Complex discrete type processing and manufacturing scheduling method and device

Abstract

The invention provides a complex discrete type processing and manufacturing scheduling method and device, and relates to the technical field of discrete processing and manufacturing, and the method comprises the steps: obtaining complex discrete type processing and manufacturing order information, and extracting a schedulable work order from the complex discrete type processing and manufacturing order information based on the external condition constraint of production processing; generating a scheduling strategy set for the schedulable work order; and based on the target function considering the equipment remodeling time, in combination with a pre-constructed processing constraint condition set, performing iterative optimization on scheduling strategies in the scheduling strategy set to determine a target scheduling strategy, the target scheduling strategy being used for describing a corresponding relationship among the schedulable work order, the processing procedure and the processing equipment. According to the method, comprehensive optimization of the complex discrete type machining and manufacturing process can be better achieved, so that the production efficiency is improved, the cost is reduced, it is ensured that the production plan can be completed on time, and the actual production requirement is better met.

Description

Technical Field

[0001] The present invention relates to the technical field of discrete processing and manufacturing, and in particular to a complex discrete processing and manufacturing scheduling method and device. Background Art

[0002] Discrete manufacturing is a production method widely used in many industries. Its core feature is to decompose the production process into a series of discrete steps, each of which involves a specific processing operation. This production method usually requires a variety of equipment and process flows to complete the conversion from raw materials to finished products. However, the current discrete manufacturing scheduling method is difficult to meet actual production needs. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide a complex discrete processing and manufacturing scheduling method and device, which can better realize the comprehensive optimization of the complex discrete processing and manufacturing process, thereby improving production efficiency, reducing costs, and ensuring that the production plan can be completed on time to better meet actual production needs.

[0004] In a first aspect, an embodiment of the present invention provides a complex discrete manufacturing scheduling method, comprising: Obtain complex discrete manufacturing order information, and extract schedulable work orders from the complex discrete manufacturing order information based on external constraints of production and processing; the schedulable work orders are divided into multiple batches, and the parts corresponding to each batch involve multiple processing procedures; Generate a set of scheduling strategies for schedulable work orders; Based on the objective function that takes equipment changeover time into consideration, combined with a pre-constructed set of processing constraints, the scheduling strategies in the scheduling strategy set are iteratively optimized to determine the target scheduling strategy; wherein the processing constraints include one or more of the constraints on personnel and equipment matching, equipment processing capacity, and actual processing conditions. The target scheduling strategy is used to describe the correspondence between schedulable work orders, processing procedures, and processing equipment.

[0005] In one implementation, the external condition constraints include delivery constraints, inventory constraints, and material constraints. Based on the external condition constraints of production and processing, schedulable work orders are extracted from complex discrete processing and manufacturing order information, including: For any work order in the complex discrete processing and manufacturing order information, determine whether the work order meets the delivery constraint, inventory constraint and material constraint; If yes, the work order is determined as a dispatchable work order; If not, split it based on the delivery constraint, inventory constraint and material constraint to obtain a sub-work order that meets the delivery constraint, inventory constraint and material constraint, and take the sub-work order as a schedulable work order.

[0006] In one embodiment, based on the objective function considering the equipment changeover time and in combination with the pre-built processing constraint condition set, the scheduling strategies in the scheduling strategy set are iteratively optimized to determine the target scheduling strategy, including: Based on the objective function that considers the equipment changeover time, multiple scheduling strategies are selected from the scheduling strategy set. The scheduling strategies are expressed in a three-dimensional matrix, which includes the dimensions of schedulable work orders, process dimensions, and equipment dimensions. Cross and mutate each scheduling strategy obtained by screening, and modify each scheduling strategy after cross and mutation based on the processing constraint condition set to obtain a new scheduling strategy set; Continue to select multiple scheduling strategies from the new scheduling strategy set based on the objective function until the preset iteration stop condition is met, and determine the target scheduling strategy from the new scheduling strategy set when the iteration stops.

[0007] In one implementation, multiple scheduling strategies are selected from the scheduling strategy set based on an objective function that takes equipment changeover time into consideration, including: For any scheduling strategy in the scheduling strategy set, based on the objective function considering the equipment changeover time, multiple initial function values ​​corresponding to the scheduling strategy are determined, and the multiple initial function values ​​are merged to obtain the objective function value; Based on the objective function value corresponding to each scheduling strategy, multiple scheduling strategies are selected from the scheduling strategy set.

[0008] In one implementation, based on the objective function considering the equipment changeover time, multiple initial function values ​​corresponding to the scheduling strategy are determined, including: For any scheduling strategy in the scheduling strategy set, based on the objective function considering the equipment changeover time, the production capacity efficiency corresponding to the scheduling strategy is determined, as well as the equipment utilization rate corresponding to the scheduling strategy, the processing sequence continuity index value of a single processing equipment corresponding to the scheduling strategy, and the work order processing cost corresponding to the scheduling strategy are determined; wherein, the processing sequence continuity index value and the work order processing cost are both related to the equipment changeover time.

[0009] In one implementation, determining the processing continuity index value of a single processing device corresponding to the scheduling strategy includes: According to the schedulable work orders corresponding to a single processing equipment in the scheduling strategy and the processing procedures therein, determine the equipment changeover time corresponding to the scheduling strategy; Based on the equipment changeover time, determine the number of process types corresponding to a single processing equipment in the scheduling strategy; According to the number of process types, the processing continuity index value corresponding to a single processing equipment in the scheduling strategy is determined.

[0010] In one implementation, determining the work order processing cost corresponding to the scheduling strategy includes: Determine the equipment changeover time corresponding to the scheduling strategy based on the schedulable work orders corresponding to a single processing equipment in the scheduling strategy and the processing procedures therein; and determine the equipment processing time corresponding to the scheduling strategy based on the process difficulty coefficient corresponding to each processing procedure in the scheduling strategy; Based on the equipment changeover time, the equipment processing time, the working time period when the processing equipment in the scheduling strategy processes the processing procedure and its corresponding penalty coefficient, the work order processing cost corresponding to the scheduling strategy is determined.

[0011] In one implementation, a plurality of initial function values ​​are fused to obtain a target function value, including: The objective function value corresponding to the scheduling strategy is obtained by taking the weighted sum of the production capacity efficiency, equipment utilization, processing procedure continuity index value and work order processing cost.

[0012] In one embodiment, the personnel and equipment matching constraint is: the matching degree between personnel and processing equipment is positively correlated with the processing efficiency of the processing equipment, and is used to schedule personnel and processing equipment based on the personnel and equipment matching constraint when the processing equipment is not in a maintenance state and the personnel is in a working state; The equipment processing capacity constraints are: the equipment processing time corresponding to the scheduling strategy is less than or equal to the maximum working time of the processing equipment; and the time and value of the working time period when the processing equipment processes the processing procedure in the scheduling strategy is less than or equal to the maximum working time of the processing equipment; The actual processing conditions are as follows: the current processing step is processed after the corresponding previous processing step is completed; and, within a period of time, a single processing device only processes one processing step; and, each processing step is processed once.

[0013] In a second aspect, an embodiment of the present invention further provides a complex discrete manufacturing scheduling device, comprising: The work order extraction module is used to obtain complex discrete manufacturing order information and extract schedulable work orders from the complex discrete manufacturing order information based on the external constraints of production and processing; the schedulable work orders are divided into multiple batches, and the parts corresponding to each batch involve multiple processing procedures; A strategy generation module is used to generate a set of scheduling strategies for schedulable work orders; The strategy iteration module is used to iteratively optimize the scheduling strategies in the scheduling strategy set based on the objective function that takes into account the equipment changeover time and in combination with a pre-built set of processing constraints to determine the target scheduling strategy; wherein the processing constraints include one or more of the constraints on personnel and equipment matching, equipment processing capacity, and actual processing situation constraints; the target scheduling strategy is used to describe the correspondence between schedulable work orders, processing procedures, and processing equipment.

[0014] A complex discrete processing and manufacturing scheduling method and device provided by an embodiment of the present invention first obtains complex discrete processing and manufacturing order information, and based on the external condition constraints of production and processing, extracts schedulable work orders from the complex discrete processing and manufacturing order information, the schedulable work orders are divided into multiple batches, and the parts corresponding to each batch involve multiple processing procedures; then a scheduling strategy set is generated for the schedulable work orders; finally, based on an objective function considering equipment changeover time and combined with a pre-constructed processing constraint condition set, the scheduling strategies in the scheduling strategy set are iteratively optimized to determine a target scheduling strategy, the processing constraint conditions include one or more of personnel and equipment matching constraints, equipment processing capacity constraints, and actual processing situation constraints, and the target scheduling strategy is used to describe the correspondence between schedulable work orders, processing procedures, and processing equipment. The above method fully considers the equipment changeover time, the matching of personnel and equipment, and at the same time incorporates external condition constraints, equipment processing capacity constraints, actual processing situation constraints, etc. into the scheduling optimization scope. On this basis, the scheduling strategies in the scheduling strategy set are iteratively optimized to obtain a better target scheduling strategy. The embodiment of the present invention can better realize the comprehensive optimization of complex discrete processing and manufacturing processes, thereby improving production efficiency, reducing costs, and ensuring that production plans can be completed on time to better meet actual production needs.

[0015] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A schematic diagram of a flow chart of a complex discrete manufacturing scheduling method provided by an embodiment of the present invention; Figure 2 A schematic diagram of a change in the objective function value of a scheduling strategy set provided by an embodiment of the present invention; Figure 3 A schematic diagram of an algorithm simulation and production scheduling result provided by an embodiment of the present invention; Figure 4 A schematic diagram of the structure of a complex discrete manufacturing scheduling device provided by an embodiment of the present invention; Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described in combination with the embodiments below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] A common problem in current discrete manufacturing scheduling methods is the failure to consider equipment changeover time and the degree of matching between personnel capabilities and equipment processing capabilities. This neglect often leads to problems in the production and processing process such as failure to meet delivery conditions, overtime, and outsourcing. Once these problems occur, they will not only affect the company's production efficiency and cost control, but will also have a certain impact on the supply chain security of downstream companies, thereby damaging the company's reputation. In addition, complex constraints such as work order BOM constraints, process difficulty constraints, and part processing priority constraints are also less considered, and very few scheduling methods simultaneously consider scheduling problems under the existence of the above-mentioned multiple constraints. As a result, existing scheduling methods are difficult to meet actual production needs.

[0021] Based on this, the implementation of the present invention provides a complex discrete processing and manufacturing scheduling method and device, which can better realize the comprehensive optimization of complex discrete processing and manufacturing processes, thereby improving production efficiency, reducing costs, and ensuring that production plans can be completed on time to better meet actual production needs.

[0022] To facilitate understanding of this embodiment, a complex discrete manufacturing scheduling method disclosed in an embodiment of the present invention is first described in detail. Figure 1 The flowchart of a complex discrete manufacturing scheduling method shown in FIG. 1 mainly includes the following steps S102 to S106: Step S102, obtaining complex discrete processing and manufacturing order information, and extracting schedulable work orders from the complex discrete processing and manufacturing order information based on external condition constraints of production and processing.

[0023] Among them, complex discrete processing and manufacturing order information is used to record multiple work orders of complex discrete processing and manufacturing and the product quantity and delivery time required for each work order; external condition constraints may include delivery time constraints, inventory constraints and BOM (material) constraints; schedulable work orders are work orders that can be delivered on time using existing inventory and materials. Scheduling work orders are divided into multiple batches, and the parts corresponding to each batch involve multiple processing procedures.

[0024] In one example, a work order that meets external constraints can be determined as a schedulable work order; for a work order that does not meet external constraints, it can be split to obtain sub-work orders that meet external constraints and sub-work orders that do not meet external constraints, and the sub-work orders that meet external constraints are determined as schedulable work orders.

[0025] Step S104: Generate a scheduling strategy set for the schedulable work order.

[0026] The scheduling strategy set includes multiple scheduling strategies, which are expressed in a three-dimensional matrix. The three-dimensional matrix includes the schedulable work order dimension, the process dimension, and the equipment dimension. The scheduling strategy can be used to describe the initial correspondence between the schedulable work order, the processing process, and the processing equipment. In one embodiment, the batch corresponding to the schedulable work order and the multiple processing processes involved in each batch of products are known, and multiple scheduling strategies are randomly generated to allocate initial processing equipment and processing personnel to each processing process involved in each schedulable work order.

[0027] Step S106, based on the objective function taking into account the equipment changeover time and in combination with the pre-constructed processing constraint condition set, the scheduling strategies in the scheduling strategy set are iteratively optimized to determine the target scheduling strategy.

[0028] Among them, the processing constraints include one or more of the constraints of personnel and equipment matching, equipment processing capacity, and actual processing situation. The target scheduling strategy is used to describe the optimal correspondence between the schedulable work orders, processing procedures, and processing equipment. In one example, the NSGA2 algorithm or other optimization algorithms can be used to iteratively optimize the scheduling strategies in the scheduling strategy set based on the objective function considering the equipment changeover time and in combination with the pre-constructed processing constraint set. When the iteration stop condition is met, the target scheduling strategy can be selected from the scheduling strategy set at this time to control the processing equipment to process multiple processing procedures involved in the schedulable work orders according to the target scheduling strategy.

[0029] The complex discrete processing and manufacturing scheduling method provided by the embodiment of the present invention fully considers the equipment changeover time and the matching of personnel and equipment, and at the same time incorporates external condition constraints, equipment processing capacity constraints, actual processing situation constraints, etc. into the scheduling optimization range. On this basis, the scheduling strategies in the scheduling strategy set are iteratively optimized to obtain a better target scheduling strategy. The embodiment of the present invention can better realize the comprehensive optimization of the complex discrete processing and manufacturing process, thereby improving production efficiency, reducing costs, and ensuring that the production plan can be completed on time to better meet actual production needs.

[0030] For ease of understanding, the embodiment of the present invention first defines the variables involved in the complex discrete manufacturing scheduling method, as shown in Table 1.

[0031] Table 1 Variable table

[0032]

[0033] The embodiment of the present invention defines the problem of complex discrete processing and manufacturing: according to the actual production and processing situation, the total Work order, split into Batch, equivalent to Each processing equipment produces in batches. For discrete manufacturing, a batch corresponds to a component of the same type. Therefore, in the current case, the production batch, or the number of pallets, corresponds to the concept of abstract parts. The current model assumes that the delivery date of the work order is known, and it is not allowed to be later than the delivery date, that is, the delivery date constraint is a hard constraint.

[0034] On this basis, an embodiment of the present invention provides a specific implementation method of a complex discrete processing and manufacturing scheduling method.

[0035] 1. Before scheduling, fully consider the BOM constraints of production and processing and other processing constraints. Other processing constraints may include delivery constraints, inventory constraints, delivery constraints, etc.

[0036] In one example, for any work order in complex discrete processing and manufacturing order information, determine whether the work order meets the delivery constraints, inventory constraints and material constraints; if so, determine the work order as a schedulable work order; if not, split it based on the delivery constraints, inventory constraints and material constraints to obtain sub-work orders that meet the delivery constraints, inventory constraints and material constraints, and treat the sub-work orders as schedulable work orders.

[0037] Specifically, the production quantity is determined based on factors such as the inventory of work-in-process and the number of work orders. Assuming pull production, no production forecast is made, simple inventory management, and no inventory forecast is made. The production quantity of each processing link of the work order is generally determined based on the safety stock, production line operation efficiency (equipment OEE), and production line qualification rate. For the product output part, it is currently known that: the positive number of the process, the number of process releases, the number of post-process releases, fixed losses and other forecasts are determined. Without considering other forecasts, this part is determined based on product production experience. The work order can be determined according to the following formula Yield: ; in, For Work Order The total demand output, It is the process positive number, process release number, process post-release number, and fixed loss. The output is determined by the demand and inventory.

[0038] Assume that the order that meets the BOM constraint is a dispatchable work order. The material constraint defined by the work order BOM. Given a work order, the BOM quantity corresponding to each work order can be obtained. The BOM contains levels, and one work order corresponds to several levels of BOM lists. In the production process, whether the material of the top-level BOM is met needs to be decomposed from each level. Total material demand of the work order: If for a work order , which meets the production conditions of material constraints, can be screened and dispatchable work orders according to the above requirements: ; in, For Work Order Whether the processing conditions are met, For Work Order Required layer The BOM quantity of the material, layer The material already has a BOM quantity.

[0039] Consider the situation that if the BOM is insufficient, a part of it will be arranged first. If the BOM is insufficient, the original work order will be arranged according to the existing BOM and work order. Convert to a new sub-ticket And sub-ticket , where sub-work orders Can satisfy the BOM order part, sub-work order For the unsatisfied part. For the sub-work orders for the satisfied part It is considered as a schedulable work order and is scheduled.

[0040] Work orders that meet the above work order BOM constraints are considered schedulable work orders, and the next step of work order and equipment scheduling can be carried out.

[0041] (II) During the scheduling process, the actual constraints are taken into consideration to complete the modeling of the scheduling process. For ease of understanding, the embodiment of the present invention takes the NSGA2 algorithm as an example to provide a specific implementation method for generating a target scheduling strategy.

[0042] First, a set of scheduling strategies is generated for the schedulable work orders. In the specific implementation, the batches corresponding to the schedulable work orders and the multiple processing procedures involved in each batch of products are known, and multiple scheduling strategies are randomly generated to allocate initial processing equipment to each processing procedure involved in each schedulable work order.

[0043] Then, based on the objective function that takes into account the equipment changeover time, combined with the pre-built set of processing constraints, the scheduling strategies in the scheduling strategy set are iteratively optimized to determine the target scheduling strategy. This includes the following steps 1 to 3: Step 1: Select multiple scheduling strategies from the scheduling strategy set based on the objective function that considers the equipment changeover time. See steps 1.1 to 1.2 below: Step 1.1, for any scheduling strategy in the scheduling strategy set, based on the objective function considering the equipment changeover time, determine multiple initial function values ​​corresponding to the scheduling strategy, and fuse the multiple initial function values ​​to obtain the objective function value.

[0044] The process of determining the initial function value is as follows: for any scheduling strategy in the scheduling strategy set, based on the objective function considering the equipment changeover time, determine the capacity efficiency corresponding to the scheduling strategy, determine the equipment utilization rate corresponding to the scheduling strategy, determine the processing continuity index value of a single processing equipment corresponding to the scheduling strategy, and determine the work order processing cost corresponding to the scheduling strategy. The processing continuity index value and the work order processing cost are both related to the equipment changeover time.

[0045] In one example, goal 1 is to maximize capacity efficiency, that is, to minimize the maximum completion time. The capacity efficiency corresponding to this scheduling strategy can be determined according to the following formula: ; or, ; in, is the decision variable, indicating the Batch of products A process, Is it on the device Processing or , For production Batch of products The process, in the equipment The completion time of processing.

[0046] In one example, goal 2 is to balance the equipment utilization rate, using the minimum variance of the average equipment running time to balance the processing running time of all equipment. The equipment utilization rate corresponding to this scheduling strategy can be determined according to the following formula: ; in, For equipment The total running time, is the total number of processes.

[0047] In one example, target 3 is the process continuity target of a single processing equipment, which is calculated using the equipment changeover time constraint. The process continuity index value of a single processing equipment corresponding to the scheduling strategy can be determined according to the following steps, including (a1) to (a3): (a1) Determine the equipment changeover time corresponding to the scheduling strategy based on the schedulable work orders corresponding to a single processing equipment in the scheduling strategy and the processing steps therein.

[0048] The process of determining the equipment changeover time is as follows: Consider the equipment changeover time, which is related to the current state of the equipment and the model processed by the equipment. Change a process parameter The time is That is, it is necessary to maintain a changeover table to determine the changeover time. Assuming that the changeover time is a fixed value, that is, the equipment Adjust a process parameter Time is a fixed value. Then the static condition is the current state of the equipment, and the model to be processed on the current equipment is scheduled. Time required for changeover The determination method is: determine the process parameter status of the current equipment , determine the product batches to be processed Required process parameters if , then a change is needed, and the corresponding decision variable is , only one person can operate the changeover of a device at the same time, and parallel adjustments cannot be made.

[0049] The equipment changeover time can be determined according to the following formula: ; in, For the Batch of products Process equipment When processing on the machine, if the equipment cannot be processed, the changeover time involved is For equipment Change a process parameter time.

[0050] (a2) Based on the equipment changeover time, determine the number of process types corresponding to a single processing equipment in the scheduling strategy. The number of process types can be determined according to the following formula: ; in, is the number of process types, represents the union operation. For the Batch of products Process equipment When processing on the surface, if the equipment cannot be processed, the changeover time involved.

[0051] (a3) According to the number of process types, determine the processing continuity index value corresponding to a single processing equipment in the scheduling strategy. The processing continuity index value can be determined according to the following formula: ; in, is the number of process types for a single device, is the penalty coefficient for the processing technology type. In the production process, in order to ensure the shortest changeover time, the continuity of production and processing is considered: the product types processed on the same equipment should be kept consistent as much as possible. The processing type of the same equipment, that is, the number of process types of the equipment, is summed up to define the penalty coefficient. , find the number of process types processed by the equipment , if the number of process types If it is 1, no penalty will be imposed. If it is more than 1, a penalty will be imposed.

[0052] In one example, goal 4 is to minimize overtime and total cost. The work order processing cost corresponding to the scheduling strategy can be determined according to the following steps, including (b1) to (b2): (b1) determining the equipment changeover time corresponding to the scheduling strategy based on the schedulable work orders corresponding to a single processing equipment in the scheduling strategy and the processing procedures therein; and determining the equipment processing time corresponding to the scheduling strategy based on the process difficulty coefficient corresponding to each processing procedure in the scheduling strategy.

[0053] The specific process of determining the equipment changeover time can be found in the above (a1), and will not be described in detail in the embodiment of the present invention.

[0054] The process of determining the equipment processing time considering the process difficulty is as follows: The theoretical capacity is the effective maximum capacity. Theoretically, the equipment cannot be opened to the maximum. How many blocks the equipment can open depends on the difficulty of the product. The more complex the process, the slower the equipment processing speed. In the actual production site, there will be an assessment of the process difficulty coefficient. It is estimated that the first The process difficulty coefficient of the process is . Process difficulty coefficient The larger it is, the longer the processing time is. In fact, it basically conforms to the linear increase relationship: is the coefficient of variation of processing time with difficulty coefficient, It is a hyperparameter, that is, an exogenous variable. The equipment processing time can be determined according to the following formula: ; in, For production Batch of products The process, in the equipment The processing time of the equipment used for processing, is the time of processing time changing with the difficulty coefficient, is the process difficulty coefficient, This is the theoretical fastest processing time.

[0055] (b2) Determine the work order processing cost corresponding to the scheduling strategy based on the equipment changeover time, the equipment processing time, the working time period when the processing equipment in the scheduling strategy processes the processing procedure, and the corresponding penalty coefficient.

[0056] In one embodiment, the working time period (i.e., the operation time) is prioritized according to the equipment. The available time of the operation, divided into normal time periods and overtime hours , the part exceeding the limit is considered to be outsourced, and the processing time of the outsourced part is . Introduce decision variables H, including , , , representing the completion of processing within the normal time period, the completion of processing within the overtime period, and the completion of processing through outsourcing. and Different penalty costs are arranged for each stage. The penalty cost calculation formula for each stage is as follows: ; in, For the full penalty of overtime costs and outsourcing costs, For the Batch of products Process equipment When processing on the machine, if the equipment cannot be processed, the changeover time involved is For production Batch of products The process, in the equipment The processing time of the equipment used for processing, is a decision variable to determine whether the production time is normal production time, overtime time, or outsourced processing time. Penalties for overtime and outsourcing.

[0057] Based on this, the work order processing cost can be determined according to the following formula: .

[0058] The process of determining the objective function value is as follows: weighted sum of capacity efficiency, equipment utilization, processing continuity index value and work order processing cost is performed to obtain the objective function value corresponding to the scheduling strategy. In the specific implementation, the capacity efficiency, equipment utilization, processing continuity index value and work order processing cost are comprehensively considered to obtain the objective function value. The objective function value can be determined according to the following formula: ; ; in, is the weight.

[0059] Step 1.2: Based on the objective function value corresponding to each scheduling strategy, multiple scheduling strategies are selected from the scheduling strategy set. In one implementation, multiple scheduling strategies can be selected in order of the objective function value from small to large. Figure 2 The schematic diagram of the change of the objective function value of a scheduling strategy set is shown. Since the embodiment of the present invention solves the minimum value, the smaller the objective function value, the better the scheduling strategy. This is different from the traditional technology in which the greater the fitness, the better the individual population.

[0060] Step 2: Cross and mutate each scheduling strategy obtained through screening, and modify each scheduling strategy after cross and mutation based on the processing constraint condition set to obtain a new scheduling strategy set.

[0061] Crossover: Each scheduling strategy sets two crossover points during the crossover process, and crosses the elements inside the crossover points. Specifically, a crossover point is set at a certain point in the three-dimensional matrix, and the elements in this interval in one scheduling strategy must cross with the interval at the corresponding position of the next scheduling strategy. The purpose of the crossover is to ensure the diversity of the population.

[0062] Mutation: Set different mutation points in the three dimensions of each scheduling strategy to avoid premature state. For example, in each dimension, randomly determine multiple points for mutation and change the value of the point from 0 to 1 or from 1 to 0.

[0063] Correction: For the scheduling strategies that do not meet the processing constraint set after crossover and mutation, they are corrected to ensure that the scheduling strategies after crossover and mutation meet the processing constraint set.

[0064] Furthermore, the embodiment of the present invention also provides a specific implementation of a processing constraint condition set, including: (I) Personnel and equipment matching constraints are: the matching degree between personnel and processing equipment is positively correlated with the processing efficiency of the processing equipment. It is used to schedule personnel and processing equipment based on the personnel and equipment matching constraints when the processing equipment is not in maintenance status and the personnel are in working status. Specifically: The matching status of personnel and equipment, that is, personnel have different personnel capabilities and different working hours, that is, the work calendar. When personnel and equipment are matched, production and processing can be carried out, so the matching part of personnel and equipment. It is necessary to schedule the operation in advance. The Hungarian algorithm matches personnel and equipment. The first thing to determine is the distributable and dispatchable status of personnel and equipment: it is agreed that when the equipment is not in maintenance and the personnel are in working status, the personnel and equipment can be dispatched. In order to determine the best scheduling personnel option, it is assumed that the personnel can operate the equipment, that is, the equipment can reach the theoretical operating time. Assume that the matching degree matrix of personnel and equipment, the more the personnel and equipment are matched, the higher the processing efficiency. The processing time of the equipment itself is fixed. Relying on the operation of the equipment by the personnel, the highest completion efficiency is achieved. Indicates the device Processing procedures production time. Assuming that personnel The operation proficiency is sufficient, mainly considering the equipment production and processing time. . Then the staff processing time is. Suppose the efficiency matrix is personnel Operating Equipment The efficiency of the decision variables Representatives Operating Equipment , you can define the following personnel and equipment matching constraints: ; ; in, For production staff About equipment The degree of matching, is the decision variable Representatives Operating Equipment .

[0065] (II) Equipment processing capacity constraints are: (II.1) The equipment processing time corresponding to the scheduling strategy is less than or equal to the maximum working time of the processing equipment: Maximum working time of the equipment: The equipment has a maximum working time limit, and the total processing time of the equipment scheduled to the relevant equipment must be less than the maximum working time allowed by the equipment. The expression of this constraint is as follows: ; in, For production Batch of products The process, in the equipment The processing time of the equipment used for processing, is the total number of production and processing equipment, For equipment The total running time.

[0066] (II.2) The time and value of the working time period in which the processing equipment performs processing operations in the scheduling strategy are less than or equal to the maximum working time of the processing equipment: In the entire planned time interval, in addition to the regular working hours, there are some time segments that can be expanded and used (overtime). If overtime is used, additional production costs will be incurred; the delivery period of the workpiece cannot be in the overtime time interval. One of the goals is to optimize the arrangement of overtime time, determine the processing sequence and start time of each process, so as to minimize the duration and total overtime time for completing all workpieces. Considering overtime, there needs to be a constraint target to minimize overtime. Assumption: The overtime cost per unit time in all overtime time intervals is the same. Add a defined time interval to distinguish between normal working hours and overtime time periods. Considering overtime, the operating time of the machine equipment shall not exceed the maximum overtime time. In addition: considering the equipment conditions and the working status of the personnel, the time slice needs to be considered. Based on this, the expression of the constraint is as follows: ; in, , , The processing time for normal time period, overtime time period and outsourced part, is the total number of production and processing equipment, For equipment The total running time.

[0067] (III) The actual processing conditions are: (III.1) The current processing step is processed after the corresponding previous processing step is completed. Define the processing order constraint: that is, in the same (a batch of production) workpieces, there is a processing order constraint between adjacent processes, and the subsequent process can only be processed after the previous process is completed.

[0068] (III.2) In a period of time, a single processing equipment can only process one processing step. Each equipment can only process one process in a period of time, that is, for the batch (workpiece) being processed by the equipment and artifacts , workpiece The processing steps are , workpiece The processing steps are , due to the changeover time, processing When it is not possible to process If the workpiece On the workpiece Before processing, at least the workpiece must be The processing completion time should be greater than the workpiece After processing, the workpiece When starting processing, the workpiece Process on equipment l Processing time t. If the workpiece On the workpiece After processing, a penalty coefficient A is added, where A is a large constant term, to avoid the existence of such a solution.

[0069] (III.3) Each processing step is processed once. The processing step constraints must be defined to ensure that each step of each device is processed once and only once.

[0070] Step 3, continue to select multiple scheduling strategies from the new scheduling strategy set based on the objective function until the preset iteration stop condition is met, and determine the target scheduling strategy from the new scheduling strategy set when the iteration stops. Repeat the above steps 1 to 2, and when the iteration stops, a target scheduling strategy can be determined based on the objective function value corresponding to each scheduling strategy in the scheduling strategy set at this time.

[0071] Furthermore, the embodiment of the present invention provides an application example of a complex discrete manufacturing scheduling method. Assume that there are 5 processing equipment at the production site, each processing equipment can process 6 processes (actually some equipment can process some processes, the principle is the same), and there are actually 7 orders. The processing time of each equipment in different processes is different, and the specific processing time is shown in Table 2 below: Table 2 Processing schedule

[0072] Each device has different conversion times between different processes. Taking device 1 as an example, the conversion timetable 3 of device 1 is as follows. The conversion time of other devices is not listed: Table 3 Conversion schedule for device 1

[0073] Different processes need to be stationary or wait for a period of time before producing the next process, as shown in Table 4. It is assumed that except for the first process, there is a waiting time. In special cases where there is no waiting time, the waiting time can be 0.

[0074] Table 4 Waiting timetable for each process

[0075] For example, the actual constraints considered are: delivery time constraint; BOM constraint; process difficulty constraint; production continuity, that is, the shortest equipment changeover time; priority of parts processing; overtime and outsourcing constraints, unfinished processing can be outsourced; processing sequence constraint; parts processing equipment constraint; The optimization goals are: maximum production efficiency, that is, the shortest total processing time; maximum equipment utilization; minimum overtime and outsourcing costs; shortest single equipment changeover time; and the equipment processing technology and procedures are as consistent as possible; The scheduling strategy set is iteratively optimized based on the above practical constraints and optimization objectives, and the results are as follows: Figure 3 The diagram of an algorithm simulation and production scheduling result shown in the figure includes: J represents the workpiece, that is, the work order (order), and O represents the processing procedure. The left vertical axis represents the production and processing equipment, and different grayscales represent the processing procedures of different work orders. For example, the second row J2O1 on the far left represents the first (O1) process of the No. 2 work order (J2) processed by mechanical equipment 4. The same applies to other equipment and work order models.

[0076] For the production scheduling result after iteration (that is, the target scheduling strategy), calculate its fitness change value (that is, the objective function value): the fitness change value represents the optimization of the objective function. The smaller the fitness change value Fitness, the better the production scheduling effect and the shorter the processing time.

[0077] Based on the above embodiments, the present invention provides a complex discrete manufacturing scheduling device. Figure 4 The schematic diagram of the structure of a complex discrete manufacturing scheduling device is shown in FIG. 1 , and the device mainly includes the following parts: The work order extraction module 402 is used to obtain complex discrete manufacturing order information and extract schedulable work orders from the complex discrete manufacturing order information based on external constraints of production and processing; wherein the schedulable work orders are divided into multiple batches, and the parts corresponding to each batch involve multiple processing procedures; A strategy generation module 404, used to generate a scheduling strategy set for a schedulable work order; The strategy iteration module 406 is used to iteratively optimize the scheduling strategies in the scheduling strategy set based on the objective function that takes into account the equipment changeover time, combined with a pre-constructed set of processing constraints, to determine the target scheduling strategy; wherein the processing constraints include one or more of the constraints on personnel and equipment matching, equipment processing capacity, and actual processing situation constraints; the target scheduling strategy is used to describe the correspondence between schedulable work orders, processing procedures, and processing equipment.

[0078] The complex discrete processing and manufacturing scheduling device provided by the embodiment of the present invention fully considers the equipment changeover time and the matching of personnel and equipment, and at the same time incorporates external condition constraints, equipment processing capacity constraints, actual processing situation constraints, etc. into the scheduling optimization range. On this basis, the scheduling strategies in the scheduling strategy set are iteratively optimized to obtain a better target scheduling strategy. The embodiment of the present invention can better realize the comprehensive optimization of the complex discrete processing and manufacturing process, thereby improving production efficiency, reducing costs, and ensuring that the production plan can be completed on time to better meet actual production needs.

[0079] In one implementation, the external condition constraints include delivery constraints, inventory constraints, and material constraints. The work order extraction module 402 is specifically used to: For any work order in the complex discrete processing and manufacturing order information, determine whether the work order meets the delivery constraint, inventory constraint and material constraint; If yes, the work order is determined as a dispatchable work order; If not, split it based on the delivery constraint, inventory constraint and material constraint to obtain a sub-work order that meets the delivery constraint, inventory constraint and material constraint, and take the sub-work order as a schedulable work order.

[0080] In one implementation, the policy iteration module 406 is specifically configured to: Based on the objective function that considers the equipment changeover time, multiple scheduling strategies are selected from the scheduling strategy set. The scheduling strategies are expressed in a three-dimensional matrix, which includes the dimensions of schedulable work orders, process dimensions, and equipment dimensions. Cross and mutate each scheduling strategy obtained by screening, and modify each scheduling strategy after cross and mutation based on the processing constraint condition set to obtain a new scheduling strategy set; Continue to select multiple scheduling strategies from the new scheduling strategy set based on the objective function until the preset iteration stop condition is met, and determine the target scheduling strategy from the new scheduling strategy set when the iteration stops.

[0081] In one implementation, the policy iteration module 406 is specifically configured to: For any scheduling strategy in the scheduling strategy set, based on the objective function considering the equipment changeover time, multiple initial function values ​​corresponding to the scheduling strategy are determined, and the multiple initial function values ​​are merged to obtain the objective function value; The objective function value corresponding to the scheduling strategy is obtained by weighted summing the production efficiency, equipment utilization, processing continuity index value and work order processing cost; Based on the objective function value corresponding to each scheduling strategy, multiple scheduling strategies are selected from the scheduling strategy set.

[0082] In one implementation, the policy iteration module 406 is specifically configured to: For any scheduling strategy in the scheduling strategy set, based on the objective function considering the equipment changeover time, the production capacity efficiency corresponding to the scheduling strategy is determined, as well as the equipment utilization rate corresponding to the scheduling strategy, the processing sequence continuity index value of a single processing equipment corresponding to the scheduling strategy, and the work order processing cost corresponding to the scheduling strategy are determined; wherein, the processing sequence continuity index value and the work order processing cost are both related to the equipment changeover time.

[0083] In one implementation, the policy iteration module 406 is specifically configured to: According to the schedulable work orders corresponding to a single processing equipment in the scheduling strategy and the processing procedures therein, determine the equipment changeover time corresponding to the scheduling strategy; Based on the equipment changeover time, determine the number of process types corresponding to a single processing equipment in the scheduling strategy; According to the number of process types, the processing continuity index value corresponding to a single processing equipment in the scheduling strategy is determined.

[0084] In one implementation, the policy iteration module 406 is specifically configured to: Determine the equipment changeover time corresponding to the scheduling strategy based on the schedulable work orders corresponding to a single processing equipment in the scheduling strategy and the processing procedures therein; and determine the equipment processing time corresponding to the scheduling strategy based on the process difficulty coefficient corresponding to each processing procedure in the scheduling strategy; Based on the equipment changeover time, the equipment processing time, the working time period when the processing equipment in the scheduling strategy processes the processing procedure and its corresponding penalty coefficient, the work order processing cost corresponding to the scheduling strategy is determined.

[0085] In one implementation, the policy iteration module 406 is specifically configured to: The objective function value corresponding to the scheduling strategy is obtained by taking the weighted sum of the production capacity efficiency, equipment utilization, processing procedure continuity index value and work order processing cost.

[0086] In one embodiment, the personnel and equipment matching constraint is: the matching degree between personnel and processing equipment is positively correlated with the processing efficiency of the processing equipment, and is used to schedule personnel and processing equipment based on the personnel and equipment matching constraint when the processing equipment is not in a maintenance state and the personnel is in a working state; The equipment processing capacity constraints are: the equipment processing time corresponding to the scheduling strategy is less than or equal to the maximum working time of the processing equipment; and the time and value of the working time period when the processing equipment processes the processing procedure in the scheduling strategy is less than or equal to the maximum working time of the processing equipment; The actual processing conditions are as follows: the current processing step is processed after the corresponding previous processing step is completed; and, within a period of time, a single processing device only processes one processing step; and, each processing step is processed once.

[0087] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0088] An embodiment of the present invention provides an electronic device. Specifically, the electronic device includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above-mentioned implementation methods.

[0089] Figure 5 A structural diagram of an electronic device provided in an embodiment of the present invention, the electronic device 100 includes: a processor 50, a memory 51, a bus 52 and a communication interface 53, wherein the processor 50, the communication interface 53 and the memory 51 are connected via the bus 52; the processor 50 is used to execute an executable module stored in the memory 51, such as a computer program.

[0090] The memory 51 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 53 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0091] The bus 52 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0092] Among them, the memory 51 is used to store programs, and the processor 50 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 50 or implemented by the processor 50.

[0093] The processor 50 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 50. The above processor 50 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to be executed, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 51, and the processor 50 reads the information in the memory 51 and completes the steps of the above method in combination with its hardware.

[0094] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be referred to the previous method embodiments, which will not be repeated here.

[0095] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0096] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A complex discrete manufacturing scheduling method, characterized in that: include: Obtaining complex discrete manufacturing order information, and extracting schedulable work orders from the complex discrete manufacturing order information based on external constraints of production and processing; wherein the schedulable work orders are divided into multiple batches, and the parts corresponding to each batch involve multiple processing procedures; Generate a scheduling strategy set for the schedulable work order; Based on the objective function taking into account the equipment changeover time and in combination with a pre-constructed set of processing constraints, the scheduling strategies in the scheduling strategy set are iteratively optimized to determine a target scheduling strategy; wherein the processing constraints include one or more of personnel and equipment matching constraints, equipment processing capacity constraints, and actual processing situation constraints; the target scheduling strategy is used to describe the correspondence between schedulable work orders, processing procedures, and processing equipment.

2. The complex discrete manufacturing scheduling method according to claim 1 is characterized in that: The external condition constraints include delivery constraints, inventory constraints and material constraints. Based on the external condition constraints of production and processing, schedulable work orders are extracted from the complex discrete processing and manufacturing order information, including: For any work order in the complex discrete processing and manufacturing order information, determine whether the work order meets the delivery constraint, the inventory constraint and the material constraint; If yes, the work order is determined as a dispatchable work order; If not, splitting is performed based on the delivery constraint, the inventory constraint and the material constraint to obtain a sub-work order that satisfies the delivery constraint, the inventory constraint and the material constraint, and the sub-work order is used as a schedulable work order.

3. The complex discrete manufacturing scheduling method according to claim 1, characterized in that: Based on the objective function considering the equipment changeover time and in combination with the pre-built processing constraint condition set, the scheduling strategies in the scheduling strategy set are iteratively optimized to determine the target scheduling strategy, including: Based on the objective function considering the equipment changeover time, a plurality of scheduling strategies are selected from the scheduling strategy set, wherein the scheduling strategies are expressed in a three-dimensional matrix, and the three-dimensional matrix includes a schedulable work order dimension, a process dimension, and an equipment dimension; Crossing and mutating each of the scheduling strategies obtained by screening, and modifying each of the scheduling strategies after crossing and mutating based on the processing constraint condition set to obtain a new scheduling strategy set; Continue to screen out multiple scheduling strategies from the new scheduling strategy set based on the objective function until a preset iteration stop condition is met, and determine the target scheduling strategy from the new scheduling strategy set when the iteration stops.

4. The complex discrete manufacturing scheduling method according to claim 3 is characterized in that: Based on the objective function considering the equipment changeover time, multiple scheduling strategies are selected from the scheduling strategy set, including: For any scheduling strategy in the scheduling strategy set, based on the objective function considering the equipment changeover time, multiple initial function values ​​corresponding to the scheduling strategy are determined, and the multiple initial function values ​​are merged to obtain the objective function value; Based on the objective function value corresponding to each of the scheduling strategies, multiple scheduling strategies are screened out from the scheduling strategy set.

5. The complex discrete manufacturing scheduling method according to claim 4 is characterized in that: Based on the objective function considering the equipment changeover time, multiple initial function values ​​corresponding to the scheduling strategy are determined, including: For any scheduling strategy in the scheduling strategy set, based on the objective function taking into account the equipment changeover time, the production capacity efficiency corresponding to the scheduling strategy is determined, as well as the equipment utilization rate corresponding to the scheduling strategy, the processing continuity index value of the single processing equipment corresponding to the scheduling strategy, and the work order processing cost corresponding to the scheduling strategy are determined; wherein the processing continuity index value and the work order processing cost are both related to the equipment changeover time.

6. The complex discrete manufacturing scheduling method according to claim 5, characterized in that: Determine the processing continuity index value of a single processing equipment corresponding to the scheduling strategy, including: Determine the equipment changeover time corresponding to the scheduling strategy according to the schedulable work order corresponding to the single processing equipment in the scheduling strategy and the processing steps therein; Based on the equipment changeover time, determine the number of process types corresponding to a single processing equipment in the scheduling strategy; According to the number of process types, a processing procedure continuity index value corresponding to a single processing equipment in the scheduling strategy is determined.

7. The complex discrete manufacturing scheduling method according to claim 5, characterized in that: Determine the work order processing cost corresponding to the scheduling strategy, including: Determine the equipment changeover time corresponding to the scheduling strategy according to the schedulable work order corresponding to a single processing equipment in the scheduling strategy and the processing steps therein; and determine the equipment processing time corresponding to the scheduling strategy according to the process difficulty coefficient corresponding to each processing step in the scheduling strategy; The work order processing cost corresponding to the scheduling strategy is determined based on the equipment changeover time, the equipment processing time, the working time period when the processing equipment in the scheduling strategy processes the processing procedure, and the corresponding penalty coefficient.

8. The complex discrete manufacturing scheduling method according to claim 5, characterized in that: The target function value is obtained by fusing the multiple initial function values, including: The production capacity efficiency, the equipment utilization rate, the processing procedure continuity index value and the work order processing cost are weightedly summed to obtain the objective function value corresponding to the scheduling strategy.

9. The complex discrete manufacturing scheduling method according to claim 1 or 3, characterized in that: The personnel and equipment matching constraint is: the matching degree between the personnel and the processing equipment is positively correlated with the processing efficiency of the processing equipment, and is used to schedule the personnel and the processing equipment based on the personnel and equipment matching constraint when the processing equipment is not in a maintenance state and the personnel is in a working state; The equipment processing capacity constraint is: the equipment processing time corresponding to the scheduling strategy is less than or equal to the maximum working time of the processing equipment; and the time and value of the working time period when the processing equipment in the scheduling strategy processes the processing procedure is less than or equal to the maximum working time of the processing equipment; The actual processing conditions are as follows: the current processing step is processed after the corresponding previous processing step is completed; and within a period of time, a single processing device only processes one processing step; and each processing step is processed once.

10. A complex discrete processing and manufacturing scheduling device, characterized in that: include: A work order extraction module is used to obtain complex discrete manufacturing order information and extract schedulable work orders from the complex discrete manufacturing order information based on external constraints of production and processing; wherein the schedulable work orders are divided into multiple batches, and the parts corresponding to each batch involve multiple processing procedures; A strategy generation module, used to generate a scheduling strategy set for the schedulable work order; The strategy iteration module is used to iteratively optimize the scheduling strategies in the scheduling strategy set based on the objective function that takes into account the equipment changeover time, combined with a pre-constructed set of processing constraints, so as to determine the target scheduling strategy; wherein the processing constraints include one or more of the constraints on personnel and equipment matching, equipment processing capacity, and actual processing situation constraints; the target scheduling strategy is used to describe the correspondence between schedulable work orders, processing procedures, and processing equipment.

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