Tire vulcanization workshop intelligent flexible scheduling method and system oriented to dynamic disturbance

Abstract

The invention belongs to the field of workshop dynamic scheduling, and provides a dynamic disturbance-oriented tire vulcanization workshop intelligent flexible scheduling method and system, and the method comprises the steps: building a production scheduling model with the minimization of the maximum completion time as an optimization target; obtaining an initial processing sequence and a selectable machine set of each workpiece process, and solving by taking the production scheduling model as a target function to obtain an optimal initial scheduling scheme; when a dynamic event occurs in the production process, detecting the type of the dynamic event and the occurrence time of the event, and extracting a completed process, a processing process and an unstarted process; and determining a to-be-scheduled process based on the influence of the dynamic event on the processing process and the unstarted process, retaining the process processing sequence of the optimal initial scheduling scheme, updating the optimal processing machine of the to-be-scheduled process, and generating a rescheduling scheme. The device only replaces a machine in an unprocessed process, is embedded into a cooling time buffer area between the same workpiece processes, solves the problem of insufficient cooling of the rubber material, and guarantees the quality and process stability.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of workshop dynamic scheduling, and particularly relates to a tire vulcanization workshop intelligent flexible scheduling method and system for dynamic disturbance. BACKGROUND

[0002] The statements in this section merely provide background information related to the application and do not necessarily constitute prior art.

[0003] With the increase of per capita car ownership, the demand for tires continues to grow, and tire manufacturing is facing the dual pressures of capacity expansion and efficiency improvement. Tire production has the characteristics of large scale, multi-category and high customization, especially in the mixing workshop, the processing quality directly determines the performance of the finished product. Mixing scheduling not only needs to consider basic elements such as workpiece quantity, process sequence, equipment processing time, etc., but also needs to deal with process characteristics such as equipment switching rules, cooling time constraints, resource conflicts, etc. The scheduling complexity is high. At the same time, dynamic disturbances such as order insertion, cancellation and equipment failure frequently occur in the production process. If the scheduling strategy response is not timely and flexible, it will lead to production delay, resource idling and overall efficiency decline. Therefore, in the mixing scheduling, it is necessary to achieve a better scheduling result, improve equipment utilization, enhance the flexibility of equipment selection, shorten the workpiece completion time, and have a fast and stable adjustment capability in the event of an emergency, so as to improve the overall production efficiency and system flexibility of the workshop.

[0004] The current scheduling problem of the mixing workshop is usually modeled as a flexible job shop scheduling problem (FJSP, Flexible job-shop scheduling problem), which is a typical NP-hard problem, and it is difficult to obtain the optimal solution by traditional algorithms. Although some researches based on genetic algorithm, ant colony algorithm, particle swarm optimization and deep learning have made some progress, most of the methods still take the static environment as the premise, lack of adaptability to dynamic disturbance, and it is difficult to achieve efficient application in actual production. In addition, many researches ignore the special process requirements in tire mixing production, resulting in poor feasibility and weak continuity of the scheduling scheme in actual scene. In the current processing of dynamic events for workshop scheduling, the following methods are mainly used in the prior art: (1) Dynamic adjustment scheme based on heuristic algorithm, which realizes a more robust rescheduling strategy under the condition of sudden events by introducing a hybrid event-driven strategy and three types of stability indicators. However, due to the large number of parameters in workshop scheduling, it is difficult to optimize and the scheduling result is unstable.

[0005] (2) Dynamic adjustment scheme based on deep reinforcement learning, focusing on the impact of operation processing time uncertainty on job shop scheduling to deal with the scheduling instability problem caused by such single dynamic event, and improving the training efficiency and generalization ability of the scheduling strategy in uncertain environment through the mixed priority experience replay mechanism. The strategy of this method is complex, has strong training dependence, and has different effects for different scenarios, and the application event type is single, and the generalization ability is limited.

[0006] Therefore, based on the field of job shop dynamic scheduling, a large number of works are concentrated on the excellence of the solution and various algorithms, and the complexity of the problem is mainly aimed at the processing sequence and machine selection of the workpiece process, and in the existing method, compared with the heuristic algorithm, the scheduling strategy of the reinforcement learning is better, but the training difficulty is great, the generalization ability is weak, and the influence of the dynamic event on the processing sequence and machine selection of the workpiece process in the final optimal scheduling scheme is ignored, so that the solving efficiency is low and the scheduling scheme is inaccurate. SUMMARY

[0007] In order to overcome the deficiencies of the existing scheduling strategy in dealing with dynamic disturbance and resource allocation, the present application proposes a dynamic disturbance-oriented intelligent flexible scheduling method and system for a tire vulcanization workshop, which comprehensively considers the maximum machine processing capacity and the minimum machine load imbalance, and dynamically allocates the optimal machine for each process. This method can effectively improve the machine utilization efficiency and avoid resource waste such as equipment overload or idling; at the same time, through the real-time machine selection mechanism, when dynamic events such as order insertion, cancellation or equipment failure occur, the processing equipment can be flexibly adjusted on the basis of maintaining the original process sequence, ensuring that the scheduling scheme has stronger stability and faster response capability.

[0008] According to some embodiments, the first aspect of the present application provides a dynamic disturbance-oriented intelligent flexible scheduling method for a tire vulcanization workshop, which adopts the following technical scheme: The dynamic disturbance-oriented intelligent flexible scheduling method for a tire vulcanization workshop comprises: Establish a production scheduling model with the optimization objective of minimizing the maximum completion time and considering the continuous processing buffer of workpieces; Obtain the initial processing sequence of each workpiece process and the set of selectable machines, and solve the production scheduling model as the objective function to obtain the optimal initial scheduling scheme; Based on the optimal initial scheduling scheme, schedule the production, detect the type and occurrence time of the dynamic event when a dynamic event occurs during production, extract the completed process, the processing process and the unstarted process; Based on the influence of the dynamic event on the processing process and the unstarted process, determine the to-be-scheduled process, retain the process processing sequence of the optimal initial scheduling scheme, update the optimal processing machine of the to-be-scheduled process, and generate a rescheduling scheme.

[0009] Further, the constraint conditions of the production scheduling model aiming at minimizing the maximum completion time include: machine unique occupation constraint, i.e. each process can only be processed by a machine once; machine capacity constraint, i.e. each machine can only process one process at the same time; workpiece processing sequence constraint, i.e. the next process can only start processing after the previous process is completed; process time buffer constraint, i.e. the same workpiece process cannot be continuously processed, and there is a cooling buffer time between each process; all processes need to be processed once.

[0010] Further, the initial processing sequence of each workpiece process and the optional machine set are obtained, and the production scheduling model is solved as the objective function to obtain an optimal initial scheduling scheme, including: the initial processing sequence of each workpiece process is obtained, and an ascending position mapping coding strategy is used for coding to obtain the optimal processing sequence of each workpiece process; based on the optimal processing sequence of each workpiece process and the optional machine set, the production scheduling model is solved as the objective function to obtain an optimal initial scheduling scheme.

[0011] Further, the influence of the dynamic event on the process being processed and the process not started is determined to determine the process to be scheduled, including: if the dynamic event is a machine failure type, stop the subsequent processing of the process being processed, and take the process being processed and the process not started as the process to be scheduled; if the dynamic event is a workpiece cancellation type, stop the process being processed, and take the process not started as the process to be scheduled; if the dynamic event is an order insertion type, optimize the scheduling of the workpiece process of the inserted order, and take the workpiece process of the inserted order, the process being processed and the process not started as the process to be scheduled.

[0012] Further, the optimal processing machine of the process to be scheduled is updated to generate a rescheduling scheme, including: based on the current state, processing capacity and load of all machines, an available machine set is obtained; based on the production scheduling model, the optimal processing machine of the process to be scheduled is determined from the available machine set, and based on the optimal processing machine of the process to be scheduled, the processing start and end time and the optimal processing machine number of each process to be scheduled are determined to generate a rescheduling scheme.

[0013] Further, based on the current state, processing capacity and load of all machines, an available machine set is obtained, including: based on the current state of all machines, idle machines are selected as the target of balancing machine load; When there are multiple idle machines, select a machine with strong processing capacity; When there are multiple machines with strong processing capacity and are selectable, select a machine with a large machine number as an available machine; Get the set of available machines.

[0014] According to some embodiments, the second aspect of the present application provides a dynamic disturbance-oriented intelligent flexible scheduling system for a tire vulcanization workshop, which adopts the following technical solution: The dynamic disturbance-oriented intelligent flexible scheduling system for a tire vulcanization workshop comprises: The production scheduling model establishment module is configured to establish a production scheduling model with the optimization objective of minimizing the maximum completion time and considering the continuous processing buffer of workpieces; The initial scheduling module is configured to obtain the initial processing sequence of each workpiece process and the set of selectable machines, solve the production scheduling model as the objective function, and obtain the optimal initial scheduling scheme; The dynamic event detection module is configured to schedule production based on the optimal initial scheduling scheme, detect the type of dynamic event and the time of event occurrence when a dynamic event occurs in the production process, and extract the completed process, the processing process and the unstarted process; The rescheduling module is configured to determine the to-be-scheduled process based on the influence of the dynamic event on the processing process and the unstarted process, retain the process processing sequence of the optimal initial scheduling scheme, update the optimal processing machine of the to-be-scheduled process, and generate a rescheduling scheme.

[0015] According to some embodiments, the third aspect of the present application provides a computer readable storage medium.

[0016] A computer readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the steps in the dynamic disturbance-oriented intelligent flexible scheduling method for a tire vulcanization workshop according to the first aspect described above.

[0017] According to some embodiments, the fourth aspect of the present application provides a computer device.

[0018] A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps in the dynamic disturbance-oriented intelligent flexible scheduling method for a tire vulcanization workshop according to the first aspect described above when executing the program.

[0019] According to some embodiments, the fifth aspect of the present application provides a computer program product or a computer program.

[0020] The computer program product or the computer program comprises computer instructions stored in a computer readable storage medium, and a processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device executes the steps in the intelligent flexible scheduling method for dynamic disturbance-oriented tire vulcanization plant as described in the first aspect.

[0021] Compared with the prior art, the beneficial effects of the present application are: The intelligent scheduling method of the present application considers the characteristics of the mixing process and the dynamic response capability, can maintain the stability of the overall scheduling in an uncertain environment, realize local rapid optimization and efficient operation of the system, and thus meet the urgent needs of modern tire production for flexible and intelligent scheduling.

[0022] The present application provides a scheduling scheme based on minimizing the completion time and comprehensively considering the maximum processing capacity of the machine and the minimum machine load imbalance, which meets the full utilization of each machine, avoids machine overload and excessive machine idle, and through the scheduling selection mode based on the machine processing capacity and the current state of the machine, a more efficient, more stable and more suitable scheduling scheme for factory processing requirements is obtained.

[0023] The present application provides a new rescheduling scheme, which retains the original scheduling scheme of the workpiece process processing sequence, maximizes the use of each processing machine, and ensures that after the occurrence of a dynamic event, the system can quickly and efficiently respond to the disturbance event, and on the basis of retaining the original plan, the machine is flexibly scheduled and the processing machine selection is adjusted in real time.

[0024] The present application provides a strategy for alternate processing of processes, and different workpiece processes can be continuously processed, and there is a certain time buffer between different workpiece processes, which effectively avoids the quality problem of finished rubber caused by insufficient cooling during continuous processing. The process and individual code are constructed by using ascending position mapping mode, and the cooperative optimization of workpiece process sequence and machine selection is completed through improved algorithm.

[0025] The present application provides a rescheduling strategy suitable for various dynamic events, which can be applied to disturbance events such as machine failure, workpiece insertion and workpiece cancellation encountered in actual production process. BRIEF DESCRIPTION OF DRAWINGS

[0026] The drawings accompanying the specification of the present application serve to provide a further understanding of the present application, and the illustrative embodiments of the present application and their descriptions serve to explain the present application, and do not constitute an improper limitation on the present application.

[0027] Figure 1 The flowchart of the intelligent flexible scheduling method for dynamic disturbance-oriented tire vulcanization plant in the embodiments of the present application; Figure 2Mapping relationship between processing sequence of workpiece process and individual element in the embodiment of the present application Figure 3 Selection diagram of dynamic machine selection rule in the embodiment of the present application Figure 4 Original production scheduling Gantt chart of workshop instance one, 15 workpieces and 6 machines in the embodiment of the present application Figure 5 Rescheduling Gantt chart after machine failure of workshop instance one, 15 workpieces and 6 machines in the embodiment of the present application Figure 6 Original production scheduling Gantt chart of workshop instance two, 10 workpieces and 6 machines in the embodiment of the present application Figure 7 Rescheduling Gantt chart after insertion of 5 new workpieces of workshop instance two, 10 workpieces and 6 machines in the embodiment of the present application Figure 8 Original production scheduling Gantt chart of workshop instance three, 15 workpieces and 6 machines in the embodiment of the present application Figure 9 Rescheduling Gantt chart after cancellation of workpiece 9 of workshop instance three, 15 workpieces and 6 machines in the embodiment of the present application DETAILED DESCRIPTION

[0028] The present application will be further described below with reference to the accompanying drawings and embodiments.

[0029] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application pertains.

[0030] It should be noted that the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form unless the context clearly indicates otherwise, and it should be further understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of a feature, step, operation, device, component and / or combinations thereof.

[0031] The embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0032] Embodiment one The embodiment provides a dynamic disturbance-oriented intelligent flexible scheduling method for a tire vulcanization plant, and the method is applied to a server for example, and it can be understood that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server and is realized through interaction of the terminal and the server. The server can be a stand-alone physical server, a server cluster or a distributed system formed by multiple physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communication, middleware services, domain name services, security services CDN, and basic cloud computing services such as big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch and the like, but is not limited thereto. The terminal and the server can be directly or indirectly connected through wired or wireless communication, which is not limited in the application. In the embodiment, the method comprises the following steps: establishing a production scheduling and dispatching model with an optimization objective of minimizing the maximum completion time and considering workpiece continuous processing buffer; obtaining an initial processing sequence of each workpiece process and a set of optional machines, and solving the production scheduling and dispatching model as a target function to obtain an optimal initial scheduling scheme; scheduling production based on the optimal initial scheduling scheme, detecting the type of a dynamic event and the time of occurrence of the dynamic event when a dynamic event occurs in the production process, and extracting a completed process, a processing process and an unstarted process; determining a to-be-scheduled process based on the influence of the dynamic event on the processing process and the unstarted process, retaining the process processing sequence of the optimal initial scheduling scheme, updating the optimal processing machine of the to-be-scheduled process, and generating a rescheduling scheme.

[0033] As shown in Figure 1 , the specific process of the method is as follows: Step S1: establishing a production scheduling and dispatching model; establishing a production scheduling and dispatching model with an optimization objective of minimizing the maximum completion time and considering workpiece continuous processing buffer; the parameter variables in the production scheduling and dispatching model are set as shown in Table 1.

[0034] Table 1 Variable Description

[0035] The production scheduling and dispatching model for minimizing the maximum completion time is as follows: (1) wherein, the minimum maximum completion time, is the completion time of the workpiece .

[0036] Step S102: The constraint conditions of the scheduling model of the workpiece continuous processing buffer are: Constraint condition 1: machine unique occupation constraint, each process can only be processed by a processing machine once.

[0037] (2); Constraint condition 2: machine capacity constraint, each machine can only process one process at the same time.

[0038] Constraint condition 3: workpiece processing sequence constraint, workpiece processes meet sequential processing, i.e. after the previous process is completed, the next process can start processing; process time buffer constraint, the same workpiece process cannot be continuously processed, and a cooling buffer time is required between each process As follows: (3); If there is a 4-hour cooling buffer time between each process, the specific value can be dynamically adjusted according to process requirements. The workpiece processing method with cooling time buffer avoids the quality problems of rubber material caused by continuous processing of workpieces, while ensuring that different workpiece processes can be alternately processed, improving production efficiency on the basis of ensuring buffer cooling time between processes.

[0039] (4); wherein, is the process of the workpiece on the machine on the machine Constraint condition 4: all processes need to be processed once.

[0040] (5).

[0041] Step S2: Solve the optimal initial scheduling scheme - obtain the initial processing order of each workpiece process and the optional machine set, solve the scheduling model as the objective function to obtain the optimal initial scheduling scheme, including: Obtain the initial processing order of each workpiece process, use ascending position mapping coding strategy for coding to obtain the optimal processing order of each workpiece process; Based on the optimal processing order of each workpiece process and the optional machine set, solve the scheduling model as the objective function to obtain the optimal initial scheduling scheme.

[0042] The optimal initial scheduling scheme includes the start and end time of each process of each workpiece and the processing machine.

[0043] It should be noted that when the objective function is solved, the initial processing sequence of each workpiece process and the optional machine set are used as the data basis, therefore, the embodiment adopts ascending order sorting of individual positions to realize the mapping relationship between discrete solutions and continuous solutions, so as to realize the mapping of individual positions to the processing sequence of the workpiece process. Based on the initial processing sequence of each workpiece process, the ascending position mapping coding strategy is used to convert the individual position vector into the optimized processing sequence of the workpiece process, and the ascending position mapping coding strategy is used to convert the individual position vector into the optimized processing sequence of the workpiece process. The coding mode can realize the conversion of continuous-discrete solution, ensure the one-to-one correspondence relationship between process and machine allocation, and facilitate scheduling modeling and objective function solving.

[0044] In a preferred embodiment of the application, the mapping relationship between the processing sequence of the workpiece process and the individual element is as shown in Figure 2 The decoding process of converting the individual position vector into the optimized processing sequence of the workpiece process by using the ascending position mapping coding strategy, specifically includes the following steps: Obtaining the initial processing sequence of each workpiece process: In this embodiment, three workpieces (1, 2, 3) are scheduled as an example, workpiece 1 has two processes, workpiece 2 has two processes, and workpiece 3 has one process, so the total number of processes is 5, and the initial processing sequence set is: {1-1, 1-2, 2-1, 2-2, 3}, that is Figure 2 Gray 1, Blue 1, Gray 2, Green 2, Orange 3 in

[0045] Based on the initial processing sequence set, a continuous value individual position vector is generated or received: An optimization algorithm (such as genetic algorithm) is used to generate a continuous value vector as a chromosome of an individual, the dimension of the vector is the same as the total number of processes, and an individual position vector containing 5 random continuous values is generated: X = [-2.3, 2.1, 1.2, 1.0, 0.5]; The vector values are sorted in ascending order and the index is obtained, including: All elements in the individual position vector X are sorted in ascending order according to the numerical value, and the position index (starting from 1) of each element in the original vector is recorded after sorting, so as to obtain an index sequence, as shown in Table 2.

[0046] Table 2 Ascending order sorting mapping table

[0047] The above table shows the sorting process. Finally, what is needed is not the sorted value, but the original index order corresponding to the sorted value; According to the sorting result, the index sequence I = [1, 5, 4, 3, 2] is obtained; the meaning of this sequence is that the position of the smallest value (-2.3) in the original vector is 1, the position of the second smallest value (0.5) is 5, and so on, and the position of the largest value (2.1) is 2.

[0048] Map the index sequence to the workpiece process processing sequence: Replace each index in the index sequence I with the workpiece process it represents.

[0049] First, the correspondence between the index number and the process needs to be established. In this embodiment, the following convention is adopted: the index position in the vector is assigned according to the workpiece order. That is: Index 1 corresponds to process 1-1; index 2 corresponds to process 1-2; index 3 corresponds to process 2-1; index 4 corresponds to process 2-2; and index 5 corresponds to process 3.

[0050] Replace each number in the index sequence I = [1, 5, 4, 3, 2] with the specific process according to the correspondence above: 1 corresponds to process 1-1; 5 corresponds to process 3; 4 corresponds to process 2-2; 3 corresponds to process 2-1; and 2 corresponds to process 1-2. Thus, the optimized processing sequence S of the workpiece process is obtained: S = [1-1, 3, 2-2, J2-1, 2-1, 1-2], that is, Figure 2 Gray 1, orange 3, green 2, gray 2, blue 1.

[0051] In the verification phase, this embodiment uses a genetic algorithm (Genetic Algorithm, GA) to solve the instance of the scheduling model to verify the adaptability and optimization effect of the proposed scheduling strategy under typical working conditions. That is, this embodiment is based on the basic framework of the genetic algorithm for scheduling optimization, but the core point is still in the subsequent rescheduling optimization process, rather than relying on the specific optimization algorithm itself.

[0052] Step S3: Schedule production based on the optimal initial scheduling scheme and detect production in real time, i.e., detect in real time whether a dynamic event occurs.

[0053] In this embodiment, a dynamic event refers to an unexpected disturbance or unplanned change that may interfere with the current scheduling scheme during the production execution process in the tire mixing or vulcanization workshop, which triggers the dynamic response and rescheduling mechanism. In order to improve the stability and flexibility of the scheduling system, this embodiment focuses on the following three types of dynamic events: 1. Workpiece cancellation class: including the case of part of the workpiece being suspended, reworked or cancelled due to raw material shortage, capacity quality problem or process abnormality; 2. Machine failure class: including the case of equipment failure, insufficient maintenance, personnel change, energy consumption limitation and other factors leading to equipment operation interruption or availability decline; 3. Order insertion class: referring to the temporary reception of emergency orders or high-priority tasks in the production process, which need to be dynamically inserted and adjusted on the basis of the current scheduling and resource allocation.

[0054] The scheduling method and system proposed in this embodiment can quickly identify and respond to the above dynamic events, realize process-level adjustment and optimal allocation of resources through local rescheduling and real-time machine selection mechanism, and ensure the continuous and stable operation of the workshop under disturbance conditions.

[0055] Dynamic event state simulation, simulating the new order reception situation under the condition of sufficient production capacity in real production, the case of non-ideal workpiece stopping production, the case of insufficient machine maintenance, and the case of machine failure. The arrival time of the above dynamic events changes with the machine load; To simulate the dynamic events in actual production process, the following settings are made: The occurrence time of all types of dynamic events is set as: (6); Wherein, and are constant coefficients between 0 and 1, and the values are , , is the maximum completion time.

[0056] Wherein, the machine failure setting: only one machine fails, the failure probability increases with the increase of machine load, and the maintenance time is related to the order completion time.

[0057] The machine failure probability when the machine failure event occurs is set as: (7); Wherein, is the running failure probability of machine , is the load time of machine , is the total machine load time.

[0058] The maintenance time range after machine failure is set as: (8); Wherein, is and is a constant factor between 0 and 1, and is , , is the maximum completion time.

[0059] Step S4: If a dynamic event occurs, go to step S5; if no dynamic event occurs, go to S8; Among them, the processing priority of the workpiece process in the order insertion class dynamic event is the highest, and the remaining processing processes of the canceled workpiece in the workpiece cancellation class dynamic event are all canceled.

[0060] Step S5: Detect the type of dynamic event and the time of event occurrence, extract the completed process, the processing process and the unstarted process; Step S6: Determine the to-be-scheduled process based on the influence of the dynamic event on the processing process and the unstarted process, retain the process processing order of the optimal initial scheduling scheme, update the optimal processing machine of the to-be-scheduled process, and generate a rescheduling scheme, which is specifically: Step S601: Determine the to-be-scheduled process based on the influence of the dynamic event on the processing process and the unstarted process, including: If the dynamic event is a machine failure class, stop the subsequent processing of the processing process, and take the processing process and the unstarted process as the to-be-scheduled process; If the dynamic event is a workpiece cancellation class, stop the processing process, and take the unstarted process as the to-be-scheduled process; If the dynamic event is an order insertion class, optimize the scheduling of the workpiece process of the inserted order, and take the workpiece process of the inserted order, the processing process and the unstarted process as the to-be-scheduled process.

[0061] In the scheduling process, not only machine failure will cause the process processing to be affected, but also workpiece cancellation, insertion and other dynamic events will affect the scheduling scheme, so the influence of the dynamic event here more accurately reflects the influence of various real-time changes in the scheduling process on the process processing.

[0062] The workshop event log can be obtained in real time, which is used to judge whether the rescheduling mechanism is triggered, and the scheduling priority is adjusted in dynamic rescheduling, wherein the priority of the inserted order is higher than that of the original order, and the remaining processing processes of the canceled workpiece are deleted.

[0063] The triggering conditions of the rescheduling mechanism include: machine failure or maintenance, workpiece cancellation or rework, emergency order insertion, and scheduling blocking due to resource conflict. When the original optimal initial scheduling scheme cannot be normally executed or affects the overall progress, the system will start the rescheduling mechanism to ensure the continuity and rapid response of the process level scheduling.

[0064] If a workpiece has a dynamic event such as machine failure during processing, the current processing machine cannot continue to complete the process, and the process is considered to be disturbed. At this time, the process cannot be successfully completed on the original processing machine, and the rescheduling mechanism needs to be triggered to reevaluate the available processing resources and select a new processing machine from the candidate machine set to complete the process, ensuring the continuity and executability of the overall production plan.

[0065] Step S602: Real-time detection of processing machine state, reservation of optimal initial scheduling scheme process processing order, priority balance of load and minimum completion time, update of optimal processing machine selection of the to-be-scheduled process, generation of rescheduling scheme, specific measures as shown below: Based on the current state, processing capacity and load of all machines, the available machine set is obtained, including: Based on the current state of all machines, idle machines are selected as the target of balancing machine load; When there are multiple idle machines, select the machine with strong processing capacity; When there are multiple machines with strong processing capacity, select the machine with large machine number as the available machine; Get the available machine set.

[0066] Based on the scheduling model, determine the optimal processing machine of the to-be-scheduled process from the available machine set, and generate the rescheduling scheme, including: As shown in Figure 3 , based on the dynamic machine selection rule of the to-be-scheduled process sequence, when the to-be-scheduled process is scheduled, the machine that minimizes the global completion time is selected from the available machine set , the optimal processing machine of the to-be-scheduled process is determined , that is: (9); (10); Wherein, is the theoretical completion time of the machine processing, is the maximum value of the machine number in the available machine set, , , , is the machine set in the available machine set that selects the machine with the minimum estimated completion time that can process the workpiece process, is the machine set in the available machine set that selects the machine with the shortest estimated processing time that can process the workpiece process.

[0067] As shown in Figure 3 ​As shown in the illustration, the selection process of dynamic machine selection rules is as follows: Step 1: First, determine the next optimal machine to be allocated, i.e. Figure 3 middle This is process 3 for workpiece 1; Step 2: The set of machines that can be used in this process is as follows: Machine 1, the processing time for this step is 2 seconds; Machine 2, the processing time for this step is 1 minute; Machine 3, the processing time for this step is 1 minute; Step 3: The estimated completion time for this process on machine 1 is 7 minutes. The estimated completion time for machine 2 to process this step is 7 minutes; The estimated completion time for machine 3 to process this step is 7 minutes; If we need to select the machine with the shortest estimated completion time, then... The set of machines remains the same: {Machine 1, Machine 2, Machine 3}; If you need to select the machine with the shortest estimated processing time, then... The set of machines is: {Machine 2, Machine 3}; Step 4: Final Selection The machine with the largest machine number is selected as the optimal processing machine, which is machine 3.

[0068] Based on the optimal processing machine for each scheduled process, determine the start and end times of each scheduled process and the optimal processing machine number, generate a rescheduling scheme, and output a visualization result in the form of a Gantt chart.

[0069] The selection of processing machines for the scheduled operations follows a dynamic machine selection rule. Idle machines are prioritized among available machines, followed by processing capacity, and finally, allocation is achieved through numbering and sorting to achieve optimal processing efficiency and load balancing. The rescheduling optimization strategy provided in this embodiment avoids algorithm re-optimization and offers a faster response. If an event occurs, the rescheduling strategy is selectively executed, preserving the original optimal initial scheduling scheme's process order and allowing for flexible machine changes.

[0070] Step S7: Reschedule production according to the rescheduling plan; Step S8: All production tasks are scheduled and the scheduling ends. The final scheduling result generated based on the rescheduling scheme includes the start and end times of each process, the processing machine number, and whether it is a rescheduling process, and outputs a visual result in the form of a Gantt chart.

[0071] The embodiment is based on a genetic algorithm framework, and fuses cooling buffer processing, ascending coding strategy, dynamic machine selection rule and disturbance event response mechanism, so that stable solution of a high-quality scheduling scheme in a dynamic flexible scheduling scene can be realized. It should be noted that although the optimization framework is based on GA, the core innovation point of the embodiment is the construction of the scheduling strategy and the optimization design of the process-machine allocation logic, rather than relying on the specific optimization algorithm itself.

[0072] The embodiment gives an intelligent flexible scheduling optimization method for a tire vulcanization workshop, aiming to improve production efficiency, reduce resource waste, and enhance the response capability to dynamic disturbances (such as insertion, cancellation, equipment failure, etc.). The method dynamically selects the optimal processing equipment based on the processing order of the workpiece and the machine state, taking into account the maximum processing capacity and minimum load imbalance of the machine, effectively improving equipment utilization and shortening the completion time. In view of the process requirements such as cooling time and continuous processing constraints in actual production, the invention introduces a time buffer mechanism to ensure the quality of the rubber and the continuity of the process. When dynamic events occur, a local rescheduling strategy is used to flexibly adjust the machine while maintaining the original process order, improving scheduling stability and system flexibility. The method can output visual scheduling results in the form of a Gantt chart, with the advantages of fast response, high adaptability and strong feasibility, and is suitable for dynamic production environments in modern tire manufacturing.

[0073] Simulation experiment The implementation case of the simulation specific factory is that the fault machine in the embodiment is machine 2, the fault duration is 29h to 47h, and the rework process is process 1 of workpiece 7; workpieces 11 to 15 are inserted as emergency orders; workpiece 9 is cancelled after the subsequent process.

[0074] As shown in Figure 4 and Figure 5 , the scheduling Gantt chart of the machine fault case extracted from the actual production data of the factory, Figure 4 is the original scheduling scheme Gantt chart, Figure 5 is the scheduling Gantt chart after the machine fault occurs. As shown in Figure 6 and Figure 7 , the scheduling Gantt chart of the workpiece insertion case extracted from the actual production data of the factory, Figure 6 is the original scheduling scheme Gantt chart, Figure 7 is the scheduling Gantt chart after the workpiece insertion occurs. As shown in Figure 8 and Figure 9 , the scheduling Gantt chart of a workpiece cancellation case extracted from the actual production data of the factory, Figure 8 is the original scheduling scheme Gantt chart, Figure 9 is the scheduling Gantt chart after the workpiece cancellation occurs.

[0075] The embodiment is directed to a dynamic production environment of a tire vulcanization workshop, and a scheduling response mechanism is constructed to cope with disturbances such as insertion, cancellation, and machine failure, so as to realize rapid rescheduling at the process level, improve scheduling stability and flexibility, and the like. Through dynamic machine selection rules, the embodiment combines processing capacity and current load, prioritizes idle high-capacity equipment, and introduces number discrimination to enhance feasibility and balance. A local rescheduling method that preserves the original process sequence is used to replace unprocessed process machines to reduce fluctuations. A cooling time buffer is embedded between workpiece processes to solve the problem of insufficient rubber cooling and ensure quality and process stability. A composite scheduling and dispatching model is used to minimize the maximum completion time, maximize machine capacity utilization, minimize load imbalance, and improve the overall quality of the solution. A visual Gantt chart output is provided to support real-time rescheduling feedback triggered by dynamic events, and to facilitate human-machine collaboration and real-time intervention.

[0076] Embodiment Two The embodiment provides a dynamic disturbance-oriented intelligent flexible scheduling system for a tire vulcanization workshop, which comprises: A scheduling and dispatching model establishment module is configured to establish a scheduling and dispatching model with the optimization objective of minimizing the maximum completion time and considering workpiece continuous processing buffer. An initial scheduling module is configured to obtain the initial processing sequence of each workpiece process and the set of selectable machines, and to solve the scheduling and dispatching model as the objective function to obtain an optimal initial scheduling scheme. A dynamic event detection module is configured to schedule the production based on the optimal initial scheduling scheme, detect the type and occurrence time of the dynamic event when a dynamic event occurs during production, and extract the completed process, the process being processed, and the process not started. A rescheduling module is configured to determine the to-be-scheduled process based on the influence of the dynamic event on the process being processed and the process not started, retain the process processing sequence of the optimal initial scheduling scheme, update the optimal processing machine of the to-be-scheduled process, and generate a rescheduling scheme.

[0077] The above modules and the examples and application scenarios realized by the corresponding steps are the same, but are not limited to the content disclosed in the above embodiment one. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer executable instructions.

[0078] The description of each embodiment in the above embodiments has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0079] The proposed system can be implemented in other ways. For example, the system embodiments described above are only illustrative, and for example, the division of the above modules is only a logical functional division, and in actual implementation, other division manners can be used, for example, a plurality of modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0080] Embodiment three The embodiment provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement steps in the intelligent flexible scheduling method for dynamic disturbance-oriented tire vulcanization plant as described in the above embodiment one.

[0081] Embodiment four The embodiment provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements steps in the intelligent flexible scheduling method for dynamic disturbance-oriented tire vulcanization plant as described in the above embodiment one when executing the program.

[0082] Embodiment five The embodiment provides a computer program product or a computer program, which includes computer instructions stored in a computer readable storage medium, and a processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to enable the computer device to execute steps in the intelligent flexible scheduling method for dynamic disturbance-oriented tire vulcanization plant as described in the above embodiment one.

[0083] Those skilled in the art should understand that the embodiments of the present application can provide a method, a system or a computer program product. Therefore, the present application can take the form of a hardware embodiment, a software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer usable program code.

[0084] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system) and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams and the combination of the flows and / or blocks can be implemented by computer program instructions. These computer program instructions can be provided to a general purpose computer, a special purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the computer or other programmable data processing device produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 One flow or multiple flows and / or blocks Figure 1apparatuses that carry out the specified functions in one or more of the blocks or a number of blocks.

[0085] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 one or more of the flows and / or blocks Figure 1 the functions specified in one or more of the blocks.

[0086] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 one or more of the flows and / or blocks Figure 1 the functions specified in one or more of the blocks.

[0087] Those of ordinary skill in the art can understand that all or part of the flow of the above-mentioned embodiment method can be completed by a computer program instructing relevant hardware, and the program can be stored in a computer readable storage medium. When the program is executed, it can include the flow of the above-mentioned embodiment method. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM), a random access memory (RAM), or the like.

[0088] Although the specific embodiments of the present application are described above with reference to the drawings, the description is not a limitation on the scope of protection of the present application. Those of ordinary skill in the art should understand that various modifications or changes made on the basis of the technical solutions of the present application without creative labor are still within the scope of protection of the present application.

Claims

1. A smart and flexible scheduling method for tire vulcanizing workshops under dynamic disturbances, characterized in that, include: Establish a production scheduling model with the optimization objective of minimizing the maximum completion time and considering the buffer of continuous workpiece processing; Obtain the initial processing sequence and selectable machine set for each workpiece process, and solve the optimal initial scheduling scheme by using the production scheduling model as the objective function; Production scheduling is based on the optimal initial scheduling scheme. When dynamic events occur during the production process, the type and occurrence time of the dynamic events are detected, and the completed processes, processes in progress, and processes not yet started are extracted. Based on the impact of dynamic events on ongoing processes and processes that have not yet started, processes to be scheduled are determined. The processing order of processes in the optimal initial scheduling scheme is retained, the optimal processing machine for processes to be scheduled is updated, and a rescheduling scheme is generated.

2. The intelligent flexible scheduling method for tire vulcanizing workshops oriented towards dynamic disturbances as described in claim 1, characterized in that, The constraints of the production scheduling model aimed at minimizing the maximum completion time include: The machine unique occupancy constraint means that each process can only be processed once by the processing machine; Machine capacity constraint, which means that each machine can only process one operation at a time; Workpiece processing sequence constraint, that is, the next process can only begin after the previous process is completed; Process time buffer constraint means that the same workpiece cannot be processed continuously in the same process, and there is a cooling buffer time between each process. Each process requires one treatment.

3. The intelligent flexible scheduling method for tire vulcanizing workshops oriented towards dynamic disturbances as described in claim 1, characterized in that, The process of obtaining the initial processing sequence and selectable machine set for each workpiece operation, and solving the optimal initial scheduling scheme using a production scheduling model as the objective function, includes: The initial processing sequence of each workpiece is obtained, and an ascending position mapping coding strategy is used to encode it to obtain the optimized processing sequence of each workpiece. Based on the optimized processing sequence of each workpiece and the set of available machines, the optimal initial scheduling scheme is obtained by solving the production scheduling model as the objective function.

4. The intelligent flexible scheduling method for tire vulcanizing workshops oriented towards dynamic disturbances as described in claim 1, characterized in that, The process of determining the scheduling process based on the impact of dynamic events on the ongoing processing process and the processes that have not yet started includes: If the dynamic event is a machine failure, then stop the subsequent processing of the process that is currently being processed, and use the process that is currently being processed and the process that has not yet started as the processes to be scheduled. If the dynamic event is a job cancellation type, then the current processing step is stopped, and the unstarted step is set as the scheduled step. If the dynamic event is an order insertion type, optimize the scheduling of the workpiece process for the inserted order, and use the workpiece process for the inserted order, the process in progress, and the process that has not yet started as the processes to be scheduled.

5. The intelligent flexible scheduling method for tire vulcanizing workshops oriented towards dynamic disturbances as described in claim 1, characterized in that, The process of updating the optimal processing machine for the scheduled process and generating a rescheduling scheme includes: Based on the current status, processing capacity, and load of all machines, a set of available machines is obtained; With the production scheduling model as the objective, the optimal processing machine for the scheduled process is determined from the set of available machines. Based on the optimal processing machine for the scheduled process, the start and end times of each scheduled process and the optimal processing machine number are determined, and a rescheduling scheme is generated.

6. The intelligent flexible scheduling method for tire vulcanizing workshops oriented towards dynamic disturbances as described in claim 5, characterized in that, Based on the current status, processing capacity, and load of all machines, the set of available machines is obtained, including: Based on the current status of all machines, idle machines are selected with the goal of balancing machine load. When multiple machines are available, choose the one with the highest processing capacity. When there are machines with strong processing capabilities and multiple options, select the machine with the larger machine number as the usable machine. Obtain the set of available machines.

7. An intelligent flexible scheduling system for tire vulcanizing workshops accommodating dynamic disturbances, characterized in that: include: The production scheduling model building module is configured to build a production scheduling model with the optimization objective of minimizing the maximum completion time and taking into account the buffer of continuous processing of workpieces; The initial scheduling module is configured to obtain the initial processing sequence and the set of available machines for each workpiece, and solve the optimal initial scheduling scheme by using the production scheduling model as the objective function. The dynamic event detection module is configured to schedule production based on the optimal initial scheduling scheme. When a dynamic event occurs during the production process, it detects the type of dynamic event and the time of the event, and extracts the completed processes, the processes being processed, and the processes that have not yet started. The rescheduling module is configured to determine the processes to be scheduled based on the impact of dynamic events on the processes currently being processed and the processes that have not yet started. It retains the process processing order of the optimal initial scheduling scheme, updates the optimal processing machine for the processes to be scheduled, and generates a rescheduling scheme.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the intelligent flexible scheduling method for tire vulcanizing workshops oriented towards dynamic disturbances as described in any one of claims 1-6.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the intelligent flexible scheduling method for tire vulcanizing workshops oriented towards dynamic disturbances as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps in the intelligent flexible scheduling method for tire vulcanizing workshops oriented towards dynamic disturbances as described in any one of claims 1-6.

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