Production scheduling decision-making method, device and equipment
The production scheduling model is constructed through a hybrid integer planning algorithm, the processing time is determined using product process data, and the global optimal scheduling solution is generated, which solves the problems of insufficient production scheduling accuracy and capacity utilization, and realizes efficient and automated production scheduling.
Patent Information
- Application Number
- CN202510505506.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-22
- Publication Date
- 2025-07-18
AI Technical Summary
In the prior art, the accuracy of production scheduling is low, resulting in poor floor-based capacity and insufficient capacity utilization, and manual methods cannot effectively deal with fluctuations and uncertainties in product processing time.
The production scheduling model is constructed using the hybrid integer planning algorithm framework, the product process data is used to determine the product processing time, and the solution variables are solved through the optimal method, and the global optimal target production scheduling plan is generated, taking into account production and demand constraints, equipment capacity constraints and product profit data to improve scheduling accuracy and implementation.
It improves the accuracy and implementation of production scheduling, improves capacity utilization, and improves production efficiency and profit margins through automated production scheduling.
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Figure CN120338423A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of production scheduling, and more particularly, to a production scheduling decision method, apparatus, and device. Background Art
[0002] The specific time required for a production factory to process products using resources (such as equipment, technicians, production lines) is called man-hour occupancy. The time during which resources can be used for production in a day is called production capacity. Each product can be processed on one or more resources, and different products may use different resources. A production factory usually has a lot of resources and many products, so how to perform production scheduling is very important for the production factory.
[0003] In related technologies, production scheduling is usually performed manually based on the experience of staff.
[0004] However, this manual method has low accuracy and is likely to result in poor feasibility of production scheduling or insufficient production capacity utilization. Summary of the Invention
[0005] In view of the above problems, this application aims to provide a production scheduling decision method, apparatus, and device to improve the accuracy of production scheduling, thereby enhancing the feasibility of production scheduling and production capacity utilization.
[0006] In a first aspect, this application provides a production scheduling decision method, the method including:
[0007] Receiving a production scheduling request from a user, and determining scheduling-related data based on the production scheduling request; wherein, the scheduling-related data includes at least one of the following: scheduling period, demand data, equipment data, product profit data, product processing time; wherein, the product processing time is determined based on product process data;
[0008] Using the scheduling-related data as the input of a pre-constructed production scheduling model, and solving the decision variables of the production scheduling model through a prepared optimization method to obtain a target production scheduling plan; wherein, the production scheduling model is constructed based on a mixed integer programming algorithm framework;
[0009] Performing production scheduling based on the target production scheduling plan.
[0010] In a possible implementation, the constraint conditions of the production scheduling model include at least one of the following: production-demand constraint, equipment production capacity constraint, and equipment production capacity exclusivity constraint.
[0011] In a possible implementation, the scheduling-related data further includes: confidence level; the equipment data includes equipment production capacity; the product processing time includes triangular fuzzy processing time;
[0012] The equipment production capacity constraint is determined based on the confidence level, the triangular fuzzy processing time, and the equipment production capacity.
[0013] In a possible implementation, the scheduling-related data further includes: efficiency risk; the triangular fuzzy processing time includes a lower processing time limit, a most likely processing time, and an upper processing time limit;
[0014] When the efficiency risk is greater than or equal to the risk threshold, the equipment production capacity constraint is determined based on the most likely processing time, the upper processing time limit, the confidence level, and the equipment production capacity.
[0015] In a possible implementation, the scheduling-related data further includes: efficiency risk; the triangular fuzzy processing time includes a lower processing time limit, a most likely processing time, and an upper processing time limit;
[0016] When the efficiency risk is less than the risk threshold, the equipment production capacity constraint is determined based on the lower processing time limit, the most likely processing time, the confidence level, and the equipment production capacity.
[0017] In a possible implementation, the objective function of the production scheduling model includes at least one of the following: a profit term and a demand satisfaction rate term.
[0018] In a second aspect, the present application provides a production scheduling decision device, which includes:
[0019] A receiving unit, configured to receive a user's production scheduling request and determine scheduling-related data based on the production scheduling request; wherein, the scheduling-related data includes at least one of the following: a scheduling period, demand data, equipment data, product profit data, product processing time; wherein, the product processing time is determined based on product process data;
[0020] A decision-making unit, configured to use the scheduling-related data as an input to a pre-constructed production scheduling model, solve the decision variables of the production scheduling model through a prepared optimization method, and obtain a target production scheduling plan; wherein, the production scheduling model is constructed based on a mixed integer programming algorithm framework;
[0021] A scheduling unit, configured to perform production scheduling based on the target production scheduling plan.
[0022] In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, and a memory communicatively connected to the processor;
[0023] The memory stores computer-executable instructions;
[0024] The processor executes the computer-executable instructions stored in the memory to implement the method in any possible implementation manner of the above first aspect.
[0025] Fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the method in any possible implementation manner of the above first aspect.
[0026] According to the fifth aspect of the present disclosure, there is provided a computer program product, including a computer program, which when executed by a processor, implements the method in any possible implementation manner of the first aspect.
[0027] A production scheduling decision method, device, and equipment provided by the present application. The method includes: receiving a production scheduling request from a user and determining scheduling-related data based on the production scheduling request; where the scheduling-related data includes at least one of the following: scheduling period, demand data, equipment data, product profit data, product processing time; where the product processing time is determined based on product process data; using the scheduling-related data as the input of a pre-constructed production scheduling model, solving the decision variables of the production scheduling model through a prepared optimization method to obtain a target production scheduling plan; where the production scheduling model is constructed based on a mixed-integer programming algorithm framework; performing production scheduling based on the target production scheduling plan. This solution pre-constructs a production scheduling model based on a mixed-integer programming algorithm framework, and then can use the production scheduling model to obtain a globally optimal target production scheduling plan. Using this target production scheduling plan for production scheduling has high accuracy, and thus can improve the feasibility of production scheduling and the production capacity utilization rate. The product processing time determined by this solution based on product process data is more accurate, and the target production scheduling plan determined using the more accurate product processing time further improves the scheduling accuracy. This solution also takes into account the product profit data, which is beneficial to improving the profit rate. This solution can automatically complete production scheduling with high efficiency. Description of the Drawings
[0028] The drawings here are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.
[0029] Figure 1 It is a schematic flowchart of a production scheduling decision method provided in Embodiment 1 of the present application;
[0030] Figure 2 It is a schematic flowchart of another production scheduling decision method provided in Embodiment 2 of the present application;
[0031] Figure 3A structural schematic diagram of a production scheduling decision-making device provided in Embodiment 3 of this application;
[0032] Figure 4 A hardware structure diagram of an electronic device provided in Embodiment 4 of this application.
[0033] Through the above-mentioned drawings, specific embodiments of this application have been shown, and there will be more detailed descriptions hereinafter. These drawings and textual descriptions are not intended to limit the scope of the concept of this application in any way, but to illustrate the concept of this application to those skilled in the art by referring to specific embodiments.
[0034] Explanation of reference numerals:
[0035] 300 - Production scheduling decision-making device; 301 - Receiving unit; 302 - Decision-making unit; 304 - Scheduling unit; 401 - Processor; 402 - Memory; 403 - Communication interface; 404 - Communication bus. Detailed implementation manners
[0036] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with this application. On the contrary, they are merely examples of devices and methods consistent with some aspects of this application as detailed in the appended claims.
[0037] It should be noted that in the embodiments of this application, words such as "exemplary" or "for example" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" or "for example" in this application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, using words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner. In the embodiments of this application, "at least one" means one or more, and "a plurality" means two or more.
[0038] For the same product, when it is produced using different devices or different workers, the product processing time is not the same. Even for the same worker, due to different states at different times, the product processing time for processing the same product is not the same. Similarly, for the same device, due to different states at different times, such as parameter changes, tool wear of the device, etc., the product processing time for processing the same product is not the same. Therefore, in the actual production process, the product processing time is not a fixed value.
[0039] In the related art, production scheduling is usually carried out manually based on the experience of staff. For example, a fixed processing time for products is given based on the experience of staff, and then the fixed processing time for products is used for production scheduling. This method cannot handle the fluctuations in the processing time of products in the actual production process. For example, in scenarios such as equipment anomalies, process adjustments, and different worker proficiencies, the production scheduling plan obtained manually is often inaccurate, difficult to implement, resulting in a large deviation between the actual execution result and the planned result. Low accuracy is also likely to cause insufficient capacity utilization.
[0040] To solve the above technical problems, the embodiments of the present application provide a production scheduling decision method, device, and equipment. A production scheduling model is pre-constructed based on a mixed integer programming algorithm framework. Then, a globally optimal target production scheduling plan can be obtained using the production scheduling model. Using the target production scheduling plan for production scheduling has high accuracy, which can improve the implementability of production scheduling and the capacity utilization rate. The product processing time determined based on product process data in this solution is more accurate, and the target production scheduling plan determined using the more accurate product processing time further improves the scheduling accuracy. This solution also takes into account product profit data, which is beneficial to improving the profit rate. This solution can automatically complete production scheduling with high efficiency.
[0041] Figure 1 FIG. is a schematic flowchart of a production scheduling decision method provided in Embodiment 1 of the present application. This embodiment is applicable to the production scheduling scenario of a production factory. This method can be executed by a production scheduling decision device, which can be implemented in software and / or hardware and is specifically configured in an electronic device. As Figure 1 shown, the method includes:
[0042] Step 101, receive a production scheduling request from a user, and determine scheduling-related data based on the production scheduling request; where the scheduling-related data includes at least one of the following: scheduling period, demand data, equipment data, product profit data, product processing time; where the product processing time is determined based on product process data.
[0043] In implementation, this solution does not limit the scheduling period. For example, the scheduling period can be one week or half a month, etc.
[0044] Among them, the demand data represents the demand data of the products to be produced. The demand data may include the total demand quantity corresponding to each product.
[0045] Among them, the equipment data may include at least one device and the production capacity corresponding to each device. The equipment data may also include the products that each device can produce.
[0046] Among them, the product profit data may include the unit profit corresponding to each product.
[0047] Among them, the product processing time refers to the processing time required for the product to be processed on the equipment.
[0048] Among them, the product process data refers to various information and data related to the process during the product production process.
[0049] In implementation, to determine the product processing time using the product process data, methods such as analyzing the process step time, considering equipment and personnel factors, and combining batch size can be adopted.
[0050] Analyze the process flow: The product process data details the entire production steps and sequence of the product from raw materials to finished products, that is, the process flow. Clearly defining the specific operation content and sequence of each process helps determine the time required for each process.
[0051] Determine the process step time: The process data usually contains the standard time or time range for each process step. These times are determined through past production experience, experimental tests, or process design. For example, in machining, each machining operation such as cutting, drilling, and milling has corresponding standard machining times, and the specific time can be determined according to factors such as the material, size, and machining accuracy of the workpiece.
[0052] Consider equipment factors: The performance and production capacity of the equipment in the process equipment information will affect the processing time. Different models and specifications of equipment may have different processing speeds and efficiencies. For example, the processing speed of a high-speed CNC machine tool may be much faster than that of an ordinary machine tool. Therefore, when determining the processing time, it is necessary to adjust according to the specific parameters of the equipment used. At the same time, the failure rate and maintenance requirements of the equipment will also indirectly affect the processing time, and it is necessary to appropriately consider reserving a certain buffer time in the calculation.
[0053] Consider personnel factors: The skill level and proficiency of the operators will also affect the processing time. Skilled workers may be able to complete the work more efficiently, while novices may require more time. In the process data, there may be reference to the average processing time of personnel with different skill levels, and comprehensive evaluation can be carried out based on this. If it is mass production, factors such as the working time arrangement and shift system of personnel also need to be considered.
[0054] Combine batch size: The processing batch of the product has an important impact on the processing time. Generally speaking, the larger the batch, the average processing time of a single product may be reduced due to the sharing of production preparation time. However, at the same time, it is necessary to consider the impact of problems such as equipment wear and quality control in batch production on time. The processing time of a single product can be corrected through the batch adjustment coefficient or empirical formula in the process data to obtain the accurate total processing time for batch production.
[0055] Adding auxiliary time: In addition to the direct processing operation time, some auxiliary times need to be considered, such as the time for clamping and unclamping workpieces, the time for changing tools and dies, and the time for equipment debugging and inspection. These auxiliary times may have separate records in the process data or can be estimated based on experience, and they should be reasonably added to the total processing time.
[0056] Statistical analysis of historical data: If there are production historical data of previous similar products, these data can be statistically analyzed to find the relationship and rules between the actual processing time and the process data. For example, analyze the processing time fluctuations of different batches of products, find the key factors affecting the processing time, and optimize and adjust the current process data to more accurately determine the product processing time.
[0057] Step 102: Use the scheduling correlation data as the input of a pre-constructed production scheduling model, solve the decision variables of the production scheduling model through a prepared optimization method, and obtain the target production scheduling plan; among them, the production scheduling model is constructed based on the mixed-integer programming algorithm framework.
[0058] In implementation, based on the mixed-integer programming algorithm framework, the constraint conditions and objective function of the production scheduling model can be pre-constructed. Input the scheduling correlation data into the constraint conditions and the objective function, and search for the maximum or minimum value of the objective function on the premise of meeting the constraint conditions, and then determine the target production scheduling plan corresponding to the maximum or minimum value of the objective function.
[0059] Among them, the prepared optimization method refers to the pre-prepared optimization method. This solution does not limit the optimization method. For example, the optimization method can be a solver or a heuristic method. Among them, the solver can use a mature commercial solver, such as the Cardinal Optimizer (COPT).
[0060] Step 103: Conduct production scheduling based on the target production scheduling plan.
[0061] Specifically, this solution does not limit the size of the time granularity of production scheduling. Assuming that the time granularity of production scheduling is in days, the target production scheduling plan should at least include: the quantity of each product produced by each device every day during the scheduling period.
[0062] The production scheduling decision-making method provided by the above embodiments pre-constructs a production scheduling model based on the mixed-integer programming algorithm framework. Furthermore, a globally optimal target production scheduling plan can be obtained using the production scheduling model. Using this target production scheduling plan for production scheduling has high accuracy, which can further improve the feasibility of production scheduling and the production capacity utilization rate. In this solution, the product processing time determined based on product process data is more accurate. The target production scheduling plan determined using the more accurate product processing time further improves the scheduling accuracy. This solution also takes into account product profit data, which is beneficial to increasing the profit rate. This solution can automatically complete production scheduling with high efficiency.
[0063] Figure 2 It is a flowchart showing another production scheduling decision-making method provided in the second embodiment of this application. This embodiment is an improvement to the production scheduling decision-making method based on the Figure 1 embodiment shown.
[0064] As Figure 2 shown, a production scheduling decision-making method may include the following steps:
[0065] Step 201, receive the user's production scheduling request and determine scheduling-related data based on the production scheduling request; wherein, the scheduling-related data includes at least one of the following: scheduling period, demand data, equipment data, product profit data, product processing time; wherein, the product processing time is determined based on product process data; the scheduling-related data further includes: confidence level; the equipment data includes equipment production capacity; the product processing time includes triangular fuzzy processing time.
[0066] Among them, assuming that the time granularity of production scheduling is in days, the equipment production capacity may refer to the maximum available working hours of the equipment per day. The equipment production capacity of the same equipment on different dates is not the same.
[0067] In implementation, after obtaining the scheduling-related data, the scheduling-related data can be preprocessed. Specifically, the scheduling-related data is converted into a data format recognizable and dockable by the algorithm and necessary association relationship processing is performed.
[0068] Step 202, use the scheduling-related data as the input of a pre-constructed production scheduling model, and solve the decision variables of the production scheduling model through a prepared optimization method to obtain a target production scheduling plan; wherein, the production scheduling model is constructed based on the mixed-integer programming algorithm framework; the constraint conditions of the production scheduling model include at least one of the following: production-demand constraint, equipment production capacity constraint, and equipment production capacity exclusivity constraint; the equipment production capacity constraint is determined based on the confidence level, triangular fuzzy processing time, and equipment production capacity; the objective function of the production scheduling model includes at least one of the following: profit item and demand satisfaction rate item.
[0069] Specifically, production-demand constraint refers to the relationship constraint between product production volume and demand volume. Under different scenarios, the production-demand constraints are not the same. For example, in the scenario of production based on sales, usually the product production volume should try to meet the demand volume and not exceed it to prevent excessive production of products, resulting in inventory waste and economic losses. For the unmet demand volume, the method of deferred production can be adopted. In the fast-moving consumer goods industry, usually the production volume of products cannot be less than the demand volume to meet the demand.
[0070] For the scenario of production based on sales, the production-demand constraint can be expressed by the following formula:
[0071]
[0072] where y i,m,t represents the product production volume obtained by producing product i on equipment m at time t; y i,m,t is a decision variable, and y i,m,t ≥0; M represents the set of equipment; T represents the scheduling period; t represents the time granularity, for example, t can be one day; d i represents the total demand quantity of product i; I represents the set of products.
[0073] Specifically, the above method can conveniently determine the production-demand constraint.
[0074] Among them, the equipment production capacity constraint is used to ensure that the working hours provided by the equipment do not exceed its equipment production capacity.
[0075] Among them, the triangular fuzzy processing time is a method used to describe the uncertainty of product processing time. The triangular fuzzy processing time can be expressed by the following formula: h i,m =(a i,m , b i,m , c i,m );
[0076] where h i,m represents the triangular fuzzy processing time of product i on equipment m; a i,m represents the lower limit of the processing time of product i on equipment m; b i,m represents the most likely processing time of product i on equipment m; c i,m represents the upper limit of the processing time of product i on equipment m.
[0077] Among them, in the triangular fuzzy processing time, the confidence level is a measure of the reliability of the product processing time estimate, which can help people better handle the uncertainty of the product processing time.
[0078] In implementation, based on the triangular fuzzy processing time, confidence level, equipment production capacity and y i,m,t the equipment production capacity constraint can be determined, and based on this equipment production capacity constraint, the decision variable yi,m,t Solve it. This method can conveniently determine the equipment production capacity constraint.
[0079] Specifically, the method of using triangular fuzzy processing time is adopted to simulate the product processing time, which improves the accuracy of the product processing time; the equipment production capacity constraint determined by using the more accurate product processing time is also more accurate, and the obtained target production scheduling plan is also more accurate. Using the more accurate target production scheduling plan for production scheduling can improve the scheduling accuracy.
[0080] In an implementable manner, the scheduling-related data further includes: efficiency risk; the triangular fuzzy processing time includes the lower limit of processing time, the most likely processing time, and the upper limit of processing time; when the efficiency risk is greater than or equal to the risk threshold, the equipment production capacity constraint is determined based on the most likely processing time, the upper limit of processing time, the confidence level, and the equipment production capacity.
[0081] In an implementable manner, the scheduling-related data further includes: efficiency risk; the triangular fuzzy processing time includes the lower limit of processing time, the most likely processing time, and the upper limit of processing time; when the efficiency risk is less than the risk threshold, the equipment production capacity constraint is determined based on the lower limit of processing time, the most likely processing time, the confidence level, and the equipment production capacity.
[0082] Considering that usually when the management situation of the production factory is good, the production efficiency is high; when the management situation of the production factory is poor, such as frequent equipment failures, high worker turnover rate, etc., the production efficiency is low. And the efficiency risk in this solution refers to the risk of the production efficiency of the production factory. When the efficiency risk is high, it means that the production efficiency is low, which is likely to cause an increase in the product processing time. When the efficiency risk is low, it means that the production efficiency is high, which is likely to reduce the product processing time. Therefore, this solution introduces efficiency risk. When the efficiency risk is greater than or equal to the risk threshold, the equipment production capacity constraint is determined based on the most likely processing time, the upper limit of processing time, the confidence level, and the equipment production capacity, and when the efficiency risk is less than the risk threshold, the equipment production capacity constraint is determined based on the lower limit of processing time, the most likely processing time, the confidence level, and the equipment production capacity. This method can further improve the accuracy of the product processing time.
[0083] In implementation, when the efficiency risk is greater than or equal to the risk threshold, the equipment production capacity constraint can be determined by the following formula.
[0084]
[0085] Among them, c i,m represents the upper limit of the processing time of product i on equipment m; b i,m represents the most likely processing time of product i on equipment m; α represents the confidence level; y i,m,tDenote the production volume of product \(i\) produced by using equipment \(m\) at time \(t\); \(C\) m,t Denote the maximum available working hours of equipment \(m\) at time \(t\), that is, the equipment production capacity of equipment \(m\) at time \(t\); \(T\) represents the scheduling period; \(t\) represents the time granularity; \(I\) represents the set of products; \(M\) represents the set of equipment.
[0086] In implementation, when the efficiency risk is less than the risk threshold, the equipment production capacity constraint can be determined by the following formula.
[0087]
[0088] Among them, \(a\) i,m Denote the lower limit of the processing time of product \(i\) on equipment \(m\); \(b\) i,m Denote the most likely processing time of product \(i\) on equipment \(m\); \(\alpha\) represents the confidence level; \(y\) i,m,t Denote the production volume of product \(i\) produced by using equipment \(m\) at time \(t\); \(C\) m,t Denote the maximum available working hours of equipment \(m\) at time \(t\), that is, the equipment production capacity of equipment \(m\) at time \(t\); \(T\) represents the scheduling period; \(t\) represents the time granularity; \(I\) represents the set of products; \(M\) represents the set of equipment.
[0089] Specifically, the above method can conveniently determine the equipment production capacity constraint.
[0090] Among them, the equipment production capacity exclusive constraint is used to ensure that the same equipment can only produce one product at the same time. The equipment production capacity exclusive constraint can be represented by the following formula:
[0091]
[0092] Among them, \(x\) i,m,t Denote whether product \(i\) is produced by using equipment \(m\) at time \(t\); if product \(i\) is produced by using equipment \(m\) at time \(t\), then \(x\) can be set i,m,t to 1; if product \(i\) is not produced by using equipment \(m\) at time \(t\), then \(x\) can be set i,m,t to 0; \(x\) i,m,t belongs to the decision variable; \(T\) represents the scheduling period; \(t\) represents the time granularity; \(I\) represents the set of products; \(M\) represents the set of equipment.
[0093] Specifically, the above method can conveniently determine the equipment production capacity constraint.
[0094] In implementation, the objective function includes a profit term and a demand satisfaction rate term, and can achieve a balance between maximizing profit and the demand satisfaction rate.
[0095] In implementation, the objective function of the production scheduling model can be determined by the following formula.
[0096]
[0097] Among them, Z represents the objective function; the first term on the right side of the equation represents the profit term; the second term on the right side of the equation represents the demand satisfaction rate term; λ represents the weight coefficient for algorithm tuning; p i represents the unit profit of product i; y i,m,t represents the production volume of product i produced by using equipment m at time t; d i represents the total demand quantity of product i; T represents the scheduling period; t represents the time granularity; I represents the set of products; M represents the set of equipment.
[0098] Specifically, through this method, the objective function of the production scheduling model can be conveniently determined.
[0099] Specifically, through the above method, the production scheduling model can be conveniently determined.
[0100] Specifically, the above method for modeling the uncertainty of processing time based on triangular fuzzy numbers combines the fastest (i.e., the lower limit of processing time), the most likely, and the longest processing time (i.e., the upper limit of processing time) with the customer's risk preference (i.e., efficiency risk) and confidence level (the confidence level represents the probability that the processing time of the product tends to the most likely processing time) to convert the fuzzy processing time into a deterministic model for solution. In this way, the feasibility of the scheduling plan is improved, thereby enhancing the production capacity utilization rate.
[0101] In this solution, the product processing time is converted into a deterministic model, and then the exact solution algorithm is used to call the COPT solver for exact solution to obtain the optimal solution. The exact solution algorithm enables users to achieve the balance of objectives under multiple objectives and multiple constraints, ensuring that the enterprise obtains a production schedule that meets the equipment production capacity constraints and is conducive to business improvement, such as profit increase, which is different from the non-optimal solution obtained by the traditional heuristic algorithm.
[0102] The manual method mainly solves the scheduling result based on the deterministic product processing time. This solution can implement scheduling under uncertain conditions, which better meets the matching degree between the workshop plan and execution, and avoids the situation where the planned result arranged under the most ideal situation cannot be executed and implemented, resulting in the infeasibility of the plan or waste of resources (such as unutilized production capacity or unmet demand).
[0103] Step 203: Perform production scheduling based on the target production scheduling plan.
[0104] In implementation, the principle and implementation method of step 203 are similar to those of step 103, and will not be elaborated here.
[0105] Figure 3 This is a schematic structural diagram of a production scheduling decision-making device provided in Embodiment 3 of the present application. The device can be in the form of software and / or hardware. Refer toFigure 3 As shown in the figure, a production scheduling decision-making device 300 includes: a receiving unit 301, a decision-making unit 302, and a scheduling unit 303.
[0106] The receiving unit 301 is configured to receive a production scheduling request from a user and determine scheduling-related data based on the production scheduling request; wherein, the scheduling-related data includes at least one of the following: a scheduling period, demand data, equipment data, product profit data, and product processing time; wherein, the product processing time is determined based on product process data.
[0107] The decision-making unit 302 is configured to use the scheduling-related data as an input to a pre-constructed production scheduling model, solve the decision variables of the production scheduling model through a prepared optimization method, and obtain a target production scheduling plan; wherein, the production scheduling model is constructed based on a mixed integer programming algorithm framework.
[0108] The scheduling unit 303 is configured to perform production scheduling based on the target production scheduling plan.
[0109] In one implementable manner, the constraint conditions of the production scheduling model include at least one of the following: production-demand constraint, equipment production capacity constraint, and equipment production capacity exclusivity constraint.
[0110] In one implementable manner, the scheduling-related data further includes: confidence level; the equipment data includes equipment production capacity; the product processing time includes triangular fuzzy processing time.
[0111] The equipment production capacity constraint is determined based on the confidence level, triangular fuzzy processing time, and equipment production capacity.
[0112] In one implementable manner, the scheduling-related data further includes: efficiency risk; the triangular fuzzy processing time includes a lower processing time limit, a most likely processing time, and an upper processing time limit.
[0113] When the efficiency risk is greater than or equal to the risk threshold, the equipment production capacity constraint is determined based on the most likely processing time, the upper processing time limit, the confidence level, and the equipment production capacity.
[0114] In one implementable manner, the scheduling-related data further includes: efficiency risk; the triangular fuzzy processing time includes a lower processing time limit, a most likely processing time, and an upper processing time limit.
[0115] When the efficiency risk is less than the risk threshold, the equipment production capacity constraint is determined based on the lower processing time limit, the most likely processing time, the confidence level, and the equipment production capacity.
[0116] In one implementable manner, the objective function of the production scheduling model includes at least one of the following: a profit term and a demand satisfaction rate term.
[0117] The production scheduling decision-making device provided in the embodiments of the present application has the same implementation principle and technical effects as those in the foregoing embodiments of the production scheduling decision-making method. For the sake of brief description, for the parts not mentioned in the device embodiments, reference may be made to the corresponding content in the foregoing embodiments of the production scheduling decision-making method.
[0118] Figure 4 This is a hardware structure diagram of an electronic device provided in Embodiment 4 of the present application. This embodiment provides an electronic device, including: at least one processor 401, and a memory 402 communicatively connected to at least one processor 401; the memory 402 stores computer-executable instructions; the processor 401 executes the computer-executable instructions stored in the memory 402 to implement the production scheduling decision-making method described in any of the foregoing embodiments.
[0119] Figure 4 The illustrated electronic device further includes a communication interface 403 and a communication bus 404. Among them, the processor 401, the memory 402, and the communication interface 403 are connected to each other through the communication bus 404. The communication bus 404 can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 4 only a thick line is used to represent the communication bus 404 in the figure, but it does not mean that there is only one communication bus 404 or one type of communication bus 404. The processor 401 can also be called a controller, and there is no limitation on the name.
[0120] In the embodiments of the present application, the memory 402 stores instructions executable by at least one processor 401. By executing the instructions stored in the memory 402, at least one processor 401 can execute the production scheduling decision-making method discussed above. The processor 401 can implement Figure 4 the functions of each module in the illustrated device.
[0121] Among them, the processor 401 is the control center of the device. It can connect various parts of the entire control device through various interfaces and lines. By running or executing the instructions stored in the memory 402 and calling the data stored in the memory 402, various functions of the device and process data, so as to monitor the device as a whole.
[0122] In a possible design, the processor 401 may include one or more processing units. The processor 401 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, user interface, application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor may not be integrated into the processor 401. In some embodiments, the processor 401 and the memory 402 can be implemented on the same chip or separately on independent chips.
[0123] The processor 401 may be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application specific integrated circuit, a field programmable gate array, or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the production scheduling decision method disclosed in combination with the embodiments of the present application may be directly embodied as being executed by a hardware processor, or executed by a combination of hardware and software modules in the processor.
[0124] As a non-volatile computer-readable storage medium, the memory 402 can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The memory 402 may include at least one type of storage medium, for example, it may include flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (RAM), a static random access memory (SRAM), a programmable read-only memory (PROM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 402 is any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 402 in the embodiments of the present application may also be a circuit or any other device capable of implementing a storage function, for storing program instructions and / or data.
[0125] By designing and programming the processor 401, the code corresponding to the production scheduling decision method introduced in the foregoing embodiments can be solidified into the chip, so that the chip can execute Figure 1 or Figure 2 the steps of the production scheduling decision method of the embodiments shown. How to design and program the processor 401 is a well-known technology to those skilled in the art and will not be elaborated here.
[0126] The embodiments of the present application also provide a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the production scheduling decision-making method described in any of the previous embodiments. Therefore, details will not be repeated here. In addition, the beneficial effects of using the same method will not be described again. For the technical details not disclosed in the embodiments of the computer storage medium involved in the present invention, please refer to the description of the method embodiments of the present invention.
[0127] In some possible implementation manners, various aspects of the production scheduling decision-making method provided by the present application can also be implemented in the form of a program product, which includes program code. When the program product runs on a device, the program code is used to cause the control device to execute the steps in the production scheduling decision-making method according to various exemplary embodiments of the present application described above in this specification.
[0128] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.
[0129] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.
[0130] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.
[0131] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process or a plurality of processes and / or blocks Figure 1 one process or a plurality of processes and / or blocks Figure 1 steps of a block or a plurality of blocks.
[0132] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.
Claims
1. A production scheduling decision-making method, characterized in that, The method includes: Receiving a production scheduling request from a user, and determining scheduling-related data based on the production scheduling request; wherein, the scheduling-related data includes at least one of the following: scheduling period, demand data, equipment data, product profit data, product processing time; wherein, the product processing time is determined based on product process data; Using the scheduling-related data as an input to a pre-constructed production scheduling model, and solving decision variables of the production scheduling model through a prepared optimization method to obtain a target production scheduling plan; wherein, the production scheduling model is constructed based on a mixed-integer programming algorithm framework; Performing production scheduling based on the target production scheduling plan.
2. The method according to claim 1, characterized in that, The constraint conditions of the production scheduling model include at least one of the following: production-demand constraint, equipment production capacity constraint, and equipment production capacity exclusivity constraint.
3. The method according to claim 2, characterized in that, The scheduling-related data further includes: confidence level; the equipment data includes equipment production capacity; the product processing time includes triangular fuzzy processing time; The equipment production capacity constraint is determined based on the confidence level, the triangular fuzzy processing time, and the equipment production capacity.
4. The method according to claim 3, wherein The scheduling-related data further includes: efficiency risk; the triangular fuzzy processing time includes a lower processing time limit, a most likely processing time, and an upper processing time limit; When the efficiency risk is greater than or equal to a risk threshold, the equipment production capacity constraint is determined based on the most likely processing time, the upper processing time limit, the confidence level, and the equipment production capacity.
5. The method according to claim 3, characterized in that, The scheduling-related data further includes: efficiency risk; the triangular fuzzy processing time includes a lower processing time limit, a most likely processing time, and an upper processing time limit; When the efficiency risk is less than the risk threshold, the equipment production capacity constraint is determined based on the lower processing time limit, the most likely processing time, the confidence level, and the equipment production capacity.
6. The method according to claim 1, wherein The objective function of the production scheduling model includes at least one of the following: profit term and demand satisfaction rate term.
7. A production scheduling decision-making device, characterized in that The apparatus includes: A receiving unit, configured to receive a production scheduling request from a user, and determine scheduling-related data based on the production scheduling request; wherein, the scheduling-related data includes at least one of the following: scheduling period, demand data, equipment data, product profit data, product processing time; wherein, the product processing time is determined based on product process data; A decision-making unit, configured to use the scheduling-related data as an input to a pre-constructed production scheduling model, and solve decision variables of the production scheduling model through a prepared optimization method to obtain a target production scheduling plan; wherein, the production scheduling model is constructed based on a mixed-integer programming algorithm framework; A scheduling unit, configured to perform production scheduling based on the target production scheduling plan.
8. An electronic device, characterized in that, It includes a processor and a memory communicatively connected to the processor; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory, so that the processor executes the production scheduling decision method according to any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that, A computer program is stored in the storage medium, and when the computer program is executed by a processor, the production scheduling decision method according to any one of claims 1 to 6 is implemented.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the production scheduling decision method described in any one of the above claims 1-6.