A flexible job shop dynamic scheduling optimization method and system considering material preparation plan

Abstract

The present application relates to the technical field of workshop scheduling, in particular to a flexible job shop dynamic scheduling optimization method and system considering material preparation plan. The scheduling optimization method comprises reading of fault machine information, obtaining of to-be-adjusted process set and formation of optimal combined scheduling scheme. When the scheduling optimization method of the present application is used for material preparation plan adjustment problem, the scheduler can quickly establish a flexible job shop dynamic scheduling model with material preparation plan associated adjustment in real time when machine fault occurs, obtain a rescheduling scheme according to the proposed improved affected process selection method, i.e. multi-stage based cooperative game strategy rescheduling optimization method, the rescheduling scheme has good robustness and stability, and on the basis of maintaining the original pre-scheduling target, the material preparation plan adjustment is minimized to improve production efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of job shop scheduling, in particular to a flexible job shop dynamic scheduling optimization method and system considering material preparation plan. BACKGROUND

[0002] In recent years, with the upgrading of consumption structure, the buy market and the consumer's individualization, customization, timeliness requirements are increasingly pressing, and the flexible production, flexible manufacturing that meet the "diversification, small scale, controllable cycle" are the key to the survival and victory of enterprises in the future, so the flexible job shop scheduling problem has become one of the main problems faced by manufacturing enterprises.

[0003] In actual production process, dynamic events such as machine failure, order urgency, order cancellation often occur, making the actual production environment more and more complex, so the flexible job shop dynamic scheduling problem is extended. Influenced by dynamic events, the unprocessed process needs to adjust the processing equipment, which further causes the change of the corresponding material demand, and excessive material preparation plan adjustment will further reduce the production efficiency.

[0004] The flexible job shop scheduling problem under machine failure is described as follows:

[0005] The workshop has m machines for work, including n workpieces to be processed, each workpiece has j processes, each process can be processed on different machines, and the processing time is determined according to the performance of the machine.

[0006] The flexible job shop scheduling problem under machine failure is specified as follows:

[0007] (1) At the initial zero time, all workpieces can be processed;

[0008] (2) Each machine can only process one workpiece at the same time;

[0009] (3) The processing of each workpiece is in strict processing process order and cannot be interrupted;

[0010] (4) Each process of each workpiece can only be processed on one machine at the same time;

[0011] (5) Different workpieces have the same priority;

[0012] (6) There is no order between the processes of different workpieces;

[0013] (7) Once the workpiece starts processing on the machine, it cannot be interrupted;

[0014] (8) The workpiece on the fault machine stops processing immediately and cannot be transferred, and continues to process after the fault is repaired successfully;

[0015] (9) Except for the fault machine, the workpieces being processed on the remaining machines are still processed on the originally assigned machines until completion.

[0016] Currently, there are few material preparation plan adjustment schemes for machine failure, and there are many flexible job shop dynamic scheduling schemes for dynamic events. Most of the rescheduling schemes mainly start from a single target or from the perspective of multiple targets, and do not take into account the stability and robustness of the scheduling system, which can easily cause pressure on the workshop processing adjustment, is difficult to maintain the stability of the original pre-scheduling system, and brings a lot of pressure to the production planning personnel and production processing personnel. The commonly used optimization algorithms such as genetic algorithm, particle swarm algorithm, and imperialist algorithm, although the results are good, but the running time is long, and it is difficult to adapt to the fast production workshop rhythm, and the real-time performance is poor. The methods with better real-time performance at present mainly adopt right shift rescheduling, route change rescheduling and Nash equilibrium game theory rescheduling mode, but right shift rescheduling is difficult to take into account other optimization objectives, route change rescheduling can ensure a certain stability of the scheduling system, but often cannot well take into account the robustness, and the rescheduling mode using Nash equilibrium game theory has better effect, but the randomness effect is relatively strong, and the result is not stable enough.

[0017] At present, the common dynamic scheduling scheme does not consider the adjustment problem of the material preparation plan, which increases the work burden of the production workshop staff and affects the work efficiency and enterprise income. In view of the influence of dynamic factors such as machine failure, the better way to consider the associated adjustment problem of the material preparation plan is to adopt the route change mode to reasonably adjust the affected processing procedures, which has better scheduling system stability, but this traditional route change mode is difficult to take into account the robustness of the system. SUMMARY

[0018] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art upon examination of the following or can be learned by practice of the application. The objects and other advantages of the application can be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

[0019] The present application aims to overcome the above-mentioned deficiencies and provides a flexible job shop dynamic scheduling optimization method and system considering material preparation plan. For the material preparation plan adjustment problem, when the scheduling optimization method of the present application is used, the scheduler can quickly establish a flexible job shop dynamic scheduling model with material preparation plan associated adjustment in real time when machine failure occurs, and obtain a rescheduling scheme according to the proposed improved method of selection of the affected process (IMSAP) - multi-stage cooperative game strategy (MCGS) rescheduling optimization method. The rescheduling scheme has good robustness and stability, and on the basis of maintaining the original pre-scheduling target, realizes the least material preparation plan adjustment, so as to improve the production efficiency.

[0020] To achieve the above-mentioned purposes, the technical solution of the present application is a flexible job shop dynamic scheduling optimization method considering material preparation plan, comprising

[0021] Reading of fault machine information;

[0022] Obtaining of the set of processes to be adjusted, obtaining the set of processes to be adjusted according to the fault machine information by using the improved method of selection of the affected process (IMSAP);

[0023] Forming of the optimal combined scheduling scheme, placing the set of processes to be adjusted in the multi-stage flexible job shop dynamic scheduling model for phased dynamic scheduling, and solving the optimal combined scheduling scheme by using the multi-stage cooperative game strategy (MCGS).

[0024] In some embodiments, the forming of the optimal combined scheduling scheme step is

[0025] Selecting g processes from the set of processes to be adjusted as processes to be processed in stages and placing them in the task pool to be scheduled;

[0026] Judging whether there is only one process to be processed in the task pool to be scheduled in the tth stage, if yes, selecting the optimal machine by using the enumeration method and outputting the final multi-stage cooperative game optimal solution, and if not, entering the next step;

[0027] Selecting the process in the decision pool by the player in the task pool to be scheduled;

[0028] Solving the optimal combination by using the multi-stage cooperative game method;

[0029] Determine whether all processes are selected, if yes, output the final multi-stage cooperative game optimal solution, if not, continue to determine whether there is only one process left in the t+1 stage task pool.

[0030] In some embodiments, the player in the decision pool selects the process in the decision pool by constructing a multi-stage based flexible job shop dynamic scheduling model, and the construction steps of the multi-stage based flexible job shop dynamic scheduling model are

[0031] The processes in the task pool are taken as the game player set P t ;

[0032] The available machine set is taken as the decision pool S t ;

[0033] The utility set of all players after strategy selection is denoted as U t ,

[0034] Construct a multi-stage based flexible job shop dynamic scheduling model

[0035] G t ={P t ;S t ;U t}t=1,2,...,L

[0036] In the formula: L is the number of game stages.

[0037] In some embodiments, the multi-stage cooperative game is used to solve the optimal combination, which is to coordinate among each game player in each stage of cooperative game to achieve the optimal value of the overall effect:

[0038]

[0039] If the above formula is satisfied, S t is the optimal solution of the current t stage dynamic game, which also represents the optimal decision made by each player in cooperation.

[0040] In some embodiments, the enumeration method is used to select the optimal machine and output the final multi-stage cooperative game optimal solution, which is: in the t stage, the process to be processed is taken out from the task pool to be scheduled, and then the transferable machine set S t Select the transferable machine, and select the optimal min(Y) for all possible combinations.

[0041] In some embodiments, when there are multiple optimal combinations, the combination with the smallest σ 2 is selected, wherein the calculation method of σ 2 is as follows:

[0042]

[0043]

[0044] wherein: denotes the mean of all machine end times, σ 2 denotes the variance of the end times of the machine run.

[0045] In some embodiments, the set of transferable machines is obtained by introducing a condition to filter the set of available machines for the transfer step,

[0046] ST ijk* + P ijk* ≤ ST rijk + P ijk || TP ijk* ≤ TP ijk (k * ∈ (1, 2,..., m))

[0047] wherein: k is the machine of the process O ij in the pre-schedule, k * is the machine selected for this phase. There is a set of transferable machines S t only if the above equation is satisfied for each phase of the process.

[0048] In some embodiments, the improved affected process selection approach (IMSAP) specifically comprises

[0049] a process shift step, wherein, after a machine failure occurs, the subsequent process on the failed machine is shifted, specifically according to the following algorithm

[0050] t' cko = t cko + T bk

[0051] t' ck(ο+1) = t ck(o+1) + max(0, t' cko - t sk(ο+1) )

[0052] t' ck(ο+1) = t' sk(o+1) + PT ko

[0053] wherein: t cko , t ck(o+1) are the end times of the preceding and the following process in the pre-schedule on machine k, respectively, t sk(ο+1) is the start time of the next process scheduled on machine k in the pre-schedule, PT ko is the processing time of the corresponding process on machine k, and T bkThe estimated recovery time for machine failure is given by t', where t' represents the state of the process shifting after the failure.

[0054] The process classification step involves categorizing and grading the subsequent processes following the failure.

[0055] The process selection process involves selecting subsequent processes based on the identified faults using a window position update method to obtain a set of processes to be adjusted.

[0056] In some implementations, the process classification step specifically involves classifying the subsequent processes of a fault into processes that are weakly affected, processes that are strongly affected, and processes that are adjustable, and assigning them to levels 1, 2, and 3, respectively.

[0057] In some implementations, the window position is updated in the following specific ways:

[0058] Initial window position t 0k ,

[0059] t bk ≤t 0k ≤t bk +T bk ||t bk ≤t 0k ≤t cko +T bk )

[0060] The updated position of the window after the h-th iteration is υ.

[0061] υ={O|min||t sko -|t (h-1)k |||}

[0062] t bk ≤t hk ≤t' ckυ (h>0)

[0063] In the formula: Indicates t hk The upper bound of the window is υ, which represents the sequence number of the last process contained in the window.

[0064] In some implementations, the determination of the optimal combined scheduling scheme is based on a dynamic scheduling model that considers the correlation adjustment of the material preparation plan. The specific algorithm of the dynamic scheduling model that considers the correlation adjustment of the material preparation plan is as follows:

[0065]

[0066] f2=C' max -C max

[0067]

[0068]

[0069] In the formula: C' max M represents the maximum completion time after dynamic scheduling. ij and M' ij For process O ij Machines allocated during pre-scheduling and rescheduling;

[0070] Based on the dynamic scheduling characteristics, a weighted sum approach can be used to balance robustness and stability. The evaluation index Y of generative formula (5) is:

[0071] Y = r1f2 + r2f3

[0072] In the formula: r1 and r2 are the penalty factors for robustness and stability, respectively, r1 + r2 = 1, and 0 <r1,r2<1。

[0073] This invention also provides a flexible workshop dynamic scheduling optimization system that takes into account material preparation plans, including...

[0074] The reading module is used to read information about faulty machines;

[0075] The acquisition module is used to acquire the set of processes to be adjusted based on the faulty machine information using the Improved Affected Process Selection Method (IMSAP).

[0076] The module forms a set of processes to be adjusted, which is placed in a multi-stage flexible workshop dynamic scheduling model for phased dynamic scheduling, and solves the optimal combination scheduling scheme through a multi-stage cooperative game strategy (MCGS).

[0077] By adopting the above technical solution, the beneficial effects of the present invention are:

[0078] To address the issue of adjusting material preparation plans, when using the scheduling optimization method of this invention, schedulers can quickly establish a flexible workshop dynamic scheduling model with material preparation plan-related adjustments in real time when machine failures occur. Based on the proposed improved method for selecting affected processes—a rescheduling optimization method based on multi-stage cooperative game strategy—a rescheduling scheme is obtained. This rescheduling scheme has good robustness and stability, and achieves minimal material preparation plan adjustments while maintaining the original pre-scheduling objectives, thereby improving production efficiency.

[0079] In response to the failure of the original scheduling plan due to machine failure dynamic events during the production process in flexible operation workshops, the scheduling optimization method of this invention can make the change of the target value after scheduling small, reduce the pressure of adjusting the workshop material preparation plan, and thus greatly shorten the maximum completion time of the original scheduling, thereby greatly improving production efficiency.

[0080] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0081] Undoubtedly, such and other objects of the present invention will become more apparent after the following detailed description of the preferred embodiments, which are illustrated in various accompanying drawings and figures.

[0082] To make the above and other objects, features and advantages of the present invention more apparent and understandable, one or more preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0083] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0084] In the accompanying drawings, the same parts use the same reference numerals, and the drawings are schematic and not necessarily drawn to actual scale.

[0085] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only one or more embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on such drawings without creative effort.

[0086] Figure 1 This is a diagram illustrating pre-scheduling.

[0087] Figure 2 This is a schematic diagram of the process being moved to the next stage;

[0088] Figure 3 The critical path graph for right-shift rescheduling;

[0089] Figure 4 This is a schematic diagram illustrating the selection of processes in stages;

[0090] Figure 5 This is a schematic diagram of the dynamic scheduling optimization method for flexible workshops that takes into account material preparation plans, as described in this invention.

[0091] Figure 6 For pre-scheduled Gantt charts;

[0092] Figure 7 For IMSAP-MCGS rescheduling Gantt chart;

[0093] Figure 8 For right-shift rescheduling of the Gantt chart;

[0094] Figure 9For a complete rescheduling of the Gantt chart. Detailed Implementation

[0095] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0096] Furthermore, in the description of this invention, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0097] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral unit; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. However, specifying a direct connection indicates that the two main bodies are not connected through a transitional structure, but rather formed as a whole through a connecting structure. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0098] In this invention, unless otherwise expressly specified and limited, the first feature "on" or "below" the second feature may be in direct contact with the first and second features, or indirect contact through an intermediate medium. In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0099] According to some embodiments of the present invention, the present invention provides a method for dynamic scheduling optimization of flexible workshops considering material preparation plans. The method includes the following steps:

[0100] S1. Reading information about faulty machines;

[0101] S2. Obtaining the set of operations to be adjusted: Based on the faulty machine information, the improved affected operations selection method (IMSAP) is used to obtain the set of operations to be adjusted.

[0102] S3. Formation of the optimal combined scheduling scheme: The set of processes to be adjusted is placed in a dynamic scheduling model of flexible workshop based on multi-stage scheduling and the optimal combined scheduling scheme is solved by a multi-stage cooperative game strategy (MCGS).

[0103] According to some embodiments of the present invention, optionally, the steps for forming the optimal combined scheduling scheme are as follows:

[0104] S31. Select g processes from the set of processes to be adjusted in stages as the processes to be processed, and put them into the task pool to be scheduled;

[0105] S32. Determine whether there is only one processing step left in the task pool to be scheduled in stage t. If so, use the enumeration method to select the optimal machine and output the final optimal solution of the multi-stage cooperative game. If not, proceed to the next step.

[0106] S33. Players in the task pool to be scheduled select a process from the decision pool;

[0107] S34. Solve the optimal combination using a multi-stage cooperative game approach;

[0108] S35. Determine whether all processes have been selected. If so, output the final optimal solution for the multi-stage cooperative game. If not, return to step S32 to determine whether there is only one process left in the task pool to be processed in stage t+1.

[0109] According to some embodiments of the present invention, optionally, the selection of a process from the decision pool by players in the task pool to be scheduled is achieved by constructing a multi-stage flexible job shop dynamic scheduling model. The construction steps of the multi-stage flexible job shop dynamic scheduling model are as follows:

[0110] S331. Treat the processes in the task pool to be scheduled as the player set P. t ;

[0111] S332. Use the available machine set as the decision pool S t ;

[0112] S333. The set of all utilities after a player makes a strategy choice is denoted as U. t ,

[0113] S334. Construct a dynamic scheduling model for flexible job shops based on multiple stages.

[0114] G t ={Pt S t ;U t} t=1,2,...,L

[0115] In the formula: L represents the number of game stages.

[0116] According to some embodiments of the present invention, optionally, the solution to the optimal combination using a multi-stage cooperative game approach specifically involves, in each stage of the cooperative game, the players coordinate with each other to achieve the optimal overall effect:

[0117]

[0118] If the above formula is satisfied, then it is S. t The optimal solution to a dynamic game in stage t represents the optimal decision made by all players in cooperation.

[0119] According to some embodiments of the present invention, optionally, the method of selecting the optimal machine and outputting the final optimal solution of the multi-stage cooperative game using enumeration specifically involves: in stage t, retrieving the process to be processed from the pool of tasks to be scheduled, and then transferring the process from the set of transferable machines S. t Select a transferable machine and choose the optimal min(Y) for all possible combinations.

[0120] Optionally, according to some embodiments of the present invention, when multiple optimal combinations exist, the smallest σ is selected. 2 The combination of σ, where σ 2 The calculation method is as follows:

[0121]

[0122]

[0123] In the formula: σ represents the mean of the completion times of all machines. 2 This represents the variance of the machine's end time.

[0124] According to some embodiments of the present invention, optionally, the transferable machine set is obtained by introducing conditions to screen the available machine set for the transfer process.

[0125] ST ijk* +P ijk* ≤ST rijk +P ijk ||TP ijk* ≤TP ijk (k * ∈(1,2,...,m))

[0126] In the formula: k represents process O in the pre-scheduling process. ij The machine, k* The machines selected for this stage. For each stage's process, a transferable machine set S exists only if the above formula is satisfied. t .

[0127] According to some embodiments of the present invention, optionally, the improved Influenced Process Selection Method (IMSAP) specifically includes:

[0128] S21. Process relocation step: After a machine failure, the subsequent processes on the failed machine are relocated. The specific algorithm is as follows:

[0129] t' cko =t cko +T bk

[0130] t' ck(ο+1) =t ck(o+1) +max(0,t' cko -t sk(ο+1) )

[0131] t' ck(ο+1) =t' sk(o+1) +PT ko

[0132] In the formula: t cko t ck(o+1) Let t be the time of the process currently being processed on machine k during pre-scheduling, and the end time of the next process, respectively. sk(ο+1) PT is the start time of the next process scheduled on machine k in the pre-schedule. ko T represents the processing time of the corresponding process on machine k. bk The estimated recovery time for machine failure is given by t', where t' represents the state of the process shifting after the failure.

[0133] S22. Process classification steps: classify and classify the subsequent processes of the fault;

[0134] S23. Process selection steps: Based on the subsequent processes after the fault is classified, the selection is carried out by updating the window position to obtain the set of processes to be adjusted.

[0135] In the process selection step, only processes less affected by the fault can escape selection; otherwise, they represent effective processes that have eliminated the impact of the fault. The ST (Scheduled Time) step for pre-scheduled workpiece processes. ijk At the window position t hk If at least one process is weakly affected, that type of process cannot be selected; select other types of processes, update the window position, and then proceed to the next process judgment. Otherwise, it can be selected. When t hkIf the window no longer contains any weakly affected processes, the window position update ends. It is only necessary to determine whether the subsequent processes are adjustable processes. If they are, they are selected; otherwise, they are not selected, until the subsequent processes have been traversed.

[0136] According to some embodiments of the present invention, optionally, the process classification step specifically involves: classifying the subsequent processes of the fault into processes that are weakly affected, processes that are strongly affected, and adjustable processes, and assigning them to levels 1, 2, and 3, respectively.

[0137] A process that is less affected refers to a process whose relocation does not directly cause a delay in the start time of the next process.

[0138] t' cij ≤t ci(j+1) (0≤j <h i )

[0139] The process that is most affected refers to the process that is moved to the next stage, causing a direct delay in the start time of the next process.

[0140] t' cij ≥t ci(j+1) (0≤j <h i )

[0141] Adjustable processes refer to processes that are the last process or processes on the critical path after right shift and rescheduling.

[0142]

[0143] In the formula: t' cij Indicates process O ij The completion time after the process is moved to the later stage, t ci(j+1) Indicates process O in pre-scheduling i(j+1) The end time.

[0144] According to some embodiments of the present invention, optionally, the window position is updated in the following manner:

[0145] Initial window position t 0k ,

[0146] t bk ≤t 0k ≤t bk +T bk ||t bk ≤t 0k ≤t cko +T bk )

[0147] The updated position of the window after the h-th iteration is υ.

[0148] υ={O|min||t sko-|t (h-1)k |||}

[0149] t bk ≤t hk ≤t' ckυ (h>0)

[0150] In the formula: Indicates t hk The upper bound of the window is υ, which represents the sequence number of the last process contained in the window.

[0151] For example, in a 3x3 calculation case, assume the process O... 21 Machine M1 malfunctioned at 8 minutes, with an estimated repair time of 7 minutes. Using the aforementioned impact transfer method, the final selected process was O. 32 O 13 A specific demonstration is as follows: Figures 1-3 As shown in Table 1-2.

[0152] Table 1 shows the processes selected using the window position update method.

[0153]

[0154] Table 2 Selected Processes After Window Position Update is Completed

[0155]

[0156] According to some embodiments of the present invention, optionally, the determination of the optimal combined scheduling scheme is based on a dynamic scheduling model that considers the correlation adjustment of the material preparation plan. The specific algorithm of the dynamic scheduling model that considers the correlation adjustment of the material preparation plan is as follows:

[0157]

[0158] f2=C' max -C max

[0159]

[0160]

[0161] In the formula: C' max M represents the maximum completion time after dynamic scheduling. ij and M' ij For process O ij Machines allocated during pre-scheduling and rescheduling;

[0162] Based on the dynamic scheduling characteristics, a weighted sum approach can be used to balance robustness and stability. The evaluation index Y of generative formula (5) is:

[0163] Y = r1f2 + r2f3

[0164] In the formula: r1 and r2 are the penalty factors for robustness and stability, respectively, r1 + r2 = 1, and 0 <r1,r2<1。

[0165] According to some embodiments of the present invention, the present invention also provides a flexible workshop dynamic scheduling optimization system that takes into account material preparation plans, including...

[0166] The reading module is used to read information about faulty machines;

[0167] The acquisition module is used to acquire the set of processes to be adjusted based on the faulty machine information using the Improved Affected Process Selection Method (IMSAP).

[0168] The module forms a set of processes to be adjusted, which is placed in a multi-stage flexible workshop dynamic scheduling model for phased dynamic scheduling, and solves the optimal combination scheduling scheme through a multi-stage cooperative game strategy (MCGS).

[0169] This invention addresses the problem of material preparation plan adjustment. With minimizing the maximum completion time as the pre-scheduling objective, it rapidly establishes a flexible dynamic scheduling model for the job shop, incorporating adjustments to the material preparation plan, in the event of machine failure. Using IMSAP-MCGS rescheduling not only demonstrates good robustness and stability but also provides real-time handling of machine failures, minimizing material preparation plan adjustments. Experiments were conducted using Brandimarte's standard example MK1. Figure 6 For the pre-scheduled scheme, the maximum completion time is 42. When machine 3 fails at time 15 minutes, the predicted recovery time is 20 minutes. The rescheduling results using the right-shift rescheduling, full rescheduling, and IMSAP-MCGS rescheduling methods are shown below. Figures 7-9 , and Table 3.

[0170] Table 3 Comparison of Simulation Results

[0171]

[0172] This shows that when machine failures occur, the evaluation index Y obtained by IMSAP-MCGS rescheduling is superior to right-shift rescheduling and full rescheduling. Its robustness index is lower than both full and right-shift rescheduling, reducing the impact of machine failures on production targets. Furthermore, its runtime is faster than full rescheduling, ensuring a timely and effective response in flexible workshops when machine failures occur. Regarding stability, while right-shift rescheduling has the best stability index, its robustness is poor, while IMSAP-MCGS rescheduling strikes a better balance between the two. Therefore, IMSAP-MCGS rescheduling can effectively balance robustness and stability, and can promptly and effectively resolve the issue of material preparation plan adjustments caused by machine failures in flexible workshops.

[0173] It should be understood that the embodiments disclosed herein are not limited to the specific processing steps or materials disclosed herein, but should be extended to equivalent substitutions of such features as understood by those skilled in the art. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0174] The term "embodiment" in this specification refers to a specific feature or characteristic described in connection with an embodiment that is included in at least one embodiment of the invention. Therefore, phrases or "embodiments" appearing in various places throughout the specification do not necessarily refer to the same embodiment.

[0175] Furthermore, the described features or characteristics can be incorporated into one or more embodiments in any other suitable manner. In the above description, specific details, such as thickness, quantity, etc., are provided to provide a comprehensive understanding of embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented without the aforementioned specific details or may be implemented using other methods, components, materials, etc.

Claims

1. A dynamic scheduling optimization method for flexible workshops considering material preparation plans, characterized in that, include: (1) Reading information about faulty machines; (2) Obtaining the set of processes to be adjusted: Based on the faulty machine information, an improved method for selecting affected processes is used to obtain the set of processes to be adjusted; wherein, the improved method for selecting affected processes specifically includes: The process relocation step involves shifting subsequent processes on the faulty machine to the next step after a machine malfunction. The specific algorithm is as follows: ， ， ， In the formula: , In the pre-scheduling process, the machines are respectively k The time of completion of the process currently being processed and the time of completion of the next process. For machines in pre-schedule k The start time of the next process arranged above. For the corresponding process in the machine k Processing time on Estimate recovery time for machine malfunctions, and This indicates the state where the process is moved to the next stage after a failure occurs; The process classification step involves categorizing and grading the subsequent processes following the failure. The process selection process involves selecting subsequent processes based on the identified faults using a window position update method to obtain a set of processes to be adjusted. (3) Formation of the optimal combined scheduling scheme: The set of processes to be adjusted is placed in a dynamic scheduling model based on a multi-stage flexible workshop for phased dynamic scheduling, and the optimal combined scheduling scheme is solved through a multi-stage cooperative game strategy; the specific steps are as follows: (3.1) Select g processes from the set of processes to be adjusted in stages as the processes to be processed, and put them into the task pool to be scheduled; (3.2) Determine whether there is only one processing step left in the task pool to be scheduled in stage t. If so, use the enumeration method to select the optimal machine and output the final optimal solution of the multi-stage cooperative game. If not, proceed to the next step. (3.3) Players in the task pool to be scheduled select processes from the decision pool. This is achieved by constructing a dynamic scheduling model for a flexible job shop based on a multi-stage approach. The construction steps of the dynamic scheduling model for a flexible job shop based on a multi-stage approach are as follows: The processes in the task pool to be scheduled are considered as the set of game players. P t ; Use the available machine set as the decision pool S t ; The set of all utilities after a player makes a strategy choice is denoted as: U t , Constructing a dynamic scheduling model for flexible workshops based on multiple stages. ， In the formula: L The number of game phases; (3.4) The optimal combination is solved by a multi-stage cooperative game approach. Specifically, in each stage of the cooperative game, the players coordinate with each other to achieve the optimal value for the overall effect: ， If the above formula is satisfied, then it is current t The optimal solution of a dynamic game at a given stage also represents the optimal decision made by the players through cooperation. The determination of the optimal combined scheduling scheme is based on a dynamic scheduling model that considers the correlation adjustment of the material preparation plan. The specific algorithm of the dynamic scheduling model that considers the correlation adjustment of the material preparation plan is as follows: ， ， ， ， In the formula: This indicates the maximum completion time after dynamic scheduling. and For process O ij Machines allocated during pre-scheduling and rescheduling; Based on the characteristics of dynamic scheduling, a weighted sum approach can be used to balance robustness and stability, generating evaluation metrics. : ， In the formula: , These are the penalty factors for robustness and stability, respectively. ,and ; (3.5) Determine whether all processes have been selected. If so, output the final optimal solution of the multi-stage cooperative game. If not, continue to determine whether there is only one process left in the task pool to be processed in stage t+1.

2. The method for dynamic scheduling optimization of flexible workshops considering material preparation plans according to claim 1, characterized in that, The specific steps for selecting the optimal machine and outputting the final optimal solution for the multi-stage cooperative game using the enumeration method are as follows: t In this phase, the process to be processed is retrieved from the pool of tasks to be scheduled, and then transferred from the set of transferable machines. Select a transferable machine and choose the optimal min(Y) for all possible combinations.

3. The method for dynamic scheduling optimization of flexible workshops considering material preparation plans according to claim 2, characterized in that, When multiple optimal combinations exist, choose the smallest one. The combination, in which, The calculation method is as follows: ， ， In the formula: This represents the average completion time of all machines. This represents the variance of the machine's end time.

4. The method for dynamic scheduling optimization of flexible workshops considering material preparation plans according to claim 2, characterized in that, The transferable machine set is obtained by introducing conditions to filter the available machine set for the transfer process. ， In the formula: k For pre-scheduled processes O ij The machine, For the machines selected for this stage, there is a transferable machine set only if the above formula is satisfied for each stage's process. .

5. The method for dynamic scheduling optimization of flexible workshops considering material preparation plans according to claim 1, characterized in that, The specific steps for classifying processes are as follows: the processes following a failure are divided into processes that are weakly affected, processes that are strongly affected, and processes that are adjustable, and are respectively classified as Level 1, Level 2, and Level 3.

6. The method for dynamic scheduling optimization of flexible workshops considering material preparation plans according to claim 1, characterized in that, The specific method for updating the window position is as follows: Initial window position , ， The position of the window after the hth update , ， ， In the formula: express The upper realm, This indicates the sequence number of the last process involved in the window.

7. A dynamic scheduling optimization system for flexible workshops that considers material preparation planning, characterized in that, The system is a dynamic scheduling optimization method for flexible workshops that takes into account material preparation plans, as described in any one of claims 1-6, and the system includes: The reading module is used to read information about faulty machines; The acquisition module is used to acquire the set of processes to be adjusted based on the faulty machine information and an improved method for selecting affected processes. The module forms a set of processes to be adjusted, which is placed in a dynamic scheduling model of a flexible workshop based on a multi-stage approach for phased dynamic scheduling, and solves the optimal combination scheduling scheme through a cooperative game strategy based on a multi-stage approach.

Images