CN114460908A - Method for scheduling flexible production workshop of spiral lion powder production enterprise - Google Patents
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Abstract
The invention provides a method for scheduling a flexible production workshop of a spiral lion powder production enterprise, which comprises the following steps: 1) generating an initial scheduling scheme through a genetic algorithm according to the workshop state at the initial moment; 2) executing a production task according to the initial scheduling scheme, detecting the production state of a workshop in real time, and executing the step 3) if a disturbance event occurs in the execution process of the production task, or executing the step 4) if the disturbance event does not occur; 3) executing a rescheduling trigger mechanism, judging whether the offset coefficient in the production process exceeds a threshold value set by a system, if so, executing a complete rescheduling strategy, if not, processing according to a right-shift rescheduling strategy, and returning to the step 2 after the processing is finished); 4) and executing the production tasks until all the production tasks are completed. The method can solve the technical problems that the disturbance factors of the snail powder production workshop are many, the fluctuation of the production process is large, and rescheduling cannot be effectively carried out in real time.
Description
Technical Field
The invention relates to the technical field of a workshop scheduling method, in particular to a method for scheduling a flexible workshop of a lion powder production enterprise.
Background
With the rapid development of economy in China, the market is complex and changeable, the competition is extremely fierce, certain impact is brought to manufacturing enterprises, and manufacturing enterprises are undergoing a deep manufacturing mode change from 'multi-variety, large-batch' to 'multi-variety, variable-batch' so as to quickly respond to the manufacturing requirements of diversified customized products of a large number of customers and face huge challenges. On one hand, under the current market environment, the wind direction of market products gradually tends to be dominant in the market of buyers, manufacturing enterprises need to meet diversified customized demands of customers, and the flexibility of the products produced by the enterprises is aggravated. On the other hand, the increase of the production scale of the manufacturing enterprise makes the production management and production scheduling of the enterprise more difficult, and at the same time, the random disturbance event of the production process needs to be dealt with.
Actual production plants often have various disturbance events that cause the production state to change, and the task scheduling plans generated by static scheduling may become infeasible, which is a disadvantage of the static scheduling model. The dynamic scheduling refers to an adjusting measure for changing the production state when a workshop disturbance event occurs, different disturbance events damage the production state to different degrees, and the disturbance events are divided into dominant disturbance and recessive disturbance according to the damage degree.
The occurrence of the dominant disturbance can cause a relatively obvious influence on the workshop production process, and the original scheduling scheme can be rendered infeasible. Common dominant disturbance scenarios are: related to processing tasks such as urgent insertion of orders, change of delivery date, cancellation of orders, rework of workpieces, etc.; associated with production resources such as machine maintenance, material shortages, staff absenteeism, etc.
The occurrence of the implicit disturbance can not obviously affect the production state of the workshop in a short period, but can affect the performance of the original scheduling scheme, and the original scheduling scheme can also become unusable after accumulating for a period of time. Common implicit disturbance scenarios are: errors occur in the actual processing time of the workpiece, the performance of machine equipment is reduced, the efficiency of workers is different, and the like.
The dynamic scheduling problem is an extension of the static scheduling problem, and the method and the device can simulate and analyze the random dynamic scene of the production workshop and establish a more comprehensive scheduling model on the basis of the static scheduling research of the flexible workshop of the enterprise.
The genetic algorithm is an intelligent search algorithm for simulating natural selection and genetic mechanism, and searches for an optimal solution in a problem solution space by simulating the behaviors of selection, mating, variation and the like in the natural biological evolution process, and a flow chart is shown in fig. 1.
At present, the methods commonly adopted for when rescheduling are periodic rescheduling, event-driven rescheduling and hybrid rescheduling. The periodic rescheduling refers to rescheduling for one time at regular intervals, and mainly aims at an implicit disturbance scene. Event-driven rescheduling is to initiate rescheduling according to the occurrence of a disturbance event, mainly aiming at an explicit disturbance scene. Hybrid rescheduling is a combination of periodic and event-driven rescheduling mechanisms.
The rescheduling strategy is a strategy for implementing specific rescheduling operation after rescheduling is triggered, and reducing or eliminating the influence of a disturbance event. Currently, common rescheduling strategies include right-shift rescheduling, local rescheduling, and full rescheduling.
The idea of the right shift rescheduling strategy is as follows: for the disturbance with smaller influence, the original scheduling scheme is maintained, namely the processing machine and the processing sequence of the workpiece are not changed, and the processing time of the influenced working procedure is delayed, namely the work is started after the delay so as to process the disturbance. The strategy is simple to operate, the stability of the production system can be maintained, but the performance of the scheduling scheme is degraded after being influenced by the disturbance event, and the scheduling scheme has a large potential delay risk.
The idea of the local rescheduling strategy is as follows: and the original scheduling scheme is not damaged as much as possible, and only the affected workpiece procedures are rescheduled. The strategy has better stability, but is more complex to operate, and only partial information is considered by the strategy, so that the generated rescheduling scheme has poor performance and is generally not as good as complete rescheduling.
The idea of the full rescheduling strategy is: and (4) taking the influence of disturbance into consideration, and rescheduling all the working procedures of the machinable workpieces. The strategy can generate a better rescheduling scheme, can well eliminate the influence caused by disturbance, but has larger calculated amount and can generate extra production cost. Such as the carrying of a workshop, the clamping cost of equipment and the like.
Disclosure of Invention
The technical problem that this application will solve is: the method for scheduling the flexible production workshop of the lion powder production enterprise is provided, and the technical problems that the lion powder production workshop with production characteristics of multiple varieties, order-oriented production and the like has multiple disturbance factors, the fluctuation of the production process is large, and rescheduling cannot be effectively carried out in real time are solved.
In order to solve the technical problems, the technical scheme of the invention is as follows: a method for scheduling a flexible production workshop of a screw lion powder production enterprise comprises the following steps:
1) generating an initial scheduling scheme through a genetic algorithm according to the workshop state at the initial moment;
2) executing the production task according to the scheduling scheme, detecting the production state of the workshop in real time, executing the step 3) if a disturbance event occurs in the execution process of the production task, and executing the step 4) if the disturbance event does not occur in the execution process of the production task;
3) executing a rescheduling trigger mechanism, quantitatively representing the influence degree of the disturbance event on the original scheduling scheme by using an offset coefficient theta, and passing through a threshold value thetasIndicates the acceptable variation degree of the workshop production condition if theta is larger than or equal to thetasThen execute the full rescheduling strategy if theta<θsThen, the right-shift rescheduling strategy is used for processingAfter finishing, returning to execute the step 2);
4) and executing the production tasks to complete all the production tasks.
Preferably, in step 2), the change of the production state of the workshop includes machine failure, emergency insertion and completion of the workpiece process.
Preferably, in the step 2), when triggering complete rescheduling, information of a finished workpiece set FJ, a currently-processed workpiece set BJ, an unprocessed workpiece set UJ, and an unscheduled workpiece set NJ needs to be obtained first, a workshop production state is updated according to the workpiece set, and then a dynamic scheduling model in a current workshop state is solved through a genetic algorithm to obtain a rescheduling scheme with excellent performance.
Preferably, when the rescheduling trigger mechanism is executed in step 3), the method includes:
1) the high efficiency means that the delay of production tasks is avoided by changing processing equipment and processing sequence of the working procedures;
2) the stability refers to that the processing equipment and the processing sequence of the workpiece procedure in the original scheduling scheme are preferentially maintained unchanged, and dynamic adjustment is made.
Preferably, the expression of the offset coefficient θ is:
θ＝θc+γθd
θcrepresenting the difference, theta, between the actual maximum completion time and the maximum completion time of the original scheduling scheme after being affected by the disturbance eventdThe difference value of the actual total workpiece delay and the total workpiece delay of the original scheduling scheme is represented, the penalty factor gamma represents that the workpiece cannot be completed in time to cause delay penalty, the penalty is usually set to be greater than 1, and the larger the value is, the lower the tolerance of the workpiece delay is.
Preferably, the completion time of the workpiece affected by the disturbance event is estimated by using a method of a subsequent process correlation tree, and the estimation sequence is as follows:
1) taking the process directly influenced by the disturbance event as a root node;
2) finding out the indirectly influenced subsequent processes according to the original scheduling scheme, and taking the found processes as new nodes;
3) and constructing the affected subsequent process association tree layer by layer until the nodes on the association tree do not have the subsequent process.
Preferably, in step 1), the initial scheduling scheme is generated by a genetic algorithm, and a crossover operator in the genetic algorithm is executed before a selection operator.
The technical effect obtained by adopting the technical scheme is as follows:
the rescheduling triggering mechanism based on the offset coefficient can avoid frequent rescheduling triggering to cause instability of a production system, can also detect the deviation degree of an original scheduling scheme in real time in the actual production process, and avoids potential influence of implicit disturbance. The method is very necessary for rescheduling effectively in real time due to the fact that the number of disturbance factors of the production workshop of the lion powder with the production characteristics of multiple varieties, order-oriented production and the like is large, and the fluctuation of the production process is large. The scheduling method can effectively maintain the stability of the production system, well eliminate the influence caused by disturbance, determine the rescheduling strategy according to the size of the offset coefficient, greatly reduce the calculated amount and effectively reduce the extra production cost.
In the step 2), when the complete rescheduling is triggered, the information of the finished workpiece set FJ, the currently processed workpiece set BJ, the unprocessed workpiece set UJ and the unscheduled workpiece set NJ needs to be obtained first, the workshop production state is updated according to the workpiece set, then the dynamic scheduling model in the current workshop state is solved through the genetic algorithm, the rescheduling scheme with better performance is obtained, the workshop production state is updated, and the effective dynamic adjustment is realized according to the change of the workshop production state.
When the rescheduling trigger mechanism is executed in the step 3), the method has high efficiency and stability, and can effectively reduce extra production cost caused by workshop transportation, equipment clamping cost and the like.
In the step 1), the initial scheduling scheme is generated through a genetic algorithm, and the crossover operator in the genetic algorithm is executed before the selection operator. The algorithm adopts a matrix coding mode, and designs a PBX and LOX mixed cross operator based on matrix rows/columns and a random position mutation operator based on matrix elements according to the structural form of a matrix, so that the basic operation of the genetic algorithm is improved, and simultaneously, a population evolution strategy for effectively improving the population diversity and convergence is designed. For the multi-objective optimization scheduling problem, on the basis of the improved genetic algorithm, in order to effectively process the optimization problem of a high-dimensional target, the multi-objective scheduling optimization algorithm designed by the application can effectively maintain the diversity of the population, and an external database is additionally arranged and used for updating and storing the Pareto optimal solution set of each iteration. Through example verification analysis, the algorithm provided by the application shows good convergence and diversity.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a basic flow diagram of a genetic algorithm;
FIG. 2 is a flow chart of a scheduling method of the present application;
fig. 3 is a flow chart of scheduling policy generation according to the present application.
Detailed Description
As shown in fig. 2, a method for scheduling a flexible production workshop of a lion powder production enterprise comprises the following steps:
(1) and generating an initial scheduling scheme through a genetic algorithm according to the workshop state at the initial moment.
(2) And (3) executing the production task according to the scheduling scheme, detecting the production state of the workshop in real time, executing the step (3) if a disturbance event occurs in the production task executing process, and executing the step (4) if the disturbance event does not occur in the production task executing process, wherein the workshop production state changes and comprises machine faults, emergency insertion, workpiece procedure processing completion and the like.
(3) A rescheduling trigger mechanism is performed.
And at the moment of rescheduling, dividing the workpieces into a finished workpiece set, a processing workpiece set, an unprocessed workpiece set and an unscheduled workpiece set according to the states of the workpieces. Wherein, the unprocessed workpiece set refers to the workpieces which are added into the production plan but are not processed yet; an unscheduled workpiece set refers to a new workpiece waiting to be scheduled for production. The machine may then be in three states: idle, in-process, and fault conditions. When rescheduling is carried out, the production state of a workshop needs to be updated, and the following two aspects are mainly considered, namely machinable equipment information and process information of machinable workpieces, namely scheduling initial information.
In order to better describe the dynamic scheduling mathematical model, the following processing is performed:
1) for the workpiece set being machined, if the workpiece procedure is machining, the release time of the workpiece is the remaining machining time of the current procedure, and if the workpiece procedure is not machining, the rescheduling time is the release time of the workpiece;
2) for the machine in the processing state, the release time of the machine is the residual processing time of the processing procedure;
3) for a machine in a fault state, if the machine is in fault during machining, the release time of the machine is the sum of the maintenance time and the residual machining time of a machining process, and if not, the maintenance time of the machine is the release time of the machine;
4) for machines and work orders in other states, the release time is the rescheduling time.
It can be found that the working procedure of the workpiece being processed is matched with the processing machine, so that the normal processing equipment information can be embodied by the processing state information of the working procedure of the workpiece. In particular, the term "remaining processing time of a process" as used herein refers to the product of the planned processing time of the process and the remaining processing schedule.
By combining the above contents, the method and the device complete the variable definition, the constraint condition and the optimization target of the model on the basis of the static scheduling model, and establish the dynamic scheduling model.
To build the dynamic scheduling model, the following variables are defined, as shown in table 1.
Table 1 variable definitions
The rescheduling scheme should have the following constraints:
1) the starting time of each machine for processing the first workpiece needs to be more than or equal to the release time of the machine, as shown in the following formula:
2) the starting time of each workpiece is more than or equal to the releasing time of the workpiece, as shown in the following formula:
in the step (2), when complete rescheduling is triggered, information of a finished workpiece set FJ, a currently-processed workpiece set BJ, an unprocessed workpiece set UJ and an unscheduled workpiece set NJ is acquired, a workshop production state is updated according to the workpiece set, and then a dynamic scheduling model in the current workshop state is solved through a genetic algorithm to acquire a rescheduling scheme with better performance.
Quantitatively representing the influence degree of the disturbance event on the original scheduling scheme by using an offset coefficient theta, wherein the expression of the offset coefficient theta is as follows:
θc＝max{Cij(Tr)}-max{Cij(Tr-1)}
θd＝∑max{Cij(Tr)-di(Tr),0}-∑max{Cij(Tr-1)-di(Tr-1),0}
θ＝θc+γθd
θcrepresenting the difference, theta, between the actual maximum completion time and the maximum completion time of the original scheduling scheme after being affected by the disturbance eventdRepresenting the difference between the actual total workpiece delay and the total workpiece delay of the original scheduling schemeThe penalty factor γ represents a penalty for a delay due to a failure to complete the workpiece in time, and is usually set to a value greater than 1, with a greater value indicating a lower tolerance for workpiece delay. Threshold value thetasIndicating an acceptable degree of variation in plant production conditions. Penalty factor gamma and threshold thetasThe value of (A) is determined by workshop management personnel according to the actual production condition. Calculating the completion time of the workpiece affected by the disturbance event by adopting a method of a subsequent process association tree, wherein the specific method comprises the following steps:
1) taking the process directly influenced by the disturbance event as a root node;
2) finding out the indirectly influenced subsequent processes according to the original scheduling scheme, and taking the found processes as new nodes;
3) and constructing the affected subsequent process association tree layer by layer until the nodes on the association tree do not have the subsequent process.
Passing threshold value thetasIndicating acceptable variation in plant conditions, thetasThe value of (A) is determined by workshop management personnel according to the actual production condition, whether the offset coefficient of the production process exceeds the threshold value set by the system is judged, and if theta is more than or equal to thetasThen execute the full rescheduling strategy if theta<θsAnd processing according to the right-shift rescheduling strategy, and returning to the step (2) after the processing is finished.
When the rescheduling trigger mechanism is executed, the rescheduling scheme should have:
1) high efficiency means that delay of production tasks is avoided by changing processing equipment and processing sequence of the working procedures;
2) the stability refers to that the processing equipment and the processing sequence of the workpiece procedure in the original scheduling scheme are preferentially maintained unchanged, and dynamic adjustment is made.
The application considers the offset degree of the processing equipment as a stability index of dynamic scheduling. The offset degree of the processing equipment refers to the minimum change of the workpiece processing equipment selection of the rescheduling scheme and the original scheduling scheme, and the expression is as follows:
if the emergency order is inserted, the emergency order needs to be completed preferentially, so the preferential completion of the emergency order is considered by the high-efficiency index of dynamic scheduling, wherein e represents the workpiece number of the emergency order, and the expression is as follows:
f＝min(Cep(Tr))
(4) and executing the production tasks to complete all the production tasks.
As shown in fig. 3, in the genetic algorithm, the crossover operator is performed before the selection operator due to the steps 1) and 3). The algorithm adopts a matrix coding mode, and designs a PBX and LOX mixed cross operator based on matrix rows/columns and a random position mutation operator based on matrix elements according to the structural form of a matrix, so that the basic operation of the genetic algorithm is improved, and a population evolution strategy for effectively improving population diversity and convergence is designed. For the multi-objective optimization scheduling problem, on the basis of the improved genetic algorithm, in order to effectively process the optimization problem of a high-dimensional objective, Pareto non-dominated sorting in the NSGA-III algorithm, a reference point-based selection strategy and the like are introduced. The multi-objective scheduling optimization algorithm can effectively maintain the diversity of the population, and an external database is additionally arranged and used for updating and storing the Pareto optimal solution set iterated every time. The algorithm presented herein shows good convergence and diversity through example validation analysis.
The disturbance event can be pertinently responded by adopting real-time dynamic scheduling, so that more accurate production guidance can be provided for a workshop, and the ordered execution of production tasks is guaranteed.
Claims (8)
1. A method for scheduling a flexible production workshop of a spiral lion powder production enterprise is characterized by comprising the following steps:
1) generating an initial scheduling scheme through a genetic algorithm according to the workshop state at the initial moment;
2) executing the production task according to the scheduling scheme, detecting the production state of the workshop in real time, executing the step 3) if a disturbance event occurs in the execution process of the production task, and executing the step 4) if the disturbance event does not occur in the execution process of the production task;
3) executing a rescheduling trigger mechanism, quantitatively representing the influence degree of the disturbance event on the original scheduling scheme by using an offset coefficient theta, and passing through a threshold value thetasIndicates the acceptable variation degree of the workshop production condition if theta is larger than or equal to thetasThen execute the full rescheduling strategy if theta<θsIf yes, processing according to the right-shift rescheduling strategy, and returning to execute the step 2) after the processing is finished;
4) and executing the production tasks to complete all the production tasks.
2. The method for scheduling the flexible production workshop of the lion powder production enterprise as claimed in claim 1, wherein in the step 2), the workshop production state changes include machine failures and emergency insertion.
3. The method for scheduling the flexible production workshop of the lion powder production enterprise as claimed in claim 1, wherein in the step 3), when the complete rescheduling is triggered, information of a finished workpiece set, a processed workpiece set, an unprocessed workpiece set and an unscheduled workpiece set is acquired, a workshop production state is updated according to the workpiece set, and then a dynamic scheduling model in the current workshop state is solved through a genetic algorithm to acquire a rescheduling scheme with excellent performance.
4. The method for scheduling the flexible production workshop of the screw lion powder production enterprise according to claim 1, wherein when the rescheduling trigger mechanism is executed in the step 3), the method comprises the following steps:
1) the high efficiency means that the delay of production tasks is avoided by changing processing equipment and processing sequence of the working procedures;
2) the stability refers to that the processing equipment and the processing sequence of the workpiece procedure in the original scheduling scheme are preferentially maintained unchanged, and dynamic adjustment is made.
5. The method for scheduling the flexible production workshop of the screw lion powder production enterprise as claimed in claim 1, wherein the expression of the offset coefficient θ is as follows:
θ＝θc+γθd
θcrepresenting the difference, theta, between the actual maximum completion time and the maximum completion time of the original scheduling scheme after being affected by the disturbance eventdThe difference value of the actual total workpiece delay and the total workpiece delay of the original scheduling scheme is represented, and the penalty factor gamma represents the penalty of delay caused by the fact that the workpieces cannot be completed in time.
6. The method for scheduling the flexible production workshop of the screw lion powder production enterprise as claimed in claim 5, wherein the completion time of the workpiece affected by the disturbance event is calculated by a method of a correlation tree of subsequent processes, and the calculation sequence is as follows:
1) taking the process directly influenced by the disturbance event as a root node;
2) finding out the indirectly influenced subsequent processes according to the original scheduling scheme, and taking the found processes as new nodes;
3) and constructing the affected subsequent process association tree layer by layer until the nodes on the association tree do not have the subsequent process.
7. The method for scheduling the flexible production workshop of the lion powder production enterprise as claimed in claim 3, wherein in the step 1), an initial scheduling scheme is generated through a genetic algorithm, and a crossover operator in the genetic algorithm is executed before a selection operator.
8. The method for scheduling the flexible production workshop of the lion powder production enterprise as claimed in claim 3, wherein in the step 3), the rescheduling scheme is generated and obtained through a genetic algorithm, and a crossover operator in the genetic algorithm is executed before a selection operator.
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