Material pre-assignment method for intelligent scheduling before discrete manufacturing scenarios
By employing ATP checks and greedy algorithms to optimize material pre-allocation in discrete manufacturing scenarios, the problem of inaccurate material pre-allocation was solved, achieving balanced production equipment and optimal allocation of key materials, thereby improving production efficiency and equipment utilization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHANGHAI BINTONG INTELLIGENT TECH CO LTD
- Filing Date
- 2022-07-22
- Publication Date
- 2026-06-02
AI Technical Summary
Existing material pre-allocation strategies cannot cope with the diverse needs of manufacturing scenarios. They do not take into account materials in transit and material procurement plans that may change at any time. Factors such as changes in order delivery dates, changes in order priorities, material shortages, shortages of machinery and equipment, and fluctuations in the value of finished products in manufacturing orders lead to inaccurate material pre-allocation results and failure to effectively utilize equipment capacity, resulting in excessive manpower and inefficient output.
We adopt a material pre-allocation method for intelligent production scheduling in discrete manufacturing scenarios. Through ATP checks, greedy algorithms, and critical material calculations, we optimize the material allocation process to ensure that the materials in stock and in transit meet order requirements, rationally allocate manufacturing orders, and take into account equipment capacity and material attributes to achieve multi-objective optimization, ensuring balanced production equipment and optimal allocation of critical materials.
It solves the problem of inaccurate material pre-allocation results, achieves balanced operation of production equipment, prioritizes order delivery time, maximizes machine and equipment capacity, maximizes utilization rate, and optimizes the allocation of key materials without disrupting the existing production plan, thereby improving production efficiency and equipment utilization.
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Figure CN115310784B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of material allocation technology, and in particular to a material pre-allocation method for intelligent scheduling in discrete manufacturing scenarios. Background Technology
[0002] In discrete manufacturing scenarios, whether the established production plan is short of materials, or whether an optimal production schedule can be formulated based on limited materials, depends on the reasonable and effective pre-allocation of materials.
[0003] Currently, common material pre-allocation schemes in the market involve obtaining material requirements based on production orders (Manufacturing Orders, MOs) and bills of materials (partially occupied materials and scheduled into non-freeze-out orders, BOMs), and then combining this with inventory for material pre-allocation. However, actual production and manufacturing scenarios are always diverse, and current material pre-allocation strategies cannot cope with the needs of multiple scenarios and have many shortcomings, such as: First, they only consider materials in stock and do not consider materials in transit or material procurement plans that may change at any time;
[0004] Second, it did not take into account the possible changes in material pre-allocation results caused by changes in order delivery dates, changes in order priorities, material shortages, machine and equipment shortages, and fluctuations in the value of finished products in manufacturing orders (MOs).
[0005] Third, it does not consider the dependence of orders on materials (material-occupied, partially material-occupied, and non-material-occupied), nor the specific attributes of the materials themselves (critical materials, non-critical materials, etc.) on the delivery of orders of different value.
[0006] Fourth, the principle of "non-disruption" of the (previously) established production plan was not considered;
[0007] Fifth, it did not take into account the requirements for equipment balancing and full utilization of limited production capacity;
[0008] Sixth, excessive manpower leads to low output. In view of this, this application proposes a material pre-allocation method for intelligent scheduling in discrete manufacturing scenarios. Summary of the Invention
[0009] The purpose of this invention is to address the shortcomings of existing technologies by proposing a material pre-allocation method for intelligent production scheduling in discrete manufacturing scenarios.
[0010] To achieve the above objectives, the present invention adopts the following technical solution:
[0011] A material pre-allocation method for intelligent scheduling in discrete manufacturing scenarios includes the following steps:
[0012] S1. Select the list of manufacturing orders (MO) to be allocated, and select the materials in transit and in stock, as well as the orders A1 that are fully equipped and placed in the freeze period and B0 that are partially occupied and placed in the non-freeze period as the list of manufacturing orders (MO).
[0013] S2, ATP check, select the manufacturing orders (MO) list to participate in the allocation in step S1.
[0014] ATP testing is performed, and those that do not meet the ATP test results are further converted to bushing and side-by-side arrangement.
[0015] Order A1, which is in the freeze period, and a portion of the materials are placed in order B0, which is not in the freeze period, thus satisfying the requirements.
[0016] After deducting the materials occupied by the complete set and placing them into the frozen period order A1 inventory, the remaining inventory after deduction is output.
[0017] S3. Manufacturing Order (MO) Material Occupancy Calculation: The material occupancy process in step S1 is calculated by using a greedy algorithm to calculate the manufacturing order (MO) material occupancy. The results are: complete material occupancy A, partial material occupancy B, no material occupancy C, and inventory deduction remainder. Simultaneously, the partial material occupancy that does not meet the ATP check in step S2 is calculated by using a greedy algorithm to calculate the manufacturing order (MO) material occupancy. The results are: complete material occupancy A, partial material occupancy B, no material occupancy C, inventory deduction remainder, and no material occupancy C0. Among these, complete material occupancy A is the work order A' that occupies the equipment capacity. The manufacturing order (MO) material occupancy process is recalculated after the ATP check result does not meet the requirement, and the output results are: complete material occupancy A, partial material occupancy B, no material occupancy C, and inventory deduction remainder.
[0018] S4. Calculate the critical material occupancy. Calculate the critical material occupancy by taking the partially occupied material B, the unoccupied material C, and the inventory deduction remainder from step S3. Output the calculated partially occupied material B, the unoccupied material C, and the inventory deduction remainder as the final result. Also output the final result as well as the complete set occupancy A after calculating the occupancy of the manufacturing order MO using the greedy algorithm, and the complete set occupancy in step S1 and the frozen order A1.
[0019] Preferably, the ATP check in step S2 includes the following steps:
[0020] The first step is to classify and statistically analyze the materials and demand of manufacturing orders (MO) with material occupancy information, and to classify and statistically analyze the materials in transit and in stock to obtain the total amount of materials.
[0021] The second step is to check the material types and quantities based on the material, demand and total quantity statistics obtained in the previous step, and determine whether the materials in transit and in stock meet the order requirements.
[0022] The third step is to use a greedy algorithm to calculate the material requirements of order MO for results that are not met in the second step, while directly deducting the material requirements for complete sets and placing them into the frozen order A1 inventory.
[0023] Preferably, the material occupancy process calculation for the manufacturing order (MO) in step S3 includes the following steps:
[0024] Step 1: Based on the greedy algorithm, assign all Manufacturing Orders (MOs) to the machinery. After filtering a portion of the Manufacturing Orders (MOs) on each machine using the minimum Manufacturing Orders (MOs) filtering rules and parameters, check if any machine does not have a Manufacturing Orders (MOs) list. If so, report an error. Simultaneously, after deducting materials and remaining machine capacity from the locked Manufacturing Orders (MOs), the Manufacturing Orders (MOs) list, in-transit and in-stock materials, available machine capacity, and Manufacturing Orders (MOs) capacity vector, output the following results: remaining Manufacturing Orders (MOs) list, remaining in-transit and in-stock material information, and remaining machine capacity.
[0025] The second step involves selecting each of the remaining Manufacturing Orders (MOs), remaining inventory and in-transit materials, and remaining machine capacity obtained in the previous step, placing them on machines with remaining capacity, and then locking the production-ready Manufacturing Orders (MOs) again, outputting the final result.
[0026] Preferably, the critical material inventory calculation in step S4 includes the following steps:
[0027] Step 1: Release the material occupancy of Manufacturing Orders (MOs). Sort the list of Manufacturing Orders (MOs) by Manufacturing Order (MO), update the original material occupancy results of Manufacturing Orders (MOs), and output the material occupancy information of Manufacturing Orders (MOs), the material occupancy information of critical materials in Manufacturing Orders (MOs), and the material occupancy information of non-critical materials in Manufacturing Orders (MOs).
[0028] The second step is to deduct the materials in stock and in transit from the original material holding results of the updated Manufacturing Order (MO) and then check whether the material information has been reduced for unpaid items. If so, keep it immediately; otherwise, output the non-critical material holdings of the Manufacturing Order (MO).
[0029] Preferably, the manufacturing order (MO) information assigned to the machinery includes the manufacturing order MO on the list, materials in transit and in stock, the relationship information between the manufacturing order MO and the available machine, and the list of existing manufacturing order MOs on the machine. The manufacturing order MO is then selected one by one from the list of existing manufacturing order MOs on the machine and placed on the machine with spare capacity. The production manufacturing order MO is then locked again, and the final result is output.
[0030] Preferably, the critical material holding information of the manufacturing order MO includes manufacturing orders MO with no material holding, manufacturing orders MO with critical material holding, and manufacturing orders MO with all critical materials in place. After deducting the materials in stock and in transit, non-critical material records and critical material records are output. The non-critical material holding information of the manufacturing order MO includes manufacturing orders MO with non-critical materials not in place and manufacturing orders MO with all materials in place.
[0031] The present invention has the following beneficial effects:
[0032] By deeply optimizing common material pre-allocation strategies in the market based on multiple objectives, this invention solves the problem of unquantifiable control over material availability caused by changes in inventory, procurement plans, order delivery dates, order priorities, material shortages, production equipment shortages, fluctuations in finished product value, changes in material attributes (critical / non-critical materials), and capacity variations before AS scheduling. During the deep optimization process, this invention focuses on achieving several optimization objectives: balanced production equipment operation, priority for order delivery dates, maximum machine capacity, highest utilization rate, optimal allocation of critical materials, and without violating the principle of "orders in production." Attached Figure Description
[0033] Figure 1 This is a schematic diagram illustrating the implementation process of the material pre-allocation method for intelligent production scheduling in a discrete manufacturing scenario proposed in this invention.
[0034] Figure 2 This is a schematic diagram of the ATP inspection process for in-stock and in-transit materials in this invention for complete material-occupied orders A1 and partially material-occupied orders B0 that are not in the frozen period.
[0035] Figure 3 This is a schematic diagram of the manufacturing order (MO) material occupancy calculation logic based on the greedy algorithm in this invention;
[0036] Figure 4 This is a schematic diagram of the logic flow for calculating the critical material occupancy of manufacturing orders (MO) of types B and C in this invention. Detailed Implementation
[0037] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.
[0038] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.
[0039] Material pre-allocation methods for intelligent scheduling in discrete manufacturing scenarios, such as Figure 1 As shown, it includes the following steps:
[0040] S1. Select the list of manufacturing orders (MO) to be allocated, and select the materials in transit and in stock, as well as the orders A1 that are fully equipped and placed in the freeze period and B0 that are partially occupied and placed in the non-freeze period as the list of manufacturing orders (MO).
[0041] S2, ATP check, select the manufacturing orders (MO) list to participate in the allocation in step S1.
[0042] ATP testing is performed, and those that do not meet the ATP test results are further converted to bushing and side-by-side arrangement.
[0043] Order A1, which is in the freeze period, and a portion of the materials are placed in order B0, which is not in the freeze period, thus satisfying the requirements.
[0044] After deducting the materials occupied by the complete set and placing them into the frozen period order A1 inventory, the remaining inventory after deduction is output.
[0045] S3. Manufacturing Order (MO) Material Occupancy Calculation: The material occupancy process in step S1 is calculated by using a greedy algorithm to calculate the manufacturing order (MO) material occupancy. The results are: complete material occupancy A, partial material occupancy B, no material occupancy C, and inventory deduction remainder. Simultaneously, the partial material occupancy that does not meet the ATP check in step S2 is calculated by using a greedy algorithm to calculate the manufacturing order (MO) material occupancy. The results are: complete material occupancy A, partial material occupancy B, no material occupancy C, inventory deduction remainder, and no material occupancy C0. Among these, complete material occupancy A is the work order A' that occupies the equipment capacity. The manufacturing order (MO) material occupancy process is recalculated after the ATP check result does not meet the requirement, and the output results are: complete material occupancy A, partial material occupancy B, no material occupancy C, and inventory deduction remainder.
[0046] S4. Calculate the critical material occupancy. Calculate the critical material occupancy by taking the partially occupied material B, the unoccupied material C, and the inventory deduction remainder from step S3. Output the calculated partially occupied material B, the unoccupied material C, and the inventory deduction remainder as the final result. Also output the final result as well as the complete set occupancy A after calculating the occupancy of the manufacturing order MO using the greedy algorithm, and the complete set occupancy in step S1 and the frozen order A1.
[0047] Reference Figure 2 The ATP check in step S2 includes the following steps:
[0048] The first step is to classify and statistically analyze the materials and demand of manufacturing orders (MO) with material occupancy information, and to classify and statistically analyze the materials in transit and in stock to obtain the total amount of materials.
[0049] The second step is to check the material types and quantities based on the material, demand and total quantity statistics obtained in the previous step, and determine whether the materials in transit and in stock meet the order requirements.
[0050] In addition, the manufacturing order (MO) information assigned to the machinery includes the manufacturing order MO on the list, materials in transit and in stock, the relationship information between the manufacturing order MO and the available machines, and the list of existing manufacturing order MOs on the machines. The manufacturing order MOs are then selected one by one from the list of existing manufacturing order MOs on the machines and placed on machines with spare capacity. The production manufacturing order MOs are then locked again, and the final result is output.
[0051] It should be noted that, for the in-stock and in-transit material inspection process for A1 and B0, there are two possible results:
[0052] The first method: If the materials in stock and in transit meet the needs of A1 and B0, the deduction is made directly from the inventory (only A1 is deducted);
[0053] The second scenario: If the conditions are not met, a greedy algorithm-based MO material occupancy calculation needs to be performed (see 3.2.3). The output will be different material occupancy results for three types of MOs: A, B, and C. However, whether a MO is included in the freeze period depends on the AS calculation after material pre-allocation. Only after the AS calculation is completed will the three types of MOs (A, B, and C) be marked with 0 / 1.
[0054] The third step is to use a greedy algorithm to calculate the material requirements of order MO for results that are not met in the second step, while directly deducting the material requirements for complete sets and placing them into the frozen order A1 inventory.
[0055] Reference Figure 3 The calculation of the material occupancy process for the manufacturing order (MO) in step S3 includes the following steps:
[0056] Step 1: Based on the greedy algorithm, assign all Manufacturing Orders (MOs) to the machinery. After filtering a portion of the Manufacturing Orders (MOs) on each machine using the minimum Manufacturing Orders (MOs) filtering rules and parameters, check if any machine does not have a Manufacturing Orders (MOs) list. If so, report an error. Simultaneously, after deducting materials and remaining machine capacity from the locked Manufacturing Orders (MOs), the Manufacturing Orders (MOs) list, in-transit and in-stock materials, available machine capacity, and Manufacturing Orders (MOs) capacity vector, output the following results: remaining Manufacturing Orders (MOs) list, remaining in-transit and in-stock material information, and remaining machine capacity.
[0057] The second step involves selecting each of the remaining Manufacturing Orders (MOs), remaining inventory and in-transit materials, and remaining machine capacity obtained in the previous step, placing them on machines with remaining capacity, and then locking the production-ready Manufacturing Orders (MOs) again, outputting the final result.
[0058] It should be noted that the material occupancy calculation process for this manufacturing order (MO) innovatively considers the following factors, which are not covered in conventional industry strategies:
[0059] (1) The real-time changes of materials in stock and in transit were taken into account, which affected the previous (material pre-allocation) material occupancy results, thus ensuring that the MO list to be processed in this material pre-allocation is complete.
[0060] (2) When allocating MOs to machines, the MOs are first sorted. The sorting process takes into account factors such as the capacity requirements of each MO for multiple available machines, material requirements, MO priority, MO finished product value, equipment scarcity, and material scarcity, to ensure that the MO sorting is as accurate, reasonable, and unique as possible. This includes, but is not limited to, allocating scarce machines and scarce materials to MOs with high priority or high value, as well as based on the capacity requirements of each MO on each available machine (which may have different capacities), to achieve precise and optimized allocation.
[0061] (3) Ensure that the MO capacity already occupied on the machinery and equipment can be produced on schedule (without disrupting the principle of past production plans);
[0062] (4) Abstract the screening rules from the physical scene to select the minimum MOs to be done on each machine; and consider the energy consumption threshold of each machine during the screening process; for MOs that exceed the threshold, select the machines with remaining capacity and the relatively optimal remaining capacity one by one to do advantageous MO affiliation.
[0063] (5) It ensures that the capacity of each machine is utilized and that the workload of all machines is relatively balanced.
[0064] (6) It ensured that the MO orders (Class A minimum + incremental) that were locked in for production had a certain number of machines available for production.
[0065] Reference Figure 4 The critical material inventory calculation in step S4 includes the following steps:
[0066] Step 1: Release the material occupancy of Manufacturing Orders (MOs). Sort the list of Manufacturing Orders (MOs) by Manufacturing Order (MO), update the original material occupancy results of Manufacturing Orders (MOs), and output the material occupancy information of Manufacturing Orders (MOs), the material occupancy information of critical materials in Manufacturing Orders (MOs), and the material occupancy information of non-critical materials in Manufacturing Orders (MOs).
[0067] The second step is to deduct the materials in stock and in transit from the original material holding results of the updated Manufacturing Order (MO) and then check whether the material information has been reduced for unpaid items. If so, keep it immediately; otherwise, output the non-critical material holdings of the Manufacturing Order (MO).
[0068] Specifically, the critical material inventory information for Manufacturing Orders (MOs) includes MOs with no inventory, MOs with critical material inventory, and MOs with all critical materials in stock. After deducting materials in inventory and in transit, non-critical material records and critical material records are output. The non-critical material inventory information for Manufacturing Orders (MOs) includes MOs with incomplete non-critical material stock and MOs with all materials in stock. It is worth mentioning that the critical material inventory calculation method has an advantage over others in the industry:
[0069] (1) This calculation gives customers enough operational freedom to support users to release the material occupancy information of MO (which comes from the result of the previous material pre-allocation) to support this calculation and obtain the key material occupancy result that the customer expects to output this time.
[0070] (2) Users can also independently calibrate key materials at any time according to actual production needs or market changes;
[0071] (3) Information on the availability of key materials can help customers make decisions on the next period's procurement plan;
[0072] (4) When it is calculated that the critical and non-critical materials of a MO are all available, as long as the machines are available (or new machines are purchased), these MOs can be included in the AS production schedule at any time. However, the existing solutions on the market will only make these MOs wait for the start of the next round of material pre-allocation calculation (because a new start will be included in the new capacity), which does not meet the application scenarios of the production line.
[0073] In summary, this paper proposes a material pre-allocation solution for use in discrete manufacturing scenarios before intelligent scheduling (AS). This solution overcomes the limitations of existing material allocation strategies on the market and has been proven effective in production planning and execution. By performing deep optimization based on multiple objectives on common material pre-allocation strategies, it solves the problem of unquantifiable control over material availability before AS scheduling due to changes in inventory, procurement plans, order delivery dates, order priorities, material shortages, production equipment shortages, fluctuations in finished product value, changes in material attributes (critical / non-critical materials), and capacity variations. During the deep optimization process, this invention focuses on achieving several optimization objectives: balanced production equipment operation, priority for order delivery dates, maximum machine capacity, highest utilization rate, optimal allocation of critical materials, and without violating the principle of "orders in production."
[0074] Furthermore, in this article, AP stands for Advanced Planning.
[0075] AS: Advanced Scheduling;
[0076] APS: Advanced Planning and Scheduling;
[0077] MO: Manufacture order;
[0078] ATP: Available to Promise;
[0079] A / B / C represent the completeness status of MO orders (A indicates complete set with materials occupied / B indicates partial set with materials occupied / C indicates no set with materials occupied);
[0080] 1 / 0 represents MO orders that are in a frozen period (1) or not frozen period (0);
[0081] MO orders that are in the freeze period will definitely be put into production after AS scheduling if there is no problem with insufficient equipment capacity; MO orders that are not in the freeze period will also be put into production after AS scheduling if there is sufficient equipment capacity.
[0082] There are typically three types of MO orders:
[0083] A1: Complete sets of materials are placed in the freeze period;
[0084] B0: Partial material is occupied and discharged during the non-freezing period;
[0085] C0: Unoccupied material is discharged into the non-freezing period.
[0086] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.
Claims
1. A material pre-allocation method for intelligent scheduling in discrete manufacturing scenarios, characterized in that, Includes the following steps: S1. Select the list of manufacturing orders (MO) to be allocated, and select the materials in transit and in stock, as well as the orders A1 that are fully equipped and placed in the freeze period and B0 that are partially occupied and placed in the non-freeze period as the list of manufacturing orders (MO). S2. ATP check: The list of manufacturing orders (MO) selected in step S1 for allocation is checked by ATP. If the result of the ATP check is not met, the orders are converted into complete sets of materials and placed into frozen order A1, and partially materials are placed into non-frozen order B0. If the result is met, the inventory of complete sets of materials placed into frozen order A1 is deducted and the inventory deduction remainder is output. S3. Manufacturing Order (MO) Material Occupancy Calculation: The material occupancy process in step S1 is calculated by using a greedy algorithm to calculate the manufacturing order (MO) material occupancy. The results are: complete material occupancy A, partial material occupancy B, no material occupancy C, and inventory deduction remainder. Simultaneously, the partial material occupancy that does not meet the ATP check in step S2 is calculated by using a greedy algorithm to calculate the manufacturing order (MO) material occupancy. The results are: complete material occupancy A, partial material occupancy B, no material occupancy C, inventory deduction remainder, and no material occupancy C0. Among these, complete material occupancy A is the work order A' that occupies the equipment capacity. The manufacturing order (MO) material occupancy process is recalculated after the ATP check result does not meet the requirement, and the output results are: complete material occupancy A, partial material occupancy B, no material occupancy C, and inventory deduction remainder. S4. Calculate the critical material occupancy. Calculate the critical material occupancy by taking the partially occupied material B, the unoccupied material C, and the inventory deduction remainder from step S3. Output the calculated partially occupied material B, the unoccupied material C, and the inventory deduction remainder as the final result. Also output the final result as well as the complete set occupancy A after calculating the occupancy of the manufacturing order MO using the greedy algorithm, and the complete set occupancy in step S1 and the frozen order A1.
2. The material pre-allocation method for intelligent scheduling in discrete manufacturing scenarios according to claim 1, characterized in that, The ATP check in step S2 includes the following steps: The first step is to classify and statistically analyze the materials and demand of manufacturing orders (MO) with material occupancy information, and to classify and statistically analyze the materials in transit and in stock to obtain the total amount of materials. The second step is to check the material types and quantities based on the material, demand and total quantity statistics obtained in the previous step, and determine whether the materials in transit and in stock meet the order requirements. The third step is to use a greedy algorithm to calculate the material requirements of order MO for results that are not met in the second step, while directly deducting the material requirements for complete sets and placing them into the frozen order A1 inventory.
3. The material pre-allocation method for intelligent scheduling in discrete manufacturing scenarios according to claim 1, characterized in that, The calculation of the material occupancy process for the manufacturing order (MO) in step S3 includes the following steps: Step 1: Based on the greedy algorithm, assign all Manufacturing Orders (MOs) to the machinery. After filtering a portion of the Manufacturing Orders (MOs) on each machine using the minimum Manufacturing Orders (MOs) filtering rules and parameters, check if any machine does not have a Manufacturing Orders (MOs) list. If so, report an error. Simultaneously, after deducting materials and remaining machine capacity from the locked Manufacturing Orders (MOs), the Manufacturing Orders (MOs) list, in-transit and in-stock materials, available machine capacity, and Manufacturing Orders (MOs) capacity vector, output the following results: remaining Manufacturing Orders (MOs) list, remaining in-transit and in-stock material information, and remaining machine capacity. The second step involves selecting each of the remaining Manufacturing Orders (MOs), remaining inventory and in-transit materials, and remaining machine capacity obtained in the previous step, placing them on machines with remaining capacity, and then locking the production-ready Manufacturing Orders (MOs) again, outputting the final result.
4. The material pre-allocation method for intelligent scheduling in discrete manufacturing scenarios according to claim 1, characterized in that, The critical material inventory calculation in step S4 includes the following steps: Step 1: Release the material occupancy of Manufacturing Orders (MOs). Sort the list of Manufacturing Orders (MOs) by Manufacturing Order (MO), update the original material occupancy results of Manufacturing Orders (MOs), and output the material occupancy information of Manufacturing Orders (MOs), the material occupancy information of critical materials in Manufacturing Orders (MOs), and the material occupancy information of non-critical materials in Manufacturing Orders (MOs). The second step is to deduct the materials in stock and in transit from the original material holding results of the updated Manufacturing Order (MO) and then check whether the material information has been reduced for unpaid items. If so, keep it immediately; otherwise, output the non-critical material holdings of the Manufacturing Order (MO).
5. The material pre-allocation method for intelligent scheduling in discrete manufacturing scenarios according to claim 2, characterized in that, The manufacturing order (MO) information assigned to the machinery includes the list of manufacturing order MOs, materials in transit and in stock, the relationship information between manufacturing order MOs and available machines, and the list of existing manufacturing order MOs on the machinery. The manufacturing order MOs are then selected one by one from the list of existing manufacturing order MOs on the machinery and placed on machines with spare capacity. The production manufacturing order MOs are then locked again, and the final result is output.
6. The material pre-allocation method for intelligent scheduling in discrete manufacturing scenarios according to claim 3, characterized in that, Manufacturing Order MO's critical material holding information includes manufacturing orders MO with no material holding, manufacturing orders MO with critical material holding, and manufacturing orders MO with all critical materials in place. After deducting materials in stock and in transit, it outputs non-critical material records and critical material records. Manufacturing Order MO's non-critical material holding information includes manufacturing orders MO with non-critical materials not in place and manufacturing orders MO with all materials in place.