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

Abstract

The invention discloses a transmission shaft assembly part production scheduling method and system based on multi-objective constraint planning and electronic equipment, and the method comprises the steps: obtaining the annual production scheduling plan data of a whole vehicle, and constructing a first objective function and a first constraint condition of an assembly annual plan; obtaining assembly annual plan data; constructing a second objective function and a second constraint condition of the component annual plan; obtaining component annual plan data; constructing a third objective function and a third constraint condition of the monthly assembly plan based on the annual assembly plan data; obtaining assembly monthly plan data according to a third objective function and a third constraint condition; constructing a fourth objective function and a fourth constraint condition of the monthly plan of the component; monthly plan data of the component is obtained; compared with the prior art, the production scheduling method provided by the embodiment of the invention has the advantages that the production plan can be quickly adjusted according to the market demand change, the product quality fluctuation is reduced, and the production efficiency is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of software development, and in particular to a method, system and electronic equipment for scheduling production of transmission shaft assembly parts based on multi-objective constraint programming. Background Art

[0002] The drive shaft is a key component in the automobile transmission system, responsible for transmitting the engine power to the drive wheels. Its performance directly affects the power output and driving stability of the vehicle. With the rapid development of the automobile industry, the market has higher and higher requirements for automobile performance, and the assembly quality of the drive shaft has become particularly important. The assembly of the drive shaft involves the precise matching of multiple parts, including shaft tubes, universal joints, bearings and other key parts. The processing and assembly accuracy of these parts is directly related to the performance and life of the drive shaft.

[0003] The traditional transmission shaft assembly production scheduling method mainly relies on manual experience, lacks systematicity and scientificity, and leads to low production efficiency and waste of resources. Under the high-efficiency and high-precision production requirements, the traditional method can no longer meet the development needs of the modern automobile manufacturing industry, and there are technical problems such as difficult quality control, serious waste of resources, and low production efficiency.

[0004] Therefore, how to provide a production scheduling method for drive shaft assembly parts based on multi-objective constraint planning, which can overcome the above-mentioned technical problems, realize the reasonable allocation and scheduling of parts in the drive shaft assembly process through optimization algorithm, improve production efficiency, reduce production costs and ensure product quality, has become a technical problem that needs to be urgently solved by technical personnel in this field. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method for scheduling the production of transmission shaft assembly parts based on multi-objective constraint programming, which can overcome the above technical problems and realize the reasonable allocation and scheduling of parts in the transmission shaft assembly process through optimization algorithms, thereby improving production efficiency, reducing production costs and ensuring product quality.

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

[0007] The present invention provides a transmission shaft assembly parts production scheduling method based on multi-objective constraint programming, comprising: obtaining annual production scheduling data of a complete vehicle, constructing a first objective function and a first constraint condition of an annual assembly plan;

[0008] Obtaining assembly annual planning data according to the first objective function and the first constraint condition;

[0009] According to the assembly annual plan data, construct a second objective function and a second constraint condition for the component annual plan;

[0010] Obtaining annual component planning data according to the second objective function and the second constraint condition;

[0011] Acquire the monthly production plan data of the whole vehicle, and construct the third objective function and the third constraint condition of the monthly assembly plan based on the annual assembly plan data;

[0012] Obtaining assembly monthly planning data according to the third objective function and the third constraint condition;

[0013] According to the assembly monthly plan data, construct a fourth objective function and a fourth constraint condition of the parts monthly plan;

[0014] Obtaining parts monthly planning data according to the fourth objective function and the fourth constraint condition;

[0015] Output and check the optimization plan based on the monthly planned data of the parts.

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

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

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

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

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

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

[0022] The “obtaining annual component planning data according to the second objective function and the second constraint condition” comprises the following steps:

[0023] Based on the second constraint condition, the second objective function is optimized to obtain annual component planning data;

[0024] The “obtaining assembly monthly planning data according to the third objective function and the third constraint condition” specifically comprises the following steps:

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

[0026] The “obtaining monthly component planning data according to the fourth objective function and the fourth constraint condition” comprises the following steps:

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

[0028] Furthermore, in a preferred embodiment of the present invention, the “constructing the first objective function and the first constraint condition of the annual assembly plan” specifically includes the following steps:

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

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

[0031]

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

[0033] st:S min ≤s i ≤S max

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

[0035] Furthermore, in a preferred embodiment of the present invention, the “constructing the second objective function and the second constraint condition of the annual component plan” specifically includes the following steps:

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

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

[0038]

[0039] Based on the requirements of 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, o ij is the number of overtime days for the jth component in month i, α is the weight of the overtime days, β is the weight of the total output, s ij is the inventory of the jth component in month i, S minj is the minimum inventory threshold of the jth component, S maxj is the maximum inventory threshold of the jth component.

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

[0043] Extracting the production relationship of model type, model capacity gap, difference fluctuation between actual inventory and ideal inventory, model service priority, and model switching frequency based on the vehicle monthly production plan data;

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

[0045]

[0046] Among them, m is the number of different models, n is the number of days in the month, and b ij is the production capacity gap of model i on day j, s ij is the absolute value of the difference between the actual inventory and the ideal inventory of model i on day j, c j is the number of switching times of all models on day j, q j is the duty priority of the jth day, o j is the overtime hours of the jth day, α, β, γ, δ, ε are the elasticity coefficients of the five objective functions respectively;

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

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

[0049] Where i is the i-th month, when i is 1, the inventory is the initial inventory entered;

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

[0051]

[0052] Among them, a to b are dates in the same week, c j That is the total number of switches this week;

[0053] The total production of all models on that day is equal to the daily production capacity of the day's service

[0054]

[0055] Among them, cap is the daily capacity of the service on that day, which is the input constant, and p i is the output of the i-th model on that day.

[0056] Furthermore, in a preferred embodiment of the present invention, the “constructing the fourth objective function and the fourth constraint condition of the parts monthly plan” specifically includes the following steps:

[0057] Extracting the production relationship of machine type, component type, component capacity gap, difference fluctuation between actual component inventory and ideal component inventory, component service priority, and component switching frequency according to the monthly assembly plan data;

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

[0059]

[0060] Among them, m is the number of different models, n is the number of days in the month, p is the number of component types, and b ijk is the production capacity gap of model i on day j, s ijk is the absolute value of the difference between the actual inventory and the ideal inventory of model i on day j, c jk is the number of switching times of all models on day j, q jk is the duty priority of the jth day, o jk is the overtime hours of the jth day, and α, β, γ, δ, and ε are the elasticity coefficients of the five objective functions respectively.

[0061] In addition, the present invention also provides a production scheduling system based on transmission shaft assembly parts, which is used to execute the production scheduling method based on transmission shaft assembly parts as described above;

[0062] The system includes:

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

[0064] a first analysis module, the first analysis module being used to obtain annual assembly plan data according to the first objective function and the first constraint condition, obtain annual component plan data according to the second objective function and the second constraint condition, obtain monthly assembly plan data according to the third objective function and the third constraint condition, and obtain monthly component plan data according to the fourth objective function and the fourth constraint condition;

[0065] A first execution module is used to output a screening optimization plan according to the parts monthly plan data.

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

[0067] A computer program, the computer program being used to execute the method for scheduling production of transmission shaft assembly parts as described above;

[0068] A memory, the memory being used to store the computer program;

[0069] A processor is used to execute the computer program.

[0070] The present invention provides a transmission shaft assembly parts scheduling method, system and electronic device based on multi-objective constraint programming. Compared with the prior art, the method includes: obtaining annual production scheduling data for a complete vehicle, constructing a first objective function and a first constraint condition for an annual assembly plan; obtaining annual assembly plan data according to the first objective function and the first constraint condition; constructing a second objective function and a second constraint condition for an annual component plan according to the annual assembly plan data; obtaining annual component plan data according to the second objective function and the second constraint condition; obtaining monthly production scheduling data for a complete vehicle, and constructing a third objective function and a third constraint condition for a monthly assembly plan based on the annual assembly plan data; obtaining monthly assembly plan data according to the third objective function and the third constraint condition; , construct the fourth objective function and the fourth constraint condition of the component monthly plan; obtain the component monthly plan data according to the fourth objective function and the fourth constraint condition; output the optimization plan according to the component monthly plan data. Compared with the prior art, the systematic and scientific production scheduling method provided by the embodiment of the present invention can quickly adjust the production plan according to the changes in market demand, and has high flexibility. Through the optimization algorithm, it realizes the reasonable allocation and scheduling of resources, reduces the waste of resources, improves the stability of product quality through precise production scheduling, reduces the idle time of equipment and workers on the production line through scientific scheduling, optimizes the processing and assembly process of parts, reduces the backlog of raw materials and semi-finished products, reduces inventory costs, more effectively controls quality, reduces product quality fluctuations, and significantly improves production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0072] Figure 1 A flowchart of the steps of a method for scheduling production of transmission shaft assembly parts based on multi-objective constraint programming provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0073] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present invention.

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

[0075] It should be understood that the terms "length", "width", "up", "down", "front", "back", "first", "second", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, and 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, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0076] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" and "several" is two or more, unless otherwise clearly and specifically defined.

[0077] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0078] like Figure 1As shown, the transmission shaft assembly parts scheduling method, system and electronic device based on multi-objective constraint programming provided by the embodiments of the present invention, wherein the method includes the steps of: obtaining the annual production scheduling data of the whole vehicle, constructing the first objective function and the first constraint condition of the annual assembly plan; obtaining the annual assembly plan data according to the first objective function and the first constraint condition; constructing the second objective function and the second constraint condition of the annual component plan according to the annual assembly plan data; obtaining the annual component plan data according to the second objective function and the second constraint condition; obtaining the monthly production scheduling data of the whole vehicle, and constructing the third objective function and the third constraint condition of the monthly assembly plan based on the annual assembly plan data; obtaining the monthly assembly plan data according to the third objective function and the third constraint condition; , construct the fourth objective function and the fourth constraint condition of the component monthly plan; obtain the component monthly plan data according to the fourth objective function and the fourth constraint condition; output the optimization plan according to the component monthly plan data. Compared with the prior art, the systematic and scientific production scheduling method provided by the embodiment of the present invention can quickly adjust the production plan according to the changes in market demand, and has high flexibility. Through the optimization algorithm, it realizes the reasonable allocation and scheduling of resources, reduces the waste of resources, improves the stability of product quality through precise production scheduling, reduces the idle time of equipment and workers on the production line through scientific scheduling, optimizes the processing and assembly process of parts, reduces the backlog of raw materials and semi-finished products, reduces inventory costs, more effectively controls quality, reduces product quality fluctuations, and significantly improves production efficiency.

[0079] Specifically, in an embodiment of the present invention, the “obtaining annual production plan data for a complete vehicle” specifically includes the following steps: obtaining annual production plan data for a complete vehicle; performing a first preprocessing on the annual production plan data for the complete vehicle to obtain annual production plan data for the complete vehicle;

[0080] Furthermore, the first preprocessing is to identify the model data in the vehicle annual plan data.

[0081] Among them, annual data preprocessing will first clean the input raw data, remove duplicate, erroneous or invalid data, integrate data from different data sources, ensure data integrity and consistency, and extract key features that affect production scheduling from the raw data, such as vehicle models, component requirements, inventory thresholds, etc. Through feature selection technology, retain features that have important contributions to the optimization of the objective function, eliminate irrelevant or redundant features, convert data into a unified format and unit, eliminate data inconsistencies, and standardize data to make different features comparable and calculable. Annual data preprocessing helps to build a more accurate and reliable prediction model, and provide strong support for the formulation of annual assembly plans and component annual plans.

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

[0083] Furthermore, the second preprocessing is to identify the model data and corresponding component data in the vehicle monthly plan data.

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

[0085] Specifically, in an embodiment of the present invention, the “obtaining annual assembly plan data according to the first objective function and the first constraint condition” specifically includes:

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

[0087] The “obtaining annual component planning data according to the second objective function and the second constraint condition” comprises the following steps:

[0088] Based on the second constraint condition, the second objective function is optimized to obtain annual component planning data;

[0089] The “obtaining assembly monthly planning data according to the third objective function and the third constraint condition” specifically comprises the following steps:

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

[0091] The “obtaining monthly component planning data according to the fourth objective function and the fourth constraint condition” comprises the following steps:

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

[0093] Specifically, in an embodiment of the present invention, the “constructing the first objective function and the first constraint condition of the annual assembly plan” specifically includes the following steps:

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

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

[0096]

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

[0098] st:S min ≤s i ≤S max

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

[0100] Specifically, in an embodiment of the present invention, the “constructing the second objective function and the second constraint condition of the annual component plan” specifically includes the following steps:

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

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

[0103]

[0104] Based on the requirements of 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, o ij is the number of overtime days for the jth component in month i, α is the weight of the overtime days, β is the weight of the total output, s ij is the inventory of the jth component in month i, S minj is the minimum inventory threshold of the jth component, S maxj is the maximum inventory threshold of the jth component.

[0107] Specifically, in an embodiment of the present invention, the “constructing the third objective function and the third constraint condition of the vehicle assembly monthly plan” specifically includes the following steps:

[0108] Extracting the production relationship of model type, model capacity gap, difference fluctuation between actual inventory and ideal inventory, model service priority, and model switching frequency based on the vehicle monthly production plan data;

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

[0110]

[0111] Among them, m is the number of different models, n is the number of days in the month, and b ij is the production capacity gap of model i on day j, s ij is the absolute value of the difference between the actual inventory and the ideal inventory of model i on day j, c j is the number of switching times of all models on day j, q j is the duty priority of the jth day, o j is the overtime hours of the jth day, α, β, γ, δ, ε are the elasticity coefficients of the five objective functions respectively;

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

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

[0114] Where i is the i-th month, when i is 1, the inventory is the initial inventory entered;

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

[0116]

[0117] Among them, a to b are dates in the same week, c j That is the total number of switches this week;

[0118] The total production of all models on that day is equal to the daily production capacity of the day's service

[0119]

[0120] Among them, cap is the daily capacity of the service on that day, which is the input constant, and p i is the output of the i-th model on that day.

[0121] Specifically, in an embodiment of the present invention, the “constructing the fourth objective function and the fourth constraint condition of the parts monthly plan” specifically includes the following steps:

[0122] Extracting the production relationship of machine type, component type, component capacity gap, difference fluctuation between actual component inventory and ideal component inventory, component service priority, and component switching frequency according to the monthly assembly plan data;

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

[0124]

[0125] Among them, m is the number of different models, n is the number of days in the month, p is the number of component types, and b ijk is the production capacity gap of model i on day j, s ijk is the absolute value of the difference between the actual inventory and the ideal inventory of model i on day j, c jk is the number of switching times of all models on day j, q jk is the duty priority of the jth day, o jk is the overtime hours of the jth day, and α, β, γ, δ, and ε are the elasticity coefficients of the five objective functions respectively.

[0126] In addition, an embodiment of the present invention further provides a production scheduling system based on transmission shaft assembly parts, the system is used to execute the production scheduling method based on transmission shaft assembly parts as described above;

[0127] The system includes:

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

[0129] a first analysis module, the first analysis module being used to obtain annual assembly plan data according to the first objective function and the first constraint condition, obtain annual component plan data according to the second objective function and the second constraint condition, obtain monthly assembly plan data according to the third objective function and the third constraint condition, and obtain monthly component plan data according to the fourth objective function and the fourth constraint condition;

[0130] A first execution module is used to output a screening optimization plan according to the parts monthly plan data.

[0131] In addition, an embodiment of the present invention further provides an electronic device, including:

[0132] A computer program, the computer program being used to execute the method for scheduling production of transmission shaft assembly parts as described above;

[0133] A memory, the memory being used to store the computer program;

[0134] A processor is used to execute the computer program.

[0135] To elaborate more specifically, in order to realize the assembly and production scheduling of transmission shaft parts, the embodiment of the present invention divides the production scheduling process into two stages: assembly and parts. These two stages are further divided into two parts: annual plan and monthly plan. The monthly plan will depend on the result of the annual plan. First, according to the production schedule of the whole vehicle and the initial inventory, inventory threshold, and attendance calendar of the relevant models, the daily production volume required for different models is obtained, and according to the constraints of the assembly process, the daily assembly plan of different models is output; the second stage determines the daily demand for each part according to the assembly plan, and outputs the daily production plan of each part as the final output according to the restrictions of production lines, inventory, etc. Specifically, the following technical steps are involved:

[0136] Assembly annual plan: The assembly annual plan takes the annual production plan of the whole vehicle as the input of the production demand. The main constraints considered are the upper and lower limits of inventory, attendance days, overtime days, service priority, etc. The objective function is to minimize the number of overtime days and maximize the service priority. The service priority is inversely proportional to the production capacity, that is, the objective function can be converted into minimizing the number of overtime days and total output. The objective function and constraints of the constructed model algorithm are as follows:

[0137]

[0138] st:S min ≤s i ≤S max

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

[0140] Parts annual plan: The demand input of the parts annual plan is the planned output of the assembly. According to the demand of the assembly, the required production of a part each month is output. The main constraints are the same as those of the assembly. The difference from the assembly annual plan is the objective function. The objective function of the part is the minimum overtime days and the difference between the monthly inventory and the average of the upper and lower limits of the inventory. 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, ij is the number of overtime days for the jth component in month i, α is the weight of the overtime days, β is the weight of the total output, s ij is the inventory of the jth component in month i, S minj is the minimum inventory threshold of the jth component, S maxj is the maximum inventory threshold of the jth component.

[0144] Assembly monthly plan: The assembly monthly plan uses the monthly plan of the whole vehicle as the demand input to generate the attendance plan and daily production plan for the month. At the same time, the inventory of the assembly annual plan for the month is used as the standard, and the standard deviation between the inventory at the end of the month and the corresponding annual inventory is limited to no more than 3000. The production scheduling of the assembly monthly plan contains multiple soft constraints, that is, there is no clear target threshold, and it is hoped that the optimal value can be achieved. For such soft constraints, the present invention uses them as the objective function, unifies the measurement range, and performs multi-objective optimization solution. This production scheduling system has the following goals: minimize the production gap, make the inventory ratio between different models closer to the ratio between demand, minimize the number of switches between different models on a production line, try to use the highest priority service, and minimize overtime. For the goals of this system, the model is constructed as follows:

[0145]

[0146] Among them, m is the number of different models, n is the number of days in the month, and b ij is the production capacity gap of model i on day j, s ij is the absolute value of the difference between the actual inventory and the ideal inventory of model i on day j, c j is the number of switching times of all models on day j, q j is the duty priority of the jth day, o jis the overtime hours on the jth day, α, β, γ, δ, and ε are the elasticity coefficients of the five objective functions respectively. The purpose is to ensure that different objective functions are in the same measurement range. For example, the daily capacity gap may be tens of thousands of units, and the number of daily switches may be in the single digit. Optimizing this may result in a result that is more inclined to minimize the daily capacity gap and ignore the number of switches. Therefore, elasticity coefficients are needed to expand and contract the measurement range of different objective functions.

[0147] Constraint modeling, first specify the variables to be solved. The main variables are as follows: monthly attendance days variable, monthly total output variable, attendance variable, and monthly excess inventory limit variable. The second step is to calculate the monthly inventory including the above variables based on the initial inventory data and the initialized variables, store i =prod i +store i-1 -sale i , where i is the i-th month. When i is 1, the inventory is the input initial inventory. For the production scheduling of the transmission shaft, there are the following restrictions:

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

[0149]

[0150] Where a to b are dates in the same week, c j This is the total number of switches this week.

[0151] The total production of all models on that day is equal to the daily production capacity of the day's service

[0152]

[0153] Where cap is the daily capacity of the service on that day, is the input constant, and p i is the output of the i-th model on that day.

[0154] The third step is to solve the objective function. Through the backtracking search algorithm, the optimal solution found within the set time range is obtained and this solution is input into the next stage as the demand for parts.

[0155] Parts monthly plan: The parts production scheduling stage is divided into 5 parts production scheduling. The demand data of each production scheduling plan is the result of the previous stage. According to the planning data generated in the assembly stage, the demand data of the corresponding parts of the corresponding accessories are generated, and the initial inventory and inventory threshold data of the corresponding parts are input. The objective function is also composed of the following parts: minimize the production gap, the inventory ratio between different models is closer to the ratio between demand, the number of switching between different models on a production line is minimized, the highest priority service is used as much as possible, and the overtime is minimized. For the goals of this system, the model is constructed as follows:

[0156]

[0157] Among them, m is the number of different models, n is the number of days in the month, p is the number of component types, and b ijk is the production capacity gap of model i on day j, s ijk is the absolute value of the difference between the actual inventory and the ideal inventory of model i on day j, c jk is the number of switching times of all models on day j, q jk is the duty priority of the jth day, o jk is the overtime hours on the jth day, α, β, γ, δ, and ε are the elasticity coefficients of the five objective functions respectively. The purpose is to ensure that different objective functions are in the same measurement range. For example, the daily capacity gap may be tens of thousands of units, and the number of daily switches may be in the single digit. Optimizing this may result in a result that is more inclined to minimize the daily capacity gap and ignore the number of switches. Therefore, elasticity coefficients are needed to expand and 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 technical points:

[0159] First, the innovation of the production scheduling strategy: the present invention proposes a production scheduling method based on transmission shaft assembly parts. The method uses a scientific method to reasonably schedule the parts in the transmission shaft assembly process to maximize production efficiency and minimize costs. Second, multi-stage production scheduling model: the production scheduling process is divided into two stages: assembly and parts. Each stage is divided into annual plan and monthly plan. This multi-stage production scheduling model is the core of the present invention. Third, the application of optimization algorithm: through optimization algorithms such as backtracking search algorithm, the optimal solution searched within the set time range is achieved, which is one of the technical keys of the present invention. Fourth, multi-objective optimization: the present invention considers multiple objective functions, including minimizing production gaps, approaching the ratio between inventory ratio and demand, minimizing the number of switching between different models on the production line, using the highest priority service, and minimizing overtime. The balance and optimization of these objectives is another key point of the present invention. Fifth, the application of elasticity coefficient: in order to ensure that different objective functions are in the same measurement range, the present invention introduces elasticity coefficient to balance the importance of different objective functions. Sixth, the processing of soft constraints. The present invention uses soft constraints as objective functions and unifies the measurement range to perform multi-objective optimization solutions. This is an innovation of the present invention. Seventh, modeling of constraints: The present invention models in detail a variety of constraints including inventory constraints, production line switching constraints, daily capacity constraints, etc. The accurate modeling of these conditions is crucial to achieve optimized production scheduling. Eighth, data-driven decision-making: The present invention relies on data inputs such as vehicle production scheduling, inventory data, and attendance calendars, emphasizing the importance of data in decision-making. Compared with the prior art, the systematic and scientific scheduling method provided by the embodiment of the present invention can quickly adjust the production plan according to changes in market demand, has high flexibility, and realizes reasonable allocation and scheduling of resources through optimization algorithms, reduces resource waste, improves product quality stability through accurate production scheduling, reduces idle time of equipment and workers on the production line through scientific scheduling, optimizes the processing and assembly process of parts, reduces the backlog of raw materials and semi-finished products, reduces inventory costs, more effectively controls quality, reduces product quality fluctuations, and significantly improves production efficiency. Compared with the prior art, it has outstanding substantive characteristics and significant technological progress.

[0160] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be 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 present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for scheduling production of transmission shaft assembly parts based on multi-objective constraint programming, characterized in that: The method comprises the following steps: Obtain the annual production plan data for the whole vehicle and construct the first objective function and the first constraint condition of the annual assembly plan; Obtaining assembly annual planning data according to the first objective function and the first constraint condition; According to the assembly annual plan data, construct a second objective function and a second constraint condition for the component annual plan; Obtaining annual component planning data according to the second objective function and the second constraint condition; Acquire the monthly production plan data of the whole vehicle, and construct the third objective function and the third constraint condition of the monthly assembly plan based on the annual assembly plan data; Obtaining assembly monthly planning data according to the third objective function and the third constraint condition; According to the assembly monthly plan data, construct a fourth objective function and a fourth constraint condition of the parts monthly plan; Obtaining parts monthly planning data according to the fourth objective function and the fourth constraint condition; Output and check the optimization plan based on the monthly planned data of the parts.

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

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

4. The method for scheduling production of transmission shaft assembly parts based on multi-objective constraint programming according to claim 1, characterized in that: The "obtaining assembly annual plan data according to the first objective function and the first constraint condition" specifically includes: Based on the first constraint condition, optimizing the first objective function to obtain assembly annual plan data; The "obtaining annual component planning data according to the second objective function and the second constraint condition" comprises the following steps: Based on the second constraint condition, the second objective function is optimized to obtain annual component planning data; The "obtaining assembly monthly planning data according to the third objective function and the third constraint condition" specifically comprises the steps of: Based on the third constraint condition, optimizing the third objective function to obtain assembly monthly plan data; The "obtaining parts monthly planning data according to the fourth objective function and the fourth constraint condition" comprises the steps of: Based on the fourth constraint condition, the fourth objective function is optimized to obtain the parts monthly planning data.

5. The method for scheduling production of transmission shaft assembly parts based on multi-objective constraint programming according to any one of claims 1 to 4, characterized in that: The "constructing the first objective function and the first constraint condition of the assembly annual plan" specifically includes the following steps: Extract the production relationship of upper and lower limits of vehicle inventory, attendance days, overtime days, and duty priority. Identify that maximizing duty priority is inversely proportional to production capacity, and obtain the decision variables of the first objective function as minimizing overtime days and total output. The first objective function is constructed as follows: Based on the vehicle inventory management requirements, the first constraint is as follows: st:S min ≤s i ≤S max Where n is the number of months, o i is the number of overtime days in the ith month, d i is the number of attendance days in the ith month, p i is the daily production capacity of the ith month, α is the weight of overtime days, β is the weight of total output, s i is the inventory in the ith month, S min is the minimum inventory threshold, S max is the maximum inventory threshold.

6. The method for scheduling production of transmission shaft assembly parts based on multi-objective constraint programming according to claim 5, characterized in that: The "constructing the second objective function and the second constraint condition of the annual component plan" specifically includes the following steps: Extract the production relationship of upper and lower limits of parts inventory, overtime days, and monthly parts inventory; The decision variables of the second objective function are to minimize the number of overtime days and the difference between the monthly inventory and the mean of the upper and lower limits of inventory. The second objective function is constructed as follows: Based on the requirements of parts inventory management, the second constraint is as follows: st:S min ≤s i ≤S max Where n is the number of months, m is the number of parts, o ij is the number of overtime days for the jth component in month i, α is the weight of the overtime days, β is the weight of the total output, s ij is the inventory of the jth component in month i, S minj is the minimum inventory threshold of the jth component, S maxj is the maximum inventory threshold of the jth component.

7. The method for scheduling production of transmission shaft assembly parts based on multi-objective constraint programming according to claim 6, characterized in that: The "constructing the third objective function and the third constraint condition of the vehicle assembly monthly plan" specifically includes the following steps: Extracting the production relationship of model type, model capacity gap, difference fluctuation between actual inventory and ideal inventory, model service priority, and model switching frequency based on the vehicle monthly production plan data; The third objective function is constructed as follows: Among them, m is the number of different models, n is the number of days in the month, and b ij is the production capacity gap of model i on day j, s ij is the absolute value of the difference between the actual inventory and the ideal inventory of model i on day j, c j is the number of switching times of all models on day j, q j is the duty priority of the jth day, o j is the overtime hours on the jth day, α, β, γ, δ, and ε are the elastic coefficients of the five objective functions respectively; Based on inventory restrictions, switching times restrictions and service daily capacity restrictions, the third constraint is as follows: store i =prod i +store i-1 -sale i Where i is the i-th month, when i is 1, the inventory is the initial inventory entered; The total number of switches per week is less than or equal to 3 times: Among them, a to b are dates in the same week, c j That is the total number of switches this week; The total production of all models on that day is equal to the daily production capacity of the day's service Among them, cap is the daily capacity of the service on that day, which is the input constant, and p i is the output of the i-th model on that day.

8. The method for scheduling production of transmission shaft assembly parts based on multi-objective constraint programming according to claim 7, characterized in that: The "constructing the fourth objective function and fourth constraint condition of the parts monthly plan" specifically includes the following steps: Extracting the production relationship of machine type, component type, component capacity gap, difference fluctuation between actual component inventory and ideal component inventory, component service priority, and component switching frequency according to the monthly assembly plan data; The fourth objective function is constructed as follows: Among them, m is the number of different models, n is the number of days in the month, p is the number of component types, and b ijk is the production capacity gap of model i on day j, s ijk is the absolute value of the difference between the actual inventory and the ideal inventory of model i on day j, c jk is the number of switching times of all models on day j, q jk is the duty priority of the jth day, o jk is the overtime hours of the jth day, and α, β, γ, δ, and ε are the elasticity coefficients of the five objective functions respectively.

9. The production scheduling system based on transmission shaft assembly parts is characterized by: The system is used to execute the transmission shaft assembly parts scheduling method based on multi-objective constraint programming as described in any one of claims 1 to 8; The system includes: A first construction module, the first construction module is used to construct a first objective function and a first constraint condition for an annual assembly plan, a second objective function and a second constraint condition for an annual component plan, a third objective function and a third constraint condition for a monthly vehicle assembly plan, and a fourth objective function and a fourth constraint condition for a monthly component plan; a first analysis module, the first analysis module being used to obtain annual assembly plan data according to the first objective function and the first constraint condition, obtain annual component plan data according to the second objective function and the second constraint condition, obtain monthly assembly plan data according to the third objective function and the third constraint condition, and obtain monthly component plan data according to the fourth objective function and the fourth constraint condition; A first execution module is used to output a screening optimization plan according to the parts monthly plan data.

10. An electronic device, characterized in that include: A computer program, the computer program being used to execute the method for scheduling production of transmission shaft assembly parts based on multi-objective constraint programming as claimed in any one of claims 1 to 8; A memory, the memory being used to store the computer program; A processor is used to execute the computer program.

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