A capacity assessment method for heat treatment workshop considering capacity constraints

Abstract

The present invention relates to the technical field of capacity assessment of heat treatment workshops, and solves the technical problem that the traditional method has a low degree of rationality in batch planning for heat treatment of workpieces, and the heat treatment time in production cannot be minimized, thereby reducing production efficiency. In particular, it relates to a capacity assessment method for a heat treatment workshop that takes capacity constraints into consideration, including data collection in the early stage; furnace classification according to the temperature compatibility of the workpieces to be treated; establishing a batch grouping model in the furnace; using a greedy algorithm to find the optimal batch grouping solution in the furnace; and performing capacity assessment after obtaining the optimal solution. The present invention classifies various types of workpieces in an order into furnaces according to temperature compatibility, and uses a greedy algorithm to find the optimal batch grouping solution in the furnace, providing a capacity assessment method for the workshop, which has practical significance for optimizing resource allocation, improving production efficiency, and enhancing the accuracy of capacity assessment, and is of great significance for improving the overall operation level and competitiveness of the workshop.

Description

Technical Field

[0001] The present invention relates to the technical field of capacity assessment of a heat treatment workshop, and in particular to a method for assessing the capacity of a heat treatment workshop taking capacity constraints into consideration. Background Art

[0002] Heat treatment is a metal processing technology that changes the internal microstructure of metal through heating and cooling processes, thereby improving the physical and chemical properties of metal, such as hardness, toughness, wear resistance, fatigue strength, etc. The heat treatment process generally includes workpiece loading, heating, heat preservation, cooling and workpiece removal.

[0003] At present, there are few studies on the methods of heat treatment workshop capacity assessment. Some related studies focus on the calculation of the time of a certain link in the heat treatment process, while others focus on the batching and packaging of orders. However, in existing studies, it is impossible to combine the two levels of order batching and heat treatment time calculation, resulting in a low degree of rationality in the batch planning of workpiece heat treatment, and the heat treatment time in production cannot be minimized in a real sense, thereby reducing production efficiency. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a method for evaluating the capacity of a heat treatment workshop taking into account capacity constraints, which solves the technical problems that the traditional method has a low degree of rationality in batch planning for heat treatment of workpieces, the heat treatment time in production cannot be minimized, and production efficiency is reduced.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for evaluating the capacity of a heat treatment workshop considering capacity constraints, the method comprising the following processes:

[0006] S1 obtains heat treatment data corresponding to the order produced by the heat treatment workshop;

[0007] S2 classifies all types of workpieces to be processed into furnaces according to heat treatment data;

[0008] S3 takes minimizing the heat treatment time as the goal, and establishes an in-furnace batch model for seeking the best batch allocation plan in each furnace classification. The in-furnace batch model includes the objective function of minimizing the heat treatment time and the corresponding constraints;

[0009] S4 uses a greedy algorithm to solve the in-furnace batching model to obtain the optimal in-furnace batching scheme. The optimal in-furnace batching scheme is the sum of the minimized heat treatment time of all in-furnace batching schemes.

[0010] S5 compares the delivery deadline of the heat treatment workshop orders based on the optimal batching plan in the furnace and evaluates the production capacity of the heat treatment workshop.

[0011] Furthermore, the heat treatment data includes the size of each workpiece to be processed. , Target temperature range of heat treatment ,quality Specific heat capacity ,in, , is the total number of workpiece types;

[0012] And, the capacity V of the heat treatment furnace, the average space utilization U, the delivery deadline .

[0013] Furthermore, the delivery period The longest heat treatment time.

[0014] Furthermore, the group furnace classification includes allocating workpieces with overlapping target temperature ranges to a category according to the target temperature ranges of the heat treatment of the workpieces to be processed, and such workpieces can be loaded into the furnace at the same time and heated to the same temperature according to the same heating power;

[0015] Workpieces without temperature overlap are assigned to another category; if the target temperature range of a certain workpiece overlaps with the ranges of multiple categories, the category with the highest temperature overlap is selected to group the furnaces.

[0016] Furthermore, the furnace group classification method comprises the following steps:

[0017] S21, define the workpiece category and initialize the workpiece parameters, including size and target temperature range of heat treatment;

[0018] S22. Define temperature coincidence function , calculate the overlap of the temperature range of each two workpieces, through The function finds the starting point with the highest temperature in the workpiece and the lowest end point , the return value is the length of the overlapping part or 0;

[0019] S23. Define furnace group classification function And initialize an empty dictionary , traverse the categories and workpieces, determine the overlap of the temperature range, if the current overlap is greater than the maximum overlap previously recorded, update the maximum overlap and the best category, if no suitable category is found, create a new category, add the workpiece to the selected category, and update the target temperature range of the category, and finally return dictionary;

[0020] S24. Use of After the function inputs the workpiece size, mass, and specific heat capacity of the order, it calls the furnace group classification method to output the workpiece category in each furnace category and detailed information about the workpiece.

[0021] Furthermore, the objective function is:

[0022] ;

[0023] In the formula, Heat treatment time to achieve the target output of all workpieces; For this batch number in the furnace classification, ; For the Heat treatment time of the batch; The total number of batches in this group of furnace classifications;

[0024] The following assumptions are made for the capacity assessment problem of a heat treatment workshop considering capacity constraints: in the workshop production tasks, detailed information such as the output of workpieces of various sizes and the heat treatment process are known; the capacity of the heat treatment furnace and the average space utilization are known; the case of only one furnace is considered; since the heat treatment target temperature of the workpiece has a temperature range, the heating target temperature is operated according to the median of the classified temperature range; the heat treatment furnace can be used continuously without interruption, and the equipment maintenance time is not considered; the target output of the workpiece must be completed;

[0025] A mixed integer programming mathematical model is established with the goal of minimizing the time required to complete the heat treatment of all workpieces in a certain furnace group classification. The constraints are as follows:

[0026] Load into this category The total size of the pieces to be processed in a batch must not exceed the available capacity of the furnace, i.e.:

[0027] ;

[0028] All workpieces to be processed must complete the heat treatment of the target output, that is:

[0029] ;

[0030] The calculation of loading time for each batch is:

[0031] ;

[0032] The calculation of heating time for each batch is:

[0033] ;

[0034] ∆T is calculated based on the median of the final temperature range of the furnace classification group minus the room temperature, that is:

[0035] ;

[0036] The calculation of the holding time for each batch is:

[0037] ;

[0038] The calculation of the cooling time for each batch is:

[0039] ;

[0040] The calculation of the take-out time for each batch is:

[0041] ;

[0042] The total heat treatment time for each batch is calculated as:

[0043] ;

[0044] The value range of the decision variable is a positive integer, that is:

[0045] ;

[0046] In the above formula, For this group of furnaces, the workpieces are classified. ; For this batch number in the furnace classification, ; is the decision variable, representing the artifact in this category In the The quantity of the batch to be loaded; Represents the workpiece Size; is the volume of the heat treatment furnace; is the average space utilization of the heat treatment furnace; Represents the workpiece target production volume; It means the The loading time of the batch; is the loading rate; It means the Heating time of the batch; It indicates the heating power of the heat treatment furnace; For workpiece The specific heat capacity, For workpiece quality; It is the maximum value of the final overlap temperature range in this group of furnace classifications; It is the minimum value of the final overlap temperature range in this group of furnace classifications; is the average indoor temperature; It means the The holding time of the batch; It means Workpieces in a batch of holding time, and The batch insulation time should be the maximum insulation time of all workpieces in the batch; It means Cooling time of the batch; It represents the cooling rate; It indicates the removal time; The take-out rate.

[0047] Furthermore, taking minimizing the heat treatment time as an indicator, a greedy algorithm is used to find the best batching scheme in the furnace. In order to maximize the capacity of the heat treatment furnace and save production time and resources, the present invention considers using a greedy algorithm to find the optimal solution for the batching scheme in the furnace. The specific process includes the following steps:

[0048] S41. Create an empty list Store the workpiece information of each furnace batch loading, initialize List, used to record the loading quantity of each workpiece in the current furnace;

[0049] S42, outer cycle: when any workpiece does not reach the target output, continue to cycle; inner cycle: when there is remaining space in the furnace and there are workpieces that do not reach the target output, continue to try to load the workpiece;

[0050] S43, select the workpiece that has the shortest heat treatment time among all the workpieces currently loaded into the furnace and load it into the furnace. If there is no suitable workpiece to be loaded, end the loading of the current furnace and add the loading status of the current furnace to List;

[0051] S44, check the last furnace: if If there is a positive number in the list, it means that the last furnace still has workpieces loaded, and it needs to be added to the result list;

[0052] S45, Return List containing information about the workpieces loaded into each furnace.

[0053] Furthermore, the production capacity of the heat treatment workshop is evaluated by comparing the delivery deadline of the heat treatment workshop order according to the optimal batching scheme in the furnace, which specifically includes:

[0054] According to the optimal batching scheme in each furnace classification, calculate the sum of the minimized heat treatment time for all batches, compare it with the delivery deadline planned by the workshop to make a capacity assessment and determine whether the time meets the delivery deadline;

[0055] If the conditions are met, the workpieces to be processed will be heat treated according to the optimal batching plan in the furnace; if not, adjustments will be made based on the actual situation. The workshop can consider increasing the number of heat treatment furnaces to speed up production.

[0056] When multiple heat treatment furnaces are performing heat treatment at the same time, it is necessary to comprehensively consider factors such as the task priority of the workpiece to be processed to allocate resources and carry out production.

[0057] By means of the above technical solution, the present invention provides a method for evaluating the capacity of a heat treatment workshop taking into account capacity constraints, which has at least the following beneficial effects:

[0058] 1. The present invention groups and packages the workpieces according to their temperature compatibility, and uses a greedy algorithm to find the best batching scheme in each category, which minimizes the heat treatment time, can effectively improve the production efficiency of the workshop, and reasonably utilize the internal space of the heat treatment furnace. It can not only provide a method for capacity evaluation for the heat treatment workshop, but also help the workshop to reasonably plan the furnace schedule to a certain extent, save time and resources, and improve production efficiency, which has certain practical significance.

[0059] 2. The present invention combines the two aspects of order batch packaging and heat treatment time calculation, and can minimize the heat treatment time in production through reasonable batch planning, thereby improving production efficiency.

[0060] 3. The present invention provides a capacity assessment method for a workshop based on the workshop's orders, which has practical significance for optimizing resource allocation, improving production efficiency, and enhancing the accuracy of capacity assessment, and is of great significance for improving the overall operation level and competitiveness of the workshop. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0062] Figure 1 It is a flow chart of the method for evaluating the capacity of a heat treatment workshop in the present invention;

[0063] Figure 2 It is a schematic diagram of furnace assembly and batch assembly in the furnace in the present invention;

[0064] Figure 3 The present invention is a flow chart of the greedy algorithm for solving the optimal batching scheme in the furnace. DETAILED DESCRIPTION

[0065] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific implementation methods, so that the implementation process of how the present application uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly. Embodiment 1

[0066] Please refer to Figure 1-Figure 3 This embodiment proposes a capacity assessment method for a heat treatment workshop that takes capacity constraints into consideration. The workpieces are grouped and packaged according to their temperature compatibility, and a greedy algorithm is used in each category to find the optimal batching solution in the furnace, thereby minimizing the heat treatment time. This can effectively improve the production efficiency of the workshop and make rational use of the internal space of the heat treatment furnace. Therefore, the present invention can not only provide a capacity assessment method for heat treatment workshops, but also help workshops to reasonably plan furnace schedules to a certain extent, save time and resources, and improve production efficiency, which has certain practical significance. Please refer to Figure 1 , the method comprises the following steps:

[0067] S1. Obtain heat treatment data corresponding to the orders produced by the heat treatment workshop. Specifically, according to the orders produced by the workshop, obtain the target output, size, target temperature range, quality and other related attributes of the workpiece to be processed, as well as the capacity, average space utilization rate, delivery deadline and other information of the heat treatment furnace.

[0068] The number of the workpiece to be processed is , ,in is the total number of workpiece types; the size of the workpiece to be processed is , ; The target temperature range of the workpiece to be processed is , ; The mass of the workpiece to be processed is , ; The specific heat capacity of the workpiece to be processed is , .

[0069] The capacity of the heat treatment furnace is V, the average space utilization rate of the heat treatment furnace is U; the actual available capacity of the heat treatment furnace is V×U.

[0070] The delivery deadline is , the delivery period here is the longest heat treatment time.

[0071] S2. Classify all types of workpieces to be processed into groups according to heat treatment data, such as Figure 2As shown, according to the target temperature range of the heat treatment of the workpiece to be processed, the workpieces with overlapping target temperature ranges are allocated to one category. Such workpieces can be loaded into the furnace at the same time and heated to the same temperature according to the same heating power; workpieces without temperature intersections are allocated to another category; if the target temperature range of a certain workpiece overlaps with the ranges of multiple categories, the category with the highest temperature overlap is selected to form the furnace.

[0072] S3. With the goal of minimizing the heat treatment time, an in-furnace batching model is established to seek the best batch allocation plan in each furnace classification. The in-furnace batching model includes the objective function of minimizing the heat treatment time and the corresponding constraints.

[0073] S4. Use the greedy algorithm to solve the furnace batching model to obtain the optimal furnace batching scheme. The optimal furnace batching scheme is the sum of the minimized heat treatment time of all furnace batching schemes, such as Figure 3 shown.

[0074] S5. Compare the delivery deadline of the heat treatment workshop orders based on the optimal batching plan in the furnace and evaluate the production capacity of the heat treatment workshop.

[0075] The method first determines detailed information such as the output of workpieces that the enterprise plans to produce, and then classifies the furnaces according to the temperature compatibility of the workpieces. Within each furnace category, a greedy algorithm is used to find the optimal allocation scheme for the batches in the furnace to minimize the heat treatment time, which serves as the basis for the enterprise's capacity assessment. Furthermore, this embodiment will construct an example of heat treatment workshop production and manufacturing, and combine the method of the present invention to perform capacity assessment.

[0076] A production workshop has received a heat treatment order. Now the workshop needs to plan a production plan to determine how long it will take to complete the target output of the workpiece to be processed and whether the order production can be completed according to the delivery deadline. Now assume that there is only one heat treatment furnace available in the company's heat treatment workshop. The capacity of the heat treatment furnace is 80cm and the average space utilization rate is 85%. The delivery deadline is 4 hours. The loading rate is 80cm / min, the cooling rate is 50cm / min, the removal rate is 60cm / min, the heating power is 500w, and the room temperature is 20℃. The specific information of the workpiece to be processed is shown in Table 1:

[0077]

[0078] According to the above workpiece information, the algorithm in step S2 of the present invention is used to solve the problem. The specific process is as follows:

[0079] S21, define the workpiece category and initialize the workpiece parameters, including size, target temperature range of heat treatment, etc.;

[0080] S22. Define temperature coincidence function , calculate the overlap of the temperature range of each two workpieces, through The function finds the starting point with the highest temperature in the workpiece and the lowest end point , the return value is the length of the overlapping part or 0;

[0081] S23. Define furnace group classification function And initialize an empty dictionary , traverse the categories and workpieces, determine the overlap of the temperature range, if the current overlap is greater than the maximum overlap previously recorded, update the maximum overlap and the best category, if no suitable category is found, create a new category, add the workpiece to the selected category, and update the target temperature range of the category, and finally return dictionary;

[0082] S24. Use of After the function inputs the workpiece size, mass, and specific heat capacity of the order, it calls the furnace group classification method to output the workpiece category in each furnace category and detailed information about the workpiece.

[0083] According to the above algorithm steps and the specific information of the workpiece, the output results are shown in Table 2:

[0084]

[0085] In order to maximize the capacity of the heat treatment furnace and save production time and resources, the present invention considers using a greedy algorithm to find the optimal solution for the batching scheme in the furnace. The following are the solution steps of the greedy algorithm proposed in step S4, and the specific process is as follows:

[0086] S41. Definition function, It is the core function of furnace loading. According to the space of each furnace and the properties of each workpiece, it selects the appropriate workpieces to fill the furnace. Create an empty list furnaces to store the workpiece information loaded in each furnace;

[0087] S42, main loop: when When there are unfinished workpieces in the furnace, load the furnace and create List, record the number of workpieces loaded in the current furnace. Initialization for , indicating the current remaining available space for the furnace. Reset and Set to 0 and prepare for heat treatment time calculation.

[0088] S43, internal circulation: the furnace is not filled and If there are still unfinished workpieces, continue to select workpieces to load, and the variable and Initialization: Represents the best choice of the current workpiece and its time, set to None and inf respectively.

[0089] Traverse each artifact, according to and Calculate the loading, cooling, unloading, and heating times for a workpiece, and then calculate the total processing time for that workpiece: Indicates the time of complete heat treatment of the workpiece. As the workpiece processing time excluding the holding time, it is used for the cumulative heating processing time. ; If the current workpiece Less than , then Set to the index of this artifact , and update and .

[0090] For example, if the workpiece type is 1, the size is 7, and the loading rate is 80, then the loading time is: 7÷80=0.0875, and the calculation method of cooling and unloading time is the same;

[0091] The calculation of heating time is: 0.88×6.6×(605+273.15)÷500÷60=0.17, where 605 is the difference between the average of the target maximum and minimum temperatures of the furnace group category and the room temperature.

[0092] S44, add the best workpiece to the furnace, if If a suitable artifact is found, update the following:

[0093] , that is: increase the loading quantity of the workpiece;

[0094] , namely: update the remaining space of the furnace;

[0095] , that is: reduce the target output of the workpiece;

[0096] , i.e.: cumulative heating treatment time;

[0097] , that is: update the maximum holding time in the furnace;

[0098] If no suitable workpiece is found, the inner loop is exited.

[0099] S45. After loading is completed, add the current furnace result to the furnaces list. If If there are still unsatisfied workpiece requirements, reset the furnace loading parameters to enter the next heat. of The function input information and the above time calculation and loop will return furnaces, showing the number of workpieces loaded in each furnace and its corresponding heat treatment time.

[0100] According to the above algorithm steps, the optimal loading solution of the greedy algorithm can be output, as shown in Table 3:

[0101]

[0102] It can be seen that to complete the heat treatment of all the workpieces to be processed based on one heat treatment furnace, the time required is: 67+88+44=199 minutes. Since 199<240, the order task can be completed on time according to the current plan. Embodiment 2

[0103] On the basis of Example 1, which is a specific implementation method of the present invention in a small-scale case, Example 2 is now given to demonstrate a specific implementation method in a large-scale workpiece case.

[0104] A workshop has received a heat treatment order. Now the workshop needs to plan the production plan and determine how long it will take to complete the target output of the workpiece to be processed. Assume that the workshop has only one heat treatment furnace available, the capacity of the heat treatment furnace is 8000cm, and the average space utilization rate is 80%. The delivery deadline is 72h. The loading rate is 8000cm / h, the cooling rate is 2000cm / h, the removal rate is 5000cm / h, the heating power is 40kw, and the room temperature is 20℃. The specific information of the workpiece to be processed is shown in Table 4:

[0105]

[0106] According to the above workpiece information, the algorithm in step S2 of the present invention is used to solve the problem. The specific process is as follows:

[0107] S21, define the workpiece category and initialize the workpiece parameters, including size, target temperature range of heat treatment, etc.;

[0108] S22. Define temperature coincidence function , calculate the overlap of the temperature range of each two workpieces, through The function finds the starting point with the highest temperature in the workpiece and the lowest end point , the return value is the length of the overlapping part or 0;

[0109] S23. Define furnace group classification function And initialize an empty dictionary , traverse the categories and workpieces, determine the overlap of the temperature range, if the current overlap is greater than the maximum overlap previously recorded, update the maximum overlap and the best category, if no suitable category is found, create a new category, add the workpiece to the selected category, and update the target temperature range of the category, and finally return dictionary;

[0110] S24. Use of After the function inputs the workpiece size, mass, and specific heat capacity of the order, it calls the furnace group classification method to output the workpiece category in each furnace category and detailed information about the workpiece.

[0111] According to the above algorithm steps and the specific information of the workpiece, the output results are shown in Table 5:

[0112]

[0113] In order to maximize the use of the capacity of the heat treatment furnace and save production time and resources, the present invention considers using a greedy algorithm to find the optimal solution for the batching scheme in the furnace. The following are the solution steps of the greedy algorithm. The detailed solution steps have been shown in Example 1, and Example 2 is simplified as follows:

[0114] S41. Create an empty list Store the workpiece information of each furnace batch loading, initialize List, used to record the loading quantity of each workpiece in the current furnace;

[0115] S42, outer cycle: when any workpiece does not reach the target output, continue to cycle; inner cycle: when there is remaining space in the furnace and there are workpieces that do not reach the target output, continue to try to load the workpiece;

[0116] S43, select the workpiece that has the shortest heat treatment time among all the workpieces currently loaded into the furnace and load it into the furnace. If there is no suitable workpiece to be loaded, end the loading of the current furnace and add the loading status of the current furnace to List;

[0117] S44, check the last furnace: if If there is a positive number in the list, it means that the last furnace still has workpieces loaded, and it needs to be added to the result list;

[0118] S45, Return List containing information about the workpieces loaded into each furnace.

[0119] According to the above algorithm steps, the optimal loading solution of the greedy algorithm can be output, as shown in Table 6:

[0120]

[0121] Therefore, the total heat treatment time based on a single heat treatment furnace is: 17+15+9+17=58 hours. Since 58<72 hours, the workshop can complete the production plan within the delivery period; and the heat treatment is carried out according to the above loading plan with the shortest time.

[0122] In summary, the capacity assessment method of the heat treatment workshop considering capacity constraints of the present invention classifies and packages the workpieces into furnaces according to their temperature compatibility, and uses a greedy algorithm to find the optimal batching scheme in each category, minimizing the heat treatment time, which can effectively improve the production efficiency of the workshop and make rational use of the internal space of the heat treatment furnace. Therefore, the present invention can not only provide a capacity assessment method for the heat treatment workshop, but also help the workshop production to reasonably plan the furnace schedule to a certain extent, save time and resources, and improve production efficiency, which has certain practical significance.

[0123] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, so the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0124] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the above embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0125] The above implementation methods have been described in detail. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for evaluating the capacity of a heat treatment workshop considering capacity constraints, characterized in that: The Method The process includes: S1 obtains heat treatment data corresponding to the order produced by the heat treatment workshop; S2 classifies all types of workpieces to be processed into groups according to the heat treatment data. The group classification method includes the following steps: S21, define the workpiece category and initialize the workpiece parameters, including size and target temperature range of heat treatment; S22, define a temperature overlap function calculate_overlap, calculate the overlap of the temperature ranges of each two workpieces, find the starting point overlap_start with the highest temperature and the ending point overlap_end with the lowest temperature in the workpieces through the min&max function, and return the length of the overlapped part or 0; S23, define a furnace classification function classify_workpieces and initialize an empty dictionary categories = {}, traverse the categories and workpieces, determine the overlap of the temperature range, if the current overlap is greater than the maximum overlap recorded previously, update the maximum overlap and the best category, if no suitable category is found, create a new category, add the workpiece to the selected category, and update the target temperature range of the category, and finally return the categories dictionary; S24, after inputting the workpiece size, mass, and specific heat capacity of the order using the input function of Python, the furnace group classification method is called to output the workpiece category in each furnace category and detailed information of the workpiece; S3 takes minimizing the heat treatment time as the goal, and establishes an in-furnace batch model for seeking the best batch allocation scheme in each furnace classification. The in-furnace batch model includes an objective function of minimizing the heat treatment time and corresponding constraints. The objective function is: Where Z is the heat treatment time to complete the target output of all workpieces; j is the batch number in this group of furnace classification, j = 1, 2, ..., J'; t_total j is the heat treatment time of the jth batch; J′ is the total number of batches in this group of furnace classifications; The constraints are as follows: The total size of the workpieces to be processed loaded into batch j of this category must not exceed the available capacity of the furnace, that is: All workpieces to be processed must complete the heat treatment of the target output, that is: The calculation of loading time for each batch is: The calculation of heating time for each batch is: The calculation of ΔT is based on the median of the final temperature range of the furnace classification group minus the room temperature, that is: The calculation of the holding time for each batch is: The calculation of the cooling time for each batch is: The calculation of the take-out time for each batch is: The total heat treatment time for each batch is calculated as: The value range of the decision variable is a positive integer, that is: In the above formula, i is the workpiece in this group of furnace classification, i = 1, 2, ..., I'; j is the batch number in this group of furnace classification, j = 1, 2, ..., J'; x ij is a decision variable, representing the number of workpieces i loaded in batch j in this category; L i represents the size of the workpiece i; V is the volume of the heat treatment furnace; U is the average space utilization rate of the heat treatment furnace; G i It represents the target output of workpiece i; t_load j represents the loading time of the jth batch; v load is the loading rate; t_heat j represents the heating time of the jth batch; P represents the heating power of the heat treatment furnace; c i is the specific heat capacity of workpiece i, m i is the mass of workpiece i; T j max is the maximum value of the final overlap temperature range in this group of furnace classifications; T j min is the minimum value of the final overlap temperature range in this group of furnace classifications; T room is the average indoor temperature; t_keep j It represents the holding time of the jth batch; t_keep ij It represents the holding time of workpiece i in batch j, and the holding time of batch j should be the maximum holding time of all workpieces in the batch; t_cool j represents the cooling time of batch j; v cool It indicates the cooling rate; t_unload j It indicates the removal time; v unload is the removal rate; S4 uses a greedy algorithm to solve the furnace batching model to obtain the optimal furnace batching scheme. The optimal furnace batching scheme is the sum of the minimized heat treatment time of all furnace batching schemes. S5 compares the delivery deadline of the heat treatment workshop orders based on the optimal batching plan in the furnace and evaluates the production capacity of the heat treatment workshop.

2. The heat treatment workshop capacity assessment method according to claim 1, characterized in that: The heat treatment data includes the size L of each workpiece to be processed i , Target temperature range of heat treatment Mass m i Specific heat capacity c i , where i = 1, 2, ..., I, I is the total number of workpiece types; Also, the capacity V of the heat treatment furnace, the average space utilization U, and the delivery deadline T_deadline.

3. The heat treatment workshop capacity assessment method according to claim 2, characterized in that: The delivery deadline T_deadline is the longest heat treatment time.

4. The heat treatment workshop capacity assessment method according to claim 1, characterized in that: The group furnace classification includes allocating workpieces with overlapping target temperature ranges to a category according to the target temperature ranges of the heat treatment of the workpieces to be processed, and such workpieces can be loaded into the furnace at the same time and heated to the same temperature according to the same heating power; Workpieces without temperature overlap are assigned to another category; if the target temperature range of a certain workpiece overlaps with the ranges of multiple categories, the category with the highest temperature overlap is selected to group the furnaces.

5. The heat treatment workshop capacity assessment method according to claim 1, characterized in that: The method of using a greedy algorithm to solve the in-furnace batching model to obtain an optimal in-furnace batching solution includes the following steps: S41, create an empty list furnaces to store the workpiece information of each furnace batch loaded, initialize the loaded_workpieces list to record the loading quantity of each workpiece in the current furnace; S42, outer cycle: when any workpiece does not reach the target output, continue to cycle; inner cycle: when there is remaining space in the furnace and there are workpieces that do not reach the target output, continue to try to load the workpiece; S43, selecting a workpiece that has the shortest heat treatment time among all the workpieces currently loaded into the furnace and loading it into the furnace; if there is no suitable workpiece to be loaded, the loading of the current furnace is terminated, and the loading status of the current furnace is added to the furnaces list; S44, check the last furnace: if there is a positive number in the loaded_workpieces list, it means that the last furnace still has loaded workpieces, which need to be added to the result list; S45. Return the furnaces list, which includes the workpiece information loaded in each furnace.

6. The heat treatment workshop capacity assessment method according to claim 1, characterized in that: The capacity of the heat treatment workshop is evaluated by comparing the delivery deadline of the heat treatment workshop order based on the optimal batching plan in the furnace, specifically including: According to the optimal batching scheme in each furnace classification, calculate the sum of the minimized heat treatment time for all batches, compare it with the delivery deadline planned by the workshop to make a capacity assessment and determine whether the time meets the delivery deadline; If the conditions are met, the workpieces to be processed will be heat treated according to the optimal batching plan in the furnace; if not, adjustments will be made based on the actual situation.

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