Production plan scheduling method, device, equipment, medium and program product
Through linear planning and integer planning algorithm combined with metaheuristic algorithms, production planning and production scheduling is optimized, and the problems of low efficiency and poor quality in the existing technology are solved, and an efficient and reliable production scheduling plan is achieved.
Patent Information
- Application Number
- CN202510501631.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-21
- Publication Date
- 2025-07-25
AI Technical Summary
In the prior art, the production schedule determination method is slow in efficiency and poor in the quality of solutions, making it difficult to meet complex constraints.
The objective function is constructed using linear programming or integer programming algorithms, combined with metaheuristic algorithms to find the optimization, and the nonlinear constraints are optimized through linear constraints as limits to obtain efficient and high-quality production scheduling solutions.
It achieves efficient and high-quality production schedules in production plans, can effectively meet a variety of complex constraints, and improves production efficiency and the reliability of the plan.
Smart Images
Figure CN120373558A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of intelligent production and manufacturing, and in particular, to a production plan scheduling method, device, equipment, medium and program product. Background Art
[0002] Production plan scheduling plays a crucial role in modern manufacturing. It not only affects the production efficiency and product quality of an enterprise, but also is directly related to the cost control, customer satisfaction and market competitiveness of the enterprise.
[0003] Currently, most use operations research optimization algorithms, meta-heuristic algorithms, and deep reinforcement learning algorithms to determine the scheduling plan, but the above algorithms have their own disadvantages respectively. For example, the operations research optimization algorithm is slow in dealing with large-scale problems, and the quality of the solution is often poor when dealing with complex constraints and conflict constraints; the meta-heuristic algorithm is sensitive to parameters and may fall into a local optimal solution; for the deep reinforcement learning algorithm, the model training converges very slowly or is difficult to converge, and it is difficult for the model to obtain a relatively good performance. Therefore, there is an urgent need for an efficient and high-quality method for determining the scheduling plan. Summary of the Invention
[0004] The present invention provides a production plan scheduling method, device, equipment, medium and program product, which is used to solve the defects of slow efficiency and poor solution quality in the existing method for determining the scheduling plan, and to achieve efficient and high-quality scheduling of the plan.
[0005] The present invention provides a production plan scheduling method, including the following steps.
[0006] Construct an objective function with the balance of the production quantity of products per time unit as the scheduling optimization objective of the production plan; Based on the linear constraint conditions of the production plan, use a linear programming algorithm or an integer programming algorithm to solve the objective function to obtain a first optimal solution; Perform target transformation on the non-linear constraint conditions of the production plan to obtain a transformed target; Using the linear constraint conditions as the limiting conditions and the first optimal solution as the initial solution, use a meta-heuristic algorithm to optimize the transformed target and the scheduling optimization target to obtain a second optimal solution; Determine the scheduling plan corresponding to the production plan according to the second optimal solution.
[0007] According to the production plan scheduling method provided by the present invention, constructing an objective function with the balance of the production quantity of products per time unit in the production plan as the scheduling optimization objective includes: Determine the average production quantity of products per time unit according to the production plan; Construct the objective function based on the difference between the production quantity of the product in each time unit and the average production quantity.
[0008] According to a production plan scheduling method provided by the present invention, based on the linear constraint conditions of the production plan, use a linear programming algorithm or an integer programming algorithm to solve the objective function, including: Screen out the critical linear constraint conditions according to the importance degree of the linear constraint conditions; Based on the critical linear constraint conditions, use a linear programming algorithm or an integer programming algorithm to solve the objective function.
[0009] According to a production plan scheduling method provided by the present invention, use a meta-heuristic algorithm to optimize the transformed objective and the scheduling optimization objective, including: Allocate proportional coefficients to each optimization objective according to the importance degree of the optimization objective; the optimization objectives include the transformed objective and the scheduling optimization objective; Determine a comprehensive evaluation index according to each optimization objective and the corresponding proportional coefficients; Based on the comprehensive evaluation index, use a meta-heuristic algorithm to optimize the transformed objective and the scheduling optimization objective.
[0010] According to a production plan scheduling method provided by the present invention, use a meta-heuristic algorithm to optimize the transformed objective and the scheduling optimization objective, including: Use multiple meta-heuristic algorithms to optimize the transformed objective and the scheduling optimization objective to obtain multiple groups of second optimal solutions; Determine the scheduling plan corresponding to the production plan according to the second optimal solution, including: Evaluate each group of second optimal solutions based on the objective function; Select a group of second optimal solutions as the scheduling plan corresponding to the production plan based on the evaluation results.
[0011] According to a production plan scheduling method provided by the present invention, it further includes: Verify the scheduling plan. If the verification result indicates that the scheduling plan does not meet the preset hard constraint conditions, fine-tune the scheduling plan so that the scheduling plan meets the preset hard constraint conditions.
[0012] The present invention also provides a production plan scheduling device, including the following modules: An objective function construction module, configured to construct an objective function with the balance of the production quantity of the product in each time unit as the scheduling optimization objective of the production plan; The first optimization module is used to solve the objective function based on the linear constraint conditions of the production plan by using a linear programming algorithm or an integer programming algorithm to obtain a first optimal solution; The objective transformation module is used to transform the non - linear constraint conditions of the production plan into a transformed objective to obtain a transformed objective; The second optimization module is used to use the linear constraint conditions as constraint conditions and the first optimal solution as an initial solution, and adopt a meta - heuristic algorithm to optimize the transformed objective and the production scheduling optimization objective to obtain a second optimal solution; The production scheduling plan determination module is used to determine the production scheduling plan corresponding to the production plan according to the second optimal solution.
[0013] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the production plan scheduling method as described in any one of the above.
[0014] The present invention also provides a non - transitory computer - readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the production plan scheduling method as described in any one of the above.
[0015] The present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the production plan scheduling method as described in any one of the above.
[0016] The production plan scheduling method, device, equipment, medium, and program product provided by the present invention construct an objective function with the balance of the production quantity of products in each time unit of the production plan as the production scheduling optimization objective, and based on the linear constraint conditions corresponding to the production plan, use a linear programming algorithm or an integer programming algorithm to solve the objective function to obtain a first optimal solution; then, use the linear constraint conditions as constraint conditions and the first optimal solution as an initial solution, and adopt a meta - heuristic algorithm to optimize the objective transformed from the non - linear constraint conditions and the production scheduling optimization objective to obtain a second optimal solution; finally, determine the production scheduling plan corresponding to the production plan according to the second optimal solution. The present invention uses a linear programming algorithm or an integer programming algorithm in combination with a meta - heuristic algorithm, and applies the linear constraint conditions and non - linear constraint conditions in the above - mentioned specific manner in the linear programming algorithm or integer programming algorithm and the meta - heuristic algorithm, realizing efficient and high - quality production plan scheduling. Description of the Drawings
[0017] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.
[0018] Figure 1 It is a schematic flowchart of the production plan scheduling method provided by an embodiment of the present invention.
[0019] Figure 2 It is a schematic flowchart of the optimization of the meta-heuristic algorithm provided by an embodiment of the present invention.
[0020] Figure 3 It is a schematic structural diagram of the production plan scheduling device provided by an embodiment of the present invention.
[0021] Figure 4 It is a schematic structural diagram of the electronic device provided by an embodiment of the present invention. Detailed implementation manners
[0022] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention with reference to the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.
[0023] It should be noted that the serial numbers assigned to the objects described in the present invention itself, such as "first", "second", etc., are only used to distinguish the described objects and do not have any sequential or technical meaning.
[0024] The following will be described in conjunction with Figure 1 the production plan scheduling method of the present invention. This production plan scheduling method can be applied to electronic devices such as terminal devices or servers. Among them, the terminal device can include mobile phones, computers, tablet computers, smart terminals, etc.; the server can include independent servers, cluster servers, or cloud servers, etc. This production plan scheduling method can also be applied to the production plan scheduling device provided in the electronic device such as the terminal device or the server. The production plan scheduling device can be implemented by software, hardware, or a combination of both.
[0025] Figure 1 Exemplarily shows a schematic flowchart of the production plan scheduling method provided by an embodiment of the present invention. Referring to Figure 1 as shown, the production plan scheduling method may include the following steps 101 to step 105.
[0026] Step 101: Construct an objective function with the goal of balancing the production quantity of products per time unit for optimizing the production scheduling of the production plan.
[0027] The production plan is the basis for organizing and controlling the production activities in the production workshop, mainly including information such as the production quantity, time range, and overall resources required. It provides the overall framework and basic parameters for production scheduling. Production scheduling, on the other hand, is to make specific arrangements and scheduling for the production process on the basis of the production plan to ensure the effective coordination and cooperation of various equipment, manpower, materials, and space, and to ensure the smooth execution of the production plan.
[0028] The production plan can be a long-term production plan, a medium-term production plan, or a short-term production plan. Among them, the long-term production plan refers to a production plan with a time span of more than one year, and its time unit is year; the medium-term production plan uses month as the time unit and is used to specify the overall annual production plan; the short-term production plan is a production scheduling plan responsible for short-term production scheduling after the medium-term plan is formulated and before the scheduling plan is made. It uses day as the time unit, can schedule the daily plan from one week to one month, and is responsible for decomposing the medium-term production plan to obtain the daily production plan required for the scheduling plan.
[0029] The balance of the production quantity of products refers to the balance of the production quantity of products on the production line, that is, the production quantity of products produced in each time unit is as close as possible to reduce production fluctuations and resource waste. For example, during periods of relatively loose production, there may be idle personnel and equipment, resulting in resource waste, while during periods of intense production, overtime may be required; problems such as inventory backlog or insufficient production may occur.
[0030] In an exemplary embodiment, the construction of the objective function includes: determining the average production quantity of products per time unit according to the production plan; constructing the objective function based on the difference between the production quantity arranged for each time unit and the average production quantity. Specifically, taking the short-term production plan as an example, the short-term production plan of a certain production line is to produce a total of 400 pieces of four products, namely Model 1, Model 2, Model 3, and Model 4, within 10 days. Using day as the time unit, then the average production quantity per day is 40 pieces, and the scheduling goal is that the production quantity arranged every day should be as close as possible to the average production quantity.
[0031] Based on this, the form of the objective function can be as follows: Or Among them, represents the production quantity arranged for the th model on the th day, represents the average daily production quantity, represents the total number of days of the production plan, Indicates the number of models produced by the production line.
[0032] Step 102: Based on the linear constraint conditions of the production plan, use the linear programming algorithm or integer programming algorithm to solve the objective function and obtain the first optimal solution.
[0033] It should be noted that the constraint conditions of the production plan include linear constraint conditions and non-linear constraint conditions. Linear constraint conditions such as delivery date constraints and critical component constraints, and non-linear constraint conditions (also known as complex constraints) such as interval constraints and proportional distribution constraints.
[0034] Specifically, for example, scheduling the production line for the time period from No. 1 to No. 10. Delivery date constraint: For example, Model 1 needs to deliver 10 pieces on the 4th. Then, when scheduling production, 10 pieces need to be scheduled before the 4th. The corresponding delivery date constraint is represented by the following linear relationship , where respectively represent the production quantities of Model 1 on the 1st, 2nd, 3rd, and 4th. Critical component constraint: For example, a critical component for producing Model 2 can only be put into production on the 2nd. The corresponding critical component constraint is represented by the following linear relationship , where represents the production quantity of Model 2 on the 1st. Interval constraint: For example, Model 3 is expected to be scheduled for production every other day. Proportional constraint: For example, Model 4 is expected to be scheduled for production at a certain proportion within the time period from No. 1 to No. 10. For example, 33% is scheduled for production from No. 1 to No. 3, 33% from No. 4 to No. 6, and 34% from No. 7 to No. 10.
[0035] The sources of the constraint conditions are mainly the constraints brought by the upstream production plan or the actual production environment. Taking the short-term production plan as an example, its constraint conditions generally come from the medium-term production plan and the actual production environment. For example, the above-mentioned delivery date constraints, critical component constraints, interval constraints, and proportional distribution constraints are all constraints brought by the actual production environment. For the constraints brought by the medium-term production plan, such as the production quantity of a certain model in a certain month. Of course, the constraint conditions may also come from the downstream plan. Similarly, taking the short-term production plan as an example, its constraint conditions will also include the constraints brought by the scheduling plan, which are specifically determined according to the actual situation.
[0036] In the embodiments of the present invention, based on the linear constraint conditions of the production plan, use the linear programming algorithm or integer programming algorithm to solve the objective function and obtain the first optimal solution. That is, the embodiments of the present invention use a mature linear programming solver to quickly obtain a feasible solution that satisfies the linear constraints (subsequent meta-heuristic algorithms will be used for further optimization based on this feasible solution), improving the calculation efficiency.
[0037] In an exemplary embodiment, according to the importance of linear constraint conditions, critical linear constraint conditions are screened out from multiple linear constraint conditions; based on the critical linear constraint conditions, a linear programming algorithm or an integer programming algorithm is used to solve the objective function.
[0038] Since there are many linear constraints, there may be conflicting or less important constraints among them. Therefore, in this embodiment, based on the importance of the constraints, relatively more critical constraints will be selected for linear programming solution. As for the evaluation criteria for critical importance, they can be set according to the actual situation, and the present invention does not limit this.
[0039] Step 103: Perform objective transformation on the non-linear constraint conditions of the production plan to obtain a transformed objective.
[0040] Before solving the objective function based on the constraint conditions in the embodiment of the present invention, it further includes a step of preprocessing the constraints, and this preprocessing step includes performing objective transformation on the non-linear constraint conditions, that is, converting the non-linear constraint conditions into independent objectives.
[0041] Specifically, taking the interval scheduling constraint as an example: Model 3 is expected to be scheduled once every other day. The interval distance between the adjacent non-zero scheduling positions of Model 3 can be used as the objective: score = j 2 - j 1 + j 3 - j 2 + j 4 - j 3, where j 1, j 2, j 3, j 4 respectively represent the positions of the 1st, 2nd, 3rd, and 4th non-zero scheduling. The above is only a simple example, intended to illustrate that for complex constraints such as non-linear constraint conditions, they can be transformed into the form of objectives.
[0042] In the embodiment of the present invention, complex constraints such as non-linear constraint conditions are transformed into independent objectives, intended to perform objective optimization together with the scheduling optimization objective by using a meta-heuristic algorithm later, so as to obtain the optimal solution of the scheduling plan.
[0043] Step 104: Using the linear constraint conditions as limiting conditions and the first optimal solution as the initial solution, a meta-heuristic algorithm is used to optimize the transformed objective and the scheduling optimization objective to obtain a second optimal solution.
[0044] Metaheuristic Algorithms are a class of efficient algorithmic frameworks for solving complex optimization problems. They perform well in solving large-scale, nonlinear, multi-peak, multi-constrained problems that are difficult to handle with traditional optimization methods. In order to further optimize the target and meet nonlinear complex constraints, metaheuristic algorithms are introduced for further optimization and solution.
[0045] In the embodiment of the present invention, the optimal solution (i.e., the first optimal solution) obtained by solving the objective function based on the linear programming algorithm or the integer programming algorithm and the linear constraints in step 102 is used as the initial solution of the metaheuristic algorithm, and the metaheuristic algorithm is used to optimize the production scheduling optimization target and the target converted from the nonlinear constraints in step 103 together to obtain the second optimal solution.
[0046] Since the linear programming algorithm or integer programming algorithm has obtained the optimal solution that satisfies the linear constraints, and these linear constraints contain many of the most important core constraints, such as delivery constraints, critical parts constraints, and total quantity constraints, on this basis, with the linear constraints in step 102 as the restriction, the meta-heuristic algorithm is used to perform secondary optimization through multiple trial and error, and the second optimal solution obtained can not only ensure that the linear constraints are satisfied, but also can maximize the satisfaction of complex constraints such as nonlinear constraints. Compared with using the linear programming algorithm or integer programming algorithm alone, or using the meta-heuristic algorithm alone to solve the objective function (production scheduling plan), the above-mentioned linear programming algorithm or integer programming algorithm combined with the meta-heuristic algorithm can obtain a higher-quality production scheduling plan. Moreover, the linear programming algorithm or integer programming algorithm is used to perform an initial solution first, and a solution that satisfies the basic linear constraints is quickly obtained, which reduces the problem scale, saves the time of the meta-heuristic algorithm processing, and improves the efficiency of the solution.
[0047] It should be noted that since linear programming algorithms or integer programming algorithms cannot cope with nonlinear complex constraints, the production scheduling solution cannot be solved by using linear programming algorithms or integer programming algorithms alone. The production scheduling solution obtained by using the metaheuristic algorithm alone can meet 80% of the constraints, with low quality and low efficiency.
[0048] In an example embodiment, Figure 2 The schematic diagram of the process flow of the meta-heuristic algorithm optimization provided by the embodiment of the present invention is exemplarily shown, referring to Figure 2 As shown in the figure, the meta-heuristic algorithm is used to optimize the conversion target and the production scheduling optimization target, which involves the following steps: Step 201: Allocate a proportional coefficient to each optimization target according to its importance; the optimization targets include conversion targets and production scheduling optimization targets.
[0049] Step 202: Determine the comprehensive evaluation index according to each optimization objective and the corresponding proportionality coefficient.
[0050] Step 203: Based on the comprehensive evaluation index, use a meta-heuristic algorithm to optimize the transformed objective and the production scheduling optimization objective.
[0051] It should be noted that the preprocessing step of the constraint conditions mentioned in the above embodiments may further include: the step of marking the priority of the constraints according to the importance of the constraints. When using a meta-heuristic algorithm to optimize the transformed objective and the production scheduling optimization objective, the proportionality coefficient can be allocated to the objectives transformed from the constraint conditions according to the priorities marked on the constraint conditions.
[0052] Specifically, taking the interval constraint and the proportional distribution constraint as examples, if the priority of the interval constraint is higher than that of the proportional distribution constraint, the proportionality coefficient allocated to the transformed objective corresponding to the interval constraint will be greater than the proportionality coefficient allocated to the transformed objective corresponding to the proportional distribution constraint. For example, the proportionality coefficient allocated to the transformed objective corresponding to the interval constraint is 3, and the proportionality coefficient allocated to the transformed objective corresponding to the proportional distribution constraint is 2. Of course, the proportionality coefficient here can also be a weight less than 1, such as 0.3 and 0.2.
[0053] The comprehensive evaluation index is a value obtained by summing the products of each optimization objective and its corresponding proportionality coefficient. It should be noted that the optimization objectives include both transformed objectives and production scheduling optimization objectives. Among them, the importance of the production scheduling optimization objective is the greatest, so its proportionality coefficient is higher than that of the transformed objective.
[0054] Step 105: Determine the production scheduling plan corresponding to the production plan according to the second optimal solution.
[0055] In an exemplary embodiment, the meta-heuristic algorithm can use algorithms such as genetic algorithm, simulated annealing algorithm, particle swarm optimization algorithm or tabu search algorithm as the optimization algorithm. The production scheduling optimization objective and the transformed objective (converted from non-linear constraint conditions) are regarded as multiple independent objectives, and they are optimized simultaneously by finding the Pareto optimal solution. Specifically, the linear constraint is used as the optimization strategy limit of the meta-heuristic algorithm, the transformed objective is added to the optimization objective function of the meta-heuristic algorithm, and the proportionality coefficient between the optimization objectives is set. The first optimal solution obtained in step 102 based on the linear programming algorithm or the integer programming algorithm is input into any one of the algorithms such as genetic algorithm, simulated annealing algorithm, particle swarm optimization algorithm or tabu search algorithm, and the parameter adaptive adjustment technology is introduced for multi-objective optimization. The obtained optimal solution (i.e., the second optimal solution) is the production scheduling plan corresponding to the production plan.
[0056] In an exemplary embodiment, the metaheuristic algorithm can use at least two optimization algorithms among algorithms such as genetic algorithm, simulated annealing algorithm, particle swarm optimization algorithm or tabu search algorithm to perform multi-objective optimization on the production scheduling optimization objective and the transformation objective (converted from non-linear constraint conditions) simultaneously. Specifically, the linear constraints are used as the optimization strategy limit of the metaheuristic algorithm, the transformation objective is added to the optimization objective function of the metaheuristic algorithm, and the proportionality coefficient between the optimization objectives is set. The first optimal solution obtained in step 102 based on the linear programming algorithm or integer programming algorithm is input into at least two optimization algorithms such as genetic algorithm, simulated annealing algorithm, particle swarm optimization algorithm or tabu search algorithm. The parameter adaptive adjustment technology is introduced, and the above at least two optimization algorithms are run simultaneously with multi-process calculation to perform multi-objective optimization. Each algorithm obtains a set of second optimal solutions, and the excellent solution is selected as the final solution by comparing the quality scores of multiple sets of second optimal solutions. The final solution is the production scheduling plan corresponding to the production plan.
[0057] In view of the parameter sensitivity and optimization randomness of the metaheuristic algorithm, the introduced parameter adaptive adjustment technology and multi-process calculation can effectively improve the solution quality and greatly reduce the probability of local optimal solutions, enabling the production scheduling plan of the production plan to be significantly improved in terms of balance and adaptability, and being able to better meet various complex constraints in actual production.
[0058] It should be noted that the quality score of the second optimal solution is obtained based on the objective function, that is, each second optimal solution is input into the objective function respectively, and the quality of the second optimal solution is determined by comparing the magnitudes of the objective function values.
[0059] In an exemplary embodiment, after obtaining the production scheduling plan, it is necessary to verify the production scheduling plan. If the verification result indicates that the production scheduling plan does not meet the preset hard constraint conditions, the production scheduling plan is fine-tuned to make the production scheduling plan meet the preset hard constraint conditions.
[0060] Since the production scheduling optimization problem belongs to the NP-hard problem, existing algorithms cannot ensure finding the global optimal solution and usually can only obtain approximate feasible solutions. Therefore, in this embodiment, through fine-tuning and verification, it is ensured that all preset hard constraint conditions are strictly met. Specific measures include adjusting the production quantity, reallocating resources, etc., to correct the deviation that may be brought by approximate solution, and ensuring that the final production scheduling plan is operable and effective in the actual production environment. This process not only verifies the legality of the production scheduling plan of the production plan, but also improves the practicality and reliability of the production scheduling plan of the production plan.
[0061] The production plan scheduling method provided by the embodiment of the present invention first sets the scheduling optimization goal, preprocesses the constraint conditions, and then uses the linear programming algorithm or the integer programming algorithm to perform an initial solution first, quickly obtaining a solution that satisfies the basic linear constraint conditions, reducing the problem scale. Then, the solution output by the linear programming algorithm is used as the initial solution of the meta-heuristic algorithm, and the former linear constraint conditions are used as the optimization limit conditions. The relatively non-linear complex constraint conditions are transformed into the optimization goal of the meta-heuristic algorithm, and then a secondary solution is performed to achieve the purpose of taking into account the advantages of both the linear programming and the meta-heuristic algorithm, while avoiding the disadvantages of both.
[0062] The production plan scheduling device provided by the present invention will be described below. The production plan scheduling device described below can be mutually corresponding and referred to the production plan scheduling method described above.
[0063] Figure 3 The structural schematic diagram of the production plan scheduling device provided by the embodiment of the present invention is exemplarily shown. Refer to Figure 3 As shown, the production plan scheduling device may include: The objective function construction module 301 is used to construct an objective function with the balanced production quantity of products per time unit as the scheduling optimization goal of the production plan.
[0064] The first optimization module 302 is used to solve the objective function by using a linear programming algorithm or an integer programming algorithm based on the linear constraint conditions of the production plan, and obtain a first optimal solution.
[0065] The objective transformation module 303 is used to transform the non-linear constraint conditions of the production plan into a target, and obtain a transformed target.
[0066] The second optimization module 304 is used to use the linear constraint conditions as the limit conditions, use the first optimal solution as the initial solution, and use a meta-heuristic algorithm to optimize the transformed target and the scheduling optimization goal, and obtain a second optimal solution.
[0067] The scheduling plan determination module 305 is used to determine the scheduling plan corresponding to the production plan according to the second optimal solution.
[0068] In an exemplary embodiment, the objective function construction module 301 may include: an average production quantity determination unit for determining the average production quantity of products per time unit according to the production plan; an objective function construction unit for constructing the objective function according to the difference between the production quantity of products in each time unit and the average production quantity.
[0069] In an exemplary embodiment, the first optimization module 302 may include: a critical linear constraint screening unit configured to screen critical linear constraint conditions according to the importance of the linear constraint conditions; and a first optimization unit configured to solve the objective function based on the critical linear constraint conditions by using a linear programming algorithm or an integer programming algorithm.
[0070] In an exemplary embodiment, the second optimization module 304 may include: a proportional coefficient allocation unit configured to allocate proportional coefficients to each optimization objective according to the importance of the optimization objective; the optimization objectives include the conversion-type objective and the production scheduling optimization objective; a comprehensive evaluation index determination unit configured to determine a comprehensive evaluation index according to each of the optimization objectives and the corresponding proportional coefficients; and a multi-objective optimization unit configured to perform optimization on the conversion-type objective and the production scheduling optimization objective based on the comprehensive evaluation index by using a meta-heuristic algorithm.
[0071] In an exemplary embodiment, the second optimization module 304 may include: a multi-objective optimization unit configured to perform optimization on the conversion-type objective and the production scheduling optimization objective by using a plurality of meta-heuristic algorithms to obtain multiple groups of second optimal solutions. The production scheduling plan determination module 305 may include: a quality evaluation unit configured to evaluate each group of second optimal solutions based on the objective function; and a production scheduling plan determination unit configured to select one group of second optimal solutions as the production scheduling plan corresponding to the production plan based on the evaluation result.
[0072] In an exemplary embodiment, the production plan scheduling device may further include: a verification and fine-tuning module configured to verify the production scheduling plan, and if the verification result indicates that the production scheduling plan does not meet the preset hard constraint conditions, fine-tune the production scheduling plan so that the production scheduling plan meets the preset hard constraint conditions.
[0073] Figure 4 An entity structure diagram of an electronic device is exemplified, as Figure 4As shown in the figure, the electronic device may include: a processor 410, a communications interface 420, a memory 430, and a communication bus 440. Among them, the processor 410, the communications interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call the logical instructions in the memory 430 to execute the production plan scheduling method. The method includes: constructing an objective function with the balance of the production quantity of products per time unit as the scheduling optimization objective of the production plan; based on the linear constraint conditions of the production plan, using a linear programming algorithm or an integer programming algorithm to solve the objective function to obtain a first optimal solution; performing objective transformation on the non-linear constraint conditions of the production plan to obtain a transformed objective; using the linear constraint conditions as the limiting conditions and the first optimal solution as the initial solution, and using a meta-heuristic algorithm to optimize the transformed objective and the scheduling optimization objective to obtain a second optimal solution; determining the scheduling plan corresponding to the production plan according to the second optimal solution. For a more specific method, reference may be made to the production plan scheduling method described above, which will not be elaborated here.
[0074] In addition, when the logical instructions in the above-mentioned memory 430 are implemented in the form of software functional units and sold or used as an independent product, they may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.
[0075] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the production plan scheduling method provided by each of the above methods. The method includes: constructing an objective function with the balance of the production quantity of products per time unit as the scheduling optimization objective of the production plan; based on the linear constraint conditions of the production plan, using a linear programming algorithm or an integer programming algorithm to solve the objective function to obtain a first optimal solution; performing objective transformation on the non-linear constraint conditions of the production plan to obtain a transformed objective; using the linear constraint conditions as limiting conditions and the first optimal solution as the initial solution, and using a meta-heuristic algorithm to optimize the transformed objective and the scheduling optimization objective to obtain a second optimal solution; determining the scheduling plan corresponding to the production plan according to the second optimal solution. As for more specific methods, reference can be made to the production plan scheduling method described above, which will not be elaborated here.
[0076] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it realizes the production plan scheduling method provided by each of the above methods. The method includes: constructing an objective function with the balance of the production quantity of products per time unit as the scheduling optimization objective of the production plan; based on the linear constraint conditions of the production plan, using a linear programming algorithm or an integer programming algorithm to solve the objective function to obtain a first optimal solution; performing objective transformation on the non-linear constraint conditions of the production plan to obtain a transformed objective; using the linear constraint conditions as limiting conditions and the first optimal solution as the initial solution, and using a meta-heuristic algorithm to optimize the transformed objective and the scheduling optimization objective to obtain a second optimal solution; determining the scheduling plan corresponding to the production plan according to the second optimal solution. As for more specific methods, reference can be made to the production plan scheduling method described above, which will not be elaborated here.
[0077] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.
[0078] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.
[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A production plan scheduling method, characterized in that, Including: Constructing an objective function with the balanced production quantity of products per time unit as the scheduling optimization objective of the production plan; Based on the linear constraint conditions of the production plan, using a linear programming algorithm or an integer programming algorithm to solve the objective function to obtain a first optimal solution; Performing objective transformation on the non-linear constraint conditions of the production plan to obtain a transformed objective; Using a meta-heuristic algorithm to optimize the transformed objective and the scheduling optimization objective with the linear constraint conditions as the limiting conditions and the first optimal solution as the initial solution to obtain a second optimal solution; Determining the scheduling plan corresponding to the production plan according to the second optimal solution.
2. The production plan scheduling method according to claim 1, characterized in that Constructing an objective function with the balanced production quantity of products per time unit as the scheduling optimization objective of the production plan, including: Determining the average production quantity of products per time unit according to the production plan; Constructing the objective function according to the difference between the production quantity of products in each time unit and the average production quantity.
3. The production plan scheduling method according to claim 1, characterized in that Based on the linear constraint conditions of the production plan, using a linear programming algorithm or an integer programming algorithm to solve the objective function, including: Screening out critical linear constraint conditions according to the importance degree of the linear constraint conditions; Based on the critical linear constraint conditions, using a linear programming algorithm or an integer programming algorithm to solve the objective function.
4. The production plan scheduling method according to claim 1, wherein Using a meta-heuristic algorithm to optimize the transformed objective and the scheduling optimization objective, including: Allocating proportional coefficients to each optimization objective according to the importance degree of the optimization objectives; the optimization objectives include the transformed objective and the scheduling optimization objective; Determining a comprehensive evaluation index according to each optimization objective and the corresponding proportional coefficient; Based on the comprehensive evaluation index, using a meta-heuristic algorithm to optimize the transformed objective and the scheduling optimization objective.
5. The production plan scheduling method according to claim 1 or 4, characterized in that, Using a meta-heuristic algorithm to optimize the transformed objective and the scheduling optimization objective, including: Using multiple meta-heuristic algorithms to optimize the transformed objective and the scheduling optimization objective to obtain multiple groups of second optimal solutions; Determining the scheduling plan corresponding to the production plan according to the second optimal solution, including: Evaluating each group of second optimal solutions based on the objective function; Selecting a group of second optimal solutions as the scheduling plan corresponding to the production plan based on the evaluation results.
6. The production plan scheduling method according to claim 1, wherein Also including: Verifying the scheduling plan, if the verification result indicates that the scheduling plan cannot meet the preset hard constraint conditions, then making fine adjustments to the scheduling plan so that the scheduling plan meets the preset hard constraint conditions.
7. A production plan scheduling device, characterized in that, Including: An objective function construction module for constructing an objective function with the balanced production quantity of products per time unit as the scheduling optimization objective of the production plan; A first optimization module for solving the objective function based on the linear constraint conditions of the production plan using a linear programming algorithm or an integer programming algorithm to obtain a first optimal solution; An objective transformation module for transforming the non-linear constraint conditions of the production plan to obtain a transformed objective; A second optimization module, configured to use the linear constraint conditions as limiting conditions, use the first optimal solution as an initial solution, and employ a metaheuristic algorithm to optimize the transformed objective and the production scheduling optimization objective to obtain a second optimal solution; A production scheduling plan determination module, configured to determine a production scheduling plan corresponding to the production plan according to the second optimal solution.
8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and running on the processor, wherein, When the processor executes the program, the production plan scheduling method according to any one of claims 1 to 6 is implemented.
9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the production plan scheduling 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, the production plan scheduling method according to any one of claims 1 to 6 is implemented.