Production plan generation method and device, electronic equipment and storage medium

Abstract

The invention discloses a production plan generation method and device, electronic equipment and a storage medium, and relates to the technical field of production plan generation. The method comprises the following steps: acquiring basic information related to a production plan; based on the basic information, constructing a constraint condition and a plan target corresponding to the production plan; wherein the constraint conditions comprise order dimension constraint, equipment dimension constraint and man-machine coordination dimension constraint; the planning target comprises an order outgoing cost dimension, an equipment use cost dimension and an order processing waiting time dimension; and generating a production plan corresponding to the basic information through a mixed integer rule MIP model based on the constraint condition and the plan target. Therefore, the limitation of a traditional artificial planning method in processing large-scale orders and complex man-machine coordination tasks is effectively solved, the plan generation efficiency is improved, the resource utilization is optimized, the production cost is reduced, and the response capability and quality of the plan are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of product manufacturing technology, and in particular to a production plan generation method, device, electronic device and storage medium. Background Art

[0002] In many manufacturing companies, thousands of orders of different product types are produced every day. Each production equipment that completes the order has its own equipment type. Each equipment type can produce one or more products, and each product can be produced by one or more equipment types. When formulating a specific production plan, planners usually need to consider historical production scheduling data, combine personal forecasts, and make the most accurate assessment of the production line's capacity, and accordingly break down the orders in detail. At the same time, according to the urgency and importance of the product, the production process priority can be flexibly arranged.

[0003] However, the above-mentioned production plan formulation scheme is highly dependent on the historical experience and subjective judgment of on-site planners and lacks unified standards. When dealing with complex large-scale orders and application scenarios with multiple production lines, the production plan formulation is inefficient and has poor stability, and the planning effect is difficult to guarantee. Summary of the invention

[0004] The embodiments of the present application disclose a production plan generation method, device, electronic device and storage medium, which are used to solve the problems of low efficiency and poor stability of production plan generation in application scenarios with high concurrent requests and large data volumes, and improve the planning effect of the production plan.

[0005] On the one hand, an embodiment of the present application provides a production plan generation method, including:

[0006] Obtain basic information related to production planning;

[0007] Based on the basic information, the constraints and planning objectives corresponding to the production plan are constructed; the constraints include order dimension constraints, equipment dimension constraints, and human-machine coordination dimension constraints; the planning objectives include order outsourcing cost dimension, equipment use cost dimension, and order processing waiting time dimension;

[0008] Based on the constraints and planning objectives, a production plan corresponding to the basic information is generated through the mixed integer programming MIP model.

[0009] On the one hand, an embodiment of the present application provides a production plan generating device, including:

[0010] Acquisition module, used to obtain basic information related to production planning;

[0011] A construction module is used to construct the constraints and planning objectives corresponding to the production plan based on the basic information; wherein the constraints include order dimension constraints, equipment dimension constraints, and human-machine coordination dimension constraints; the planning objectives include order outbound cost dimension, equipment use cost dimension, and order processing waiting time dimension;

[0012] The generation module is used to generate a production plan corresponding to the basic information based on constraints and planning objectives through a mixed integer programming MIP model.

[0013] In a possible embodiment, the acquisition module is used to:

[0014] Based on the business information corresponding to the production plan, determine the planning data that conforms to the business logic; the planning data covers all aspects of the production process, including product demand, equipment capacity, personnel arrangement, and equipment availability;

[0015] Obtain the basic information corresponding to the planned data, including product work order information, outbound order information, equipment processing information, equipment basic information, equipment maintenance information, equipment earliest available time information, and equipment unavailable time information.

[0016] In one possible embodiment, the building blocks are used to:

[0017] Based on the basic information, order dimension constraints corresponding to the production plan are constructed; wherein the order dimension constraints include order delivery time requirement constraints, order merging and processing constraints, order splitting constraints, order outbound rule constraints, order completion time constraints, and order production demand constraints; and / or,

[0018] Based on the basic information, construct equipment dimension constraints corresponding to the production plan; wherein the equipment dimension constraints include equipment allocation rule constraints, equipment unavailable time constraints, equipment maintenance plan constraints, equipment production capacity constraints, and equipment and production batch matching constraints; and / or,

[0019] Based on the basic information, construct the human and coordination dimension constraints corresponding to the production plan.

[0020] In a possible embodiment, the generating module is used to:

[0021] Generate an initial solution of the production plan through the MIP model; the initial solution satisfies the constraints and is close to the plan target, and includes the type of products produced by each device, the data of the products produced, and the order in which the products are produced;

[0022] Based on the initial solution, a production plan corresponding to the basic information is generated.

[0023] In a possible embodiment, the generating module is used to:

[0024] Performing a preset number of heuristic searches on the initial solution to optimize the initial solution and obtain an optimized solution;

[0025] The fitness corresponding to the optimized solution is calculated, and when the fitness is not optimized compared with the fitness calculated when the heuristic search was performed last time, a production plan corresponding to the basic information is generated based on the optimized solution.

[0026] In a possible embodiment, the generating module is used to:

[0027] Construct heuristic search operators, including job movement operator, exchange operator, 2-shfit operator, direct exchange operator, order splitting operator, balanced output operator, and equipment production sequence exchange operator;

[0028] Based on the heuristic search operator, a preset number of heuristic searches are performed on the initial solution.

[0029] In a possible embodiment, the generating module is used to:

[0030] Define multiple optimization goals, including order outbound cost, equipment idle time, equipment quantity, and order processing waiting time;

[0031] After weighting multiple optimization objectives, unified dimension processing is performed to obtain a comprehensive optimization objective;

[0032] Based on the comprehensive optimization goal, a comprehensive fitness function is constructed;

[0033] The fitness corresponding to the optimized solution is calculated through the comprehensive fitness function.

[0034] On the one hand, an embodiment of the present application provides an electronic device, which includes a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes any one of the above-mentioned production plan generation methods.

[0035] On the one hand, the present application provides a computer-readable storage medium, which includes a program code. When the storage medium is run on an electronic device, the program code is used to enable the electronic device to execute any of the above-mentioned production plan generation methods.

[0036] The beneficial effects of this application are as follows:

[0037] Improve planning efficiency: Through the mixed integer programming model, high-quality production plans can be generated in a short time, significantly improving the speed of plan formulation, reducing manual intervention, avoiding planning delays and errors caused by human factors, and improving the accuracy and reliability of plans. In addition, in the face of emergencies (such as equipment failures, emergency orders), production plans can be quickly adjusted to generate the best response plan to ensure the continuity and stability of production.

[0038] Optimize resource utilization: Through equipment dimension constraints and optimization goals, ensure that the equipment can meet order requirements while minimizing idle time and improving the overall utilization of the equipment. At the same time, consider the constraints of human-machine coordination dimension, reasonably arrange personnel operation tasks, avoid personnel idleness or overwork, and improve personnel work efficiency.

[0039] Reduce costs: Through the mixed integer programming model, global factors can be considered to generate a better production plan, improve order satisfaction and equipment utilization. By optimizing the objective function, it is ensured that the production plan can achieve balanced utilization of equipment and personnel while meeting order requirements, avoiding local overload or idleness.

[0040] In summary, the production plan generation method provided in this application uses the basic data obtained by the MIP model to establish constraints and planning goals in multiple dimensions, and generates relevant production plans based on this, which effectively solves the limitations of traditional manual planning methods in dealing with large-scale orders and complex human-machine coordination tasks. It not only improves the efficiency of plan generation, optimizes resource utilization, and reduces production costs, but also significantly improves the responsiveness and quality of the plan, which can better adapt to the complex needs of modern factory production and improve the overall competitiveness of the factory.

[0041] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0043] Figure 1 This is a schematic diagram of an application scenario in an embodiment of the present application;

[0044] Figure 2This is a flowchart of a method for generating a production plan in an embodiment of the present application;

[0045] Figure 3 A schematic diagram of a job moving operator in an embodiment of the present application;

[0046] Figure 4 A schematic diagram of an exchange operator in an embodiment of the present application;

[0047] Figure 5 This is a schematic diagram of a 2-shift operator in an embodiment of the present application;

[0048] Figure 6 This is a schematic diagram of the structure of a production plan generating device in an embodiment of the present application;

[0049] Figure 7 A schematic diagram of the hardware structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described below in conjunction with the drawings in the embodiment of the present application. Obviously, the described embodiment is only a part of the embodiment of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be arbitrarily combined with each other. In addition, although the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in an order different from that here.

[0051] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the invention described herein can be implemented in sequences other than those illustrated or described herein.

[0052] The following is a brief introduction to the design concept of the embodiment of the present application:

[0053] In many manufacturing companies, thousands of orders of different product types are produced every day. Each production equipment that completes the order has its own equipment type. Each equipment type can produce one or more products, and each product can be produced by one or more equipment types. When formulating a specific production plan, planners usually need to consider historical production scheduling data, combine personal forecasts, and make the most accurate assessment of the production line's capacity, and accordingly break down the orders in detail. At the same time, according to the urgency and importance of the product, the production process priority can be flexibly arranged.

[0054] However, the above production planning scheme is highly dependent on the historical experience and subjective judgment of the on-site planners and lacks unified standards. When dealing with complex large-scale orders and multi-production line application scenarios, there are the following technical problems:

[0055] (1) Limitations of manual planning: When faced with large-scale decisions to allocate thousands of order batches to hundreds of production equipment, manual planning has limitations and one-sidedness. Human brain calculations cannot take global factors into account and cannot maximize the order demand satisfaction and machine capacity utilization.

[0056] (2) Manual planning is slow: Manual planning takes a long time and is slow, and it is impossible to allocate all orders to appropriate devices at the minute level.

[0057] (3) Unable to respond quickly to special situations: Various special situations may occur in actual factory production, such as sudden machine failures and high-priority orders that need to be completed immediately. Manual response can be made quickly, but there is no way to achieve the best response based on the current situation.

[0058] (4) The effectiveness of the plan cannot be guaranteed: When making decisions on large-scale orders and hundreds of devices, if various constraints need to be considered, the balance of the plan made manually will be difficult to control and may even break through some of the original constraints, requiring the plan to be modified and adjusted from time to time.

[0059] In view of this, the embodiment of the present application provides a production plan generation method, device, electronic device and storage medium. Among them, the production plan generation method includes: obtaining basic information related to the production plan; based on the basic information, constructing the constraints and planning goals corresponding to the production plan; wherein, the constraints include order dimension constraints, equipment dimension constraints, and human-machine coordination dimension constraints; the planning goals include order outbound cost dimensions, equipment use cost dimensions, and order processing waiting time dimensions; based on the constraints and planning goals, a production plan corresponding to the basic information is generated through a mixed integer programming (Mixed Integer Programming, MIP) model. In this way, through the MIP model, the limitations of traditional manual planning methods in processing large-scale orders and complex human-machine coordination tasks are effectively solved, which not only improves the efficiency of plan generation, optimizes resource utilization, reduces production costs, but also significantly improves the responsiveness and quality of the plan, and can better adapt to the complex needs of modern factory production and improve the overall competitiveness of the factory.

[0060] The preferred embodiments of the present application are described below in conjunction with the drawings in the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application. In addition, the embodiments and features in the embodiments of the present application may be combined with each other if there is no conflict.

[0061] like Figure 1 , which is a schematic diagram of an application scenario provided by an embodiment of the present application. In the schematic diagram of the application scenario, a terminal device 101 and a server 102 are included. The terminal device 101 and the server 102 communicate with each other through a communication network.

[0062] The terminal device 101 is an electronic device used by the target object, and the electronic device may be a personal computer, a mobile phone, a tablet computer, a notebook, an e-book reader, a vehicle-mounted terminal, etc. In addition, a client related to production plan generation may be installed on the terminal device 101, and the client may be software (for example, an APP, a browser, etc.), or a web page, a small program, etc. The target object may use the above-mentioned client related to production plan generation through the terminal device 101 to perform operations related to production plan generation.

[0063] The server 102 may be an independent physical server or an edge device 102 in the field of cloud computing. It may also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, cloud functions, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (English name: Content Delivery Network, abbreviated as CDN), as well as big data and artificial intelligence platforms.

[0064] There is no restriction on the number of the terminal devices 101 and / or servers 102 .

[0065] It should be noted that the production plan generation method in the embodiment of the present application can be executed by the terminal device 101 or the server 102 alone, or can be executed by the terminal device 101 and the server 102 together. For example, when executed by the server 102 alone, the server 102 obtains basic information related to the production plan; based on the basic information, the constraints and planning goals corresponding to the production plan are constructed; wherein the constraints include order dimension constraints, equipment dimension constraints, and human-machine coordination dimension constraints; the planning goals include order outbound cost dimensions, equipment use cost dimensions, and order processing waiting time dimensions; based on the constraints and planning goals, a production plan corresponding to the basic information is generated through a mixed integer programming MIP model.

[0066] The following describes the production plan generation method provided by the exemplary embodiment of the present application in combination with the above-mentioned application scenarios and with reference to the accompanying drawings. It should be noted that the above-mentioned application scenarios are only shown to facilitate understanding of the spirit and principles of the present application, and the implementation methods of the present application are not subject to any limitations in this regard.

[0067] refer to Figure 2 , is an implementation flow chart of a production plan generation method provided in an embodiment of the present application. Here, the server is used as the execution subject for introduction. The specific implementation process of the method is as follows:

[0068] S201, obtaining basic information related to the production plan.

[0069] In an embodiment of the present application, when acquiring basic data, the planning department can make any adjustments that conform to business logic based on actual business conditions, so as to determine planning data that conforms to business logic based on the business information corresponding to the production plan; wherein the basic planning data covers all aspects of the production process, including product demand, equipment capacity, personnel arrangements, and equipment availability; then, the basic information corresponding to the planning data is obtained, and the basic information includes product work order information, outbound order information, equipment processing information, basic equipment information, equipment maintenance information, equipment earliest available time information, and equipment unavailable time information.

[0070] Among them, product work order information includes production batch, product model, product name, and product quantity information; outbound order information includes production batch, product model, outbound order cost, and outbound order cycle information; product equipment processing information includes production batch, product model, equipment group type, and equipment single-trip processing time information; equipment basic information includes equipment name, type, number of axes, and area information; equipment maintenance information includes equipment name and maintenance time information; equipment earliest available time information includes equipment name, type, and earliest available time; equipment unavailable time information includes equipment name, type, date, unavailable start time, and unavailable end time.

[0071] In the embodiment of the present application, the basic information of the production plan is the core data source for formulating the production plan, and this information covers all aspects of the production process, including product demand, equipment capacity, personnel arrangement, and equipment availability, etc. Through this basic information, constraints and objective functions that meet actual production needs can be constructed to generate the optimal production plan.

[0072] S202, based on the basic information, construct the constraints and planning objectives corresponding to the production plan; wherein the constraints include order dimension constraints, equipment dimension constraints, and human-machine coordination dimension constraints; the planning objectives include order outsourcing cost dimension, equipment use cost dimension, and order processing waiting time dimension.

[0073] In the embodiments of the present application, actual production rules and resource constraints are converted into constraints and planning goals in the production planning model to ensure that the plan is consistent with the actual production situation and to ensure the rationality of the plan.

[0074] In one embodiment, a specific method of constructing constraint conditions includes:

[0075] Based on the basic information, the order dimension constraints corresponding to the production plan are constructed; wherein the order dimension constraints include order delivery time requirement constraints, order merging and processing constraints, order splitting constraints, order outsourcing rule constraints, order completion time constraints, and order production demand constraints; and / or, based on the basic information, the equipment dimension constraints corresponding to the production plan are constructed; wherein the equipment dimension constraints include equipment allocation rule constraints, equipment unavailable time constraints, equipment maintenance plan constraints, equipment production capacity constraints, and equipment and production batch matching constraints; and / or, based on the basic information, the people and coordination dimension constraints corresponding to the production plan are constructed.

[0076] In one embodiment, the specific method of constructing the planning goal includes:

[0077] After adding the actual production rules and resource constraints into the model, the actual production target is converted into an objective function with specific mathematical meaning and used as the planning target of the MIP model.

[0078] Optionally, the objective functions include: minimum equipment usage function, minimization of outsourcing cost function, equipment usage constraint function, production trip requirement constraint function, basic constraint function of the relationship between order outsourcing and order splitting variables, and basic constraint function of the relationship between order outsourcing and drilling rig allocation variables.

[0079] Optionally, the minimum number of devices used function is:

[0080]

[0081] In formula (1), U m : Indicates whether device m is used; U is the minimum number of devices used.

[0082] Optionally, the outgoing cost function is minimized as:

[0083]

[0084] In formula (2), Z n : Indicates whether order n is to be shipped out; Cost n : represents the outsourcing cost of order n; Z: represents the total minimum outsourcing cost.

[0085] Optionally, whether the device uses the constraint function is:

[0086]

[0087] In formula (3), X nm : Indicates whether there is equipment m to produce order n; Max_value: indicates the large M value commonly seen in operations research models.

[0088] Optional, production run requirement constraint function, that is, the number of production runs allocated to the single-axis machine, dual-axis machine, and six-axis machine for each order must be equal to the number of production runs required by each type of drilling rig in the selected allocation scheme. The specific function is:

[0089]

[0090] In formula group (4), Y ni : Indicates whether order n chooses order splitting scheme i. There are multiple splitting schemes for an order, and the model decides to choose one; a ni : represents the number of trips required for the single-axis machine in the splitting scheme i of order n; b ni : represents the number of trips required for the biaxial machine in the splitting scheme i of order n; c ni: represents the number of trips required for the six-axis machine in the splitting scheme i of order n; A nm : represents the number of trips of single-axis machine m producing order n; B nm : represents the number of trips of the double-axis machine m to produce order n; C nm : Indicates the number of passes of six-axis machine m for production order n.

[0091] Optionally, the basic constraint function of the relationship between order outbound and order splitting variables is:

[0092]

[0093] Optionally, the basic constraint function of the relationship between order outbound and rig allocation variables is:

[0094]

[0095] Through the above process, based on the various rules and resource constraints in the actual production process, they are converted into constraints and planning goals in the production planning model to ensure that the plan is consistent with the actual production situation and the rationality of the plan.

[0096] S203, based on the constraints and planning objectives, a production plan corresponding to the basic information is generated through a mixed integer programming MIP model.

[0097] In the embodiment of the present application, after constructing the constraints and planning objectives corresponding to the production plan, further, a production plan corresponding to the basic information is generated through a mixed integer programming MIP model. The specific method includes:

[0098] First, the initial solution of the production plan is generated through the MIP model; wherein the initial solution satisfies the constraints and is close to the planning goals, and the initial solution includes the type of products produced by each device, the data of the products produced, and the order in which the products are produced.

[0099] Secondly, based on the initial solution, a production plan corresponding to the basic information is generated. Specifically, a preset number of heuristic searches are performed on the initial solution to optimize the initial solution and obtain an optimized solution, including: constructing a heuristic search operator, the heuristic search operator includes a job movement operator, an exchange operator, a 2-shfit operator, a direct exchange operator, an order splitting operator, a balanced output operator, and an equipment production sequence exchange operator; based on the heuristic search operator, a preset number of heuristic searches are performed on the initial solution to obtain an optimized solution.

[0100] like Figures 3 to 5 , which are respectively a schematic diagram of a job movement operator, a schematic diagram of a swap operator, and a schematic diagram of a 2-shift operator provided in an embodiment of the present application. x 、m yRespectively represent device x and device y, j1~j9 represent job indexes. Figure 3 In the device m x The corresponding job index j3 is moved to device m y The job index is between j1 and j9; Figure 4 In the device m x The corresponding job index j1 is moved to device m y Between job index j6 and j7, device m y The corresponding job index j9 is moved to device m x Between job indexes j3 and j3; Figure 5 In the device m x The corresponding job index j1 and device m y The positions of job index j8 are directly exchanged.

[0101] Preferably, the preset number of times is 50, so as to realize that the number of consecutive iterations without improving the existing solution is used as the termination condition on the basis of taking into account the solution time. Specifically, when our algorithm iterates 50 times in a row, the fitness change does not exceed the threshold, indicating that after 50 times of various heuristic searches, the solution result is still not improved, and the iteration is stopped at this time. Of course, the preset number of times can also be designed to be other values, such as 51, 52, 53, etc., which are not specifically limited here.

[0102] In this way, by constructing a variety of heuristic search operators and performing a preset number of heuristic searches on the initial solution, it is possible to gradually improve the initial solution through local adjustment and optimization in complex production planning problems, and ultimately generate a better production plan, including improving the optimization effect, adaptability and flexibility of the production plan, as well as the feasibility and reliability, so as to better meet the complex needs of modern factory production and improve the overall competitiveness of the factory.

[0103] Finally, the fitness corresponding to the optimized solution is calculated, and when the fitness is not optimized compared to the fitness calculated during the last heuristic search, a production plan corresponding to the basic information is generated based on the optimized solution, including: defining multiple optimization targets, including order outsourcing cost, equipment idle time, equipment quantity, and order processing waiting time; weighting multiple optimization targets and performing unified dimension processing to obtain a comprehensive optimization target; constructing a comprehensive fitness function based on the comprehensive optimization target; and calculating the fitness corresponding to the optimized solution through the comprehensive fitness function. In this way, this method can effectively balance multiple optimization targets and generate a better production plan, including improving the optimization effect, adaptability and flexibility of the production plan, as well as feasibility and reliability, so as to better meet the complex needs of modern factory production and improve the overall competitiveness of the factory.

[0104] At this time, the goals of minimizing outsourcing order costs, minimizing the time for processing empty shafts of powered-on equipment, minimizing the number of powered-on equipment, and minimizing the waiting time for order processing are weighted and unified, and used as the evaluation criteria for the quality of the solution. Here, the optimization direction of minimization is adopted. In this problem, the lower the fitness, the better the business objectives of the production plan are met.

[0105] The technical effects that can be achieved through the above production plan generation method are as follows:

[0106] Improve planning efficiency: Through the mixed integer programming model, high-quality production plans can be generated in a short time, significantly improving the speed of plan formulation, reducing manual intervention, avoiding planning delays and errors caused by human factors, and improving the accuracy and reliability of plans. In addition, in the face of emergencies (such as equipment failures, emergency orders), production plans can be quickly adjusted to generate the best response plan to ensure the continuity and stability of production.

[0107] Optimize resource utilization: Through equipment dimension constraints and optimization goals, ensure that the equipment can meet order requirements while minimizing idle time and improving the overall utilization of the equipment. At the same time, consider the constraints of human-machine coordination dimension, reasonably arrange personnel operation tasks, avoid personnel idleness or overwork, and improve personnel work efficiency.

[0108] Reduce costs: Through the mixed integer programming model, global factors can be considered to generate a better production plan, improve order satisfaction and equipment utilization. By optimizing the objective function, it is ensured that the production plan can achieve balanced utilization of equipment and personnel while meeting order requirements, avoiding local overload or idleness.

[0109] Based on the same inventive concept, the embodiment of the present application also provides a production plan generating device. Figure 6 As shown, it is a schematic diagram of the structure of the production plan generating device 600, which may include:

[0110] Acquisition module 601, used to acquire basic information related to the production plan;

[0111] A construction module 602 is used to construct the constraint conditions and planning objectives corresponding to the production plan based on the basic information; wherein the constraint conditions include order dimension constraints, equipment dimension constraints, and human-machine coordination dimension constraints; and the planning objectives include order outbound cost dimensions, equipment use cost dimensions, and order processing waiting time dimensions;

[0112] The generation module 603 is used to generate a production plan corresponding to the basic information based on the constraints and the planning objectives through a mixed integer programming MIP model.

[0113] In a possible embodiment, the acquisition module 601 is used to:

[0114] Based on the business information corresponding to the production plan, determine the planning data that conforms to the business logic; wherein the planning data covers all aspects of the production process, including product demand, equipment capacity, personnel arrangement, and equipment availability;

[0115] Obtain the basic information corresponding to the planned data, including product work order information, outbound order information, equipment processing information, equipment basic information, equipment maintenance information, equipment earliest available time information, and equipment unavailable time information.

[0116] In a possible embodiment, the construction module 602 is used to:

[0117] Based on the basic information, order dimension constraints corresponding to the production plan are constructed; wherein the order dimension constraints include order delivery time requirement constraints, order merging and processing constraints, order splitting constraints, order outbound rule constraints, order completion time constraints, and order production demand constraints; and / or,

[0118] Based on the basic information, construct equipment dimension constraints corresponding to the production plan; wherein the equipment dimension constraints include equipment allocation rule constraints, equipment unavailable time constraints, equipment maintenance plan constraints, equipment production capacity constraints, and equipment and production batch matching constraints; and / or,

[0119] Based on the basic information, construct the human and coordination dimension constraints corresponding to the production plan.

[0120] In a possible embodiment, the generating module 603 is used to:

[0121] Generate an initial solution of the production plan through the MIP model; the initial solution satisfies the constraints and is close to the plan target, and includes the type of products produced by each device, the data of the products produced, and the order in which the products are produced;

[0122] Based on the initial solution, a production plan corresponding to the basic information is generated.

[0123] In a possible embodiment, the generating module 603 is used to:

[0124] Performing a preset number of heuristic searches on the initial solution to optimize the initial solution and obtain an optimized solution;

[0125] The fitness corresponding to the optimized solution is calculated, and when the fitness is not optimized compared with the fitness calculated when the heuristic search was performed last time, a production plan corresponding to the basic information is generated based on the optimized solution.

[0126] In a possible embodiment, the generating module 603 is used to:

[0127] Construct heuristic search operators, including job movement operator, exchange operator, 2-shfit operator, direct exchange operator, order splitting operator, balanced output operator, and equipment production sequence exchange operator;

[0128] Based on the heuristic search operator, a preset number of heuristic searches are performed on the initial solution.

[0129] In a possible embodiment, the generating module 603 is used to:

[0130] Define multiple optimization goals, including order outbound cost, equipment idle time, equipment quantity, and order processing waiting time;

[0131] After weighting multiple optimization objectives, unified dimension processing is performed to obtain a comprehensive optimization objective;

[0132] Based on the comprehensive optimization goal, a comprehensive fitness function is constructed;

[0133] The fitness corresponding to the optimized solution is calculated through the comprehensive fitness function.

[0134] The technical effects achieved by the above production plan generating device can be referred to the production plan generating method part, and will not be described in detail here.

[0135] In some possible implementations, the production plan generation device according to the present application may include at least a processor and a memory. The memory stores program code, and when the program code is executed by the processor, the processor executes the steps of the production plan generation method according to various exemplary implementations of the present application described in this specification. For example, the processor may execute the following steps: Figure 2 Follow the steps shown in .

[0136] Based on the same inventive concept, an electronic device is also provided in the embodiment of the present application. The electronic device can realize the functions of the aforementioned production plan generation method and device. Figure 7 , electronic equipment includes:

[0137] At least one processor 701, and a memory 702 connected to the at least one processor 701. The specific connection medium between the processor 701 and the memory 702 is not limited in the embodiment of the present application. Figure 7 In the example, the processor 701 and the memory 702 are connected via a bus 700. Figure 7 The connections between other components are shown in bold lines, and are not intended to be limiting. The bus 700 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, Figure 7Only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. Alternatively, the processor 701 can also be called a controller, and there is no limitation on the name.

[0138] In the embodiment of the present application, the memory 702 stores instructions that can be executed by at least one processor 701. The at least one processor 701 can execute the production plan generation method discussed above by executing the instructions stored in the memory 702. The processor 701 can implement Figure 6 The functions of each module in the device shown.

[0139] Among them, the processor 701 is the control center of the device, and can use various interfaces and lines to connect the various parts of the entire control device. By running or executing instructions stored in the memory 702 and calling data stored in the memory 702, the various functions of the device and process data, the device can be monitored as a whole.

[0140] In one possible design, the processor 701 may include one or more processing units, and the processor 701 may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the modem processor may not be integrated into the processor 701. In some embodiments, the processor 701 and the memory 702 may be implemented on the same chip, and in some embodiments, they may also be implemented separately on separate chips.

[0141] Processor 701 can 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, a discrete gate or transistor logic device, a discrete hardware component, and can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the production plan generation method disclosed in the embodiments of the present application can be directly embodied as a hardware processor to execute, or a combination of hardware and software modules in the processor to execute.

[0142] The memory 702 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules. The memory 702 may include at least one type of storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (Random Access Memory, RAM), a static random access memory (Static Random Access Memory, SRAM), a programmable read-only memory (Programmable Read Only Memory, PROM), a read-only memory (Read Only Memory, ROM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), a magnetic memory, a disk, an optical disk, etc. The memory 702 is any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 702 in the embodiment of the present application can also be a circuit or any other device that can realize a storage function, for storing program instructions and / or data.

[0143] By programming the processor 701, the code corresponding to the production plan generation method described in the above embodiment can be fixed into the chip, so that the chip can execute the code when running. Figure 2 The steps of the production plan generation method of the embodiment shown are as follows: How to design and program the processor 701 is a technique known to those skilled in the art and will not be described in detail here.

[0144] Based on the same inventive concept, an embodiment of the present application further provides a storage medium, which stores computer instructions. When the computer instructions are executed on a computer, the computer executes the production plan generation method discussed above.

[0145] In some possible implementations, various aspects of the production plan generation method provided in the present application can also be implemented in the form of a program product, which includes a program code. When the program product is run on an apparatus, the program code is used to enable the control device to execute the steps of the production plan generation method according to various exemplary embodiments of the present application described above in this specification.

[0146] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0147] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0148] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0149] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0150] 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 equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A production plan generation method, characterized in that: include: Obtain basic information related to production planning; Based on the basic information, the constraints and planning objectives corresponding to the production plan are constructed; wherein the constraints include order dimension constraints, equipment dimension constraints, and human-machine coordination dimension constraints; the planning objectives include order outbound cost dimensions, equipment use cost dimensions, and order processing waiting time dimensions; Based on the constraints and the planning objectives, a production plan corresponding to the basic information is generated through a mixed integer programming MIP model.

2. The method according to claim 1, characterized in that: The basic information related to the production plan is obtained, including: Based on the business information corresponding to the production plan, determine the plan data that conforms to the business logic; wherein the plan data covers all aspects of the production process, including product demand, equipment capacity, personnel arrangement, and equipment availability; The basic information corresponding to the plan data is obtained, wherein the basic information includes product work order information, outbound order information, equipment processing information, equipment basic information, equipment maintenance information, equipment earliest available time information, and equipment unavailable time information.

3. The method according to claim 1, characterized in that: The step of constructing constraint conditions corresponding to the production plan based on the basic information includes: Based on the basic information, construct order dimension constraints corresponding to the production plan; wherein the order dimension constraints include order delivery time requirement constraints, order merging and processing constraints, order splitting constraints, order outbound rule constraints, order completion time constraints, and order production demand constraints; and / or, Based on the basic information, construct equipment dimension constraints corresponding to the production plan; wherein the equipment dimension constraints include equipment allocation rule constraints, equipment unavailable time constraints, equipment maintenance plan constraints, equipment production capacity constraints, and equipment and production batch matching constraints; and / or, Based on the basic information, the personnel and coordination dimension constraints corresponding to the production plan are constructed.

4. The method according to claim 1, characterized in that: The generating a production plan corresponding to the basic information based on the constraint conditions and the planning objectives through a mixed integer programming MIP model includes: Generate an initial solution of the production plan through the MIP model; wherein the initial solution satisfies the constraint conditions and is close to the plan target, and the initial solution includes the type of product produced by each device, the data of the produced products, and the order of producing the products; Based on the initial solution, a production plan corresponding to the basic information is generated.

5. The method according to claim 4, characterized in that: The generating a production plan corresponding to the basic information based on the initial solution includes: Performing a preset number of heuristic searches on the initial solution to optimize the initial solution and obtain an optimized solution; The fitness corresponding to the optimization solution is calculated, and when the fitness is not optimized compared with the fitness calculated when the heuristic search was performed last time, a production plan corresponding to the basic information is generated based on the optimization solution.

6. The method according to claim 5, characterized in that: The performing a preset number of heuristic searches on the initial solution comprises: Constructing heuristic search operators, the heuristic search operators including a job movement operator, an exchange operator, a 2-shfit operator, a direct exchange operator, an order splitting operator, a balanced output operator, and an equipment production sequence exchange operator; Based on the heuristic search operator, a preset number of heuristic searches are performed on the initial solution.

7. The method according to claim 5, characterized in that: The calculating the fitness corresponding to the optimization solution includes: Define multiple optimization goals, including order outbound cost, equipment idle time, equipment quantity, and order processing waiting time; After weighting the multiple optimization objectives, unified dimension processing is performed to obtain a comprehensive optimization objective; Based on the comprehensive optimization objective, construct a comprehensive fitness function; The fitness corresponding to the optimization solution is calculated through the comprehensive fitness function.

8. A production plan generating device, characterized in that: include: Acquisition module, used to obtain basic information related to production planning; A construction module, for constructing the constraint conditions and planning objectives corresponding to the production plan based on the basic information; wherein the constraint conditions include order dimension constraints, equipment dimension constraints, and human-machine coordination dimension constraints; the planning objectives include order outbound cost dimensions, equipment use cost dimensions, and order processing waiting time dimensions; A generation module is used to generate a production plan corresponding to the basic information based on the constraints and the planning objectives through a mixed integer programming MIP model.

9. An electronic device, characterized in that: The device comprises a processor and a memory, wherein the memory stores program codes, and when the program codes are executed by the processor, the processor executes any one of the methods in claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium comprises a program code, and when the storage medium is run on an electronic device, the program code is used to enable the electronic device to execute any one of the methods described in claims 1 to 7.

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