Intelligent scheduling method for steel structure production fusing full set and equipment resource constraints

Abstract

The application belongs to the field of steel component production and optimal scheduling, and discloses a steel structure production intelligent scheduling method combining set matching and equipment resource constraints, which is used for solving the scheduling difficulty problem caused by part set matching constraints and complex machining processes in a steel structure factory. The method comprises the following steps: acquiring data such as production line information, PBOM information, set matching information, machining quota, production state and plan, and constructing an optimal scheduling model with the target of minimizing the maximum completion time. The model introduces constraint conditions such as blanking sequence, equipment allocation, machining sequence and set matching, and adopts a compound chromosome genetic algorithm for solution. In combination with process sequencing and equipment allocation, scheduling decoding is completed to generate a feasible scheduling scheme that meets the set matching requirements. The method can effectively optimize task allocation in the component production process, improve resource utilization and overall scheduling efficiency, and has strong 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, 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:

[0007] S1: Information acquisition and preprocessing: acquiring steel component production related information;

[0008] S2: Construction of 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;

[0009] S3: Through the chromosome coding mechanism, the scheduling model established in step S2 is solved by combining the complex chromosome genetic algorithm;

[0010] S4: output the optimized scheduling scheme of steel components.

[0011] Preferably, the information in step S1 includes production line information, PBOM information, nesting optimization information, process quota time, production progress status, and production scheduling plan.

[0012] The production line information includes date, production line name, contained processes, equipment number, equipment type, planned working time, effective working time, personnel configuration number, and type of work.

[0013] The PBOM information includes component number, processing process, processing frequency, input material number, input material specification, input material quantity, output material number, output material specification, and output material quantity.

[0014] The nesting optimization information includes nesting number, nesting frequency, blanking type, blanking time, belonging project, belonging department, component number, component part number, component size, and component quantity.

[0015] The process quota time includes process name, equipment type, measurement attribute, measurement unit, and unit time

[0016] The production progress status includes project name, belonging department, component number, component ID, and progress situation

[0017] The production scheduling plan includes project name, partition name, order name, component model, component ID, specification, single net weight, order requirement completion date, component priority, component planned completion date, production line allocation, process name, production equipment allocation, process start date, and process end date.

[0018] Preferably, the constraint conditions include blanking 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.

[0019] The specific method of the blanking process scheduling constraint includes the following steps:

[0020] S211, based on the information obtained in step S1, set the scheduling target to minimize the maximum completion time ;

[0021] S212, calculate the blanking completion time, and the specific expression is as follows:

[0022] ;

[0023] wherein, is the blanking completion time of scheme d; is the blanking completion time of scheme the start time of the unloading of the part; to be able to perform the unloading plan by the set of machines, ; to be the unloading machine; is 1 if the plan is unloaded by the machine , otherwise 0; is the unloading plan of the machine ; is the unloading plan; is the set of plans that need to be unloaded,

[0024] S213, define that the unloading plan can only be executed by one machine, ensure that the unloading process is executed uniquely, the specific expression is:

[0025] ;

[0026] S214, further define the unloading sequence, the specific expression is:

[0027] ;

[0028] wherein, is 1 if the plans and are unloaded by the machine , and the unloaded by the machine is processed before , otherwise 0; is 1 if the plans and are unloaded by the machine , and the unloaded by the machine is processed before , otherwise 0; is 1 if the plan is assigned to the machine , otherwise 0; is 1 if the plan is assigned to the machine , otherwise 0;

[0029] S215, define the conflict constraint, the specific expression is:

[0030]

[0031] wherein, is the unloading completion time of the plan ; is the start-up time of the scheme ; is a very large positive number.

[0032] As a preference, the parts fitting constraint is defined by defining the fitting of parts constraint for constraining the components Only after all the required parts of the component are blanked can the subsequent processing begin, and the specific expression is as follows:

[0033] ;

[0034] ;

[0035] wherein, is the blanking completion time of the scheme , is the fitting time of the steel plate required for processing the component ; is the set of schemes required for processing the component ; ; is the set of components, , ; is the start-up time of the process of the component ;

[0036] The processing sequence constraint is defined by defining that the main body and the part processing are strictly performed according to the predetermined sequence, and the specific expression is as follows:

[0037] ;

[0038] ;

[0039] wherein, is the completion time of the process of the component ; is the first main body processing process passed by the component , , ; is the first part processing process passed by the component , , ; is the first +1 main body processing process passed by the component , is the first +1 part processing process passed by the component .

[0040] As a preference, the process time calculation and assignment constraints include the following steps:

[0041] S241, calculate the process completion time, the specific expression is:

[0042] ;

[0043] Wherein, is 1 if the component is processed by the machine of the production line , otherwise 0; ; is the time required for the process of the machining component of the machine ; is the machine set, ;

[0044] S242, define that the final assembly must be carried out after the component processing is completed (applicable to components that need final assembly), the specific expression is:

[0045] ;

[0046] Wherein, is the completion time of the last process of the component ; ; is the start time of the final assembly process of the component ;

[0047] S243, define the process machine assignment to be unique, the specific expression is:

[0048] ;

[0049] Wherein, is the set of actual production lines, .

[0050] As a preference, the processing exclusion constraint ensures that the processing time does not conflict by defining the processing order of multiple components in the same machine, the specific expression is as follows:

[0051] ;

[0052] ;

[0053] Wherein, is 1 if the process of the component and ​are all processed by machines are all processed by machines are all processed by machines are all processed by machines is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0; is 1 if the process is processed by machines, otherwise 0;

[0054] The line process shared constraint is processed by defining multiple line shared or selective shared processes, specifically including the following steps:

[0055] S261, judging whether the shared process can be executed on the i-th line, and the specific expression is:

[0056] ;

[0057] wherein, is the dependent process of the process; is 1 if the process is executed on the machine of the i-th line, otherwise 0; is 1 if the process is executed on the machine of the i-th line, otherwise 0; is 1 if the process is executed on the machine of the i-th line, otherwise 0; is 1 if the process is executed on the machine of the i-th line, otherwise 0; is 1 if the process is executed on the machine of the i-th line, otherwise 0; is 1 if the process is executed on the machine of the i-th line, otherwise 0; is 1 if the process is executed on the machine of the i-th line, otherwise 0; is 1 if the process is executed on the machine of the i-th line, otherwise 0; is 1 if the process is executed on the machine of the i-th line, otherwise 0; is 1 if the process is executed on the machine of the i-th line, otherwise 0; is 1 if the process is executed on the machine of the i-th line, otherwise 0; is 1 if the process is executed on the machine of the i-th line, otherwise 0; is 1 if the process is executed on the machine of the i-th line, otherwise 0; is 1 if the process is executed on the machine of the i-th line, otherwise 0; is 1 if the process is executed on the machine of the i-th line, otherwise 0;​ The process can be performed on the first production line 1 if, and 0 otherwise; represents a component In the first The machine of the first production line The shared process has been performed on the machine of the first production line 1 if, and 0 otherwise; represents a component In the first The machine of the first production line The process has been performed on the machine of the first production line 1 if, and 0 otherwise;

[0058] S262, determining whether the shared process can be performed on the first production line, is specifically expressed as:

[0059] ;

[0060] wherein, represents a component In the first production line The machine of the first production line The process has been performed on the machine of the first production line 1 if the first production line can perform the process, and 0 otherwise; 1 if the first production line can perform the process, and 0 otherwise; represents a component In the second production line The machine of the second production line The process has been performed on the machine of the second production line represents a component In the first production line The machine of the first production line The process has been performed on the machine of the first production line

[0061] S263, determining whether the shared process can be performed on the last production line, is specifically expressed as:

[0062] ;

[0063] wherein, represents a component In the first production line The machine of the first production line The process has been performed on the machine of the first production line 1 if, and 0 otherwise; 1 if the first production line can perform the process, and 0 otherwise; 1 if the first production line can perform the process, and 0 otherwise; represents a component In the first production line The machine of the first production line ​Machines of the production line The process is performed on the last machine 1 if, otherwise 0; denotes if the component In the first Machines of the production line The process is performed on the last machine 1 if, otherwise 0;

[0064] The completion time constraint defines the completion time with 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:

[0065] ;

[0066] ;

[0067] wherein, is the completion time of the component denotes the time spent by the th part of the component to complete the process on the last machine of the production line.

[0068] As a preferred, the step S3 comprises the following steps:

[0069] S31, process chromosome coding design: using a complex chromosome coding method, the scheduling problem is divided into process chromosome and machine chromosome;

[0070] S32, machine chromosome mutation design includes: process chromosome mutation, machine chromosome mutation, and nesting scheme chromosome mutation, so as to randomly replace the equipment number in the processing equipment set for a certain process;

[0071] S33, chromosome decoding process: according to the process processing order and machine allocation information, combined with the matching constraint, the chromosome is decoded to obtain the start and end time of each process.

[0072] As a preferred, the process chromosome in the step S31 is used to represent the processing order of all processes in component production, and the chromosome length is the total number of all scheduled processes. Each gene position is encoded with process number and arranged according to processing order;

[0073] 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 machine number selected for the process, and the coding needs to meet the processable mapping relationship between process and machine.

[0074] ​​As preferred, the procedure chromosome mutation in step S32 is to select an arbitrary continuous gene segment on the chromosome, and a procedure gene segment is randomly selected for rearrangement without violating the procedure dependency constraint to generate a new sequencing scheme;

[0075] The machine chromosome mutation is to randomly replace the current machine number in the processing equipment set for a certain procedure, thereby realizing the adjustment of the processing resource;

[0076] The nesting scheme chromosome mutation is to randomly select another scheme from the nesting scheme set of the component to replace the current coding under the premise of satisfying the matching constraint, thereby forming a new nesting allocation structure; if the mutation causes the change of the part set corresponding to the selected component, the related procedure and equipment mapping are updated synchronously to maintain the consistency of the scheduling model.

[0077] As preferred, the chromosome decoding process in step S33 includes nesting scheduling decoding and component production scheduling decoding. The nesting scheduling decoding calculates the nesting completion time of each component in the order of the components . The component production scheduling decoding determines the earliest feasible start time of each procedure according to the procedure order and machine allocation, combined with the process sequence and equipment occupation state , and specifically includes the following steps:

[0078] S331, for the current procedure with a preceding procedure, the start time is the maximum value of the end time of the preceding procedure and the earliest available time of the selected equipment, and the expression is:

[0079] ;

[0080] wherein, 、 is the completion time of the immediately preceding procedure; is the earliest idle time of the selected machine ;

[0081] S332, if the procedure is the first procedure of the component , it is constrained by the nesting completion time of the component , and the specific expression is:

[0082] ;

[0083] S333, the fitness function calculation takes the minimization of the maximum completion time of all components as the optimization objective, normalizes the fitness, and the individual fitness function expression is:

[0084] , ;

[0085] wherein, is the maximum completion time of the individual; is the maximum completion time of the population; is the minimum completion time of the population; is the minimum completion time of the population; is 0, and the maximum is 1, and the greater the value represents the better performance of the individual;

[0086] S334, according to the fitness calculation result, judge whether to meet the termination condition, if meet, output optimization scheduling scheme, if not meet, continue to carry out the process, machine, sleeve material chromosome mutation, get the optimal chromosome, and decode to get the optimization scheduling scheme.

[0087] Overall, compared with the prior art, the above technical solutions conceived by the present application have the following beneficial effects:

[0088] 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, 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 members, thereby improving the executability and scientificity of the scheduling result, and realizing the minimization of the maximum completion time of the steel member production. BRIEF DESCRIPTION OF DRAWINGS

[0089] Figure 1 is the flow chart of the steel structure production intelligent scheduling method of the present application.

[0090] Figure 2 is the steel member production scheduling model structure diagram considering the part set.

[0091] Figure 3 is the flow chart of solving the production scheduling problem of steel members by using the composite chromosome genetic algorithm.

[0092] Figure 4 is the processing flow chart of two components needing processing.

[0093] Figure 5 is the process and machine chromosome.

[0094] Figure 6 is the sleeve material scheme chromosome. DETAILED DESCRIPTION

[0095] ​In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0096] Please refer to Figures 1-6 The embodiment provides a steel member intelligent scheduling method based on a composite chromosome genetic algorithm, comprising the following steps:

[0097] 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;

[0098] S11, production line information: date (such as 20xx0410), production line name (such as xx line), containing 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] S15, production progress state: project name, belonging department, member number, member ID, and progress condition.

[0103] S16, production scheduling plan: project name, partition name, order name, member model, member ID, specification, single net weight, order requirement completion date, member priority, member plan completion date, production line allocation, process name, production equipment allocation, process start date, and process end date.

[0104] S2 Constructing scheduling model: based on the information obtained in step S1, a steel member production scheduling model is established in combination with the constraints of part completeness and actual production, and the objective function is to minimize the maximum completion time (Tmax) ). The model is shown in Figure 2 , and the constraints include blanking process scheduling constraints, part completeness constraints, processing sequence constraints, process time calculation and distribution constraints, processing exclusion constraints, production line process sharing constraints, and completion time constraints; during the scheduling modeling process, the constraint condition of member part completeness time is introduced to ensure that all parts are ready before starting processing, avoid equipment idling and plan delay caused by incomplete parts, and improve the actual executability of scheduling and resource utilization. At the same time, through multi-level process modeling and sequence constraint definition, it can adapt to the needs of multi-type production line and multi-stage assembly process, and consider the complex constraints of main body and part processing sequence, assembly timing, etc.

[0105] S21, blanking process scheduling constraints: ensure that the blanking process is executed by a unique device, and consider device conflicts and processing sequence, including the following steps:

[0106] S211, based on the information obtained in step S1, set the optimization scheduling target to minimize the maximum completion time, which is expressed as:

[0107] ;

[0108] S212, calculate the blanking completion time, the specific expression is as follows:

[0109] ;

[0110] Wherein, is the blanking completion time of scheme d; is the start blanking time of scheme ; is the machine set capable of carrying out blanking scheme , ; is the blanking machine; is 1 if scheme is blanked by machine , otherwise 0; is the time required for blanking scheme of machine ; is the blanking scheme; is the scheme set that needs to be blanked, ;

[0111] S213, define the blanking scheme can only be executed by a machine, to ensure that the blanking process is executed only, the specific expression is:

[0112] ;

[0113] S214, further define the blanking sequence, the specific expression is:

[0114] ;

[0115] wherein, represents if the scheme and are blanked by machine , and the blanked by machine is prior to processing, then 1, otherwise 0; represents if the scheme and are blanked by machine , and the blanked by machine is prior to processing, then 1, otherwise 0; represents if the scheme is assigned to machine , then 1, otherwise 0; represents if the scheme is assigned to machine , then 1, otherwise 0.

[0116] S215, define the conflict constraint, the specific expression is:

[0117] ;

[0118] wherein, is the blanking completion time of scheme ; is the start blanking time of scheme ; is a very large positive number.

[0119] S22, part set constraint by defining the set of parts of the constraint, used to constrain components only after all the parts needed to blanking completion can begin subsequent processing, the specific expression is as follows:

[0120] ;

[0121] ;

[0122] wherein, For the plan The material unloading completion time, For components The fitting time for the steel plates required for processing; For processing components A collection of solutions requiring material cutting. ; For a set of components, , ; For components process The start date of the project;

[0123] S23. Processing sequence constraints are defined by strictly adhering to a predetermined processing order for the main body and components. The specific expression is as follows:

[0124] ;

[0125] ;

[0126] in, For components process Completion time; For components After the first Main processing steps of the channel , ; For components After the first Processing steps for components, , ; For components After the first +1 main processing step, For components After the first +1 component processing step.

[0127] S24. Process Time Calculation and Allocation Constraints: Considering multi-production line equipment resource constraints and unique process allocation, the following steps are included:

[0128] S241. Calculate the process completion time, the specific expression is as follows:

[0129] ;

[0130] 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, ;

[0131] S242. Define final assembly as having to be performed after component manufacturing is complete (applicable to components requiring final assembly), the specific expression is:

[0132] ;

[0133] in, Representing components Components The completion time of the final process; Representing components Final assembly process The start time.

[0134] S243. Define the machine allocation for each process as unique; the specific expression is:

[0135] ;

[0136] in, For the actual production line assembly, .

[0137] 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:

[0138] ;

[0139] ;

[0140] 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;

[0141] 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:

[0142] S261. Determine whether a shared process can be performed. The execution is performed online, and the specific expression is:

[0143] ;

[0144] 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.

[0145] S262. Determine whether a shared process can be executed on the first production line. The specific expression is:

[0146] ;

[0147] 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.

[0148] S263. Determine whether the shared process can be carried out on the last production line. The above is executed; the specific expression is:

[0149] ;

[0150] 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 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 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.

[0151] S27. Completion Time Constraint: Defines completion time and maximum completion time constraints, meaning the completion time of all components must not exceed the current maximum completion time. The specific expression is as follows:

[0152] ;

[0153] ;

[0154] wherein, is the completion time of the component ; represents the time spent by the th part of the component on the last line to complete the process .

[0155] S3: Solve the scheduling model established in step S2 by the chromosome coding mechanism combined with the compound chromosome genetic algorithm, establish a line process sharing mechanism and a device unique allocation mechanism, consider the selectable sharing of different line boundary processes, which helps to improve the flexibility of device configuration and the degree of load balancing of the line, and optimize the configuration efficiency of the overall processing resource; Taking the minimization of the maximum completion time of the component as the optimization objective, combined with the compound 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:

[0156] S31, process chromosome coding design: adopt a compound chromosome coding mode, and 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:

[0157] The process chromosome is used to represent the processing sequence of all processes in the component production, and the length of the chromosome is the total number of all processes to be scheduled. Each gene site is coded with a process number (such as Y1, Y5, Y2, Y3, Y6, Y4), and arranged in the processing sequence;

[0158] 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 the process and the machine.

[0159] S32, machine chromosome mutation design includes: process chromosome mutation, machine chromosome mutation, and nesting scheme chromosome mutation, which selects a random device number in the processing equipment set for a certain process;

[0160] The process chromosome mutation is to select any continuous gene segment (such as position [2, 4]) on the chromosome, and randomly select a segment of 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;

[0161] Machine chromosome mutation refers to the process of randomly replacing the current machine number of one machine in the set of processing equipment for a specific process, thereby adjusting the processing resources.

[0162] The nesting scheme chromosome mutation is a nesting scheme gene locus for a certain component in the chromosome. Under the premise of satisfying the homogeneity constraint, another scheme is randomly selected from the set of nesting schemes selected for that component to replace the current code, thereby forming a new nesting allocation structure. If the mutation causes the set of parts corresponding to the selected component to change, the relevant process and equipment mapping is updated synchronously to maintain the consistency of the scheduling model.

[0163] S33. Chromosome Decoding Process: Based on the processing sequence and machine allocation information, and combined with homogeneity constraints, the chromosome is scheduled and decoded to obtain the start and end times of each process.

[0164] Specifically, the chromosome decoding process in this application includes nesting scheduling decoding and component production scheduling decoding. Nesting scheduling decoding calculates the completion time for nesting each component according to their order. Component production scheduling decoding determines the earliest feasible start time for each process based on the sequence of operations and machine allocation, combined with the process order and equipment occupancy status. Specifically, it includes the following steps:

[0165] S331. For the current process that has 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:

[0166] ;

[0167] in, , The completion time of the preceding process; For the selected machine The earliest free time;

[0168] S332. If the process is a component The first step is affected by the time required to complete the component kitting. The constraint, specifically expressed as:

[0169] ;

[0170] 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 time on the corresponding machine is 0.5, 0.7, 1.4, 1, 1, and all machines are available all day on that day, that is, in the time window [0, 9] all machines are in the idle state. The specific steps of decoding are as follows:

[0171] Decoding the gene , its start time can be represented as ; its start time is 0, that is, in the time window [0, 0.5] process is processed on machine , and the release time of machine is updated to 0.5;

[0172] Decoding the gene , its start time can be represented as ; its start time is 1, that is, in the time window [1, 1.7] process is processed on machine , and the release time of machine is updated to 1.7.

[0173] Decoding the gene , since there is no immediate relationship between processes , its start time can be represented as ; its start time is 0, that is, in the time window [0, 1.4] process is processed on machine , and the release time of machine is updated to 1.4.

[0174] Decoding the gene , its start time can be represented as ; its start time is 1.4, that is, in the time window [1.4, 1.8] process is processed on machine , and the release time of machine is updated to 1.8.

[0175] Decoding the gene , its start time can be represented as ; its start time is 0.7, that is, in the time window [1.7, 2.7] process is processed on machine , and the release time of machine is updated to 2.7.

[0176] 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.

[0177] 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:

[0178] , ;

[0179] 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.

[0180] 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.

[0181] 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).

[0182] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

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; the constraints include blanking process scheduling constraints, part set matching constraints, processing sequence constraints, process time calculation and distribution constraints, processing exclusion constraints, production line process sharing constraints, and completion time constraints; S3: solving the scheduling model established in step S2 by using a chromosome coding mechanism combined with a complex chromosome genetic algorithm; S3 specifically comprises the following steps: S31, process chromosome coding design: using a complex chromosome coding method, the scheduling problem is divided into process chromosomes and machine chromosomes; S32, machine chromosome mutation design, including 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; S33, chromosome decoding process: according to the process processing sequence and machine distribution information, combining the set matching constraint, the chromosome is decoded to obtain the start and end time of each process; S4: output the optimized scheduling scheme of the steel component.

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 number, 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 status; The production scheduling plan includes project name, partition name, order name, component model, component ID, specification, single net weight, order requirement completion date, component priority, component planned 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 specific method of the blanking process scheduling constraint in step S2 comprises the following steps: S211, based on the information obtained in step S1, a scheduling model is constructed, and a scheduling target is set as minimizing the maximum completion time ; S212, calculating the blanking completion time, 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 machine that performs the scheme ; is 1 if the scheme is performed by the machine ; is the time required for the machine to perform the scheme ; is the scheme that needs to be performed ; S213, defining that the blanking scheme can only be executed by one machine to ensure that the blanking process is executed uniquely, the specific expression is as follows: S214, further defining the blanking sequence, the specific expression is as follows: wherein, is 1 if and are both fed by machines and machined by machines fed by machines are machined before machined, otherwise 0; is 1 if and are both fed by machines and machined by machines fed by machines are machined before machined, otherwise 0; is 1 if the plan is assigned to machines , otherwise 0; is 1 if the plan is assigned to machines , otherwise 0; S215, defining the conflict constraint, the specific expression is as follows: wherein is the unloading completion time of the blanking; is the start unloading time of the blanking; 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 of parts Only after all the parts required under it are blanked out can the subsequent processing begin, and the specific expression is as follows: wherein, the blanking completion time of the scheme , the steel sheet required for the machining of the component ; the machining component required blanking scheme set, ; the component set, , ; the start time of the process of the component ; The processing sequence constraint is defined by strictly following the predetermined sequence for processing the main body and the part, 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 +1 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 distribution constraint comprises the following steps: S241, calculating the process completion time, the specific expression is as follows: wherein, represents if the component is processed by the machine of the production line then 1, otherwise 0; is the time required for the process of the component by the machine of the production line ; is the set of machines, ; S242. It is defined that final assembly must be carried out after the component processing is completed. 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. Define the machine allocation for each process as unique; the specific expression is: 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 mutual exclusion constraint ensures 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. Representing components process Completion time; Representation of 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, judging whether the sharing procedure can be performed on the production line or not, which is expressed as: the production line, which is 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. 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 sharing process can be performed at the last production line The above is executed, and the specific expression is: wherein, represents 1 if the component performed a process on the machine of the i-th production line, and 0 otherwise; represents 1 if the component performed a process on the machine of the i-th production line, and 0 otherwise; represents 1 if the component performed a process on the machine of the i-th production line, and 0 otherwise; represents 1 if the component performed a process on the machine of the i-th production line, and 0 otherwise; represents 1 if the component performed a process on the machine of the i-th production line, and 0 otherwise; represents 1 if the component performed a process on the machine of the i-th production line, and 0 otherwise; 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: 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 integrating just-in-time and equipment resource constraints as claimed in claim 1, characterized in that, In step S31, the process chromosome is used to represent the processing sequence of all processes in the component production. Its chromosome length is the total number of all processes to be scheduled. Each gene position is encoded by the process number and arranged according to the processing sequence. The machine chromosome is used to specify the processing equipment assigned to each process. Its chromosome length is the same as that of the process chromosome. Each gene position records the machine number selected for that process. This encoding must satisfy the process-machine machinability mapping relationship.

8. The intelligent scheduling method for steel structure production integrating just-in-time and equipment resource constraints as claimed in claim 1, characterized in that, In step S32, the process chromosome mutation involves selecting any continuous gene segment on the chromosome, and, without violating the process dependency constraint, randomly selecting a process gene segment for rearrangement to generate a new sorting scheme. Machine chromosome mutation refers to the process of randomly replacing the current machine number of one machine in the set of processing equipment for a specific process, thereby adjusting the processing resources. The nesting scheme chromosome variation is a nesting scheme gene locus targeting a certain component in the chromosome. Under the premise of satisfying the homogeneity constraint, another scheme is randomly selected from the set of nesting schemes selected for that component to replace the current code, thereby forming a new nesting allocation structure. If the mutation causes a change in the set of parts corresponding to the selected component, the relevant process and equipment mappings are updated synchronously to maintain the consistency of the scheduling model.

9. 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 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 a 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, expressed as: 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: 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: ， 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 a value between 0 and 1, where a higher value indicates a better performance of the individual. 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.

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