CN114742377A - Method, apparatus, medium, and program product for processing production task of product - Google Patents
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Abstract
The application provides a method, equipment, medium and program product for processing production tasks of products. The method comprises the following steps: receiving input production constraints of the products, and randomly setting the starting time and the finishing time of each production task according to the production period of each product, the task time corresponding to each production task and the final finishing time of all the products; randomly setting the production sequence of the production tasks according to the production number of the products, the completion of the production task of each product and the precedence relationship among the production tasks; randomly generating a first preset number of initialization task scheduling tables according to the starting time and the finishing time of each production task and the production sequence of the production tasks; and performing iterative training for preset times on the initialization task scheduling lists with the first preset number by using a genetic algorithm, and determining the final task scheduling list of the product. The method can enable the task scheduling list to meet the production requirements of a plurality of products on the production line, and therefore efficient streamlined production is achieved.
Description
Technical Field
The present application relates to the field of manufacturing, and in particular, to a method, an apparatus, a medium, and a program product for processing a production task of a product.
Background
Due to the advantages of high working efficiency, labor resource saving and the like, the production line is more and more popular in the fields of industrial manufacturing and the like, and the task scheduling list is an important foundation of the production line.
The task scheduling table of the existing production line is determined for a single product. First, the computer may receive production constraints for a single product input by a worker, which may include time constraints, resource constraints, and production task order constraints for producing a single product, among other constraints. The computer may build a formal model based on the received production constraints. Then, the computer can obtain an optimal task scheduling list by solving the optimal solution of the formal model, wherein the optimal task scheduling list is the minimum total time consumption for completing the production of the product under the condition of meeting the production constraint condition and comprises the production sequence of each production task, the start time and the completion time of the task and the like. The computer can output the task scheduling list to the product production control equipment, so that the product production control equipment can utilize the optimal task scheduling list to perform production control.
However, the production line produces a plurality of products in batch, and the task scheduling list generated in the prior art cannot meet the production requirements of a plurality of products on the production line, and cannot realize efficient streamlined production.
Disclosure of Invention
The application provides a production task processing method, equipment, medium and program product of a product, which are used for solving the problems that a task scheduling list generated in the prior art cannot meet the production requirements of a plurality of products on a production line and cannot realize efficient streamlined production.
In a first aspect, the present application provides a method for processing a production task of a product, including:
receiving input production constraints of products, wherein the production constraints of the products comprise the production number of the products, the production period of each product, the final completion time of all the products, the production task of each product, the task time corresponding to each production task and the precedence relationship among the production tasks;
randomly setting the starting time and the finishing time of each production task according to the production period of each product, the task time corresponding to each production task and the final finishing time of all the products;
randomly setting the production sequence of the production tasks according to the production number of the products, the completion of the production task of each product and the precedence relationship among the production tasks;
randomly generating a first preset number of initialization task scheduling lists according to the starting time and the finishing time of each production task and the production sequence of the production tasks;
and performing iterative training for a preset number of times on the initialization task scheduling lists with the first preset number by using a genetic algorithm, and determining the final task scheduling list of the product.
In a possible implementation manner, the performing, by using a genetic algorithm, iterative training for a preset number of times on the first preset number of initialization task scheduling tables to determine a final task scheduling table of the product specifically includes:
calculating a non-adaptability value of each initialization task scheduling table, wherein the non-adaptability value is set according to the production requirement of the product;
sequencing the initialization task scheduling list according to the sequence of the inadaptability values from small to large to generate a task scheduling list sequence;
determining a reserved task scheduling list with a first preset number and the remaining tasks to be processed in the task scheduling list sequence;
generating an updated task scheduling list set according to the reserved task scheduling list and the task scheduling list to be processed;
continuing iterative training according to the updated task scheduling list set until the iterative training of preset times is finished;
and determining a task scheduling table corresponding to the minimum inadaptability value in the inadaptability values obtained by the last iterative training.
In a possible implementation manner, the generating an updated task scheduling list set according to the reserved task scheduling list and the to-be-processed task scheduling list specifically includes:
combining the task scheduling lists to be processed in pairs at random to generate a combined task scheduling list with a third preset number, wherein the third preset number is equal to the difference between the first preset number and the second preset number;
performing cross operation on the combined task scheduling list according to a preset cross probability to generate a crossed task scheduling list, wherein the cross operation is performed based on the precedence relationship;
performing exchange variation operation on the crossed task scheduling list according to a preset variation probability to generate a varied task scheduling list;
and generating an updated task scheduling list set according to the reserved task scheduling list and the mutated task scheduling list.
In a possible implementation manner, the calculating the inadaptation value of each initialization task scheduling table specifically includes:
calculating a non-adaptability value of each initialization task scheduling table by using the following formula:
wherein f represents a non-fitness value, λ represents a weight parameter, R represents a set of resources required to complete the product, and R representskRepresents the k-th resource in the set of resources, the UkIndicating the resource utilization of the k-th resource,
t represents the production cycle of the product, piIndicating the task duration of the ith production task; b iskIndicating the amount of demand on the kth resource,
said E represents the final finish time of the entire product, said
Indicating the start time of the ith production task of the jth product; the above-mentioned
Indicating the completion time of the ith production task of the jth product; b isikRepresents the required quantity of the kth resource of the ith production task, the t represents the time, the
Representing an indicative function when satisfied
When the temperature of the water is higher than the set temperature,
when not satisfied with
When the temperature of the water is higher than the set temperature,
in one possible embodiment, the method further comprises:
and if the inadaptation values of the initialization task scheduling tables are the same, determining the total production time corresponding to the initialization task scheduling tables, and sequencing the initialization task scheduling tables according to the sequence of the total production time from small to large.
In a possible implementation manner, after the calculating of the non-adaptability value of each of the initialization task scheduling tables, the method further includes:
determining a minimum one of the non-fitness values;
judging whether the minimum inadaptation value is larger than a preset inadaptation value or not;
if not, updating the preset inadaptability value according to the minimum inadaptability value;
and if so, updating the required quantity of the resources of the initialization task shift schedule, and recalculating the non-adaptability values of the initialization task shift schedule according to the updated required quantity of the resources until the minimum non-adaptability value in the non-adaptability values is less than or equal to the preset non-adaptability value.
In a possible implementation manner, the updating the required quantity of the resource of the initialization task scheduling table specifically includes:
determining the resource utilization rate of each resource of the initialization task scheduling table corresponding to the minimum inadaptability value;
judging whether the minimum resource utilization rate in the resource utilization rates is larger than a preset threshold value or not;
if so, determining the required quantity of the resources corresponding to the minimum resource utilization rate, and subtracting a preset quantity difference value from the required quantity of the resources to generate the updated required quantity of the resources;
if not, determining the resource utilization rate with the minimum difference value from the minimum resource utilization rate, taking the resource utilization rate with the minimum difference value as the minimum resource utilization rate, and executing the step of judging whether the minimum resource utilization rate is greater than a preset threshold value.
In a second aspect, the present application provides a production task processing apparatus for a product, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer execution instructions;
the processor executes the computer-executable instructions stored by the memory to implement the methods described above.
In a third aspect, the present application provides a computer-readable storage medium having stored thereon computer-executable instructions for implementing the above-mentioned method when executed by a processor.
In a fourth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the method described above.
The method for processing the production task of the product can receive input production constraints of the product, wherein the production constraints of the product comprise the production number of the product, the production period of each product, the final completion time of all the products, the completion of the production task of each product, the task time corresponding to each production task and the precedence relationship among the production tasks; randomly setting the starting time and the finishing time of each production task according to the production period of each product, the task time corresponding to each production task and the final finishing time of all the products; randomly setting the production sequence of the production tasks according to the production number of the products, the completion of the production task of each product and the precedence relationship among the production tasks; randomly generating a first preset number of initialization task scheduling tables according to the starting time and the finishing time of each production task and the production sequence of the production tasks; and performing iterative training for preset times on the initialization task scheduling lists with the first preset number by using a genetic algorithm, and determining the final task scheduling list of the product. The method of the application increases the production number of products, the production period of each product and the final completion time of all the products on the basis of the existing production constraint. Because the production of the product has periodicity, and the requirements of different production moments on resources also have periodicity, the production period of the product can be increased in production constraint, so that the task scheduling table of the product also has periodicity. The periodic task scheduling table can adapt to the actual infinite production of a production line, and the production requirements of a plurality of products on the production line are met, so that efficient streamlined production is realized. Further, there are yield and time limitations in the production of products on a production line, i.e., a certain amount of product needs to be produced within a certain time period. Therefore, the production number of the products and the final completion time of all the products can be increased in the production constraint, so that the task scheduling list can further meet the requirements of product production on the production line. After a plurality of task scheduling tables are generated according to the random setting of the production constraints, an optimal task scheduling table of the task scheduling tables can be calculated by using a genetic algorithm. Through the arrangement, the optimal task scheduling table can meet production constraints and production requirements of a plurality of products on a production line, and efficient streamlined production is achieved. Furthermore, the production cycle of each product is increased in the existing production constraint, and the production efficiency and the orderliness of assembly line production can be ensured, so that the production of a plurality of products on the assembly line can be orderly carried out according to certain production efficiency.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
FIG. 1 is a system architecture diagram according to an embodiment of the present application;
FIG. 2 is a flow chart of a method for processing a production task of a product according to an embodiment of the present application;
FIG. 3 is a flow chart of a method for processing a production task of a product according to another embodiment of the present application;
fig. 4 is a schematic structural diagram of a production task processing device for a product according to an embodiment of the present application.
Reference numerals: 1. a computer; 2. a production control device; 3. production line.
Specific embodiments of the present application have been shown by way of example in the drawings and will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The task scheduling table of the existing production line is determined for a single product. The computer receives the production constraint conditions of a single product input by the staff, wherein the production constraint conditions can comprise all production tasks for producing the product, the task time length corresponding to each production task, the precedence relationship of each production task, the type of resources required by each production task and the required quantity of each resource. The computer may build a formal model, such as a mathematical model including a set of tasks, a task duration vector, a set of relationships, and the like, based on the received production constraints. Then, the computer can obtain an optimal task scheduling list by solving an optimal solution of the formal model, wherein the optimal solution is the minimum total time consumed for completing the production of the product under the condition of meeting the production constraint condition. The optimal task scheduling list comprises the production sequence of each production task of the product, the starting time and the finishing time corresponding to each production task, the resource type and the resource quantity corresponding to each production task and the like. The computer can output the task scheduling list to the product production control equipment, so that the product production control equipment can utilize the optimal task scheduling list to perform production control, and the production of a product is completed.
However, the line does not produce only one product at a time, but rather runs continuously to produce a near infinite number of products in batch, with multiple products being produced on the line. The task scheduling table for a single product generated in the prior art cannot meet the production requirements of a plurality of products on a production line, and efficient streamlined production cannot be realized. When a plurality of products are produced simultaneously on a production line, the existing task scheduling table for a single product cannot be utilized, and the data in the task scheduling table cannot be directly multiplied by the number of the products to be utilized. For example, a third production task requires 1 worker and 3 materials a and 4 materials B when a product is produced separately, while a third production task is not simple requiring only 5 workers and 15 materials a and 20 materials B when 5 products are produced. Because the production line has characteristics such as orderliness and beat nature, these 5 staff and 15 material A and 20 material B will possess certain precedence and go on in grades, need 1 staff and 3 material A and 4 material B at every turn, for example, first staff uses 3 material A1 and 4 material B1, and second staff uses 3 material A2 and 4 material B2 to go on in grades in order.
The application provides a production task processing method of a product, and aims to solve the technical problems in the prior art. The method increases the production number of products, the production period of each product and the final completion time of all the products on the basis of the existing production constraint. Because the production of the product has periodicity, and the requirements of different production moments on resources also have periodicity, the production period of the product can be increased in production constraint, so that the task scheduling table of the product also has periodicity. The periodic task scheduling table can adapt to the reality of infinite production of a production line, and meets the production requirements of a plurality of products on the production line, thereby realizing efficient streamlined production. Further, there are yield and time limitations in the production of products on a production line, i.e., a certain amount of product needs to be produced within a certain time period. Therefore, the production number of the products and the final completion time of all the products can be increased in the production constraint, so that the task scheduling list can further meet the requirements of product production on the production line. After a plurality of task scheduling tables are generated according to the random setting of the production constraints, an optimal task scheduling table of the task scheduling tables can be calculated by using a genetic algorithm. Through the arrangement, the optimal task scheduling table can meet production constraints and production requirements of a plurality of products on a production line, and efficient streamlined production is achieved. In addition, the production cycle of each product is increased in the existing production constraint, and the production efficiency and the orderliness of assembly line production can be ensured, so that the production of a plurality of products on the assembly line can be orderly carried out according to certain production efficiency.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 is a system architecture diagram according to an embodiment of the present application, and as shown in fig. 1, a computer 1 receives production constraints based on multiple product productions input by a worker, and after the computer 1 randomly generates a certain task shift table based on the received production constraints, the task shift table is optimized with a resource minimization as an optimization target to obtain an optimal task shift table. The computer 1 outputs the task scheduling list to the product production control device 2, and the production control device 2 controls the production assembly line 3 to complete production of a plurality of products according to the task scheduling list.
Example one
Fig. 2 is a flowchart of a method for processing a production task of a product according to an embodiment of the present application, where an execution main body of the method for processing a production task of a product according to the embodiment of the present application may be a production task processing device or a server, and the method for processing a production task of a product according to the present embodiment is described with the execution main body being a production task processing device. As shown in fig. 2, the method for processing the production task of the product may include the steps of:
s101: receiving input production constraints of the products, wherein the production constraints of the products can include the production number of the products, the production period of each product, the final completion time of all the products, the production task of completing each product, the task time corresponding to each production task and the precedence relationship among the production tasks.
In this embodiment, since the production of the product has periodicity, and the demands for resources at different production times also have periodicity, the production period of the product can be increased in the production constraint, so that the task scheduling table of the product also has periodicity. The periodic task scheduling table can adapt to the reality of infinite production of a production line, and meets the production requirements of a plurality of products on the production line, thereby realizing efficient streamlined production.
Further, there are yield and time limitations in the production of products on a production line, i.e., a certain amount of product needs to be produced within a certain time period. Therefore, the production number of the products and the final completion time of all the products can be increased in the production constraint, so that the task scheduling list can further meet the requirements of product production on the production line.
Illustratively, a factory will produce 10 trolleys, which input production constraints as follows: the production cycle of the vehicle is 1h, the final completion time is 10h, and the production constraints of 1 trolley are shown in the following table 1:
TABLE 1
| Production tasks | Making holes at connecting positions | Structural inspection | Component mounting | Polishing of |
| Task duration | 5min | 10min | 15min | 30min |
| Resource categories | Hole making gun | Structural group member | Installing group personnel; screw set | Polishing group member |
| Amount of resources | 1 | 1 | 1；4 | 1 |
The precedence relationship of each task is as follows: the assembly installation must precede the structural inspection.
In the present embodiment, the above-mentioned production constraints can be flexibly set by those skilled in the art according to the actual production, and are not limited herein.
In the present embodiment, the production cycle of each product can be regarded as the tact of industrial production of the product. The industrial production cycle is usually considered with the craft (product) as a whole, for example, the whole process cycle of manufacturing an airplane is one week, which means that a fixed cycle exists in all the sub-processes between the airplane with the level 1 and the airplane with the level 2. Taking the assembly wing as an example, the station 1 executes the assembly wing of the rack 1, the station 1 can assemble the assembly wing of the rack 2 at the same time after a week, and similarly, the station 2 executes the assembly machine head of the rack 1, and the station 2 can execute the assembly machine head of the rack 2 at the same time after a week. The time interval (tempo) for all the same procedures for rack 1 and rack 2 was one week.
In the present embodiment, by introducing the concept of a production cycle (tact) in the production constraints of products, and setting the tact of each product to be the same, the time to complete any one product is fixed and calculable.
S102: and randomly setting the starting time and the finishing time of each production task according to the production period of each product, the task time corresponding to each production task and the final finishing time of all the products.
In this embodiment, when the start time and the completion time of each production task are randomly set, the following time constraint conditions need to be satisfied:
wherein n represents the number of productions of a product, m represents the last production job of the product,
indicating the completion time of the last production task of the last product.
Indicating the start time of a first production job for a first product. E represents the final finish time of the entire product.
Indicating the start time of the ith production task of the jth product;
indicating the completion time of the ith production task for the jth product；piThe task duration corresponding to the ith production task is represented;
indicating a start time of an ith production job for the first product;
indicating a completion time of an ith production task for the first product; t represents the production cycle of each product.
For example, if the start time of the third production task of the first product is 10:00, and the task time corresponding to the third production task is 20min, the completion time of the third production task of the first product may be set to 10:20, or may be set to 10:21 or 10: 22.
In this embodiment, the production cycle of each product is the same, so that the time for completing one product is fixed, the start time and the completion time of each production task are conveniently set, the production efficiency and the orderliness of the flow line production are ensured, and the production practice of the flow line is met.
S103: and randomly setting the production sequence of each production task according to the production number of the products, the completion of the production task of each product and the precedence relationship among the production tasks.
In this embodiment, the randomly set production sequence is to satisfy that the production tasks corresponding to each product are continuous, and the production tasks of different products cannot be set at intervals, for example: the production task of a second product cannot be inserted in the sequence of production tasks of a first product. The tasks of a product are required to conform to the precedence relationship, for example, if task A is constrained to precede task B, the production order can be task A, task C, task B, but not task B, task A, task C.
S104: and randomly generating a first preset number of initialization task scheduling lists according to the starting time and the finishing time of each production task and the production sequence of each production task.
In this embodiment, the start time and the completion time of each production task generated in step S102 and the production sequence of each production task generated in step S103 may be randomly combined, so that the generated initialization task shift list has more possibilities, and a sufficient number of samples are generated for subsequent training.
For example, a task scheduling table randomly set according to the input production constraints may be shown in table 2 below:
TABLE 2
| Production tasks | Starting time | Completion time | Resource categories | Amount of resources |
| 1 st trolley connecting position hole making | 8:30 | 8:35 | Hole making gun | 1 |
| 1 st trolley assembly installation | 8:35 | 8:50 | Installing group personnel; nut | 1；4 |
| 1 st trolley structural inspection | 8:50 | 9:00 | Structural group member | 1 |
| 1 st trolley polishing | 9:00 | 9:30 | Polishing group member | 1 |
| 2 nd trolley connecting position hole making | 9:30 | 9:35 | Hole making gun | 1 |
| 2 nd trolley assembly installation | 9:35 | 9:50 | Installing group personnel; nut | 1；4 |
| 2 nd trolley structural inspection | 9:50 | 10:00 | Structural group member | 1 |
| 2 nd trolley polishing | 10:00 | 10:30 | Polishing group member | 1 |
| 3 rd trolley connecting position hole making | 11:30 | 11:35 | Hole making gun | 1 |
| 3 rd trolley component installation | 11:35 | 11:50 | Installing group personnel; nut | 1；4 |
| 3 rd trolley structural inspection | 13:00 | 13:10 | Structural group member | 1 |
| 3 rd trolley polishing | 13:10 | 13:40 | Polishing group member | 1 |
| ... | ... | ... | ... | ... |
It should be noted that, the task scheduling table in table 2 only lists the start time and the completion time of the production tasks of some vehicles, and since the production cycle of the vehicle is 1h, the start time and the completion time of each production task of the remaining vehicles can be obtained according to the production cycle.
S105: and performing iterative training for a preset number of times on the initialization task scheduling lists with the first preset number by using a genetic algorithm, and determining the final task scheduling list of the product. Please see example two for the detailed determination of the final task scheduling list of the product.
In this embodiment, a person skilled in the art can flexibly set the first preset number and the preset times, which is not limited herein.
In the present embodiment, the number of products produced, the production period of each product, and the final completion time of the entire product are increased based on the existing production constraints. Because the production of the product has periodicity, and the requirements of different production moments on resources also have periodicity, the production period of the product can be increased in production constraint, so that the task scheduling table of the product also has periodicity. The periodic task scheduling table can adapt to the actual infinite production of a production line, and the production requirements of a plurality of products on the production line are met, so that efficient streamlined production is realized. Further, there are yield and time limitations in the production of products on a production line, i.e., a certain amount of product needs to be produced within a certain time period. Therefore, the production number of the products and the final completion time of all the products can be increased in the production constraint, so that the task scheduling list can further meet the requirements of product production on the production line. After a plurality of task scheduling tables are generated according to the random setting of the production constraints, an optimal task scheduling table of the task scheduling tables can be calculated by using a genetic algorithm. Through the arrangement, the optimal task scheduling table can meet production constraints and production requirements of a plurality of products on a production line, and efficient streamlined production is achieved. Furthermore, the production cycle of each product is increased in the existing production constraint, and the production efficiency and the orderliness of the flow line production can be ensured, so that the production of a plurality of products on the flow line can be orderly carried out according to certain production efficiency, and the production practice of the flow line is met.
In the following, step S105 in the first embodiment is implemented by using a genetic algorithm to perform iterative training on the initialization task shift table with the first preset number for the preset number of times, and specific contents of the task shift table for determining the final product are described in detail.
Example two
Fig. 3 is a flowchart of a method for processing a production task of a product according to an embodiment of the present application, where an execution main body of the method for processing a production task of a product according to the embodiment of the present application may be a production task processing device or a server, and the method for processing a production task of a product according to the embodiment is described with the execution main body being a production task processing device. As shown in fig. 3, the method for processing the production task of the product may include the steps of:
s201: and calculating the inadaptation value of each initialization task scheduling table, wherein the inadaptation value is set according to the production requirement of the product.
In this embodiment, the inadaptability value may be set according to the production requirement of the product, for example, if the production requirement of a certain product is that the required amount of the resource is the minimum, the inadaptability value may be the total amount of the resource required to produce the product; if the production demand of a certain product is that the utilization rate of the resource is maximum, the inadaptability value can be the utilization rate of the resource when the product is produced; if the production requirement for a product is that the production time be the shortest, then the incompatibility value may be the total time to produce the product.
In one possible embodiment, the calculating of the inadaptation value of each initialization task shift table in step S201 may include: the inadaptation value of each initialization task shift table is calculated using the following equation (1):
wherein f represents a non-fitness value, λ represents a weight parameter, R represents a set of resources required to complete the product, R represents a weight parameter, andkrepresents the k-th resource, U, in the set of resourceskDenotes the resource utilization of the k-th resource, BkRepresenting the amount of demand on the kth resource.
It should be noted that the production constraint of the product also includes a resource constraint, and the resource constraint includes the resources required by each task and the required amount of each resource. The initialization task scheduling list also comprises the quantity of the demands of each task on a certain resource, and the quantity of the demands of each task on the certain resource is randomly set according to resource constraints. As shown in table 1 in embodiment 1, different tasks require different types of resources (personnel, materials, etc.) during vehicle production, and each resource also requires a certain amount of resources, for example, during vehicle production, 1 installation crew and 4 screw components are required for component installation, and 1 installation crew is required for structural inspection.
In this embodiment, the inadaptability value may represent that the production requirement of the product is the minimum quantity of the required resources while maximizing the resource utilization rate, and the weight parameter λ is a balance between the quantity of the required resources and the resource utilization rate, which may be flexibly set by a person skilled in the art according to the production requirement.
In the present embodiment, the resource utilization rate U of the kth resourcekIs calculated by the following formula (2):
wherein T represents the production cycle of the product, piIndicating the task duration of the ith production task; b iskRepresenting the number of demands on the k-th resource, bikIndicating the required amount of the kth resource by the ith production task.
In the present embodiment, the required amount of the kth resource is calculated by using the following formula (3):
wherein E represents the final finish time of the entire product,
indicating the start time of the ith production task of the jth product;
indicating the completion time of the ith production task of the jth product; bikIndicating the required quantity of the kth resource by the ith production task, t indicating the time,
represents an indicative function when satisfied
When the utility model is used, the water is discharged,
when it is not satisfied
When the temperature of the water is higher than the set temperature,
in the embodiment, the non-adaptive value is set to be the minimum resource demand quantity through the formula (1) and simultaneously the resource utilization rate is maximized, so that the optimal task scheduling table which requires the minimum resources and maximizes the resource utilization rate on the basis of meeting the production constraint can be solved by utilizing the non-adaptive value subsequently, the resources required by the production of a product assembly line are saved, and the resource utilization rate is improved. Furthermore, the relation between the resource demand quantity and the resource utilization rate can be balanced by utilizing the weight parameter lambda, when the production demand of the product is biased to the minimum resource demand quantity, the weight parameter lambda can be increased, and when the production demand of the product is biased to the maximum resource utilization rate, the weight parameter lambda can be reduced.
In a possible embodiment, after calculating the non-adaptability value of each initialization task shift table in step S201, the method may further include: determining a minimum non-adaptability value of the non-adaptability values; judging whether the minimum inadaptation value is larger than a preset inadaptation value or not; if not, updating the preset inadaptability value according to the minimum inadaptability value; and if so, updating the required quantity of the resources of the initialization task shift schedule, and recalculating the non-adaptability values of the initialization task shift schedule according to the updated required quantity of the resources until the minimum non-adaptability value in the non-adaptability values is less than or equal to a preset non-adaptability value.
In this embodiment, a person skilled in the art may set a preset inadaptation value in advance according to experience or an actual production process, and the task shift table corresponding to the preset inadaptation value may be used as a basic task shift table generated according to experience. And then, after the inadaptability value is calculated in each round of iterative training, selecting the minimum inadaptability value, wherein the task scheduling table corresponding to the minimum inadaptability value is the optimal task scheduling table in the round of training. And finally, comparing the minimum inadaptability value with a preset inadaptability value, and if the minimum inadaptability value is smaller than the preset inadaptability value, indicating that the task scheduling table corresponding to the minimum inadaptability value is superior to the task scheduling table corresponding to the preset inadaptability value. At this time, the minimum inadaptation value can be used as a new preset inadaptation value, and is compared with the minimum inadaptation value calculated by subsequent iterative training, and an optimal task scheduling list is obtained by continuous comparison. If the minimum inadaptation value is not smaller than the preset inadaptation value, it is indicated that the task scheduling table corresponding to the minimum inadaptation value is not good as a basic task scheduling table generated by technicians according to experience, and the required quantity of resources of the initialized task scheduling table needs to be updated in time, so that the task scheduling table with the minimum required quantity of resources is solved.
In the present embodiment, since the resource utilization rate is calculated based on the required amount of the resource, the minimum inadaptation degree can be regarded as the minimization of the required amount of the resource. Therefore, when the minimum inadaptation value is not less than the preset inadaptation value, the inadaptation value can be reduced by updating the required quantity of the resources of the initialization task scheduling table.
In this embodiment, since the required quantity of each task in the initialization task shift table for a certain resource is randomly set according to the resource constraint in the production constraint, it is cumbersome to calculate the optimal solution of the required quantity of the resource from the numerous initialization task shift tables, and the calculation amount is large. Therefore, technicians can set the required quantity of a group of resources in advance according to experience or an actual production process, can calculate a preset inadaptation value according to the required quantity of the resources, and then compare the preset inadaptation value with the inadaptation value calculated by each iteration training, so that the calculation efficiency of the optimal solution of the required quantity of the resources is improved, and the task scheduling table with the minimum required quantity of the resources is efficiently and accurately calculated.
In a possible embodiment, the updating the required quantity of the resources of the initialization task shift table may specifically include: determining the resource utilization rate of each resource by the initialization task scheduling table corresponding to the minimum inadaptability value; judging whether the minimum resource utilization rate in the resource utilization rates is greater than a preset threshold value or not; if so, determining the required quantity of the resources corresponding to the minimum resource utilization rate, and subtracting a preset quantity difference value from the required quantity of the resources to generate the updated required quantity of the resources; if not, determining the resource utilization rate with the minimum difference value from the minimum resource utilization rate, taking the resource utilization rate with the minimum difference value as the minimum resource utilization rate, and executing the step of judging whether the minimum resource utilization rate is greater than a preset threshold value.
In this embodiment, the preset threshold may be flexibly set by a person skilled in the art, for example, the preset threshold may be 1, that is, the resource utilization rate is already maximum at this time, and the number cannot be reduced any more. In addition, the preset number difference can be flexibly set by a person skilled in the art, for example, the preset number difference can be 1, that is, the number required each time is reduced by one, and is gradually reduced to the minimum number.
In this embodiment, the minimum inadaptability value is not smaller than the preset inadaptability value, which indicates that the number of the required resources of the initialization task scheduling table corresponding to the minimum inadaptability value is large, so that the number of the required resources is not required during actual production, and the required number of the resources is required to be reduced. When the required quantity of the resources is reduced, the required quantity of the resources corresponding to the minimum resource utilization rate can be reduced firstly, if the required quantity of the resources corresponding to the minimum resource utilization rate cannot be reduced, the required quantity of the resources corresponding to the second smallest resource utilization rate is reduced, and by analogy, the required quantity of the resources of the initialization task scheduling list is updated. Through the arrangement, the required quantity of the corresponding resources is reduced by utilizing the resource utilization rate of each resource, the calculation efficiency of the optimal solution of the required quantity of the resources can be further improved, and the task scheduling list with the minimum required quantity of the resources can be efficiently and accurately calculated.
S202: and sequencing the initialized task scheduling list according to the sequence of the inadaptability values from small to large to generate a task scheduling list sequence.
In this embodiment, the production requirements of the products in the above formula (1) are the minimum values corresponding to the production requirements of the products in the order from small to large of the inadaptability values, and then the inadaptability values are arranged in the order from small to large; if the production requirement of the product is the maximum value, the inadaptability values are arranged in the order from large to small.
In a possible embodiment, if the inadaptability values of the initialization task shift tables calculated by using the above formula (1) are the same, the total production time length corresponding to the initialization task shift tables is determined, and the initialization task shift tables are sorted according to the sequence of the total production time length from small to large.
In this embodiment, when the inadaptability value is calculated, there may be a case where the inadaptability values of some two or more initialization task shift tables are the same, at this time, the initialization task shift tables may be sorted according to the next production requirement of the product, for example, the production duration, and by such setting, the optimization degrees of the initialization task shift tables are further distinguished and sorted, so that a sample of the next round of iterative training is generated according to the task shift table sequence in the following.
In this embodiment, the initialization task scheduling tables with the same inadaptability values may also be sorted according to other numerical values, and the numerical values according to the inadaptability values may be set according to the production requirements of the products.
S203: and determining the reserved task scheduling lists with the first second preset number and the remaining task scheduling lists to be processed in the task scheduling list sequence.
In this embodiment, the inadaptability values are arranged in the order from small to large, and the task shift table is better when the inadaptability values are smaller, so that the task shift tables with the second preset number in the task shift table sequence can be regarded as better individuals, and can be retained, and the remaining individuals need to be processed to generate samples for the next round of iterative training. The second predetermined number can be flexibly set by those skilled in the art, and is not limited thereto.
S204: and generating an updated task scheduling list set according to the reserved task scheduling list and the task scheduling list to be processed.
In this embodiment, the updated task scheduling list set may be used as a sample for the next round of iterative training.
In a possible embodiment, the step S204 of generating an updated task scheduling list set according to the reserved task scheduling list and the task scheduling list to be processed may include: combining the task scheduling lists to be processed in pairs randomly to generate a combined task scheduling list with a third preset number, wherein the third preset number is equal to the difference between the first preset number and the second preset number; performing cross operation on the combined task scheduling list according to a preset cross probability to generate a crossed task scheduling list, wherein the cross operation is performed based on an order relation; performing exchange variation operation on the crossed task scheduling list according to a preset variation probability to generate a varied task scheduling list; and generating an updated task scheduling list set according to the reserved task scheduling list and the mutated task scheduling list.
In this embodiment, the number of training samples in each iteration training is the same, that is, the number of task shift tables is the same, so after the second preset number of task shift tables is reserved, the task shift tables obtained by subtracting the second preset number from the first preset number are generated.
In the embodiment, the better task scheduling list is reserved, the worse task scheduling list to be processed can be randomly combined in pairs to generate a combined task scheduling list, and then the combined task scheduling list is subjected to cross variation to generate a new task scheduling list. Through the arrangement, more new individuals can be generated on the premise of keeping better individuals, and the calculation efficiency of the optimal task scheduling list is improved.
S205: and continuing the iterative training according to the updated task scheduling list set until the iterative training of the preset times is completed.
S206: and determining a task scheduling table corresponding to the minimum inadaptability value in the inadaptability values obtained by the last iterative training, wherein the task scheduling table is an optimal task scheduling table with the minimum resource demand quantity and the maximum resource utilization rate.
In this embodiment, after the initialization task scheduling table satisfying the production constraint is randomly generated, the initialization task scheduling table may be optimized by using the non-adaptive value set according to the production requirement of the product as an optimization target, so as to calculate an optimal task scheduling table. In the optimization process, a better reserved task scheduling table and a poorer task scheduling table to be processed can be determined according to the inadaptability value of the initialized task scheduling table. And randomly combining and cross-mutating the poor task scheduling list to be processed to generate new individuals, taking the new individuals and the better reserved individuals as samples of the next round of iterative training, and repeating the iterative training in the same way, so that the task scheduling list corresponding to the minimum inadaptability value in the last iterative training is the optimal task scheduling list. Through the arrangement, the optimal task scheduling list with the minimum resource demand quantity and the maximum resource utilization rate can be efficiently and accurately obtained.
The following describes a method for processing a production task of a product according to the present application with a specific example.
EXAMPLE III
In a specific embodiment, a factory needs to complete the flow-line production of a certain number of airplanes, so that staff needs to set a specific task scheduling list of airplane production work through a computer integrated with a production task processing device, which includes the following specific steps:
the method comprises the steps of firstly, receiving production constraints of a product input by workers, wherein the production constraints comprise the production number of airplanes, the assembly period of one airplane, the assembly completion time of all the airplanes, various assembly tasks of one airplane, the task duration corresponding to each assembly task and the precedence relationship among the assembly tasks.
And secondly, randomly setting the starting time and the finishing time of each assembly task according to the assembly period of one airplane, the task time corresponding to each assembly task and the assembly finishing time of all airplanes.
And thirdly, randomly setting the assembly sequence of each assembly task according to the production number of the airplanes, each assembly task of one airplane and the precedence relationship among the assembly tasks.
And fourthly, randomly generating 50 initialization task shift lists according to the starting time and the finishing time of each assembly task and the assembly sequence of each assembly task.
And fifthly, calculating the inadaptability value of each initialization task shift schedule, wherein the inadaptability value is the quantity of resources required by the aircraft production.
And sixthly, sequencing the initialized task scheduling list according to the sequence of the inadaptability values from small to large so as to generate a task scheduling list sequence.
And seventhly, determining the reserved task shift list of the first 10 tasks and the remaining 40 to-be-processed task shift lists in the task shift list sequence.
And eighthly, generating an updated task scheduling list set according to the 10 reserved task scheduling lists and the 40 to-be-processed task scheduling lists, wherein the set comprises 50 task scheduling lists.
And step nine, continuing iterative training according to the 50 updated task scheduling table sets until 50 times of iterative training is completed.
And step ten, determining a task scheduling table corresponding to the minimum inadaptability value in the inadaptability values obtained by the 50 th iterative training, wherein the task scheduling table is the optimal aircraft production task scheduling table with the minimum resource demand quantity.
Fig. 4 is a schematic structural diagram of a production task processing device of a product according to an embodiment of the present application, and as shown in fig. 4, the production task processing device of the product includes: a processor 101, and a memory 102 communicatively coupled to the processor 101; the memory 102 stores computer execution instructions; the processor 101 executes the computer execution instructions stored in the memory 102 to implement the steps of the production task processing method of the product in the above method embodiments.
The production task processing device of the product may be a stand-alone device or a part of a production control device of the product, and the processor 101 and the memory 102 may adopt existing hardware of a communication network.
In the production task processing device of the above product, the memory 102 and the processor 101 are electrically connected directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines, such as a bus. The memory 102 stores computer-executable instructions for implementing the data access control method, including at least one software functional module that can be stored in the memory 102 in the form of software or firmware, and the processor 101 executes various functional applications and data processing by running software programs and modules stored in the memory 102.
The Memory 102 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 102 is used for storing programs, and the processor 101 executes the programs after receiving the execution instructions. Further, the software programs and modules within the memory 102 may also include an operating system, which may include various software components and/or drivers for managing system tasks (e.g., memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide an operating environment for other software components.
The processor 101 may be an integrated circuit chip having signal processing capabilities. The Processor 101 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and so on. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
An embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored, and the computer-executable instructions are executed by a processor to implement the steps of the method embodiments of the present application.
An embodiment of the present application also provides a computer program product comprising a computer program that, when being executed by a processor, performs the steps of the method embodiments of the present application.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A method for processing a production task of a product, comprising:
receiving input production constraints of products, wherein the production constraints of the products comprise the production number of the products, the production period of each product, the final completion time of all the products, the production task of each product, the task time corresponding to each production task and the precedence relationship among the production tasks;
randomly setting the starting time and the finishing time of each production task according to the production period of each product, the task time corresponding to each production task and the final finishing time of all the products;
randomly setting the production sequence of the production tasks according to the production number of the products, the completion of the production task of each product and the precedence relationship among the production tasks;
randomly generating a first preset number of initialization task scheduling lists according to the starting time and the finishing time of each production task and the production sequence of the production tasks;
and performing iterative training for preset times on the first preset number of initialization task scheduling tables by using a genetic algorithm, and determining the final task scheduling table of the product.
2. The method according to claim 1, wherein the performing a predetermined number of iterative trainings on the first predetermined number of initialization task shift lists using a genetic algorithm to determine a final task shift list of the product specifically includes:
calculating a non-adaptability value of each initialization task scheduling table, wherein the non-adaptability value is set according to the production requirement of the product;
sequencing the initialization task scheduling list according to the sequence of the inadaptability values from small to large to generate a task scheduling list sequence;
determining a reserved task scheduling list with a first preset number and the remaining tasks to be processed in the task scheduling list sequence;
generating an updated task scheduling list set according to the reserved task scheduling list and the task scheduling list to be processed;
continuing to perform iterative training according to the updated task scheduling list set until the iterative training of preset times is completed;
and determining a task scheduling table corresponding to the minimum inadaptability value in the inadaptability values obtained by the last iterative training.
3. The method according to claim 2, wherein generating an updated task shift list set according to the reserved task shift list and the to-be-processed task shift list specifically comprises:
combining the task scheduling tables to be processed in pairs randomly to generate a combined task scheduling table with a third preset number, wherein the third preset number is equal to the difference between the first preset number and the second preset number;
performing cross operation on the combined task scheduling list according to a preset cross probability to generate a crossed task scheduling list, wherein the cross operation is performed based on the precedence relationship;
performing exchange variation operation on the crossed task scheduling list according to a preset variation probability to generate a varied task scheduling list;
and generating an updated task scheduling list set according to the reserved task scheduling list and the mutated task scheduling list.
4. The method of claim 2, wherein the calculating the non-fitness value for each of the initialization task schedules comprises:
calculating a non-adaptability value of each initialization task scheduling table by using the following formula:
wherein f represents an inadaptation value, λ represents a weighting parameter, R represents a set of resources required to complete the product, and R representskRepresents the k-th resource, the U, in the set of resourceskIndicating the resource utilization of the k-th resource,
t represents the production cycle of the product, piIndicating the task duration of the ith production task; b iskIndicating the amount of demand on the kth resource,
said E represents the final finish time of the entire product, said
Indicating the start time of the ith production task of the jth product; the above-mentioned
Indicating the completion time of the ith production task of the jth product; b isikRepresenting the quantity of the demand of the ith production task on the kth resource, wherein t represents the time, and
represents an indicative function when satisfied
When the temperature of the water is higher than the set temperature,
when it is not satisfied
When the temperature of the water is higher than the set temperature,
5. the method of claim 4, further comprising:
and if the inadaptability values of the initialization task shift list are the same, determining the total production time length corresponding to the initialization task shift list, and sequencing the initialization task shift list according to the sequence of the total production time length from small to large.
6. The method according to any one of claims 2-5, further comprising, after said calculating a non-fitness value for each of said initialization task schedule:
determining a minimum one of the non-fitness values;
judging whether the minimum inadaptation value is larger than a preset inadaptation value or not;
if not, updating the preset inadaptability value according to the minimum inadaptability value;
and if so, updating the required quantity of the resources of the initialization task shift schedule, and recalculating the non-adaptability values of the initialization task shift schedule according to the updated required quantity of the resources until the minimum non-adaptability value in the non-adaptability values is less than or equal to the preset non-adaptability value.
7. The method of claim 6, wherein the updating the required number of resources of the initialization task shift table specifically comprises:
determining the resource utilization rate of each resource by the initialization task scheduling table corresponding to the minimum inadaptation value;
judging whether the minimum resource utilization rate in the resource utilization rates is larger than a preset threshold value or not;
if so, determining the required quantity of the resources corresponding to the minimum resource utilization rate, and subtracting a preset quantity difference value from the required quantity of the resources to generate the updated required quantity of the resources;
if not, determining the resource utilization rate with the minimum difference value from the minimum resource utilization rate, taking the resource utilization rate with the minimum difference value as the minimum resource utilization rate, and executing the step of judging whether the minimum resource utilization rate is greater than a preset threshold value.
8. A production task processing device for a product, comprising a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored by the memory to implement the method of any of claims 1 to 7.
9. A computer-readable storage medium having computer-executable instructions stored therein, which when executed by a processor, are configured to implement the method of any one of claims 1 to 7.
10. A computer program product comprising a computer program which, when executed by a processor, implements the method of any one of claims 1-7.
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