Production scheduling optimization management method and device, electronic equipment and storage medium

Abstract

The invention provides a production scheduling optimization management method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining production line information, production bottleneck weight information and at least two to-be-produced order information, determining the emergency level and the process complexity level of a to-be-produced order according to the to-be-produced order information, and setting different weights; the method comprises the steps of obtaining production priority scores of to-be-produced orders, sorting the production priority scores to obtain a plurality of production order sequences, performing production scheduling on the production order sequences according to production line information and production bottleneck weight information to obtain a plurality of initial production scheduling schemes, and inputting the initial production scheduling schemes into a preset optimization function for solving to obtain an optimized production scheduling scheme; according to the method, production scheduling planning is carried out in combination with the data of the emergency level, the process complexity level and the production bottleneck weight information, the influence of human factors is reduced, production scheduling consideration factors are more comprehensive and accurate, the optimal scheme is solved through the preliminary production scheduling and the optimization function, and the reliability and adaptability of production scheduling are improved.

Description

Technical Field

[0001] The present application relates to the field of factory management technology, and in particular to a production scheduling optimization management method, device, electronic equipment and storage medium. Background Art

[0002] In the process of intelligent factory management, efficient production scheduling is a key management link. Production scheduling is the key link to reasonably allocate limited resources to different production tasks to achieve optimal production efficiency and product quality. With the increasing diversification and personalization of market demand and the increasing complexity of production systems, traditional production scheduling methods have gradually shown their limitations. The existing technology mainly relies on empirical rules or simple rules for production scheduling, which not only leads to low resource utilization and inability to fully allocate equipment and human resources, but also makes enterprises respond slowly when facing rapidly changing market demands, making it difficult to adjust production plans in a timely manner. In addition, traditional production scheduling methods often fail to effectively evaluate and manage bottlenecks and delay risks in the production process, resulting in delays in key orders. Secondly, the process complexity and urgency of different orders vary significantly, but these factors have not been fully considered in traditional production scheduling methods, resulting in unreasonable production scheduling, which further affects the flexibility and efficiency of production. Summary of the invention

[0003] The purpose of the embodiments of the present invention is to provide a production scheduling optimization management method, device, electronic device and storage medium to solve the above technical problems.

[0004] The present invention provides a production scheduling optimization management method, which includes: obtaining production line information, production bottleneck weight information and at least two to-be-produced order information, wherein the production bottleneck weight information is used to characterize the delay risk weight of the component production process; determining the urgency level and process complexity level of the to-be-produced order according to the to-be-produced order information; setting different weights for the urgency level and process complexity level of each to-be-produced order, respectively, to obtain a production priority score for each to-be-produced order, and sorting the orders according to the production priority scores of each to-be-produced order to obtain a plurality of production order sequences; scheduling each of the production order sequences according to the production line information and the production bottleneck weight information to obtain a plurality of initial production scheduling plans; inputting the plurality of initial production scheduling plans into a preset optimization function for solving, to obtain an optimized production scheduling plan, and scheduling according to the optimized production scheduling plan.

[0005] In one embodiment of the present invention, the acquisition of the production bottleneck weight information includes: acquiring historical production data, and performing statistics on the historical production data to obtain weight factors of the production processes of each component, wherein the weight factors include production complexity and historical delay frequency; performing weighted summation calculations on the weight factors according to preset bottleneck factor weight coefficients to obtain weight scores of the production processes of each component, and normalizing the weight scores of the production processes of each component to obtain comprehensive weight parameters of the production processes of each component, and determining the comprehensive weight parameters of the production processes of each component as the production bottleneck weight information.

[0006] In one embodiment of the present invention, different weights are set for the urgency level and process complexity level of each of the orders to be produced, and the production priority score of each order to be produced is obtained, including: setting weights for the urgency level and process complexity level of each of the orders to be produced in turn according to preset production scheduling grading weight gradient information, the preset production scheduling grading weight gradient information including an urgency level weight parameter and a process complexity level weight parameter; performing weighted summation of the urgency level and the process complexity level according to the urgency level weight parameter and the process complexity level weight parameter, to obtain the production priority score of each order to be produced.

[0007] In one embodiment of the present invention, scheduling each of the production order sequences according to the production line information and the production bottleneck weight information includes: scheduling each to-be-produced order in the production order sequence based on the production line information according to the production order sequence to obtain an original production scheduling plan, wherein the production line information includes production line identity information and production line component production information; performing a weighted sorting operation on the order of component production processes in the original production scheduling plan according to the production bottleneck weight information to obtain an initial production scheduling plan, wherein the weighted sorting operation includes obtaining the production rhythm parameters of each component production process, and performing a weighted calculation on the production rhythm parameters of each component production process according to the production bottleneck weight information to obtain the production bottleneck optimization parameters of each component production process, and adjusting the order of each component production process based on the production bottleneck optimization parameters to obtain an initial production scheduling plan.

[0008] In one embodiment of the present invention, multiple initial production scheduling plans are input into a preset optimization function for solution, including: obtaining the maximum capacity information of the production line and the completion deadline of each order to be produced; constructing a set of production constraints based on the production line information, the maximum capacity information of the production line, and the completion deadline of each order to be produced; adjusting the preset optimization function based on the production constraint set, and setting the maximization of production line utilization and the minimum number of employees as optimization goals to obtain a target optimization function; solving multiple initial production scheduling plans based on the target optimization function to obtain an optimized production scheduling plan.

[0009] In one embodiment of the present invention, solving multiple initial production scheduling plans according to the target optimization function includes: initializing each of the initial production scheduling plans as an optimizing particle to obtain an initial optimizing particle population, wherein the position of the optimizing particle is composed of the start time, end time and production line used of the order to be produced; calculating the fitness value of each optimizing particle in the initial optimizing particle population, wherein the fitness value is determined based on the production line utilization rate and the number of employees utilized; performing iterative optimization calculations on the initial optimizing particle population, and updating the speed and position of each optimizing particle until the number of iterations reaches a preset maximum iteration number threshold or the fitness value of each optimizing particle reaches a preset fitness threshold, and outputting an optimized production scheduling plan.

[0010] In one embodiment of the present invention, the set of production constraint conditions includes a delivery time constraint used to characterize that each order to be produced must be completed within the specified completion period, a production line capacity constraint used to characterize that each production line cannot exceed the maximum production capacity of the production line, a production line component production information constraint used to characterize that each production line can only produce corresponding components, a resource availability constraint used to characterize that equipment and workers are always available during the production process, and a process sequence constraint used to characterize that the production processes are carried out in the correct process production sequence.

[0011] An embodiment of the present invention also provides a production scheduling optimization management device, which includes: a production scheduling information acquisition module, which is used to acquire production line information, production bottleneck weight information and at least two orders to be produced information, wherein the production bottleneck weight information is used to characterize the delay risk weight of the component production process; the urgency level and process complexity level of the orders to be produced are determined according to the orders to be produced information; an initial production scheduling calculation module, which is used to set different weights for the urgency level and process complexity level of each order to be produced, respectively, to obtain a production priority score for each order to be produced, and to sort the orders according to the production priority scores of each order to be produced to obtain multiple production order sequences; each production order sequence is scheduled according to the production line information and the production bottleneck weight information to obtain multiple initial production scheduling plans; a production scheduling plan optimization module, which is used to input the multiple initial production scheduling plans into a preset optimization function for solution to obtain an optimized production scheduling plan, so as to schedule according to the optimized production scheduling plan.

[0012] An embodiment of the present invention also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the electronic device implements a production scheduling optimization management method as described in any one of the above embodiments.

[0013] An embodiment of the present invention further provides a computer-readable storage medium on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor of a computer, the computer executes the production scheduling optimization management method as described in any one of the above embodiments.

[0014] The present invention provides a production scheduling optimization management method, device, electronic device and storage medium. By acquiring production line information, production bottleneck weight information and at least two to-be-produced order information, the urgency level and process complexity level of the to-be-produced order are determined according to the to-be-produced order information and different weights are set respectively, the production priority score of each to-be-produced order is obtained and sorted, and multiple production order sequences are obtained. Each production order sequence is scheduled according to the production line information and the production bottleneck weight information to obtain multiple initial production scheduling plans, which are input into a preset optimization function for solution to obtain an optimized production scheduling plan; the application performs production scheduling planning based on data and algorithms for the urgency level and process complexity level of the to-be-produced order in combination with the production bottleneck weight information, reduces the influence of human factors, and makes the production scheduling considerations more comprehensive and accurate. By quantifying the risk weight of the production bottleneck, the potential delay risk can be considered in advance during the production scheduling, and the possibility of order delay can be reduced. Secondly, by setting different weights for each to-be-produced order to calculate the production priority score, it is ensured that more urgent or more complex orders are given priority. The optimal production scheduling plan is found by solving the optimization function, avoiding the limitations of a single plan, and improving the flexibility and adaptability of the production scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1 is a schematic diagram of an exemplary system architecture shown in an exemplary embodiment of the present application; Figure 2 is a flow chart of a production scheduling optimization management method shown in an exemplary embodiment of the present application; Figure 3 is a schematic diagram of a production scheduling optimization management device shown in an exemplary embodiment of the present application; Figure 4 It is a structural diagram of a computer system of an electronic device shown in an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0016] The following will describe the embodiments of the present invention with reference to the accompanying drawings and specific embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0017] It should be noted that the illustrations provided in the following embodiments are only used to illustrate the basic concept of the present invention in a schematic manner, and thus the illustrations only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0018] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0019] The "and / or" mentioned in this application describes the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0020] The present application may also provide the following beneficial effects: configuring the target production line for line change according to the line change configuration list, including multiple configuration nodes, and the line change configuration status of each configuration node is synchronously displayed on the first-level authority control terminal to visually manage the progress of the line change process, thereby avoiding the occurrence of management blind spots in the line change process and affecting production management efficiency; setting a management intervention division threshold to facilitate timely timeout processing, minimize production efficiency loss caused by line change timeout, and clarify management levels to facilitate the classification of timeout events for subsequent management improvements; reporting can be performed based on statistical information on abnormal line change records within a cycle, so that relevant technical personnel at the second-level authority control terminal and relevant technical personnel at the third-level authority control terminal can summarize the causes of abnormal line change records within the cycle, formulate line change optimization management strategies, and improve the management efficiency of the line change process.

[0021] Figure 1 It is a schematic diagram of an exemplary system architecture shown in an exemplary embodiment of the present application.

[0022] Reference Figure 1 As shown, the system architecture may include a production system 110 and a computer device 120. The computer device 120 obtains production line information, production bottleneck weight information and at least two pending production order information through the production system 110, and the production bottleneck weight information is used to characterize the delay risk weight of the component production process; the urgency level and process complexity level of the pending production order are determined according to the pending production order information; different weights are set for the urgency level and process complexity level of each pending production order, and the production priority score of each pending production order is obtained, and the production priority scores of each pending production order are sorted to obtain multiple production order sequences; each production order sequence is scheduled according to the production line information and the production bottleneck weight information, and multiple initial production scheduling plans are obtained; the multiple initial production scheduling plans are input into the preset optimization function for solution, and the optimized production scheduling plan is obtained, so as to schedule according to the optimized production scheduling plan. The above-mentioned computer device 120 may be at least one of a microcomputer, an embedded computer, a network computer, etc.; the above-mentioned production system 110 includes at least two production lines, an order management system, a production line management system, a production database, and a process database.

[0023] Schematically, the computer device 120 obtains production line information, production bottleneck weight information and at least two pending production order information through the production system 110, determines the urgency level and process complexity level of the pending production order according to the pending production order information and sets different weights respectively, obtains the production priority score of each pending production order and sorts it, obtains multiple production order sequences, schedules each production order sequence according to the production line information and production bottleneck weight information, obtains multiple initial production scheduling plans, inputs them into a preset optimization function for solution, and obtains an optimized production scheduling plan; the present application combines the urgency level and process complexity level of the pending production order with the production bottleneck weight information based on data and algorithms to perform production scheduling planning, reduces the influence of human factors, and makes the production scheduling considerations more comprehensive and accurate, wherein by quantifying the risk weight of the production bottleneck, the potential delay risk can be considered in advance during scheduling, and the possibility of order delays can be reduced, and secondly, by setting different weights for each pending production order to calculate the production priority score, it is ensured that more urgent or more complex orders are given priority, and the best scheduling plan is found by solving the optimization function, avoiding the limitations of a single plan, and improving the flexibility and adaptability of scheduling.

[0024] Figure 2 is a flowchart of a production scheduling optimization management method shown in an exemplary embodiment of the present application. The production scheduling optimization management method can be Figure 1 The implementation environment shown in the figure can also be implemented in other implementation environments, and the above implementation environment is not specifically limited here. Figure 2 As shown, the flowchart of the production scheduling optimization management method includes at least steps S210 to S250, which are described in detail as follows: In step S210, production line information, production bottleneck weight information and information of at least two pending production orders are obtained.

[0025] In one embodiment of the present application, the above-mentioned production bottleneck weight information is used to characterize the delay risk weight of the component production process.

[0026] In one embodiment of the present application, the acquisition of production bottleneck weight information includes acquiring historical production data, and statistically analyzing the historical production data to obtain weight factors of each component production process, the weight factors include production complexity and historical delay frequency, and weighted summing the weight factors according to the preset bottleneck factor weight coefficients to obtain weight scores of each component production process, and normalizing the weight scores of each component production process to obtain comprehensive weight parameters of each component production process, and determining the comprehensive weight parameters of each component production process as production bottleneck weight information. In some other embodiments, the production bottleneck weight information can also be based on statistics of error messages that cause delays in the historical data process, and relevant technical personnel confirm and sort the component production processes that are prone to delays, and set weights from high to low based on the sorting.

[0027] In one embodiment of the present application, the production line information includes production line identity information and production line component production information, wherein the production line identity information is the relevant information of the production line's label, logo, and signature information used to authenticate the identity content of the production line; the production line component production information includes the type of components produced by the production line, the model information produced by the production line, and the production rhythm of the production line.

[0028] In one embodiment of the present application, the source of the above-mentioned acquired data is the production-related parameters collected by the relevant subsystems in the production system, such as obtaining relevant information of orders in the ERP system, obtaining relevant information such as production plans in the MES management system, and at the same time, in some feasible environments, based on the impact of the production scheduling process on raw materials, relevant information of material inventory can also be obtained from the WMS warehouse management system.

[0029] It should be noted that after the above information is obtained, the data needs to be preprocessed. The preprocessing process includes using the isolation forest model to detect and remove outliers, and filling in missing values ​​based on the linear interpolation method.

[0030] In step S220, the urgency level and process complexity level of the order to be produced are determined according to the information of the order to be produced.

[0031] In one embodiment of the present application, the process of determining the urgency level of the order to be produced according to the information of the order to be produced includes assigning an urgency level to the current order according to the difference between the completion deadline in the order and the current date, wherein the urgency level can be specifically set according to the production scheduling requirements of the production order, for example, it is set to level 1 within 3 days, level 2 within 5 days, level 3 within 10 days, and level 4 above 10 days. The setting of the specific level can be set according to specific needs and is not specifically limited here. After determining the urgency level, the urgency level can be represented based on the corresponding digital identifier, for example, the first-level urgency level is set to digital identifier 1, the second-level urgency level is set to digital identifier 2, and so on. In some other feasible environments, the relevant urgency level can also be determined in the process of determining the urgency level in combination with the customer's priority setting and the business production priority setting as reference factors. In the case of other urgency level reference factors, the difference in delivery date is used as the main reference factor, and a higher weight level is set. The customer priority and business production priority are used as secondary reference factors, and the same or different weight levels are set respectively, and the final urgency level is calculated in this way. This calculation process can adopt weighted summation and other methods.

[0032] In one embodiment of the present application, the process of determining the above-mentioned process complexity level first needs to analyze the content of the parts to be produced in each to-be-produced order information, and call the corresponding production process in the process database for each production link of the parts, where the production process includes the number of processes, special processing conditions and production time. The weights of each factor in the production process are set separately, and the level range is set for each factor separately, so as to perform a level evaluation and a final weighted sum or weighted average on each item in the production process to obtain the process complexity level.

[0033] In step S230, different weights are set for the urgency level and process complexity level of each order to be produced, so as to obtain a production priority score of each order to be produced, and the orders to be produced are sorted according to their production priority scores to obtain a plurality of production order sequences.

[0034] In some embodiments of the present application, the urgency level and the process complexity level of each order to be produced are weighted in turn according to preset production scheduling grading weight gradient information, and the preset production scheduling grading weight gradient information includes an urgency level weight parameter and a process complexity level weight parameter. The urgency level and the process complexity level are weighted and summed according to the urgency level weight parameter and the process complexity level weight parameter to obtain a production priority score for each order to be produced.

[0035] In some embodiments of the present application, the above-mentioned production scheduling classification weight gradient information is a weight gradient setting formed by the difference between the manufacturer's emphasis on the urgency level and the process complexity level. If the manufacturer pays more attention to the time limit requirements for production scheduling, a higher weight is set for the urgency level, for example, the urgency level weight is set to 0.7, and the process complexity level weight is set to 0.3. In other manufacturers that pay more attention to production quality, the weight of the process complexity level is set higher, and the urgency level weight is set lower. In the embodiments of the present application, different weight settings are made for the urgency level and the process complexity level, respectively, and a plurality of division combinations with different weight settings can be formed, so that a plurality of initial production scheduling plans can be obtained after the property of each combination.

[0036] In step S240, each production order sequence is scheduled according to the production line information and the production bottleneck weight information to obtain a plurality of initial production scheduling plans.

[0037] In one embodiment of the present application, each to-be-produced order in the production order sequence is scheduled based on the production line information according to the production order sequence to obtain an original production scheduling plan, the production line information includes production line identity information and production line component production information, and the order of component production processes in the original production scheduling plan is weighted sorted according to the production bottleneck weight information to obtain an initial production scheduling plan, the weighted sorting operation includes obtaining the production rhythm parameters of each component production process, and performing weighted calculation on the production rhythm parameters of each component production process according to the production bottleneck weight information to obtain the production bottleneck optimization parameters of each component production process, and adjusting the order of each component production process based on the production bottleneck optimization parameters to obtain an initial production scheduling plan.

[0038] In a specific embodiment of the present application, it is assumed that there are two orders to be produced (order A and order B), each order contains multiple parts: Order A includes: Part 1: needs to be produced on production line 1; Part 2: needs to be produced on production line 2; Part 3: needs to be produced on production line 3.

[0039] Order B includes: Part 4: needs to be produced on production line 1; Part 5: needs to be produced on production line 2; Part 6: needs to be produced on production line 3.

[0040] Production line information includes but is not limited to: A. Production line identity information: production line number (such as production line 1, production line 2, production line 3); production line type; current status of the production line.

[0041] B. Production line component production information: the types of components that each production line can produce; the production cycle parameters of each component on the production line; the available time and capacity limit of the production line.

[0042] For example, production line 1 can produce component 1 and component 4, with production cycles of 10 minutes / piece and 12 minutes / piece respectively; production line 2 can produce component 2 and component 5, with production cycles of 8 minutes / piece and 15 minutes / piece respectively; production line 3 can produce component 3 and component 6, with production cycles of 14 minutes / piece and 10 minutes / piece respectively. Based on the above production line information, the components in each order are preliminarily scheduled to obtain the original production scheduling plan. Assuming that the production is scheduled according to order priority, the initial order is: component 1 of order A is production line 1, component 2 is production line 2, and component 3 is production line 3; component 4 of order B is production line 1, component 5 is production line 2, and component 6 is production line 3. A preliminary original production scheduling plan can be obtained, but the impact of production bottlenecks has not yet been considered.

[0043] Assume that the production bottleneck weight information includes: the production bottleneck weight of component 1 is 0.7; the production bottleneck weight of component 2 is 0.5; the production bottleneck weight of component 3 is 0.9; the production bottleneck weight of component 4 is 0.6; the production bottleneck weight of component 5 is 0.8; and the production bottleneck weight of component 6 is 0.4. These weights reflect the delay risk that each component may cause during the production process, and the larger the value, the higher the delay risk.

[0044] The production cycle parameters of each component (i.e., the average production time of each component) are extracted from the production line information. It is assumed that component 1 is 10 minutes / piece; component 2 is 8 minutes / piece; component 3 is 14 minutes / piece; component 4 is 12 minutes / piece; component 5 is 15 minutes / piece; and component 6 is 10 minutes / piece. In order to reflect the impact of delay risk on production efficiency, the production cycle parameters are combined with the delay risk weight to calculate the production bottleneck optimization parameters of each component. The results are: component 1 is 10 minutes / piece × 0.7 = 7.0; component 2 is 8 minutes / piece × 0.5 = 4.0; component 3 is 14 minutes / piece × 0.9 = 12.6; component 4 is 12 minutes / piece × 0.6 = 7.2; component 5 is 15 minutes / piece × 0.8 = 12.0; and component 6 is 10 minutes / piece × 0.4 = 4.0. According to the calculated production bottleneck optimization parameters, the production order of each component is readjusted. The goal is to arrange the components with higher production bottleneck optimization parameters as early as possible in the production plan to reduce the potential risk of delays. The adjusted order in the example is: 1. Component 3 (12.6) is on production line 3; 2. Component 5 (12.0) is on production line 2; 3. Component 4 (7.2) in production line 1; 4. Component 1 (7.0) is on production line 1; 5. Component 2 (4.0) is on production line 2; 6. Component 6 (4.0) is on production line 3.

[0045] Through the above weighted sorting operation, an optimized initial production scheduling plan can be obtained. This plan not only takes into account the priority of the order, but also combines the production bottleneck weight information to ensure that those parts that are prone to become bottlenecks can be produced as early as possible, thereby reducing the risk of delays in the overall production process.

[0046] In step S250, a plurality of initial production scheduling plans are input into a preset optimization function for solution to obtain an optimized production scheduling plan, and production is scheduled according to the optimized production scheduling plan.

[0047] In one embodiment of the present application, the maximum capacity information of the production line and the completion deadline of each pending production order are obtained, and a set of production constraints are constructed based on the production line information, the maximum capacity information of the production line, and the completion deadline of each pending production order. The preset optimization function is adjusted according to the production constraint set, and the maximization of the production line utilization and the minimum number of employees are set as the optimization goals to obtain the target optimization function. According to the target optimization function, multiple initial production scheduling plans are solved to obtain an optimized production scheduling plan.

[0048] In one embodiment of the present application, Particle Swarm Optimization (PSO) is a global optimization technology that simulates the foraging behavior of bird groups. In PSO, each possible solution is regarded as a "particle" in the search space. These particles fly at a certain speed and adjust their trajectories according to their own and their companions' historical best positions. Each particle records the best solution it has found (individual extreme value pBest), and the entire group shares the currently discovered best solution (global extreme value gBest). By iteratively updating the speed and position of the particles, PSO aims to converge to the optimal or approximate optimal solution to the problem. It is suitable for optimization problems in continuous space and is widely used in engineering, economics and other fields.

[0049] In one embodiment of the present application, solving multiple initial production scheduling plans according to the target optimization function is to initialize each initial production scheduling plan as an optimizing particle to obtain an initial optimizing particle population, the position of the optimizing particle is composed of the start time, end time and production line used of the order to be produced, and the fitness value of each optimizing particle in the initial optimizing particle population is calculated. The fitness value is determined based on the production line utilization rate and the number of employees utilized. The initial optimizing particle population is iteratively optimized and calculated, and the speed and position of each optimizing particle are updated until the number of iterations reaches a preset maximum iteration number threshold or the fitness value of each optimizing particle reaches a preset fitness threshold, and the optimized production scheduling plan is output.

[0050] The initial production schedule is converted into a search particle in the particle swarm optimization algorithm. Each search particle represents a production schedule, and its position includes the start time of the order to be produced, the end time of the order to be produced, and the production line to be used. In order to evaluate the quality of each search particle, its fitness value needs to be calculated. In the embodiment of the present application, the fitness value is determined based on two main indicators: 1. Production line utilization rate is a measure of the efficiency of each production line in the entire production scheduling cycle. The higher the utilization rate, the more balanced the workload of the production line and the more fully the resources are utilized; 2. The number of employees utilized is a measure of the number of employees required in the production scheduling process. The fewer the number of employees utilized, the more efficient the production scheduling plan is and the less labor costs are.

[0051] In the particle swarm optimization algorithm, the optimization particle will find the optimal solution through continuous iterative optimization. The specific process includes that in each iteration, each optimization particle will adjust its speed and position according to its current state (position and speed) and the state of other particles in the group. The speed determines the direction and distance of the particle movement, and the position reflects the new production scheduling plan. The speed update includes that the particle will refer to its own historical best position (that is, the best solution ever found) and the global best position of all particles in the group (that is, the best solution found in the entire population) to adjust the particle speed so as to get closer to a better solution faster; the position update includes that according to the updated speed, the particle will move to a new position to form a new production scheduling plan. After each iteration, the fitness value of each optimization particle will be recalculated to evaluate the pros and cons of the new position. In each iteration, the particle with the highest fitness value in the current group will be recorded as the current optimal solution.

[0052] When the optimization process is over, the production scheduling plan corresponding to the optimal particle with the highest fitness value is selected as the final optimized production scheduling plan. This plan not only takes into account the utilization rate of the production line and the number of employees, but also ensures the rationality and efficiency of the production scheduling plan through multiple iterative optimizations.

[0053] In one embodiment of the present application, the above-mentioned set of production constraint conditions includes a delivery time constraint used to characterize that each order to be produced must be completed within the specified completion period, a production line capacity constraint used to characterize that each production line cannot exceed the maximum production capacity of the production line, a production line component production information constraint used to characterize that each production line can only produce corresponding components, a resource availability constraint used to characterize that equipment and workers are always available during the production process, and a process sequence constraint used to characterize that the production processes are carried out in the correct process production order.

[0054] The present invention provides a production scheduling optimization management method, device, electronic device and storage medium. By acquiring production line information, production bottleneck weight information and at least two to-be-produced order information, the urgency level and process complexity level of the to-be-produced order are determined according to the to-be-produced order information and different weights are set respectively, the production priority score of each to-be-produced order is obtained and sorted, and multiple production order sequences are obtained. Each production order sequence is scheduled according to the production line information and the production bottleneck weight information to obtain multiple initial production scheduling plans, which are input into a preset optimization function for solution to obtain an optimized production scheduling plan; the application performs production scheduling planning based on data and algorithms for the urgency level and process complexity level of the to-be-produced order in combination with the production bottleneck weight information, reduces the influence of human factors, and makes the production scheduling considerations more comprehensive and accurate. By quantifying the risk weight of the production bottleneck, the potential delay risk can be considered in advance during the production scheduling, and the possibility of order delay can be reduced. Secondly, by setting different weights for each to-be-produced order to calculate the production priority score, it is ensured that more urgent or more complex orders are given priority. The optimal production scheduling plan is found by solving the optimization function, avoiding the limitations of a single plan, and improving the flexibility and adaptability of the production scheduling.

[0055] The following describes an embodiment of the device of the present application, which can be used to execute the production scheduling optimization management method in the above embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the production scheduling optimization management method in the above embodiment of the present application.

[0056] Figure 3 is a schematic diagram of a production scheduling optimization management device shown in an exemplary embodiment of the present application. The device can be applied to Figure 2 The method implementation process shown in the figure can be based on Figure 1 The present invention is executed in the implementation environment shown in the figure, and may also be applicable to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the apparatus is applicable.

[0057] like Figure 3 As shown, the exemplary production scheduling optimization management device includes: a production scheduling information acquisition module 301, an initial production scheduling calculation module 302 and a production scheduling scheme optimization module 303.

[0058] Among them, the production scheduling information acquisition module 301 is used to obtain production line information, production bottleneck weight information and at least two orders to be produced, the production bottleneck weight information is used to characterize the delay risk weight of the component production process; the urgency level and process complexity level of the orders to be produced are determined according to the information of the orders to be produced; the initial production scheduling calculation module 302 is used to set different weights for the urgency level and process complexity level of each order to be produced, obtain the production priority score of each order to be produced, and sort the orders according to the production priority score of each order to be produced to obtain multiple production order sequences; each production order sequence is scheduled according to the production line information and production bottleneck weight information to obtain multiple initial production scheduling plans; the production scheduling plan optimization module 303 is used to input multiple initial production scheduling plans into a preset optimization function for solution, obtain an optimized production scheduling plan, and schedule according to the optimized production scheduling plan.

[0059] In one embodiment of the present application, according to the above-mentioned production scheduling optimization management method and device, production scheduling optimization management can be further developed and a production scheduling dashboard can be designed to ensure the visualization of production scheduling results. Among them, the interface of the production scheduling dashboard includes a display box for displaying the current production date and time; a display box for clarifying order information; a notification box for displaying the production scheduling optimization plan, and the early warning notification box will specifically display the optimized production scheduling plan details.

[0060] An embodiment of the present application also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by one or more processors, the electronic device implements the production scheduling optimization management method provided in the above-mentioned embodiments.

[0061] Figure 4 is a schematic diagram of the structure of a computer system of an electronic device according to an exemplary embodiment of the present application. It should be noted that: Figure 4 The computer system 400 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0062] like Figure 4As shown, computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in read-only memory (ROM) 402 or the program loaded from the storage part to random access memory (RAM) 403, such as executing the method in the above embodiment. In RAM 403, various programs and data required for system operation are also stored. CPU 401, ROM 402 and RAM 403 are connected to each other through a bus. Input / output (I / O) interface 405 is also connected to bus 404.

[0063] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0064] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 409, and / or installed from a removable medium 411. When the computer program is executed by a central processing unit (CPU) 401, various functions defined in the system of the present application are executed.

[0065] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. A computer program contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0066] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0067] In the corresponding drawings of the above embodiments, connecting lines may indicate the connection relationship between the various components to indicate more constituent signal paths (constituent_signalpath) and / or one or more ends of some lines may have arrows to indicate the main information flow direction. Connecting lines, as a mark, are not a limitation to the scheme itself, but the use of these lines in combination with one or more exemplary embodiments helps to connect circuits or logic units more easily. Any represented signal (determined by design requirements or preferences) may actually include one or more signals that can be transmitted in either direction and implemented with any appropriate type of signal scheme.

[0068] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0069] Another aspect of the present application further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device.

[0070] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0071] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0072] It should be noted that the present application can be used in many general or special computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0073] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0074] It should be understood that the above content is only a preferred exemplary embodiment of the present application and is not intended to limit the implementation scheme of the present application. A person skilled in the art can easily make corresponding changes or modifications based on the main concept and spirit of the present application. Therefore, the scope of protection of the present application should be the scope of protection required by the claims.

Claims

1. A production scheduling optimization management method, characterized in that: The production scheduling optimization management method comprises: Acquire production line information, production bottleneck weight information, and information on at least two pending production orders, wherein the production bottleneck weight information is used to characterize the delay risk weight of the component production process; Determine the urgency level and process complexity level of the order to be produced according to the information of the order to be produced; Different weights are set for the urgency level and process complexity level of each of the to-be-produced orders to obtain a production priority score for each to-be-produced order, and the orders to-be-produced are sorted according to the production priority scores to obtain multiple production order sequences; Scheduling each of the production order sequences according to the production line information and the production bottleneck weight information to obtain multiple initial production scheduling plans; The multiple initial production scheduling plans are input into a preset optimization function for solution to obtain an optimized production scheduling plan, and production is scheduled according to the optimized production scheduling plan.

2. The production scheduling optimization management method according to claim 1, characterized in that: The acquisition of the production bottleneck weight information includes: Acquire historical production data, and perform statistics on the historical production data to obtain weight factors of the production process of each component, wherein the weight factors include production complexity and historical delay frequency; The weight factors are weighted and summed according to the preset bottleneck factor weight coefficients to obtain the weight scores of the production processes of each component, and the weight scores of the production processes of each component are normalized to obtain the comprehensive weight parameters of the production processes of each component. The comprehensive weight parameters of the production processes of each component are determined as the production bottleneck weight information.

3. The production scheduling optimization management method according to claim 1, characterized in that: Different weights are set for the urgency level and process complexity level of each of the pending production orders, and the production priority scores of each pending production order are obtained, including: The urgency level and process complexity level of each of the to-be-produced orders are weighted in turn according to the preset production scheduling grading weight gradient information, wherein the preset production scheduling grading weight gradient information includes an urgency level weight parameter and a process complexity level weight parameter; The urgency level and the process complexity level are weighted and summed according to the urgency level weight parameter and the process complexity level weight parameter to obtain a production priority score for each order to be produced.

4. The production scheduling optimization management method according to claim 1, characterized in that: Scheduling each of the production order sequences according to the production line information and the production bottleneck weight information includes: According to the production order sequence, each to-be-produced order in the production order sequence is scheduled based on the production line information to obtain an original production scheduling plan, wherein the production line information includes production line identity information and production line component production information; According to the production bottleneck weight information, a weighted sorting operation is performed on the order of the component production process in the original production schedule to obtain an initial production schedule. The weighted sorting operation includes obtaining the production rhythm parameters of each component production process, and performing weighted calculation on the production rhythm parameters of each component production process according to the production bottleneck weight information to obtain the production bottleneck optimization parameters of each component production process, and adjusting the order of each component production process based on the production bottleneck optimization parameters to obtain an initial production scheduling plan.

5. The production scheduling optimization management method according to claim 1, characterized in that: Inputting the plurality of initial production scheduling plans into a preset optimization function for solving includes: Obtain the maximum production capacity information of the production line and the completion deadline of each pending production order; Constructing a set of production constraint conditions according to the production line information, the maximum production capacity information of the production line, and the completion deadline of each pending production order; The preset optimization function is adjusted according to the set of production constraints, and the maximum utilization rate of the production line and the minimum number of employees are set as the optimization goals to obtain the target optimization function; The multiple initial production scheduling plans are solved according to the target optimization function to obtain an optimized production scheduling plan.

6. The production scheduling optimization management method according to claim 5, characterized in that: Solving the plurality of initial production scheduling plans according to the target optimization function includes: Initializing each of the initial production scheduling plans as an optimization particle to obtain an initial optimization particle population, wherein the position of the optimization particle is composed of the start time, end time and production line used for the order to be produced; Calculate the fitness value of each optimal particle in the initial optimal particle population, where the fitness value is determined based on the production line utilization rate and the number of employees utilized; Iterative optimization calculation is performed on the initial optimization particle population, and the speed and position of each optimization particle are updated until the number of iterations reaches a preset maximum iteration number threshold or the fitness value of each optimization particle reaches a preset fitness threshold, and an optimized production scheduling plan is output.

7. The production scheduling optimization management method according to claim 5, characterized in that: The set of production constraint conditions includes a delivery date constraint used to characterize that each order to be produced must be completed within the specified completion period, a production line capacity constraint used to characterize that each production line cannot exceed the maximum production capacity of the production line, a production line component production information constraint used to characterize that each production line can only produce corresponding components, a resource availability constraint used to characterize that equipment and workers are always available during the production process, and a process sequence constraint used to characterize that the production processes are carried out in the correct process production sequence.

8. A production scheduling optimization management device, characterized in that: The production scheduling optimization management device comprises: A production scheduling information acquisition module is used to acquire production line information, production bottleneck weight information and at least two pending production order information, wherein the production bottleneck weight information is used to characterize the delay risk weight of the component production process; and determine the urgency level and process complexity level of the pending production order according to the pending production order information; The initial production scheduling calculation module is used to set different weights for the urgency level and process complexity level of each of the pending production orders, obtain the production priority score of each of the pending production orders, and sort the orders according to the production priority scores of each of the pending production orders to obtain multiple production order sequences; schedule each of the production order sequences according to the production line information and production bottleneck weight information to obtain multiple initial production scheduling plans; The production scheduling optimization module is used to input the multiple initial production scheduling plans into a preset optimization function for solution to obtain an optimized production scheduling plan, so as to schedule production according to the optimized production scheduling plan.

9. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the electronic device to implement the production scheduling optimization management method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Computer-readable instructions are stored thereon, and when the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the production scheduling optimization management method as described in any one of claims 1 to 7.

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