Multi-product distributed comprehensive scheduling method for asymmetric three workshops

Abstract

The invention provides a multi-product distributed comprehensive scheduling method for three asymmetric workshops. According to the method, a product is used as an optimization object, a pressing value strategy and a multi-level feedback scheduling strategy are designed, and on the basis of following a layer priority principle, the conflict problem of simultaneous processing of multiple products and characteristics such as the position and the processing time of a pressing process in a complex product structure are fully considered to determine the scheduling sequence of the process; the workshop is used as an optimization object, a workshop allocation procedure strategy is designed, the migration frequency between different workshops is reduced, the time consumption of the whole machining process is further shortened, and the overall utilization rate of equipment is improved. The characteristics of a slender tree structure with more levels and longitudinal extension of nodes are considered, and the characteristics of a flat tree structure with less levels and more transverse branches are also considered.

Description

Technical Field

[0001] The invention belongs to the technical field of computer integrated manufacturing, and in particular relates to a multi-product distributed comprehensive scheduling method for asymmetric three-workshop. Background Art

[0002] Traditional workshop scheduling mainly solves pure processing problems where there is no sequential constraint relationship between workpieces, mainly including flow-shop scheduling problems and job-shop scheduling problems. Many experts and scholars have conducted in-depth research on this. For example, for large-scale multi-flexible job-shop scheduling problems with sequence-dependent installation time, a hybrid fluid master-apprentice evolutionary method is proposed to minimize the maximum completion time; for multi-objective flow-shop problems, an improved multi-objective grey wolf optimization method is proposed to improve the global and local search capabilities of the algorithm; for multi-objective zero-wait flow-shop scheduling problems, a mixed integer linear programming model is constructed, and a tensor evolutionary method based on GPU acceleration is proposed to effectively solve the problem. As the demand for products continues to develop towards personalization and diversification, the orders for customized and small-batch products are increasing. Different from traditional flow-shop scheduling and workshop scheduling, a new scheduling mode is proposed, which is a comprehensive scheduling of product processing and assembly synchronous processing.

[0003] At present, the research on comprehensive scheduling problems by researchers at home and abroad mainly includes general comprehensive scheduling problems, flexible comprehensive scheduling problems, distributed comprehensive scheduling problems, multi-objective scheduling research and multi-workshop comprehensive scheduling problems. However, there are few research results on multi-workshop comprehensive scheduling problems. For example, for the comprehensive scheduling problem where process migration time affects product processing time, a two-workshop equipment-driven comprehensive scheduling method using process migration time is proposed; for the problem of multi-workshop collaborative comprehensive scheduling with similar processing attributes, a hybrid teaching optimization method is proposed. However, these methods all consider how the processes should be allocated for processing when the equipment resources of a single product are symmetrical in the workshop, ignoring the special cases of asymmetric equipment resources and simultaneous processing of multiple products. Summary of the invention

[0004] The purpose of the present invention is to solve the problems in the prior art and propose a multi-product distributed comprehensive scheduling method for asymmetric three-workshop. Specifically, in the distributed scheduling process of multiple complex products with a tree structure where "processing and assembly are carried out simultaneously", the complex multi-product structure attributes and the asymmetric processing capabilities of multiple workshops are taken as dual optimization objects, achieving the optimization effect of shorter total processing time for multiple products and higher overall equipment utilization.

[0005] The present invention is implemented by the following technical scheme. The present invention proposes a multi-product distributed integrated scheduling method for an asymmetric three-workshop, and the method comprises the following steps:

[0006] Step 1: Form a virtual processing tree from the processing technology trees of multiple products;

[0007] Step 2: Calculate the urgency value P of each product according to the urgency value strategy i ;

[0008] Step 3: Adopt a multi-level feedback scheduling strategy to form a set of pre-scheduling processes;

[0009] Step 4: Use the workshop allocation process strategy to select the processing workshop of the process in the pre-scheduled process set;

[0010] Step 5: Store the process of the determined processing workshop into the set of schedulable processes, and delete the process from the set of pre-scheduled processes;

[0011] Step 6: Determine whether the pre-scheduled process set is empty. If yes, go to step 7; if not, go to step 4.

[0012] Step7: Scheduling is completed, exit.

[0013] Furthermore, assuming that the number of products is Z, and the jth process of product i is U ij (1≤i≤n, 1≤j≤n), the product's processing workshop x is W x (1≤x≤n), the equipment resource set of each workshop is M={M x , 1≤x≤n}, where the equipment resources of each workshop are asymmetric; let the processing start time of product i be S i , TB ij TE ij Respectively represent the starting processing time and the completion processing time of the jth process of product i, and V represents the number of migrations of the processed product; under the condition of satisfying the constraints, select the processable equipment of the appropriate processing workshop for each process of all products to ensure that the number of migrations is as small as possible and the maximum completion time of all products is minimized; thus, it can be obtained:

[0014]

[0015] stminTB ij (2)

[0016] TB ijk ≥S i (3)

[0017] TB (i+1)(j+1) ≥max(TE ij ) (4)

[0018] min(V)(5)

[0019] Where: (1) represents minimizing the maximum completion time of the product, which is the optimization goal; (2) represents that the process starts processing as early as possible; (3) represents that the starting processing time of each process must not be earlier than the starting processing time of the product; (4) represents that the subsequent process can only be processed after the processing of the preceding process is completed; (5) represents that the number of process migrations during the scheduling process is as small as possible.

[0020] Furthermore, the urgency value strategy is specifically:

[0021] The critical value P of product i i for:

[0022]

[0023] Where: t ri represents the total processing time of the urgent process of product i; Represents the sum of the processing time of all processes of product i; R i represents the total number of processes for product i; r i Represents the number of urgent processes for product i.

[0024] Furthermore, the multi-level feedback scheduling strategy is specifically:

[0025] The number of workshops that can process the process G ij As a benchmark, the processes at the same level in the virtual processing technology tree are hierarchically scheduled. The processes with more processing workshops are assigned higher priority levels. The higher the level R, the higher the priority for processing. The processes at the same level are scheduled from large to small according to the product urgency value. The larger the product urgency value, the higher the priority for processing. The processes with the same urgency value are scheduled from small to large according to the short-time strategy. The shorter the product processing time, the higher the priority for processing.

[0026] Furthermore, the workshop allocation process strategy is specifically as follows: the process needs to select the processable equipment in the appropriate workshop for processing;

[0027] Case 1: When the equipment that can be processed in the process is idle in only one workshop, two cases are considered:

[0028] If the process equipment is in the same workshop as the processing equipment of the previous process, the process does not need to be moved and can only be processed when the equipment in this workshop is idle, that is, A i M x1k1 With A (i-1) M x2k2 middle, Then α=0;

[0029] If the processing equipment of a process and the processing equipment of the process immediately before it with the latest processing completion time are not in the same workshop, the process needs to be moved to the equipment of other workshops for processing, that is, Ai M x1k1 With A (i-1) M x2k2 middle, Then α=1;

[0030] Case 2: When the equipment that can be processed in the process is idle in multiple workshops, two cases are considered:

[0031] If the process equipment is in the same workshop as the process equipment of the previous process, the process will be assigned to this workshop for processing, that is, A i M x1k1 With A (i+1) M x2k2 middle, Then α=0;

[0032] If the process equipment is not in the same workshop as the process equipment of the previous process, it is allocated to the equipment corresponding to the workshop with the shorter migration time for processing, that is, A i M x1k1 With A (i+1) M x2k2 middle, Then α=1, If the migration time is the same, it will be allocated to the workshop where the process starts earliest.

[0033] Furthermore, the migration time of a process is equal to the ratio of the migration capacity of the starting workshop to that of the migrated workshop multiplied by the processing time of the process, that is, Migration capability refers to the speed at which processes in a processing workshop can be migrated to other processing workshops, expressed as λ x express.

[0034] Furthermore, the migration coefficient of the process is represented by α, where a value of 1 indicates that migration occurs, and a value of 0 indicates that migration does not occur.

[0035] Furthermore, the urgency value is used to measure the urgency of multiple products in the time dimension. The larger the urgency value of a product is, the higher the urgency of processing the product is.

[0036] The present invention also proposes an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the asymmetric three-workshop multi-product distributed integrated scheduling method are implemented.

[0037] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the asymmetric three-workshop multi-product distributed integrated scheduling method.

[0038] The beneficial effects of the present invention are:

[0039] (1) In the comprehensive scheduling of multiple products, a multi-level feedback scheduling strategy is proposed to schedule all processes in a hierarchical manner, effectively solving the conflict problems arising from the joint processing of multiple products.

[0040] (2) In multi-product integrated scheduling, the concept of product urgency value is introduced to determine the priority of each process at the same level.

[0041] (3) For the comprehensive scheduling of the asymmetric three workshops, a workshop allocation process strategy is proposed to always maintain the allocation principle of processing in the same workshop as the immediately preceding process and reduce the number of migrations. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0043] Figure 1 This is a flow chart of the multi-product distributed integrated scheduling method of the asymmetric three-workshop according to the present invention;

[0044] Figure 2 It is a multi-level feedback scheduling strategy diagram;

[0045] Figure 3 Assign process strategy flow chart to the workshop;

[0046] Figure 4 This is the multi-workshop processing network diagram of Example 1;

[0047] Figure 5 This is a schematic diagram of the processing technology tree for product A;

[0048] Figure 6 This is a schematic diagram of the processing technology tree for product B;

[0049] Figure 7 This is a schematic diagram of the processing technology tree for product C;

[0050] Figure 8 The diagram of the virtual processing tree composed of products A, B, and C;

[0051] Fig. 9 Gantt chart for the scheduling of products A, B, and C;

[0052] Fig.10 This is the multi-workshop processing network diagram of Example 2;

[0053] Fig.11 This is a schematic diagram of the processing technology tree for product P;

[0054] Fig.12 This is a schematic diagram of the processing technology tree for product Q;

[0055] Fig.13 This is a schematic diagram of the processing technology tree for product R;

[0056] Fig.14 It is a schematic diagram of the virtual processing tree composed of products P, Q, and R;

[0057] Fig.15 Gantt chart for scheduling of products P, Q, and R. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] Combination Figure 1-Figure 15 The present invention proposes an asymmetric three-workshop multi-product distributed integrated scheduling method, the method comprising the following steps:

[0060] Step 1: Form a virtual processing tree from the processing technology trees of multiple products;

[0061] Step 2: Calculate the urgency value P of each product according to the urgency value strategy i ;

[0062] Step 3: Adopt a multi-level feedback scheduling strategy to form a set of pre-scheduling processes;

[0063] Step 4: Use the workshop allocation process strategy to select the processing workshop of the process in the pre-scheduled process set;

[0064] Step 5: Store the process of the determined processing workshop into the set of schedulable processes, and delete the process from the set of pre-scheduled processes;

[0065] Step 6: Determine whether the pre-scheduled process set is empty. If yes, go to step 7; if not, go to step 4.

[0066] Step7: Scheduling is completed, exit.

[0067] In the process of multi-product distributed integrated scheduling of the asymmetric three-workshop, there are some special constraints between the processes. The specific requirements are as follows:

[0068] (1) If each process and its immediate predecessor are not processed in the same workshop, the process will be migrated once. The more processes are migrated, the longer the migration time will be, and the longer the overall scheduling process will be.

[0069] (2) The equipment resources owned by asymmetric workshops are different, which means that the processes on certain equipment can only be processed in fixed workshops.

[0070] For the convenience of description, the relevant concepts are defined as follows:

[0071] Definition 1 Migration capability: The speed at which processes in a processing workshop migrate to other processing workshops, expressed as λ x The more equipment there is in a machining shop, the greater the migration capacity.

[0072] Definition 2 Migration time: The migration time of the process is equal to the ratio of the migration capacity of the starting workshop and the migration workshop multiplied by the processing time of the process, that is,

[0073] Definition 3 Migration coefficient: α represents the migration coefficient of the process. If it is 1, it means migration occurs, and if it is 0, it means no migration occurs.

[0074] Definition 4: Urgency value: It is used to measure the urgency of multiple products in the time dimension. The larger the urgency value of a product, the higher the processing urgency of the product.

[0075] Assume that the number of products is Z and the jth process of product i is U ij (1≤i≤n, 1≤j≤n), the product's processing workshop x is W x (1≤x≤n), the equipment resource set of each workshop is M={M x , 1≤x≤n}, where the equipment resources of each workshop are asymmetric; let the processing start time of product i be S i , TB ij TE ij Respectively represent the starting processing time and the completion processing time of the jth process of product i, and V represents the number of migrations of the processed product; under the condition of satisfying the constraints, select the processable equipment of the appropriate processing workshop for each process of all products to ensure that the number of migrations is as small as possible and the maximum completion time of all products is minimized; thus, it can be obtained:

[0076]

[0077] stminTB ij (2)

[0078] TB ijk ≥S i (3)

[0079] TB(i+1)(j+1) ≥max(TE ij ) (4)

[0080] min(V)(5)

[0081] Where: (1) represents minimizing the maximum completion time of the product, which is the optimization goal; (2) represents that the process starts processing as early as possible; (3) represents that the starting processing time of each process must not be earlier than the starting processing time of the product; (4) represents that the subsequent process can only be processed after the processing of the preceding process is completed; (5) represents that the number of process migrations during the scheduling process is as small as possible.

[0082] The present invention adopts a variety of strategies to optimize the scheduling process. First, a multi-level feedback scheduling strategy is adopted. By scheduling the processes in a graded manner, the parallel processing capability of the equipment is significantly improved, and the process-centered optimization goal is achieved; secondly, a product contribution value strategy is adopted to effectively reduce the processing time of parallel processes; finally, a workshop allocation process strategy is adopted, which is a strategy based on reducing the number of migrations and processing the processes as early as possible, thereby improving the compact processing strength of the processes and the overall utilization rate of the equipment.

[0083] The urgency value strategy is specifically:

[0084] The critical value P of product i i for:

[0085]

[0086] Where: t ri represents the total processing time of the urgent process of product i; Represents the sum of the processing time of all processes of product i; R i represents the total number of processes for product i; r i Represents the number of urgent processes for product i.

[0087] The multi-level feedback scheduling strategy is specifically:

[0088] like Figure 2 As shown, the number of workshops that can process the process is G ij As a benchmark, the processes at the same level in the virtual processing technology tree are hierarchically scheduled. The processes with more processing workshops are assigned higher priority levels. The higher the level R, the higher the priority for processing. The processes at the same level are scheduled from large to small according to the product urgency value. The larger the product urgency value, the higher the priority for processing. The processes with the same urgency value are scheduled from small to large according to the short-time strategy. The shorter the product processing time, the higher the priority for processing.

[0089] like Figure 3 As shown, the workshop allocation process strategy is specifically as follows: the process needs to select the processable equipment in the appropriate workshop for processing;

[0090] Case 1: When the equipment that can be processed in the process is idle in only one workshop, two cases are considered:

[0091] If the process equipment is in the same workshop as the processing equipment of the previous process, the process does not need to be moved and can only be processed when the equipment in this workshop is idle, that is, A i M x1k1 With A (i-1) M x2k2 middle, Then α=0;

[0092] If the processing equipment of a process and the processing equipment of the process immediately before it with the latest processing completion time are not in the same workshop, the process needs to be moved to the equipment of other workshops for processing, that is, A i M x1k1 With A (i-1) M x2k2 middle, Then α=1;

[0093] Case 2: When the equipment that can be processed in the process is idle in multiple workshops, two cases are considered:

[0094] If the process equipment is in the same workshop as the process equipment of the previous process, the process will be assigned to this workshop for processing, that is, A i M x1k1 With A (i+1) M x2k2 middle, Then α=0;

[0095] If the process equipment is not in the same workshop as the process equipment of the previous process, it is allocated to the equipment corresponding to the workshop with the shorter migration time for processing, that is, A i M x1k1 With A (i+1) M x2k2 middle, Then α=1, If the migration time is the same, it will be allocated to the workshop where the process starts earliest.

[0096] Method complexity analysis: The time complexity of calculating the number of urgent processes and the total duration of a product is O(n); the time complexity of calculating the urgent value of a product is O(n 2 ); The time complexity of forming a pre-scheduled process set through a multi-level feedback scheduling strategy is O(n); The time complexity of calling a workshop allocation process strategy is O(n 2 ); In summary, the time complexity of the method of the present invention is max(O(n),O(n 2 ))=O(n 2 ).

[0097] For the asymmetric three-workshop multi-product distributed integrated scheduling method studied in this invention, two different types of examples are selected for scheduling demonstration to comprehensively evaluate the performance and advantages of the method. Example 1 selects a complex product with a "slender" tree structure, which has a deeper level, the nodes extend in sequence along the longitudinal depth direction, and the relationship between each level is close and the levels are clear; Example 2 selects a complex product with a "flat" tree structure, which has a shallower level, has more branches in the horizontal dimension, and the nodes are widely distributed at the same level.

[0098] Each node in the diagram includes three elements: the processing procedure, the corresponding processing equipment and the required processing time. The three elements are separated by " / ". The arrows in the diagram represent the constraint relationship between adjacent procedures when they are processed closely. The starting point of the arrow represents the immediately preceding procedure, and the arrow points to the immediately succeeding procedure.

[0099] Example 1 Scheduling Analysis and Results

[0100] Assume that there are multiple varieties and small batches of products A, B, and C, and their processing tree is as follows: Figures 5 to 7 As shown. There are three asymmetric workshops, namely workshop a, workshop b, and workshop c. Workshop a has equipment M2, M4, and M5, workshop b has equipment M1, M4, and M5, and workshop c has equipment M3 and M5. Figure 4 shown.

[0101] Step 1: Align products A, B, and C with the root node to form a virtual processing tree, such as Figure 8 shown.

[0102] Step 2: Calculate the number of urgent processes and the total processing time, the total number of processes and the total processing time of each product. According to the urgent value strategy, the urgent value of product A is 2.857, the urgent value of product B is 2.571, and the urgent value of product C is 1.5, as shown in Table 1. Therefore, the product scheduling order is A, B, C.

[0103] Table 1 Product emergency value information table

[0104]

[0105] Step 3: For Figure 8 In the virtual processing technology tree, the levels of processes A8, A9, A10, and B11 are all 5, and a multi-level feedback scheduling strategy is used for classification: A8 and B11 are processed on M5 equipment, A9 is processed on M1 equipment, and A10 is processed on M4 equipment, so G A8 =3, G B11 =3 for the first level, G A10 =2 is the second level, G A9=1 is the third level, and the urgency value of the same-level process A8 is greater than the urgency value of B11. Therefore, the scheduling order of the processes at level 5 is {A8, B11, A10, A9}.

[0106] Step 4: Add processes A8, B11, A10, and A9 to the pre-scheduling set. There are 7 processes at level 4, namely A6, A7, B8, B9, B10, C5, and C6. They are classified using a multi-level feedback scheduling strategy: G C5 =3 is the first level, G A6 =2, G B9 =2, G C6 =2 for the second level, G A7 =1, G B8 =1, G B10 =1 is the third level, where the urgency value A>B>C, so the process scheduling order of level 4 is {C5, A6, B9, C6, A7, B8, B10}.

[0107] Step 5: Similarly, all processes at levels 3, 2, and 1 are hierarchically scheduled using a multi-level feedback scheduling strategy. Therefore, the scheduling order of processes at level 3 is {B7, B5, C4, A5, A3, A4, B4, B6, C3}; the scheduling order of processes at level 2 is {A2, C2, B2, B3}; and the scheduling order of processes at level 1 is {C1, A1, B1}. Therefore, the set of pre-scheduled processes is {A8, B11, A10, A9, C5, A6, B9, C6, A7, B8, B10, B7, B5, C4, A5, A3, A4, B4, B6, C3, A2, C2, B2, B3, C1, A1, B1}.

[0108] Step 6: According to the workshop allocation strategy, the workshops are allocated for the processes in the pre-scheduled process set: if process A8 and its immediate successor process A6 and its immediate successor process A3 are processed in the same workshop a, they are allocated to workshop a; if process B11 and its immediate successor process B8 are processed in the same workshop b, they are allocated to workshop b; if process A10 and its immediate successor process A7 are not processed in the same workshop, and the migration time is It is assigned to the workshop b that started processing the earliest; process A9 is processed on equipment M1 and can only be assigned to workshop a; process C5 and its immediate subsequent process C3 are processed in the same workshop, so it is assigned to workshop c; process A6 and its immediate previous process A8 are processed in the same workshop a, so it is assigned to workshop a; process B9 and its immediate subsequent process B5 are processed in the same workshop a, so it is assigned to workshop a; process C6 and its immediate subsequent process C4 are processed in multiple workshops a and b, and the earliest processing time in workshop b is earlier, so it is assigned to workshop b; A7 is processed on equipment M3, so it can only be assigned to workshop c; process B8 and its immediate previous process B11 are processed in the same workshop b, so it is assigned to workshop b; process B10 is processed on equipment M2, so it can only be assigned to workshop a. Similarly, select the workshops that can be processed for the remaining processes in the pre-scheduling, as shown in Table 2.

[0109] Table 2 Workshop Allocation Execution Table

[0110]

[0111]

[0112] In summary, the total scheduling time for products A, B, and C is 120 hours, the number of migrations V is 6, and the scheduling Gantt chart is as follows: Fig. 9 shown.

[0113] Example 2 Scheduling Analysis and Results

[0114] Assume that there are multiple varieties and small batch products P, Q, and R, and their processing tree is as follows: Figures 11 to 13 As shown. There are three asymmetric workshops, namely workshop e, workshop f, and workshop g. Among them, workshop e has equipment M1, workshop f has equipment M2, M3, and M4, and workshop g has equipment M2, M4, and M5. Fig.10 shown.

[0115] Step 1: Align products P, Q, and R with the root node to form a virtual processing tree, such as Fig.14 shown.

[0116] Step 2: Calculate the number of urgent processes and the total processing time, the total number of processes and the total processing time of each product. According to the urgent value strategy, the urgent value of product A is 2.75, the urgent value of product B is 3.478, and the urgent value of product C is 1.455, as shown in Table 3. Therefore, the product scheduling order is Q, P, R.

[0117] Table 3 Product emergency value information table

[0118]

[0119] Step 3: For Fig.14 In the virtual processing technology tree, the levels of processes P6, P7, P8, P9, Q10, Q11, Q12, and R6 are all 4, and a multi-level feedback scheduling strategy is used for classification: P6 =2, G P9 =2, G Q10 =2, G Q11 =2 is the first level, G P7 =1, G P8 =1, G Q12 =1, G R6 =1 is the second level, where the urgency value Q>P>R, so the process scheduling order for level 4 is {Q11, Q10, P6, P9, Q12, P7, P8, R6}.

[0120] Step 4: Add processes Q11, Q10, P6, P9, Q12, P7, P8, and R6 to the pre-scheduling set. There are 10 processes at level 3, namely P4, P5, Q5, Q6, Q7, Q8, Q9, R3, R4, and R5. They are graded using a multi-level feedback scheduling strategy: G Q5 =2, G Q8 =2, G Q7 =2, G P5 =2, G P4 =2, G R3 =2 for the first level, G Q6 =1, G Q9 =1, G R4 =1, G R5 =1 is the second level, where the urgency value Q>P>R, so the process scheduling order for level 4 is {Q5, Q8, Q7, P5, P4, R3, Q6, Q9, R5, R4}.

[0121] Step 5: Similarly, all processes at levels 2 and 1 are hierarchically scheduled using a multi-level feedback scheduling strategy: the scheduling order of processes at level 2 is {Q3, Q4, P3, R2, Q2, P2}; the scheduling order of processes at level 1 is {R1, Q1, P1}. Therefore, the set of pre-scheduled processes is {Q11, Q10, P6, P9, Q12, P7, P8, R6, Q5, Q8, Q7, P5, P4, R3, Q6, Q9, R5, R4, Q3, Q4, P3, R2, Q2, P2, R1, Q1, P1}.

[0122] Step 6: According to the workshop allocation strategy, the processes in the pre-scheduled process set are allocated to workshops for processing: process Q11 and its subsequent process Q7 are processed by multiple workshops f and g, and another immediate predecessor process Q12 of process Q7 can only be processed in workshop g. In order to reduce the number of migrations, Q11 is allocated to workshop g for processing; process Q10 and its subsequent process Q5 are processed by multiple workshops f and g. Since workshop f has the earliest start time, Q10 is allocated to workshop f; process P6 and its subsequent process P4 are processed by multiple workshops f and g, and another immediate predecessor process P7 of process P4 can only be processed in workshop f, so P6 is allocated to workshop f; process P9 and its subsequent process P5 are processed by multiple workshops f and g In the case of, since workshop g has the earliest start time, Q10 is allocated to workshop g; process Q12 is processed in equipment M5, so it is only allocated to workshop g; process P7 is processed in equipment M3, so it is only allocated to workshop f; process P8 is processed in equipment M1, so it is only allocated to workshop e; process R6 is processed in equipment M3, so it is only allocated to workshop f; process Q5 and its immediate predecessor process Q10 are processed in the same workshop f, so it can be allocated to workshop f; process Q8 and its immediate successor process Q4 are processed in multiple workshops f and g, and the start time of workshop f is the earliest, so Q8 is allocated to workshop f; process Q7 and its immediate predecessor processes Q11 and Q12 are processed in the same workshop g, so Q7 is allocated to workshop g. Similarly, select the workshops that can be processed for the remaining processes in the pre-scheduling, as shown in Table 4.

[0123] Table 4 Workshop Allocation Execution Table

[0124]

[0125]

[0126] In summary, the total scheduling time for products P, Q, and R is 130 hours, the number of migrations V is 6, and the scheduling Gantt chart is as follows: Fig.15 shown.

[0127] The asymmetric three-workshop multi-product distributed integrated scheduling method proposed in the present invention has the following main advantages:

[0128] (1) From the perspective of achieving compact process scheduling and improving overall equipment utilization, the urgency value strategy and multi-level feedback scheduling strategy are adopted to determine the scheduling order of the processes, effectively shortening the equipment idle period and product parallel processing time, and achieving the rapid scheduling effect of tight processing of processes on the equipment. Fig. 9 For example, device W b M1 was processed intensively from t=0 to t=120, and its equipment utilization rate reached 100%. Fig.15 For example, device W eM1 was processed intensively from t=0 to t=110, and its equipment utilization rate reached 100%, as shown in Table 5.

[0129] Table 5 Scheduling results and equipment utilization of two examples

[0130] Examples Total processing time for multiple products Overall equipment utilization 1 120 87.1％ 2 130 80.6％

[0131] (2) From the perspective of reducing the number of migrations and shortening the processing time, the present invention adopts a workshop allocation process strategy to reasonably allocate processing workshops to processes. During the scheduling period, the immediately preceding process is always processed in the same workshop as the process, which greatly reduces the migration phenomenon and migration frequency of processes between different workshops, thereby achieving more efficient resource allocation and significantly shortening the total processing time of the product. Fig. 9 For example, the total processing time of products A, B, and C is 120 hours, and the number of migrations V is 6; Fig.15 For example, the total processing time for products P, Q, and R is 130 working hours, and the number of migrations V is 6.

[0132] The present invention proposes an asymmetric three-workshop multi-product distributed integrated scheduling method: taking "product" as the optimization object, an urgency value strategy and a multi-level feedback scheduling strategy are designed. On the basis of following the layer priority principle, the conflict problem of simultaneous processing of multiple products and the position and processing time of the urgent process itself in the complex product structure are fully considered to determine the scheduling order of the process; taking "workshop" as the optimization object, a workshop allocation process strategy is designed to reduce the number of migrations between different workshops, further shorten the time consumption of the entire processing process, and improve the overall utilization rate of the equipment.

[0133] The present invention not only takes into account the characteristics of the "slender" tree structure with more levels and vertically extended nodes, but also takes into account the characteristics of the "flat" tree structure with fewer levels and more horizontal branches. The experimental results show that the present invention is applicable to different types of complex product structures, which can not only ensure the minimization of the total processing time of multiple products in the distributed scheduling process, but also achieve the dual optimization effect of "product + workshop" to the greatest extent.

[0134] The present invention also proposes an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the asymmetric three-workshop multi-product distributed integrated scheduling method are implemented.

[0135] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the asymmetric three-workshop multi-product distributed integrated scheduling method.

[0136] The memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DRRAM). It should be noted that the memory of the method described in the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0137] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disc (SSD)).

[0138] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software. The steps of the method disclosed in conjunction with the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software modules in a processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it is not described in detail here.

[0139] It should be noted that the processor in the embodiment of the present application can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method embodiment can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to perform, or the hardware and software modules in the decoding processor can be combined and performed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0140] The above is a detailed introduction to the asymmetric three-workshop multi-product distributed integrated scheduling method proposed in the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. Asymmetric three-workshop multi-product distributed integrated scheduling method, characterized in that: The method comprises the following steps: Step 1: Form a virtual processing tree from the processing technology trees of multiple products; Step 2: Calculate the urgency value P of each product according to the urgency value strategy i ; Step 3: Adopt a multi-level feedback scheduling strategy to form a set of pre-scheduling processes; Step 4: Use the workshop allocation process strategy to select the processing workshop of the process in the pre-scheduled process set; Step 5: Store the process of the determined processing workshop into the set of schedulable processes, and delete the process from the set of pre-scheduled processes; Step 6: Determine whether the pre-scheduled process set is empty. If yes, go to step 7; if not, go to step 4. Step7: Scheduling is completed, exit.

2. The method according to claim 1, characterized in that Assume that the number of products is Z and the jth process of product i is U ij (1≤i≤n, 1≤j≤n), the product's processing workshop x is W x (1≤x≤n), the equipment resource set of each workshop is M={M x , 1≤x≤n}, where the equipment resources of each workshop are asymmetric; let the processing start time of product i be S i , TB ij TE ij Respectively represent the starting processing time and the completion processing time of the jth process of product i, and V represents the number of migrations of the processed product; under the condition of satisfying the constraints, select the processable equipment of the appropriate processing workshop for each process of all products to ensure that the number of migrations is as small as possible and the maximum completion time of all products is minimized; thus, it can be obtained: s.t.minTB ij (2) TB ijk ≥S i (3) TB (i+1)(j+1) ≥max(TE ij ) (4) min(V)(5)In the formula: (1) represents minimizing the maximum completion time of the product, which is the optimization goal; (2) represents that the process should start processing as early as possible; (3) represents that the starting processing time of each process should not be earlier than the starting processing time of the product; (4) represents that the subsequent process can only be processed after the preceding process is completed; (5) represents that the number of process migrations during the scheduling process should be as small as possible.

3. The method according to claim 2, characterized in that The urgency value strategy is specifically: The urgency value P of product i i for: Where: t ri represents the total processing time of the urgent process of product i; Represents the sum of the processing time of all processes of product i; R i represents the total number of processes for product i; r i Represents the number of urgent processes for product i.

4. The method according to claim 3, characterized in that The multi-level feedback scheduling strategy is specifically: The number of workshops that can process the process G ij As a benchmark, the processes at the same level in the virtual processing technology tree are hierarchically scheduled. The processes with more processing workshops are assigned higher priority levels. The higher the level R, the higher the priority for processing. The processes at the same level are scheduled from large to small according to the product urgency value. The larger the product urgency value, the higher the priority for processing. The processes with the same urgency value are scheduled from small to large according to the short-time strategy. The shorter the product processing time, the higher the priority for processing.

5. The method according to claim 4, characterized in that The workshop allocation process strategy is specifically as follows: the process needs to select the processable equipment in the appropriate workshop for processing; Case 1: When the equipment that can be processed in the process is idle in only one workshop, two cases are considered: If the process equipment is in the same workshop as the processing equipment of the previous process, the process does not need to be moved and can only be processed when the equipment in this workshop is idle, that is, A i M x1k1 With A (i-1) M x2k2 middle, Then α=0; If the processing equipment of a process and the processing equipment of the process immediately before it with the latest processing completion time are not in the same workshop, the process needs to be moved to the equipment of other workshops for processing, that is, A i M x1k1 With A (i-1) M x2k2 middle, Then α=1; Case 2: When the equipment that can be processed in the process is idle in multiple workshops, two cases are considered: If the process equipment is in the same workshop as the process equipment of the previous process, the process will be assigned to this workshop for processing, that is, A i M x1k1 With A (i+1) M x2k2 middle, Then α=0; If the process equipment is not in the same workshop as the process equipment of the previous process, it is allocated to the equipment corresponding to the workshop with the shorter migration time for processing, that is, A i M x1k1 With A (i+1) M x2k2 middle, Then α=1, If the migration time is the same, it will be allocated to the workshop where the process starts earliest.

6. The method according to claim 5, characterized in that The migration time of a process is equal to the ratio of the migration capacity of the starting workshop to that of the migrated workshop multiplied by the processing time of the process, that is, Migration capability refers to the speed at which processes in a processing workshop can be migrated to other processing workshops, expressed as λ x express.

7. The method according to claim 6, characterized in that The migration coefficient of the process is represented by α, where 1 indicates that migration occurs, and 0 indicates that migration does not occur.

8. The method according to claim 7, characterized in that The urgency value is used to measure the urgency of multiple products in the time dimension. The larger the urgency value of a product, the more urgent the processing of the product.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium for storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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