A Multi-Objective Flexible Spraying Workshop Scheduling Method and System under Dynamic Events

Abstract

The present invention provides a multi-objective flexible spraying workshop scheduling method and system under dynamic events. Based on the particle swarm algorithm with the beetle antennae search strategy and combined with the rescheduling response mechanism, the multi-objective dynamic flexible spraying workshop scheduling model is optimized and solved to obtain the global optimal value. And based on the global optimal value, a multi-objective flexible spraying workshop scheduling scheme is obtained. An optimization algorithm based on the beetle antennae search strategy is proposed to reduce the number of solution calculations and improve the algorithm efficiency. Combined with the rescheduling response mechanism, the effectiveness and accuracy of the multi-objective flexible spraying workshop scheduling scheme are guaranteed. Finally, through the analysis of the dynamic characteristics of the workshop, the flexibility and production efficiency of the manufacturing industry are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of process scheduling, and particularly relates to a multi-objective flexible spraying workshop scheduling method and system under dynamic events. Background Art

[0002] Currently, the assembly line operation mode has gradually become the mainstream mode in automobile production. An automobile production line includes welding, stamping, painting, powertrain, etc. Among them, the spraying workshop, as a key link, has a great impact on the appearance and quality of automobile products. An automobile spraying workshop includes multiple processes such as primer sanding and wiping, intermediate coat spraying, drying, strong cooling, topcoat spraying, topcoat drying, topcoat strong cooling, inspection and finishing, and spot repair. Basically, industrial robots and auxiliary equipment are used to replace manual work, which has the characteristics of high automation, high flexibility, and strong daily production capacity. Its production rhythm and flexibility are crucial for the production of the whole vehicle.

[0003] With the continuous development of the automobile industry, a single-model automobile spraying workshop can no longer fully meet the market demand. To meet the personalized needs of customers, the method of integrating multiple vehicle model spraying lines into a flexible spraying system has been widely used. During the production process of a flexible spraying workshop, in order to improve the flexibility and production efficiency of the manufacturing industry simultaneously, it is necessary to study the flexible job shop scheduling problem. The existing optimization methods for workshop scheduling problems mainly focus on static problems and simplified job shop environments, and often cannot adapt to the dynamic characteristics of the actual workshop. Summary of the Invention

[0004] The present invention provides a multi-objective flexible spraying workshop scheduling method and system under dynamic events to solve the problems raised in the background art.

[0005] A multi-objective flexible spraying workshop scheduling method under dynamic events includes:

[0006] S1: Based on the equipment parameters and related working parameters of a multi-objective flexible spraying workshop, build a spraying workshop simulation model, and conduct static scheduling simulation on the spraying workshop simulation model to determine the influence of dynamic events on the original scheduling plan;

[0007] S2: Based on the dynamic events of machine failures and emergency order insertions in a multi-objective flexible spraying workshop, establish a multi-objective dynamic flexible spraying workshop scheduling model with the makespan, maximum machine load, and total machine load as the objectives;

[0008] S3: Based on the influence of dynamic events on the original scheduling plan, combined with the constraint conditions, establish a rescheduling response mechanism corresponding to the dynamic events;

[0009] S4: Based on the beetle antenna search strategy particle swarm optimization algorithm, combined with the rescheduling response mechanism, optimize and solve the multi-objective dynamic flexible spraying workshop scheduling model to obtain the global optimal value, and based on the global optimal value, obtain the multi-objective flexible spraying workshop scheduling plan.

[0010] Preferably, in S1, based on the equipment parameters and related working parameters of the multi-objective flexible spraying workshop, build a spraying workshop simulation model, including:

[0011] Based on the basic processes of the flexible spraying workshop, use simulation software to establish a model layer corresponding to the flexible spraying process;

[0012] Insert a material outputter, a material terminator, and a material processor into the model layer in sequence, and use connectors to connect the material outputter, the material terminator, and the material processor to obtain the workstations corresponding to the basic processes, and set up a material form module and a process route table module to construct the basic flexible spraying workshop model;

[0013] Set production parts in the basic flexible spraying workshop model, and establish a data table for the production parts at different workstations;

[0014] Establish an index relationship between the workstations and the data table, and add the index relationship to the basic flexible spraying workshop model to obtain the spraying workshop simulation model.

[0015] Preferably, in S1, conduct static scheduling simulation on the spraying workshop simulation model to determine the impact of dynamic events on the original scheduling plan, including:

[0016] Use the event controller to perform static thorough running, single-step running, and reset running operations on the spraying workshop simulation model, collect the statistical data during the running process, and determine whether the spraying workshop simulation model is running normally;

[0017] After determining that the spraying workshop simulation model is running normally, set the algorithm parameters of the genetic algorithm, and based on the workshop data, combine the algorithm parameters to simulate the spraying workshop simulation model to obtain an initial scheduling plan;

[0018] Carry out custom machine failures and custom emergency orders at the workstations of the spraying workshop simulation model to obtain the output results, and compare the output results with the original scheduling plan to obtain the impact of dynamic events on the original scheduling plan.

[0019] Preferably, in S2, based on the machine failure dynamic event and the emergency order insertion dynamic event of the multi-objective flexible spraying workshop, establish a multi-objective dynamic flexible spraying workshop scheduling model with the makespan, the maximum machine load, and the total machine load as the objectives, including:

[0020] Determine the time impact characteristics and load impact characteristics of machine failure dynamic events and emergency order insertion dynamic events on the processing process;

[0021] Take the maximum completion time, the maximum machine load, and the total machine load as the main optimization objectives, establish the constraint conditions for the processing process of the flexible spraying workshop, and determine the objective function for the main optimization objectives based on the constraint conditions;

[0022] Establish a weight selection mechanism for the maximum completion time, the maximum machine load, and the total machine load, and combine it with the objective function of the main optimization objectives to construct a basic scheduling model;

[0023] Add the failure dynamic event and the emergency order insertion dynamic event to the basic scheduling model to establish a basic dynamic scheduling model;

[0024] Perform specific dynamic impact marking on the time impact characteristics and load impact characteristics in the basic dynamic scheduling model to obtain a multi-objective dynamic flexible spraying workshop scheduling model.

[0025] Preferably, perform specific dynamic impact marking on the time impact characteristics and load impact characteristics in the basic dynamic scheduling model to obtain a multi-objective dynamic flexible spraying workshop scheduling model, including:

[0026] Add the time impact characteristics and load impact characteristics to the corresponding objective function in the basic dynamic scheduling model, perform dynamic marking on the objective function to obtain a dynamic objective function;

[0027] Perform dynamic marking on the weight selection mechanism based on the dynamic objective function to obtain a dynamic weight selection mechanism;

[0028] Based on the dynamic objective function and the dynamic weight selection mechanism, obtain a multi-objective dynamic flexible spraying workshop scheduling model.

[0029] Preferably, in S3, based on the influence of the dynamic event on the original scheduling plan, combine the constraint conditions to establish a rescheduling response mechanism for the dynamic event, including:

[0030] Based on the influence of the dynamic event on the original scheduling plan, combine the constraint conditions to determine the influence value between the dynamic event and the plan characteristics, and based on the corresponding relationship between the dynamic event and the plan characteristics, classify the dynamic event to obtain passive influence events and active influence events, and determine the influence weights of the passive influence events and active influence events based on the influence value between the dynamic event and the plan characteristics;

[0031] Determine the rescheduling method and the rescheduling adjustment range based on the passive influence events, the active influence events, and their corresponding influence weights;

[0032] An initial rescheduling model is established based on the rescheduling method and the rescheduling adjustment range, and a rescheduling plan for a preset single dynamic event under the initial rescheduling model is obtained. Comprehensive analysis is performed on multiple preset single dynamic events and their corresponding rescheduling plans to determine the interaction characteristics between different single dynamic events;

[0033] Based on the interaction characteristics, multiple action branches are established for the initial rescheduling model, and the action priorities between the multiple action branches are set. The initial rescheduling model is upgraded and optimized based on the multiple action branches and action priorities to obtain the target rescheduling model;

[0034] The dynamic event is input into the target rescheduling model to obtain the rescheduling response mechanism corresponding to the dynamic event.

[0035] Preferably, the determination of the rescheduling method and the rescheduling adjustment range based on the passive impact event and the active impact event and their corresponding impact weights includes:

[0036] Determine that the rescheduling method for the passive impact event is the periodic rescheduling method, and determine the adjustment range of the rescheduling period based on the impact weight;

[0037] Determine that the rescheduling method for the active impact event is the event-driven rescheduling, and determine the adjustment range of the driven rescheduling based on the impact weight.

[0038] Preferably, in S4, the multi-objective dynamic flexible spray painting workshop scheduling model is optimized and solved based on the particle swarm algorithm with the beetle antennae search strategy, combined with the rescheduling response mechanism, to obtain the global optimal value, including:

[0039] Based on the coding crossover strategy, crossover and mutation operations are performed on the existing workshop processes to obtain new processes;

[0040] The beetle antennae search strategy and the particle swarm algorithm are fused to obtain the beetle antennae search particle swarm algorithm. The beetle antennae search particle swarm algorithm is added to the multi-objective dynamic flexible spray painting workshop scheduling model, and the model parameters are initialized. Based on the new processes, combined with the rescheduling response mechanism, the reverse generational distance and the hypervolume are used as evaluation indicators to obtain the initial global optimal solution;

[0041] Randomly obtain the position and speed of each beetle to obtain the fitness value of each beetle, and update each speed and position based on the update rule and the fitness value for iteration;

[0042] Obtain the updated initial individual optimal solution and the initial global optimal solution after iteration, and compare them with the values of the previous iteration to obtain the global optimal value.

[0043] Preferably, the multi-objective flexible spray painting workshop scheduling plan is obtained based on the global optimal value, including:

[0044] Determine the model parameters of the multi-objective dynamic flexible spraying workshop scheduling model based on the global optimal value;

[0045] Determine the specific values of the makespan, the maximum machine load, and the total machine load, and process the operations of the workshop to be scheduled with the multi-objective dynamic flexible spraying workshop scheduling model under the model parameters to obtain a multi-objective flexible spraying workshop scheduling plan.

[0046] A scheduling system for a multi-objective flexible spraying workshop scheduling method under dynamic events, including:

[0047] A simulation module, which is used to build a spraying workshop simulation model based on the equipment parameters and relevant working parameters of the multi-objective flexible spraying workshop, and perform static scheduling simulation on the spraying workshop simulation model to determine the impact of dynamic events on the original scheduling plan;

[0048] A model determination module, which is used to establish a multi-objective dynamic flexible spraying workshop scheduling model with the makespan, the maximum machine load, and the total machine load as objectives based on the machine failure dynamic event and the emergency order insertion dynamic event of the multi-objective flexible spraying workshop;

[0049] A response determination module, which is used to establish a rescheduling response mechanism corresponding to dynamic events based on the impact of dynamic events on the original scheduling plan and in combination with constraint conditions;

[0050] A plan determination module, which is used to optimize and solve the multi-objective dynamic flexible spraying workshop scheduling model based on the particle swarm algorithm of the beetle antennae search strategy, in combination with the rescheduling response mechanism, to obtain the global optimal value, and based on the global optimal value, obtain a multi-objective flexible spraying workshop scheduling plan.

[0051] Compared with the prior art, the present invention has achieved the following beneficial effects:

[0052] By building a spraying workshop simulation model based on the equipment parameters and related working parameters of a multi-objective flexible spraying workshop, and conducting static scheduling simulation on the spraying workshop simulation model to determine the impact of dynamic events on the original scheduling plan, providing a simulation basis for the scheduling of a multi-objective flexible spraying workshop under dynamic events. Based on the dynamic events of machine failures and emergency order insertions in a multi-objective flexible spraying workshop, a multi-objective dynamic flexible spraying workshop scheduling model with the makespan, maximum machine load, and total machine load as objectives is established to explore the impact of different dynamic events on the original optimal scheduling plan of the spraying workshop, laying a foundation for the subsequent research on the rescheduling mechanism under dynamic events. Based on the impact of dynamic events on the original scheduling plan and combined with the constraint conditions, a corresponding rescheduling response mechanism for dynamic events is established. By establishing the rescheduling response mechanism, it provides a basis for determining the scheduling plan of a multi-objective flexible spraying workshop in the future. Based on the particle swarm algorithm with the beetle antennae search strategy and combined with the rescheduling response mechanism, the multi-objective dynamic flexible spraying workshop scheduling model is optimized and solved to obtain the global optimal value, and based on the global optimal value, the scheduling plan of the multi-objective flexible spraying workshop is obtained. An optimization algorithm based on the beetle antennae search strategy is proposed to reduce the number of solution calculations and improve the algorithm efficiency. Combined with the rescheduling response mechanism, it ensures the effectiveness and accuracy of the scheduling plan of the multi-objective flexible spraying workshop. Finally, through the analysis of the dynamic characteristics of the workshop, the flexibility and production efficiency of the manufacturing industry are improved.

[0053] Other features and advantages of the present invention will be described in the following specification, and in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in this application document.

[0054] The technical solutions of the present invention will be further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the accompanying drawings:

[0056] Figure 1 is a flowchart of a method for scheduling a multi-objective flexible spraying workshop under dynamic events in an embodiment of the present invention;

[0057] Figure 2 is a flowchart of building a spraying workshop simulation model in an embodiment of the present invention;

[0058] Figure 3 is a structural diagram of a system for scheduling a multi-objective flexible spraying workshop under dynamic events in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] The preferred embodiments of the present invention will be described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0060] Embodiment 1:

[0061] The embodiment of the present invention provides a multi-objective flexible spraying workshop scheduling method under dynamic events. As Figure 1 shown, it includes:

[0062] S1: Based on the equipment parameters and related working parameters of the multi-objective flexible spraying workshop, build a spraying workshop simulation model, and conduct static scheduling simulation on the spraying workshop simulation model to determine the impact of dynamic events on the original scheduling plan;

[0063] S2: Based on the machine failure dynamic event and the emergency order insertion dynamic event in the multi-objective flexible spraying workshop, establish a multi-objective dynamic flexible spraying workshop scheduling model with the makespan, maximum machine load, and total machine load as the objectives;

[0064] S3: Based on the impact of dynamic events on the original scheduling plan, combined with the constraint conditions, establish a rescheduling response mechanism corresponding to the dynamic events;

[0065] S4: Based on the particle swarm algorithm of the beetle antennae search strategy, combined with the rescheduling response mechanism, optimize and solve the multi-objective dynamic flexible spraying workshop scheduling model to obtain the global optimal value, and based on the global optimal value, obtain the multi-objective flexible spraying workshop scheduling plan.

[0066] In this embodiment, the dynamic events include machine failure dynamic events and emergency order insertion dynamic events.

[0067] In this embodiment, the impact of dynamic events on the original scheduling plan includes the impact on time, load, etc.

[0068] In this embodiment, the constraint conditions are, for example, makespan constraint, process start time constraint, sequence constraint, machine constraint, etc.

[0069] In this embodiment, the rescheduling response mechanism satisfies the determination of the departure and adjustment of rescheduling.

[0070] The beneficial effects of the above design scheme are as follows: Based on the equipment parameters and relevant working parameters of the multi-objective flexible spraying workshop, a simulation model of the spraying workshop is built, and static scheduling simulation is carried out on the simulation model of the spraying workshop to determine the impact of dynamic events on the original scheduling scheme, providing a simulation basis for the scheduling of the multi-objective flexible spraying workshop under dynamic events. Based on the dynamic events of machine failures and emergency order insertions in the multi-objective flexible spraying workshop, a multi-objective dynamic flexible spraying workshop scheduling model with the makespan, maximum machine load, and total machine load as objectives is established to explore the impact of different dynamic events on the original optimal scheduling scheme of the spraying workshop, laying a foundation for the subsequent research on the rescheduling mechanism under dynamic events. Based on the impact of dynamic events on the original scheduling scheme and combined with the constraint conditions, a corresponding rescheduling response mechanism for dynamic events is established. By establishing the rescheduling response mechanism, a basis is provided for subsequently determining the scheduling scheme of the multi-objective flexible spraying workshop. Based on the particle swarm algorithm with the beetle antennae search strategy and combined with the rescheduling response mechanism, the multi-objective dynamic flexible spraying workshop scheduling model is optimized and solved to obtain the global optimal value, and based on the global optimal value, a scheduling scheme for the multi-objective flexible spraying workshop is obtained. An optimization algorithm based on the beetle antennae search strategy is proposed to reduce the number of solution calculations and improve the algorithm efficiency. Combined with the rescheduling response mechanism, the effectiveness and accuracy of the scheduling scheme for the multi-objective flexible spraying workshop are ensured. Finally, through the analysis of the dynamic characteristics of the workshop, the flexibility and production efficiency of the manufacturing industry are improved.

[0071] Embodiment 2:

[0072] Based on Embodiment 1, the embodiment of the present invention provides a multi-objective flexible spraying workshop scheduling method under dynamic events, as Figure 2 shown. In S1, based on the equipment parameters and relevant working parameters of the multi-objective flexible spraying workshop, a simulation model of the spraying workshop is built, including:

[0073] Based on the basic processes of the flexible spraying workshop, using simulation software, a model layer corresponding to the flexible spraying process is established;

[0074] In the model layer, a material output device, a material terminator, and a material processor are sequentially inserted, and the material output device, the material terminator, and the material processor are connected using a connector to obtain the workstations corresponding to the basic processes, and a material form module and a process route table module are set to construct a basic flexible spraying workshop model;

[0075] Production parts are set in the basic flexible spraying workshop model, and data tables of the production parts at different workstations are established;

[0076] An index relationship between the workstations and the data tables is established, and the index relationship is added to the basic flexible spraying workshop model to obtain the simulation model of the spraying workshop.

[0077] In this embodiment, the basic processes of the flexible spraying workshop include four major categories: PVC sealing, primer process, topcoat process, and body finishing. One flexible spraying process corresponds to one model layer.

[0078] In this embodiment, simulation software, such as Plant Simulation, provides various convenient and fast modules that can help users quickly model and simulate, and provides rich interfaces.

[0079] In this embodiment, material producers, material terminators, and material processors are used for the generation, end, and processing of attacks.

[0080] The beneficial effects of the above design scheme are as follows: By building a spraying workshop simulation model based on the equipment parameters and related working parameters of the multi-objective flexible spraying workshop, it provides a simulation basis for the scheduling of the multi-objective flexible spraying workshop under dynamic events.

[0081] Embodiment 3:

[0082] Based on Embodiment 2, the embodiment of the present invention provides a method for scheduling a multi-objective flexible spraying workshop under dynamic events. In S1, static scheduling simulation is performed on the spraying workshop simulation model to determine the impact of dynamic events on the original scheduling scheme, including:

[0083] Use the event controller to perform static thorough running, single-step running, and reset running operations on the spraying workshop simulation model, collect statistical data during the running process, and determine whether the spraying workshop simulation model is running normally;

[0084] After determining that the spraying workshop simulation model is running normally, set the algorithm parameters of the genetic algorithm, and based on the workshop data, combine the algorithm parameters to simulate the spraying workshop simulation model to obtain an initial scheduling scheme;

[0085] Customize machine failures and custom emergency orders at the workstations of the spraying workshop simulation model to obtain the output results, and compare the output results with the original scheduling scheme to obtain the impact of dynamic events on the original scheduling scheme.

[0086] The beneficial effects of the above design scheme are as follows: Customize machine failures and custom emergency orders at the workstations of the spraying workshop simulation model to obtain the output results, and compare the output results with the original scheduling scheme to obtain the impact of dynamic events on the original scheduling scheme, providing a reference basis for the determination of the subsequent rescheduling response mechanism.

[0087] Embodiment 4:

[0088] Based on Embodiment 1, an embodiment of the present invention provides a multi-objective flexible spraying workshop scheduling method under dynamic events. In S2, based on the machine failure dynamic event and the emergency order insertion dynamic event in the multi-objective flexible spraying workshop, a multi-objective dynamic flexible spraying workshop scheduling model with the makespan, the maximum machine load, and the total machine load as the objectives is established, including:

[0089] Determine the time impact characteristics and load impact characteristics of the machine failure dynamic event and the emergency order insertion dynamic event on the processing process;

[0090] Take the makespan, the maximum machine load, and the total machine load as the main optimization objectives, and establish the constraint conditions for the processing process of the flexible spraying workshop. Based on the constraint conditions, determine the objective function for the main optimization objectives;

[0091] Establish a weight selection mechanism for the makespan, the maximum machine load, and the total machine load. Combine with the objective function of the main optimization objectives to construct a basic scheduling model;

[0092] Add the failure dynamic event and the emergency order insertion dynamic event to the basic scheduling model to establish a basic dynamic scheduling model;

[0093] Perform specific dynamic impact marking on the time impact characteristics and load impact characteristics in the basic dynamic scheduling model to obtain a multi-objective dynamic flexible spraying workshop scheduling model.

[0094] The beneficial effects of the above design are as follows: By determining the time impact characteristics and load impact characteristics of the machine failure dynamic event and the emergency order insertion dynamic event on the processing process, taking the makespan, the maximum machine load, and the total machine load as the main optimization objectives, and establishing the constraint conditions for the processing process of the flexible spraying workshop. Based on the constraint conditions, determine the objective function for the main optimization objectives, establish a weight selection mechanism for the makespan, the maximum machine load, and the total machine load. Combine with the objective function of the main optimization objectives to construct a basic scheduling model to realize simulation scheduling. Then add the failure dynamic event and the emergency order insertion dynamic event to the basic scheduling model to establish a basic dynamic scheduling model; perform specific dynamic impact marking on the time impact characteristics and load impact characteristics in the basic dynamic scheduling model to obtain a multi-objective dynamic flexible spraying workshop scheduling model, realizing the simulation of dynamic scheduling, laying a foundation for exploring the impact of different dynamic events on the original optimal scheduling plan of the spraying workshop and for studying the rescheduling mechanism under dynamic events.

[0095] Embodiment 5:

[0096] Based on Embodiment 4, an embodiment of the present invention provides a multi-objective flexible spraying workshop scheduling method under dynamic events. The time influence feature and load influence feature are specifically dynamically marked in the basic dynamic scheduling model to obtain a multi-objective dynamic flexible spraying workshop scheduling model, including:

[0097] The time influence feature and load influence feature are added to the corresponding objective function in the basic dynamic scheduling model, and the objective function is dynamically marked to obtain a dynamic objective function;

[0098] Based on the dynamic objective function, the weight selection mechanism is dynamically marked to obtain a dynamic weight selection mechanism;

[0099] Based on the dynamic objective function and the dynamic weight selection mechanism, a multi-objective dynamic flexible spraying workshop scheduling model is obtained.

[0100] The beneficial effect of the above design solution is that by adding the time influence feature and load influence feature to the corresponding objective function in the basic dynamic scheduling model, the objective function is dynamically marked to obtain a dynamic objective function; based on the dynamic objective function, the weight selection mechanism is dynamically marked to obtain a dynamic weight selection mechanism, and based on the dynamic objective function and the dynamic weight selection mechanism, a multi-objective dynamic flexible spraying workshop scheduling model is obtained. Dynamic marking is carried out from two aspects: the dynamic change of the function and the dynamic change of the function participation weight, ensuring the flexibility and accuracy of the multi-objective dynamic flexible spraying workshop scheduling model.

[0101] Embodiment 6:

[0102] Based on Embodiment 1, an embodiment of the present invention provides a multi-objective flexible spraying workshop scheduling method under dynamic events. In S3, based on the influence of dynamic events on the original scheduling plan and combined with the constraint conditions, a rescheduling response mechanism corresponding to the dynamic events is established, including:

[0103] Based on the influence of dynamic events on the original scheduling plan and combined with the constraint conditions, the influence value between the dynamic events and the plan features is determined, and based on the corresponding relationship between the dynamic events and the plan features, the dynamic events are classified to obtain passive influence events and active influence events. Based on the influence value between the dynamic events and the plan features, the influence weights of the passive influence events and active influence events are determined;

[0104] Based on the passive influence events, active influence events and their corresponding influence weights, the rescheduling method and rescheduling adjustment range are determined;

[0105] An initial rescheduling model is established based on the rescheduling method and the rescheduling adjustment range, and a rescheduling plan for a preset single dynamic event under the initial rescheduling model is obtained. Comprehensive analysis is performed on multiple preset single dynamic events and their corresponding rescheduling plans to determine the interaction characteristics between different single dynamic events;

[0106] Based on the interaction characteristics, multiple action branches are established for the initial rescheduling model, and the action priorities between the multiple action branches are set. The initial rescheduling model is upgraded and optimized based on the multiple action branches and action priorities to obtain a target rescheduling model;

[0107] The dynamic event is input into the target rescheduling model to obtain a rescheduling response mechanism corresponding to the dynamic event.

[0108] The beneficial effects of the above design scheme are as follows: By establishing an initial rescheduling model based on the rescheduling method and the rescheduling adjustment range, obtaining a rescheduling plan for a preset single dynamic event under the initial rescheduling model, performing comprehensive analysis on multiple preset single dynamic events and their corresponding rescheduling plans, determining the interaction characteristics between different single dynamic events, establishing multiple action branches for the initial rescheduling model based on the interaction characteristics, setting the action priorities between the multiple action branches, upgrading and optimizing the initial rescheduling model based on the multiple action branches and action priorities to obtain a target rescheduling model, ensuring the comprehensiveness and accuracy of the obtained target rescheduling model under multi-objective dynamic events, and inputting the dynamic event into the target rescheduling model to obtain a rescheduling response mechanism corresponding to the dynamic event, ensuring the accuracy of the obtained rescheduling response mechanism, providing a basis for subsequent determination of the multi-objective flexible spraying workshop scheduling plan.

[0109] Example 7:

[0110] Based on Example 6, an embodiment of the present invention provides a multi-objective flexible spraying workshop scheduling method under dynamic events. The rescheduling method and the rescheduling adjustment range are determined based on passive influence events, active influence events, and their corresponding influence weights, including:

[0111] Determine that the rescheduling method for passive influence events is a periodic rescheduling method, and determine the adjustment range of the rescheduling period based on the influence weight;

[0112] Determine that the rescheduling method for active influence events is event-driven rescheduling, and determine the adjustment range of the driven rescheduling based on the influence weight.

[0113] The beneficial effects of the above design scheme are as follows: By determining the rescheduling method and the rescheduling adjustment range based on passive influence events, active influence events, and their corresponding influence weights, a basis is provided for the establishment of the rescheduling model.

[0114] Example 8:

[0115] Based on Example 1, an embodiment of the present invention provides a multi-objective flexible spraying workshop scheduling method under dynamic events. In S4, based on the beetle antennae search strategy particle swarm optimization algorithm, combined with the rescheduling response mechanism, the multi-objective dynamic flexible spraying workshop scheduling model is optimized and solved to obtain the global optimal value, including:

[0116] Based on the coding crossover strategy, crossover and mutation operations are performed on the existing workshop processes to obtain new processes;

[0117] The beetle antennae search strategy and the particle swarm optimization algorithm are fused to obtain the beetle antennae search particle swarm optimization algorithm. The beetle antennae search particle swarm optimization algorithm is added to the multi-objective dynamic flexible spraying workshop scheduling model, and the model parameters are initialized. Based on the new processes, combined with the rescheduling response mechanism, the reverse generational distance and hypervolume are used as evaluation indicators to obtain the initial global optimal solution;

[0118] Randomly obtain the positions and velocities of each beetle, obtain the fitness values of each beetle, and update each velocity and position based on the update rules and fitness values for iteration;

[0119] Obtain the updated initial individual optimal solution and initial global optimal solution after iteration, and compare them with the values of the previous iteration to obtain the global optimal value.

[0120] The beneficial effects of the above design are as follows: Through the beetle antennae search strategy particle swarm optimization algorithm, combined with the rescheduling response mechanism, the multi-objective dynamic flexible spraying workshop scheduling model is optimized and solved to obtain the global optimal value. Using beetles instead of particles, the position information is updated in real time during the iteration process to prevent the problem that the particle swarm optimization algorithm is prone to falling into local optimization, and it is no longer limited to single-objective parameter optimization, making the dynamic flexible job shop parameter optimization more effective and simpler, reducing the number of calculations, improving the algorithm efficiency, combined with the rescheduling response mechanism, ensuring the effectiveness and accuracy of the multi-objective flexible spraying workshop scheduling scheme. Finally, through the analysis of the workshop dynamic characteristics, the flexibility and production efficiency of the manufacturing industry are improved.

[0121] Example 9:

[0122] Based on Example 1, an embodiment of the present invention provides a multi-objective flexible spraying workshop scheduling method under dynamic events. Based on the global optimal value, a multi-objective flexible spraying workshop scheduling scheme is obtained, including:

[0123] Based on the global optimal value, determine the model parameters of the multi-objective dynamic flexible spraying workshop scheduling model;

[0124] Determine the specific values of the maximum completion time, the maximum machine load, and the total machine load, and process the operations of the workshop to be scheduled with the multi-objective dynamic flexible spray painting workshop scheduling model under the model parameters to obtain a multi-objective flexible spray painting workshop scheduling plan.

[0125] The beneficial effects of the above design are as follows: Ensure the effectiveness and accuracy of the obtained multi-objective flexible spray painting workshop scheduling plan. Finally, through the analysis of the dynamic characteristics of the workshop, improve the flexibility and production efficiency of the manufacturing industry.

[0126] Embodiment 10:

[0127] Based on Embodiment 1, an embodiment of the present invention provides a scheduling system for a multi-objective flexible spray painting workshop scheduling method under dynamic events, as Figure 3 shown, including:

[0128] A simulation module, which is used to build a spray painting workshop simulation model based on the equipment parameters and related working parameters of the multi-objective flexible spray painting workshop, and perform static scheduling simulation on the spray painting workshop simulation model to determine the impact of dynamic events on the original scheduling plan;

[0129] A model determination module, which is used to establish a multi-objective dynamic flexible spray painting workshop scheduling model with the maximum completion time, the maximum machine load, and the total machine load as the objectives based on the machine failure dynamic event and the emergency order insertion dynamic event of the multi-objective flexible spray painting workshop;

[0130] A response determination module, which is used to establish a rescheduling response mechanism corresponding to the dynamic event based on the impact of the dynamic event on the original scheduling plan and in combination with the constraint conditions;

[0131] A plan determination module, which is used to optimize and solve the multi-objective dynamic flexible spray painting workshop scheduling model based on the particle swarm algorithm of the beetle antennae search strategy, in combination with the rescheduling response mechanism, to obtain the global optimal value, and obtain a multi-objective flexible spray painting workshop scheduling plan based on the global optimal value.

[0132] In this embodiment, the dynamic events include machine failure dynamic events and emergency order insertion dynamic events.

[0133] In this embodiment, the impact of the dynamic event on the original scheduling plan includes the impact on time, load, etc.

[0134] In this embodiment, the constraint conditions are, for example, completion time constraints, operation start time constraints, sequence constraints, and machine constraints, etc.

[0135] In this embodiment, the rescheduling response mechanism satisfies the determination of the departure and adjustment conditions of the rescheduling.

[0136] The beneficial effects of the above design are as follows: Based on the equipment parameters and related working parameters of the multi-objective flexible spraying workshop, a simulation model of the spraying workshop is built, and static scheduling simulation is carried out on the simulation model of the spraying workshop to determine the impact of dynamic events on the original scheduling scheme, providing a simulation basis for the scheduling of the multi-objective flexible spraying workshop under dynamic events. Based on the dynamic events of machine failures and emergency order insertions in the multi-objective flexible spraying workshop, a multi-objective dynamic flexible spraying workshop scheduling model with the makespan, maximum machine load, and total machine load as the objectives is established to explore the impact of different dynamic events on the original optimal scheduling scheme of the spraying workshop, laying a foundation for the subsequent study of the rescheduling mechanism under dynamic events. Based on the impact of dynamic events on the original scheduling scheme and combined with the constraint conditions, a corresponding rescheduling response mechanism for dynamic events is established. By establishing the rescheduling response mechanism, it provides a basis for determining the scheduling scheme of the multi-objective flexible spraying workshop in the future. Based on the particle swarm algorithm with the beetle antennae search strategy and combined with the rescheduling response mechanism, the multi-objective dynamic flexible spraying workshop scheduling model is optimized and solved to obtain the global optimal value, and based on the global optimal value, the scheduling scheme of the multi-objective flexible spraying workshop is obtained. An optimization algorithm based on the beetle antennae search strategy is proposed to reduce the number of solution calculations and improve the algorithm efficiency. Combined with the rescheduling response mechanism, it ensures the effectiveness and accuracy of the scheduling scheme of the multi-objective flexible spraying workshop. Finally, through the analysis of the dynamic characteristics of the workshop, the flexibility and production efficiency of the manufacturing industry are improved.

[0137] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of this application document and its equivalent technologies, the present invention also intends to include these changes and modifications.

Claims

1. A multi-objective flexible spray shop scheduling method under dynamic events, characterized in that: include: S1: Based on the equipment parameters and related working parameters of the multi-objective flexible spraying workshop, a spraying workshop simulation model is built, and a static scheduling simulation is performed on the spraying workshop simulation model to determine the impact of dynamic events on the original scheduling plan; S2: Based on the dynamic events of machine failure and urgent order insertion in the multi-objective flexible spray shop, a multi-objective dynamic flexible spray shop scheduling model with maximum completion time, maximum machine load and total machine load as the objectives is established; S3: Based on the impact of dynamic events on the original scheduling plan and combined with constraints, a rescheduling response mechanism corresponding to dynamic events is established; specifically, it includes: Based on the impact of the dynamic event on the original scheduling plan, combined with the constraint conditions, the impact value between the dynamic event and the plan characteristics is determined, and based on the corresponding relationship between the dynamic event and the plan characteristics, the dynamic event is classified to obtain passive impact events and active impact events, and based on the impact value between the dynamic event and the plan characteristics, the impact weights of the passive impact events and the active impact events are determined; Determine the rescheduling method and rescheduling adjustment range based on passive impact events and active impact events and their corresponding impact weights; An initial rescheduling model is established based on the rescheduling mode and the rescheduling adjustment range, and a rescheduling plan for a preset single dynamic event under the initial rescheduling model is obtained. A comprehensive analysis is performed on multiple preset single dynamic events and their corresponding rescheduling plans to determine the interaction characteristics between different single dynamic events; Based on the interaction characteristics, multiple action branches are established for the initial rescheduling model, and action priorities are set between the multiple action branches. Based on the multiple action branches and action priorities, the initial rescheduling model is upgraded and optimized to obtain a target rescheduling model; Input the dynamic events into the target rescheduling model to obtain the rescheduling response mechanism corresponding to the dynamic events; S4: Based on the particle swarm algorithm of the beetle whisker search strategy and combined with the rescheduling response mechanism, the multi-objective dynamic flexible spray workshop scheduling model is optimized and solved to obtain the global optimal value, and based on the global optimal value, the multi-objective flexible spray workshop scheduling solution is obtained; In S2, based on the dynamic events of machine failure and urgent order insertion in the multi-objective flexible spray shop, a multi-objective dynamic flexible spray shop scheduling model with maximum completion time, maximum machine load and total machine load as targets is established, including: Determine the time and load impact characteristics of machine failure dynamic events and emergency order insertion dynamic events on the machining process; The maximum completion time, the maximum load of the machine and the total load of the machine are taken as the main optimization targets, and the constraint conditions of the processing process of the flexible spraying workshop are established, and the objective function of the main optimization target is determined based on the constraint conditions; A weight selection mechanism for maximum completion time, maximum machine load and total machine load is established, and a basic scheduling model is constructed by combining the objective function of the main optimization goal. Adding fault dynamic events and urgent order insertion dynamic events to the basic scheduling model to establish a basic dynamic scheduling model; The time influence characteristics and load influence characteristics are specifically marked with dynamic influence in the basic dynamic scheduling model to obtain a multi-objective dynamic flexible spray shop scheduling model; The time impact characteristics and load impact characteristics are specifically marked in the basic dynamic scheduling model to obtain a multi-objective dynamic flexible spray shop scheduling model, including: Adding the time impact characteristics and load impact characteristics to the corresponding objective function in the basic dynamic scheduling model, dynamically marking the objective function, and obtaining a dynamic objective function; Dynamically marking the weight selection mechanism based on the dynamic objective function to obtain a dynamic weight selection mechanism; Based on the dynamic objective function and dynamic weight selection mechanism, a multi-objective dynamic flexible spray workshop scheduling model is obtained.

2. The multi-objective flexible spray shop scheduling method under dynamic events according to claim 1 is characterized in that: In S1, a spray painting workshop simulation model is constructed based on the equipment parameters and related working parameters of the multi-objective flexible spray painting workshop, including: Based on the basic process of the flexible spraying workshop, the model layer corresponding to the flexible spraying process is established using simulation software; Insert the material generator, material terminator and material processor in the model layer in sequence, connect the material generator, material terminator and material processor with the connector to obtain the workstation corresponding to the basic process, set the material form module and process route table module, and construct the basic flexible spray workshop model; Setting production parts in the basic flexible spray shop model and establishing data tables of production parts at different workstations; An index relationship between workstations and data tables is established, and the index relationship is added to the basic flexible spray shop model to obtain a spray shop simulation model.

3. The multi-objective flexible spray shop scheduling method under dynamic events according to claim 2 is characterized in that: In S1, static scheduling simulation is performed on the spraying workshop simulation model to determine the impact of dynamic events on the original scheduling plan, including: Use the event controller to perform static and thorough operation, single-step operation and reset operation on the spray painting workshop simulation model, collect statistical data during the operation process, and determine whether the spray painting workshop simulation model is operating normally; After confirming that the spray painting workshop simulation model is running normally, the algorithm parameters of the genetic algorithm are set, and the spray painting workshop simulation model is simulated based on the workshop data and the algorithm parameters to obtain the initial scheduling plan; Customized machine failures and customized emergency orders are performed in the workstations of the spray shop simulation model, and the output results are obtained. The output results are compared with the original scheduling plan to obtain the impact of dynamic events on the original scheduling plan.

4. The multi-objective flexible spray shop scheduling method under dynamic events according to claim 1 is characterized in that: The determining of the rescheduling mode and the rescheduling adjustment range based on the passive impact events and the active impact events and their corresponding impact weights includes: Determine that the rescheduling method for the passive impact event is a periodic rescheduling method, and determine the adjustment range of the rescheduling period based on the impact weight; The rescheduling method for actively influencing events is determined to be event-driven rescheduling, and the adjustment range of the driven rescheduling is determined based on the impact weight.

5. The multi-objective flexible spray shop scheduling method under dynamic events according to claim 1 is characterized in that: In S4, the particle swarm algorithm based on the beetle whisker search strategy is combined with the rescheduling response mechanism to optimize and solve the multi-objective dynamic flexible spray shop scheduling model to obtain the global optimal value, including: Based on the coding crossover strategy, crossover and mutation operations are performed on the existing workshop processes to obtain new processes; The beetle whisker search strategy and the particle swarm algorithm are integrated to obtain the beetle whisker search particle swarm algorithm, which is added to the multi-objective dynamic flexible spray shop scheduling model, and the model parameters are initialized. Based on the new process, combined with the rescheduling response mechanism, the reverse generation distance and hypervolume are used as evaluation indicators to obtain the initial global optimal solution; Randomly obtain the position and speed of each beetle, obtain the fitness value of each beetle, and update each speed and position iteratively based on the update rule and fitness value; After obtaining the iteration, the initial individual optimal solution and the initial global optimal solution are updated and compared with the value of the previous iteration to obtain the global optimal value.

6. The multi-objective flexible spray shop scheduling method under dynamic events according to claim 1 is characterized in that: Based on the global optimal value, a multi-objective flexible spray shop scheduling scheme is obtained, including: Determining model parameters of a multi-objective dynamic flexible spray shop scheduling model based on the global optimal value; The specific values ​​of the maximum completion time, the maximum machine load and the total machine load are determined, and the workshop processes to be scheduled are processed by the multi-objective dynamic flexible spray workshop scheduling model under the model parameters to obtain a multi-objective flexible spray workshop scheduling plan.

7. The scheduling system of a multi-objective flexible spray shop scheduling method under dynamic events according to claim 1 is characterized in that: include: The simulation module is used to build a spray shop simulation model based on the equipment parameters and related working parameters of the multi-objective flexible spray shop, and to perform static scheduling simulation on the spray shop simulation model to determine the impact of dynamic events on the original scheduling plan; A model determination module is used to establish a multi-objective dynamic flexible spray shop scheduling model with maximum completion time, maximum machine load and total machine load as targets based on the dynamic events of machine failure and urgent order insertion in the multi-objective flexible spray shop; The response determination module is used to establish a rescheduling response mechanism corresponding to the dynamic event based on the impact of the dynamic event on the original scheduling plan and in combination with the constraint conditions; The scheme determination module is used to optimize and solve the multi-objective dynamic flexible spray workshop scheduling model based on the particle swarm algorithm of the longicorn beetle whisker search strategy, combined with the rescheduling response mechanism, to obtain the global optimal value, and based on the global optimal value, obtain the multi-objective flexible spray workshop scheduling scheme.

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