Production distribution method and system for solving co-production product grading and degrading use based on dimension planning

Abstract

The invention discloses a production distribution method and system for solving the classification and degradation use of linked products based on dimension planning, and the method comprises the steps: (1), initialization and data preparation: analyzing demand orders and basic static data needed by production, constructing a dimension mapping model, and carrying out the preliminary sorting of the demand orders according to a demand distribution rule; step (2), grading matching: carrying out preliminary matching of a production distribution scheme on each demand order in sequence according to the preliminary sorting of the demand orders; in the preliminary matching process of the demand order production distribution scheme, co-production product optimization is carried out; step (3), carrying out feasibility verification of capacity and material constraint on the production distribution scheme matched with each demand order; and step (4), outputting a final production distribution plan of the demand order by adopting reverse scheduling or forward scheduling. The problem that an existing production management system cannot meet the application requirements of global optimization of a production plan and maximum utilization of resources can be solved.

Description

TECHNICAL FIELD

[0001] The present invention relates to the field of production management and planning in manufacturing industry. Specifically, it is a method and system for production allocation based on dimension planning to solve the problem of co-products grading and down-binning. BACKGROUND

[0002] Product grading refers to the process of dividing the same batch of products into different grades according to their different performance or parameters. For example, 512MB chips belong to high grade; 256MB chips belong to middle grade; and 128MB chips belong to low grade. Although these chips are produced in the same way, they will automatically form different grades due to natural differences in the manufacturing process (such as different materials, temperatures, and wafer quality).

[0003] Co-products refer to the situation where, when producing a batch of high-grade products, some accompanying middle-grade or low-grade products are also produced. For example, when producing 512MB chips, some chips may only reach the level of 256MB or 128MB, which are called co-products or by-products. These co-products, although not the main products, still have some value. If managed properly, they can be allocated to other orders, reducing the need for additional production.

[0004] When customers place orders, some may need high-grade products, while others may only need middle or low-grade products. Assuming that the factory has a large inventory of high-grade products (512MB chips) and not enough middle-grade products (256MB chips), the system can allow "using high-grade products to replace low-grade requirements". This is called down-binning, also known as "using good to replace bad". This approach can avoid repeated production, reduce inventory accumulation, and improve production efficiency.

[0005] When managing these grades and attributes, the system establishes a set of dimension models. Simply put, each "dimension" represents a product feature, such as memory size, frequency, power consumption, material, etc. "Dimension planning" is to define these attributes and their relationships in advance; for example, products with a capacity of 512MB belong to "high grade"; products with a capacity of 256MB belong to "middle grade"; and the system knows that 512MB ≥ 256MB ≥ 128MB. Through dimension planning, the computer system can automatically identify the matching relationship between different grades and determine which products can be replaced by each other.

[0006] In the semiconductor, electronic device, chemical industry and precision manufacturing industries, there are often situations where graded products and associated by-products are produced simultaneously during the production process. In reality, different customers have different order requirements, and factories have limited production capacity and limited raw materials. If we rely on manual calculation to determine "which order to produce on which production line, how to use associated by-products, and whether to substitute with downgraded products", not only is the workload large, but it is also easy to make mistakes.

[0007] Existing production management systems (such as traditional ERP, MES) can usually only achieve production capacity scheduling and material matching based on a single specification, and it is difficult to handle the following more complex situations: (1) multi-dimensional attribute constraints: the key parameters of products (such as chip capacity, frequency, power consumption, etc.) have a multi-level, downgradable matching relationship; (2) down-binning: higher grade products can be used to replace lower grade requirements, but need to meet specific business rules; (3) associated by-product optimization: the same batch production may produce multiple grades of products, and how to use associated by-products to meet different order requirements becomes a key issue; (4) capacity and material coupling constraints: the production capacity of each production line, BOM configuration and calendar resource restrictions need to be considered comprehensively. Existing systems often cannot dynamically and intelligently achieve optimal allocation among "multi-grade requirements - multi-production line capacity - associated by-product utilization", resulting in wasted capacity or inventory accumulation. SUMMARY

[0008] To this end, the technical problem to be solved by the present application is to provide a production allocation method and system based on dimensional planning to solve the problem of associated by-product grading and down-binning, to solve the problem that existing production management systems cannot meet the global optimization of production planning and maximize the utilization of resources, to realize automated and intelligent production allocation and optimization, to reduce manual calculation and errors, and to improve production efficiency and resource utilization.

[0009] To solve the above technical problems, the present application provides the following technical solutions:

[0010] The production allocation method based on dimensional planning to solve associated by-product grading and down-binning includes the following steps:

[0011] Step (1), initialization and data preparation: parse the demand orders and the basic static data required for production, construct a dimensional mapping model, and preliminarily sort the demand orders according to the demand allocation rules;

[0012] Step (2), hierarchical matching: according to the preliminary order of the demand order, sequentially perform the preliminary matching of the production allocation scheme for each demand order; when matching the production allocation scheme for each demand order, preferentially perform the exact matching, if the exact matching cannot meet the demand order, then judge whether the downgrade enable parameter is a true value, if the downgrade enable parameter is a true value, then perform the downgrade matching after checking the downgrade feasibility, and generate the preliminary matching result of the demand order; during the preliminary matching of the production allocation scheme of the demand order, perform the by-product optimization;

[0013] Step (3), capacity and material constraint verification: perform the feasibility verification of the capacity and material constraints on the production allocation scheme of each demand order preliminarily matched;

[0014] Step (4), output the final production allocation plan of the demand order by using reverse scheduling or forward scheduling; the reverse scheduling means calculating the process start time from the order delivery time; the forward scheduling means calculating the process completion time from the planned start time.

[0015] The above production allocation method based on the dimension planning solves the hierarchical and downgrade use of by-products, in step (1), the basic static data includes product attributes, a bill of materials, a production line calendar and business rules;

[0016] The demand allocation rules are one or a combination of two or more of the priority rules, the delivery time rules, the inventory consumption rules, the production line efficiency rules, the product grade or attribute matching rules, the maximum profit rules and the customer category weight rules;

[0017] The dimension mapping model includes an attribute dimension definition table, a dimension group priority definition table, an attribute alias grouping table, an attribute alias value table, a production routing BOM table, a dimension allocation table and a production line calendar data table; the matching logic between different levels of attributes is defined by a relationship symbol.

[0018] The above production allocation method based on the dimension planning solves the hierarchical and downgrade use of by-products, the relationship symbol includes "EQ", "LT", "LE", "GE", "GT", "NE" and "PASS"; "EQ" means that the attribute value must be equal to the specified value; "LT" means that the attribute value must be less than the specified value; "LE" means that the attribute value must be less than or equal to the specified value; "GE" means that the attribute value must be greater than or equal to the specified value; "GT" means that the attribute value must be greater than the specified value; "NE" means that the attribute value cannot be equal to the specified value; "PASS" means that the attribute value is passed from the parent component to the child component.

[0019] The above-mentioned production allocation method based on dimensional planning to solve the classification and downgrading of by-products, in step (2), the precise matching method is as follows: check whether there is inventory or capacity that is completely consistent with the demand level of the demand order after sorting; if the inventory or capacity meets the demand order, then the precise matching is directly executed to generate the preliminary matching result of the demand order; the downgrading matching method is as follows: if the precise matching is not satisfied and the downgrading activation parameter is true, then the downgrading matching logic is started, and the downgrading matching rule is used to perform preliminary matching of the production allocation scheme for the demand order; if the downgrading activation parameter is false, then the system only executes the precise matching logic and does not perform any downgrading substitution operation.

[0020] The above-mentioned production allocation method based on dimensional planning to solve the classification and downgrading of joint and sub-products, in step (2), the method of using downgrading matching rules to perform preliminary matching of production allocation schemes for demand orders is as follows: according to the downgrading priority, starting from the inventory or capacity of high-level products, verify whether the current demand of low-level products is met, and allocate the available inventory or capacity of high-level products to the current demand of low-level products until the demand of low-level products is fully met.

[0021] The above-mentioned production allocation method based on dimensional planning to solve the classification and downgrading of joint and secondary products, in step (2), the method for optimizing joint and secondary products is as follows: check whether the demand orders with matched production allocation schemes generate joint and secondary products during the production process; if joint and secondary products are generated, calculate the quantity of joint and secondary products that can be used for other demand orders, allocate these joint and secondary products to the demand orders that need them, and update the net demand of the demand orders.

[0022] In the production allocation method based on dimensional planning to solve the classification and downgrading of joint and secondary products, in step (2), before precise matching, all dimension groups that can meet the requirements of the demand order attributes are identified and sorted according to the preset dimension group priority; then precise matching is prioritized in the sorted dimension groups.

[0023] Dimension group priorities can be configured based on factors such as enterprise business strategy, high-level resource utilization strategy, historical capacity performance, or attribute matching accuracy. By setting dimension group priorities, global optimal matching across dimension groups can be achieved while addressing product grading and downgrading. In addition, the introduction of the dimension group priority mechanism can ensure that demand orders use higher-level and more stringent dimension groups as much as possible during matching, reducing unnecessary cross-level downgrading. When resources are limited, alternatives can still be made, but the downgrading order is controlled and predictable. Furthermore, when enterprise strategy requires priority consumption of certain levels of inventory, intelligent control can be achieved through dimension group priorities.

[0024] The above-mentioned production allocation method based on dimensional planning to solve the classification and downgrading of by-products, in step (3), the capacity verification method is: based on the production line calendar and resource load, check whether the available capacity of the selected production line in the demand period meets the planned demand; the material verification method is: according to the bill of materials configuration, verify whether the raw materials required for production meet the planned demand.

[0025] The above-mentioned production allocation method based on dimensional planning to solve the classification and downgrading of by-products, in step (4), the output production allocation plan includes the work plan, material requirements plan and inventory update forecast.

[0026] A production allocation system based on dimensional planning to address the hierarchical and degraded use of joint and secondary products includes a storage module and a processor; the processor executes instructions in the storage module to implement the aforementioned production allocation method based on dimensional planning to address the hierarchical and degraded use of joint and secondary products.

[0027] The aforementioned production allocation system, based on dimensional planning to address the tiered and degraded use of by-products, comprises the following storage modules: a dimensional modeling module, a demand parsing module, a degraded matching module, a by-product optimization module, a capacity and material verification module, and a plan generation module. These modules interact via data interfaces. Specifically: the dimensional modeling module establishes the mapping relationship between attribute dimensions and groupings; the demand parsing module identifies order attributes and priority order; the degraded matching module performs matching judgments between higher-level products and lower-level demands; the by-product optimization module judges and executes the allocation of by-products during the production allocation scheme matching process; the capacity and material verification module performs resource constraint verification; and the plan generation module outputs comprehensive scheduling and material planning results.

[0028] The technical solution of the present invention achieves the following beneficial technical effects:

[0029] 1. This invention provides a production allocation method for classifying and downgrading byproducts based on dimensional planning. By constructing a multi-dimensional attribute mapping table, classifying and downgrading matching logic, byproduct output model and allocation algorithm, it can intelligently determine which products can be downgraded and substituted, which production lines have the optimal output ratio, how to use byproducts to meet other orders, and how to arrange production planning while ensuring sufficient capacity and raw materials. Furthermore, by using the combination of dimensional group priority and downgrading priority, the system can achieve cross-dimensional global optimization matching strategy in multi-level product production environments, making the production process more efficient, reducing waste, and maximizing resource utilization.

[0030] 2. This invention provides a production allocation method based on dimensional planning to address the classification and downgrading of by-products. It enables dynamic matching of multi-dimensional attributes and allows for flexible definition of attribute levels and relational operators. It supports automatic determination of downgrading logic, reducing manual intervention and erroneous allocation. It can fully utilize by-product resources to achieve optimal linkage between batch outputs. It can balance capacity and material constraints to ensure the feasibility of the plan. Thus, it improves the overall resource utilization rate and order fulfillment rate in enterprise production and reduces capacity waste. Detailed Implementation

[0031] This embodiment uses a production allocation method based on dimensional planning to address the classification and downgrading of by-products, including the following steps:

[0032] Step (1), Initialization and Data Preparation: Load and parse the demand orders, load the basic static data required for production, build a dimension mapping model, and perform preliminary sorting of the demand orders according to the demand allocation rule (demand_allocation_rule); among which, the basic static data includes product attributes, bill of materials, production line calendar and business rules; the demand allocation rule is a combination of one or more of the following: priority rule, delivery date rule, inventory consumption rule, production line efficiency rule, product grade or attribute matching rule, profit maximization rule and customer category weight rule.

[0033] The dimension mapping model includes an attribute dimension definition table (as shown in Table 1), an attribute alias grouping table (as shown in Table 2), an attribute alias value table (as shown in Table 4), a production routing BOM table (as shown in Table 5), a dimension allocation table (as shown in Table 6), and a production line calendar data table. Matching logic between attributes of different levels is defined through relational operators. These operators include "EQ", "LT", "LE", "GE", "GT", "NE", and "PASS". "EQ" indicates that the attribute value must be equal to a specified value; "LT" indicates that the attribute value must be less than a specified value; "LE" indicates that the attribute value must be less than or equal to a specified value; "GE" indicates that the attribute value must be greater than or equal to a specified value; "GT" indicates that the attribute value must be greater than a specified value; "NE" indicates that the attribute value cannot be equal to a specified value; and "PASS" indicates that the attribute value is passed from the parent component to the child component (see Table 3).

[0034] In some other embodiments, the dimension mapping model further defines a dimension group priority (dimension_grp_priority) to sort the order of multiple adaptable dimension groups. For example, when the attribute of a demand order is "256MB_ge", the system may simultaneously identify that both 256MB_ge and 128MB_ge meet the matching conditions. In this case, the dimension group priority mechanism can prioritize the use of the higher-priority 256MB_ge group to reduce the resource waste caused by potential degradation; the dimension group priority only sorts the "dimension groups that meet the conditions".

[0035] Step (2), Hierarchical Matching: According to the initial sorting of demand orders, preliminary matching of production allocation schemes is performed for each demand order in turn; when matching production allocation schemes for each demand order, if exact matching cannot meet the demand order, it is determined whether the downbinning enable parameter is true. If the downbinning enable parameter is true, downbinning matching is performed after a downbinning feasibility check to generate the preliminary matching result of the demand order; during the preliminary matching of production allocation schemes for demand orders, coproduct optimization is performed; in this embodiment, the downbinning enable parameter (downbinning_enabled) and coproduct optimization parameter (coproduct_optimization) are system control parameters used to control whether the downbinning matching logic and coproduct allocation logic are enabled respectively;

[0036] The exact matching method is as follows: for each sorted demand order, check whether there is inventory or capacity that is exactly the same as the demand level of the demand order; if the inventory or capacity meets the demand order, then perform exact matching directly to generate the preliminary matching result of the demand order.

[0037] The method for downgrading matching is as follows: If an exact match is not satisfied, and the downgrading enable parameter is true (i.e., downbinning_enabled = TRUE), then the downgrading matching logic is activated, and the production allocation plan for the demand order is initially matched using the downgrading matching rules. The method for initially matching the production allocation plan for the demand order using the downgrading matching rules is as follows: Based on the downgrading priority, starting from the inventory or capacity of high-level products, it is verified step by step whether the current demand for low-level products can be met, and the available inventory or capacity of high-level products is allocated to the current demand for low-level products until the demand for low-level products is fully met; for example: according to the downbinning_priority (such as HIGHER_FIRST) rule, starting from high-level chips, it is checked step by step to verify whether the inventory or capacity of high-level chips can meet the current demand for low-level products (using a 512MB chip to meet a 256MB demand).

[0038] The method for optimizing coproducts is as follows: check whether coproducts are generated during the production process of demand orders with matched production allocation plans; if coproducts are generated (i.e., coproduct_optimization = TRUE), calculate the quantity of coproducts available for other demand orders, allocate these coproducts to the demand orders that need them, and update the net demand of the demand orders to maximize resource utilization.

[0039] In some other embodiments, all dimension groups that can meet the requirements of the demand order attributes can be identified first, and the dimension groups that can meet the demand order can be sorted according to the preset dimension group priority; then, exact matching can be performed first in the sorted dimension groups.

[0040] Step (3), capacity and material constraint verification: verify the feasibility of the production allocation plan matched for each demand order in terms of capacity and material constraints; the capacity verification method is: based on the production line calendar and resource load, check whether the available capacity of the selected production line during the demand period meets the planned demand; the material verification method is: according to the bill of materials configuration, verify whether the raw materials required for production meet the planned demand.

[0041] Step (4): After all the above verifications are passed, the system generates a detailed allocation plan. This may use reverse scheduling (counting back the start time of each process from the order delivery date) or forward scheduling (counting forward from the order release time) logic; the output production allocation plan includes a detailed work plan, a material requirements plan, and an inventory update forecast; the detailed work plan is the start and end time of each production task on specific equipment, accurate to the second; the material requirements plan is the material feeding plan accurate to the hour based on the production plan; the inventory update forecast is the expected inventory level of each level of material after the plan is executed.

[0042] Table 1 Attribute Dimension Definition Table

[0043]

[0044] In Table 1, "dimension" is the attribute name, representing the English identifier of the attribute category, such as memory_size (memory capacity); "description" is the attribute description; "value" represents the attribute value number; and "value_des" is the attribute value description, indicating the actual displayed value of the attribute, such as 128MB, 256MB, and 512MB. The attribute dimension definition table is used to define the basic attributes and attribute values ​​of the product, and is the lowest-level data source for the system to perform classification and matching.

[0045] Table 2 Attribute Alias ​​Grouping Table

[0046]

[0047] In Table 2, "dimension_grp" is the dimension group name (i.e., the attribute group name); "description" is the group description; "value" represents the attribute value code; "relationship" is the relationship type (a logical relational operator used to define matching conditions, such as EQ, GE, LE); "and_or" is the logical connector, used to specify the logical relationship when a group contains multiple conditions: and (and) or or (or); if empty, it indicates only one condition; "Value2" is the second attribute value (if a group consists of two conditions, such as "between 100 and 102", this column represents the second attribute value); "Relationship2" is the second relationship type (used in conjunction with Value2 to define the relational operator for the second condition, such as LE);

[0048] This table defines the judgment rules for each level group. The system uses relational operators to determine whether a product meets the specified level (e.g., 512MB_only only accepts 512MB chips, 256MB_ge accepts 256MB and above chips).

[0049] Table 3 Explanation of various relation types

[0050]

[0051] Table 4 Attribute Alias ​​Value Table

[0052]

[0053] In Table 4, "dimension_grp" is the dimension group name (i.e., the attribute group name); "description" is the group description; "value" represents the attribute value number; "Value_des" is the attribute value description; and "dimension" is the attribute name. "512MB_only" indicates that only 512MB products are allowed; "256MB_ge" indicates that 256MB or higher levels (i.e., 256MB and 512MB) products are allowed; and "128MB_ge" indicates that 128MB and higher levels (128MB, 256MB, and 512MB are all allowed) are allowed. This table defines the correspondence between different level groups and attribute values, and is the core foundation for the system to implement the "leveling" and "downgrading" logic.

[0054] Table 5 Production Route BOM (Process Route Definition Table)

[0055]

[0056] Table 6 Dimension Allocation Table

[0057]

[0058] In Tables 5 and 6, "Part1" represents the production object (part / batch name); "bom_id" is the bill of materials number; "part2" is the name of the produced part (e.g., chip); "routing" is the process route number; "priority" is the priority; "Max_qty" is the maximum output quantity; "faction" is the output ratio; and "dimension_grp" is the attribute group (i.e., product level group). Table 5 defines the optional production routes and priorities for each production batch, which is an important basis for the system to automatically schedule production and allocate capacity. Table 6 describes the output ratio of each production line among different product levels, which is the basis for the system to determine "how many batches need to be produced to meet the needs of different levels".

[0059] This embodiment employs a production allocation system based on dimensional planning to address the tiered and downgraded use of by-products. The system includes a storage module and a processor. The processor executes instructions from the storage module to implement the aforementioned dimensional planning-based production allocation method for tiered and downgraded use of by-products. The storage module includes a dimensional modeling module, a demand analysis module, a downgrade matching module, a by-product optimization module, a capacity and material verification module, and a plan generation module. These modules interact via data interfaces. Specifically: the dimensional modeling module establishes the mapping relationship between attribute dimensions and groupings; the demand analysis module identifies order attributes and priority order; the downgrade matching module performs matching judgments between high-level products and low-level demands; the by-product optimization module judges and executes the allocation of by-products during the production allocation scheme matching process; the capacity and material verification module performs resource constraint verification; and the plan generation module outputs comprehensive scheduling and material planning results.

[0060] The following example, using a company's production allocation for chip-related orders, further explains the workflow of the production allocation method and system based on dimensional planning to solve the hierarchical and degraded use of related products.

[0061] Scenario 1: As shown in Table 7, a factory receives three chip orders (Order A, Order B, and Order C) simultaneously, with chip requirements of 2000, 1500, and 1000 respectively. Order A requires chips with a memory capacity of 512MB, Order B requires chips with a memory capacity greater than or equal to 256MB, and Order C requires chips with a memory capacity greater than or equal to 128MB. At this time, the inventory of chips with all three attributes is 0, and all require production. The three orders are ordered as 1, 2, and 3 according to the demand allocation rules. The priority order of the dimension groups 512MB_only, 256MB_ge, and 128MB_ge is 1, 2, and 3 respectively. The factory's dimension allocation for chips with different attributes is shown in Table 8.

[0062] Table 7

[0063]

[0064] Table 8

[0065]

[0066] For this scenario, the deductive steps for production allocation based on dimensional planning to solve the classification and downgrading of by-products are as follows:

[0067] (1) The system identifies the attributes of the materials required in the order:

[0068] Order A: Must be satisfied with a 512MB chip (EQ relationship, strict match).

[0069] Order B: Available with 256MB or 512MB chips (GE relationship, downgrade possible).

[0070] Order C: Available with 128MB, 256MB or 512MB chips (GE relationship, downgrade is possible).

[0071] (2) Optimize production allocation:

[0072] Prioritize using production line A (high output ratio of high-grade products) to meet the 512MB requirement of order A.

[0073] Calculate the required batches: 2000 pieces ÷ 500 pieces / batch (output of 512MB from production line A) = 4 batches.

[0074] Production calculation: 512MB chips: 4 batches × 500 chips / batch = 2000 chips (just enough to meet order A).

[0075] By-product calculation: 256MB chip: 4 batches × 300 chips / batch = 1200 chips; 128MB chip: 4 batches × 200 chips / batch = 800 chips.

[0076] (3) Use by-products to fulfill other orders:

[0077] Order B requires 1500 units of 256MB_ge: 1200 units have been delivered (from by-products of production line A), and 300 units are needed.

[0078] Order C requires 1000 128MB_ge units: 800 units have been delivered (from by-products of production line A), 200 units are needed.

[0079] (4) Supplement production using production line B:

[0080] Production line B produces 400 256MB chips and 300 128MB chips per batch.

[0081] To meet the shortage of 300 256MB units in order B, one batch is needed (production line B produces 400 256MB units, which is enough to cover the 300 units).

[0082] Meanwhile, production line B produces 300 128MB chips per batch, which can cover the shortage of 200 chips in order C.

[0083] Therefore, producing one batch of production line B can simultaneously meet the shortages of orders B and C (producing 400 256MB chips and 300 128MB chips, with 300 256MB chips and 200 128MB chips actually used, and the remainder kept as inventory).

[0084] (5) Final result:

[0085] Order A: Fully satisfied (2000 512MB units).

[0086] Order B: Fully fulfilled (1200 units from production line A + 300 units from production line B).

[0087] Order C: Fully satisfied (800 units from production line A + 200 units from production line B).

[0088] Inventory remaining: After production line B, there are 100 256MB chips and 100 128MB chips remaining.

[0089] Scenario 2: As shown in Table 9, a factory receives an urgent chip order. The order requires 500 chips, and the required memory capacity of the chips is greater than or equal to 256MB. The chip inventory with the three attributes is shown in Table 10.

[0090] Table 9

[0091]

[0092] Table 10

[0093]

[0094] For this scenario, the deductive steps for production allocation based on dimensional planning to solve the classification and downgrading of by-products are as follows:

[0095] (1) Check the feasibility of downgrading: The 500 512MB and 800 256MB chips in the inventory meet the requirements; however, the requirement attribute is 256MB_ge (GE relationship), which allows the use of higher-grade chips (such as 512MB).

[0096] (2) Automatic downgrade allocation: The system prioritizes using the 512MB surplus inventory to meet the 256MB demand (because downbinning_priority is set to HIGHER_FIRST, i.e., high-level products are allocated first). Therefore, the following calculation is performed: the demand for 500 chips can be directly met by 500 512MB chips (because 512MB ≥ 256MB).

[0097] (3) Output allocation result: Use 500 512MB chips to fulfill the emergency order and avoid new production. At this time, the 512MB inventory in Table 10 changes from 500 to 0. The demand pool for 256MB is still 800 chips (but the emergency order has been fulfilled, and the shortage does not affect the order).

[0098] This automatic degradation optimization method reduced excess inventory by 512MB and avoided the resource consumption of producing 256MB chips.

[0099] Scenario 3: As shown in Table 11, a factory receives three chip orders (Order A, Order B, and Order C) simultaneously, with chip requirements of 1000, 2000, and 1500 respectively. Order A requires chips with a memory capacity of 512MB, Order B requires chips with a memory capacity greater than or equal to 256MB, and Order C also requires chips with a memory capacity greater than or equal to 256MB. At this time, the inventory of chips with all three attributes is 0, and all require production. The order of the three orders according to the demand allocation rules is 1, 2, and 3 respectively. The factory's dimensional allocation for chips with different attributes is shown in Table 8.

[0100] Table 11

[0101]

[0102] For this scenario, the deductive steps for production allocation based on dimensional planning to solve the classification and downgrading of by-products are as follows:

[0103] (1) The system identifies the attributes of the materials required in the order:

[0104] Order A: Must be satisfied with a 512MB chip (EQ relationship, strict match).

[0105] Order B: Available with 256MB or 512MB chips (GE relationship, downgrade possible).

[0106] Order C: Available with 128MB, 256MB or 512MB chips (GE relationship, downgrade is possible).

[0107] (2) Optimize production allocation:

[0108] Prioritize allocating high-priority orders (512MB_EQ) to production line A (because production line A has a high output ratio of 512MB).

[0109] Calculate the required batches: 1000 pieces ÷ 500 pieces / batch = 2 batches (production line A).

[0110] Output calculation: 512MB chips: 2 batches × 500 chips / batch = 1000 chips (to meet high priority orders).

[0111] By-product calculation: 256MB chip: 2 batches × 300 chips / batch = 600 chips; 128MB chip: 2 batches × 200 chips / batch = 400 chips.

[0112] (3) Use by-products to fulfill other orders:

[0113] Medium priority order B (256MB_ge requires 2000 units): 600 units already available (from by-products of production line A), 1400 units short.

[0114] Low priority order C (128MB_ge requires 1500 units): 400 units already available (from by-products of production line A), 1100 units short.

[0115] Prioritize using related products to satisfy medium-priority order B (because demand_allocation_rule is PRIORITY_BASED, meaning the demand allocation rule is a priority rule).

[0116] (4) Supplement production using production line B:

[0117] The remaining demand is met by production line B (which has a higher capacity). Production line B produces 400 256MB chips and 300 128MB chips per batch.

[0118] Calculate the required batches: To meet the shortage of 1400 units of 256MB in medium priority order B: 1400 units ÷ 400 units / batch = 3.5 batches → round up to 4 batches (to ensure sufficient supply).

[0119] Production line B produced the following in batch 4: 256MB chips = 4 × 400 = 1600 chips; 128MB chips = 4 × 300 = 1200 chips.

[0120] The medium-priority order B requires 1400 units of 256MB each: 1400 units out of the 1600 units will be used to satisfy the demand, leaving 200 units remaining.

[0121] Low-priority order C requires 1100 units of 128MB each: use 1100 out of 1200 units to fulfill the requirement, leaving 100 units remaining.

[0122] (5) Final result:

[0123] Order A: Fully satisfied (1000 512MB units).

[0124] Order B: Fully fulfilled (600 units from production line A + 1400 units from production line B).

[0125] Order C: Fully satisfied (400 units from production line A + 1100 units from production line B).

[0126] Inventory remaining: After production line B, there are 200 256MB chips remaining and 100 128MB chips remaining.

[0127] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of the claims of this patent application.

Claims

1. A production allocation method based on dimensional planning to solve the classification and downgrading of by-products, characterized in that, Includes the following steps: Step (1), Initialization and Data Preparation: Parse the basic static data required for demand orders and production, construct a dimension mapping model, and perform preliminary sorting of demand orders according to the demand allocation rules; Step (2), Hierarchical matching: According to the initial sorting of demand orders, perform preliminary matching of production allocation schemes for each demand order in turn; when performing preliminary matching of production allocation schemes for each demand order, exact matching is performed first. If exact matching cannot meet the demand order, it is determined whether the downgrade activation parameter is true. If the downgrade activation parameter is true, downgrade matching is performed after performing downgrade feasibility check to generate preliminary matching results for demand orders. Optimize related and by-products during the initial matching process of production allocation schemes for demand orders; Step (3), capacity and material constraint verification: verify the feasibility of the capacity and material constraints of the production allocation plan that has been initially matched for each demand order; Step (4): Use reverse scheduling or forward scheduling to output the final production allocation plan for demand orders.

2. The production allocation method for solving the classification and downgrading of by-products based on dimensional programming according to claim 1, characterized in that, In step (1), the basic static data includes product attributes, bill of materials, production line calendar, and business rules; The demand allocation rule is a combination of one or more of the following: priority rule, delivery date rule, inventory consumption rule, production line efficiency rule, product grade or attribute matching rule, profit maximization rule, and customer category weighting rule. The dimension mapping model includes an attribute dimension definition table, a dimension group priority definition table, an attribute alias grouping table, an attribute alias value table, a production route BOM table, a dimension allocation table, and a production line calendar data table; it defines the matching logic between attributes of different levels through relational operators.

3. The production allocation method for solving the classification and downgrading of by-products based on dimensional programming according to claim 2, characterized in that, Relational operators include "EQ", "LT", "LE", "GE", "GT", "NE", and "PASS"; "EQ" indicates that the attribute value must be equal to the specified value; "LT" indicates that the attribute value must be less than the specified value; "LE" indicates that the attribute value must be less than or equal to the specified value; "GE" indicates that the attribute value must be greater than or equal to the specified value; "GT" indicates that the attribute value must be greater than the specified value; "NE" indicates that the attribute value cannot be equal to the specified value; and "PASS" indicates that the attribute value is passed from the parent component to the child component.

4. The production allocation method for solving the classification and downgrading of by-products based on dimensional programming according to claim 1, characterized in that, In step (2), the exact matching method is as follows: check whether there is inventory or capacity that is completely consistent with the demand level of the sorted demand orders; if the inventory or capacity meets the demand order, then the exact matching is directly executed to generate the preliminary matching result of the demand order; the downgrade matching method is as follows: if the exact matching is not satisfied and the downgrade activation parameter is true, then the downgrade matching logic is started, and the downgrade matching rules are used to perform preliminary matching of the production allocation scheme for the demand order; if the downgrade activation parameter is false, then the system only executes the exact matching logic and does not perform any downgrade substitution operation.

5. The production allocation method for solving the classification and downgrading of by-products based on dimensional programming according to claim 4, characterized in that, In step (2), the method for preliminary matching of production allocation schemes for demand orders using the downgrade matching rule is as follows: based on the downgrade priority, starting from the inventory or capacity of high-level products, verify whether the current demand for low-level products is met level by level, and allocate the available inventory or capacity of high-level products to the current demand for low-level products until all the demand for low-level products is met.

6. The production allocation method for solving the classification and downgrading of by-products based on dimensional programming according to claim 5, characterized in that, In step (2), the method for optimizing by-products is as follows: check whether by-products are generated during the production process of demand orders that have been matched with production allocation plans; if by-products are generated, calculate the quantity of by-products that can be used for other demand orders, allocate these by-products to the demand orders that need them, and update the net demand of the demand orders.

7. The production allocation method for solving the classification and downgrading of by-products based on dimensional programming according to any one of claims 1-6, characterized in that, In step (2), before precise matching, all dimension groups that can meet the requirements of the order attributes are identified and sorted according to the preset dimension group priority; then precise matching is prioritized in the sorted dimension groups.

8. The production allocation method for solving the classification and downgrading of by-products based on dimensional programming according to claim 1, characterized in that, In step (3), the capacity verification method is: based on the production line calendar and resource load, check whether the available capacity of the selected production line during the demand period meets the planned demand; the material verification method is: according to the bill of materials configuration, verify whether the raw materials required for production meet the planned demand. In step (4), the output production allocation plan includes the work plan, material requirements plan and inventory update forecast.

9. A production allocation system based on dimensional planning to solve the classification and downgrading of by-products, characterized in that, Includes storage modules and processors; The processor executes instructions in the storage module to implement the production allocation method for solving the classification and degradation of joint products based on dimensional planning as described in any one of claims 1-8.

10. The production allocation system for solving the classification and downgrading of by-products based on dimensional planning as described in claim 9, characterized in that, It also includes a dimensional modeling module, a demand analysis module, a degradation matching module, a combined product optimization module, a capacity and material verification module, and a plan generation module; the modules interact with each other through data interfaces; among which: The dimensional modeling module is used to establish the mapping relationship between attribute dimensions and grouping. The requirements parsing module is used to identify order attributes and priority order; The downgrade matching module is used to perform matching judgments between high-level products and low-level requirements; The by-product optimization module is used to determine and execute the allocation of by-products during the production allocation scheme matching process; The capacity and material verification module is used to perform resource constraint verification; The planning generation module is used to generate and output comprehensive scheduling and material planning results.

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