Method and system for production capacity allocation optimization based on mathematical model
By constructing a capacity allocation method based on mathematical models, the problem of capacity allocation in integrated circuit manufacturing was solved, the optimal capacity allocation solution was achieved, and decision inconsistency and production uncertainty were reduced.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHANGHAI HUALI MICROELECTRONICS CORP
- Filing Date
- 2023-03-31
- Publication Date
- 2026-05-12
AI Technical Summary
Existing technologies struggle to effectively balance various production indicators in integrated circuit manufacturing, leading to difficulties in capacity allocation.
A capacity allocation method based on a mathematical model is constructed. By establishing the correspondence between machine groups and process routes, the standard operating time and available production capacity of each work station are obtained, a coordination coefficient is set, and the branch and bound algorithm is used to find the optimal solution, thereby optimizing capacity allocation.
It achieves the optimal solution for capacity allocation, avoids the problem of production decision-makers relying on personal experience and making it difficult to balance indicators, and reduces decision-making inconsistency and production uncertainty.
Smart Images
Figure CN116362403B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of semiconductor manufacturing technology, and in particular to a method and system for optimizing production capacity allocation based on a mathematical model. Background Technology
[0002] Integrated circuit manufacturing systems are mainly divided into: 1) scheduling of wafer start-up and shipment plans; 2) daily equipment capacity allocation (distribution of workload across multiple production stages within the same machine group over a certain time period); and 3) machine group scheduling and dispatching. Wafer start-up and shipment plans are scheduled based on the overall factory's work-in-process inventory control and delivery targets for the next one to two months. Daily equipment capacity allocation is based on short-term (generally one-day) production targets set according to delivery targets, production cycles, and output requirements. Machine group scheduling and dispatching implements short-term (10-minute or real-time) production plans based on equipment capacity allocation targets combined with equipment capacity utilization requirements. Daily equipment capacity allocation plays a crucial role as the connecting module between upper-level production planning and lower-level execution planning. Therefore, research on capacity allocation has received considerable attention.
[0003] For example, Chung et al. studied how to generate daily capacity allocation schemes for bottleneck equipment groups by balancing work-in-process (WIP) through production planning, thereby maximizing output. Lee et al. divided all process routes into separate segments according to lithography layers and used statistical analysis to estimate the target WIP level for each lithography layer. They then continuously reduced the gap between the target WIP level and the current WIP level through real-time control, achieving production line balancing. Similarly, Bureauetal et al. also achieved this by reducing the gap between the target WIP level and the current WIP level for each lithography layer, where the target WIP level was obtained through simulation. Regardless of the method used, accurately obtaining the target WIP level is usually difficult. Therefore, some scholars have adopted scheduling rules. For example, Zhou et al. designed a new combined scheduling rule that comprehensively considers scheduling rules such as delivery date priority and shortest processing time priority to achieve comprehensive capacity allocation and coordination among multiple production indicators of the production line.
[0004] In summary, existing research mostly adopts a combination of statistical analysis, simulation rules, and heuristic scheduling rules to allocate daily production capacity. However, due to the complex production operation characteristics of integrated circuit manufacturing, the above methods are difficult to take into account various production indicators. Summary of the Invention
[0005] The purpose of this invention is to provide a capacity allocation optimization method and system based on a mathematical model, which at least solves one of the technical problems existing in the prior art.
[0006] To achieve the above objectives, this invention provides a capacity allocation optimization method based on a mathematical model, comprising:
[0007] Establish the correspondence between machine groups and process routes;
[0008] Obtain the standard operating time of each work station on the process route;
[0009] Obtain the available production capacity of each machine group for a future period of time;
[0010] Obtain the distribution of work-in-process at each work site and the film production plan for the future period;
[0011] The capacity allocation model is constructed as follows:
[0012]
[0013] Constraints:
[0014] X gtl =Y gt(l-1) g=1,2,…G; t=1,2,…T; l=2,3,…,L (2)
[0015] W g11 =IW g1 +R g1 -Y g11 g = 1, 2, ... G; (3)
[0016] W g1l =IW gl -Y g1l g=1,2,…G; l=2,3,…,L (4)
[0017] W gt1 =W g(t-1)1 +R gt -Y gt1 g=1, 2,…G; t=2, 3,…T (5)
[0018] W gtl =W g(t-1)l +Y gtl +X gtl g=1,2,…G; t=2,3,…T; l=2,3,…,L (6)
[0019]
[0020] In the formula, δ is the coordination coefficient between output level and production volume; L is the number of work stations, where l represents the l-th work station; G is the total number of product types, where g represents the g-th product type; T is the production planning cycle, where t represents the t-th day; Y gtlX represents the number of operations for the g-th product category at the l-th workstation on day t; gtl W represents the work-in-process quantity of product type g arriving at work station l on day t; gtl Let IW be the work-in-process quantity of product category g on day t at work station l; gl R represents the work-in-process inventory of the g-th product category at the beginning of the period at the l-th work station; gt Let K be the wafer input quantity for the g-th product type on day t; K is the number of workstations, where k represents the k-th workstation; C kt a represents the available production capacity of the k-th work group on day t; gl The time spent on the g-th product type at the l-th work station; OP lk This indicates that if the l-th work station is operated by the k-th work group, then it is 1; otherwise, it is 0.
[0021] By setting a coordination coefficient, the optimal solution for capacity allocation can be obtained.
[0022] Optionally, the branch and bound algorithm can be used to find the optimal solution of the capacity allocation model.
[0023] Optional, traffic balancing between upstream and downstream work sites for any product.
[0024] Optionally, a dynamic balance can be maintained between the initial wafer input at the starting work station of the initial process route, the initial work-in-process level, the output at the current station, and the work-in-process level.
[0025] Optionally, the dynamic balance between the initial work-in-process level, the output of the station, and the work-in-process level at each station in the initial process route, excluding the starting station.
[0026] Optionally, dynamic balance between work-in-process level, current station input, current station output, and wafer feed at all work stations along the process route between production days.
[0027] Optionally, for each product, the capacity requirement for each fleet of machines on each production day shall not exceed its available capacity level.
[0028] Based on the same inventive concept, this invention also provides a capacity allocation optimization system based on a mathematical model, comprising:
[0029] The building module is configured to build the correspondence between the machine group and the process route;
[0030] The data acquisition module is configured to acquire the standard operating time of each work station on the process route, acquire the available production capacity of each work group in the future, acquire the work-in-process distribution of each work station, and the wafer start plan in the future.
[0031] The capacity allocation module is configured to build a capacity allocation model as follows:
[0032]
[0033] Constraints:
[0034] X gtl =Y gt(l-1) g=1,2,…G; t=1,2,…T; l=2,3,…,L (2)
[0035] W g11 =IW g1 +R g1 -Y g11 g = 1, 2, ... G; (3)
[0036] W g1l =IW gl -Y g1l g=1,2,…G; l=2,3,…,L (4)
[0037] W gt1 =W g(t-1)1 +R gt -Y gt1 g=1, 2,…G; t=2, 3,…T (5)
[0038] W gtl =W g(t-1)l +Y gtl +X gtl g=1,2,…G; t=2,3,…T; l=2,3,…,L (6)
[0039]
[0040] In the formula, δ is the coordination coefficient between output level and production volume; L is the number of work stations, where l represents the l-th work station; G is the total number of product types, where g represents the g-th product type; T is the production planning cycle, where t represents the t-th day; Y gtl X represents the number of operations for the g-th product category at the l-th workstation on day t; gtl W represents the work-in-process quantity of product type g arriving at work station l on day t; gtl Let IW be the work-in-process quantity of product category g on day t at work station l; gl R represents the work-in-process inventory of the g-th product category at the beginning of the period at the l-th work station; gt Let K be the wafer input quantity for the g-th product type on day t; K is the number of workstations, where k represents the k-th workstation; C kt a represents the available production capacity of the k-th work group on day t;gl The time spent on the g-th product type at the l-th work station; OP lk This indicates that if the l-th work station is operated by the k-th work group, then it is 1; otherwise, it is 0.
[0041] The capacity allocation module is also configured to set a coordination coefficient to obtain the optimal solution for the capacity allocation scheme.
[0042] Optionally, the branch and bound algorithm can be used to find the optimal solution of the capacity allocation model.
[0043] Based on the same inventive concept, the present invention also provides a readable storage medium having a computer program stored thereon, which, when executed, can implement the capacity allocation optimization method based on the mathematical model as described above.
[0044] In the capacity allocation optimization method and system based on mathematical models provided by this invention, by optimizing capacity allocation based on mathematical models, production decision-makers can set coordination coefficients according to demand to obtain the optimal solution of capacity allocation scheme. This avoids the problem of production decision-makers making daily capacity allocation schemes based on their personal production experience, which makes it difficult to take into account various production indicators. It also reduces the problem of decision inconsistency caused by individual differences and increased production uncertainty. Attached Figure Description
[0045] Those skilled in the art will understand that the accompanying drawings are provided to better understand the invention and do not constitute any limitation on the scope of the invention. Wherein:
[0046] Figure 1 A flowchart illustrating a capacity allocation optimization method based on a mathematical model, as provided in an embodiment of the present invention;
[0047] Figure 2 The diagram shows the structure of a capacity allocation optimization system based on a mathematical model, as provided in an embodiment of the present invention.
[0048] In the attached image:
[0049] 1-Basic Data Module; 2-Data Processing Module; 3-Judgment Module; 4-Management Module; 5-Display Module. Detailed Implementation
[0050] To make the objectives, advantages, and features of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the drawings are in a very simplified form and use non-precise proportions, intended only to facilitate and clarify the illustration of the embodiments of this invention, and are not intended to limit the conditions for implementing this invention. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to size, without affecting the effects and objectives achieved by this invention, should still fall within the scope of the technical content disclosed in this invention.
[0051] It should also be understood that, unless otherwise specified or indicated, the terms "first," "second," "third," etc., in the specification are used only to distinguish the various components, elements, steps, etc., in the specification, and not to indicate the logical or sequential relationships between the various components, elements, steps, etc. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0052] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a capacity allocation optimization method based on a mathematical model, as provided in an embodiment of the present invention. This embodiment provides a capacity allocation optimization method based on a mathematical model, including:
[0053] S1. Establish the correspondence between the machine group and the process route;
[0054] S2. Obtain the standard operating time of each work station on the process route;
[0055] S3. Obtain the available production capacity of each machine group for a period of time in the future;
[0056] S4. Obtain the distribution of work-in-process at each work station and the film delivery plan for the future period;
[0057] S5. Construct the capacity allocation model as follows:
[0058]
[0059] Constraints:
[0060] X gtl =Y gt(l-1)g=1,2,…G; t=1,2,…T; l=2,3,…,L (2)
[0061] W g11 =IW g1 +R g1 -Y g11 g = 1, 2, ... G; (3)
[0062] W g1l =IW gl -Y g1l g=1,2,…G; l=2,3,…,L (4)
[0063] W gt1 =W g(t-1)1 +R gt -Y gt1 g=1, 2,…G; t=2, 3,…T (5)
[0064] W gtl =W g(t-1)l +Y gtl +X gtl g=1,2,…G; t=2,3,…T; l=2,3,…,L (6)
[0065]
[0066] In the formula, δ is the coordination coefficient between output level and production volume; L is the number of work stations, where l represents the l-th work station; G is the total number of product types, where g represents the g-th product type; T is the production planning cycle, where t represents the t-th day; Y gtl X represents the number of operations for the g-th product category at the l-th workstation on day t; gtl W represents the work-in-process quantity of product type g arriving at work station l on day t; gtl Let IW be the work-in-process quantity of product category g on day t at work station l; gl R represents the work-in-process inventory of the g-th product category at the beginning of the period at the l-th work station; gt Let K be the wafer input quantity for the g-th product type on day t; K is the number of workstations, where k represents the k-th workstation; C kt a represents the available production capacity of the k-th work group on day t; gl The time spent on the g-th product type at the l-th work station; OP lk This indicates that if the l-th work station is operated by the k-th work group, then it is 1; otherwise, it is 0.
[0067] S6. Set the coordination coefficient to obtain the optimal solution for the capacity allocation plan.
[0068] This embodiment optimizes capacity allocation based on a mathematical model. Production decision-makers can set coordination coefficients according to demand to obtain the optimal solution for capacity allocation. This avoids the problem of difficulty in balancing various production indicators caused by production decision-makers manually formulating daily capacity allocation plans based on their personal production experience. It also reduces the problem of decision inconsistency caused by individual differences and increased production uncertainty.
[0069] Specifically, step S1 is executed first to establish the correspondence between machine groups and process routes. Taking a process route that produces only one product as an example, i.e., G=1, the process route has 6 work stations, i.e., L=1, mainly composed of two machine groups, i.e., K=2. Work stations 2 and 6 in the process route utilize machine group 1, while the remaining stations utilize machine group 2. The correspondence between work stations and machine groups is shown in Table 1:
[0070]
[0071] Table 1
[0072] Then, step S2 is executed to obtain the standard operating time of each work station on the process route, as shown in Table 2:
[0073]
[0074] Table 2
[0075] Next, step S3 is executed to obtain the available production capacity of each machine group for a future period of time, as shown in Table 3:
[0076]
[0077] Table 3
[0078] Then, step S4 is executed to obtain the initial work-in-process distribution of each workstation and the wafer input plan (wafer input volume) for a future period, as shown in Tables 4 and 5:
[0079]
[0080] Table 4
[0081]
[0082] Table 5
[0083] Next, proceed to step S5 to construct the capacity allocation model as follows:
[0084]
[0085] Constraints:
[0086] X gtl=Y gt(l-1) g=1,2,…G; t=1,2,…T; l=2,3,…,L (2)
[0087] W g11 =IW g1 +R g1 -Y g11 g = 1, 2, ... G; (3)
[0088] W g1l =IW gl -Y g1l g=1,2,…G; l=2,3,…,L (4)
[0089] W gt1 =W g(t-1)1 +R gt -Y gt1 g=1, 2,…G; t=2, 3,…T (5)
[0090] W gtl =W g(t-1)l +Y gtl +X gtl g=1,2,…G; t=2,3,…T; l=2,3,…,L (6)
[0091]
[0092] In the formula, δ is the coordination coefficient between output level and production volume; L is the number of work stations, where l represents the l-th work station; G is the total number of product types, where g represents the g-th product type; T is the production planning cycle, where t represents the t-th day; Y gtl X represents the number of operations for the g-th product category at the l-th workstation on day t; gtl W represents the work-in-process quantity of product type g arriving at work station l on day t; gtl Let IW be the work-in-process quantity of product category g on day t at work station l; gl R represents the work-in-process inventory of the g-th product category at the beginning of the period at the l-th work station; gt Let K be the wafer input quantity for the g-th product type on day t; K is the number of workstations, where k represents the k-th workstation; C kt a represents the available production capacity of the k-th work group on day t; gl The time spent on the g-th product type at the l-th work station; OP lk This indicates that if the l-th work station is operated by the k-th work group, then it is 1; otherwise, it is 0.
[0093] In this embodiment, Equation (1) is the objective function. The first part of Equation (1) represents the total output level over multiple cycles. The output level is a measure of the output capacity of the entire production line. The second part represents the total production volume over multiple cycles. The production volume is a measure of machine utilization. The two are not positively correlated. δ is the coordination coefficient between the output level and the production volume.
[0094] In this embodiment, Equation (2) can be used to represent the flow balance of any product between upstream and downstream work stations, that is, the output of the previous work station is the input of the next work station; Equation (3) can be used to represent the dynamic balance between the initial work station's wafer input, initial work-in-process level, current station output, and work-in-process level in the initial process route; Equation (4) can be used to represent the dynamic balance between the initial work-in-process level, current station output, and work-in-process level of each station in the initial process route except for the starting station; Equations (5) and (6) can be used to represent the dynamic balance between the work-in-process level, current station input, current station output, and wafer input of all work stations in the process route between production days; Equation (7) can be used to represent that the capacity demand of each product for each work group does not exceed its available capacity level on each production day, thus forming a constraint on the objective function.
[0095] Finally, step S6 is executed to set the coordination coefficient and obtain the optimal solution for capacity allocation. For example, when the production decision-maker sets the coordination coefficient δ = 0.5, it means that output level and production volume are equally important to the production decision-maker, and the total shipment level for the next week reaches 800 units, and the total production volume reaches 3100 units. The optimal scheduling results, i.e., the operation scheduling of each site for each cycle, are shown in Table 6:
[0096]
[0097] Table 6
[0098] As shown in Table 6, in the early stage of the production cycle, the preceding process is concentrated, while in the middle and late stages of the production cycle, the following process is concentrated.
[0099] Preferably, the branch and bound algorithm is used to find the optimal solution of the capacity allocation model. Since existing dynamic capacity allocation schemes require a daily update frequency, the decision-making process directly affects the production line's output level, thus requiring high computational accuracy and low computational speed. Therefore, this application uses the branch and bound algorithm to solve for the optimal solution of the model to achieve optimal decision-making.
[0100] Based on this, please refer to Figure 2 The present invention also provides a capacity allocation optimization system based on a mathematical model, comprising:
[0101] Module 1 is configured to build the correspondence between the machine group and the process route;
[0102] Data acquisition module 2 is configured to acquire the standard operating time of each work station on the process route, acquire the available production capacity of each work group in the future, acquire the work-in-process distribution of each work station, and the wafer start plan in the future.
[0103] Capacity allocation module 3 is configured to build the capacity allocation model as follows:
[0104]
[0105] Constraints:
[0106] X gtl =Y gt(l-1) g=1,2,…G; t=1,2,…T; l=2,3,…,L (2)
[0107] W g11 =IW g1 +R g1 -Y g11 g = 1, 2, ... G; (3)
[0108] W g1l =IW gl -Y g1l g=1,2,…G; l=2,3,…,L (4)
[0109] W gt1 =W g(t-1)1 +R gt -Y gt1 g=1, 2,…G; t=2, 3,…T (5)
[0110] W gtl =W g(t-1)l +Y gtl +X gtl g=1,2,…G; t=2,3,…T; l=2,3,…,L (6)
[0111]
[0112] In the formula, δ is the coordination coefficient between output level and production volume; L is the number of work stations, where l represents the l-th work station; G is the total number of product types, where g represents the g-th product type; T is the production planning cycle, where t represents the t-th day; Y gtl X represents the number of operations for the g-th product category at the l-th workstation on day t; gtl W represents the work-in-process quantity of product type g arriving at work station l on day t; gtl Let IW be the work-in-process quantity of product category g on day t at work station l;gl R represents the work-in-process inventory of the g-th product category at the beginning of the period at the l-th work station; gt Let K be the wafer input quantity for the g-th product type on day t; K is the number of workstations, where k represents the k-th workstation; C kt a represents the available production capacity of the k-th work group on day t; gl The time spent on the g-th product type at the l-th work station; OP lk This indicates that if the l-th work station is operated by the k-th work group, then it is 1; otherwise, it is 0.
[0113] The capacity allocation module 3 is also configured to set a coordination coefficient to obtain the optimal solution for the capacity allocation scheme.
[0114] Furthermore, the branch and bound algorithm is used to obtain the optimal solution of the capacity allocation model.
[0115] Based on the same inventive concept, this invention also proposes a readable storage medium storing a computer program thereon, which, when executed, can implement the capacity allocation optimization method based on the mathematical model as described above.
[0116] The readable storage medium can be a tangible device capable of holding and storing instructions for use by an instruction execution device, such as, but not limited to, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer programs described herein can be downloaded from the readable storage medium to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. Each computing / processing device's network adapter card or network interface receives the computer program from the network and forwards it for storage in a readable storage medium within the respective computing / processing device. The computer program used to perform the operations of this invention can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as "C" or similar languages. The computer program can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuits, such as programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), are personalized by utilizing state information from a computer program. These electronic circuits can execute computer-readable program instructions, thereby realizing various aspects of the present invention.
[0117] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by a computer program. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. These computer programs can also be stored in a readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the readable storage medium storing the computer program comprises an article of manufacture including instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams.
[0118] A computer program may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the computer program executing on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.
[0119] In summary, this invention provides a capacity allocation optimization method and system based on a mathematical model. By optimizing capacity allocation based on a mathematical model, production decision-makers can set coordination coefficients according to demand to obtain the optimal solution for capacity allocation. This avoids the problem of production decision-makers manually formulating daily capacity allocation plans based on their personal production experience, which makes it difficult to balance various production indicators. It also reduces the problems of decision inconsistency caused by individual differences and increased production uncertainty.
[0120] Furthermore, it should be understood that although the present invention has been disclosed above with reference to preferred embodiments, these embodiments are not intended to limit the present invention. For any person skilled in the art, many possible variations and modifications can be made to the technical solutions of the present invention based on the disclosed technical content, or equivalent embodiments can be modified accordingly, without departing from the scope of the present invention. Therefore, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention, without departing from the content of the present invention, shall still fall within the scope of protection of the present invention.
Claims
1. A capacity allocation optimization method based on a mathematical model, characterized in that, include: Establish the correspondence between machine groups and process routes; Obtain the standard operating time of each work station on the process route; Obtain the available production capacity of each machine group for a future period of time; Obtain the distribution of work-in-process at each work site and the film production plan for the future period; The capacity allocation model is constructed as follows: F= （1） Constraints: （2） （3） （4） （5） （6） ; （7） In the formula, This is the coordination coefficient between output level and production volume; For the number of work sites, Indicates the first One work site; This represents the total number of product types. Indicates the first Product categories; For the production planning cycle, Indicates the first sky; For the first The product category in the first Heaven is in The number of jobs at each work station; For the first The product category in the first The day arrived at The quantity of work-in-process at each work station; For the first The product category in the first Heaven is in The quantity of work-in-process at each work station; For the first The product category was in the first half of the period. Work-in-process inventory at each work site; For the first The product category in the first The daily wafer input; For the number of operating machines, Indicates the first A group of operating machines; For the first Heavenly The available production capacity of each machine group; For the first The product category in the first The time spent on each work site; Indicates if the first The work station is the first one. If a group of machines is operating, the value is 1; otherwise, it is 0. By setting a coordination coefficient, the optimal solution for capacity allocation can be obtained.
2. The capacity allocation optimization method based on a mathematical model according to claim 1, characterized in that, The optimal solution of the capacity allocation model is obtained by using the branch and bound algorithm.
3. The capacity allocation optimization method based on a mathematical model according to claim 1, characterized in that, Equation (2) is used to represent the flow balance of any product between upstream and downstream work sites.
4. The capacity allocation optimization method based on a mathematical model according to claim 1, characterized in that, Equation (3) represents the dynamic balance between the initial batch quantity at the starting work station of the initial process route, the initial work-in-process level, the output quantity at the current station, and the work-in-process level.
5. The capacity allocation optimization method based on a mathematical model according to claim 1, characterized in that, Equation (4) is used to represent the dynamic balance between the initial work-in-process level, the output of the station, and the work-in-process level of each station in the initial process route, excluding the starting station.
6. The capacity allocation optimization method based on a mathematical model according to claim 1, characterized in that, Equations (5) and (6) are used to represent the dynamic balance between the work-in-process level, input quantity, output quantity, and wafer loading quantity of all work stations in the process route between production days.
7. The capacity allocation optimization method based on a mathematical model according to claim 1, characterized in that, Equation (7) is used to indicate that the capacity demand of each product on each production day does not exceed its available capacity level for each operating group.
8. A capacity allocation optimization system based on a mathematical model, characterized in that, include: The building module is configured to build the correspondence between the machine group and the process route; The data acquisition module is configured to acquire the standard operating time of each work station on the process route, acquire the available production capacity of each work group in the future, acquire the work-in-process distribution of each work station, and the wafer start plan in the future. The capacity allocation module is configured to build a capacity allocation model as follows: F= （1） Constraints: （2） （3） （4） （5） （6） ; （7） In the formula, This is the coordination coefficient between output level and production volume; For the number of work sites, Indicates the first One work site; This represents the total number of product types. Indicates the first Product categories; For the production planning cycle, Indicates the first sky; For the first The product category in the first Heaven is in The number of jobs at each work station; For the first The product category in the first The day arrived at The quantity of work-in-process at each work station; For the first The product category in the first Heaven is in The quantity of work-in-process at each work station; For the first The product category was in the first half of the period. Work-in-process inventory at each work site; For the first The product category in the first The daily wafer input; For the number of operating machines, Indicates the first A group of operating machines; For the first Heavenly The available production capacity of each machine group; For the first The product category in the first The time spent on each work site; Indicates if the first The work station is the first one. If a group of machines is operating, the value is 1; otherwise, it is 0. The capacity allocation module is also configured to set a coordination coefficient to obtain the optimal solution for the capacity allocation scheme.
9. The capacity allocation optimization system based on a mathematical model according to claim 8, characterized in that, The optimal solution of the capacity allocation model is obtained by using the branch and bound algorithm.
10. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it can implement the capacity allocation optimization method based on a mathematical model according to any one of claims 1-7.