Green scheduling method, system and device for segmented welding line hybrid flow production

Abstract

The application discloses a kind of green scheduling method, system and device for segmented welding line mixed flow production, comprising: importing product information to be processed from database, based on process and machine, establish multi-objective mixed flow shop scheduling optimization scheduling model;Using mixed sparrow algorithm containing green processing strategy to solve batch mixed flow workshop model, get scheduling scheme;Production simulation is carried out to scheduling scheme and is optimized, output scheduling plan and issue to production workshop;Production workshop is uploaded to database by production monitoring equipment group, production line processing product data, equipment state data, intermediate product logistics data, buffer capacity data;Prediction adjustment module analyzes and predicts machine bottleneck and buffer congestion, dynamically adjusts machine speed gear and material supply, generates adjustment scheme and issues.The present application can solve how to reasonably formulate scheduling scheme in segmented welding line batch mixed flow production, to realize green index optimization while shortening the completion time.

Description

Technical Field

[0001] This invention relates to the field of workshop scheduling technology, and in particular to a green scheduling method, system and device for mixed-flow production in segmented assembly and welding lines. Background Technology

[0002] In existing ship section assembly and welding line workshop scheduling processes, there is little optimization for energy consumption and carbon emissions generated during production. Given the increasing pressure for energy conservation and emission reduction, considering how to reduce energy consumption and carbon emissions in mixed-flow production has significant economic and social value. However, existing technologies for workshop operation scheduling suffer from the following problems:

[0003] 1. In segmented assembly and welding workshops, production scheduling is often done manually, resulting in low planning efficiency and difficulty in adjustment;

[0004] 2. The machine status during the production process cannot be monitored in real time. When the machine is overloaded or the processing is blocked, it cannot be reported and dealt with in a timely manner, which affects production efficiency.

[0005] 3. The processing time of each process cannot be well matched with the processing rhythm, resulting in waste of machine resources, unnecessary energy waste and carbon emissions.

[0006] To address these issues, this invention provides a green scheduling method and system for batch mixed-flow production in segmented assembly and bonding lines. Summary of the Invention

[0007] Purpose of the Invention: The purpose of this invention is to overcome the shortcomings of the aforementioned technologies. This invention provides a green scheduling method and system for batch mixed-flow production in segmented assembly and welding lines to solve the multi-objective green scheduling problem of batch production in mixed-flow workshops of segmented assembly and welding lines. In terms of modeling, a batch mixed-flow production line scheduling model is established considering modern shipbuilding grouping technology. Based on the mixed-flow production scheduling model, the batching of work groups, the adjustment and setting time of workpieces before processing at the workstation, and the selection of different machine speed levels are considered. In terms of algorithm, a three-segment real-number encoding method based on process and workpiece batching is designed. A hybrid sparrow algorithm combining the improved Sparrow Algorithm (ISSA) and the Particle Swarm Optimization (PSO) algorithm is used to solve the batch mixed-flow workshop model, further improving the algorithm's solution efficiency and obtaining a better global optimal solution set. In the scheduling system, the production workshop uploads the bottleneck machine load and buffer data of the production line to the database through the production monitoring equipment group. The prediction and adjustment module analyzes and predicts the machine bottleneck and buffer blockage, dynamically adjusts the machine speed level and material supply, generates adjustment plans, and issues them out. This invention can solve the problem of how to rationally formulate a scheduling plan in the batch mixed production of segmented assembly and welding lines, so as to achieve the optimization of green indicators while shortening the completion time.

[0008] To achieve the above-mentioned technical objectives, the present invention provides the following technical solution:

[0009] On the one hand, this invention provides a green scheduling method for batch mixed-flow production in segmented assembly and bonding lines, comprising:

[0010] S1. Import the product information to be processed from the database, and establish a multi-objective hybrid flow workshop scheduling optimization model based on the process and machine;

[0011] S2. The hybrid sparrow algorithm, which includes green processing strategies, is used to solve the batch mixed-flow workshop model to obtain the scheduling scheme;

[0012] S3. Perform production simulation and optimization on the scheduling scheme, output the scheduling plan and send it to the production workshop.

[0013] S4. The production workshop uploads data on the load and buffer zone of the bottleneck machine on the production line to the database through the production monitoring equipment group;

[0014] S5. The prediction and adjustment module analyzes and predicts machine bottlenecks and buffer blockages, dynamically adjusts machine speed levels and material supply, generates adjustment plans, and issues them out.

[0015] Optionally, the process of constructing the shop floor scheduling optimization model in S1 includes:

[0016] S11. Import the product information to be processed from the database;

[0017] S12. Set the optimization objectives and constraints for scheduling;

[0018] S13. Based on the optimization objectives and constraints, establish a green scheduling model for batch mixed-flow production.

[0019] Optionally, the constraints in S12 include:

[0020] There are multiple different types of product operations to be completed. Each operation can be divided into multiple batches for processing, and each sub-batch contains a number of workpieces. The sum of the sub-batch quantities does not exceed the total number of workpieces in the operation.

[0021] At any given time, the same process for the same workpiece can only be processed by one machine group, and the processing sequence is fixed and the processing is not interrupted.

[0022] A machine can only process one workpiece at a time, and the processing sequence is fixed.

[0023] The next process for the same workpiece only begins after the previous process is completed.

[0024] The completion time of a certain operation in a sub-batch of any job is equal to the start time of processing, the adjustment time before workpiece processing, and the unit processing time of each workpiece multiplied by the sum of the workpieces contained in the sub-batch.

[0025] The maximum completion time is the completion time of the last process of the last workpiece.

[0026] The decision variable takes the value 0 or 1.

[0027] Optionally, step S2 involves using a hybrid sparrow algorithm incorporating green processing strategies to solve the batch mixed-flow workshop model, including:

[0028] S21. Encode the workpiece based on the processing procedure, processing batch, workpiece sequence, and parallel machine speed setting;

[0029] Optionally, in one embodiment, the coding design is first carried out, and a three-segment real number coding based on process and workpiece batching is used to represent the discrete position of each sparrow individual. The batch size of each work batch is represented by Segment-1. Each work can be divided into different sub-batches, and the sub-batch size is determined by the batch ratio. The processing order of each sub-batch is represented by segment-2, and the workpieces are assigned to each workpiece by ascending order, which is the arrangement order of the workpieces in each process. The machine speed level is selected by segment-3, which multiplies the randomly generated real number between [0-1] with the speed level number and rounds up to obtain the processing speed level.

[0030] S22. Population initial solution and algorithm initialization, generating the corresponding initial scheduling scheme;

[0031] Specifically, in one embodiment, the initialization of problem parameters includes importing the job processing information matrix JobProcessTimes, the job adjustment setting time information matrix JobSetupTimes, the processing machine energy consumption information matrix MachECost, the job batching information matrix JobSpiltQty, and the job batch information JobUnitQty.

[0032] The initialization algorithm parameters include the maximum number of iterations maxGen, the population size POP, and setting the initial population nowGen to 0; sparrow population individuals are randomly generated by generating real numbers between [0-1].

[0033] S23. Calculate the detailed scheduling plan for each individual sparrow, and calculate the total completion time and total carbon emissions.

[0034] Optionally, the steps in S23 for calculating the maximum completion time and total carbon emissions include:

[0035] S2301. The first sub-batch of the first job starts processing from time 0 and begins adjusting the time.

[0036] S2302. After the first sub-batch of the first job is adjusted, processing begins, and then the processing of the next sub-batch of the job begins.

[0037] S2303. The final total completion time is denoted as the completion time of the last sub-batch and used as the first objective function.

[0038] S2304. Calculate the idle time, adjustment time, and processing time for each task.

[0039] S2305. Calculate the corresponding idle energy consumption, adjustment energy consumption, and processing energy consumption based on the duration.

[0040] S2306. Summarize the total energy consumption and calculate the corresponding carbon emissions as the second objective function.

[0041] S24. Obtain the non-dominated solution set based on the two-dimensional array of objective function values, classify the non-dominated solution set of the entire objective function array into levels, generate the optimal non-dominated solution set, and extract the first level of non-dominated solution set as the current approximate optimal solution set.

[0042] Optionally, in S24, the entire objective function array is hierarchically divided into non-dominated solution sets to generate the optimal non-dominated solution set, including:

[0043] S2401. Extract the non-dominated solution set A of the first level;

[0044] S2402. Merge the existing optimal non-dominated solution set B into C.

[0045] S2403. Sort the non-dominated solution sets of C and extract the first-level non-dominated solution sets as the optimal non-dominated solution sets.

[0046] S25. Use the improved sparrow individual position update formula to obtain the offspring population, and calculate the objective function value corresponding to each sparrow individual;

[0047] Optionally, the improved update formula in S25 includes:

[0048] To obtain better optimization results, this paper designs a global search for dynamic inertia factor balance. The formula for calculating the dynamic inertia factor ω is as follows:

[0049]

[0050] Where T represents the maximum number of iterations, and t represents the current number of iterations.

[0051] The improved formula for the explorer's position change is:

[0052]

[0053] The improved formula for follower position change is:

[0054]

[0055] in: The position of the i-th sparrow in the j-th dimension after the number of iterations t; xb t This represents the global optimum value at iteration number t; X represents the historical best value of the i-th sparrow in the j-th dimension after t iterations; w It is the worst position in the current global context; f i Let represent the fitness value of the i-th individual; α∈[0,1], R1∈[0,1], R2∈[0,1], ST∈[0.5,1]; i is the i-th sparrow individual; n is the total number of sparrow individuals; Q is a random number following a normal distribution; L is a matrix where every element is 1; c1 and c2 are learning factors.

[0056] S26. Update and iterate. If the set maximum number of iterations is reached, output the optimal solution set; otherwise, return to step S23.

[0057] Optionally, during the update iteration, if the set maximum number of iterations is reached, the optimal solution set is output, including:

[0058] S2601, Get the current iteration number nowGen;

[0059] S2602. Determine if the current iteration number nowGen is greater than maxGen;

[0060] S2603. If not, continue the update iteration; if yes, stop the iteration and output the optimal solution set.

[0061] S27. Decode the optimal solution set, output the corresponding scheduling scheme, and output the final scheduling result.

[0062] Optionally, the final scheduling result is a matrix Schedule including job number, machine number, process number, start time, completion time, adjustment start time, adjustment end time, batch number, batch number, and machine gear, and they correspond one-to-one.

[0063] S3. Perform production simulation and optimization of the scheduling scheme, output the scheduling plan and issue it for execution.

[0064] S4. This invention provides a green scheduling system for batch mixed-flow production of ship section assembly and welding lines, characterized in that it includes:

[0065] The information transceiver module includes a wireless transceiver group, an RFID collector, active RFID and QR code tags, and a cloud database;

[0066] The resource scheduling module is used to automatically encode the scheduling model, use a hybrid sparrow algorithm that includes green processing strategies to solve the workshop model, and output a scheduling scheme.

[0067] The production monitoring module is used to upload production line processing product data, equipment status data, and intermediate product logistics data to the database through the production monitoring equipment group; several AP nodes are installed to meet network coverage, and the equipped RFID collectors scan to obtain QR code and RFID tag information for pallet warehousing and transfer, thereby realizing the real-time collection of product data, equipment status data, and intermediate product logistics data.

[0068] The predictive adjustment module analyzes the current machine load, buffer capacity, and future tasks to predict machine bottlenecks and buffer blockages, and dynamically adjusts machine speed levels and material supply. If the predicted bottleneck machine is overloaded or experiencing processing blockages, the module reduces the speed of upstream machines, generates an adjustment plan, and issues it.

[0069] Beneficial effects: Compared with the prior art, the beneficial effects of the technical solution provided by the embodiments of the present invention include at least the following:

[0070] The above solution addresses the scheduling problem of batch mixed-flow production in group production of ship sectional assembly and welding lines by establishing a workshop scheduling model. The model considers modern shipbuilding group technology and establishes a batch mixed-flow production line scheduling model. Based on this model, it takes into account the batching of work processes, the adjustment and setting time of workpieces before processing at the workstation, and the selection of different machine speed levels. Algorithmically, a three-segment real-number encoding method based on process and workpiece batching is designed. A hybrid sparrow algorithm combining the improved Sparrow Algorithm (ISSA) and Particle Swarm Optimization (PSO) is used to solve the batch mixed-flow workshop model, further improving the algorithm's solution efficiency and achieving a better global optimal solution set. In the scheduling system, the production workshop uploads the bottleneck machine load and buffer data to the database through the production monitoring equipment group. The prediction and adjustment module analyzes and predicts the machine bottleneck and buffer congestion, dynamically adjusts the machine speed level and material supply, generates adjustment plans, and distributes them. This invention can solve the problem of how to rationally formulate scheduling plans in batch mixed-flow production of sectional assembly and welding lines to achieve both shortened completion time and optimized green indicators.

[0071] This invention can avoid the irrationality and inefficiency of manual scheduling decisions, and helps to improve the production efficiency of enterprises and achieve energy conservation and emission reduction. Attached Figure Description

[0072] Figure 1 This is a schematic diagram of a green scheduling method for mixed-flow production in segmented assembly and bonding lines, provided by an embodiment of the present invention.

[0073] Figure 2 A schematic diagram of the layout of the production monitoring equipment acquisition system for solving the green scheduling problem of mixed-flow production in segmented assembly and bonding lines, provided in an embodiment of the present invention;

[0074] Figure 3 This is a schematic diagram of the hybrid sparrow algorithm for solving the green scheduling problem of mixed-flow production in segmented assembly and bonding lines, provided in an embodiment of the present invention.

[0075] Figure 4 This is a schematic diagram of the hybrid sparrow algorithm encoding for a green scheduling method for mixed-flow production in segmented assembly and bonding lines, provided by an embodiment of the present invention.

[0076] Figure 5 This is a simulation comparison diagram of a green scheduling method for mixed-flow production in segmented assembly and bonding lines provided by an embodiment of the present invention. Detailed Implementation

[0077] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0078] like Figure 1 As shown, this embodiment of the invention provides a method for green scheduling of batch mixed-flow production in segmented assembly and bonding lines. Figure 1 The flowchart shown illustrates the green scheduling problem for batch mixed-flow production in ship section assembly and welding lines. The processing flow of this method may include the following steps:

[0079] S1. Import the product information to be processed from the database, and establish a multi-objective hybrid flow workshop scheduling optimization model based on the process and machine;

[0080] The information on products to be processed includes the types and quantities of assembled products to be produced, the number of processing stages, the number of parallel machines in each process, the processing speed corresponding to different speed levels and states of the processing equipment, processing energy consumption and carbon emissions, and the working state of the processing equipment includes the loaded processing state, the no-load adjustment state and the idle standby state.

[0081] Optionally, the process of establishing the workshop scheduling optimization model in S1 includes:

[0082] S11. Set the optimization objectives and constraints for scheduling;

[0083] Optionally, the optimization objective in S11 is to minimize the completion time and total carbon emissions.

[0084] S12. Based on the optimization objectives and constraints, establish a green scheduling model for batch mixed-flow production.

[0085] Optionally, the constraints in S12 include:

[0086] There are multiple different types of product operations to be completed. Each operation can be divided into multiple batches for processing, and each sub-batch contains a number of workpieces. The sum of the sub-batch quantities does not exceed the total number of workpieces in the operation.

[0087] At any given time, the same process for the same workpiece can only be processed by one machine group, and the processing sequence is fixed and the processing is not interrupted.

[0088] A machine can only process one workpiece at a time, and the processing sequence is fixed.

[0089] The next process for the same workpiece only begins after the previous process is completed.

[0090] The completion time of a certain operation in a sub-batch of any job is equal to the start time of processing, the adjustment time before workpiece processing, and the unit processing time of each workpiece multiplied by the sum of the workpieces contained in the sub-batch.

[0091] The maximum completion time is the completion time of the last process of the last workpiece.

[0092] The decision variable takes the value 0 or 1.

[0093] In one feasible implementation, the target value is calculated by combining the model to achieve batch constraints on processing order and selection of machine speed levels.

[0094] S2. The hybrid sparrow algorithm, which includes green processing strategies, is used to solve the batch mixed-flow workshop model to obtain the scheduling scheme;

[0095] Figure 3 This is a schematic diagram of the hybrid sparrow algorithm for solving the green scheduling problem of mixed-flow production in segmented assembly and bonding lines, provided in an embodiment of the present invention.

[0096] Optionally, step S2 involves using a hybrid sparrow algorithm incorporating green processing strategies to solve the batch mixed-flow workshop model, including:

[0097] S21. Encode the workpiece based on the processing procedure, processing batch, workpiece sequence, and parallel machine speed setting;

[0098] Specifically, in one embodiment, the coding design is first carried out, using a three-segment real number code based on process and workpiece batching to represent the discrete position of each individual sparrow, such as... Figure 4As shown in the figure. The batch size of each operation is represented by Segment-1. There are three operations in total, and each operation can be divided into 2, 2, and 3 batches respectively. The sub-batch size is determined by the batch ratio. The processing order of each sub-batch is represented by Segment-2. The sub-batch is assigned to each workpiece in ascending order, which is the arrangement order of the workpieces in each process. The machine speed level is selected by Segment-3. The processing speed level is obtained by multiplying a randomly generated real number between [0-1] with the speed level number and rounding up.

[0099] S22. Problem initialization and algorithm initialization, generating the corresponding initial population solution set;

[0100] Specifically, in one embodiment, the initialization of problem parameters includes importing the job processing information matrix JobProcessTimes, the job adjustment setting time information matrix JobSetupTimes, the processing machine energy consumption information matrix MachECost, the job batching information matrix JobSpiltQty, and the job batch information JobUnitQty.

[0101] The initialization algorithm parameters include the maximum number of iterations maxGen, the population size POP, and setting the initial population nowGen to 0; sparrow population individuals are randomly generated by generating real numbers between [0-1].

[0102] S23. Calculate the detailed scheduling plan for each individual sparrow, and calculate the total completion time and total carbon emissions.

[0103] Optionally, the steps in S23 for calculating the maximum completion time and total carbon emissions include:

[0104] S2301. The first sub-batch of the first job starts processing from time 0 and begins adjusting the time.

[0105] S2302. After the first sub-batch of the first job is adjusted, processing begins, and then the processing of the next sub-batch of the job begins.

[0106] S2303. The final total completion time is denoted as the completion time of the last sub-batch and used as the first objective function.

[0107] S2304. Calculate the idle time, adjustment time, and processing time for each task.

[0108] S2305. Calculate the corresponding idle energy consumption, adjustment energy consumption, and processing energy consumption based on the duration.

[0109] S2306. Summarize the total energy consumption and calculate the corresponding carbon emissions as the second objective function.

[0110] S24. Obtain the non-dominated solution set based on the two-dimensional array of objective function values, classify the non-dominated solution set of the entire objective function array into levels, generate the optimal non-dominated solution set, and extract the first level of non-dominated solution set as the current approximate optimal solution set.

[0111] Optionally, in S24, the entire objective function array is hierarchically divided into non-dominated solution sets to generate the optimal non-dominated solution set, including:

[0112] S2401. Extract the non-dominated solution set A of the first level;

[0113] S2402. Merge the existing optimal non-dominated solution set B into C.

[0114] S2403. Sort the non-dominated solution sets of C and extract the first-level non-dominated solution sets as the optimal non-dominated solution sets.

[0115] S25. Use the improved sparrow individual position update formula to obtain the offspring population, and calculate the objective function value corresponding to each sparrow individual;

[0116] Optionally, the sparrow individual position update formula in the hybrid sparrow algorithm combining the Improved Sparrow Algorithm (ISSA) and Particle Swarm Optimization (PSO) in S25 includes:

[0117] The formula for calculating the dynamic inertia factor ω is:

[0118]

[0119] Where T represents the maximum number of iterations, and t represents the current number of iterations.

[0120] The improved formula for the explorer's position change is:

[0121]

[0122] The improved formula for follower position change is:

[0123]

[0124] in: The position of the i-th sparrow in the j-th dimension after the number of iterations t; xb t This represents the global optimum value at iteration number t; X represents the historical best value of the i-th sparrow in the j-th dimension after t iterations; w It is the worst position in the current global context; f iLet represent the fitness value of the i-th individual; α∈[0,1], R1∈[0,1], R2∈[0,1], ST∈[0.5,1]; i is the i-th sparrow individual; n is the total number of sparrow individuals; Q is a random number following a normal distribution; L is a matrix where every element is 1; c1 and c2 are learning factors.

[0125] S26. Update and iterate. If the set maximum number of iterations is reached, output the optimal solution set; otherwise, return to step S23.

[0126] Optionally, during the update iteration, if the set maximum number of iterations is reached, the optimal solution set is output, including:

[0127] S2601, Get the current iteration number nowGen;

[0128] S2602. Determine if the current iteration number nowGen is greater than maxGen;

[0129] S2603. If not, continue the update iteration; if yes, stop the iteration and output the optimal solution set.

[0130] S27. Decode the optimal solution set, output the corresponding scheduling scheme, and output the final scheduling result.

[0131] Optionally, the final scheduling result is a matrix Schedule including job number, machine number, process number, start time, completion time, adjustment start time, adjustment end time, batch number, batch number, and machine gear, and they correspond one-to-one.

[0132] S3. Perform production simulation and optimization on the scheduling scheme, output the scheduling plan and send it to the production workshop.

[0133] S4. The production workshop uploads data on the load and buffer zone of the bottleneck machine on the production line to the database through the production monitoring equipment group;

[0134] S5. The prediction and adjustment module analyzes and predicts machine bottlenecks and buffer blockages, dynamically adjusts machine speed levels and material supply, generates adjustment plans, and issues them out.

[0135] Figure 2 A schematic diagram of the layout of the production monitoring equipment acquisition system for solving the green scheduling problem of mixed-flow production in segmented assembly and bonding lines, provided in an embodiment of the present invention;

[0136] This invention also provides a green scheduling system for batch mixed-flow production in segmented assembly and bonding lines, comprising:

[0137] The information transceiver module includes a wireless transceiver group, an RFID collector, active RFID and QR code tags, and a cloud database. Workshop production information can be stored in the cloud database. This production information includes a workpiece processing information matrix (JobProcessTimes), a workpiece adjustment and setting time information matrix (JobSetupTimes), a processing machine energy consumption information matrix (MachECost), a workpiece batching information matrix (JobSpiltQty), and a job batch information matrix (JobUnitQty).

[0138] The resource scheduling module is used to automatically encode the scheduling model, use a hybrid sparrow algorithm that includes green processing strategies to solve the workshop model to obtain the optimal non-dominated solution set, and output the scheduling scheme.

[0139] The production monitoring module is used to upload production line processing product data, equipment status data, and intermediate product logistics data to the database through the production monitoring equipment group; several AP nodes are installed to meet network coverage, and the equipped RFID collectors scan to obtain QR code and RFID tag information for pallet warehousing and transfer, thereby realizing the real-time collection of product data, equipment status data, and intermediate product logistics data.

[0140] The predictive adjustment module analyzes the current machine load, buffer capacity, and future tasks to predict machine bottlenecks and buffer blockages, and dynamically adjusts machine speed levels and material supply. If the predicted bottleneck machine is overloaded or experiencing processing blockages, the module reduces the speed of upstream machines, generates an adjustment plan, and issues it.

[0141] To illustrate the superiority of the green scheduling method for batch mixed-flow production in segmented assembly and welding lines provided by this invention, a three-job example is provided for explanation: Table 1 is the job processing information JobProcessTimes, Table 2 is the job adjustment and setting time JobSetupTimes, and Table 3 is the processing machine energy consumption information matrix MachECost.

[0142] Table 1 Workpiece Machining Information

[0143]

[0144] Table 2 Workpiece Adjustment Setting Time

[0145]

[0146]

[0147]

[0148] Table 3 shows the energy consumption information of the processing machines.

[0149]

[0150] Figure 5 This is a simulation comparison diagram of the green scheduling method for batch mixed-flow production of ship section assembly and welding lines provided in this embodiment of the invention, illustrating the iterative path of the optimal solution in the example. From Figure 5 It can be seen that, in the objective function values ​​of the optimal solution makespan and total carbon emissions, the hybrid sparrow search algorithm proposed in this invention has a faster solution speed and better solution performance, and can effectively reduce the maximum completion time and carbon emissions. This demonstrates the effectiveness of the proposed scheduling method.

[0151] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including instructions that can be executed by a processor in a terminal to complete the aforementioned green scheduling method for solving the batch mixed-flow production of ship section assembly and welding lines. For example, the computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0152] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0153] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A green scheduling method for mixed-flow production in segmented assembly and bonding lines, characterized in that, Includes the following steps: S1: Import the product information to be processed from the database, and establish a multi-objective hybrid flow workshop scheduling optimization model based on the process and machine; S2: The hybrid sparrow algorithm, which incorporates green processing strategies, is used to solve the scheduling optimization model for batch hybrid flow workshops, and the scheduling scheme is obtained. S3: Perform production simulation and optimization of the scheduling scheme, output the scheduling plan and send it to the production workshop; S4: The production workshop uploads data on processed products, equipment status, intermediate product logistics, and buffer capacity to the database through the production monitoring equipment group; S5: The prediction and adjustment module analyzes the current machine load, buffer capacity, and future tasks to predict machine bottlenecks and buffer blockages. It dynamically adjusts the machine speed level and material supply. If the predicted bottleneck machine is overloaded or blocked, it reduces the speed level of the upstream process machine, generates an adjustment plan, and issues it. Step S2 includes: S21: Encode the workpiece based on the processing procedure, processing batch, workpiece sequence, and parallel machine speed setting; S22: Population initial solution and algorithm initialization, generating the corresponding initial scheduling scheme; S23: Calculate and summarize the maximum completion time and corresponding energy consumption of the detailed scheduling scheme for each individual sparrow; S24: Obtain the non-dominated solution set based on the two-dimensional array of objective function values, classify the non-dominated solution set of the entire objective function array, generate the optimal non-dominated solution set, and retain the objective function with level number T0 as the current approximate optimal solution set; S25: The offspring population is obtained by using the hybrid sparrow algorithm that combines the improved sparrow algorithm and the particle swarm algorithm to update the position of individual sparrows, and the objective function value corresponding to each individual sparrow is calculated. S26: Update and iterate. If the set maximum number of iterations is reached, output the optimal solution set; otherwise, return to step S23. S27: Decode the optimal solution set, output the corresponding scheduling scheme, and output the final scheduling result to step S3; The encoding method in S21 includes: using a three-segment real number encoding based on process and workpiece batches to represent the discrete position of each individual sparrow; the batch size of each work batch is represented by Segment-1; each work can be divided into different sub-batches, and the sub-batch size is determined by the batch ratio; the processing order of each sub-batch is represented by segment-2; and the workpieces are assigned to each workpiece in ascending order, which is the arrangement order of the workpieces in each process; the machine speed gear selection is represented by segment-3; and the processing speed gear is obtained by multiplying a randomly generated real number between [0-1] with the speed gear number and rounding up. The steps in S23 for calculating the maximum completion time and total carbon emissions include: S2301: The first sub-batch of the first job starts processing from time 0 and begins adjusting the time. S2302: The first sub-batch of the first job is finished and processing begins, then the next sub-batch of the job begins processing; S2303: The final total completion time is recorded as the completion time of the last sub-batch, and is used as the first objective function; S2304: Calculate the idle time, adjustment time, and processing time for each task; S2305: Calculate the corresponding idle energy consumption, adjustment energy consumption, and processing energy consumption based on the duration; S2306: Summarize the total energy consumption as the second objective function.

2. The green scheduling method for mixed-flow production in segmented assembly and bonding lines according to claim 1, characterized in that, The process of establishing the workshop scheduling optimization model in S1 includes: S11: Import product processing information; S12: Set the optimization objectives and constraints for the scheduling; S13: Based on the optimization objectives and constraints, establish a green scheduling model for batch mixed-flow production.

3. The green scheduling method for mixed-flow production in segmented assembly and bonding lines according to claim 1, characterized in that, Different pre-processing adjustment times are set for each operation based on the corresponding machine and the connection relationship between the preceding and following operations; The rules for starting the adjustment of subsequent processes are as follows: If the machine for the subsequent process can be adjusted in advance after the previous process of the corresponding job is completed, the adjustment preparation should be submitted as soon as possible so that the subsequent process can be used immediately when the previous process is completed; if the machine for the subsequent process is occupied by other jobs after the previous process of the corresponding job is completed, the adjustment should be started after the machine for the subsequent process is completed.

4. The green scheduling method for mixed-flow production in segmented assembly and bonding lines according to claim 1, characterized in that, The step S24, which involves hierarchically classifying the non-dominated solution set of the entire objective function array to generate the optimal non-dominated solution set, includes: S2401: Extract the non-dominated solution set A of the first level; S2402: Merge the existing optimal non-dominated solution set B into C; S2403: Sort the non-dominated solution sets of C and extract the first-level non-dominated solution set as the optimal non-dominated solution set.

5. The green scheduling method for mixed-flow production in segmented assembly and bonding lines according to claim 1, characterized in that, The sparrow individual position update formula in the hybrid sparrow algorithm combining the improved sparrow algorithm and the particle swarm algorithm in S25 includes: Dynamic inertia factor The calculation formula is: in, Indicates the maximum number of iterations. This represents the current iteration number; The improved formula for the explorer's position change is: The improved formula for follower position change is: in: No. The sparrow in the first The number of dimension iterations is The position at that time; Indicates the number of iterations. The global optimal value at that time; Indicates the first Only sparrows in the first The number of dimension iterations is The historical best value; It is currently the worst position globally; This represents the fitness value of the i-th individual; , , , ; For the first A single sparrow; This represents the total number of individual sparrows. These are random numbers that follow a normal distribution. It is a matrix in which every element is 1; and For learning factors.

6. A green scheduling system for mixed-flow production in segmented assembly and bonding lines, performing the method described in claim 1, characterized in that: It includes an information sending and receiving module, a resource scheduling module, a production monitoring module, and a forecasting and adjustment module; The information transceiver module includes a wireless transceiver group, an RFID collector, active RFID and QR code tags, and a cloud database; the information transceiver module imports information about products to be processed from the database and establishes a multi-objective hybrid assembly line workshop scheduling optimization model based on processes and machines; The resource scheduling module automatically encodes the scheduling model and uses a hybrid sparrow algorithm incorporating green processing strategies to solve the batch hybrid flow workshop scheduling optimization model, thereby obtaining the scheduling scheme; the resource scheduling module The production monitoring module performs production simulation and optimization of the scheduling scheme, outputs the scheduling plan and sends it to the production workshop; the production workshop uploads the production line processing product data, equipment status data, intermediate product logistics data and buffer capacity data to the database through the production monitoring equipment group; AP nodes are installed to meet network coverage, and the equipped RFID collectors scan to obtain QR code and RFID tag information for pallet warehousing and transfer, realizing the real-time collection of product data, equipment status data and intermediate product logistics data; The prediction and adjustment module analyzes the current machine load, buffer capacity, and future tasks to predict machine bottlenecks and buffer blockages. It dynamically adjusts the machine speed level and material supply. If the bottleneck machine is predicted to be overloaded or blocked, the speed level of the upstream process machine is reduced. Then, the adjustment plan is generated and issued.

7. A green scheduling device for mixed-flow production in segmented assembly and bonding lines, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is loaded into the processor, it implements the green scheduling method for mixed-flow production of segmented assembly and bonding lines according to any one of claims 1-5.

Images