Steel structure production intelligent scheduling method fusing neat conformity and equipment resource constraint

Abstract

The invention belongs to the field of steel member production and optimal scheduling, and discloses a steel structure production intelligent scheduling method fusing neat property and equipment resource constraint, which is used for solving the problem of scheduling difficulty caused by part neat property constraint and complex processing procedures in a steel structure factory. The method comprises the following steps: acquiring production line information, PBOM information, nesting information, processing quota, production state and plan and other data, and constructing an optimal scheduling model with the goal of minimizing the maximum completion time. The model introduces constraint conditions such as a blanking sequence, equipment allocation, a processing sequence and neat set performance, a composite chromosome genetic algorithm is adopted for solving, scheduling decoding is completed in combination with process sorting and equipment allocation, and a feasible scheduling scheme meeting the neat set requirement is generated. According to the method, task allocation in the component production process can be effectively optimized, the resource utilization rate and the overall scheduling efficiency are improved, and the method has high engineering applicability.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of steel component production and optimized scheduling, in particular to a steel structure production intelligent scheduling method fusing the constraints of completeness and equipment resources. BACKGROUND

[0002] To comply with the model-driven steel component design and production trend, and realize the intelligentization of the whole production cycle of steel components, an efficient and intelligent component production scheduling mechanism needs to be established. Steel components are usually composed of a main body and several parts, and need to go through multiple processes such as cutting, beveling, assembly, submerged arc welding, end face milling, general assembly, polishing, shot blasting and painting in sequence. The processing flow is complex. In the actual production process, steel components are subject to various complex constraints such as part completeness constraints. That is, all the base steel plates of a component need to be cut by different cutting schemes, and the parts required by the same component may be distributed in multiple cutting schemes, so only when all the parts required by the component have been cut, can the subsequent processing flow be entered.

[0003] In addition, the steel component production also has the following constraints: the main body and parts of the same component need to be assembled in the general assembly process; there are multiple levels of processing dependencies between components; multiple devices can be selected for processing in some processes, and the processing times of different devices are inconsistent. The traditional scheduling method cannot meet the above-mentioned complex constraints, resulting in poor feasibility of the production scheduling scheme, low resource utilization rate, long delivery cycle and other problems.

[0004] To solve the above problems, the present application provides a steel structure production intelligent scheduling method considering part completeness constraints based on a complex chromosome genetic algorithm. SUMMARY

[0005] In view of the above defects or improvement needs of the prior art, the present application provides a steel structure production intelligent scheduling method fusing the constraints of completeness and equipment resources to solve the scheduling difficulty problem caused by part completeness constraints, complex processing procedures and other factors in steel structure factories.

[0006] To achieve the above-mentioned purpose, according to one aspect of the present application, a steel structure production intelligent scheduling method fusing the constraints of completeness and equipment resources is provided, comprising the following steps: S1: information acquisition and preprocessing: acquiring steel component production related information; S2: constructing a scheduling model: based on the information acquired in step S1, combining the part completeness and the constraints of actual production, a steel component production scheduling model is established; S3: solving the scheduling model established in step S2 by a chromosome coding mechanism combined with a complex chromosome genetic algorithm; S4: outputting the optimized scheduling scheme of the steel component.

[0007] Preferably, the information in step S1 comprises production line information, PBOM information, nesting optimization information, process quota time, production progress status, production scheduling plan; The production line information comprises date, production line name, contained process, equipment number, equipment type, planned working time, effective working time, personnel configuration number, and work type. The PBOM information comprises component number, processing process, processing times, input material number, input material specification, input material quantity, output material number, output material specification, and output material quantity. The nesting optimization information comprises nesting number, nesting times, nesting type, nesting time, belonging project, belonging department, component number, component part number, component size, and component quantity. The process quota time comprises process name, equipment type, measurement attribute, measurement unit, unit time The production progress status comprises project name, belonging department, component number, component ID, and progress situation. The production scheduling plan comprises project name, partition name, order name, component model, component ID, specification, net weight, order requirement completion date, component priority, component plan completion date, production line allocation, process name, production equipment allocation, process start date, and process end date.

[0008] Preferably, the constraint conditions comprise nesting process scheduling constraint, part nesting constraint, processing sequence constraint, process time calculation and allocation constraint, processing mutual exclusion constraint, production line process sharing constraint, and completion time constraint. The specific method of the nesting process scheduling constraint comprises the following steps: S211, based on the information obtained in step S1, setting the scheduling target as minimizing the maximum completion time ; S212, calculating the nesting completion time, and the specific expression is as follows: ; Wherein, is the nesting completion time of scheme d; is the start nesting time of scheme ; is the machine set capable of performing the nesting scheme ; ; is the nesting machine; represents 1 if the nesting scheme is performed by the machine , otherwise 0; is the time required for the nesting scheme of the machine . is a blanking scheme; is a set of schemes requiring blanking, ; S213, define that a blanking scheme can only be executed by one machine, to ensure that the blanking process is executed uniquely, the specific expression is: ; S214, further define the blanking sequence, the specific expression is: ; wherein, is 1 if scheme and are both blanked by machine , and the blanked by machine is processed before , otherwise 0; is 1 if scheme and are both blanked by machine , and the blanked by machine is processed before , otherwise 0; is 1 if scheme is assigned to machine , otherwise 0; is 1 if scheme is assigned to machine , otherwise 0; S215, define the conflict constraint, the specific expression is:

[0009] wherein, is the blanking completion time of scheme ; is the start blanking time of scheme ; is a very large positive number.

[0010] As a preferred, the part set constraint is used to constrain the component can only start subsequent processing after all the required parts are blanked, the specific expression is as follows: ; ; wherein, is the blanking completion time of scheme , is the component the required set-up time of the steel plate for processing; for processing a component a set of solutions for blanking, for a set of components, for a component the start time of the process the finish time of the process; The processing sequence constraint is that the main body and component processing are strictly performed in a predetermined order, which is expressed as: wherein, the finish time of the process the start time of the process the finish time of the process; the finish time of the process the first main body processing process, the finish time of the process the first component processing process, the finish time of the process the first +1 main body processing process, the finish time of the process the first +1 component processing process.

[0011] As a preferred embodiment, the process time calculation and distribution constraint comprises the following steps: S241, calculating the process finish time, which is expressed as: wherein, represents 1 if the component is processed by the machine of the production line for the process , otherwise 0; the time required for the process of the component by the machine ; a set of machines, S242, defining that the final assembly must be performed after the component processing is completed (applicable to components requiring final assembly), which is expressed as:​​​​​​​​​​​ ; in, Representing components Components The completion time of the final process; Representing components Final assembly process Start time; S243. Define the machine allocation for each process as unique; the specific expression is: ; in, For the actual production line assembly, .

[0012] Preferably, the processing mutual exclusion constraint ensures that processing times do not conflict by defining the processing order of multiple components in the same machine, as specifically expressed below: ; ; in, Indicates if component and process All by machine Processed by machine Processed components Prior to If processing is performed, the value is 1; otherwise, it is 0. Indicates if component and process All by machine Processed by machine Processed components Prior to The value is 1 if processing is required, otherwise it is 0. Indicates if component process By machine If processing is required, the value is 1; otherwise, it is 0. Indicates if component process By machine If processing is required, the value is 1; otherwise, it is 0. Representing components process Completion time; Representing components process Start time; The production line process sharing constraint is defined to handle processes shared or selectively shared across multiple production lines, specifically including the following steps: S261. Determine whether a shared process can be performed. The execution is performed online, and the specific expression is: ; in, For process Dependent processes; Indicates if component In the Machines on the production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if the first The production line can perform processes If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if component In the Machines on the production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if the first The production line can perform processes If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if component In the Machines on the production line Shared processes were executed. If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if component In the Machines on the production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. S262. Determine whether a shared process can be executed on the first production line. The specific expression is: ; in, Indicates if component Machines on the first production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. This indicates that if the first production line can execute the process. If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if component Machines on the second production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if component Machines on the first production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. S263. Determine whether the shared process can be carried out on the last production line. The above is executed; the specific expression is: ; in, Indicates if component In the Machines on the production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if the first The production line can perform processes If it is true, then it is 1; otherwise, it is 0. Indicates if component In the Machines on the production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if component In the Machines on the production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. The completion time constraint defines a completion time and a maximum completion time constraint, meaning that the completion time of all components shall not exceed the current maximum completion time. The specific expression is as follows: ; ; in, For components Completion time; Representation of components The Each component is on the final production line. Completed process Time spent.

[0013] Preferably, step S3 includes the following steps: S31. Process Chromosome Coding Design: A composite chromosome coding method is adopted to decompose the scheduling problem into process chromosomes and machine chromosomes; S32. Machine chromosome variation design includes: process chromosome variation, machine chromosome variation, and nesting scheme chromosome variation, so as to randomly change the equipment number in the set of processing equipment for a certain process. S33, chromosome decoding process: according to the process processing sequence and machine allocation information, combined with the set constraint, the chromosome is decoded to obtain the start and end time of each process.

[0014] As preferred, the process chromosome in step S31 is used to represent the processing sequence of all processes in component production, the chromosome length is the total number of all processes to be scheduled, and each gene position is encoded with process number and arranged according to processing sequence; The machine chromosome is used to specify the processing equipment allocated to each process, and the chromosome length is the same as that of the process chromosome. Each gene position records the selected machine number of the process, and the coding needs to meet the processable mapping relationship between process and machine.

[0015] As preferred, the process chromosome mutation in step S32 is to select any continuous gene segment on the chromosome, and randomly select a process gene segment for rearrangement without violating the process dependency constraint to generate a new ordering scheme; The machine chromosome mutation is to randomly replace the current machine number with a machine in the processing equipment set for a certain process, so as to realize the adjustment of processing resources; The nesting scheme chromosome mutation is to select the nesting scheme gene position of a certain component in the chromosome, and randomly select another scheme from the nesting scheme set of the component to replace the current coding under the premise of meeting the set constraint, so as to form a new nesting allocation structure. If the mutation causes the change of the part set corresponding to the selected component, the related process and equipment mapping are updated synchronously to maintain the consistency of the scheduling model.

[0016] As preferred, the chromosome decoding process in step S33 includes nesting scheduling decoding and component production scheduling decoding. The nesting scheduling decoding calculates the completion time of each component nesting according to the component order ; The component production scheduling decoding determines the earliest feasible start time of each process according to the process order and machine allocation, combined with the process sequence and equipment occupation state , which includes the following steps: S331, for the current process with a preceding process, the start time is the maximum value of the end time of the preceding process and the earliest available time of the selected equipment, and the expression is: ; Among them, 、 is the completion time of the immediately preceding process; is the earliest idle time of the selected machine ; S332, if the process is the first process of the component , then the component nesting completion time Constraint, the specific expression is: ; S333, fitness function calculation, taking the minimum maximum completion time of all components as the optimization goal, normalizing the fitness, and the individual fitness function expression is: , ; Wherein, The maximum completion time of the first individual; The maximum completion time in the population; The minimum completion time in the population; The minimum is 0, the maximum is 1, and the larger the value represents the better performance of the individual; S334, according to the fitness calculation result, judge whether to meet the termination condition, if meet, output the optimization scheduling scheme; if not, continue to carry out the process, machine, and sleeve material chromosome mutation, get the optimal chromosome, and decode to get the optimization scheduling scheme.

[0017] Overall, compared with the prior art, the above technical scheme conceived by the present application has the following beneficial effects: Through the double-layer chromosome coding mechanism, the process chromosome and the machine chromosome are independently coded, and the set judgment mechanism is introduced in the decoding stage, so as to solve the scheduling difficulty problem caused by the part set constraint, complex processing process and other factors in the steel structure factory. Compared with the traditional scheduling method, the method of the present application can more accurately establish the set constraint and other complex constraint relationships in the production of steel components, thereby improving the executability and scientificity of the scheduling result, and realizing the minimization of the maximum completion time of steel component production. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The flowchart of the steel structure production intelligent scheduling method of the present application.

[0019] Figure 2 The steel component production scheduling model structure diagram considering the part set.

[0020] Figure 3 The flowchart of solving the production scheduling problem of steel components by using the composite chromosome genetic algorithm.

[0021] Figure 4 The processing flowchart of two components that need to be processed.

[0022] Figure 5 The process and machine chromosome.

[0023] Figure 6 The sleeve material scheme chromosome. DETAILED DESCRIPTION

[0024] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely intended to explain the present application and should not be used to limit the present application. In addition, the technical features involved in the various embodiments of the present application described below can be combined with each other as long as there is no conflict.

[0025] Please refer to Figures 1-6 The embodiment provides a steel member complete set intelligent scheduling method based on a composite chromosome genetic algorithm, which comprises the following steps: S1, information acquisition and pretreatment: acquiring steel member production related information, the method first acquires various types of information related to steel member production, including production line information, PBOM information, nesting optimization information, process quota time, production progress state, production scheduling plan; S11, production line information: date (such as 20xx0410), production line name (such as xx line), contained process (such as hole making), equipment number, equipment type (such as planar numerical control), planned working time (such as 9h), effective working time (such as 7.5h), personnel configuration quantity (such as 1), and work type.

[0026] S12, PBOM information: member number, processing process (such as plasma cutting blanking), processing times (such as 1 time), input material number (such as Q355B steel plate), input material specification (such as 12*1500*6000), input material quantity, output material number, output material specification (such as 12*100*78), and output material quantity.

[0027] S13, nesting optimization information: nesting number (such as T01), nesting times (such as 1 time), blanking type (such as flame cutting blanking), blanking time (such as 2.5h), belonging project, belonging department, member number, member part number, member size (such as 5243*250), and member quantity.

[0028] S14, process quota time: process name (such as assembly), equipment type (such as vertical assembly machine), measurement attribute (such as length), measurement unit, and unit time.

[0029] S15, production progress state: project name, belonging department, member number, member ID, and progress condition.

[0030] S16. Production Scheduling Plan: Project Name, Zone Name, Order Name, Component Model, Component ID, Specifications, Net Weight, Order Required Completion Date, Component Priority, Component Planned Completion Date, Production Line Allocation, Process Name, Production Equipment Allocation, Process Start Date, Process End Date.

[0031] S2. Constructing the Scheduling Model: Based on the information obtained in step S1, and combined with the constraints of parts availability and actual production, a steel component production scheduling model is established. The objective function is to minimize the maximum completion time. Model as follows Figure 2 As shown, the constraints include material handling process scheduling constraints, parts matching constraints, processing sequence constraints, process time calculation and allocation constraints, processing mutual exclusion constraints, production line process sharing constraints, and completion time constraints. The scheduling modeling process incorporates constraints on component parts matching time, ensuring that processing only begins after all parts are ready. This avoids equipment idle time and plan delays caused by incomplete parts, improving the actual executability and resource utilization of the scheduling. Furthermore, through multi-level process modeling and sequence constraint definition, it can adapt to the needs of various production lines and multi-stage assembly processes, considering complex constraints such as the processing sequence of the main body and components, and the final assembly sequence.

[0032] S21. Material cutting process scheduling constraints: Ensure that the material cutting process is executed by a single piece of equipment, and consider equipment conflicts and processing sequence, including the following steps: S211. Based on the information obtained in step S1, the optimization scheduling objective is set as minimizing the maximum completion time, and its expression is: ; S212. Calculate the material cutting completion time. The specific expression is as follows: ; in, The material cutting completion time for scheme d; For the plan The start time of material feeding; In order to develop a material cutting plan A collection of machines, ; For feeding machines; Indicates if the solution By machine The value is 1 if material is being fed, otherwise it is 0. For machines Material cutting scheme Time required; For material cutting plan; This is a set of solutions that require material cutting. ; S213, define the blanking scheme can only be executed by one machine, to ensure that the blanking process is executed uniquely, the specific expression is: ; S214, further define the blanking sequence, the specific expression is: ; Wherein, represents if the scheme and are blanked by machine , and the blanked by machine is prior to processing, then it is 1, otherwise it is 0; represents if the scheme and are blanked by machine , and the blanked by machine is prior to processing, then it is 1, otherwise it is 0; represents if the scheme is assigned to machine , then it is 1, otherwise it is 0; represents if the scheme is assigned to machine , then it is 1, otherwise it is 0.

[0033] S215, define the conflict constraint, the specific expression is: ; Wherein, is the blanking completion time of scheme ; is the start blanking time of scheme ; is a very large positive number.

[0034] S22, part set constraint by defining the set constraint of parts, used to constrain the components Only after all the required parts are blanked, the subsequent processing can begin, the specific expression is as follows: ; ; Wherein, is the blanking completion time of scheme , is the set time of steel plate required for processing component ; is the set of schemes required for blanking component , ; is a component, , ; is a component is a process is a start time of a process; S23, process sequence constraint is defined that the main body and component processing are strictly performed according to a predetermined sequence, and the specific expression is: ; ; wherein, is a component is a process is a finish time of a process; is a component is a first way main body processing process, , ; is a component is a first way component processing process, , ; is a component is a first +1 way main body processing process, is a component is a first +1 way component processing process.

[0035] S24, process time calculation and distribution constraint: considering the multi-line equipment resource constraint and process unique distribution, including the following steps: S241, calculating the process completion time, and the specific expression is: ; wherein, is 1 if the component is processed by the machine of the line for the process , otherwise 0; is the time required for the process of the machine for processing the component ; is a machine set, ; S242, defining that the final assembly must be performed after the component processing is completed (applicable to the component requiring final assembly), and the specific expression is: ; wherein, is 1 if the component Components The completion time of the final process; Representation of components Final assembly process The start time.

[0036] S243. Define the machine allocation for each process as unique; the specific expression is: ; in, For the actual production line assembly, .

[0037] S25. Processing mutual exclusion constraints ensure that processing times do not conflict by defining the processing order of multiple components in the same machine. The specific expression is as follows: ; ; in, Indicates if component and process All by machine Processed by machine Processed components Prior to If processing is performed, the value is 1; otherwise, it is 0. Indicates if component and process All by machine Processed by machine Processed components Prior to The value is 1 if processing is required, otherwise it is 0. Indicates if component process By machine If processing is required, the value is 1; otherwise, it is 0. Indicates if component process By machine If processing is required, the value is 1; otherwise, it is 0. Representation of components process Completion time; Representation of components process Start time; S26. Production Line Process Sharing Constraints: By defining and handling processes shared or selectively shared across multiple production lines, this constraint addresses issues related to shared or selectively shared processes, further improving resource utilization. Specifically, it includes the following steps: S261. Determine whether a shared process can be performed. The execution is performed online, and the specific expression is: ; in, For process Dependent processes; Indicates if component In the Machines on the production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if the first The production line can perform processes If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if component In the Machines on the production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if the first The production line can perform processes If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if component In the Machines on the production line Shared processes were executed. If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if component In the Machines on the production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. S262. Determine whether a shared process can be executed on the first production line. The specific expression is: ; in, Indicates if component Machines on the first production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. This indicates that if the first production line can execute the process. If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if component Machines on the second production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if component Machines on the first production line The above process was executed 1 if the component i has finished the process on the last production line, otherwise 0; S263, judging whether the sharing process can be performed on the last production line is executed on the machine of the last production line, the specific expression is: ; wherein, indicates that the component i has finished the process on the machine of the last production line the process is executed on the machine of the last production line , the specific expression is: 1 if the component i has finished the process on the last production line, otherwise 0; indicates that the last production line can perform the process , the specific expression is: 1 if the last production line can perform the process, otherwise 0; indicates that the component i has finished the process on the machine of the last production line the process is executed on the machine of the last production line , the specific expression is: 1 if the component i has finished the process on the last production line, otherwise 0; indicates that the component i has finished the process on the machine of the last production line the process is executed on the machine of the last production line , the specific expression is: 1 if the component i has finished the process on the last production line, otherwise 0; indicates that the component i has finished the process on the machine of the last production line the process is executed on the machine of the last production line , the specific expression is: S27, completion time constraint defines the completion time and the maximum completion time constraint, that is, the completion time of all components does not exceed the current maximum completion time, the specific expression is: ; ; wherein, is the completion time of the component i; indicates that the component i has finished the process on the machine of the last production line the process is executed on the machine of the last production line , the specific expression is: the time spent by the i th part of the component i on the last production line to complete the process .

[0038] S3: through the chromosome coding mechanism, the scheduling model established in step S2 is solved by combining the complex chromosome genetic algorithm, the production line process sharing mechanism and the device unique allocation mechanism are established, the selectable sharing of different production line boundary processes is considered, which helps to improve the flexibility of device configuration and the load balancing degree of production line, and optimizes the configuration efficiency of overall processing resources; taking the minimum maximum completion time of components as the optimization objective, combined with the complex chromosome genetic algorithm for solving, the total time of component production is significantly reduced, and the production scheduling efficiency of steel components is improved. The specific steps are as follows: S31, process chromosome coding design: adopt composite chromosome coding mode, split the scheduling problem into process chromosome and machine chromosome, i.e. process sequencing problem and machine selection problem, as shown in Figure 5 Specifically, The process chromosome is used to represent the processing sequence of all processes in component production, and the chromosome length is the total number of all processes to be scheduled. Each gene position is encoded with a process number (such as Y1, Y5, Y2, Y3, Y6, Y4), arranged in processing order; The machine chromosome is used to specify the processing equipment allocated to each process, and the chromosome length is the same as the process chromosome. Each gene position records the machine number selected for the process. This encoding needs to meet the processable mapping relationship between the process and the machine.

[0039] S32, machine chromosome mutation design includes: process chromosome mutation, machine chromosome mutation, and nesting scheme chromosome mutation, to randomly replace the equipment number in the processing equipment set for a certain process; Process chromosome mutation is to select any continuous gene segment (such as position [2, 4]) on the chromosome, and randomly select a process gene segment for rearrangement without violating the process dependency constraint, for example, change the sequence [Y2, Y3, Y5] to [Y2, Y5, Y3] to generate a new sequencing scheme; Machine chromosome mutation is to randomly replace the current machine number with a machine in the processing equipment set for a certain process, thereby adjusting the processing resources; Nesting scheme chromosome mutation is to randomly select another scheme from the nesting scheme set of a certain component in the chromosome to replace the current encoding under the premise of satisfying the nesting constraint, thereby forming a new nesting allocation structure. If the mutation causes the selected component's part set to change, the relevant process and equipment mapping are updated synchronously to maintain the consistency of the scheduling model.

[0040] S33, chromosome decoding process: according to the process processing sequence and machine allocation information, and combining the nesting constraint, the chromosome is decoded to obtain the start and end time of each process.

[0041] Specifically, the chromosome decoding process in the present application includes nesting scheduling decoding and component production scheduling decoding. The nesting scheduling decoding calculates the completion time of each component nesting according to the component order ; the component production scheduling decoding determines the earliest feasible start time of each process according to the process order and machine allocation, combining the process sequence and equipment occupation state , specifically including the following steps: S331, for the current process with a preceding process Its start time is the maximum value of the end time of the preceding process and the earliest available time of the selected equipment. If the process There are multiple preceding processes (such as process) The expression is: ; in, , The completion time of the preceding process; For the selected machine The earliest free time; S332. If the process is a component The first step is affected by the time required to complete the component nesting process. The constraint, specifically expressed as: ; For ease of understanding, Figure 4 Decode the chromosome shown, assuming the component , The completion times for nesting are 0 and 1 respectively; process , , , , , The processing times on the corresponding machines are 0.5, 0.7, 1.4, 1, 1. All machines are available all day, meaning they are idle during the time window [0, 9]. The specific decoding steps are as follows: On genes If we decode it, its start time can be expressed as: Therefore, its starting time is 0, that is, in the process within the time window [0, 0.5]. In the machine Processing and upgrading machines. The release time is 0.5; On genes If we decode it, its start time can be expressed as: Therefore, its start time is time 1, that is, in the process within the time window [1, 1.7]. In the machine Processing and upgrading machines. The release time is 1.7.

[0042] On genes Decoding is performed due to the process If there is no immediate precedence relationship between them, then their start time can be expressed as Therefore, its starting time is 0, that is, in the process within the time window [0, 1.4]. In the machine Processing and upgrading machines. The release time is 1.4.

[0043] On genes If we decode it, its start time can be expressed as: Therefore, its start time is 1.4, which is the time window [1.4, 1.8] process. In the machine Processing and upgrading machines. The release time is 1.8.

[0044] On genes If we decode it, its start time can be expressed as: Therefore, its start time is 0.7, which is within the time window [1.7, 2.7] of the process. In the machine Processing and upgrading machines. The release time is 2.7.

[0045] On genes If we decode it, its start time can be expressed as: Therefore, its start time is 2.7, which is the time window [2.7, 3.7] process. In the machine Processing and upgrading machines. The release time is 3.7.

[0046] S333. Fitness function calculation: With minimizing the maximum completion time of all components as the optimization objective, the fitness is normalized, and the corresponding individual fitness function expression is: , ; in, For the first Maximum completion time for an individual; The maximum completion time in the population; The minimum completion time in the population; The minimum value is 0, and the maximum value is 1. The larger the value, the better the individual's performance. S334. Based on the fitness calculation results, determine whether the termination condition is met. If it is met, output the optimized scheduling scheme; if not, continue to perform chromosome mutation of process, machine, and nesting to obtain the optimal chromosome, and decode to obtain the optimized scheduling scheme.

[0047] S4. Output steel component optimization scheduling scheme: task number (e.g., 1), start time (e.g., 8:00), end time (e.g., 9:30), production line (xx line 1), machine number, component ID, task content (e.g., beveling).

[0048] It is to be understood that the above description is intended to be illustrative and not restrictive. Many other embodiments will be apparent to those of skill in the art upon reading and understanding the above description. The scope of the application should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims

1. An intelligent scheduling method for steel structure production that combines the tightness and equipment resource constraints, characterized in that, The method comprises the following steps: S1: information acquisition and preprocessing: acquiring information related to steel component production; S2: constructing a scheduling model: based on the information acquired in step S1, combining part set matching and actual production constraints, a steel component production scheduling model is established; S3: solving the scheduling model established in step S2 by using a chromosome coding mechanism and combining a complex chromosome genetic algorithm; S4: outputting an optimized scheduling scheme for steel components.

2. The intelligent scheduling method for steel structure production integrating just-in-time and equipment resource constraints according to claim 1, characterized in that, The information in step S1 includes production line information, PBOM information, nesting optimization information, process quota time, production progress status, and production scheduling plan; The production line information includes date, production line name, contained process, equipment number, equipment type, planned working time, effective working time, personnel configuration quantity, and work type; The PBOM information includes component number, processing process, processing times, input material number, input material specification, input material quantity, output material number, output material specification, and output material quantity; The nesting optimization information includes nesting number, nesting times, blanking type, blanking time, belonging project, belonging department, component number, component part number, component size, and component quantity; The process quota time includes process name, equipment type, measurement attribute, measurement unit, and unit time; The production progress status includes project name, belonging department, component number, component ID, and progress situation; The production scheduling plan includes project name, partition name, order name, component model, component ID, specification, net weight, order requirement completion date, component priority, component plan completion date, production line allocation, process name, production equipment allocation, process start date, and process end date.

3. The intelligent scheduling method for steel structure production integrating just-in-time and equipment resource constraints as claimed in claim 1, characterized in that, The constraints in step S2 include blanking process scheduling constraints, part set matching constraints, processing sequence constraints, process time calculation and allocation constraints, processing exclusion constraints, production line process sharing constraints, and completion time constraints; The specific method of the blanking process scheduling constraints comprises the following steps: S211. Based on the information obtained in step S1, a scheduling model is constructed, and the scheduling target is set to minimize the maximum completion time ; S212: calculating the blanking completion time, and the specific expression is as follows: ； wherein, is the completion time of the scheme ; is the start time of the scheme ; is the set of machines that can perform the scheme ; ; is the unloading machine; is 1 if the scheme is performed by the machine , otherwise it is 0; is the time required by the machine to perform the scheme ; is the unloading scheme; is the set of schemes that need to be unloaded, ; S213: defining that the blanking scheme can only be executed by one machine, ensuring that the blanking process is executed only once, and the specific expression is as follows: ； S214: further defining the blanking sequence, and the specific expression is as follows: ； wherein, is 1 if the scheme and are both fed by machines , and fed by machines , and 0 otherwise; is 1 if the scheme is fed before being processed, and 0 otherwise; is 1 if the scheme and are both fed by machines , and fed by machines , and 0 otherwise; is 1 if the scheme is fed before being processed, and 0 otherwise; is 1 if the scheme is assigned to machines , and 0 otherwise; is 1 if the scheme is assigned to machines , and 0 otherwise; S215: defining the conflict constraints, and the specific expression is as follows: ； wherein, is the unloading completion time for the scenario . is the start unloading time for the scenario . is a very large positive number.

4. The intelligent scheduling method for steel structure production integrating just-in-time and equipment resource constraints as claimed in claim 3, characterized in that, The part set constraint is used to constrain the component by defining the set property of the part Only after all the required parts are blanked out can the subsequent processing begin, and the specific expression is as follows: ； ； wherein, the blanking completion time of the scheme , the matching time of the steel plate required for processing the component ; the scheme set required for processing the component , ; the component set , ; the start time of the process of the component , ​ The processing sequence constraints are defined by strictly defining the processing sequence of the main body and the part according to the predetermined sequence, and the specific expression is as follows: ； ； wherein, is a component is a process is a completion time; is a component is a first is a main body machining process, , ; is a component is a first is a component machining process, , ; is a component is a first is a main body machining process, is a component is a first is a +1 component machining process.

5. The intelligent scheduling method for steel structure production integrating just-in-time and equipment resource constraints as claimed in claim 3, characterized in that, The process time calculation and allocation constraints comprise the following steps: S241: calculating the process completion time, and the specific expression is as follows: ； in, Indicates if component From the production line machine Process The value is 1 if it is 1, otherwise it is 0. For machines Processed components process Time required; For machine collection, ; S242: defining that the final assembly must be performed after the part processing is completed, and the specific expression is as follows: ； wherein representing member of the component the completion time of the last process; representing member the assembly process the start time; S243: defining that the process machine allocation is unique, and the specific expression is as follows: ； wherein is a set of real production lines, .

6. The intelligent scheduling method for steel structure production integrating just-in-time and equipment resource constraints as claimed in claim 3, characterized in that, The processing exclusion constraints are defined by defining the processing sequence of multiple components in the same machine, ensuring that the processing time does not conflict, and the specific expression is as follows: ； ； wherein, represents 1 if the process of the component and is processed by the machine and the component processed by the machine is processed before , otherwise 0; wherein, represents 1 if the process of the component and is processed by the machine and the component processed by the machine is processed before , otherwise 0; represents 1 if the process of the component is processed by the machine , otherwise 0; represents 1 if the process of the component is processed by the machine , otherwise 0; represents 1 if the process of the component is processed by the machine , otherwise 0; represents 1 if the process of the component is processed by the machine , otherwise 0; represents the process completion time of the component ; represents the process start time of the component ; The production line process sharing constraints are defined by processing multiple production line shared or selective shared processes, and specifically comprise the following steps: S261, judging whether the sharing procedure can be performed on the production line or not, which is specifically expressed as: the production line, which is specifically expressed as: ； in, For process Dependent processes; Indicates if component In the Machines on the production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if the first The production line can perform processes If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if component In the Machines on the production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if the first The production line can perform processes If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if component In the Machines on the production line Shared processes were executed. If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if component In the Machines on the production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. S262: judging whether the shared process can be executed on the first production line, and the specific expression is as follows: ； in, Indicates if component Machines on the first production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. This indicates that if the first production line can execute the process... If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if component Machines on the second production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if component Machines on the first production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. S263、determine whether the sharing process can be performed at the last production line The above is executed, and the specific expression is: ； in, Indicates if component In the Machines on the production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if the first The production line can perform processes If it is true, then it is 1; otherwise, it is 0. Indicates if component In the Machines on the production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. Indicates if component In the Machines on the production line The above process was executed If the value is 1, then the value is 1; otherwise, the value is 0. The completion time constraint defines the completion time and the maximum completion time constraint, i.e. the completion time of all components does not exceed the current maximum completion time, and the specific expression is: ； ； wherein, the time to complete the component; the time to complete the component; the time to complete the component; the time to complete the component; the time to complete the component; the time to complete the component; the time to complete the component; 7. The intelligent scheduling method for steel structure production with the fusion of the nestability and the equipment resource constraints according to any one of claims 1-6, characterized in that, Step S3 comprises the following steps: S31, process chromosome coding design: adopting a complex chromosome coding mode, the scheduling problem is divided into process chromosome and machine chromosome; S32, machine chromosome mutation design includes: process chromosome mutation, machine chromosome mutation, and nesting scheme chromosome mutation, so as to randomly replace the device number in the processing equipment set for a certain process; S33, chromosome decoding process: according to the process processing sequence and machine allocation information, combined with the matching constraint, the chromosome is decoded to obtain the start and end time of each process.

8. The intelligent scheduling method for steel structure production integrating just-in- sequence and equipment resource constraints as claimed in claim 7, characterized in that, The process chromosome in step S31 is used to represent the processing sequence of all processes in component production, and the length of the chromosome is the total number of all processes to be scheduled. Each gene position is encoded with a process number and arranged in processing sequence; The machine chromosome is used to specify the processing equipment allocated to each process, and the length of the chromosome is the same as that of the process chromosome. Each gene position records the machine number selected for the process. This coding needs to meet the processable mapping relationship between process and machine.

9. The intelligent scheduling method for steel structure production integrating just-in- sequence and equipment resource constraints as claimed in claim 7, characterized in that, In step S32, the process chromosome mutation is to select any continuous gene segment on the chromosome, and randomly select a process gene segment for rearrangement without violating the process dependency constraint, to generate a new sorting scheme; The machine chromosome mutation is to randomly replace the current machine number with a machine in the processing equipment set for a certain process, so as to realize the adjustment of processing resources; The nesting scheme chromosome mutation is to randomly select another scheme from the nesting scheme set of the component in the chromosome for the nesting scheme gene position of the component, to replace the current coding under the premise of meeting the matching constraint, so as to form a new nesting allocation structure; If the mutation causes the selected component part set to change, the related process and device mapping are updated synchronously to maintain the consistency of the scheduling model.

10. The intelligent scheduling method for steel structure production integrating just-in-time and equipment resource constraints as claimed in claim 7, characterized in that, The chromosome decoding process in step S33 includes a material lot scheduling decoding and a component production scheduling decoding. The material lot scheduling decoding calculates the completion time of each material lot in the order of components ; The component production scheduling decoding determines the earliest feasible start time of each process according to the process sequence and machine allocation, in combination with the process sequence and the equipment occupation state , and specifically includes the following steps: S331, for the current process with a preceding process, the start time is the maximum value of the end time of the preceding process and the earliest available time of the selected device, and the expression is: ； wherein, , is the completion time of the immediately preceding process step; is the earliest free time of the selected machine . S332, if the process is the first process of the component , the component nesting completion time is constrained, and the specific expression is: ； S333, fitness function calculation, taking the minimum maximum completion time of all components as the optimization objective, normalizing the fitness, and the individual fitness function expression is: ， ； wherein, is the maximum completion time for the individual; is the maximum completion time for the population; is the minimum completion time for the population; is the minimum completion time for the population; is the minimum completion time for the population; S334, according to the fitness calculation result, it is judged whether the termination condition is met. If it is met, the optimized scheduling scheme is output; if it is not met, the chromosome mutation of process, machine and nesting is continued to obtain the optimal chromosome, and the optimized scheduling scheme is obtained by decoding.

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