Multi-level production scheduling method based on production orders

Abstract

The invention provides a multi-level production scheduling method based on production orders, and the method comprises the following steps: S1, obtaining order information of to-be-sorted orders, constructing an order priority model which aims at minimizing importance coefficients and user waiting time of different orders, and setting a target function and a constraint condition in the order priority model; and S2, on the basis of distribution proportion constraints of production capacity, vehicle types and colors of a workshop, constructing a daily production plan model taking an order priority model as a target, and by providing an upstream and downstream linkage integrated plan production scheduling mode, on the premise of meeting hard constraints, realizing minimum violation of soft constraints and aiming at meeting production constraint conditions and target requirements. Through the method, an enterprise can better manage the production process, improve the production efficiency and optimize the resource utilization rate, so that market changes and customer requirements are met, the overall service of the enterprise is improved, and the production efficiency is improved while the stability of a supply chain is ensured.

Description

Technical Field

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

[0002] With the rapid development of the automotive industry and the increasing diversification of consumer demand, the efficiency and digitalization of automobile production scheduling have become key factors for automobile companies to maintain productivity. At present, there are many types of automobile production products, and customers are increasingly strict with delivery dates. How to produce more high-quality products with less manpower, shorter time, and lower inventory has become an urgent problem that companies need to solve. In the era of automotive digitalization, the value of the Advanced Production Scheduling APS system is becoming increasingly prominent. It can help companies quickly generate optimized production plans, improve the utilization of production resources, shorten the production cycle, and improve the overall productivity of the company.

[0003] The traditional production scheduling method has the following problems, such as the inability to implement order priority management, resulting in the inability to prioritize customers in production arrangements; low scheduling frequency, long annual or monthly plan response cycle, and inability to timely reflect changes in market demand, extending order delivery cycles and reducing customer purchase intentions; and the lack of effective digital support for issues such as whether the scheduling results are optimal and whether inventory is the lowest. In addition, with the increasing number of new digital automobile factories and new models, the complexity of the production system has increased. How to improve production efficiency and resource utilization while ensuring the stability of the supply chain has become a major challenge facing automakers. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a multi-level production scheduling method for improving production efficiency, reducing inventory costs and enhancing customer satisfaction in view of the shortcomings of the above technical solutions.

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

[0006] S1, obtaining order information of the orders to be sorted, and constructing an order priority model with the objectives of minimizing the importance coefficients of different orders and user waiting time, wherein the order priority model is provided with an objective function and constraint conditions;

[0007] S2, based on the production capacity of the workshop, the distribution ratio constraints of vehicle models and colors, a daily production planning model with the order priority model as the target is constructed, wherein the daily production planning model is provided with an objective function and constraint conditions;

[0008] S3, based on the balanced production of vehicle models and centralized production scheduling of vehicle models and colors, builds a production planning model for the general assembly workshop with the goal of minimizing production batches and minimizing the costs of vehicle model switching and color switching;

[0009] S4, based on the production plan model of the general assembly workshop, sequential adjustments are made to realize centralized production scheduling of vehicle models, and a production plan model of the welding workshop is constructed with the goal of minimizing the number of adjustments and minimizing vehicle model switching, wherein the production plan model of the welding workshop is provided with an objective function and constraint conditions;

[0010] S5, based on the welding shop production planning model, sequential adjustment is performed to realize the centralized production scheduling of vehicle colors, and a painting shop production planning model is constructed with the goal of minimizing quantity adjustment and minimizing color switching, wherein the painting shop production planning model is provided with an objective function and constraint conditions;

[0011] S6, using mathematical modeling tools to solve the daily production plan model, the 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 production time of assembly, welding and painting, and determine the final production schedule based on the production time of assembly, welding and painting.

[0012] In the multi-level production scheduling method based on production orders described in the present invention; in the step S1, the order information includes the user's required delivery date, order importance and the user's 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.

[0013] In the multi-level production scheduling method based on production orders described in the present invention, the objective function in step S1 is: Among them, J represents the total amount 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 order j, p j ∈{1, 2, 3, 4}, and the smaller the priority coefficient, the higher the priority. j represents the waiting time corresponding to order j; let user waiting time = user required delivery date - user order placement date, and normalize user waiting time by difference formula x * =1+((x-min) / (max-min))×4Map the original data result to p j ∈{1, 2, 3, 4}, where min and max represent the maximum and minimum values ​​in the order waiting time series, respectively.

[0014] In the multi-level production scheduling method based on production orders described in the present invention, the constraint condition in step S1 is: j ≤E j j = 1, 2, ..., J; where e j represents the actual delivery date of order j, E j represents the delivery date of order j.

[0015] In the multi-level production scheduling method based on production orders described in the present invention, the objective function in step S2 is:

[0016] Among them, L represents the level value of the order, and the level value L j It is expressed as: (today's date - bill of lading date) / (rank(j)×max((deadline time - current time), 0.001)), J represents the order number, D represents the production planning cycle, rank(j) represents the priority of each order, and rank(j)=ω1×p j +ω2×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 order j, E j represents the delivery date of order j.

[0017] In the multi-level production scheduling method based on production orders of the present invention; in step S2, if the number of orders that can be scheduled for production every day is within the production capacity, that is, the number of orders that can be scheduled for production every day is less than or equal to PA d In addition, during the production process, there are restrictions on the number of orders for different models produced each day. Some orders also involve production on a specified date, so for different models i, configuration a must meet the minimum value that can be produced on that day. and maximum value Satisfy the following formula:

[0018]

[0019]

[0020] Among them, I represents the type of vehicle, A represents the configuration type, D represents the production planning cycle, PA represents the production capacity, and x jiad Indicates whether model a of order j with model i and configuration a is produced on day d.

[0021] In the multi-level production scheduling method based on production orders described in the present invention, the production planning model of the general assembly workshop in step S3 is expressed as: Where I represents the type of vehicle, C represents the color of the vehicle, S represents the number of vehicles, and α 1i represents the weight corresponding to the switch of vehicle type i, α 2c Indicates the weight corresponding to the car color c switching, IW si It is a binary number, indicating whether the model i of the sth vehicle has switched. If so, it is 1, otherwise it is 0. CW sc It is a binary number, indicating whether the color c of the sth vehicle has been switched. If so, it is 1, otherwise it is 0. The minimum number of consecutive vehicle models in the same batch is IB. min The maximum number of consecutive vehicle models in the same batch is IB max When the vehicle type is switched at least once, the following formula is satisfied:

[0022]

[0023] 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 When the vehicle type is switched at least once, the following formula is satisfied:

[0024]

[0025] Among them, B represents the number of vehicles in a batch, IC si , indicating whether the s-th vehicle of model i is produced continuously, if so, it is 1, otherwise it is 0, CC si Indicates whether the s-th car of color i is produced continuously, if so, it is 1, otherwise it is 0.

[0026] In the multi-level production scheduling method based on production orders described in the present invention, the objective function in step S4 is expressed as: Among them, β 1d and β 2d represents the correlation coefficient between the number of production adjustment sequences on day d and the minimum target for vehicle model switching. Let the maximum number of adjustment digits be W max , |ss′| represents the distance of position adjustment, and the distance of position adjustment is within the given maximum adjustment digit W max Within the window range, AI ds is a binary variable, which is 1 if the position s of the d-day is adjusted, otherwise it is 0; IW ds Indicates whether a vehicle type switch occurs at position s on day d, and the sum indicates the sum of vehicle type switches at all positions on day d;

[0027] The constraints are: y(i, d, s) = 1i = 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] Among them, y(i, d, s) is a number indicating whether the model i is produced at the s position on the d day. If yes, it is 1, otherwise it is 0; x(j, i, d, s) indicates whether the model i of the jth order, the cth color of the ith order, is produced at the s position on the d day. If yes, it is 1, otherwise it is 0, and IW ds ≥y(i,d,s)-y(i,d,s-1).

[0029] In the multi-level production scheduling method based on production orders described in the present invention, the objective function in step S5 is expressed as:

[0030]

[0031] Among them, γ 1d and γ 2d represents the correlation coefficient between the number of production adjustment orders on day d and the minimum target of car color switching. Let the maximum number of adjustment digits be W max , |ss′| represents the distance of position adjustment, and the distance of position adjustment is within the given maximum adjustment digit W max Within the window range, AI ds is a binary variable, which is 1 if the position s of the d-day is adjusted, and 0 otherwise; CW ds Indicates whether color switching occurs at position s on day d, and the sum indicates the sum of color switching at all positions on day d;

[0032] The constraints are: y(c, d, s) = 1c = 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] Among them, y(c, d, s) is a 1 indicating whether the color c is produced at the s position on the d day. If yes, it is 1, otherwise it is 0. x(j, c, d, s) indicates whether the jth order i model c color d day s position is produced. If yes, it is 1, otherwise it is 0. And CW ds ≥y(c,d,s)-y(c,d,s-1).

[0034] The multi-level production scheduling method based on production orders of the present invention proposes an integrated planning and scheduling method for upstream and downstream linkage, which minimizes the violation of soft constraints while satisfying hard constraints, aiming to meet production constraints and target requirements. Through this method, enterprises can better manage production processes, improve production efficiency, optimize resource utilization, and thus respond to market changes and customer needs, improve the overall service of the enterprise, and ensure the stability of the supply chain while improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow chart of an embodiment of a multi-level production scheduling method based on production orders of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

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

[0038] like Figure 1 , which is a flow chart of an embodiment of a multi-level production scheduling method based on production orders of the present invention. 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, order information of the orders to be sorted is obtained, and an order priority model is constructed with the objectives of minimizing the importance coefficients of different orders and user waiting time, wherein the order priority model is provided with an objective function and constraint conditions;

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

[0041] In step S3, a production planning model for the general assembly workshop is constructed based on the balanced production of vehicle models and centralized production scheduling of vehicle models and colors, with the goal of minimizing production batches and minimizing the costs of vehicle model switching and color switching;

[0042] In step S4, sequential adjustments are made based on the production planning model of the general assembly workshop to realize 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, wherein the production planning model of the welding workshop is provided with an objective function and constraint conditions;

[0043] In step S5, a sequence adjustment is performed based on the welding shop production planning model to realize the centralized production scheduling of vehicle colors, and a paint shop production planning model is constructed with the goal of minimizing quantity adjustment and minimizing color switching, wherein the paint shop production planning model is provided with an objective function and constraints;

[0044] In step S6, mathematical modeling tools are used to solve the daily production plan model, the 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 production time of assembly, welding and painting, and the final production schedule is determined according to the production time of assembly, welding and painting. Among them, mathematical modeling tools include pyomo, pulp, or-tools, etc., which are used to convert the corresponding constraints and goals into a language that can be understood by the solver, and the solver is used to solve and calculate the daily production schedule, the production time of assembly, welding and painting.

[0045] In one embodiment, in step S1, the order information includes the user's required delivery date, order importance and the user's 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.

[0046] In one embodiment, the objective function in step S1 is:

[0047] Among them, J represents the total amount 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 order jj, p j ∈{1, 2, 3, 4}, and the smaller the priority coefficient, the higher the priority. j represents the waiting time corresponding to order j; let user waiting time = user required delivery date - user order placement date, and normalize user waiting time by difference formula x* =1+((x-min) / (max-min))×4Map the original data result to p j ∈{1, 2, 3, 4}, where min and max represent the maximum and minimum values ​​in the order waiting time series, respectively.

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

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

[0050] Among them, L represents the level value of the order, and the level value L j It is expressed as: (today's date - bill of lading date) / (rank(j)×max((deadline time - current time), 0.001)), J represents the order number, D represents the production planning cycle, rank(j) represents the priority of each order, and rank(j)=ω1×p j +ω2×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 order j, E j represents the delivery date of order j. Rank(j) can be understood as the priority value of a set of order sequences that we obtain by combining the importance of the order and the user's waiting time.

[0051] In one embodiment, in step S2, if the number of orders that can be arranged for production per day is within the production capacity, that is, the number of orders that can be arranged for production per day is less than or equal to PA d In addition, during the production process, there are restrictions on the number of orders for different models produced each day. Some orders also involve production on a specified date, so for different models i, configuration a must meet the minimum value that can be produced on that day. and maximum value

[0052] Satisfy the following formula:

[0053]

[0054] Among them, I represents the type of vehicle, A represents the configuration type, D represents the production planning cycle, PA represents the production capacity, and x jiad Indicates whether model a of order j with model i and configuration a is produced on day d.

[0055] In one embodiment, the production planning model of the final assembly shop in step S3 is expressed as: Where I represents the type of vehicle, C represents the color of the vehicle, S represents the number of vehicles, and α 1i represents the weight corresponding to the switch of vehicle type i, α 2c Indicates the weight corresponding to the car color c switching, IW si It is a binary number, indicating whether the model i of the sth vehicle has switched. If so, it is 1, otherwise it is 0. CW sc It is a binary number, indicating whether the color c of the sth vehicle has been switched. If so, it is 1, otherwise it is 0. The minimum number of consecutive vehicle models in the same batch is IB. min The maximum number of consecutive vehicle models in the same batch is IB max When the vehicle type is switched at least once, the following formula is satisfied:

[0056]

[0057] 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 When the vehicle type is switched at least once, the following formula is satisfied:

[0058]

[0059] Among them, B represents the number of vehicles in a batch, IC si , indicating whether the s-th vehicle of model i is produced continuously, if so, it is 1, otherwise it is 0, CC si Indicates whether the s-th car of color i is produced continuously. If so, it is 1, otherwise it is 0. Specifically, for the production plan of the general assembly workshop, the general assembly workshop mainly considers the balanced model, concentrated model and concentrated color production. The balanced model can be constructed by setting the minimum production batch. For example, if there are three models that need to meet the balance of 2:2:1, the minimum batch can be set to a multiple of 5. Therefore, the main goal of the general assembly is to minimize the cost of model and color switching.

[0060] In one embodiment, the objective function in step S4 is expressed as:

[0061] Among them, β 1d and β 2dIt represents the correlation coefficient between the number of production adjustment sequences on day d and the minimum target of vehicle model switching. It should be noted that the correlation coefficient can be adjusted according to the importance of the target. Assume that the maximum number of adjustment digits is W max , |ss′| represents the distance of position adjustment, and the distance of position adjustment is within the given maximum adjustment digit W max Within the window range, AI ds is a binary variable, which is 1 if the position s of the d-day is adjusted, otherwise it is 0; IW ds Indicates whether a vehicle type switch occurs at position s on day d, and the sum indicates the sum of vehicle type switches at all positions on day d;

[0062] The constraints are: y(i, d, s) = 1i = 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] Among them, y(i, d, s) is a number indicating whether the model i is produced at the s position on the d day. If yes, it is 1, otherwise it is 0; x(j, i, d, s) indicates whether the model i of the jth order, the cth color of the ith order, is produced at the s position on the d day. If yes, it is 1, otherwise it is 0, and IW ds ≥y(i,d,s)-y(i,d,s-1).

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

[0065] Among them, γ 1d and γ 2d It represents the correlation coefficient between the number of production adjustment orders in d days and the minimum target of car color switching. It should be noted that the correlation coefficient can be adjusted according to the importance of the target. Let the maximum number of adjustment digits be W max , |ss′| represents the distance of position adjustment, and the distance of position adjustment is within the given maximum adjustment digit W max Within the window range, AI ds is a binary variable, which is 1 if the position s of the d-day is adjusted, and 0 otherwise; CW ds Indicates whether color switching occurs at position s on day d, and the sum indicates the sum of color switching at all positions on day d;

[0066] The constraints are: y(c, d, s) = 1c = 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] Among them, y(c, d, s) is a symbol indicating whether the color c is produced at the s position on the d day. If yes, it is 1, otherwise it is 0. x(j, c, d, s) indicates whether the jth order i model c color d day s position is produced. If yes, it is 1, otherwise it is 0. And CW ds ≥y(c,d,s)-y(c,d,s-1).

[0068] By building an order priority model to optimize the order priority, we can ensure that high-priority orders can be processed in a timely manner while minimizing the waiting time for users. In actual applications, it may also be necessary to combine other constraints and business rules for adjustment.

[0069] Through the above models and constraints, a complete multi-level automobile production scheduling method can be effectively constructed, including the setting of order priorities, the formulation of daily production plans, the production planning model of the assembly workshop, the production planning model of the painting workshop, and the production planning model of the welding workshop, ensuring that the order priorities are reasonably arranged and that efficient production plans are achieved while meeting production capacity and other constraints. This will help improve production efficiency, reduce inventory costs, and increase customer satisfaction.

[0070] The beneficial effects of a multi-level production scheduling method based on production orders provided by an embodiment of the present invention are at least:

[0071] 1. Compared with traditional heuristic algorithms and original heuristic algorithms, this application uses an efficient solver to achieve fast solutions, which can improve efficiency by at least a hundred times compared with traditional algorithms.

[0072] 2. This application proposes a multi-level and top-down scheduling plan for order-based automobile production plans, which improves the analyzability of the impact of plan adjustments and the timely responsiveness of changes.

[0073] 3. Based on the actual application scenarios of automobile factories, this application can construct a multi-level production scheduling solution to address the pain points faced by automobile factories in production scheduling, such as the changeable and difficult response to sales demand, low scheduling efficiency, and difficulty in analyzing the impact of plan adjustments on upstream and downstream.

[0074] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0075] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0076] Therefore, the above is only a preferred specific implementation mode of the present invention, and the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention. The protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A multi-level production scheduling method based on production orders, characterized in that: The method comprises the following steps: S1, obtaining order information of the orders to be sorted, and constructing an order priority model with the objectives of minimizing the importance coefficients of different orders and user waiting time, wherein the order priority model is provided with an objective function and constraint conditions; S2, based on the production capacity of the workshop, the distribution ratio constraints of vehicle models and colors, a daily production planning model with the order priority model as the target is constructed, wherein the daily production planning model is provided with an objective function and constraint conditions; S3, based on the balanced production of vehicle models and centralized production scheduling of vehicle models and colors, builds a production planning model for the general assembly workshop with the goal of minimizing production batches and minimizing the costs of vehicle model switching and color switching; S4, based on the production plan model of the general assembly workshop, sequential adjustments are made to realize centralized production scheduling of vehicle models, and a production plan model of the welding workshop is constructed with the goal of minimizing the number of adjustments and minimizing vehicle model switching, wherein the production plan model of the welding workshop is provided with an objective function and constraint conditions; S5, based on the welding shop production planning model, sequential adjustment is performed to realize the centralized production scheduling of vehicle colors, and a painting shop production planning model is constructed with the goal of minimizing quantity adjustment and minimizing color switching, wherein the painting shop production planning model is provided with an objective function and constraint conditions; S6, using mathematical modeling tools to solve the daily production plan model, the 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 production time of assembly, welding and painting, and determine the final production schedule based on the production time of assembly, welding and painting.

2. The multi-level production scheduling method based on production orders according to claim 1 is characterized in that: In step S1, the order information includes the user's required delivery date, order importance and the user's 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 production scheduling method based on production orders according to claim 1 is characterized in that: In step S1, the objective function is: Among them, J represents the total amount 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 order j, p j ∈{1,2,3,4}, and the smaller the priority coefficient, the higher the priority. j represents the waiting time corresponding to order j; let user waiting time = user required delivery date - user order placement date, and normalize user waiting time by difference formula x * =1+((x-min) / (max-min))×4Map the original data result to p j ∈{1,2,3,4}, where min and max represent the maximum and minimum values ​​in the order waiting time series, respectively.

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

5. The multi-level production scheduling method based on production orders according to claim 4 is characterized in that: In step S2, the objective function is: Among them, L represents the level value of the order, and the level value L j It is expressed as: (today's date - bill of lading date) / (rank(j)×max((deadline time - current time), 0.001)), J represents the order number, D represents the production planning cycle, rank(j) represents the priority of each order, and rank(j)=ω1×p j +ω2×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 order j, E j represents the delivery date of order j.

6. The multi-level production scheduling method based on production orders according to claim 5 is characterized in that: In step S2, if the number of orders that can be arranged for production every day is within the production capacity, that is, the number of orders that can be arranged for production every day is less than or equal to PA d In addition, during the production process, there are restrictions on the number of orders for different models produced each day. Some orders also involve production on a specified date, so for different models i, configuration a must meet the minimum value that can be produced on that day. and maximum value Satisfy the following formula: Among them, I represents the type of vehicle, A represents the configuration type, D represents the production planning cycle, PA represents the production capacity, and x jiad Indicates whether model a of order j with model i and configuration a is produced on day d.

7. The multi-level production scheduling method based on production orders according to claim 6 is characterized in that: In step S3, the production planning model of the final assembly shop is expressed as: Where I represents the type of vehicle, C represents the color of the vehicle, S represents the number of vehicles, and α 1i represents the weight corresponding to the switch of vehicle type i, α 2c Indicates the weight corresponding to the car color c switching, IW si It is a binary number, indicating whether the model i of the sth vehicle has switched. If so, it is 1, otherwise it is 0. CW sc It is a binary number, indicating whether the color c of the sth vehicle has been switched. If so, it is 1, otherwise it is 0. The minimum number of consecutive vehicle models in the same batch is IB. min The maximum number of consecutive vehicle models in the same batch is IB max When the vehicle type is switched at least once, the following formula is satisfied: 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 When the vehicle type is switched at least once, the following formula is satisfied: Among them, B represents the number of vehicles in a batch, IC si , indicating whether the s-th vehicle of model i is produced continuously, if so, it is 1, otherwise it is 0, CC si Indicates whether the s-th car of color i is produced continuously, if so, it is 1, otherwise it is 0.

8. The multi-level production scheduling method based on production orders according to claim 7 is characterized in that: In step S4, the objective function is expressed as: Among them, β 1d and β 2d represents the correlation coefficient between the number of production adjustment sequences on day d and the minimum target for vehicle model switching. The maximum number of adjustment digits is W. max ,|ss ′ | represents the distance of position adjustment, and the distance of position adjustment is within the given maximum adjustment digit W max Within the window range, AI ds is a binary variable, which is 1 if the position s of the d-day is adjusted, otherwise it is 0; IW ds Indicates whether a vehicle type switch occurs at position s on day d, and the sum indicates the sum of vehicle type switches at all positions on day d; The constraints are: y(i,d,s)=1i=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; Among them, y(i,d,s) is a number indicating whether the model i is produced at the s position on the d day. If yes, it is 1, otherwise it is 0; x(j,i,d,s) indicates whether the jth order i model c color d day s position is produced. If yes, it is 1, otherwise it is 0, and IW ds ≥y(i,d,s)-y(i,d,s-1).

9. The multi-level production scheduling method based on production orders according to claim 8, characterized in that: In step S5, the objective function is expressed as: Among them, γ 1d and γ 2d represents the correlation coefficient between the number of production adjustment orders on day d and the minimum target of car color switching. Let the maximum number of adjustment digits be W max ,|Ss ′ | represents the distance of position adjustment, and the distance of position adjustment is within the given maximum adjustment digit W max Within the window range, AI ds is a binary variable, which is 1 if the position s of the d-day is adjusted, and 0 otherwise; CW ds Indicates whether color switching occurs at position s on day d, and the sum indicates the sum of color switching at all positions on day d; The constraints are: y(c, d, s) = 1c = 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; Among them, y(c,d,s) is a 1 indicating whether the color c is produced at the s position on the d day. If yes, it is 1, otherwise it is 0. x(j,c,d,s) indicates whether the jth order i model c color d day s position is produced. If yes, it is 1, otherwise it is 0. And CW ds ≥y(c,d,s)-y(c,d,s-1).

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