Fuzzy operator-based multivariate factor optimization production feeding scheduling method and system

Abstract

The invention discloses a multivariate factor optimization production feeding scheduling method and system based on a fuzzy operator, and the method comprises the steps: 1), obtaining an order set and the stock of various materials, and carrying out the ascending sorting according to the sorting reference value of orders, and obtaining an initial order queue; 2) traversing each order in the initial order queue, and performing state updating and state subtraction to obtain an intermediate order queue in which the total consumption of different materials of each traversed order is not greater than the stock and the comprehensive priority score of each traversed order is accumulated to be the highest; and 3) selecting the intermediate order queue corresponding to the highest accumulation of the comprehensive priority scores of all the orders from all the intermediate order queues as an optimal order queue so as to carry out production supply scheduling. The method has the advantages of improving the order punctual delivery rate, improving the comprehensive priority score, shortening the scheduling response time and the like.

Description

Technical Field

[0001] The present invention mainly relates to the field of production technology, and in particular to a multi-factor optimization production feeding scheduling method and system based on fuzzy operators. Background Art

[0002] In today's production operations, order sequencing is often done manually. The primary problem with manual scheduling is that it overlooks numerous complex and multifaceted factors, such as the dynamics of market demand, unstable raw material supply, sudden equipment failures, and the difficulty in temporarily mobilizing human resources. These frequently occurring situations in actual production are not fully considered, resulting in a disconnect between scheduling and actual production scenarios, making it difficult to respond to various emergencies.

[0003] Another major drawback is the rigidity of order planning. Current orders are often scheduled according to fixed rules and processes, lacking flexibility and adaptability. This makes it difficult to quickly adjust to changes in production conditions, leading to low production efficiency and potentially even decreased customer satisfaction. Summary of the Invention

[0004] In response to the technical problems existing in the prior art, the present invention provides a multi-factor optimization production feeding scheduling method and system based on fuzzy operators, which shortens the scheduling response time and improves the comprehensive priority score.

[0005] In order to solve the above technical problems, the technical solution proposed by the present invention is:

[0006] A multi-factor optimization production feed scheduling method based on fuzzy operators comprises the following steps:

[0007] 1) Get the order set Order and the inventory of various materials I, and preliminarily sort the order set Order; specifically, the sorting reference value op of the order i Sort in ascending order to obtain an initial order queue; the order set Order includes the order number, the comprehensive priority score of the order and the consumption of different materials; wherein the comprehensive priority score of the order is obtained based on multi-factor fuzzy calculation;

[0008] 2) Traverse each order in the initial order queue, perform status updates and status pruning, and obtain an intermediate order queue in which the total consumption of different materials of the traversed orders is no greater than the inventory quantity 1 and the traversed orders have the highest cumulative comprehensive priority scores;

[0009] 3) From all the intermediate order queues, the intermediate order queue corresponding to the highest cumulative comprehensive priority score of each order is selected as the optimal order queue for production supply scheduling.

[0010] Preferably, in step 1), the formula of the order set Order is:

[0011] Order = {O, Profit, Ma};

[0012] Where O is the order number, Profit is the comprehensive priority score of the order after fuzzy calculation based on multiple factors, and Ma is the quantity of different materials consumed by the order; specifically, O=[O1, O2, O3, .... O m ]; where m is the number of orders, O i Represents the i-th order number; Profit=[P o1 ,P o2 ,...,P om ];P oi Indicates order O i 's order priority score;

[0013]

[0014] om ij It represents the loss of production materials of the jth inventory material for the i-th order;

[0015] The formula for the inventory quantity I of each material is: I=[Ivt1,Ivt2,Ivt3,...,Ivt n ], where n is the type of raw material, Ivt i is the inventory of the i-th raw material.

[0016] Preferably, the sorting reference value op i The calculation formula is:

[0017]

[0018] Profit is the comprehensive priority score of the order after fuzzy calculation based on multiple factors, Ma is the quantity of different materials consumed by the order; i Refers to the consumption of the i-th inventory material by the current order; P i Refers to the net profit of the i-th order.

[0019] Preferably, when traversing each order in the initial order queue, a status queue Status is pre-built to store the node S in the process of searching for the optimal order queue. i ;

[0020]

[0021] For each intermediate state, that is, node S i , CurPr represents the current S iThe accumulated comprehensive priority score of this state node; CurQue represents the order number stored in the current state; CurMa represents the current state S i The material inventory consumption for the storage CurQue order.

[0022] Preferably, the specific steps of the status update are as follows:

[0023] 2.1.1) For the currently traversed order O i+1 , combined with the intermediate state S i With order O i+1 Generate a new state queue S' i+1 ;

[0024] 2.1.2) When new orders can be met Constraints, the state of the new queue is as follows:

[0025]

[0026] Merge refers to the operation that merges the two queue states;

[0027] For the unsatisfied For an order with constraints, perform the following steps:

[0028] 2.1.2.1) Generate a new empty order set TmpQue i ';

[0029] 2.1.2.2) For the original state queue S i The stored order queue TmpQue i The stored order queue is traversed forward in sequence, and 1-2 historical orders are randomly selected; if they meet the constraints of the new inventory, they are added to TmpQue i ';

[0030] 2.1.2.3) TmpQue′ i and TmpQue i Merged into TmpQue i+1 , and according to TmpQue i+1 Calculate the value corresponding to the corresponding optimal sorting queue;

[0031] 2.1.2.4) If the constraints are not met, a node state containing only the order is generated based on the order, added to the queue, and the state is updated at the same time:

[0032] TotalProfit=Max(TotalProfit,CruPr i ).

[0033] Preferably, the specific steps of the status update are as follows:

[0034] 2.2.1) Traverse the nodes in Status, for It satisfies the following two constraints:

[0035]

[0036] Subtract S j The corresponding node;

[0037] 2.2.2) When step 2.2.1) is completed, if it is found that all the states described in 2.2.1 cannot be met, but the current Status queue is still larger than Size, then sorting is performed and nodes are selected. The sorting is based on the following formula:

[0038]

[0039] So that it satisfies exactly equal to Size; where Size is the maximum number of branch point windows.

[0040] Preferably, the calculation formula of size is:

[0041]

[0042] Preferably, the specific process of obtaining the comprehensive priority score of an order based on fuzzy calculation of multiple factors is as follows:

[0043] Obtain the weight of the corresponding factors of each order element and the corresponding membership degree;

[0044] The weighted membership of the order on each factor is calculated according to the weight, and then the weighted membership of all factors is combined to obtain the comprehensive priority score of the order.

[0045] Preferably, the multivariate factors include profit, urgency, priority and seasonal factors.

[0046] The present invention also discloses a multivariate factor optimization production feeding scheduling system based on fuzzy operators, comprising an interconnected memory and a processor, wherein a computer program is stored on the memory, and when the computer program is run by the processor, the steps of the above method are executed.

[0047] Compared with the prior art, the advantages of the present invention are:

[0048] The present invention is applicable to the pharmaceutical manufacturing industry. By introducing multiple factors and fuzzy theory, a fuzzy matrix is constructed to evaluate the diversified evaluation factors, and the comprehensive optimization is achieved based on the optimal membership (ideal optimal situation) combined with the actual production inventory (reality). The present invention uses fuzzy algorithms to process uncertainty and fuzzy information, incorporating difficult-to-quantify multiple factors into the scheduling consideration range; through the analysis and processing of various types of fuzzy information, the scheduling plan is made more suitable for the complex and changeable actual production environment, thereby effectively improving the flexibility and adaptability of the scheduling and ensuring the efficient and stable operation of production.

[0049] The present invention aims at the problems of multi-objective coupling, frequent dynamic interference, and complex resource constraints in the production scheduling of the pharmaceutical manufacturing industry. By constructing a fuzzy matrix to quantify qualitative factors such as order urgency, material utilization, delivery deadline weight, and customer priority, the membership function is combined to calculate the closeness of each factor to the ideal production state, and the real-time inventory data is integrated to generate a dynamic priority score to achieve multi-objective collaborative optimization. In the scheduling process, a state queue iterative update mechanism is adopted to merge the current order and the historical state node to generate a new candidate sequence. The over-limit order is automatically replaced by a constraint-sensitive backtracking strategy to ensure that material consumption always meets the inventory restrictions of raw materials, excipients, etc. At the same time, a pruning algorithm is introduced to remove redundant states (such as nodes with high material consumption but low scores) based on the maximum number of windows, to achieve a balance between computational efficiency and solution quality, and to dynamically adapt to sudden scenarios such as insertion orders and equipment failures. The scheduling scheme finally generated can reduce raw material waste, improve the on-time delivery rate of orders, improve the comprehensive priority score, and shorten the scheduling response time. This method effectively resolves uncertainty and multi-factor conflicts through fuzzy theory, and is both flexible and robust. It is particularly suitable for the production of small batches, multiple batches, and GMP preparations with high compliance requirements, providing key technical support for the pharmaceutical manufacturing industry to achieve efficient resource utilization and stable production. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of an embodiment of the production material scheduling method of the present invention.

[0051] Figure 2 This is a data structure diagram of various factors of the order in the present invention.

[0052] Figure 3 FIG. 4 is a diagram illustrating an embodiment of the state array compression and merging according to the present invention. DETAILED DESCRIPTION

[0053] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0054] like Figure 1-Figure 3 As shown, the multi-factor optimization production feeding scheduling method based on fuzzy operators provided in an embodiment of the present invention specifically includes the following steps:

[0055] 1) Get the order set Order and the inventory of various materials I, and preliminarily sort the order set Order; specifically, the sorting reference value op of the order i Sort in ascending order to obtain the initial order queue;

[0056] where op is defined i is the sorting reference value of the i-th order, and its calculation formula is as follows:

[0057]

[0058] Among them i Refers to the consumption of the i-th inventory material by the current order; P i Refers to the net profit of the i-th order;

[0059] The formula for the order set Order is:

[0060] Order = {O, Profit, Ma};

[0061] Where O is the order number, Profit is the comprehensive priority score of the order after fuzzy calculation based on multiple factors, and Ma is the quantity of different materials consumed by the order; specifically, O=[O1, O2, O3, .... O m ]; where m is the number of orders, O i Represents the i-th order number; Profit = [P o1 ,P o2 ,...,P om ];P oi Indicates order O i 's order priority score;

[0062]

[0063] om ij It represents the loss of production materials for the jth inventory material for the i-th order.

[0064] The formula for the inventory quantity I of each material is: I=[Ivt1,Ivt2,Ivt3,...,Ivt n ], where n is the type of raw material, Ivt i is the inventory of the i-th raw material.

[0065] 2) Pre-design the status queue Status,

[0066] The state queue is used to store the intermediate state in the process of searching for the optimal order queue, that is, the state node S i ;

[0067]

[0068] For each intermediate state (i.e. node S i ), CurPr represents the current S i The accumulated comprehensive priority score of this state node; CurQue represents the order number stored in the current state, that is, the priority production according to this order number can bring the greatest value (the embodiment and quantification of value is reflected by the accumulated comprehensive priority score); CurMa is the current state S i The material inventory consumption for the storage CurQue order.

[0069] like Figure 3 As shown, after designing the status queue Status, perform the following operations on each order in the initial order queue:

[0070] 2.1) Status update;

[0071] 2.2) State pruning;

[0072] Specifically, the operation of the status update in step 2.1) is as follows:

[0073] 2.1.1) For the currently traversed order O i+1 , combined with the intermediate state S i With order O i+1 Generate a new state queue S' i+1 ;

[0074] 2.1.2) When new orders can be met The state of the new queue is as follows:

[0075]

[0076] Merge refers to the operation that merges the two queue states;

[0077] For the unsatisfied For orders with equal constraints, perform the following steps:

[0078] 2.1.2.1) Generate a new empty order set TmpQue i ';

[0079] 2.1.2.2) For the original state queue S i The stored order queue TmpQue i The stored order queue is traversed forward in sequence, and 1-2 historical orders are randomly selected; if they meet the constraints of the new inventory, they are added to TmpQue i ';

[0080] 2.1.2.3) TmpQue′ i and TmpQue i Merged into TmpQue i+1 , and according to TmpQue i+1 Calculate the value corresponding to the corresponding optimal sorting queue;

[0081] 2.1.2.4) If the constraints are not met, a node state containing only the order is generated based on the order, added to the queue, and the state is updated at the same time:

[0082] TotalProfit=Max(TotalProfit,CruPr i )

[0083] After traversing all nodes, find the order with the highest Profit but the required inventory usage.

[0084] For step 2.2), the state pruning operation is as follows:

[0085] Design a maximum branch window size, which is:

[0086]

[0087] The operations on the status queue are as follows:

[0088] 2.2.1) Traverse the nodes in Status, for It satisfies the following two constraints:

[0089]

[0090] Subtract S j The corresponding node;

[0091] 2.2.2) When step 2.2.1) is completed, if it is found that all the states described in 2.2.1 cannot be met, but the current Status queue is still larger than Size, then sorting is performed and nodes are selected. The sorting is based on the following formula:

[0092]

[0093] So that it satisfies exactly equal to Size.

[0094] 3) After traversing all order nodes, select the order node corresponding to the TmpQue state with the largest current TotalProfit. This is the order node with the highest priority. The current order sequence is the priority of various factors that can be obtained under the production conditions that meet the existing inventory.

[0095] In a specific embodiment, the specific process of obtaining the comprehensive priority score of an order based on fuzzy calculation of multiple factors is as follows:

[0096] 1. Define fuzzy sets

[0097] Profit: high profit, medium profit, low profit.

[0098] Urgency: urgent, relatively urgent, not urgent.

[0099] Priority: High priority, Medium priority, Low priority.

[0100] Seasonal factors: peak season, shoulder season, and off-season.

[0101] For the above factors, the priority is calculated according to the following formula:

[0102]

[0103] Where x is the quantification of profit, urgency, priority, and seasonal factors; ag is the judgment standard limit of each level of each factor.

[0104] Set the weight parameters of different factors:

[0105]

[0106] Among them, w1, w2, w3, and w4 are the weight coefficients of profit, urgency, priority, and seasonal factors;

[0107] Then construct the fuzzy matrix:

[0108]

[0109] Weighted calculation:

[0110]

[0111] Defuzzification:

[0112]

[0113] The specific fuzzy membership reference function is as follows:

[0114] High profit: profit value > 80, membership is 1; profit value is between 60 and 80, membership decreases linearly.

[0115] Medium profit: when the profit value is between 40 and 80, the membership is 1; when it is lower than 40 or higher than 80, the membership decreases linearly.

[0116] Low profit: when the profit value is <40, the membership is 1; when the profit value is between 20 and 40, the membership decreases linearly.

[0117] 2. Determine the weight

[0118] Assign a weight to each factor based on business needs. The weight indicates the importance of the factor in the decision.

[0119] Example weighting: Profit: 40%; Urgency: 30%; Priority: 20%; Seasonality: 10%.

[0120] 3. Fuzzy scoring

[0121] For each order, its membership in each fuzzy subset is calculated according to its specific values (such as profit value, urgency score, etc.).

[0122] Example calculation:

[0123] Assume that the specific data of an order is as follows: profit value: 70 points; urgency score: 85 points; priority score: 75 points; seasonal factor: shoulder season (score 60 points);

[0124] (1) Calculation of profit membership:

[0125] High profit: profit value = 70, between 60 and 80, membership = (80-70) / (80-60) = 0.5;

[0126] Medium profit: profit value = 70, between 40 and 80, membership degree = 1;

[0127] Low profit: profit value = 70, between 20 and 40, membership = 0.

[0128] (2) Calculation of membership degree of urgency:

[0129] Urgent: score = 85, between 80 and 100, membership = (85-80) / (100-80) = 0.25;

[0130] More urgent: score = 85, between 60 and 80, membership = (85-80) / (80-60) = 0.25;

[0131] No rush: score = 85, lower than 60, membership = 0.

[0132] (3) Priority membership calculation:

[0133] High priority: score = 75, between 70 and 100, membership = (75-70) / (100-70) = 0.1667;

[0134] Medium priority: score = 75, between 40 and 70, membership = (75-70) / (70-40) = 0.1667;

[0135] Low priority: score = 75, lower than 40, membership = 0.

[0136] (4) Calculation of membership of seasonal factors:

[0137] Peak season: score = 60, between 80 and 100, membership = (60-80) / (100-80) = -1 (negative value, 0);

[0138] Shoulder season: score = 60, between 60 and 80, membership = 1;

[0139] Off-season: score = 60, below 60, membership = 0.

[0140] 4. Weighted fuzzy operator

[0141] According to the weights, the weighted membership of the order on each factor is calculated, and then the weighted membership of all factors is combined to obtain the comprehensive priority score of the order.

[0142] Example calculation:

[0143] (1) Weighted membership of profit:

[0144] High profit: 0.5 × 40% = 0.2;

[0145] Medium profit: 1×40%=0.4;

[0146] Low profit: 0 × 40% = 0;

[0147] (2) Weighted membership of urgency:

[0148] Emergency: 0.25 × 30% = 0.075;

[0149] More urgent: 0.25 × 30% = 0.075;

[0150] No rush: 0×30%=0;

[0151] (3) Weighted membership of priority:

[0152] High priority: 0.1667 × 20% = 0.0333;

[0153] Medium priority: 0.1667 × 20% = 0.0333;

[0154] Low priority: 0×20%=0;

[0155] (4) Weighted membership of seasonal factors:

[0156] Peak season: 0 × 10% = 0;

[0157] Shoulder season: 1×10%=0.1;

[0158] Off-season: 0 × 10% = 0;

[0159] (5) Comprehensive priority score: Add the weighted membership of all factors to obtain the comprehensive priority score of the order: Comprehensive score = 0.2 + 0.4 + 0.075 + 0.075 + 0.0333 + 0.0333 + 0.1 = 1.2166.

[0160] The present invention is applicable to the pharmaceutical manufacturing industry. By introducing multiple factors and fuzzy theory, a fuzzy matrix is constructed to evaluate the diversified evaluation factors, and the comprehensive optimization is achieved based on the optimal membership (ideal optimal situation) combined with the actual production inventory (reality). The present invention uses fuzzy algorithms to process uncertainty and fuzzy information, incorporating difficult-to-quantify multiple factors into the scheduling consideration range; through the analysis and processing of various types of fuzzy information, the scheduling plan is made more suitable for the complex and changeable actual production environment, thereby effectively improving the flexibility and adaptability of the scheduling and ensuring the efficient and stable operation of production.

[0161] The present invention aims at the problems of multi-objective coupling, frequent dynamic interference, and complex resource constraints in the production scheduling of the pharmaceutical manufacturing industry. By constructing a fuzzy matrix to quantify qualitative factors such as order urgency, material utilization, delivery deadline weight, and customer priority, the membership function is combined to calculate the closeness of each factor to the ideal production state, and the real-time inventory data is integrated to generate a dynamic priority score to achieve multi-objective collaborative optimization. In the scheduling process, a state queue iterative update mechanism is adopted to merge the current order and the historical state node to generate a new candidate sequence. The over-limit order is automatically replaced by a constraint-sensitive backtracking strategy to ensure that material consumption always meets the inventory restrictions of raw materials, excipients, etc. At the same time, a pruning algorithm is introduced to remove redundant states (such as nodes with high material consumption but low scores) based on the maximum number of windows, to achieve a balance between computational efficiency and solution quality, and to dynamically adapt to sudden scenarios such as insertion orders and equipment failures. The scheduling scheme finally generated can reduce raw material waste, improve the on-time delivery rate of orders, improve the comprehensive priority score, and shorten the scheduling response time. This method effectively resolves uncertainty and multi-factor conflicts through fuzzy theory, and is both flexible and robust. It is particularly suitable for the production of small batches, multiple batches, and GMP preparations with high compliance requirements, providing key technical support for the pharmaceutical manufacturing industry to achieve efficient resource utilization and stable production.

[0162] The present invention also discloses a multivariate factor optimization production feed scheduling system based on fuzzy operators, comprising a memory and a processor connected to each other, the memory storing a computer program that, when executed by the processor, executes the steps of the above-described method. The system of the present invention corresponds to the above-described method and similarly possesses the advantages described above.

[0163] The present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned method embodiment. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable storage media include: any entity or device that can carry computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. The memory is used to store computer programs and / or modules, and the processor implements various functions by running or executing computer programs and / or modules stored in the memory, and calling data stored in the memory. The memory may include high-speed random access memory and non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0164] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A multi-factor optimization production feed scheduling method based on fuzzy operators, characterized in that: Including steps: 1) Get the order set Order and the inventory of various materials I, and preliminarily sort the order set Order; specifically, the sorting reference value op of the order i Sort in ascending order to obtain an initial order queue; the order set Order includes the order number, the comprehensive priority score of the order and the consumption of different materials; The comprehensive priority score of the order is obtained based on fuzzy calculation of multiple factors; 2) Traverse each order in the initial order queue, perform status updates and status pruning, and obtain an intermediate order queue in which the total consumption of different materials of the traversed orders is no greater than the inventory quantity 1 and the traversed orders have the highest cumulative comprehensive priority scores; 3) From all the intermediate order queues, the intermediate order queue corresponding to the highest cumulative comprehensive priority score of each order is selected as the optimal order queue for production supply scheduling.

2. The multi-factor optimization production feed scheduling method based on fuzzy operators according to claim 1 is characterized in that: In step 1), the formula of the order set Order is: Order = {O, Profit, Ma}; Where O is the order number, Profit is the comprehensive priority score of the order after fuzzy calculation based on multiple factors, and Ma is the quantity of different materials consumed by the order; specifically, O=[O1, O2, O3, .... O m ]; where m is the number of orders, O i Indicates the i-th order number; Profit=[P o1 ,P o2 ,...,P om ];P oi Indicates order O i 's order priority score; om ij It represents the loss of production materials of the jth inventory material for the i-th order; The formula for the inventory quantity I of each material is: I=[Ivt1,Ivt2,Ivt3,...,Ivt n ], where n is the type of raw material, Ivt i is the inventory of the i-th raw material.

3. The multi-factor optimization production feed scheduling method based on fuzzy operators according to claim 2 is characterized in that: The sorting reference value op i The calculation formula is: Profit is the comprehensive priority score of the order after fuzzy calculation based on multiple factors, Ma is the quantity of different materials consumed by the order; i Refers to the consumption of the i-th inventory material by the current order; P i Refers to the net profit of the i-th order.

4. The multi-factor optimization production feed scheduling method based on fuzzy operators according to claim 2 is characterized in that: When traversing each order in the initial order queue, a status queue Status is pre-built to store the node S in the process of searching for the optimal order queue. i ; For each intermediate state, that is, node S i , CurPr represents the current S i The accumulated comprehensive priority score of this state node; CurQue represents the order number stored in the current state; CurMa represents the current state S i The material inventory consumption for storage CurQue orders.

5. The multi-factor optimization production feed scheduling method based on fuzzy operators according to claim 4 is characterized in that: The specific steps of the status update are as follows: 2.1.1) For the currently traversed order O i+1 , combined with the intermediate state S i With order O i+1 Generate a new state queue S' i+1 ; 2.1.2) When the new order can meet the om<Ivt constraint conditions, the status of the new queue is as follows: Merge refers to the operation that merges the two queue states; For orders that cannot meet the om <Ivt constraint conditions, perform the following steps: 2.1.2.1) Generate a new empty order set TmpQue′ i ; 2.1.2.2) For the original state queue S i The stored order queue TmpQue i The stored order queue is traversed forward in sequence, and 1-2 historical orders are randomly selected; if they meet the constraints of the new inventory, they are added to TmpQue′ i ; 2.1.2.3) TmpQue′ i and TmpQue i Merged into TmpQue i+1 , and according to TmpQue i+1 Calculate the value corresponding to the corresponding optimal sorting queue; 2.1.2.4) If the constraints are not met, a node state containing only the order is generated based on the order, added to the queue, and the state is updated at the same time: TotalProfit=Max(TotalProfit,CruPr i )。 6. The multi-factor optimization production feed scheduling method based on fuzzy operators according to claim 5 is characterized in that: The specific steps of the status update are as follows: 2.2.1) Traverse the nodes in Status, for It satisfies the following two constraints: Subtract S j The corresponding node; 2.2.2) When step 2.2.1) is completed, if it is found that all the states described in 2.2.1 cannot be met, but the current Status queue is still larger than Size, then sorting is performed and nodes are selected. The sorting is based on the following formula: So that it satisfies exactly equal to Size; where Size is the maximum number of branch point windows.

7. The multi-factor optimization production feed scheduling method based on fuzzy operators according to claim 6 is characterized in that: The calculation formula for size is:

8. The multi-factor optimization production feed scheduling method based on fuzzy operators according to any one of claims 1 to 7, characterized in that: The specific process of obtaining the comprehensive priority score of an order based on fuzzy calculation of multiple factors is as follows: Obtain the weight of the corresponding factors of each order element and the corresponding membership degree; The weighted membership of the order on each factor is calculated according to the weight, and then the weighted membership of all factors is combined to obtain the comprehensive priority score of the order.

9. The multi-factor optimization production feed scheduling method based on fuzzy operators according to claim 8 is characterized in that: The multiple factors include profit, urgency, priority and seasonality.

10. A multi-factor optimization production feed scheduling system based on fuzzy operators, comprising a memory and a processor connected to each other, wherein a computer program is stored in the memory, characterized in that: When the computer program is executed by a processor, the computer program performs the steps of the method according to any one of claims 1 to 9.

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