Transmission shaft assembly part scheduling method, system and electronic equipment based on multi-target constraint programming

Abstract

The application discloses a transmission shaft assembly part scheduling method, system and electronic equipment based on multi-target constraint programming, which comprises the following steps: obtaining annual scheduling plan data of a whole vehicle, constructing a first target function and a first constraint condition of an assembly annual plan; obtaining assembly annual plan data; constructing a second target function and a second constraint condition of a part annual plan; obtaining part annual plan data; constructing a third target function and a third constraint condition of an assembly monthly plan based on the assembly annual plan data; obtaining assembly monthly plan data according to the third target function and the third constraint condition; constructing a fourth target function and a fourth constraint condition of a part monthly plan; obtaining part monthly plan data; and outputting a scheduling optimization scheme according to the part monthly plan data. Compared with the prior art, the scheduling method provided by the application can quickly adjust the production plan according to market demand changes, reduce product quality fluctuations, and significantly improve production efficiency.

Description

Technical Field

[0001] This invention relates to the field of software development technology, and more specifically, to a method, system, and electronic device for scheduling the production of drive shaft assembly components based on multi-objective constraint programming. Background Technology

[0002] The driveshaft is a key component of a car's transmission system, responsible for transmitting power from the engine to the drive wheels. Its performance directly affects the vehicle's power output and driving stability. With the rapid development of the automotive industry, market demands for vehicle performance are increasing, making the assembly quality of the driveshaft particularly important. Driveshaft assembly involves the precise fitting of multiple components, including the shaft tube, universal joints, and bearings. The machining and assembly precision of these components directly affects the driveshaft's performance and lifespan.

[0003] Traditional driveshaft assembly scheduling methods rely heavily on manual experience, lacking a systematic and scientific approach, resulting in low production efficiency and resource waste. Under the demands of high-efficiency and high-precision production, traditional methods can no longer meet the development needs of the modern automotive manufacturing industry, exhibiting technical problems such as difficulty in quality control, serious resource waste, and low production efficiency.

[0004] Therefore, how to provide a production scheduling method for drive shaft assembly parts based on multi-objective constraint programming, which can overcome the above-mentioned technical problems, and achieve reasonable allocation and scheduling of parts in the drive shaft assembly process through optimization algorithms, thereby improving production efficiency, reducing production costs and ensuring product quality, has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method for scheduling the production of drive shaft assembly components based on multi-objective constraint programming. This method overcomes the technical issues by optimizing the algorithm to achieve reasonable allocation and scheduling of components during the drive shaft assembly process, thereby improving production efficiency, reducing production costs, and ensuring product quality.

[0006] The technical solution provided by this invention is as follows:

[0007] This invention provides a method for scheduling the production of drive shaft assembly components based on multi-objective constraint programming, including: acquiring annual production plan data for the whole vehicle, and constructing a first objective function and a first constraint condition for the annual assembly plan;

[0008] The assembly annual plan data is obtained based on the first objective function and the first constraint conditions;

[0009] Based on the assembly annual plan data, construct the second objective function and the second constraint conditions for the component annual plan;

[0010] The annual plan data for components is obtained based on the second objective function and the second constraint conditions;

[0011] Obtain the monthly production schedule data for the whole vehicle, and construct the third objective function and third constraint conditions for the monthly assembly plan based on the annual assembly plan data;

[0012] The assembly monthly plan data is obtained based on the third objective function and the third constraint conditions;

[0013] Based on the assembly monthly plan data, construct the fourth objective function and the fourth constraint condition for the component monthly plan;

[0014] The monthly component plan data is obtained based on the fourth objective function and the fourth constraint conditions;

[0015] Based on the monthly plan data for the components, output an optimization plan.

[0016] Furthermore, in a preferred embodiment of the present invention, the step of "obtaining annual vehicle production schedule data" specifically includes the following steps: obtaining annual vehicle schedule data; performing a first preprocessing on the annual vehicle schedule data to obtain annual vehicle production schedule data.

[0017] Furthermore, the first preprocessing includes: identifying the model data in the annual vehicle plan data.

[0018] Furthermore, in a preferred embodiment of the present invention, the step of "obtaining monthly vehicle production schedule data" specifically includes the following steps: obtaining monthly vehicle schedule data; performing a second preprocessing on the monthly vehicle schedule data to obtain monthly vehicle production schedule data.

[0019] Furthermore, the second preprocessing includes: identifying the model data and corresponding component data in the monthly vehicle plan data.

[0020] Furthermore, in a preferred embodiment of the present invention, the step of "obtaining assembly annual plan data based on the first objective function and the first constraint condition" specifically includes:

[0021] Based on the first constraint, the first objective function is optimized to obtain the assembly annual plan data;

[0022] The step of "obtaining the annual plan data for components based on the second objective function and the second constraint condition" includes the following steps:

[0023] Based on the second constraint, the second objective function is optimized to obtain the annual plan data for parts;

[0024] The step of "obtaining the assembly monthly plan data based on the third objective function and the third constraint condition" specifically includes the following steps:

[0025] Based on the third constraint, the third objective function is optimized to obtain the assembly monthly plan data;

[0026] The step of "obtaining the monthly component plan data based on the fourth objective function and the fourth constraint condition" includes the following steps:

[0027] Based on the fourth constraint, the fourth objective function is optimized to obtain the monthly plan data for parts.

[0028] Furthermore, in a preferred embodiment of the present invention, the step of "constructing the first objective function and first constraint conditions for the annual assembly plan" specifically includes the following steps:

[0029] Extract the production relationship based on vehicle inventory upper and lower limits, attendance days, overtime days, and duty priority.

[0030] Identifying that maximizing duty priority is inversely proportional to production capacity, the decision variables for the first objective function are minimizing overtime days and total output. The first objective function is constructed as follows:

[0031]

[0032] Based on the requirements for vehicle inventory management, the first constraint is as follows:

[0033] st:S min ≤s i ≤S max

[0034] Where n is the number of months, o i Let d be the number of overtime days in the i-th month. i Let p be the number of attendance days in the i-th month. i Let be the daily production capacity for month i, α be the weight of overtime days, β be the weight of total output, and s be the daily production capacity for month i. i Let S be the inventory level for the i-th month. min S is the minimum inventory threshold. max This is the maximum inventory threshold.

[0035] Furthermore, in a preferred embodiment of the present invention, the "construction of the second objective function and second constraint conditions for the annual component plan" specifically includes the following steps:

[0036] Extract the production relationship of upper and lower limits of component inventory, overtime days, and monthly component inventory;

[0037] The decision variables for the second objective function are minimizing the number of overtime days and the difference between monthly inventory and the mean of the upper and lower inventory limits. The second objective function is constructed as follows:

[0038]

[0039] Based on the requirements for parts inventory management, the second constraint is as follows:

[0040] st:S min ≤s i ≤S max

[0041] Where n is the number of months, m is the number of parts, and o ij Let be the number of overtime days for the j-th component in month i, α be the weight of the overtime days, β be the weight of the total output, and s be the weight of the total output. ij S represents the inventory level of the j-th component in month i. minj S is the minimum inventory threshold for the j-th component. maxj Let be the maximum inventory threshold for the j-th component.

[0042] Furthermore, in a preferred embodiment of the present invention, the step of "constructing the third objective function and third constraint condition for the monthly vehicle assembly plan" specifically includes the following steps:

[0043] Based on the monthly production plan data for the complete vehicle, the production relationships are extracted as follows: model type, model capacity gap, fluctuation of the difference between actual and ideal inventory, model service priority, and model switching frequency.

[0044] The third objective function is constructed as follows:

[0045]

[0046] Where m is the number of different models, n is the number of days in the month, and b ij To address the production capacity shortfall of model i on day j, s ij c is the absolute value of the difference between the actual inventory and the ideal inventory of model i on day j. j q represents the number of times all device models were switched on day j. j For the duty priority of day j, o j Let be the overtime hours on day j, and α, β, γ, δ, and ε be the elasticity coefficients of the five objective functions, respectively.

[0047] Based on inventory constraints, switching frequency constraints, and daily service capacity constraints, the third constraint is as follows:

[0048] store i =prod i +store i-1 -sale i

[0049] Where i represents the i-th month, and when i is 1, the inventory is the initial inventory input.

[0050] The total number of switches per week is less than or equal to 3:

[0051]

[0052] Where a to b are dates within the same week, c j This is the total number of switches this week;

[0053] The total production of all aircraft types on that day equals the daily production capacity of the day's operations.

[0054]

[0055] Where cap is the daily workload of the day, and p is an input constant. i This represents the output of the i-th model on that day.

[0056] Furthermore, in a preferred embodiment of the present invention, the "construction of the fourth objective function and fourth constraint condition for the monthly component plan" specifically includes the following steps:

[0057] Based on the monthly assembly plan data, the production relationships of machine type, component type, component capacity gap, fluctuation of the difference between actual and ideal component inventory, component service priority, and component changeover frequency are extracted.

[0058] The fourth objective function is constructed as follows:

[0059]

[0060] Where m is the number of different machine models, n is the number of days in the month, p is the number of parts types, and b ijk To address the production capacity shortfall of model i on day j, s ijk c is the absolute value of the difference between the actual inventory and the ideal inventory of model i on day j. jk q represents the number of times all device models were switched on day j. jk For the duty priority of day j, o jk Let be the overtime hours on day j, and α, β, γ, δ, and ε be the elasticity coefficients of the five objective functions, respectively.

[0061] Furthermore, the present invention also provides a production scheduling system for assembling parts based on a drive shaft, which is used to perform the production scheduling method for assembling parts based on a drive shaft as described above;

[0062] The system includes:

[0063] The first construction module is used to construct a first objective function and a first constraint condition for the annual assembly plan, a second objective function and a second constraint condition for the annual component plan, a third objective function and a third constraint condition for the monthly vehicle assembly plan, and a fourth objective function and a fourth constraint condition for the monthly component plan.

[0064] The first analysis module is used to obtain assembly annual plan data according to the first objective function and the first constraint, obtain component annual plan data according to the second objective function and the second constraint, obtain assembly monthly plan data according to the third objective function and the third constraint, and obtain component monthly plan data according to the fourth objective function and the fourth constraint.

[0065] The first execution module is used to output a troubleshooting and optimization plan based on the monthly plan data of the parts.

[0066] Furthermore, the present invention also provides an electronic device, comprising:

[0067] A computer program for executing the method of scheduling assembly parts based on a drive shaft as described above;

[0068] A memory for storing the computer program;

[0069] A processor for executing the computer program.

[0070] This invention provides a method, system, and electronic device for scheduling the production of driveshaft assembly components based on multi-objective constraint programming. Compared with existing technologies, the method includes: acquiring annual production plan data for the entire vehicle; constructing a first objective function and first constraints for the annual assembly plan; obtaining annual assembly plan data based on the first objective function and first constraints; constructing a second objective function and second constraints for the annual component plan based on the annual assembly plan data; obtaining annual component plan data based on the second objective function and second constraints; acquiring monthly production plan data for the entire vehicle; constructing a third objective function and third constraints for the monthly assembly plan based on the annual assembly plan data; obtaining monthly assembly plan data based on the third objective function and third constraints; and obtaining monthly assembly plan data based on the monthly assembly plan data. This invention constructs a fourth objective function and a fourth constraint condition for the monthly component plan; obtains the monthly component plan data based on the fourth objective function and the fourth constraint condition; and outputs a troubleshooting and optimization scheme based on the monthly component plan data. Compared with the prior art, the systematic and scientific production scheduling method provided by this invention can quickly adjust the production plan according to changes in market demand, has high flexibility, achieves reasonable allocation and scheduling of resources through optimization algorithms, reduces resource waste, improves product quality stability through precise production scheduling, reduces idle time of equipment and workers on the production line through scientific production scheduling, optimizes the processing and assembly process of parts, reduces the backlog of raw materials and semi-finished products, lowers inventory costs, more effectively controls quality, reduces product quality fluctuations, and significantly improves production efficiency. Attached Figure Description

[0071] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0072] Figure 1 A flowchart illustrating the steps of a multi-objective constraint programming-based method for scheduling the production of drive shaft assembly components, as provided in an embodiment of the present invention. Detailed Implementation

[0073] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0074] It should be noted that when a component is referred to as "fixed to" or "set on" another component, it can be directly on or indirectly set on the other component; when a component is referred to as "connected to" another component, it can be directly connected to or indirectly connected to the other component.

[0075] It should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "first", "second", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention.

[0076] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" or "several" means two or more, unless otherwise explicitly specified.

[0077] It should be noted that the structures, proportions, sizes, etc., shown in the accompanying drawings of this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed in the specification, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0078] like Figure 1As shown in the embodiments of the present invention, a method, system, and electronic device for scheduling the assembly of drive shaft components based on multi-objective constraint programming are provided. The method includes the following steps: acquiring annual production plan data for the entire vehicle; constructing a first objective function and first constraint conditions for the annual assembly plan; obtaining annual assembly plan data based on the first objective function and the first constraint conditions; constructing a second objective function and second constraint conditions for the annual component plan based on the annual assembly plan data; obtaining annual component plan data based on the second objective function and the second constraint conditions; acquiring monthly production plan data for the entire vehicle; constructing a third objective function and third constraint conditions for the monthly assembly plan based on the annual assembly plan data; obtaining monthly assembly plan data based on the third objective function and the third constraint conditions; and obtaining monthly assembly plan data based on the monthly assembly plan data. This invention constructs a fourth objective function and a fourth constraint condition for the monthly component plan; obtains the monthly component plan data based on the fourth objective function and the fourth constraint condition; and outputs a troubleshooting and optimization scheme based on the monthly component plan data. Compared with the prior art, the systematic and scientific production scheduling method provided by this invention can quickly adjust the production plan according to changes in market demand, has high flexibility, achieves reasonable allocation and scheduling of resources through optimization algorithms, reduces resource waste, improves product quality stability through precise production scheduling, reduces idle time of equipment and workers on the production line through scientific production scheduling, optimizes the processing and assembly process of parts, reduces the backlog of raw materials and semi-finished products, lowers inventory costs, more effectively controls quality, reduces product quality fluctuations, and significantly improves production efficiency.

[0079] Specifically, in the embodiments of the present invention, the "obtaining annual vehicle production schedule data" specifically includes the following steps: obtaining annual vehicle plan data; performing a first preprocessing on the annual vehicle plan data to obtain annual vehicle production schedule data;

[0080] Furthermore, the first preprocessing involves identifying the model data in the annual vehicle plan data.

[0081] The annual data preprocessing process begins by cleaning the raw input data, removing duplicates, errors, or invalid data, and integrating data from different data sources to ensure data integrity and consistency. Key features that influence production scheduling, such as vehicle models, component requirements, and inventory thresholds, are extracted from the raw data. Feature selection techniques are used to retain features that significantly contribute to optimizing the objective function while eliminating irrelevant or redundant features. The data is then converted to a unified format and unit to eliminate inconsistencies and is standardized to ensure comparability and calculability between different features. This annual data preprocessing helps build more accurate and reliable predictive models, providing strong support for the development of annual assembly and component plans.

[0082] Specifically, in an embodiment of the present invention, the "acquiring monthly vehicle production schedule data" includes the following steps: acquiring monthly vehicle schedule data; performing a second preprocessing on the monthly vehicle schedule data to obtain monthly vehicle production schedule data.

[0083] Furthermore, the second preprocessing involves identifying the model data and corresponding component data in the monthly vehicle plan data.

[0084] Among them, the monthly planning data for the whole vehicle can help optimize the target function, help respond quickly to changes in market and production demand, make the production schedule more flexible and adjustable, improve production efficiency and reduce costs.

[0085] Specifically, in embodiments of the present invention, the step of "obtaining assembly annual plan data based on the first objective function and the first constraint condition" specifically includes:

[0086] Based on the first constraint, the first objective function is optimized to obtain the assembly annual plan data;

[0087] The step of "obtaining the annual plan data for components based on the second objective function and the second constraint condition" includes the following steps:

[0088] Based on the second constraint, the second objective function is optimized to obtain the annual plan data for parts;

[0089] The step of "obtaining the assembly monthly plan data based on the third objective function and the third constraint condition" specifically includes the following steps:

[0090] Based on the third constraint, the third objective function is optimized to obtain the assembly monthly plan data;

[0091] The step of "obtaining the monthly component plan data based on the fourth objective function and the fourth constraint condition" includes the following steps:

[0092] Based on the fourth constraint, the fourth objective function is optimized to obtain the monthly plan data for parts.

[0093] Specifically, in the embodiments of the present invention, the step of "constructing the first objective function and the first constraint condition of the assembly annual plan" specifically includes the following steps:

[0094] Extract the production relationship based on vehicle inventory upper and lower limits, attendance days, overtime days, and duty priority.

[0095] Identifying that maximizing duty priority is inversely proportional to production capacity, the decision variables for the first objective function are minimizing overtime days and total output. The first objective function is constructed as follows:

[0096]

[0097] Based on the requirements for vehicle inventory management, the first constraint is as follows:

[0098] st:S min ≤s i ≤S max

[0099] Where n is the number of months, o i Let d be the number of overtime days in the i-th month. i Let p be the number of attendance days in the i-th month. i Let be the daily production capacity for month i, α be the weight of overtime days, β be the weight of total output, and s be the daily production capacity for month i. i Let S be the inventory level for the i-th month. min S is the minimum inventory threshold. max This is the maximum inventory threshold.

[0100] Specifically, in the embodiments of the present invention, the "construction of the second objective function and second constraint condition for the annual plan of components" specifically includes the following steps:

[0101] Extract the production relationship of upper and lower limits of component inventory, overtime days, and monthly component inventory;

[0102] The decision variables for the second objective function are minimizing the number of overtime days and the difference between monthly inventory and the mean of the upper and lower inventory limits. The second objective function is constructed as follows:

[0103]

[0104] Based on the requirements for parts inventory management, the second constraint is as follows:

[0105] st:S min ≤s i ≤S max

[0106] Where n is the number of months, m is the number of parts, and o ij Let be the number of overtime days for the j-th component in month i, α be the weight of the overtime days, β be the weight of the total output, and s be the weight of the total output. ij S represents the inventory level of the j-th component in month i. minj S is the minimum inventory threshold for the j-th component. maxj Let be the maximum inventory threshold for the j-th component.

[0107] Specifically, in the embodiments of the present invention, the "construction of the third objective function and third constraint condition for the monthly vehicle assembly plan" specifically includes the following steps:

[0108] Based on the monthly production plan data for the complete vehicle, the production relationships are extracted as follows: model type, model capacity gap, fluctuation of the difference between actual and ideal inventory, model service priority, and model switching frequency.

[0109] The third objective function is constructed as follows:

[0110]

[0111] Where m is the number of different models, n is the number of days in the month, and b ij To address the production capacity shortfall of model i on day j, s ij c is the absolute value of the difference between the actual inventory and the ideal inventory of model i on day j. j q represents the number of times all device models were switched on day j. j For the duty priority of day j, o j Let be the overtime hours on day j, and α, β, γ, δ, and ε be the elasticity coefficients of the five objective functions, respectively.

[0112] Based on inventory constraints, switching frequency constraints, and daily service capacity constraints, the third constraint is as follows:

[0113] store i =prod i +store i-1 -sale i

[0114] Where i represents the i-th month, and when i is 1, the inventory is the initial inventory input.

[0115] The total number of switches per week is less than or equal to 3:

[0116]

[0117] Where a to b are dates within the same week, c j This is the total number of switches this week;

[0118] The total production of all aircraft types on that day equals the daily production capacity of the day's operations.

[0119]

[0120] Where cap is the daily workload of the day, and p is an input constant. i This represents the output of the i-th model on that day.

[0121] Specifically, in the embodiments of the present invention, the "construction of the fourth objective function and fourth constraint condition for the monthly plan of components" specifically includes the following steps:

[0122] Based on the monthly assembly plan data, the production relationships of machine type, component type, component capacity gap, fluctuation of the difference between actual and ideal component inventory, component service priority, and component changeover frequency are extracted.

[0123] The fourth objective function is constructed as follows:

[0124]

[0125] Where m is the number of different machine models, n is the number of days in the month, p is the number of parts types, and b ijk To address the production capacity shortfall of model i on day j, s ijk c is the absolute value of the difference between the actual inventory and the ideal inventory of model i on day j. jk q represents the number of times all device models were switched on day j. jk For the duty priority of day j, o jk Let be the overtime hours on day j, and α, β, γ, δ, and ε be the elasticity coefficients of the five objective functions, respectively.

[0126] Furthermore, embodiments of the present invention also provide a production scheduling system for assembling parts based on a drive shaft, which is used to execute the production scheduling method for assembling parts based on a drive shaft as described above;

[0127] The system includes:

[0128] The first construction module is used to construct a first objective function and a first constraint condition for the annual assembly plan, a second objective function and a second constraint condition for the annual component plan, a third objective function and a third constraint condition for the monthly vehicle assembly plan, and a fourth objective function and a fourth constraint condition for the monthly component plan.

[0129] The first analysis module is used to obtain assembly annual plan data according to the first objective function and the first constraint, obtain component annual plan data according to the second objective function and the second constraint, obtain assembly monthly plan data according to the third objective function and the third constraint, and obtain component monthly plan data according to the fourth objective function and the fourth constraint.

[0130] The first execution module is used to output a troubleshooting and optimization plan based on the monthly plan data of the parts.

[0131] Furthermore, embodiments of the present invention also provide an electronic device, comprising:

[0132] A computer program for executing the method of scheduling assembly parts based on a drive shaft as described above;

[0133] A memory for storing the computer program;

[0134] A processor for executing the computer program.

[0135] More specifically, to achieve production scheduling for driveshaft component assembly, this embodiment of the invention divides the scheduling process into two stages: assembly and component production. Each stage is further divided into annual and monthly plans. The monthly plan depends on the annual plan. First, based on the overall vehicle production plan and the initial inventory, inventory threshold, and attendance calendar for relevant models, the daily production requirements for different models are obtained. Then, based on the constraints of the assembly process, the daily assembly plan for different models is output. The second stage, based on the assembly plan, determines the daily demand for each component. Based on production line and inventory constraints, the daily production plan for each component is output as the final output. Specifically, the following technical steps are involved:

[0136] Assembly Annual Plan: The assembly annual plan uses the vehicle's annual production schedule as the input for production demand. The main constraints considered are inventory limits, attendance days, overtime days, and duty priority. The objective function is to minimize overtime days and maximize duty priority. Duty priority is inversely proportional to production capacity; that is, the objective function can be transformed into minimizing overtime days and total output. The constructed model algorithm objective function and constraints are as follows:

[0137]

[0138] st:S min ≤s i ≤S max

[0139] Where n is the number of months, o i Let d be the number of overtime days in the i-th month. i Let p be the number of attendance days in the i-th month. i Let be the daily production capacity for month i, α be the weight of overtime days, β be the weight of total output, and s be the daily production capacity for month i. i Let S be the inventory level for the i-th month. min S is the minimum inventory threshold. max This is the maximum inventory threshold.

[0140] Component Annual Plan: The demand input for the component annual plan is the assembly plan output. Based on the assembly demand, the monthly production output of a specific component is output. The main constraints are the same as for assembly. The difference between the component annual plan and the assembly annual plan is the objective function. The objective function for the component is the minimum number of overtime days and the difference between the monthly inventory and the average of the upper and lower inventory limits. The objective function and constraints of the constructed model algorithm are as follows:

[0141]

[0142] st:S min ≤s i ≤S max

[0143] Where n is the number of months, m is the number of parts, and o ij Let be the number of overtime days for the j-th component in month i, α be the weight of the overtime days, β be the weight of the total output, and s be the weight of the total output. ij S represents the inventory level of the j-th component in month i. minj S is the minimum inventory threshold for the j-th component. maxj Let be the maximum inventory threshold for the j-th component.

[0144] Assembly Monthly Plan: The monthly assembly plan uses the vehicle's monthly plan as demand input to generate the monthly attendance plan and daily production plan. Simultaneously, it uses the monthly inventory of the annual assembly plan as a standard, limiting the standard difference between the monthly inventory and the corresponding annual inventory to no more than 3000. The monthly assembly scheduling includes multiple soft constraints, meaning there are no explicit target thresholds, but the goal is to achieve optimal values. For these soft constraints, this invention treats them as objective functions, unifying the measurement range and performing multi-objective optimization. This scheduling system has the following objectives: minimizing production gaps, ensuring the inventory ratio between different models is closer to the demand ratio, minimizing the number of model changes on a production line, using the highest priority shifts as much as possible, and minimizing overtime. The model for these system objectives is constructed as follows:

[0145]

[0146] Where m is the number of different models, n is the number of days in the month, and b ij To address the production capacity shortfall of model i on day j, s ij c is the absolute value of the difference between the actual inventory and the ideal inventory of model i on day j. j q represents the number of times all device models were switched on day j. j For the duty priority of day j, o jLet denot be the overtime hours on day j, and let α, β, γ, δ, and ε be the elasticity coefficients of the five objective functions. The purpose is to ensure that different objective functions are within the same measurement range. For example, the daily production capacity gap may be tens of thousands of units, and the daily switching frequency may be in the single digits. Optimizing the solution for this may lead to the result being more inclined to minimize the daily production capacity gap and ignore the switching frequency. Therefore, elasticity coefficients are needed to expand or contract the measurement range of different objective functions.

[0147] Constraint modeling begins by specifying the solution variables. The main variables are: monthly attendance days, total monthly production, attendance, and monthly excess inventory. The second step involves calculating the monthly inventory, including the aforementioned variables, based on the initial inventory data and the initialized variables. i =prod i +store i-1 -sale i Where i represents the i-th month, and when i is 1, the inventory is the initial inventory input. For the production scheduling of drive shafts, the following constraints apply:

[0148] The total number of switches per week is less than or equal to 3.

[0149]

[0150] Where a to b represent dates within the same week, c j This represents the total number of switches this week.

[0151] The total production of all aircraft types on that day equals the daily production capacity of the day's operations.

[0152]

[0153] Where cap is the daily workload of the day, and is an input constant, p i This represents the output of the i-th model on that day.

[0154] The third step is to solve the objective function. The optimal solution found within the set time range is obtained through a backtracking search algorithm. This solution is then used as the component requirement input for the next stage.

[0155] Component Monthly Planning: The component production scheduling phase is divided into five component production schedules. The demand data for each schedule is the result of the previous phase. Based on the planning data generated in the assembly phase, the demand data for corresponding parts is generated, and the initial inventory and inventory threshold data for the corresponding parts are input. The objective function also consists of the following parts: minimizing production gaps, making the inventory ratio between different models closer to the demand ratio, minimizing the number of model changes on a production line, using the highest priority shifts as much as possible, and minimizing overtime. The model for this system's objectives is constructed as follows:

[0156]

[0157] Where m is the number of different machine models, n is the number of days in the month, p is the number of parts types, and b ijk To address the production capacity shortfall of model i on day j, s ijk c is the absolute value of the difference between the actual inventory and the ideal inventory of model i on day j. jk q represents the number of times all device models were switched on day j. jk For the duty priority of day j, o jk Let denot be the overtime hours on day j, and let α, β, γ, δ, and ε be the elasticity coefficients of the five objective functions. The purpose is to ensure that different objective functions are within the same measurement range. For example, the daily production capacity gap may be tens of thousands of units, and the daily switching frequency may be in the single digits. Optimizing the solution for this may lead to the result being more inclined to minimize the daily production capacity gap and ignore the switching frequency. Therefore, elasticity coefficients are needed to expand or contract the measurement range of different objective functions.

[0158] In summary, the technical solutions involved in the embodiments of the present invention include the following key technical points:

[0159] First, the innovation of the production scheduling strategy: This invention proposes a production scheduling method based on drive shaft assembly components. This method uses scientific methods to rationally schedule the production of components during the drive shaft assembly process, thereby maximizing production efficiency and minimizing costs. Second, the multi-stage production scheduling model: The production scheduling process is divided into two stages: assembly and component production. Each stage is further divided into annual and monthly plans. This multi-stage production scheduling model is the core of this invention. Third, the application of optimization algorithms: Optimization algorithms such as backtracking search are used to find the optimal solution within a set time range, which is one of the key technologies of this invention. Fourth, multi-objective optimization: This invention considers multiple objective functions, including minimizing production gaps, balancing inventory and demand ratios, minimizing the number of machine switching operations on the production line, prioritizing the use of the highest-priority work, and minimizing overtime hours. Balancing and optimizing these objectives is another key point of this invention. Fifth, the application of elasticity coefficients: To ensure that different objective functions are within the same measurement range, this invention introduces elasticity coefficients to balance the importance of different objective functions. Sixth, the handling of soft constraints: This invention uses soft constraints as objective functions and performs multi-objective optimization after unifying the measurement range, which is an innovation of this invention. Seventh, Modeling of Constraints: This invention models in detail various constraints, including inventory constraints, production line changeover constraints, and daily capacity constraints. Accurate modeling of these constraints is crucial for achieving optimized production scheduling. Eighth, Data-Driven Decision-Making: This invention relies on data inputs such as vehicle production plans, inventory data, and attendance calendars, emphasizing the importance of data in decision-making. Compared to existing technologies, the systematic and scientific production scheduling method provided by this invention can quickly adjust production plans according to changes in market demand, exhibiting high flexibility. Through optimization algorithms, it achieves rational allocation and scheduling of resources, reducing resource waste. Precise production planning improves product quality stability. Scientific production scheduling reduces idle time for equipment and workers on the production line, optimizes the processing and assembly of parts, reduces the backlog of raw materials and semi-finished products, lowers inventory costs, more effectively controls quality, reduces product quality fluctuations, and significantly improves production efficiency. Compared with existing technologies, it has outstanding substantive features and significant technological advancements.

[0160] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for scheduling the production of drive shaft assembly components based on multi-objective constraint programming, characterized in that, The method includes the following steps: Obtain the annual production schedule data for the complete vehicle, and construct the first objective function and first constraint conditions for the annual assembly plan; The assembly annual plan data is obtained based on the first objective function and the first constraint conditions; Based on the assembly annual plan data, construct the second objective function and the second constraint conditions for the component annual plan; The annual plan data for components is obtained based on the second objective function and the second constraint conditions; Obtain the monthly production schedule data for the whole vehicle, and construct the third objective function and third constraint conditions for the monthly assembly plan based on the annual assembly plan data; The assembly monthly plan data is obtained based on the third objective function and the third constraint conditions; Based on the assembly monthly plan data, construct the fourth objective function and the fourth constraint condition for the component monthly plan; The monthly component plan data is obtained based on the fourth objective function and the fourth constraint conditions; Output an optimization plan based on the monthly component plan data; Extract the production relationship based on vehicle inventory upper and lower limits, attendance days, overtime days, and duty priority. Identifying that maximizing duty priority is inversely proportional to production capacity, the decision variables for the first objective function are minimizing overtime days and total output. The first objective function is constructed as follows: Based on the requirements for vehicle inventory management, the first constraint is as follows: Where n is the number of months. Let i be the number of overtime days in the i-th month. Let be the number of attendance days in the i-th month. Let this be the daily production capacity for month i. As for the weighting of overtime days, This is the weighted value for total output. Let be the inventory level for month i. Minimum inventory threshold, The maximum inventory threshold; Extract the production relationship of upper and lower limits of component inventory, overtime days, and monthly component inventory; The decision variables for the second objective function are minimizing the number of overtime days and the difference between monthly inventory and the mean of the upper and lower inventory limits. The second objective function is constructed as follows: Based on the requirements for parts inventory management, the second constraint is as follows: Where n is the number of months and m is the number of parts. The number of overtime days for the j-th component in month i. As for the weighting of overtime days, This is the weighted value for total output. Let i be the inventory level of the j-th component in month i. Let j be the minimum inventory threshold for the j-th component. Let j be the maximum inventory threshold for the j-th component; Based on the monthly production plan data for the complete vehicle, the production relationships are extracted as follows: model type, model capacity gap, fluctuation of the difference between actual and ideal inventory, model service priority, and model switching frequency. The third objective function is constructed as follows: Where e represents the number of different models, and w represents the number of days in the month. This is to address the production capacity shortfall for the H model on day l. This represents the absolute value of the difference between the actual inventory and the ideal inventory of model h on day l. This represents the number of times all device models were switched on day l. Aircraft type service priority for day l. The overtime hours for day l. These are the elasticity coefficients of production relationships based on aircraft type, aircraft capacity gap, fluctuations in the difference between actual and ideal inventory for each aircraft type, aircraft service priority, and number of aircraft switchovers. Based on inventory constraints, switching frequency constraints, and daily service capacity constraints, the third constraint is as follows: Where i represents the i-th month, and when i is 1, the inventory is the initial inventory input. The total number of switches per week is less than or equal to 3: Where a to b represent dates within the same week. This is the total number of switches this week; The total production of all aircraft types on that day equals the daily production capacity of the day's operations. Where cap is the daily workload of the day, and is an input constant. This represents the output of the h-th model on that day. Based on the monthly assembly plan data, the production relationships of machine type, component type, component capacity gap, fluctuation of the difference between actual and ideal component inventory, component service priority, and component changeover frequency are extracted; the fourth objective function is constructed as follows: Where e represents the number of different machine models, w represents the number of days in the month, and p represents the number of parts types. This is to address the production capacity shortfall for the H model on day l. This represents the absolute value of the difference between the actual inventory and the ideal inventory of model h on day l. This represents the number of times all device models were switched on day l. Prioritize the parts service for day l. The overtime hours for day l. These are the elasticity coefficients of production relationships based on aircraft type, component type, component capacity gap, fluctuation of the difference between actual and ideal component inventory, component service priority, and component changeover frequency.

2. The method for scheduling the production of drive shaft assembly components based on multi-objective constraint programming according to claim 1, characterized in that, The "obtaining annual vehicle production plan data" specifically includes the following steps: obtaining annual vehicle plan data; performing a first preprocessing on the annual vehicle plan data to obtain annual vehicle production plan data.

3. The method for scheduling the production of drive shaft assembly components based on multi-objective constraint programming according to claim 2, characterized in that, The "obtaining monthly vehicle production schedule data" specifically includes the following steps: obtaining monthly vehicle production schedule data; performing a second preprocessing on the monthly vehicle production schedule data to obtain monthly vehicle production schedule data.

4. A production scheduling system for assembly parts based on a drive shaft, characterized in that, This system is used to execute the multi-objective constraint programming-based production scheduling method for drive shaft assembly components as described in any one of claims 1 to 3; The system includes: The first construction module is used to construct a first objective function and a first constraint condition for the annual assembly plan, a second objective function and a second constraint condition for the annual component plan, a third objective function and a third constraint condition for the monthly vehicle assembly plan, and a fourth objective function and a fourth constraint condition for the monthly component plan. The first analysis module is used to obtain assembly annual plan data according to the first objective function and the first constraint, obtain component annual plan data according to the second objective function and the second constraint, obtain assembly monthly plan data according to the third objective function and the third constraint, and obtain component monthly plan data according to the fourth objective function and the fourth constraint. The first execution module is used to output a troubleshooting and optimization plan based on the monthly plan data of the parts.

5. An electronic device, characterized in that, include: A computer program, the computer program being used to execute the drive shaft assembly component scheduling method based on multi-objective constraint programming as described in any one of claims 1 to 3; A memory for storing the computer program; A processor for executing the computer program.

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