A multi-stage production scheduling method based on production orders

Abstract

The application provides a multi-level production scheduling method based on production orders, which comprises the following steps: S1, obtaining order information of orders to be sequenced, and constructing an order priority model with different order importance coefficients and user waiting time minimization as targets, wherein a target function and a constraint condition are arranged in the order priority model; S2, constructing a daily production planning model with the order priority model as a target based on the production capacity, vehicle type and color distribution proportion constraints of a workshop, and realizing minimum violation of soft constraints under the premise of meeting hard constraints by proposing an integrated planning and production scheduling mode of upstream and downstream linkage, aiming to meet production constraint conditions and target demand. Through the method, enterprises can better manage production processes, improve production efficiency, and optimize resource utilization, so as to cope with market changes and customer demand, improve the overall service of enterprises, ensure the stability of the supply chain, and improve the production efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile production order management, and more particularly to a multi-level production scheduling method based on production orders. BACKGROUND

[0002] With the rapid development of the automobile industry and the increasing diversification of consumer demand, the efficiency and digitization of automobile production scheduling have become key factors for automobile enterprises to maintain productivity. Currently, there are various types of automobile production products, and customers are increasingly demanding delivery dates. How to produce more high-quality products with less manpower, shorter time and lower inventory has become a problem that enterprises need to solve. In the era of automobile digitization, the value of advanced production scheduling (APS) system is increasingly prominent. It can help enterprises quickly generate optimized production plans, improve production resource utilization, shorten production cycles and improve overall productivity.

[0003] Traditional production scheduling methods have the following problems, such as the inability to manage order priority, which leads to the inability to prioritize customers in production arrangements; low scheduling frequency, long annual or monthly plan response cycle, and inability to reflect market demand changes in a timely manner, which prolongs the order delivery cycle and reduces customer purchase intentions; and lack of effective digital support means for whether the scheduling result is optimal and whether the inventory is minimal. In addition, with the increasing number of new automobile digitization factories and new vehicle models, the complexity of the production system increases. How to ensure the stability of the supply chain while improving production efficiency and resource utilization has become a major challenge for automobile manufacturers. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a multi-level scheduling method that improves production efficiency, reduces inventory costs and improves customer satisfaction, in view of the deficiencies of the above technical solutions.

[0005] The present application provides a multi-level scheduling method based on production orders, which is applied to an automobile production line, and the method comprises the following steps:

[0006] S1, obtaining order information of orders to be sorted, and constructing an order priority model with different order importance coefficients and user waiting time minimization as the target, the order priority model being provided with a target function and a constraint condition;

[0007] S2, constructing a daily production plan model with the order priority model as the target based on the production capacity, vehicle type and color distribution ratio constraints of the workshop, the daily production plan model being provided with a target function and a constraint condition;

[0008] S3, based on the vehicle model balance, the model and the color of the centralized production scheduling to build the assembly plant production planning model with the minimum production batch, the minimum cost of model switching and color switching as the target;

[0009] S4, based on the assembly plant production planning model, the sequence adjustment is carried out to realize the centralized production scheduling of the vehicle model, and a welding plant production planning model is constructed with the minimum adjustment quantity and the minimum model switching as the target, and the target function and the constraint condition are set in the welding plant production planning model;

[0010] S5, based on the welding plant production planning model, the sequence adjustment is carried out to realize the centralized production scheduling of the vehicle color, and a coating plant production planning model is constructed with the minimum adjustment quantity and the minimum color switching as the target, and the target function and the constraint condition are set in the coating plant production planning model;

[0011] S6, the daily production planning model, the assembly plant production planning model, the coating plant production planning model and the welding plant production planning model are solved by using a mathematical modeling tool, the daily production arrangement, the up and down line production time of the assembly, welding and coating are obtained, and the final production scheduling plan is determined according to the up and down line production time of the assembly, welding and coating.

[0012] In the multi-level production scheduling method based on the production order, the order information in the step S1 includes the user required delivery date, the order importance and the user order placing date; the delivery date is set as a hard constraint, and the order priority and the order placing date are set as soft constraints.

[0013] In the multi-level production scheduling method based on the production order, the target function in the step S1 is: Wherein, J represents the total quantity of orders, ω 1j represents the weight value of the order priority target, ω 2j represents the weight value of the user waiting time target, p j represents the priority coefficient corresponding to the order j, p j ∈{1, 2, 3, 4}, and the smaller the priority coefficient is, the higher the priority is, t j represents the waiting time corresponding to the order j; the user waiting time is set as the user required delivery date-the user order placing date, and the user waiting time is mapped to p * ∈{1, 2, 3, 4} interval by a difference standardization formula x j =1+((x-min) / (max-min))×4, wherein min and max respectively represent the maximum value and the minimum value in the order waiting time sequence.

[0014] In the multi-stage production order-based scheduling method, the constraint condition in the step S1 is: e j ≤E j j=1,2,...,J; wherein, e j represents the actual delivery date of the order j, E j represents the delivery date of the order j.

[0015] In the multi-stage production order-based scheduling method, the objective function in the step S2 is:

[0016] wherein, L represents the rank value of the order, the rank value L j is represented as: (date of the day-bill date) / (rank(j)×max((deadline-current time), 0.001)), J represents the order number, D represents the scheduling plan period, rank(j) represents the priority of each order, and rank(j)=ω1×p j +ω2×t j ; wherein, ω1 represents the weight value, and ω2 represents the weight value; the constraint condition is: e j ≤E j j=1,2,...,J; wherein, e j represents the actual delivery date of the order j, E j represents the delivery date of the order j.

[0017] In the multi-stage production order-based scheduling method, in the step S2, if the number of orders that can be arranged for production every day is required to be within the production capacity range, that is, the number of orders that can be arranged for production every day is less than or equal to PA d , and there is a limit to the number of orders produced every day for different vehicle types in the production process, and some orders also involve production on a specified date, so for different vehicle types i configuration a every day, the minimum value and the maximum value that can be produced on the day d need to be met, which satisfy the following formula:

[0018]

[0019]

[0020] wherein, I represents the vehicle type, A represents the configuration type, D represents the scheduling plan period, PA represents the production capacity, x jiad represents whether the i vehicle type a configuration d day of the order j is produced.

[0021] In the multi-stage production order-based scheduling method, the production plan model of the final assembly workshop in the step S3 is represented as: where I represents the vehicle type category, C represents the vehicle color category, S represents the vehicle number, and 1i represents the weight corresponding to the switching of the vehicle type i, and 2c represents the weight corresponding to the switching of the vehicle color c, and si is a binary number, and represents whether the vehicle type i of the s-th vehicle is switched, and is 1 if switched, and is 0 otherwise, and sc is a binary number, and represents whether the vehicle color c of the s-th vehicle is switched, and is 1 if switched, and is 0 otherwise; the minimum number of consecutive vehicle types in the number of vehicles in the same batch is IB min , and the maximum number of consecutive vehicle types in the number of vehicles in the same batch is IB max ; the vehicle type is switched at least once; and the following formula is satisfied:

[0022]

[0023] the minimum number of consecutive colors in the number of vehicles in the same batch is CB min , and the maximum number of consecutive colors in the number of vehicles in the same batch is CB max ; the vehicle type is switched at least once; and the following formula is satisfied:

[0024]

[0025] where B represents the number of vehicles in a batch, IC si represents whether the i-th vehicle type is continuously produced, and is 1 if continuously produced, and is 0 otherwise, and si represents whether the i-th vehicle color is continuously produced, and is 1 if continuously produced, and is 0 otherwise.

[0026] In the multi-level production scheduling method based on production orders provided by the present application, the objective function in the step S4 is represented as: where, wherein β 1d and β 2d represent the correlation coefficients of the number of production adjustment sequences and the minimum target of vehicle type switching in d days, and the maximum adjustment bit number is W max , and |s-s' | represents the distance of position adjustment, and the distance of position adjustment is within the window range of the given maximum adjustment bit number W max , AI ds is a binary variable, and is 1 if the position s in d days is adjusted, and is 0 otherwise; and ds represents whether the vehicle type is switched at the position s in d days, and the sum represents the sum of the vehicle type switching at all positions in d days.

[0027] The constraint condition is: y(i, d, s) = 1 i = 1,..I; d = 1,..D; s = 1,..S; y(i, d, s) = max j {x(j, i, d, s)}j = 1..J; i = 1,..I; d = 1,..D; s = 1,..S;

[0028] Wherein, y(i, d, s) is one indicating that the model produced on d day s position is i, and is 1, and 0 otherwise; x(j, i, d, s) indicates whether j order i model c color d day s position is produced, and is 1 if produced, and 0 otherwise, and IW ds ≥ y(i, d, s) - y(i, d, s-1).

[0029] The multi-level production scheduling method based on production order in the application, wherein the objective function in the step S5 is represented as:

[0030]

[0031] Wherein, γ 1d And γ 2d Indicate the correlation coefficient of d day production adjustment sequence number and color switching minimum target, and the maximum adjustment bit number is W max , |s-s'| indicates the position adjustment distance, and the position adjustment distance is within the window range of the given maximum adjustment bit number W max , AI ds Is a binary variable, and is 1 if d day position s is adjusted, and 0 otherwise; CW ds Indicates whether d day s position color switching occurs, and the sum indicates the sum of color switching on all positions on d day;

[0032] The constraint condition is: y(c, d, s) = 1 c = 1,..C; d = 1,..D; s = 1,..S; y(c, d, s) = max j {x(j, c, d, s)}j = 1..J; c = 1,..C; d = 1,..D; s = 1,..S;

[0033] Wherein, y(c, d, s) is one indicating that d day s position produced is color c, and is 1, and 0 otherwise, and x(j, c, d, s) indicates whether j order i model c color d day s position is produced, and is 1 if produced, and 0 otherwise, and CW ds ≥ y(c, d, s) - y(c, d, s-1).

[0034] The multi-level production scheduling method based on production orders provided by the application realizes the minimum violation of soft constraints under the premise of meeting hard constraints, and aims to meet production constraint conditions and target demand. Through the method, enterprises can better manage production processes, improve production efficiency, optimize resource utilization, and thus cope with market changes and customer demand, improve the overall service of enterprises, ensure the stability of the supply chain while improving production efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 is a flowchart of an embodiment of the multi-level production scheduling method based on production orders of the application. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solutions and advantages of the application clearer, the application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the application and do not limit the application.

[0037] It should be noted that the terms "first", "second", etc. in the specification and claims of the application and the above-mentioned drawings are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0038] As Figure 1 shown is a flowchart of an embodiment of the multi-level production scheduling method based on production orders of the application. A multi-level production scheduling method based on production orders is provided, which is applied to an automobile production line, and the method comprises the following steps:

[0039] In step S1, the order information of the orders to be sorted is obtained, and an order priority model is constructed with the importance coefficient of different orders and the minimization of user waiting time as the target, and the order priority model is provided with a target function and a constraint condition;

[0040] In step S2, a daily production planning model is constructed based on the production capacity, vehicle type and color distribution ratio constraints of the workshop, with the order priority model as the target, and the daily production planning model is provided with a target function and a constraint condition;

[0041] In step S3, a total assembly shop production planning model is constructed based on the vehicle model balancing, the centralized production scheduling of the vehicle model and the color, aiming to minimize the production batch, the cost of minimizing the vehicle model switching and the color switching;

[0042] In step S4, the sequence adjustment is performed based on the total assembly shop production planning model to realize the centralized production scheduling of the vehicle model, and a welding shop production planning model is constructed aiming to minimize the adjustment quantity and the vehicle model switching, wherein the welding shop production planning model is provided with a target function and a constraint condition;

[0043] In step S5, the sequence adjustment is performed based on the welding shop production planning model to realize the centralized production scheduling of the vehicle color, and a painting shop production planning model is constructed aiming to minimize the adjustment quantity and the color switching, wherein the painting shop production planning model is provided with a target function and a constraint condition;

[0044] In step S6, the daily production planning model, the total assembly shop production planning model, the painting shop production planning model and the welding shop production planning model are solved by using a mathematical modeling tool to obtain the daily production arrangement, the up-line and down-line production time of the total assembly, the welding and the painting, and the final production scheduling is determined according to the up-line and down-line production time of the total assembly, the welding and the painting. The mathematical modeling tool includes pyomo, pulp, or-tools, etc., pyomo, pulp, or-tools, etc. are used to convert the corresponding constraint and target into a language that can be understood by a solver, and the daily production arrangement, the up-line and down-line production time of the total assembly, the welding and the painting are calculated by using the solver to solve.

[0045] In an embodiment, in the step S1, the order information includes the user required delivery date, the order importance and the user order placing date; the delivery date is set as a hard constraint, and the order priority and the order placing date are set as soft constraints.

[0046] In an embodiment, in the step S1, the target function is:

[0047] wherein J represents the total quantity of orders, ω 1j represents the weight value of the order priority target, ω 2j represents the weight value of the user waiting time target, p j represents the priority coefficient corresponding to the order jj, p j ∈{1,2,3,4}, and the smaller the priority coefficient is, the higher the priority is, t j represents the waiting time corresponding to the order j; the user waiting time is set as the user required delivery date-user order placing date, and the user waiting time is normalized by a difference value formula* = 1 + ((x-min) / (max-min)) x 4 maps the original data result to p j ∈ {1, 2, 3, 4} interval, where min and max represent the maximum value and the minimum value in the order waiting time sequence respectively.

[0048] In an embodiment, the constraint condition in the step S1 is: e j ≤ E j j = 1, 2, …, J; where, e j represents the actual delivery date of the order j, E j represents the delivery date of the order j.

[0049] In an embodiment, the objective function in the step S2 is:

[0050] wherein L represents the rank value of the order, the rank value L j is represented as: (date of the day - bill of lading date) / (rank(j) x max((deadline - current time), 0.001)), J represents the order number, D represents the production planning period, rank(j) represents the priority of each order, and rank(j) = ω1 x p j + ω2 x t j ; wherein ω1 represents the weight value, ω2 represents the weight value; the constraint condition is: e j ≤ E j j = 1, 2, …, J; where, e j represents the actual delivery date of the order j, E j represents the delivery date of the order j. rank(j) can be understood as a priority value of a group of order sequences obtained by comprehensively considering the importance of the order and the user waiting time

[0051] In an embodiment, in the step S2, if the number of orders that can be arranged for production per day is required to be within the production capacity range, i.e., the number of orders that can be arranged for production per day is less than or equal to PA d and there is a limit to the number of orders produced per day for different vehicle types in the production process, and some orders also involve production on a specified date, so for different vehicle types i configuration a per day d needs to meet the minimum value and the maximum value

[0052] satisfy the following formula:

[0053]

[0054] where I represents the vehicle type category, A represents the configuration category, D represents the production planning period, PA represents the production capacity, x jiad represents whether the i vehicle type a configuration d day of order j is produced.

[0055] In an embodiment, the assembly plant production planning model in the step S3 is represented as: where I represents the vehicle type category, C represents the vehicle color category, S represents the vehicle number, α 1i represents the weight corresponding to the switching of the vehicle type i, α 2c represents the weight corresponding to the switching of the vehicle color c, IW si is a binary number, representing whether the vehicle type i of the s-th vehicle switches, and is 1 if switching occurs, otherwise 0, CW sc is a binary number, representing whether the vehicle color c of the s-th vehicle switches, and is 1 if switching occurs, otherwise 0; the minimum number of consecutive vehicle types in the vehicle number of the same batch is IB min , the maximum number of consecutive vehicle types in the vehicle number of the same batch is IB max ; the vehicle type switches at least once; and the following formula is satisfied:

[0056]

[0057] The minimum number of consecutive colors in the vehicle number of the same batch is CB min , the maximum number of consecutive colors in the vehicle number of the same batch is CB max ; the vehicle type switches at least once; and the following formula is satisfied:

[0058]

[0059] where B represents the vehicle number of a batch, IC si represents whether the s-th i vehicle type is continuously produced, and is 1 if continuously produced, otherwise 0, CC si represents whether the s-th vehicle i color is continuously produced, and is 1 if continuously produced, otherwise 0. Specifically, for the assembly plant production planning, the assembly plant mainly considers vehicle type balance, vehicle type concentration and color concentration production planning, and vehicle type balance can be constructed by setting the minimum production batch, for example, there are three vehicle types that need to satisfy the balance of 2:2:1, and the minimum batch can be set to a multiple of 5. Therefore, the main goal of the assembly is to minimize the cost of vehicle type and color switching.

[0060] In an embodiment, the objective function in the step S4 is represented as:

[0061] where, β 1d and β 2dThis represents the correlation coefficient between the number of production adjustment sequences on day d and the minimum target for vehicle model switching. It should be noted that the correlation coefficient can be adjusted based on the importance of the target. Let the maximum number of adjustment bits be W. max |ss′| represents the distance of the position adjustment, and the distance of the position adjustment is within the given maximum adjustment bit width W. max Within the window range, AI ds It is a binary variable; if the position s of day d has been adjusted, it is 1; otherwise, it is 0. ds This indicates whether a vehicle model change occurred at position s on day d, and the summation represents the sum of vehicle model changes at all positions on day d.

[0062] The constraints are: y(i, d, s) = 1, i = 1, ..., I; d = 1, ..., D; s = 1, ..., S; y(i, d, s) = max j {x(j,i,d,s)}j=1..J; i=1,..I; d=1,..D; s=1,..S;

[0063] Where y(i, d, s) represents a value indicating that the model i is produced at position s on day d; y(i, d, s) is 1 if yes, 0 otherwise. x(j, i, d, s) represents whether order j for model i, color c, is produced at position s on day d; x(j, i, d, s) is 1 if yes, 0 otherwise. ds ≥y(i,d,s)-y(i,d,s-1).

[0064] In one embodiment, the objective function in step S5 is expressed as:

[0065] Where, γ 1d and γ 2d This represents the correlation coefficient between the number of production adjustment sequences on day d and the minimum target for vehicle color switching. It should be noted that the correlation coefficient can be adjusted based on the importance of the target. Let the maximum adjustment bit depth be W. max |ss′| represents the distance of the position adjustment, and the distance of the position adjustment is within the given maximum adjustment bit width W. max Within the window range, AI ds It is a binary variable; it is 1 if the position s has been adjusted on day d, and 0 otherwise. (CW) ds This indicates whether a color change occurs at position s on day d, and the summation represents the sum of color changes at all positions on day d.

[0066] The constraints are: y(c, d, s) = 1, c = 1, ..., C; d = 1, ..., D; s = 1, ..., S; y(c, d, s) = max j{x(j, c, d, s)}j = 1..J; c = 1,..C; d = 1,..D; s = 1,..S;

[0067] where y(c, d, s) is a binary variable that equals 1 if color c is produced on day d at position s, and 0 otherwise, x(j, c, d, s) is a binary variable that equals 1 if order j of model i in color c is produced on day d at position s, and 0 otherwise, and CW ds ≥ y(c, d, s) - y(c, d, s - 1).

[0068] By constructing an order priority model, the priority of orders can be optimized to ensure that high-priority orders are processed in a timely manner while minimizing user waiting time. In practical applications, other constraints and business rules may also need to be adjusted.

[0069] Through the above model and constraints, a complete multi-level automobile production scheduling method can be effectively constructed, including order priority setting, daily production planning, general assembly workshop production planning model, painting workshop production planning model, and welding workshop production planning model, to ensure that the priority of orders is reasonably arranged and efficient production planning is achieved under the satisfaction of production capacity and other constraints. This will help improve production efficiency, reduce inventory costs, and improve customer satisfaction.

[0070] The multi-level scheduling method based on production orders provided by the embodiments of the present application has at least the following beneficial effects:

[0071] 1. Compared with traditional heuristic algorithms and original heuristic algorithms, the present application uses an efficient solver to achieve fast solution, which can at least improve the efficiency by 100 times compared with traditional algorithms.

[0072] 2. The present application proposes a multi-level scheduling scheme for order-based automobile production planning, which improves the analysis of plan adjustment impact and the timeliness of response to changes.

[0073] 3. Based on the actual application scenario of automobile factories, the present application can construct a multi-level production scheduling scheme for the pain points of automobile factory scheduling, such as variable sales demand, low scheduling efficiency, and difficulty in analyzing the impact of plan adjustment on upstream and downstream.

[0074] It should be noted that, for the foregoing method embodiments, in order to simplify the description, they are all described as a series of action combinations, but those skilled in the art should know that the present application is not limited by the order of the described actions, because according to the present application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0075] Those skilled in the art can clearly understand the method according to the above-mentioned embodiments can be realized by means of software and necessary general hardware platform, of course, also can be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application essentially or say the part of the prior art to make contributions can be embodied in the form of software products, the computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc), including a number of instructions to make a terminal device (may be a mobile phone, computer, server, or network equipment, etc.) executes the method described in various embodiments of the present application.

[0076] Therefore, the above-mentioned, only for the preferred specific embodiments of the present application, the protection scope of the present application is not limited to this, any person skilled in the art in the technical field of the present application disclosed in the technical range, can easily think of the changes or replacement, should be covered in the protection scope of the present application, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A multi-level scheduling method based on production orders, characterized in that, The method includes the following steps: S1, Obtain the order information of the orders to be sorted, and construct an order priority model with the goal of minimizing the importance coefficient of different orders and the user waiting time. The order priority model is set with an objective function and constraints. S2, Based on the constraints of the workshop's production capacity, vehicle model, and color distribution ratio, a daily production plan model is constructed with an order priority model as the objective. The daily production plan model is equipped with an objective function and constraints. S3 is a final assembly workshop production planning model built on vehicle model balancing and centralized production scheduling of vehicle models and colors, with the goal of minimizing the production batch size and minimizing the costs incurred by vehicle model and color switching. S4. Based on the production planning model of the final assembly workshop, the sequential adjustment is carried out to realize the centralized production scheduling of vehicle models, and a production planning model of the welding workshop is constructed with the goal of minimizing the number of adjustments and minimizing vehicle model switching. The production planning model of the welding workshop is equipped with an objective function and constraints. S5. Based on the welding workshop production plan model, the sequence is adjusted to realize the centralized production scheduling of vehicle colors, and a painting workshop production plan model is constructed with the goal of minimizing quantity adjustment and minimizing color switching. The painting workshop production plan model is equipped with an objective function and constraints. S6. Using mathematical modeling tools, solve the daily production plan model, the final assembly workshop production plan model, the painting workshop production plan model, and the welding workshop production plan model to obtain the daily production schedule, the start and end times of final assembly, welding, and painting, and determine the final production schedule based on the start and end times of final assembly, welding, and painting. The objective function in step S1 is: Where J represents the total number of orders, ω 1j ω represents the weight value of the order priority target. 2j p represents the weight value of the user's waiting time target. j p represents the priority coefficient corresponding to order j. j ∈{1,2,3,4}, and the smaller the priority coefficient, the higher the priority. j This represents the waiting time for order j; let user waiting time = user's requested delivery date - user's order placement date, and map the user waiting time to p using the difference standardization formula. j The interval; where the standardization formula for the difference is x * =1+((x-min) / (max-min))×4, where min and max represent the maximum and minimum values ​​in the order waiting time series, respectively.

2. The multi-level scheduling method based on production orders according to claim 1, characterized in that, In step S1, the order information includes the user's requested delivery date, order importance, and user order placement date; the delivery date is set as a hard constraint, and the order priority and order placement date are set as soft constraints.

3. The multi-level scheduling method based on production orders according to claim 2, characterized in that, The constraint condition in step S1 is: e j ≤E j j = 1, 2, ..., J; where e j Indicates the actual delivery date of order j, E j This indicates the delivery date for order j.

4. The multi-level scheduling method based on production orders according to claim 3, characterized in that, The objective function in step S2 is: Where L represents the order's grade value, and the grade value L j It is expressed as: (Date of the day - Bill of lading date) / (rank(j) × max((Deadline time - Current time), 0.001)), where D represents the production scheduling cycle, rank(j) represents the priority of each order, and rank(j) = ω1 × p j +ω2×t j Where ω1 represents the weight value and ω2 represents the weight value; the constraint condition is: e j ≤E j j = 1, 2, ..., J; where e j Indicates the actual delivery date of order j, E j This indicates the delivery date for order j.

5. The multi-level scheduling method based on production orders according to claim 4, characterized in that, In step S2, if the number of orders that can be scheduled for production each day is within the production capacity, that is, the number of orders that can be scheduled for production each day is less than or equal to PA. d Furthermore, there are daily production limits for different car models, and some orders require production on specific dates. Therefore, for different car models with different configurations, each day's production capacity must meet the minimum production requirement that day. and maximum value Satisfy the following formula: Where I represents vehicle type, A represents configuration type, PA represents production capacity, and x jiad This indicates whether order j for model i, configuration a, is ready for production on day d.

6. The multi-level scheduling method based on production orders according to claim 5, characterized in that, The final assembly workshop production planning model in step S3 is represented as follows: Where I represents vehicle type, C represents vehicle color type, S represents number of vehicles, and α 1i This indicates that the weight corresponding to model i is switched, α 2c This indicates the weight corresponding to the car color c switching, IW si This is a binary number representing whether the model i of the s-th car has changed; a value of 1 indicates a change, and a value of 0 indicates no change. (CW) sc This is a binary number representing whether the color c of the s-th vehicle has changed; a value of 1 indicates a change, and a value of 0 indicates no change. The minimum number of consecutive vehicle types in the same batch is IB. min The maximum number of consecutive vehicle models in the same batch is IB. max At any time; the vehicle model must be switched at least once; the following formula must be met: The minimum number of vehicles with consecutive colors in the same batch is CB. min The maximum number of vehicles with consecutive colors in the same batch is CB. max At any time; the vehicle model must be switched at least once; the following formula must be met: Where B represents the number of vehicles in a batch, IC si , indicating whether the s-th vehicle of model i is continuously produced; 1 if continuously produced, 0 otherwise. CC si This indicates whether the i-th color of the s-th vehicle is produced continuously; if it is, it is 1, otherwise it is 0.

7. The multi-level scheduling method based on production orders according to claim 6, characterized in that, The objective function in step S4 is expressed as: Wherein, β 1d and β 2d This represents the correlation coefficient between the number of production adjustment sequences on day d and the minimum target for model switching, with the maximum number of adjustment bits set to W. max |ss′| represents the distance of the position adjustment, and the distance of the position adjustment is within the given maximum adjustment bit width W. max Within the window range, AI ds It is a binary variable; if the position s of day d has been adjusted, it is 1; otherwise, it is 0. ds Indicates whether a vehicle model change has occurred at position d-day s; The constraints are: y(i,d,s)=1; where i=1,..I; d=1,..D; s=1,..S; y(i,d,s)=max j {x(j,i,d,s)}; j=1..J; i=1,..I; d=1,..D; s=1,..S; Where y(i,d,s) represents a value indicating whether the model i is produced at position s on day d; y(i,d,s) is 1 if yes, 0 otherwise. x(j,i,d,s) represents whether order j for model i, color c, at position s on day d is produced; x(j,i,d,s) is 1 if yes, 0 otherwise. ds ≥y(i,d,s)-y(i,d,s-1).

8. The multi-level scheduling method based on production orders according to claim 7, characterized in that, The objective function in step S5 is expressed as: Where, γ 1d and γ 2d This represents the correlation coefficient between the number of production adjustment sequences on day d and the minimum target for vehicle color switching. Let the maximum number of adjustment bits be W. max |SS′| represents the distance of the position adjustment, and the distance of the position adjustment is within the given maximum adjustment bit width W. max Within the window range, AI ds It is a binary variable; it is 1 if the position s has been adjusted on day d, and 0 otherwise. (CW) ds Indicates whether the color changes at position s on day d; The constraints are: y(c,d,s)=1; where c=1,..C; d=1,..D; s=1,..S; y(c,d,s)=max j {x{j,c,d,s)}; j=1..J; c=1,..C; d=1,..D; s=1,..S; Where y(c,d,s) represents a value indicating whether color c is produced at position s on day d (1 if yes, 0 otherwise), and x(j,c,d,s) represents whether color c is produced at position s on day d for order i (1 if yes, 0 otherwise). ds ≥y(c,d,s)-y(c,d,s-1).

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