre-configuration. Figure 4 As shown, Figure 4 The figure shows a schematic diagram of the function dialog box of SSM 30 in an example of the present invention. It can be seen that the SSM 30 side can specifically include an APS rule configuration module 34. Opcenter APS10 provides some built-in APS rules, such as the shortest delivery date (EDD), the shortest processing time (SPT), the lowest critical ratio (CR), the first-in-first-out (FIFO) and other rules. In addition, in addition to the built-in rules, the APS rule library further expands the rules, such as the shortest slack time (STR), the last-in-first-out (LIFO), the least number of operations (LOPNR) and so on. The APS rule configuration module 34 presents these rules to the user, and the user selects the required rules. Of course, the rule library can also allow users to define and add custom rules, which can be achieved by setting the attributes of each production order and adding these attributes as new rules for order sorting to the library. Figure 5 FIG. 1 shows a schematic diagram of an APS rule base configuration in an example. Figure 5 As shown, the right side presents some rules in the Opcenter APS rule library. Users can perform three operations through the APS rule configuration module 34: the first operation 341: add custom rules; the second operation 342: select the required rules, such as Figure 5 On the right, EDD, LIFO, and LOPNR are selected; the third operation 343: assign weights to the selected rules, such as Figure 5 1, 2, and 3 in the right box.

[0066] In the third operation 341 of assigning weights to the selected rules, there are two implementation schemes. One is manual scheduling, i.e., the user manually assigns weights to the selected APS rules, and then calls the simulation model to verify the scheduling scheme. This method is suitable for experienced planners and personnel who need to perform rapid planning and verification. If the scheme is manual scheduling, step S404 is performed after this step, and if automatic scheduling is selected, step S405 is performed.

[0067] In step S404, the user accepts the weight configuration of the APS rules.

[0068] In step S405, the optimization algorithm submodule 33 accepts the weight configuration of the APS rules.

[0069] The weight configuration of the APS rules by the optimization algorithm submodule 33 means that the SSM 30 automatically configures the weights of the APS rules using the optimization algorithm submodule 33, and continuously searches for the best APS scheduling sequence through scheduling and simulation based on the configured weights. Automatic scheduling is designed for planners who face new production scenarios or changing targets and do not know how to select the weights of the APS rules. Therefore, the simulation closed-loop scheduling optimization scheme helps users verify the scheduling results and automatically optimize the APS rules. The specific process can be referred to steps 501 to 509 below.

[0070] In step S406, a scheduling scheme is generated based on the weight configuration of the APS rules, i.e., a scheduling Gantt chart is obtained. In the scheduling Gantt chart, the processing order of each order and its corresponding processing station and processing time are embodied.

[0071] In this step, the total score of each order can be calculated according to the following formula (1) based on the weight configuration of the APS rules.

[0072] S sum =(V fcfs ×W fcfs +V edd ×W edd +V spt ×W spt +V str ×W str ) / (W fcfs +W edd +W spt +W str ) (1)

[0073] S sum : total score of each order;

[0074] V fcfs : initial priority score of each order, the value of the first order is 1, the value of the second order is 2, and so on.

[0075] V edd : initial delivery time score of each order, the value of the order with the earliest delivery time is 1, the value of the order with the second earliest delivery time is 2, and so on;

[0076] V spt : initial processing time score of each order, the value of the order with the shortest processing time is 1, the value of the order with the second shortest processing time is 2, and so on;

[0077] V str : initial slack time score of each order, the value of the order with the shortest slack time is 1, the value of the order with the second shortest slack time is 2, and so on.

[0078] W fcfs , W edd , W spt , W str is a weight value.

[0079] The score of each order determines the entire order sequence, and finally determines the scheduling result.

[0080] Step S407, based on the scheduling scheme, derive the corresponding order sequence, and store the order sequence into a database (DB).

[0081] Step S411, extract the simulation result from the database.

[0082] Step S412, determine whether the simulation result meets the needs, if yes, execute step S413; otherwise, return to execute step S403.

[0083] Step S413, output the corresponding scheduling scheme.

[0084] It can be seen that the scheduling submodule 31 is mainly used to determine the APS rule selected for scheduling, and determine the weight configuration of the selected APS rule; generate a scheduling Gantt chart according to the weight configuration of the APS rule; derive an order sequence corresponding to the scheduling Gantt chart into a database; and obtain a simulation result based on the order sequence from the database; determine whether the simulation result meets the needs, if yes, output the corresponding scheduling Gantt chart; otherwise, return to execute the operation of selecting the APS rule for scheduling.

[0085] The simulation submodule 32 side can include the following operations:

[0086] Step S408, the simulation sub-module 32 loads simulation related data and creates a scheduled simulation model. The loading of simulation related data includes: step S4081, loading simulation model configuration data; step S4082, loading simulation model; and step S4083, loading order sequence.

[0087] In this step S408, the simulation model can be created quickly as shown in the first interface 61 and the second interface 62. Figure 6 As shown in the first interface 61 and the second interface 62. Various methods can be used through the COM interface, and the specific process can include: first, the SSM 30 loads the basic simulation model through the LoadModel() method. In the basic simulation model, there is a series of SimTalk methods and ODBC connection objects, which are remotely controlled by the ExecuteSimTalk() method, which can read data from the Opcenter APS database, and then automatically create and run the relevant production line simulation model. In addition, the order sequence generated by the scheduling sub-module 31 is also loaded into the current production line simulation model and assigned to each production workstation. In order to calibrate the model, the simulation model configuration data is also loaded in this way, which can be input by the user from the SSM 30 integrated into the Opcenter APS 10 and stored in the database. The simulation model configuration data can include the settings of workstation processing time, workstation mean time to failure (MTTF), yield rate, logistics rules, etc. In this way, a high-precision model can be obtained, and a new model can be quickly created to run the simulation by changing the production-related data in the Opcenter APS 10 each time. Then after the simulation triggers the SimulationFinished event, the SSM finally receives the KPI data through the GetValue() method. Figure 6 The first interface 61 and the second interface 62 in the simulation model creation interface 60 are only used to represent an example of the model creation carried, and do not limit the actual application interface, and do not affect the implementation of the technical scheme of the present application, so the specific content thereon is blurred.

[0088] Step S409, running the scheduled simulation model and evaluating the simulated KPI data.

[0089] In this embodiment, if the weights of the aforementioned APS rules are automatically configured by the optimization algorithm sub-module 33, the KPI data obtained in this step can be fed back to the optimization algorithm sub-module 33.

[0090] Step S410, exporting and storing the KPI data as simulation results to the database DB.

[0091] It can be seen that the simulation submodule 32 is mainly used to load the order sequence and the pre-set simulation model configuration data from the database, load the order sequence and the simulation model configuration data into the basic simulation model of PlantSimulation to obtain a scheduling simulation model, run the scheduling simulation model, and evaluate to obtain key performance indicator data, and store the key performance indicator data as simulation results in the database.

[0092] On the side of the optimization algorithm submodule 33, the following operations can be included:

[0093] In this embodiment, considering that the scheduling optimization problem is based on a discrete scenario, and the KPI results are considered to guide the weight configuration of the APS rules in this application, the optimization algorithm submodule 33 can use an optimization algorithm called comprehensive particle swarm optimization algorithm (SG-DPSO). The SG-DPSO is based on the particle swarm optimization algorithm (PSO) for iterative optimization, and combines the SA algorithm and the GA algorithm together to complete the optimization of the weight configuration during the iterative optimization process. The initialization of PSO is a group of random particles (random solutions), and then the optimal solution is found through iteration. In each iteration, the particle updates itself by tracking two "extreme values", namely the optimal position Pbest experienced by the particle itself and the optimal position Gbest experienced by the particle swarm, to update itself. After finding the two optimal solutions, the particle updates its speed and position. The reason for using the PSO algorithm is that the result of each iteration can guide the fitness value of the particle to become better by updating the speed and position of the particle. At the same time, the SA algorithm is used to process the current best particle position, and the GA algorithm is used to process the global optimal position of the particle population to update the particle population, thereby avoiding the generation of local optimal solutions. In addition, the fitness value is calculated according to the KPI data to determine Pbest and Gbest. In specific implementation, the following steps can be included:

[0094] Step S501, determine the APS rule selected by the scheduling submodule 31 through the COM interface, assign an initial weight to the APS rule, and perform particle coding on the initial weight.

[0095] For example, Figure 7 A schematic diagram of an APS rule weight allocation automatic optimization process is shown. As Figure 7 shown, the APS rule combination includes four rules: EDD, FIFO, STR, and SPT, and a possible particle after coding should be like "1342", the number of each position represents the weight value of each rule, and the maximum value represents the highest weight of the related rule. After decoding, the scheduling submodule 31 can calculate the total score of each production order according to the weight values of different APS rules.

[0096] Step S502, the initialized weight after particle coding is obtained, and the current particle swarm is obtained.

[0097] Step S503, the current particle swarm is decoded to obtain the current weight configuration of the APS rule, and the current weight configuration of the APS rule is provided to the scheduling submodule 31 through the COM interface, and the scheduling submodule 31 accepts the weight configuration of the APS rule in step S404.

[0098] Step S504, the KPI data fed back by the simulation submodule 32 after step S408 is received through the COM interface, and the fitness value is calculated according to the KPI data to determine Pbest and Gbest.

[0099] Step S505, whether the maximum iteration number is reached? If yes, step S509 is executed; otherwise, step S506 is executed, and the inertia weight value of the particle position update is adjusted according to the iteration number.

[0100] Step S506, the Pbest is evaluated by using the simulated annealing algorithm.

[0101] Step S507, the Gbest is evaluated and updated by using the mutation operation of the genetic algorithm.

[0102] Step S508, a new generation of current particle swarm is generated, and then step S503 is executed.

[0103] Step S509, the particle swarm corresponding to Gbest is selected as the current example swarm, and then step S503 is executed.

[0104] It can be seen that the optimization algorithm submodule 33 is mainly used to set the weight configuration of the selected APS rule by using the comprehensive particle swarm optimization algorithm, and the weight configuration is provided to the scheduling submodule; the comprehensive particle swarm optimization algorithm is improved on the basis of the basic particle swarm optimization algorithm, and in the iteration optimization process, the fitness value is calculated according to the key performance indicator data to determine the optimal position experienced by the particle itself and the optimal position experienced by the particle swarm, and the simulated annealing algorithm is used to evaluate the optimal position of the particle itself obtained each time, the mutation operation of the genetic algorithm is used to evaluate and update the optimal position of the particle swarm, the inertia weight value is dynamically adjusted, and finally the next generation of particle swarm is updated.

[0105] It can be seen that, as Figures 2 to 7As shown in the above embodiments, the simulation-based closed-loop APS scheduling optimization system in the embodiments of the present application can include: an advanced planning and scheduling software module, a plant design simulation software module, and a simulation-based scheduling module 30 which is configured as a component object model (COM) component, integrated into the advanced planning and scheduling software module through a COM interface, connected to the plant design simulation software module through a COM interface of the plant design simulation software module, and performs the following operations: determining APS rules selected for scheduling; determining a weight configuration of the selected APS rules; generating a scheduling Gantt chart according to the weight configuration of the APS rules; exporting an order sequence corresponding to the scheduling Gantt chart; loading the order sequence and acquired pre-set simulation model configuration data into a basic simulation model of the plant design simulation software module to obtain a scheduling simulation model; running the scheduling simulation model and evaluating to obtain key performance indicator data; exporting the key performance indicator data as a simulation result; determining whether the simulation result meets the needs, and if yes, outputting a corresponding scheduling Gantt chart; otherwise, returning to perform the step of selecting APS rules for scheduling.

[0106] Figure 8 For the structure of another simulation-based closed-loop APS scheduling optimization system in the embodiments of the present application, the device can be used to implement the method shown in Figure 1 and Figure 3 , or realize the system shown in Figure 2 . As shown in Figure 8 , the system can include: at least one memory 81, at least one processor 82, and at least one display 83. In addition, some other components such as communication ports can also be included. These components communicate through a bus 84.

[0107] Among them, at least one memory 81 is used to store a computer program. In an embodiment, the computer program can be understood as including various modules of the simulation-based closed-loop APS scheduling optimization system shown in Figure 2 . In addition, the at least one memory 81 can also store an operating system and the like. The operating system includes but is not limited to: Android operating system, Symbian operating system, Windows operating system, Linux operating system, and the like.

[0108] The at least one display 83 is used to display scheduling Gantt charts and the like.

[0109] The at least one processor 82 is used to call the computer program stored in the at least one memory 81, and execute the simulation-based closed-loop APS scheduling optimization method described in the embodiments of the present application.

[0110] Specifically, the at least one processor 82 is configured to invoke a computer program stored in the at least one memory 81 to cause the apparatus to perform corresponding operations. The operations can include: determining an APS rule selected for scheduling; determining a weight configuration set for the selected APS rule; generating a scheduling Gantt chart according to the weight configuration of the APS rule; deriving an order sequence corresponding to the scheduling Gantt chart; loading the order sequence and the obtained pre-set simulation model configuration data into a basic simulation model of Plant Simulation to obtain a scheduling simulation model; running the scheduling simulation model and evaluating to obtain KPI data; exporting the KPI data as a simulation result; determining whether the simulation result meets the requirement, and if yes, outputting a corresponding scheduling Gantt chart; otherwise, returning to perform the step of selecting an APS rule for scheduling.

[0111] In one embodiment, the determining the weight configuration set for the selected APS rule comprises: setting the weight configuration for the selected APS rule by using a comprehensive particle swarm optimization algorithm; the comprehensive particle swarm optimization algorithm is improved on the basis of a basic particle swarm optimization algorithm, and in the iterative optimization process, a fitness value is calculated according to the key performance indicator data to determine an optimal position experienced by a particle itself and an optimal position experienced by a particle swarm, a simulated annealing algorithm is used to evaluate and process the optimal position of the particle itself obtained each time, a mutation operation of a genetic algorithm is used to evaluate and update the optimal position of the particle swarm, an inertia weight value is dynamically adjusted, and finally a next generation of particle population is updated.

[0112] In one embodiment, the step of assigning weights to the selected APS rules by using the integrated particle swarm optimization algorithm comprises: assigning initial weights to the selected APS rules, and performing particle coding on the initial weights; initializing the weights after the particle coding to obtain a current particle swarm; decoding the current particle swarm to obtain a current weight configuration of the APS rules, and determining the current weight configuration as the weight configuration of the selected APS rules; after the step of executing the scheduling simulation model and obtaining the KPI data, further comprising: obtaining the KPI data, and calculating fitness values according to the KPI data to determine the optimal position experienced by the particle itself and the optimal position experienced by the particle swarm; determining whether the maximum number of iterations is reached; when the maximum number of iterations is not reached, performing evaluation processing on the optimal position experienced by the particle itself by using the simulated annealing algorithm, and performing evaluation updating on the optimal position experienced by the particle swarm by using the mutation operation of the genetic algorithm; generating a new generation of the current particle swarm, and returning to perform the operation of decoding the current particle swarm to obtain the current weight configuration of the APS rules; when the maximum number of iterations is reached, taking the particle swarm corresponding to the optimal position experienced by the particle swarm as the current example swarm, and returning to perform the operation of decoding the current particle swarm to obtain the current weight configuration of the APS rules.

[0113] In one embodiment, the step of determining the APS rules selected for scheduling comprises: selecting the rules required for the current scheduling based on the existing rules of the advanced planning and scheduling software and / or the added custom rules.

[0114] As can be seen from the above scheme, since the Opcenter APS and the PlantSimulation are integrated in the embodiment of the present application, the scheduling and dispatching scheme generated based on the Opcenter APS can create a corresponding scheduling simulation model by the PlantSimulation, the KPI data of the simulation result can be obtained by running the scheduling simulation model, and it can be determined whether the corresponding scheduling and dispatching scheme meets the requirements based on the KPI data, so that a more feasible scheduling scheme can be provided.

[0115] Further, by using the integrated particle swarm optimization algorithm to automatically assign weights to the APS rules selected for scheduling, production planning personnel who do not know how to select the weights of the APS rules when facing new production scenarios or changing production requirements can complete the scheduling.

[0116] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A simulated based closed loop advanced planning and scheduling software (APS) dispatch optimization method characterized in that, The method comprises the following steps: determining an APS rule selected for scheduling; determining a weight configuration set for the selected APS rule; generating a scheduling Gantt chart according to the weight configuration of the APS rule; deriving an order sequence corresponding to the scheduling Gantt chart; loading the order sequence and the obtained pre-set simulation model configuration data into a basic simulation model of a factory design simulation software to obtain a scheduling simulation model; running the scheduling simulation model and evaluating to obtain key performance indicator data; exporting the key performance indicator data as a simulation result; judging whether the simulation result meets the requirement, and if yes, outputting a corresponding scheduling Gantt chart; otherwise, returning to execute the step of selecting an APS rule for scheduling; wherein the step of determining a weight configuration set for the selected APS rule comprises: adopting a comprehensive particle swarm optimization algorithm to set the weight configuration for the selected APS rule; the comprehensive particle swarm optimization algorithm is improved on the basis of a basic particle swarm optimization algorithm, and in the iterative optimization process, fitness values are calculated according to the key performance indicator data to determine the optimal position experienced by the particle itself and the optimal position experienced by the particle swarm, and a simulated annealing algorithm is adopted to evaluate the optimal position of the particle itself obtained each time, a mutation operation of a genetic algorithm is adopted to evaluate and update the optimal position of the particle swarm, and the inertia weight value is dynamically adjusted, so as to finally update the next generation of particle swarm.

2. The scheduling optimization method of claim 1, wherein, The step of adopting a comprehensive particle swarm optimization algorithm to set the weight configuration for the selected APS rule comprises: allocating an initial weight for the selected APS rule, and performing particle coding on the initial weight; initializing the weight after the particle coding to obtain a current particle swarm; decoding the current particle swarm to obtain a current weight configuration of the APS rule, and determining the current weight configuration as the weight configuration set for the selected APS rule; after the step of running the scheduling simulation model and evaluating to obtain key performance indicator data, further comprising: obtaining the key performance indicator data, and calculating fitness values according to the key performance indicator data to determine the optimal position experienced by the particle itself and the optimal position experienced by the particle swarm; judging whether the maximum iteration number is reached; when the maximum iteration number is not reached, adopting a simulated annealing algorithm to evaluate the optimal position experienced by the particle itself, and adopting a mutation operation of a genetic algorithm to evaluate and update the optimal position experienced by the particle swarm; generating a new generation of current particle swarm, and returning to execute the operation of decoding the current particle swarm to obtain the current weight configuration of the APS rule; when the maximum iteration number is reached, taking the particle swarm corresponding to the optimal position experienced by the particle swarm as a current example swarm, and returning to execute the operation of decoding the current particle swarm to obtain the current weight configuration of the APS rule.

3. The dispatch optimization method of claim 1 or 2, wherein, The step of determining an APS rule selected for scheduling comprises: selecting a rule required for current scheduling based on existing rules and / or added custom rules of an advanced planning and scheduling software.

4. A simulation-based closed loop advanced planning and scheduling (APS) dispatch optimization system, characterized in that, The method comprises the following steps: at least one memory (81) and at least one processor (82), wherein: the at least one memory (81) is configured to store a computer program; the at least one processor (82) is configured to invoke the computer program stored in the at least one memory (81) to cause the system to perform corresponding operations, the operations comprising: determining an APS rule selected for scheduling; determining a weight configuration set for the selected APS rule; generating a scheduling Gantt chart according to the weight configuration of the APS rule; deriving an order sequence corresponding to the scheduling Gantt chart; loading the order sequence and the obtained pre-set simulation model configuration data into a basic simulation model of a factory design simulation software to obtain a scheduling simulation model; running the scheduling simulation model and evaluating to obtain key performance indicator data; exporting the key performance indicator data as a simulation result; determining whether the simulation result meets the needs, if yes, outputting a corresponding scheduling Gantt chart; otherwise, returning to perform the step of selecting an APS rule for scheduling; wherein the determining a weight configuration set for the selected APS rule comprises: adopting a comprehensive particle swarm optimization algorithm to set the weight configuration for the selected APS rule; the comprehensive particle swarm optimization algorithm is improved on the basis of a basic particle swarm optimization algorithm, in which a fitness value is calculated according to the key performance indicator data in an iterative optimization process to determine an optimal position experienced by a particle itself and an optimal position experienced by a particle swarm, a simulated annealing algorithm is adopted to evaluate the optimal position of the particle itself obtained each time, a mutation operation of a genetic algorithm is adopted to evaluate and update the optimal position of the particle swarm, and an inertia weight value is dynamically adjusted, and finally a next generation of particle swarm is updated.

5. The dispatch optimization system of claim 4, wherein, the adopting a comprehensive particle swarm optimization algorithm to set the weight configuration for the selected APS rule comprises: allocating an initial weight for the selected APS rule, and performing particle coding on the initial weight; initializing the weight after the particle coding to obtain a current particle swarm; decoding the current particle swarm to obtain a current weight configuration of the APS rule, and determining the current weight configuration as the weight configuration set for the selected APS rule; after performing the step of running the scheduling simulation model and evaluating to obtain key performance indicator data, further comprising: obtaining the key performance indicator data, and calculating a fitness value according to the key performance indicator data to determine an optimal position experienced by a particle itself and an optimal position experienced by a particle swarm; determining whether a maximum iteration number is reached; when the maximum iteration number is not reached, adopting a simulated annealing algorithm to evaluate the optimal position experienced by the particle itself, and adopting a mutation operation of a genetic algorithm to evaluate and update the optimal position experienced by the particle swarm; generating a new generation of current particle swarm, and returning to perform the operation of decoding the current particle swarm to obtain the current weight configuration of the APS rule. Upon reaching the maximum number of iterations, the particle swarm that has experienced the optimal position corresponding to the particle swarm is taken as the current example swarm, and the operation of decoding the current particle swarm to obtain the current weight configuration of the APS rule is returned to be performed.

6. The dispatch optimization system of claim 4 or 5, wherein, The determining of the APS rule selected for scheduling comprises selecting the rule required for the current scheduling based on the existing rule of the advanced planning and scheduling software and / or the added custom rule.

7. A simulation-based closed loop advanced planning and scheduling (APS) dispatch optimization system, characterized in that, The method comprises: an advanced planning and scheduling software module; a plant design simulation software module; and a simulation-based scheduling module (30) configured as a component object model (COM) component, integrated into the advanced planning and scheduling software module through a COM interface, connected to the plant design simulation software module through a COM interface of the plant design simulation software module, and performing the following operations: determining an APS rule selected for scheduling; determining a weight configuration of the selected APS rule; generating a scheduling Gantt chart according to the weight configuration of the APS rule; deriving an order sequence corresponding to the scheduling Gantt chart; loading the order sequence and the obtained pre-set simulation model configuration data into a basic simulation model of the plant design simulation software module to obtain a scheduling simulation model; running the scheduling simulation model and evaluating to obtain key performance indicator data; deriving the key performance indicator data as a simulation result; determining whether the simulation result meets the requirement, and if so, outputting the corresponding scheduling Gantt chart; otherwise, returning to perform the step of determining the APS rule selected for scheduling; wherein the determining of the weight configuration of the selected APS rule comprises: setting the weight configuration of the selected APS rule by using a comprehensive particle swarm optimization algorithm; the comprehensive particle swarm optimization algorithm is improved on the basis of a basic particle swarm optimization algorithm, and in the iteration optimization process, a fitness value is calculated according to the key performance indicator data to determine the optimal position experienced by the particle itself and the optimal position experienced by the particle swarm, a simulated annealing algorithm is used to evaluate and process the best position of the particle itself obtained each time, a mutation operation of a genetic algorithm is used to evaluate and update the best position of the particle swarm, the inertia weight value is dynamically adjusted, and finally the next generation of particle population is updated. The simulation-based scheduling module (30) comprises a scheduling submodule (31) and a simulation submodule (32); wherein 8. The dispatch optimization system of claim 7, wherein, the scheduling submodule (31) is configured to determine an APS rule selected for scheduling, and determine a weight configuration of the selected APS rule; generate a scheduling Gantt chart according to the weight configuration of the APS rule; derive an order sequence corresponding to the scheduling Gantt chart into a database; and obtain a simulation result based on the order sequence from the database; determine whether the simulation result meets the requirement, and if so, output the corresponding scheduling Gantt chart; otherwise, return to perform the operation of selecting the APS rule for scheduling; ​ The simulation submodule (32) is configured to load the order sequence and preset simulation model configuration data from the database, load the order sequence and the simulation model configuration data into a basic simulation model of a plant design simulation software module to obtain a scheduling simulation model, run the scheduling simulation model, and evaluate to obtain key performance indicator data; and store the key performance indicator data as a simulation result in the database.

9. The dispatch optimization system of claim 8, wherein, The simulation-based scheduling module (30) further comprises an optimization algorithm submodule (33) and a submodule configured to assign a weight configuration to the selected APS rules by using a comprehensive particle swarm optimization algorithm and provide the weight configuration to the scheduling submodule (31). The comprehensive particle swarm optimization algorithm is improved on the basis of a basic particle swarm optimization algorithm, and in an iterative optimization process, calculates fitness values according to key performance indicator data to determine an optimal position experienced by a particle itself and an optimal position experienced by a particle swarm, evaluates the optimal position experienced by the particle itself by using a simulated annealing algorithm, evaluates and updates the optimal position experienced by the particle swarm by using a mutation operation of a genetic algorithm, dynamically adjusts an inertia weight value, and finally updates to generate a next generation of particle swarms.

10. The dispatch optimization system of claim 9, wherein, The optimization algorithm submodule (33) assigns an initial weight to the selected APS rules, performs particle coding on the initial weight, initializes the weight after the particle coding to obtain a current particle swarm, decodes the current particle swarm to obtain a current weight configuration of the APS rules and provide the current weight configuration to the scheduling submodule (31), acquires key performance indicator data evaluated by the simulation submodule (32), calculates fitness values according to the key performance indicator data to determine an optimal position experienced by a particle itself and an optimal position experienced by a particle swarm, and determines whether a maximum iteration number is reached. When the maximum iteration number is not reached, the optimal position experienced by the particle itself is evaluated by using a simulated annealing algorithm, and the optimal position experienced by the particle swarm is evaluated and updated by using a mutation operation of a genetic algorithm; a new generation of current particle swarms is generated, and the operation of decoding the current particle swarms to obtain a current weight configuration of the APS rules is performed; and when the maximum iteration number is reached, the optimal position experienced by the particle swarm is used as a current example swarm, and the operation of decoding the current particle swarms to obtain a current weight configuration of the APS rules is performed.

11. The dispatch optimization system of any one of claims 7 to 10, wherein, The simulation-based scheduling module (30) further comprises an APS rule configuration module (34) configured to determine rules required for current scheduling based on existing rules of advanced planning and scheduling software and / or added custom rules.

12. A computer readable storage medium having stored thereon a computer program; characterized in that, The computer program can be executed by a processor and implement the scheduling optimization method of claim 1.

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