Blocking flow shop scheduling method based on double-layer production-maintenance iterative optimization

Abstract

The application relates to a blocking flow shop scheduling method based on double-layer production-maintenance iterative optimization, which sequentially solves a multi-objective blocking flow shop scheduling model through a multi-type search and an enhanced search; the multi-type search and the enhanced search are production-maintenance iterative optimization modes based on a processing sequence and a variable neighborhood search of a processing fragment granularity, and the production-maintenance iterative optimization mode comprises a production scheduling optimization-maintenance activity following module and a maintenance plan optimization-production activity following module, and the following steps are repeatedly performed in the former: variable neighborhood search is performed through a local search operator to obtain a processing sequence, and a corresponding maintenance sequence is generated according to a machine minimum reliability principle; the following steps are repeatedly performed in the latter: a maintenance sequence is adjusted according to a forward adjustment condition, and a processing sequence is adjusted. Compared with the prior art, the application can effectively embed maintenance information into scheduling decisions, and efficiently obtain a scientific integrated scheduling scheme.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of workshop production scheduling, and particularly relates to a blocked flow shop scheduling method based on double-layer production-maintenance iterative optimization. BACKGROUND

[0002] Flow shop production is a widely used efficient production mode in manufacturing industry, which requires all jobs to pass through each machine in the same order. However, the particularity of the steel, chemical, and robot industries often leads to a lack of buffer between adjacent machines, and space limitations or technical requirements make it impossible for the jobs completed by the upstream machine to be directly transferred to the next step when the downstream machine is busy, resulting in the current process being blocked. The blocked flow shop scheduling problem thus generated has higher complexity than the traditional flow line scheduling problem.

[0003] In this complex production environment, the segmented local optimization approach often cannot meet the global demand, and the introduction of a comprehensive perspective has become a key means to solve complex production problems. Chinese invention patent CN113902174B discloses an improved sparrow search optimization method for the blocked flow shop scheduling problem, which introduces an elite solution set and a multi-group dynamic learning strategy in the search and update of the sparrow population position, which can improve the sufficiency of global search and balance the local mining and global exploration capabilities of the sparrow algorithm. However, in order to improve the comprehensive competitiveness, enterprises not only pursue to reduce production cost and improve efficiency, but also increasingly pay attention to production safety and effective management of resources. Under this background, the close association and collaborative optimization between production scheduling and equipment maintenance activities have gradually become the research focus of the blocked flow shop scheduling problem. Through joint optimization of production and maintenance, the resource utilization rate of equipment can be improved, unnecessary downtime can be reduced, and ultimately the operating cost can be reduced. However, the blocked flow shop scheduling problem considering maintenance activities is extremely complex in constraints, and although existing research has begun to explore the combination of production and maintenance, the exploration of collaborative optimization mode is still in the preliminary stage. The existing method is insufficient in multi-objective optimization based on collaborative optimization scheduling, and it is difficult to meet the dual requirements of improving efficiency and optimizing management in modern manufacturing industry. Therefore, it is necessary to design a blocked flow shop scheduling method to effectively embed maintenance information into scheduling decisions and efficiently obtain a scientific integrated scheduling scheme to provide an innovative solution for enterprises to improve productivity and increase added value. SUMMARY

[0004] The purpose of the present application is to overcome the defects of the prior art and provide a blocked flow shop scheduling method based on double-layer production-maintenance iterative optimization, which effectively embeds maintenance information into scheduling decisions and efficiently obtains a scientific integrated scheduling scheme.

[0005] The purpose of the present application can be achieved by the following technical solutions:

[0006] The application provides a blocked flow shop scheduling method based on double-layer production-maintenance iterative optimization, comprising the following steps:

[0007] obtaining equipment basic information and equipment state data, inputting a multi-objective blocked flow shop scheduling model, and solving to obtain an optimal production-maintenance integrated scheduling scheme to schedule the blocked flow shop;

[0008] The production-maintenance integrated scheduling scheme comprises a production sequence and a maintenance sequence, the equipment basic information comprises time consumption of each machine in processing different workpieces, and processing threshold and maintenance time of each machine; the multi-objective blocked flow shop scheduling model takes minimizing completion time and total cost as the target, and is sequentially solved through a multi-type search and an enhanced search; the multi-type search and the enhanced search are production-maintenance iterative optimization modes based on processing sequence granularity variable neighborhood search and processing segment granularity variable neighborhood search, respectively, the production-maintenance iterative optimization mode comprises a production scheduling optimization-maintenance activity following module and a maintenance plan optimization-production activity following module, in the production scheduling optimization-maintenance activity following module, the following steps are repeatedly performed multiple times: variable neighborhood search is performed through a local search operator to obtain a processing sequence, and a corresponding maintenance sequence is generated according to a machine minimum reliability principle; in the maintenance plan optimization-production activity following module, the following steps are repeatedly performed multiple times: the maintenance sequence generated by the production scheduling optimization-maintenance activity following module is adjusted according to a forward adjustment condition, and the processing sequence generated by the production scheduling optimization-maintenance activity following module is adjusted.

[0009] Further, the local search operator is any one of swap, insert and reverse.

[0010] Further, the machine minimum reliability principle is that, before processing a workpiece, cumulative processing time of a machine after processing a processing task of the workpiece is calculated, if the cumulative processing time exceeds a processing threshold of the machine, the machine needs to be maintained before processing the workpiece, and after the maintenance operation is completed, the machine continues to process.

[0011] Further, the specific process of adjusting the maintenance sequence generated by the production scheduling optimization-maintenance activity following module according to the forward adjustment condition is that, if moving forward a maintenance operation does not increase the total number of maintenance operations, and a previous idle time of a corresponding machine is sufficient to arrange the maintenance activity, the maintenance operation and all production and maintenance operations after the maintenance operation are moved forward as a whole.

[0012] Further, the specific process of solving the multi-objective blocked flow shop scheduling model to obtain the optimal production-maintenance integrated scheduling scheme is as follows:

[0013] S1, randomly generate a processing sequence, and set the iteration number to 1;

[0014] S2, perform variable neighborhood search on the complete processing sequence by using a local search operator to obtain a first processing sequence, and generate a corresponding first maintenance sequence according to the principle of minimum machine reliability;

[0015] S3, adjust the first maintenance sequence according to the forward adjustment condition, and further adjust the first processing sequence, and determine whether a first termination condition is met, if not, return to step S2, and if yes, proceed to step S4;

[0016] S4, obtain a plurality of processing segments according to the first maintenance sequence and the first processing sequence, perform variable neighborhood search on the processing segments by using a local search operator to obtain a second processing sequence, and generate a corresponding second maintenance sequence according to the principle of minimum machine reliability;

[0017] S5, adjust the second maintenance sequence according to the forward adjustment condition, and further adjust the second processing sequence, and determine whether a second termination condition is met, if not, return to step S4, and if yes, proceed to step S6;

[0018] S6, generate a production-maintenance integrated scheduling scheme based on the second processing sequence and the second maintenance sequence, and determine whether an iteration termination condition is met, if not, increase the iteration number by 1 and return to step S2; if yes, output the current production-maintenance integrated scheduling scheme, which is the optimal production-maintenance integrated scheduling scheme.

[0019] Further, the first termination condition and the second termination condition are both reaching a preset execution number, and the iteration termination condition is reaching a preset iteration number.

[0020] Further, the specific process of obtaining a plurality of processing segments according to the first maintenance sequence and the first processing sequence is as follows:

[0021] Execution wherein, represents a production-maintenance integrated scheduling scheme, and represents that the production-maintenance integrated scheduling scheme is based on the first maintenance sequence 0 is inserted into the corresponding position of the first processing sequence λ;

[0022] 0 is taken as a node, and the production-maintenance integrated scheduling scheme is divided into a plurality of processing segments.

[0023] Further, the constraint condition of the multi-objective blocking flow shop scheduling model includes:

[0024] The workpieces to be processed must pass through all machines in the same order;

[0025] The workpiece completing the current operation must stay on the current machine until the next machine is idle;

[0026] At most one workpiece can be processed on one machine at a time;

[0027] One workpiece can only be processed on one machine at a time and is not allowed to be interrupted;

[0028] Maintenance is considered for all machines to ensure a minimum reliability level, i.e., maintenance activities are performed on the corresponding machine before the cumulative processing time of the machine exceeds a threshold.

[0029] Further, the specific expressions of the constraint conditions of the multi-objective blocking flow shop scheduling model are as follows:

[0030]

[0031]

[0032] D k,1 ≥D (k-1),1 +p k,1 ,k=2,…,n

[0033]

[0034] Wherein, n and m respectively represent the number of workpieces and the number of machines, i and z are corresponding indexes; k represents the position of the workpiece in the processing sequence; X i,k is a binary variable, when the workpiece J i is at the kth position in the processing sequence, the value is 1, otherwise 0; y k,z is a binary variable, when the machine z is maintained after the kth workpiece is processed, the value is 1, otherwise 0; C max represents the completion time; S k,z and D k,z respectively represent the start and leave time of the kth workpiece on the machine z; r z represents the time required for the machine z to perform maintenance, p i,z represents the processing time of the workpiece i on the machine z; d z represents the processing threshold of the machine z, G k,z represents the cumulative processing time of the machine z after processing the kth workpiece, G k,z is reset after the machine is maintained; E k,z represents the end time of the maintenance activity after the kth workpiece is processed on the machine z.

[0035] Further, the total cost includes maintenance cost and machine operation cost.

[0036] Compared with the prior art, the present application has the following beneficial effects:

[0037] 1. The application proposes a blocking flow shop scheduling method based on double-layer production-maintenance iterative optimization, which establishes a multi-objective blocking flow shop scheduling model to minimize the completion time and total cost, and obtains the optimal production-maintenance integrated scheduling scheme; the model is solved by multi-type search and enhanced search in turn, and the multi-type search and enhanced search are production-maintenance iterative optimization modes based on processing sequence granularity variable neighborhood search and processing segment granularity variable neighborhood search, respectively; through variable neighborhood search of different granularity, the depth and breadth of search can be balanced, the problem of local optimal solution can be overcome, and a solution closer to the global optimal solution can be found; the production-maintenance iterative optimization mode includes a production scheduling optimization-maintenance activity following module and a maintenance plan optimization-production activity following module; in the production scheduling optimization-maintenance activity following module, the following steps are repeated multiple times: variable neighborhood search is performed by a local search operator to obtain a processing sequence, and a corresponding maintenance sequence is generated according to the principle of minimum machine reliability; the above process can effectively insert maintenance activities while optimizing the production scheduling scheme, and realize production scheduling scheme optimization and maintenance activity following; in the maintenance plan optimization-production activity following module, the following steps are repeated multiple times: the maintenance sequence generated by the production scheduling optimization-maintenance activity following module is adjusted according to the forward adjustment condition, and the processing sequence generated by the production scheduling optimization-maintenance activity following module is adjusted; the above process can further adjust the maintenance activities, and then adjust the production activities as a whole, so as to realize maintenance plan updating and production activity following; the above method can obtain high-quality scheduling solution in a short time, and can provide decision makers with scheduling schemes with different preferences, thereby improving the comprehensive competitiveness of enterprises.

[0038] 2. The application randomly generates a processing sequence at the beginning of solving the model, which helps to cover different areas of the solution space and maintain the diversity of the population. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 The flowchart of the method of the application;

[0040] Figure 2 The structural diagram of the production-maintenance iterative optimization mode;

[0041] Figure 3 The diagram for dividing processing sequence segments;

[0042] Figure 4 The Gantt chart corresponding to a group of integrated scheduling schemes in the example,

[0043] Processing time represents processing time, blocking time represents blocking time, maintenance time represents maintenance time, J and M represent workpieces and machines, respectively.

[0044] Figure 5 a comparison chart of the method of the present application and the prior art,

[0045] wherein (5a) corresponds to the convergence index, (5b) corresponds to the diversity index, and (5c) corresponds to the dominance index. DETAILED DESCRIPTION

[0046] The present application will be described in detail below with reference to the accompanying drawings and specific embodiments. The embodiments are implemented on the premise of the technical solutions of the present application, and detailed implementation modes and specific operation processes are given, but the protection scope of the present application is not limited to the following embodiments.

[0047] Embodiment:

[0048] This embodiment needs to solve the multi-objective blocking flow shop scheduling problem, and minimize the makespan and total cost as the target. A multi-objective blocking flow shop scheduling model is established, and the specific expressions of the model and its constraints are respectively as formula (1) and formula (2)-(12):

[0049] {C max ,TC}(1)

[0050]

[0051] D k,1 ≥D (k-1),1 +p k,1 ,k=2,…,n (8)

[0052]

[0053] wherein C max represents the makespan, TC represents the total cost (including maintenance cost and machine running cost). n and m respectively represent the number of workpieces and the number of machines, i and z respectively are corresponding indexes; k represents the position of the workpiece in the processing sequence; X i,k is a binary variable, when the workpiece J i is in the kth position of the processing sequence, its value is 1, otherwise it is 0; y k,z is a binary variable, when the machine z is maintained after the kth workpiece is processed, its value is 1, otherwise it is 0; C max represents the makespan; S k,z and D k,z respectively represent the start and departure time of the kth workpiece on the machine z; r z represents the time required for one maintenance of the machine z, p i,z represents the processing time of the workpiece i on the machine z; d z represents the processing threshold of the machine z, and G k,zGk represents the cumulative processing time of the kth workpiece on machine z, and is reset after maintenance of machine z; k,z E k,z Gk represents the end time of the maintenance activity after the kth workpiece is processed on machine z.

[0054] Equations (2) and (3) are used to ensure that each workpiece must be assigned into the processing sequence; equation (4) indicates that the completion time is greater than or equal to the time when each workpiece is completed; equation (5) indicates that the start time of each workpiece on the first machine must be greater than 0; equations (6) and (7) ensure that the kth workpiece cannot be started on machine z before the (k-1)th workpiece on machine z is completed and the maintenance activity of machine z is completed; equations (8) and (9) describe the time when the kth workpiece leaves the machine; equation (10) shows the completion time of each job; equation (11) ensures that the cumulative processing time of each machine does not exceed the corresponding threshold; and equation (12) provides that the maintenance activity needs to be performed after the current workpiece is completed.

[0055] The embodiment provides a blocking flow shop scheduling method based on double-layer production-maintenance iterative optimization, and the general idea is to take specific business optimization and another business follow-up response as a single business optimization approach, as shown in Figure 2 The optimization of the production and maintenance businesses is iteratively performed, and specifically includes the following steps:

[0056] 1) Production scheduling optimization-maintenance activity follow-up: access to device state data and device basic information to obtain the latest maintenance time of the device. The device maintenance requirement reflected by the device basic information is converted into a production scheduling constraint, so that the maintenance activity can be effectively inserted while optimizing the production scheduling scheme, and an integrated scheduling scheme is obtained.

[0057] 2) Maintenance plan optimization-production activity follow-up: under the premise of keeping the number of maintenance activities unchanged, the operation time of the maintenance activity is adjusted according to the downtime of the machine, and then the production scheduling is adjusted, so as to update the integrated scheduling scheme.

[0058] Meanwhile, on the basis of the production-maintenance iterative optimization mode formed by the combination of 1) and 2), variable neighborhood search of two different search granularities is introduced, and a multi-type search and an enhanced search are constructed, and then a double-layer optimization method is formed to effectively solve the problem to be solved in the embodiment.

[0059] The method of the embodiment includes the following steps, as shown in Figure 1

[0060] Step 1: Initialize the population. Randomly generate individuals with a length of n, which can be represented as λ=(λ1, λ2, …, λn), where λi represents the completion time of the ith workpiece. n i ​​Let (i = 1,..., n) denote the ith processed workpiece. The population size is PS. By this random generation method, it is helpful to cover different areas of the solution space and maintain the diversity of the population.

[0061] Decode in the order of individual workpieces, calculate the fitness value (i.e. the objective function value) of each individual. Calculate the cumulative processing time of each machine, and obtain the corresponding maintenance node according to the processing threshold. Since the maintenance node of each machine may be different, a two-dimensional sequence of maintenance plans will be obtained after decoding wherein represents that the machine z is maintained after processing the ith workpiece, and 0 otherwise. At the same time, let the iteration number gen = 1.

[0062] Step 2: Update all individuals by diversity search, and update according to the production-maintenance iterative optimization mode shown in Figure 2 until the preset execution number is reached.

[0063] Step 2.1: Randomly select one from the three local search operators swap, insert, and reverse, and perform local search on the complete processing sequence to obtain the first processing sequence. Then, convert the equipment maintenance requirement into a production scheduling constraint, and obtain the first maintenance sequence based on the machine minimum reliability principle, so as to realize the optimized production scheduling scheme and make the maintenance activities effectively follow. The machine minimum reliability principle is to calculate the cumulative processing time of the machine after completing the processing task of a workpiece before processing the workpiece, and if it exceeds the processing threshold of the machine, maintenance operation is required before processing the workpiece; after the maintenance operation is completed, the machine continues to process. The machine minimum reliability principle can ensure that the cumulative processing time of the machine does not exceed the processing threshold, and ensure that the workpiece processing process will not be interrupted.

[0064] Step 2.2: Adjust the first maintenance sequence while keeping the number of maintenance activities unchanged and the starting time of the current workpiece unchanged. According to the downtime of the machine, adjust the maintenance activities that meet the forward adjustment condition (the total number of maintenance activities does not increase after the activity is moved forward, and the idle time of the previous machine is sufficient to arrange the maintenance activities). Then, move all activities after the workpiece originally corresponding to the maintenance activity forward by the whole, and adjust the first processing sequence.

[0065] By rolling out steps 2.1 and 2.2, the maintenance plan is updated and the production activities are followed.

[0066] Step 3: Update all individuals by enhanced search, and update according to the production-maintenance iterative optimization mode shown in Figure 2 until the preset execution number is reached.

[0067] Step 3.1: First, divide the processing segments. As shown in Figure 3 the processing segments are determined by inserting the maintenance activities of each machine in a production sequence. The processing segments are executed wherein, represents the integrated sequence in which the workpieces are integrated with the maintenance activities, and represents the maintenance schedule of each machine 0 is inserted at the corresponding position of the processing sequence λ. Take for example, which contains four processing segments, [4, 8, 1, 9], [10, 5, 6], [3] and [7, 2] inside. Then steps 3.1 and 3.2 are repeatedly executed multiple times.

[0068] Step 3.2: Randomly select one from the three local search operators, swap, insert and reverse, and perform local search on the random processing segment to obtain a second processing sequence. Then, the maintenance requirements of the machines are converted into production scheduling constraints, and a maintenance plan is obtained based on the principle of minimum reliability of the machines to obtain a second maintenance sequence, so as to optimize the production scheduling scheme and enable the maintenance activities to effectively follow.

[0069] Step 3.3: Under the premise of keeping the number of maintenance activities unchanged and the starting time of the current workpiece unchanged, the maintenance activities that meet the conditions are adjusted forward according to the downtime of the machine, the second maintenance sequence is adjusted, and then the production activities are adjusted as a whole, and the second processing sequence is adjusted.

[0070] Steps 3.1 and 3.2 are executed by rolling, so as to realize maintenance plan updating and production activity following.

[0071] Step 4: Obtain the current non-dominated solution set and update the global Pareto archive set. Determine whether the termination condition (for example, the preset number of iterations is met) is met, if the termination condition is met, output the global Pareto archive set as the final result; otherwise, gen = gen + 1, and return to step 2.

[0072] To verify the effectiveness of the above method, this embodiment verifies an example with 20 workpieces (J1, J2, …, J 20 ), 5 machines (M1, M2, …, M5). The cumulative processing time threshold of each machine is 300, the maintenance activity lasts for 18 each time, and the specific processing time table of each workpiece is shown in Table 1:

[0073] Table 1 Basic information of equipment in the example

[0074]

[0075] Figure 4The Gantt chart corresponding to the set of integrated scheduling schemes of this example. The above method is compared with the classic and excellent multi-objective optimization algorithm NSGA2 and SPEA2, and is evaluated from three angles of convergence, diversity and dominance, wherein the smaller the convergence and diversity index values are, the better, and the larger the dominance index value is, the better.

[0076] For the above example, the three algorithms are independently run for 20 times in turn at the running time of 2.5 seconds, 5 seconds and 10 seconds. The interval graph of the results of each algorithm is shown in Figure 5 , wherein (5a) corresponds to the convergence index, (5b) corresponds to the diversity index, and (5c) corresponds to the dominance index. Obviously, the method proposed in this embodiment can more effectively solve the blocking flow shop scheduling problem considering maintenance.

[0077] When the above method is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the present application or the part of the prior art that essentially contributes or the part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0078] The above description of the embodiments is for the convenience of the ordinary skilled person in the art to understand and use the application. Those skilled in the art can easily make various modifications to these embodiments, and apply the general principles described herein to other embodiments without having to go through creative labor. Therefore, the present application is not limited to the above embodiments, and those skilled in the art can make improvements and modifications to the present application without departing from the scope of the present application.

Claims

1. A blocking flow shop scheduling method based on double-layer production-maintenance iterative optimization, characterized in that, The method comprises the following steps: obtaining equipment basic information and equipment state data, inputting a multi-objective blocked flow shop scheduling model, and solving an optimal production-maintenance integrated scheduling scheme to schedule the blocked flow shop; The production-maintenance integrated scheduling scheme comprises a production sequence and a maintenance sequence, the equipment basic information comprises time consumed by each machine in processing different workpieces, and processing thresholds and maintenance time of each machine; the multi-objective blocked flow shop scheduling model aims to minimize completion time and total cost, and is solved by multi-type search and enhanced search in sequence; the multi-type search and the enhanced search are production-maintenance iterative optimization modes based on processing sequence granularity variable neighborhood search and processing segment granularity variable neighborhood search, respectively; the production-maintenance iterative optimization mode comprises a production scheduling optimization-maintenance activity following module and a maintenance plan optimization-production activity following module; in the production scheduling optimization-maintenance activity following module, the following steps are repeated multiple times: variable neighborhood search is performed by using a local search operator to obtain a processing sequence, and a corresponding maintenance sequence is generated according to a machine minimum reliability principle; in the maintenance plan optimization-production activity following module, the following steps are repeated multiple times: the maintenance sequence generated by the production scheduling optimization-maintenance activity following module is adjusted according to a forward adjustment condition, and the processing sequence generated by the production scheduling optimization-maintenance activity following module is adjusted; The specific process of solving the multi-objective blocked flow shop scheduling model to obtain the optimal production-maintenance integrated scheduling scheme is as follows: S1, a processing sequence is randomly generated, and the iteration number is set to 1; S2, variable neighborhood search is performed on the complete processing sequence by using a local search operator to obtain a first processing sequence, and a corresponding first maintenance sequence is generated according to a machine minimum reliability principle; S3, the first maintenance sequence is adjusted according to a forward adjustment condition, and then the first processing sequence is adjusted, and it is judged whether a first termination condition is met; if not, the step S2 is returned; if yes, the step S4 is entered; S4, a plurality of processing segments are obtained according to the first maintenance sequence and the first processing sequence, variable neighborhood search is performed on the processing segments by using a local search operator to obtain a second processing sequence, and a corresponding second maintenance sequence is generated according to a machine minimum reliability principle; S5, the second maintenance sequence is adjusted according to a forward adjustment condition, and then the second processing sequence is adjusted, and it is judged whether a second termination condition is met; if not, the step S4 is returned; if yes, the step S6 is entered; S6, a production-maintenance integrated scheduling scheme is generated based on the second processing sequence and the second maintenance sequence, and it is judged whether an iteration termination condition is met; if not, the iteration number is increased by 1 and the step S2 is returned; if yes, the current production-maintenance integrated scheduling scheme is output, which is the optimal production-maintenance integrated scheduling scheme.

2. The method of claim 1, wherein, The local search operator is any one of swap, insert and reverse.

3. The method of claim 1, wherein, The minimum reliability principle of the machine is that before processing a workpiece, the cumulative processing time of the machine after completing the processing task of the workpiece is calculated, and if the cumulative processing time exceeds the processing threshold of the machine, a maintenance operation needs to be performed on the machine before processing the workpiece; after the maintenance operation is completed, the machine continues to process.

4. The method of claim 1, wherein, The specific process of adjusting the maintenance sequence generated by the production scheduling optimization-maintenance activity following module according to the forward adjustment condition is that if moving forward a maintenance operation does not increase the total number of maintenance operations, and the previous idle time of the corresponding machine is sufficient to arrange the maintenance activity, the maintenance operation and all production and maintenance operations after the maintenance operation are moved forward as a whole.

5. The method of claim 1, wherein, The first termination condition and the second termination condition are both reaching a preset execution number, and the iteration termination condition is reaching a preset iteration number.

6. The method of claim 1, wherein, The specific process of obtaining a plurality of processing segments according to the first maintenance sequence and the first processing sequence is as follows: performing wherein, denotes the production-maintenance integrated scheduling scheme, denotes the first maintenance sequence inserting 0 at the corresponding position of the first processing sequence of the first processing sequence. With 0 as the node, the production-maintenance integrated scheduling scheme is divided into multiple processing segments.

7. The method for scheduling a blocked flow shop based on two-layer production-maintenance iterative optimization according to claim 1, characterized in that: The production scheduling constraints include: The workpieces to be processed must be processed through all the machines in the same order; The workpiece completing the current operation must be left on the current machine until the next machine is idle; At most one workpiece can be processed by one machine at a time; One workpiece can be processed on one machine at a time, and interruption is not allowed; Maintenance of all machines is considered to ensure the minimum reliability level, that is, maintenance activities are performed on the corresponding machine before the cumulative processing time of each machine exceeds the threshold.

8. The method of claim 1, wherein, The specific expression of the production scheduling constraint is as follows: in, and Represent the number of workpieces and machines respectively, and are the corresponding indexes respectively; Indicates the position of the workpiece in the processing sequence; is a binary variable, when the workpiece In the processing sequence When the position is 0, its value is 1, otherwise it is 0. is a binary variable, when the machine In the When a workpiece is processed and maintained, its value is 1, otherwise it is 0; Indicates completion time; and Respectively represent Workpieces in the machine Start and leave time on the Indicates the machine The time required to perform a maintenance Represents workpiece In the machine Processing time on Indicates the machine The processing threshold, Indicates the machine Finished The cumulative processing time of each workpiece is Perform a reset; Indicates the machine Processing The end time of maintenance activities after the artifact is completed.

9. The method of claim 1, wherein, The total cost includes maintenance cost and machine operation cost.

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