CN114995318A - Flexible workshop dynamic batch scheduling method aiming at quality inspection disturbance - Google Patents
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Abstract
The invention discloses a flexible workshop dynamic batch scheduling method aiming at quality inspection disturbance, which comprises the following steps: firstly, aiming at the problem of production operation disturbance caused by quality problems in workshop production, establishing a flexible job workshop dynamic batch scheduling model taking the minimum workshop production cost, the minimum total machine tool energy consumption cost and the minimum difference cost of a dynamic and static scheduling scheme as optimization targets; then, aiming at the problem of production operation disturbance caused by quality inspection disturbance, a rescheduling strategy is designed, aiming at the problem that process routes of the same type of workpieces in different batches are inconsistent due to quality inspection disturbance, the rescheduling workpieces are recombined and batched, and a double-layer search frame is adopted to improve the Husky algorithm for solving. The method can effectively cope with the quality inspection disturbance of the workshop, simultaneously shorten the completion time, reduce the energy consumption of the workshop, ensure the robustness of the static scheduling scheme and bring better economic benefit for enterprises.
Description
Technical Field
The invention belongs to a flexible job shop dynamic batch scheduling neighborhood, and particularly relates to a flexible shop dynamic batch scheduling method aiming at quality inspection disturbance.
Background
Various dynamic emergencies exist in the actual production process of a workshop, and can interfere with a static production scheduling scheme before starting operation, so that an operation plan is disconnected from the actual production, and the dynamic scheduling has great research significance. Meanwhile, energy conservation and emission reduction become strategic requirements of manufacturing industries of various countries, and manufacturing enterprises at home and abroad must consider energy consumption factors while pursuing economic benefits, so that research aiming at the workshop scheduling problem is not limited to indexes reflecting production benefits such as completion time, delivery period and machine utilization rate, and more researches at home and abroad take the energy consumption factors into consideration, and energy conservation and emission reduction and green manufacturing become important research contents at present and in future.
Disclosure of Invention
Aiming at the problem of quality inspection disturbance interference in the actual production process of a workshop, the invention provides a flexible workshop dynamic batch scheduling method aiming at quality inspection disturbance.
The invention relates to a flexible workshop dynamic batch scheduling method aiming at quality inspection disturbance, which comprises the following steps:
step 1: a description of a flexible job shop dynamic batch scheduling problem is determined.
Is provided with M machine tools M ═ M k I k 1,2, …, m, n-type workpiece J { J } i 1,2, …, n, and the number of i-th workpieces is R i And each type of workpiece has O i ＝{O il |l＝1,2,…,w i The method comprises M procedures, wherein the machine tool capable of processing each procedure comprises M il ，M il The processing time of each process changes along with the difference of the machine tool performance; each type of work being divided into a plurality of sub-batches F i ＝{F is |s＝1,2,…,u i Processing on different machines, treating each sub-batch as a whole and occupying the same auxiliary time.
Step 2: and establishing a mathematical model for optimizing the dynamic batch scheduling target of the flexible job shop.
The objective functions are workshop production cost, machine tool energy consumption cost and dynamic and static scheduling scheme difference cost, and the three objective function values are processed in a weighted normalization mode.
An objective function:
min{ω 1 ·f 1 +ω 2 ·f 2 +ω 3 ·f 3 } (4)
constraint conditions are as follows:
T＝max(Z il ·E islk ) (6)
if S islk ＜T,DS islk -S islk ＝0 (7)
wherein, C is Indicating the completion time, t, of the ith lot of the i-th workpiece islk In the machine M, the first step of the s-th lot representing the i-th workpiece k One-piece man-hour of ilk Showing the i-th workpiece in the machine tool M k Auxiliary time of (S) islk The first process of the s batch showing the i-th workpieces is performed in the machine M k Starting time of (1), E islk In the machine M, the first step of the s-th lot representing the i-th workpiece k Time of completion of working of (d), γ ilk Represents the machine selection decision variable when process O il Can be on machine M k Gamma ray at working ilk 1, otherwise γ ilk ＝0，
Represents the auxiliary time decision variable when in the machine M k The class of the workpiece to be machined is the same as the class of the last workpiece to be machined by the machine tool,
otherwise, the reverse is carried out
Q k Representing the maximum completion time, W, of the machine k k Indicating the load, PC, of machine k k Indicating machine M k Average energy consumption cost per unit time under load, NC k Indicating machine M k Average energy consumption cost in unit time, RC, when no load k Indicating machine M k Average energy consumption cost per unit time during preparation, eta represents completion cost per unit time in a workshop, omega 1 Represents the completion cost weight coefficient, ω 2 Representing the energy consumption cost weight coefficient, ω 3 Representing the differential cost weight coefficient, DS, of a dynamic and static scheduling scheme islk Lot s of work in machine M representing class i work in dynamic scheduling scheme k Upper start of processing time, DW islk Lot s of work in machine M representing class i work in dynamic scheduling scheme k Time of completion of working of (D) islk Representing procedure O in a dynamic scheduling scheme il Machine M still in a static scheduling scheme k During machining, D islk Not all right 0, otherwise D islk ＝1，S islk Lot s of work in machine M representing class i workpieces in a static scheduling scheme k Upper start-up time, ω 3 Representing a difference cost weight coefficient, V representing the difference unit cost of the dynamic and static scheduling scheme, T representing a disturbance time node, DF is Representing the s-th sub-batch, SF, of the ith class of workpieces in a dynamic scheduling scheme is Representing the s-th sub-batch of the i-th workpiece after the disturbance time node in the static scheduling scheme, R representing the number of newly added workpieces caused by disturbance, Z il Represents a step O il Is a perturbation process, D il 1, otherwise D il ＝0。
Wherein, the formula (5) represents that the processing completion time of one process of a batch of workpieces in the dynamic scheduling scheme is more than or equal to the sum of the processing start time of the process, the auxiliary time and the working hours of all the workpieces in the batch; equation (6) shows that the disturbance time node is the ith batch of the ith workpiece in the first process in the machine M k The machining completion time of (1); the formula (7) shows that the processes before the disturbance time node and at the beginning of the processing do not participate in the dynamic scheduling; equation (8) represents the sum of the number of unprocessed workpieces and the number of newly added workpieces caused by disturbance when the total batch number of workpieces in the dynamic scheduling scheme is equal to the disturbance time node.
And step 3: rescheduling for quality inspection disturbances.
The quality inspection disturbance processing flow is to execute procedures according to a static scheduling scheme, and after a key procedure needing quality inspection is executed, the quality inspection result of the workpieces in the current processing batch is judged: 1) continuing to execute subsequent processes for the workpiece with qualified quality inspection result;
2) the quality inspection result is a repaired workpiece, secondary processing needs to be carried out on the current key working procedure, and the secondary processing is added to the unprocessed workpiece set; 3) the quality inspection result is a scrapped workpiece, the machining is required to be restarted from the first procedure, and the scrapped workpiece is added to the unmachined workpiece set; after all the workpiece information is obtained, the workpieces in the processed workpiece set are not processed, and the workpieces in the unprocessed workpiece set need to be rescheduled.
And 4, step 4: a recombined batch aiming at quality inspection disturbance;
when rescheduling is carried out, various workpieces need to be batched to generate a new processing task so as to facilitate rescheduling; the specific process of the recombination batch is shown in FIG. 3, and the process is as follows: after the quality inspection disturbance occurs, the to-be-processed processes of all the workpieces are different, but the processing can be arranged at the rescheduling time, so that the workpieces with the same process route need to be re-batched, namely, the workpieces with the same to-be-processed processes under all the workpieces are combined into a new batch of workpieces, and a new scheduling problem is formed.
And 5: optimizing and solving by improving a gray wolf algorithm;
and designing coding and decoding aiming at the recombined batch workpieces subjected to dynamic disturbance. The method is characterized in that the coding framework consists of three layers, namely a batch layer, a process layer and a machine layer, wherein the batch layer codes represent workpiece batch information, the process layer codes represent workpieces and processes, the times of different digital codes represent different batches, and the machine layer represents a processing machine of each process; the decoding mode is divided into two parts, firstly, the batch layer is decoded to obtain a specific batch dividing strategy so as to obtain the specific batch number of each batch of workpieces, then, the process layer and the machine layer are decoded, the target value of the scheduling scheme is calculated, and when the process layer is decoded, the workpieces after batch recombination need to be calculated according to the original workpiece information;
the improved grey wolf algorithm is designed and mainly divided into two layers, wherein the outer layer search mainly solves the batch scheme, the inner layer search solves the scheduling scheme, and when the grey wolf algorithm is solved, bernoulli chaotic mapping is adopted for population initialization, so that population distribution is more uniform, the convergence rate and the optimal solution quality of the algorithm are improved, and when control parameters are updated, nonlinear control parameters are adopted, so that the algorithm has strong global search capability in the early period and high convergence rate of local optimization in the later period. The beneficial technical effects of the invention are as follows:
(1) the quality inspection disturbance rescheduling strategy is designed, a rescheduling scheme can be given in time when disturbance occurs, the interference of workshop quality inspection disturbance on a static scheduling scheme is solved, and meanwhile, the obtained rescheduling scheme can effectively reduce the energy consumption of a machine tool and maintain the stability of the static scheduling scheme while shortening the completion time. In the prior art, when dynamic interference is processed, a periodic rescheduling mode is usually adopted, response cannot be timely performed, and an energy consumption index is not considered.
(2) The invention designs a batch recombination technology, and effectively solves the problem of rescheduling of the same workpiece with different residual process routes caused by disturbance. In the prior art, when the rescheduling problem is solved, the workpiece is not considered to be recombined, so that the workpiece scale is increased, and an optimal solution is difficult to find. Compared with the prior art, the method improves population initialization and parameter control of the wolf algorithm, and greatly improves the solving efficiency of the algorithm.
Drawings
FIG. 1 is a flow chart of a quality control disturbance rescheduling strategy;
FIG. 2 is a flow chart of an improved Grey wolf algorithm;
FIG. 3 is a schematic diagram of a reconstitution batch strategy;
FIG. 4 is a diagram illustrating an encoding method;
FIG. 5 is a chart of scheduling Gantt for a quality inspection perturbed static scheme;
fig. 6 is a scheduling gantt chart for a quality inspection perturbation rescheduling scheme.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments.
The invention relates to a flexible workshop dynamic batch scheduling method aiming at quality inspection disturbance, which comprises the following steps:
step 1: a description of a flexible job shop dynamic batch scheduling problem is determined.
The flexible job shop dynamic batch scheduling problem is a scheduling problem for rescheduling aiming at dynamic disturbance in the workshop production process, and is specifically described as follows:
is provided with M machine tools M ═ M k I k 1,2, …, m, n-type workpiece J { J } i 1,2, …, n, and the number of i-th workpieces is R i And each type of workpiece has O i ＝{O il |l＝1,2,…,w i The method comprises M procedures, wherein the machine tool capable of processing each procedure comprises M il ，M il The processing time of each process changes along with the difference of the machine tool performance; each type of work being divided into a plurality of sub-batches F i ＝{F is |s＝1,2,…,u i Processing on different machines, treating each sub-batch as a whole and occupying the same auxiliary time. The problem is that the static scheduling scheme is adopted, disturbance can occur in the execution process of the static scheduling scheme, the process started before the disturbance time node is divided into a finished workpiece and a machined workpiece, rescheduling is not needed, and the process started after the disturbance time node is needed to be dynamically scheduled. When dynamic scheduling is carried out, the available time of all machine tools is changed from the original zero moment to a disturbance time node orThe original machining end time of the workpiece being machined.
Step 2: and establishing a mathematical model for optimizing the dynamic batch scheduling target of the flexible job shop.
The objective functions are workshop production cost, machine tool energy consumption cost and dynamic and static scheduling scheme difference cost, and the three objective function values are processed in a weighted normalization mode.
An objective function:
min{ω 1 ·f 1 +ω 2 ·f 2 +ω 3 ·f 3 } (4)
constraint conditions are as follows:
T＝max(Z il ·E islk ) (6)
if S islk ＜T,DS islk -S islk ＝0 (7)
wherein, C is Indicating the completion time, t, of the ith lot of the i-th workpiece islk The first process of the s batch showing the i-th workpieces is performed in the machine M k One-piece man-hour of ilk I-channel tool for indicating i-th workpieceMachine tool M k Auxiliary time of (S) islk The first process of the s batch showing the i-th workpieces is performed in the machine M k Starting time of (1), E islk In the machine M, the first step of the s-th lot representing the i-th workpiece k Time of completion of working of (d), γ ilk Indicating machine selection decision variables, when Process O il Can be on machine M k Gamma-ray during machining ilk 1, otherwise γ ilk ＝0，
Represents the auxiliary time decision variable when in the machine M k The class of the workpiece processed at the upper stage is the same as the class of the workpiece processed at the upper stage by the machine tool,
otherwise, the reverse is carried out
Q k Represents the maximum completion time, W, of the machine k k Indicating the load, PC, of machine k k Indicating machine M k Average energy consumption cost per unit time under load, NC k Indicating machine M k Average energy consumption cost in unit time, RC, when no load k Indicating machine M k Average energy consumption cost per unit time during preparation, eta represents completion cost per unit time in a workshop, omega 1 Represents the completion cost weight coefficient, ω 2 Representing the energy consumption cost weight coefficient, ω 3 Representing the differential cost weight coefficient, DS, of a dynamic and static scheduling scheme islk Lot s of work in machine M representing class i work in dynamic scheduling scheme k Upper start of processing time, DW islk Lot s of work in machine M representing class i work in dynamic scheduling scheme k Time of completion of working of (D) islk Representing procedure O in a dynamic scheduling scheme il Machine M still in a static scheduling scheme k During machining, D islk Not all right 0, otherwise D islk ＝1，S islk Lot s of work in machine M representing class i workpieces in a static scheduling scheme k Upper start-up time, ω 3 Representing a difference cost weight coefficient, V representing the difference unit cost of the dynamic and static scheduling scheme, T representing a disturbance time node, DF is Representing the s-th sub-batch, SF, of the ith class of workpieces in a dynamic scheduling scheme is Representing the s-th sub-batch of the i-th workpiece after the disturbance time node in the static scheduling scheme, R representing the number of newly added workpieces caused by disturbance, Z il Represents a step O il Is a perturbation process, D il 1, otherwise D il ＝0。
Wherein, the formula (5) represents that the processing completion time of one process of a batch of workpieces in the dynamic scheduling scheme is more than or equal to the sum of the processing start time of the process, the auxiliary time and the working hours of all the workpieces in the batch; equation (6) shows that the disturbance time node is the ith batch of the ith workpiece in the first process in the machine M k The machining completion time of (1); the formula (7) shows that the processes before the disturbance time node and at the beginning of the processing do not participate in the dynamic scheduling; and (8) the total batch number of the workpieces in the dynamic scheduling scheme is equal to the sum of the number of unprocessed workpieces and the number of newly added workpieces caused by disturbance when the disturbance time node.
And step 3: rescheduling for quality inspection disturbances.
In the current research of the workshop dynamic scheduling problem, documents considering the influence of quality inspection disturbance on the execution of a static scheduling scheme are few, while in the research of the flexible job workshop batch dynamic scheduling problem, no documents exist yet, but the quality inspection disturbance is important production disturbance existing in the actual production, has important influence on the workshop scheduling and has great research significance. Meanwhile, the rescheduling scheme generated according to quality inspection disturbance also involves the problem of energy consumption, and further research is needed. Aiming at the problem of workpiece quality, the existing literature usually estimates the rejection rate of workpieces, increases the scheduling quantity of the workpieces during static scheduling, improves the tolerance of a static scheduling scheme to the problem of workpiece quality, and reduces the interference of quality inspection disturbance. However, the processing method has major disadvantages that firstly, the estimated rejection rate is not always accurate, the excessive estimation leads to excessive productivity and resource waste, and the insufficient estimation leads to insufficient productivity, so that the process cannot be completed on time; and secondly, the quality inspection process may generate a repaired workpiece, if the workpiece is directly scrapped, resources are wasted, and if the repair is arranged, the workpiece needs to be rescheduled, so that the processing mode can cause the scheduling scheme to be disjointed from the actual production and cannot continuously guide the actual production. Therefore, a rescheduling strategy is provided for the quality inspection disturbance method. The specific flow chart is shown in the attached figure 1.
The quality inspection disturbance processing flow is to execute procedures according to a static scheduling scheme, and after a key procedure needing quality inspection is executed, the quality inspection result of the workpieces in the current processing batch is judged: 1) continuing to execute subsequent processes for the workpiece with qualified quality inspection result; 2) the quality inspection result is a repaired workpiece, secondary processing needs to be carried out on the current key working procedure, and the secondary processing is added to the unprocessed workpiece set; 3) the quality inspection result is a scrapped workpiece, the workpiece needs to be machined from the first procedure again, and the workpiece is added to the unmachined workpiece set; after all the workpiece information is obtained, the workpieces in the processed workpiece set are not processed, and the workpieces in the unprocessed workpiece set need to be rescheduled.
And 4, step 4: a recombined batch aiming at quality inspection disturbance;
after a quality inspection disturbance event occurs, due to the fact that the processing process is carried out, the residual processing procedures of the same type of workpieces in different batches are different, and meanwhile, quality inspection disturbance can also be caused to be workpieces with unqualified quality inspection, so that during rescheduling, various workpieces need to be batched to generate a new processing task, and rescheduling is facilitated. The quality inspection disturbed reorganization batch of the invention is shown in fig. 3, which specifically comprises: after the quality inspection disturbance occurs, the to-be-processed procedures of all the workpieces are different, and the workpieces with the same process route are recombined and batched, namely, the workpieces with the same to-be-processed procedures under all the workpieces are combined into a new batch of workpieces to form a new scheduling problem.
And 5: improving the optimization solution of the wolf algorithm by a double-layer search frame;
the invention designs a coding and decoding mode aiming at a recombined batch workpiece after dynamic disturbance, a coding framework consists of three layers, namely a batch layer, a process layer and a machine layer, the process layer is coded into the workpiece and the process, the times of the occurrence of different digital codes represent different batches, as shown in figure 4, a grey number 23 in the figure is taken as an example, 23 represents a third process of the workpiece 2, and the number is firstly appeared in the process layer, namely represents a first batch.
Secondly, because the similar workpieces after the batch regrouping are formed into different batches for processing, and the workpieces of each batch are regrouped again during the rescheduling, redundancy of a coding mode can be caused, so that the similar workpieces of different batches after the batch regrouping are endowed with new workpiece numbers, but the original workpiece information is still used for calculation during decoding.
The decoding mode is divided into two parts, firstly, the batch layer is decoded to obtain a specific batch dividing strategy so as to obtain the specific batch number of each batch of workpieces, then, the process layer and the machine layer are decoded, the target value of the scheduling scheme is calculated, and when the process layer is decoded, the workpieces after batch recombination need to be calculated according to the original workpiece information.
In order to reschedule the workpieces after being recombined and batched, the improved grayish wolf algorithm is mainly divided into two layers, wherein the outer layer search mainly solves the batch scheme, and the inner layer search solves the scheduling scheme, and the solving process shown in fig. 2 is as follows: when the wolf algorithm is used for solving, the bernoulli chaotic mapping is used for population initialization, so that population distribution is more uniform, the convergence rate and the optimal solution quality of the algorithm are improved, and when control parameters are updated, nonlinear control parameters are used, so that the algorithm is strong in global search capability in the early stage and high in convergence rate of local optimization in the later stage.
The embodiment is as follows:
in order to apply theoretical research to actual production management and control of a workshop, the scheduling status and scheduling requirements of a flexible job workshop of an aeronautical structure factory are analyzed, according to the problems of the existing scheduling mode and processing flow of the workshop, a static scheduling scheme is designed by using the flexible job workshop static batch scheduling model considering energy consumption, and meanwhile, a satisfactory workshop dynamic scheduling scheme is obtained by using a policy of inserting orders and a policy of quality inspection and disturbance according to a workshop dynamic disturbance event and the flexible job workshop dynamic batch scheduling model considering energy consumption.
The workpieces needing to be machined in the workshop are 18 types, each type of workpiece needs to be machined by 10 workpieces in one production cycle, two types of workpieces are only subjected to two working procedures, the rest workpieces are subjected to four working procedures, the machining time of the various workpieces on a machine tool is different along with the performance difference of the machine tool, and specific machining information is shown in table 1.
Table 1 workpiece processing information table
When the disturbance time T of the workshop is 3325, the 4 th batch of workpieces 15 needs to be subjected to quality inspection in process 1, the batch has two workpieces, the quality inspection result is that 1 workpiece is qualified, 1 workpiece is repaired, the results obtained by each algorithm under the working condition are shown in table 2, and the results of the original dynamic scheduling strategy of the workshop are shown in table 2.
TABLE 2 engineering example quality control disturbance experiment results
Therefore, the solving result of the algorithm provided by the invention is respectively reduced by 1.5%, 0.6% and 3.8% in the comprehensive cost, the completion time cost and the energy consumption cost compared with the conventional workshop scheduling strategy, and compared with the algorithm compared with the invention, the scheduling scheme provided by the invention is also better, the effectiveness and feasibility of the algorithm are verified, meanwhile, the conventional workshop scheduling strategy is applied to the optimized static scheduling scheme for comparison, and if the conventional workshop scheduling strategy is directly compared with the original static scheduling scheme of a workshop, the comprehensive cost, the completion time cost and the energy consumption cost are respectively improved by 27.3%, 34.8% and 24.5%. Fig. 5 is a gantt chart of a static scheduling scheme for quality inspection disturbances, and fig. 6 is a dynamic scheduling scheme in which workpiece 1 is actually workpiece 2, workpiece 19 is actually workpiece 10, workpiece 20 is actually workpiece 12, workpiece 21 is actually workpiece 15, workpiece 22 is actually workpiece 17, workpiece 23 is actually workpiece 18, workpiece 24 is actually workpiece 15, and workpiece 25 is actually workpiece 14.
Aiming at the influence of quality inspection disturbance on a static scheduling scheme in the workshop production process, the invention establishes a flexible job workshop dynamic batch scheduling problem model considering energy consumption by taking the minimum production cost of the workshop, the minimum total energy consumption cost of a machine tool and the minimum difference cost of a dynamic and static scheduling scheme as optimization targets, designs a specific rescheduling strategy aiming at the quality inspection disturbance, performs batch regrouping on a rescheduling workpiece, and adopts an improved Husky algorithm to perform optimization solution after the batch regrouping is completed.
Claims (1)
1. A flexible workshop dynamic batch scheduling method aiming at quality inspection disturbance is characterized by comprising the following steps:
step 1: determining the description of the flexible job shop dynamic batch scheduling problem;
is provided with M machine tools M ═ M k I k 1,2, …, m, n-type workpiece J { J } i 1,2, …, n, and the number of i-th workpieces is R i And each type of workpiece has O i ＝{O il |l＝1,2,…,w i The method comprises M procedures, wherein the machine tool capable of processing each procedure comprises M il ，M il The processing time of each process changes along with the difference of the machine tool performance; each type of work being divided into a plurality of sub-batches F i ＝{F is |s＝1,2,…,u i Processing on different machines, and treating each sub batch as a whole and occupying the same auxiliary time;
step 2: establishing a mathematical model for optimizing a dynamic batch scheduling target of the flexible job shop;
the objective functions are workshop production cost, machine tool energy consumption cost and dynamic and static scheduling scheme difference cost, and the three objective function values are processed in a weighted normalization mode;
an objective function:
min{ω 1 ·f 1 +ω 2 ·f 2 +ω 3 ·f 3 } (4)
constraint conditions are as follows:
T＝max(Z il ·E islk ) (6)
if S islk ＜T,DS islk -S islk ＝0 (7)
wherein, C is Indicating the completion time, t, of the ith lot of the i-th workpiece islk In the machine M, the first step of the s-th lot representing the i-th workpiece k One-piece man-hour of ilk Showing the i-th workpiece in the machine tool M k Auxiliary time of (S) islk Of the s-th batch representing the i-th class of workpiecesThe first step is carried out in machine M k Starting time of (1), E islk In the machine M, the first step of the s-th lot representing the i-th workpiece k Time of completion of working of (d), γ ilk Indicating machine selection decision variables, when Process O il Can be on machine M k Gamma ray at working ilk 1, otherwise γ ilk ＝0，
Represents the auxiliary time decision variable when in the machine M k The class of the workpiece processed at the upper stage is the same as the class of the workpiece processed at the upper stage by the machine tool,
otherwise, the reverse is carried out
Q k Representing the maximum completion time, W, of the machine k k Indicating the load, PC, of machine k k Indicating machine M k Average energy consumption cost per unit time under load, NC k Indicating machine M k Average energy consumption cost in unit time, RC, when no load k Indicating machine M k Average energy consumption cost per unit time during preparation, eta represents completion cost per unit time in a workshop, omega 1 Represents the completion cost weight coefficient, ω 2 Representing the energy consumption cost weight coefficient, ω 3 Representing the differential cost weight coefficient, DS, of a dynamic and static scheduling scheme islk Lot s of work in machine M representing class i work in dynamic scheduling scheme k Upper start of processing time, DW islk Lot s of work in machine M representing class i work in dynamic scheduling scheme k Time of completion of working of (D) islk Representing procedure O in a dynamic scheduling scheme il Machine M still in a static scheduling scheme k During machining, D islk Not greater than 0, otherwise D islk ＝1，S islk Lot s of work in machine M representing class i workpieces in a static scheduling scheme k Upper start-up time, ω 3 RepresentA difference cost weight coefficient, V represents the difference unit cost of the dynamic and static scheduling scheme, T represents a disturbance time node, DF is Representing the s-th sub-batch, SF, of the ith class of workpieces in a dynamic scheduling scheme is Representing the s-th sub-batch of the i-th workpiece after the disturbance time node in the static scheduling scheme, R representing the number of newly added workpieces caused by disturbance, Z il Represents a step O il Is a perturbation process, D il 1, otherwise D il ＝0；
Wherein, the formula (5) represents that the processing completion time of one process of a batch of workpieces in the dynamic scheduling scheme is more than or equal to the sum of the processing start time of the process, the auxiliary time and the working hours of all the workpieces in the batch; equation (6) shows that the disturbance time node is the ith batch of the ith workpiece in the first process in the machine M k The machining completion time of (1); the formula (7) shows that the processes before the disturbance time node and at the beginning of the processing do not participate in the dynamic scheduling; formula (8) represents the sum of the number of unprocessed workpieces and the number of newly added workpieces caused by disturbance when the total batch number of the workpieces in the dynamic scheduling scheme is equal to the disturbance time node;
and step 3: rescheduling for quality inspection disturbances;
the quality inspection disturbance processing flow is to execute procedures according to a static scheduling scheme, and after a key procedure needing quality inspection is executed, the quality inspection result of the workpieces in the current processing batch is judged: 1) continuing to execute subsequent processes for the workpiece with qualified quality inspection result; 2) the quality inspection result is that the repaired workpiece needs to be subjected to secondary processing on the current key process and is added to the unprocessed workpiece set; 3) the quality inspection result is a scrapped workpiece, the workpiece needs to be machined from the first procedure again, and the workpiece is added to the unmachined workpiece set; after all the workpiece information is obtained, the workpieces of the processed workpiece set are not processed, and the workpieces of the unprocessed workpiece set need to be rescheduled;
and 4, step 4: a recombined batch aiming at quality inspection disturbance;
when rescheduling is carried out, various workpieces need to be batched to generate a new processing task so as to facilitate rescheduling; the specific process of the recombination batch comprises the following steps: after the quality inspection disturbance occurs, workpieces of the same process route are recombined and batched when the to-be-processed working procedures of the workpieces are different, namely the workpieces of the same to-be-processed working procedures of various workpieces are combined into a new batch of workpieces to form a new scheduling problem;
and 5: improving the optimization solution of the wolf algorithm by a double-layer search frame;
designing a coding and decoding mode aiming at the recombined batch workpieces after dynamic disturbance, wherein a coding framework consists of three layers, namely a batch layer, a process layer and a machine layer, the batch layer codes represent workpiece batch information, the process layer codes represent the workpieces and the processes, the times of different digital codes represent different batches, and the machine layer represents a processing machine of each process; the decoding mode is divided into two parts, firstly, the batch layer is decoded to obtain a specific batch dividing strategy so as to obtain the specific batch number of each batch of workpieces, then, the process layer and the machine layer are decoded, the target value of the scheduling scheme is calculated, and when the process layer is decoded, the workpieces after batch recombination need to be calculated according to the original workpiece information;
an improved wolf algorithm is designed and mainly divided into two layers, wherein the outer layer search mainly solves the batch scheme, the inner layer search solves the scheduling scheme, and the solving process is as follows: when the wolf algorithm is used for solving, the bernoulli chaotic mapping is used for population initialization, so that population distribution is more uniform, the convergence rate and the optimal solution quality of the algorithm are improved, and when control parameters are updated, nonlinear control parameters are used, so that the algorithm is strong in global search capability in the early stage and high in convergence rate of local optimization in the later stage.
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