A method, apparatus, and equipment for allocating manufacturing resources.

Abstract

This application discloses a method, apparatus, and equipment for configuring manufacturing resources, relating to the field of mechanical manufacturing technology. The method includes: acquiring a set of production processes to be scheduled, a set of manufacturing resources, currently existing manufacturing resource-unit combination information, and a currently existing set of manufacturing resource units within a flexible workshop; initializing a population for the set of production processes to be scheduled based on the set of manufacturing resources, the currently existing manufacturing resource-unit combination information, and the currently existing set of manufacturing resource units, using a priority allocation rule; and executing a non-dominated genetic algorithm on the initialized population with the optimization objectives of minimizing the maximum completion time and the minimum number of cross-unit steps, and with a pre-established dynamic multi-objective unit construction model oriented towards manufacturing resources as constraints, to obtain the optimal resource allocation scheme corresponding to the set of production processes to be scheduled. The solution of this application improves the working efficiency of workshop equipment and shortens the processing cycle of workpieces.

Description

Technical Field

[0001] This application relates to the field of mechanical manufacturing technology, and in particular to a method, apparatus and equipment for configuring manufacturing resources. Background Technology

[0002] With rapid economic development, the manufacturing industry is facing unprecedented competition. The current market is no longer a market with a single demand, but rather one with a constant stream of emerging and rapidly changing products. Traditional mass production models are no longer suitable for today's market competition. Variety, batch size, and short cycle time are increasingly becoming the mainstream of manufacturing development. How to better respond to customers' personalized needs while ensuring efficiency has become a common concern in the market.

[0003] Cellular manufacturing systems are an advanced production model that combines the high efficiency of rigid production systems with the high flexibility of flexible production systems. The core idea is to treat manufacturing resources within the workshop as a shared resource pool. Based on changes in production tasks, appropriate manufacturing resources are selected from this pool to form different manufacturing cells. Manufacturing cell reconfiguration optimizes the spatial allocation of processing tasks, enabling dynamic optimization and combination of manufacturing resources according to changing demands. While ensuring task completion, it also offers advantages such as shortening manufacturing time, reducing work-in-process inventory, and lowering transportation costs, making it more suitable for the market's demand for multi-variety, variable-batch, and customized production. However, existing manufacturing resources are generally statically allocated based on manufacturing tasks, resulting in a lack of resource inheritance, low workshop equipment efficiency, and long workpiece processing cycles. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus and equipment for configuring manufacturing resources, thereby solving the problem that the current method of allocating manufacturing resources lacks continuity, resulting in low working efficiency of workshop equipment and long processing cycles of workpieces.

[0005] Firstly, in order to achieve the above objectives, embodiments of this application provide a method for configuring manufacturing resources, including:

[0006] Obtain the set of pending production processes, the set of manufacturing resources, the information on the current combination of manufacturing resources and units, and the set of current manufacturing resource units within the flexible operation workshop;

[0007] Based on the set of manufacturing resources, the currently existing manufacturing resource-unit combination information, and the currently existing set of manufacturing resource units, a population is initialized for the set of processes to be scheduled using a priority allocation rule. The initialized population includes the manufacturing resources allocated to each process in the set of processes to be scheduled and the units to which the allocated manufacturing resources belong.

[0008] With the optimization objectives of minimizing the maximum completion time and the minimum number of cross-unit steps, and with the constraints of a pre-established dynamic multi-objective unit construction model oriented towards manufacturing resources, a non-dominated genetic algorithm is executed on the initialized population to obtain the optimal resource allocation scheme corresponding to the set of production processes to be scheduled.

[0009] Optionally, the initialized population includes workpiece-device chromosomes and unit-device chromosomes.

[0010] Optionally, the population is initialized for the set of processes to be scheduled using priority assignment rules, including:

[0011] Based on the manufacturing resource set and the priority allocation rule, initialize the workpiece-equipment chromosome for each process in the set of processes to be scheduled;

[0012] The unit-device chromosome is initialized based on the manufacturing resource set, the currently existing manufacturing resource-unit combination information, the currently existing manufacturing resource unit set, and the priority allocation rule.

[0013] Optionally, based on the manufacturing resource set and the priority allocation rule, the workpiece-equipment chromosome is initialized for each process in the set of processes to be scheduled, including:

[0014] Using the earliest start time (AT) rule and the minimum work order slack time (SL) rule, select the processes that need to be scheduled from the set of processes to be scheduled.

[0015] From the set of manufacturing resources associated with the processes that need to be scheduled, the types of resources that need to be scheduled are selected using the AT rule and the Shortest Processing Time (SPT) rule.

[0016] A workpiece-equipment gene is formed based on the required processes and the required resource types;

[0017] Based on the required processes and the required resource types, update the set of processes to be scheduled and the set of manufacturing resources, and return to the step of selecting the processes to be scheduled in the set of processes to be scheduled using AT rules and SL rules, until all processes to be scheduled have been assigned manufacturing resources.

[0018] Based on the formed workpiece-equipment gene, the workpiece-equipment chromosome is generated.

[0019] Optionally, the unit-device chromosome is initialized based on the manufacturing resource set, the currently existing manufacturing resource-unit combination information, the currently existing manufacturing resource unit set, and the priority allocation rule, including:

[0020] Based on the set of manufacturing resources, the set of currently existing manufacturing resource units, and the information on the combination of currently existing manufacturing resources and units, determine the probability of each type of manufacturing resource appearing in each unit;

[0021] Update the currently existing manufacturing resource-unit combination based on the probability;

[0022] The unit-device chromosome is generated based on the updated set of manufacturing resource units.

[0023] Optionally, with minimizing the maximum completion time and minimum number of cross-cell steps as the optimization objectives, and using a pre-established dynamic multi-objective cell construction model oriented towards manufacturing resources as constraints, a non-dominated genetic algorithm is executed on the initialized population to obtain the optimal resource allocation scheme corresponding to the set of production processes to be scheduled, including:

[0024] The non-dominated genetic algorithm is used to iteratively optimize the workpiece-device chromosome and unit-device chromosome of the initialized population to obtain the optimal resource allocation scheme.

[0025] Optionally, the non-dominated genetic algorithm is used to iteratively optimize the workpiece-device chromosome and unit-device chromosome of the initialized population to obtain the optimal resource allocation scheme, including:

[0026] By performing the first operation, a child population is formed, wherein the first operation includes: performing multi-point crossover on the parent population, performing single-point mutation on the workpiece-equipment chromosome, and performing single-column mutation on the unit-equipment chromosome, wherein the parent population is the initialized population;

[0027] In the merged population of the offspring population and the parent population, a fast non-dominated sort is performed to obtain the non-dominated level of each individual in the merged population.

[0028] Calculate the crowding level of individuals in each non-dominant hierarchy;

[0029] Based on the non-dominance level and the crowding degree, the offspring of the next iteration are determined using an elite retention strategy, and the process returns to the step of forming the offspring population by performing the first operation.

[0030] When the number of iterations reaches the preset number of iterations, the optimal resource allocation is determined based on the current merged population.

[0031] Optionally, in the merged population of the offspring and the parent population, a fast non-dominated sort is performed to obtain the non-dominated level of each individual in the merged population, including:

[0032] The workpiece-equipment chromosome and unit-equipment chromosome in the merged population are decoded to obtain the optimal decoding scheme, wherein the optimal decoding scheme is the scheme with the most consecutive processes in the same unit;

[0033] Calculate the maximum completion time and minimum number of cross-unit operations when using the optimal decoding scheme;

[0034] Based on the maximum completion time and the minimum number of cross-units, a fast non-dominated sort is performed on the individuals in the merged population.

[0035] Optionally, the workpiece-equipment chromosome and unit-equipment chromosome in the merged population are decoded to obtain the optimal decoding scheme, including:

[0036] Based on the workpiece-equipment chromosome, determine the manufacturing resource type corresponding to each process in the set of processes to be scheduled for production;

[0037] Based on the unit-equipment chromosome, determine the unit set to which the manufacturing resource type corresponding to each process belongs;

[0038] The optimal decoding scheme is obtained by establishing the permutation number of the unit sets to which the manufacturing resource types corresponding to each process belong.

[0039] Optionally, calculating the maximum completion time and minimum number of cell jumps when processing using the optimal decoding scheme includes:

[0040] According to the formula Calculate the maximum completion time, where C max ET represents the maximum completion time. ij =ST ij +T ij ×N ij ET ij O ij End time, O ij T represents the j-th process of workpiece i. ij O ij Processing time, N ij O ij The number of ST ij O ij The start time;

[0041] According to the formula Calculate the minimum number of cross-cell steps; where CE min Let R represent the minimum number of steps across units, C represent the number of existing manufacturing resource units, I represent the number of jobs, n(i) represent the number of operations for job i, and R represent the minimum number of steps across units. ijc A value of 1 indicates O ijIt was assigned to unit C for processing.

[0042] Optionally, based on the maximum completion time and the minimum number of cross-units, a fast non-dominated sort is performed on the individuals in the merged population, including:

[0043] Calculate all non-dominant individuals in the merged population and define them as the Nth rank;

[0044] In the remaining population after removing all non-dominant individuals of level N, all non-dominant individuals are calculated and defined as level N+1, until all individuals in the merged population have the corresponding level, where N is a positive integer variable.

[0045] Optionally, the crowding degree of individuals in each non-dominant rank is calculated, including:

[0046] According to the formula Calculate the congestion degree, where w dist This represents the crowding level value of the i-th individual. Let represent the objective function value of the d-dimensional objective of the two individuals closest to the i-th individual when the i-th individuals are on the same Pareto front.

[0047] Secondly, in order to achieve the above objectives, embodiments of this application provide a manufacturing resource configuration apparatus, comprising:

[0048] The acquisition module is used to acquire the set of production processes to be scheduled, the set of manufacturing resources, the information on the current manufacturing resource-unit combinations, and the set of current manufacturing resource units in the flexible operation workshop;

[0049] An initialization module is used to initialize a population for the set of processes to be scheduled for production based on the set of manufacturing resources, the currently existing manufacturing resource-unit combination information, and the currently existing set of manufacturing resource units, using a priority allocation rule. The initialized population includes the manufacturing resources allocated to each process in the set of processes to be scheduled for production and the units to which the allocated manufacturing resources belong.

[0050] The execution module is used to perform a non-dominated genetic algorithm on the initialized population with the optimization objectives of minimizing the maximum completion time and the minimum number of cross-units, and with the constraints of a pre-established dynamic multi-objective unit construction model oriented towards manufacturing resources, to obtain the optimal resource allocation scheme corresponding to the set of production processes to be scheduled.

[0051] Thirdly, in order to achieve the above objectives, embodiments of this application provide a manufacturing resource configuration device, including a transceiver, a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the manufacturing resource configuration method as described in the first aspect.

[0052] Fourthly, in order to achieve the above objectives, embodiments of this application provide a readable storage medium having a program or instructions stored thereon, which, when executed by a processor, implement the manufacturing resource configuration method as described in the first aspect.

[0053] The above-mentioned technical solution of this application has at least the following beneficial effects:

[0054] The manufacturing resource allocation method of this application embodiment first obtains the set of production processes to be scheduled, the set of manufacturing resources, the information on currently existing manufacturing resource-unit combinations, and the set of currently existing manufacturing resource units within a flexible workshop. Second, based on the set of manufacturing resources, the information on currently existing manufacturing resource-unit combinations, and the set of currently existing manufacturing resource units, a population is initialized for the set of production processes to be scheduled using a priority allocation rule. The initialized population includes the manufacturing resources allocated to each process in the set of production processes to be scheduled and the units to which the allocated manufacturing resources belong. This achieves the allocation of manufacturing resources to the production processes to be scheduled based on the allocated units, making the allocation process inheritable. Finally, with the optimization objective of minimizing the maximum completion time and the minimum number of cross-unit steps, and with a pre-established dynamic multi-objective unit construction model for manufacturing resources as a constraint, a non-dominated genetic algorithm is executed on the initialized population to obtain the optimal resource allocation scheme corresponding to the set of production processes to be scheduled. This shortens the processing cycle of workpieces using the finally determined optimal resource allocation scheme, improving the working efficiency of vehicle manufacturing resources. Attached Figure Description

[0055] Figure 1 This is one of the flowcharts illustrating a method for configuring manufacturing resources according to an embodiment of this application;

[0056] Figure 2 This is a second schematic flowchart illustrating the method for configuring manufacturing resources according to an embodiment of this application;

[0057] Figure 3 This is a flowchart illustrating the improved non-dominated genetic algorithm based on priority assignment rules in an embodiment of this application.

[0058] Figure 4 This is a schematic diagram of the chromosome encoding method in the embodiments of this application;

[0059] Figure 5This is a schematic diagram of chromosome crossing over in an embodiment of this application;

[0060] Figure 6 This is a schematic diagram of the structure of a manufacturing resource configuration device according to an embodiment of this application;

[0061] Figure 7 This is a schematic diagram of the structure of a manufacturing resource configuration device according to an embodiment of this application. Detailed Implementation

[0062] To make the technical problems, technical solutions, and advantages of this application clearer, a detailed description will be provided below in conjunction with the accompanying drawings and specific embodiments. In the following description, specific details such as particular configurations and components are provided merely to aid in a comprehensive understanding of the embodiments of this application. Therefore, those skilled in the art should understand that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Furthermore, for clarity and brevity, descriptions of known functions and structures have been omitted.

[0063] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0064] In the various embodiments of this application, it should be understood that the sequence number of each process described below does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0065] In the embodiments provided in this application, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined based on A. However, it should also be understood that determining B based on A does not mean determining B solely based on A; B can also be determined based on A and / or other information.

[0066] like Figure 1 As shown, this application provides a method for configuring manufacturing resources, specifically a dynamic reconfiguration and scheduling method for manufacturing resources, which includes:

[0067] Step 101: Obtain the set of production processes to be scheduled, the set of manufacturing resources, the information on the current combination of manufacturing resources and units, and the set of current manufacturing resource units in the flexible operation workshop;

[0068] In this step, the set of production processes to be scheduled can be the set of currently processable processes selected from the set of processes of all orders in the flexible operation workshop according to the process route; the set of currently existing manufacturing resource units is the unit allocated to the manufacturing resources in the flexible operation workshop according to the previous processing plan; the information on the combination of currently existing manufacturing resources and units specifically refers to the correspondence between each unit and the manufacturing resources, that is: the information on the manufacturing resources included in each unit.

[0069] Step 102: Based on the set of manufacturing resources, the information on the combination of existing manufacturing resources and units, and the set of existing manufacturing resource units, initialize a population for the set of processes to be scheduled using priority allocation rules. The initialized population includes the manufacturing resources allocated to each process in the set of processes to be scheduled and the units to which the allocated manufacturing resources belong.

[0070] In this step, Priority Dispatch rules (PDR) are the earliest approximate method. This method assigns a priority to all processing operations, then the operation with the highest priority is selected and sorted first, followed by operations in order of priority. This method is very easy to implement and has low computational complexity, and is often used in practical scheduling problems.

[0071] In this step, the priority allocation rule is used, and the population of the set of production processes to be scheduled is initialized based on the existing manufacturing resource-unit combination information and the existing set of manufacturing resource units. This is to obtain the initial manufacturing resources and the units mentioned in the manufacturing resources for each process in the set of production processes to be scheduled, so that the allocation of manufacturing resources has inheritance, realizes the movement of manufacturing resources as little as possible, and improves the efficiency of workshop scheduling.

[0072] Step 103: With minimizing the maximum completion time and the minimum number of cross-unit steps as the optimization objectives and the pre-established dynamic multi-objective unit construction model oriented towards manufacturing resources as the constraint, a non-dominated genetic algorithm is executed on the initialized population to obtain the optimal resource allocation scheme corresponding to the set of production processes to be scheduled.

[0073] In this step, the non-dominated genetic algorithm, also known as the non-dominated sorting genetic algorithm, differs from the simple genetic algorithm in that it stratifies individuals based on their dominance relationship before the selection operator is executed.

[0074] In this step, the optimal resource allocation scheme in the set of production processes is obtained with the optimization objective of minimizing the maximum completion time and the minimum number of cross-units. On the one hand, the processing time of the process is shortened, and on the other hand, the number of workpieces is reduced, ultimately improving the production efficiency of the workshop.

[0075] Therefore, as Figure 2 As shown, the specific implementation process of the manufacturing resource configuration method in this application embodiment includes:

[0076] S201, Receive the set of work workshop processes, resource set, and unit set;

[0077] S202, based on the process route, select the set of currently processable processes (set of processes to be scheduled) and the status of each resource in the workshop;

[0078] S203, based on the current production schedule of the workshop, obtain the allocation of equipment resources and workshop unit layout, that is: obtain the current manufacturing resource-unit combination information and the current manufacturing resource unit set;

[0079] S204, using the priority allocation rule, resources are allocated to the process in sequence, forming a resource allocation unit, to obtain a preliminary manufacturing resource allocation scheme (initialized population);

[0080] S205 uses a non-dominated genetic algorithm to optimize the unit configuration scheme of manufacturing resources, clarify the resources selected for each process, and obtain the final production scheduling scheme.

[0081] The manufacturing resource allocation method of this application embodiment first obtains the set of production processes to be scheduled, the set of manufacturing resources, the information on currently existing manufacturing resource-unit combinations, and the set of currently existing manufacturing resource units within a flexible workshop. Second, based on the set of manufacturing resources, the information on currently existing manufacturing resource-unit combinations, and the set of currently existing manufacturing resource units, a population is initialized for the set of production processes to be scheduled using a priority allocation rule. The initialized population includes the manufacturing resources allocated to each process in the set of production processes to be scheduled and the units to which the allocated manufacturing resources belong. This achieves the allocation of manufacturing resources to the production processes to be scheduled based on the allocated units, making the allocation process inheritable. Finally, with the optimization objective of minimizing the maximum completion time and the minimum number of cross-unit steps, and with a pre-established dynamic multi-objective unit construction model for manufacturing resources as a constraint, a non-dominated genetic algorithm is executed on the initialized population to obtain the optimal resource allocation scheme corresponding to the set of production processes to be scheduled. This shortens the processing cycle of workpieces using the finally determined optimal resource allocation scheme, improving the working efficiency of vehicle manufacturing resources.

[0082] As an optional implementation, the initialized population includes workpiece-device chromosomes and unit-device chromosomes. The chromosomes in this initialized population, such as... Figure 4 As shown, a two-dimensional integer matrix encoding method is used. Specifically, Figure 4 [X] ij] represents the gene of workpiece-equipment information, where the subscripts i and j represent the j-th process of workpiece i, i∈(1,2,3…,I), j∈(1,2,3…,Ji); Figure 4 [Y] pq ] represents the gene of device-unit information, where the subscripts p and q indicate that unit p contains devices of type q, p∈(1,2,3…,C), q∈(1,2,3…,M); where I represents the number of workpieces, Ji represents the number of processes, C represents the number of units, and M represents the number of device types.

[0083] As an optional implementation, step 102 initializes the population for the set of processes to be scheduled using priority assignment rules, including:

[0084] Based on the manufacturing resource set and priority allocation rules, the workpiece-equipment chromosome is initialized for each process in the set of processes to be scheduled for production; that is, this step is to allocate manufacturing resources to each process in the set of processes to be scheduled for production based on the manufacturing resource set in the workshop and the status of each manufacturing resource in the workshop, using priority allocation rules.

[0085] Based on the set of manufacturing resources, the current manufacturing resource-unit combination information, the current set of manufacturing resource units, and the priority allocation rules, the unit-device chromosome is initialized; that is, this step is to reconfigure the manufacturing resources according to the existing units and the manufacturing resources in each unit, so as to reallocate units to each manufacturing resource.

[0086] As a specific implementation, based on the manufacturing resource set and priority allocation rules, the workpiece-equipment chromosome is initialized for each process in the set of processes to be scheduled, including:

[0087] (1) Using the earliest start time AT rule (earliest start time ST) ij The process to be scheduled is selected from the set of pending production processes according to the rules of "earlier is higher priority" and "minimum work order slack time (SL)". The work order slack time is expressed by the following formula:

[0088]

[0089] Where, d i t represents the delivery date of workpiece i. i Indicates the scheduling time of workpiece i, O sij T represents the remaining operations for workpiece i. ij Point O ij Processing time, O ij Refers to the j-th process of workpiece i.

[0090] It should be noted that in this step, AT rules and SL rules can be used to select the process to be arranged, but it is not limited to the above two rules. Other heuristic rules can also be used, which will not be listed here.

[0091] (2) From the set of manufacturing resources related to the processes that need to be scheduled, select the type of resource to be scheduled using the AT rule and the Shortest Processing Time (SPT) rule; specifically, this step can use a combination of the AT rule and the SPT rule, such as ST... ij +T ij ×N ij Select the resource type to be arranged, where N ij O ij The processing quantity.

[0092] (3) Form a workpiece-equipment gene based on the required processes and the required resource types; that is, this step is: form a workpiece-equipment gene X based on the selected processes (processes to be arranged) and equipment (resource types to be arranged). ij =x, the workpiece-equipment gene represents O ij The device is assigned to process the equipment of type x.

[0093] (4) Update the set of processes to be scheduled and the set of manufacturing resources according to the required processes and resource types, and return to the step of selecting the processes to be scheduled in the set of processes to be scheduled using AT rules and SL rules, until all processes to be scheduled are assigned manufacturing resources; In this step, updating the set of processes to be scheduled and the set of manufacturing resources specifically means removing the processes to be scheduled that have been selected in step (1) and the resources of the resource types to be scheduled in step (2).

[0094] (5) Generate the workpiece-equipment chromosome based on the formed workpiece-equipment gene.

[0095] In other words, the specific implementation process of this optional implementation method is as follows: First, heuristic rules (AT rules, SL rules, etc.) are used to select the processes to be arranged. Second, in the set of manufacturing resources mapped to the processes to be arranged, the combination of AT rules and SPT rules is used to select the resource types to be arranged to form a workpiece-equipment gene. Third, the set of processes to be arranged and the set of manufacturing resources are updated, and the above steps are repeated until all processes are arranged with manufacturing resources. Finally, the workpiece-equipment chromosome is obtained based on the formed workpiece-equipment gene.

[0096] As another specific implementation, based on the manufacturing resource set, the currently existing manufacturing resource-unit combination information, the currently existing manufacturing resource unit set, and the priority allocation rules, the unit-device chromosome is initialized, including:

[0097] (1) Based on the set of manufacturing resources, the set of existing manufacturing resource units, and the information on the combination of existing manufacturing resources and units, determine the probability of each type of manufacturing resource appearing in each unit; that is, this step is to calculate the probability of each type of equipment appearing in each unit based on the current production plan, specifically using the following formula to calculate this probability:

[0098]

[0099] Where, δ cm N represents the probability of a device of type m appearing in cell c. cm The number of devices of type m in unit c.

[0100] (2) Update the current manufacturing resource-unit combination based on this probability; that is, the unit set is updated according to the probability δ of the equipment type. cm Update the set again, and randomly select a unit c from the unit set as the unit to which the device of type m belongs. Then the unit-device gene Y is obtained. pq =y+1, indicating that unit c contains y+1 devices of type m. Repeat this step until all devices are assigned to their respective units.

[0101] (3) Generate the unit-device chromosome based on the updated set of manufacturing resource units.

[0102] As an optional implementation, step 103, with the optimization objectives of minimizing the maximum completion time and the minimum number of cross-cell steps, and with the constraints of a pre-established dynamic multi-objective cell construction model oriented towards manufacturing resources, executes a non-dominated genetic algorithm on the initialized population to obtain the optimal resource allocation scheme corresponding to the set of production processes to be scheduled, including:

[0103] The non-dominated genetic algorithm is used to iteratively optimize the workpiece-device chromosome and unit-device chromosome of the initialized population to obtain the optimal resource allocation scheme.

[0104] This optional implementation optimizes the chromosomes in the initialized population through iterative optimization, so that the number of times the workpiece is transferred between different units during the final configuration of resources and the processing time of each workpiece is shortened, thereby improving the processing efficiency of the workshop.

[0105] As a specific implementation, a non-dominated genetic algorithm is used to iteratively optimize the workpiece-device chromosomes and unit-device chromosomes of the initialized population to obtain the optimal resource allocation scheme, including:

[0106] (1) By performing a first operation, a offspring population is formed, wherein the first operation includes: performing multi-point crossover on the parent population, performing single-point mutation on the workpiece-equipment chromosome, and performing single-column mutation on the unit-equipment chromosome, wherein the parent population is the initialized population; that is, by following... Figure 5 The parent population is subjected to multi-point crossover and single-point mutation of the [X] chromosome and single-column mutation of the [Y] chromosome to form the offspring population; wherein, as mentioned above, the [X] chromosome is the workpiece-equipment chromosome formed by the workpiece-equipment gene, and the [Y] chromosome is the equipment-unit chromosome formed by the equipment-unit gene.

[0107] (2) In the merged population of the offspring population and the parent population, a fast non-dominated sort is performed to obtain the non-dominated level of each individual in the merged population. In this step, by performing a fast non-dominated sort on the individuals in the merged population, a level is defined for each non-dominated individual, which facilitates the subsequent optimization of manufacturing resources.

[0108] (3) Calculate the crowding level of individuals in each non-dominant level;

[0109] (4) Update the parent population using an elite retention strategy based on the non-dominance level and the crowding degree, and return to the step of forming the offspring population by performing the first operation; In this step, updating the parent population using an elite retention strategy specifically means using individuals in the Pareto set as offspring for the next generation iteration through an elite retention strategy.

[0110] (5) When the number of iterations reaches the preset number of iterations, determine the optimal resource allocation based on the current merged population.

[0111] As a specific implementation, in the merged population of offspring and parent populations, a fast non-dominated sort is performed to obtain the non-dominated level of each individual in the merged population, including:

[0112] 1) Decode the workpiece-equipment chromosome and unit-equipment chromosome in the merged population to obtain the optimal decoding scheme, wherein the optimal decoding scheme is the scheme with the most consecutive processes in the same unit;

[0113] Specifically, the implementation process of this step is as follows:

[0114] (a) Determine the manufacturing resource type corresponding to each process in the set of processes to be scheduled for production based on the workpiece-equipment chromosome; that is, find the processing equipment type of the process through chromosome [X].

[0115] (b) Based on the unit-equipment chromosome, determine the unit set to which the manufacturing resource type corresponding to each process belongs; that is: find the unit set for each equipment type based on the chromosome [Y];

[0116] (c) By establishing the permutation number of the unit set to which the manufacturing resource type belongs to each process, the optimal decoding scheme is obtained; that is, establish the permutation tree of the optional unit set of the process, and select the maximum number of consecutive processes in the same unit as the decoding scheme output.

[0117] 2) Calculate the maximum completion time and minimum number of cell crossings when using the optimal decoding scheme;

[0118] Specifically, the implementation process of this step is as follows:

[0119] (a) According to the formula Calculate the maximum completion time, where C max ET represents the maximum completion time. ij =ST ij +T ij ×N ij ET ij O ij End time, O ij T represents the j-th process of workpiece i. ij O ij Processing time, N ij O ij The number of ST ij O ij The start time;

[0120] Here, it should be noted that C max It is the most fundamental indicator for measuring scheduling schemes, mainly used to reflect workshop production efficiency, and can be continuously optimized through algorithm iteration. max .

[0121] (b) According to the formula Calculate the minimum number of cell-crossing operations; where CE min Let R represent the minimum number of steps across units, C represent the number of existing manufacturing resource units, I represent the number of jobs, n(i) represent the number of operations for job i, and R represent the minimum number of steps across units. ijc A value of 1 indicates O ij It is assigned to unit C for processing. That is, in unit O... ij When assigned to unit c for processing, R ijc =1; in Oij When not assigned to unit c for processing, R ijc It is 0.

[0122] It should be noted here that CE min It is a standard used to measure the quality of manufacturing resource reconfiguration. At the same time, it is used to bind the equipment in the unit based on the probability of the current production plan equipment appearing in the unit, which can play a good role in resource inheritance within the unit.

[0123] 3) Based on the maximum completion time and the minimum number of cross-units, perform a fast non-dominated sort on the individuals in the merged population.

[0124] As a specific implementation, a fast non-dominated sort is performed on the individuals in the merged population based on the maximum completion time and the minimum number of cell jumps, including:

[0125] Calculate all non-dominant individuals in the merged population and define them as the Nth rank;

[0126] In the remaining population after removing all non-dominant individuals of level N, calculate all non-dominant individuals and define them as level N+1, until all individuals in the merged population have the corresponding level, where N is a positive integer variable.

[0127] For example, the implementation process of this specific method is as follows: First, calculate all non-dominant individuals in the current population and assign them to the first level; second, calculate all non-dominant individuals in the remaining population after removing all individuals of the first level and assign them to the second level; then, repeat the above process until all individuals have the corresponding level.

[0128] As an optional implementation, the crowding degree of individuals in each non-dominant hierarchy is calculated, including:

[0129] According to the formula Calculate the congestion degree, where w dist This represents the crowding score of the i-th individual. Individuals with higher crowding scores are more likely to be selected. Let represent the objective function value of the d-th dimension of the objective of the two individuals closest to the i-th individual, given that the i-th individuals are on the same Pareto front. Here, Pareto specifically refers to the optimal set obtained using an elite preservation strategy.

[0130] Below, in conjunction with Figure 3 The population optimization process in the embodiments of this application will be described in detail below:

[0131] S310, Population initialization using assignment priority rules;

[0132] S320, generation number Gen = 1;

[0133] S330, crossover and mutation operations;

[0134] S340, merge parent and offspring populations;

[0135] S350, Fast Nondominated Sort;

[0136] S360, congestion calculation;

[0137] S370, elite strategy selects the parent generation;

[0138] S380: Determine if Gen is less than the maximum algebra. If yes, return to step S330; otherwise, end.

[0139] Specifically, S310 includes the following steps:

[0140] S3101, obtain the optional process pool and optional equipment pool, that is: obtain the manufacturing resources that have not been scheduled in the set of processes to be scheduled and the set of manufacturing resources;

[0141] S3102, using a heuristic algorithm to select the process step;

[0142] S3103, Select the process equipment type using combination rules;

[0143] S3104, Generate workpiece-equipment genes;

[0144] S3105, Determine whether all processes in the set of processes to be scheduled have been selected. If yes, proceed to step S3106; otherwise, proceed to step S3102.

[0145] S3106, Chromosome generation equipment.

[0146] S3107, Obtain the unit set and the optional equipment pool; that is: obtain the currently existing manufacturing resource unit set and the manufacturing resources in the manufacturing resource set that have not been scheduled for work.

[0147] S3108, Calculate the probability that a certain piece of equipment exists in a certain unit based on the combination of unit equipment in the workshop;

[0148] S3109, select unit-device matching based on probability; that is: generate unit-device genes based on probability.

[0149] S3110, Generating Unit - Device Chromosome.

[0150] like Figure 6 The present application embodiment also provides a manufacturing resource configuration device, including:

[0151] The acquisition module 601 is used to acquire the set of production processes to be scheduled, the set of manufacturing resources, the information on the current manufacturing resource-unit combination, and the set of the current manufacturing resource units in the flexible operation workshop.

[0152] The initialization module 602 is used to initialize a population for the set of processes to be scheduled for production based on the set of manufacturing resources, the currently existing manufacturing resource-unit combination information and the currently existing set of manufacturing resource units, using priority allocation rules. The initialized population includes the manufacturing resources allocated to each process in the set of processes to be scheduled for production and the units to which the allocated manufacturing resources belong.

[0153] The execution module 603 is used to perform a non-dominated genetic algorithm on the initialized population with the optimization objectives of minimizing the maximum completion time and the minimum number of cross-units, and with the constraints of a pre-established dynamic multi-objective unit construction model oriented towards manufacturing resources, to obtain the optimal resource allocation scheme corresponding to the set of production processes to be scheduled.

[0154] Optionally, the initialized population includes workpiece-device chromosomes and unit-device chromosomes.

[0155] Optionally, the initialization module 602 includes:

[0156] The first initialization submodule is used to initialize the workpiece-equipment chromosome for each process in the set of processes to be scheduled for production, according to the manufacturing resource set and the priority allocation rule;

[0157] The second initialization submodule is used to initialize the unit-device chromosome based on the manufacturing resource set, the currently existing manufacturing resource-unit combination information, the currently existing manufacturing resource unit set, and the priority allocation rule.

[0158] Optionally, the first initialization submodule includes:

[0159] The first selection unit is used to select the processes to be scheduled from the set of processes to be scheduled, using the earliest start time (AT) rule and the minimum work order slack time (SL) rule.

[0160] The second selection unit is used to select the type of resource to be arranged from the set of manufacturing resources related to the process to be arranged, using the AT rule and the shortest processing time SPT rule.

[0161] The first generation unit is used to form a workpiece-equipment gene according to the required process and the required resource type;

[0162] The first update unit is used to update the set of processes to be scheduled and the set of manufacturing resources according to the processes to be scheduled and the types of resources to be scheduled, and return to the step of selecting the processes to be scheduled in the set of processes to be scheduled using AT rules and SL rules, until all processes to be scheduled have been assigned manufacturing resources.

[0163] The second generation unit is used to generate the workpiece-equipment chromosome based on the formed workpiece-equipment gene.

[0164] Optionally, the second initialization submodule includes:

[0165] The first determining unit is used to determine the probability of each type of manufacturing resource appearing in each unit based on the manufacturing resource set, the currently existing manufacturing resource unit set, and the currently existing manufacturing resource and unit combination information.

[0166] The second update unit is used to update the currently existing manufacturing resource-unit combination according to the probability.

[0167] The third generation unit is used to generate the unit-device chromosome based on the updated set of manufacturing resource units.

[0168] Optionally, the execution module 603 is specifically used to iteratively optimize the workpiece-device chromosome and unit-device chromosome of the initialized population using the non-dominated genetic algorithm to obtain the optimal resource allocation scheme.

[0169] Optionally, the execution submodule includes:

[0170] The first execution submodule is used to form a child population by performing a first operation, wherein the first operation includes: performing multi-point crossover on the parent population, performing single-point mutation on the workpiece-equipment chromosome, and performing single-column mutation on the unit-equipment chromosome, and the parent population is the initialized population;

[0171] The second execution submodule is used to perform fast non-dominated sorting in the merged population of the offspring population and the parent population to obtain the non-dominated level of each individual in the merged population.

[0172] The calculation submodule is used to calculate the crowding degree of individuals in each non-dominant level;

[0173] The update submodule is used to update the parent population based on the non-dominance level and the crowding degree using an elite retention strategy, and return to the step of forming the offspring population by performing the first operation;

[0174] The determination submodule is used to determine the optimal resource configuration based on the current merged population when the number of iterations reaches a preset number of iterations.

[0175] Optionally, the second execution submodule includes:

[0176] The decoding unit is used to decode the workpiece-equipment chromosome and the unit-equipment chromosome in the merged population to obtain the optimal decoding scheme, wherein the optimal decoding scheme is the scheme with the most consecutive processes in the same unit;

[0177] The calculation unit is used to calculate the maximum completion time and minimum number of cross-unit operations when processing using the optimal decoding scheme.

[0178] An execution unit is used to perform fast non-dominated sorting on individuals in the merged population based on the maximum completion time and the minimum number of cross-units.

[0179] Optionally, the decoding unit includes:

[0180] The first determining subunit is used to determine the manufacturing resource type corresponding to each process in the set of processes to be scheduled for production based on the workpiece-equipment chromosome.

[0181] The second determining subunit is used to determine the set of units to which the manufacturing resource type corresponding to each process belongs, based on the unit-equipment chromosome.

[0182] Obtain sub-units to obtain the optimal decoding scheme by establishing the permutation number of the unit sets to which the manufacturing resource types corresponding to each process belong.

[0183] Optionally, the computing unit is specifically used for:

[0184] According to the formula Calculate the maximum completion time, where C max ET represents the maximum completion time. ij =ST ij +T ij ×N ij ET ij O ij End time, O ij T represents the j-th process of workpiece i. ij O ij Processing time, N ij O ij The number of ST ij O ij The start time;

[0185] According to the formula Calculate the minimum number of cross-cell steps; where CEmin Let R represent the minimum number of steps across units, C represent the number of existing manufacturing resource units, I represent the number of jobs, n(i) represent the number of operations for job i, and R represent the minimum number of steps across units. ijc A value of 1 indicates O ij It was assigned to unit C for processing.

[0186] Optionally, the execution unit includes:

[0187] The first calculation subunit is used to calculate all non-dominant individuals in the merged population and define them as the Nth level;

[0188] The second calculation subunit is used to calculate all non-dominant individuals in the remaining population after removing all non-dominant individuals of level N and define them as level N+1, until all individuals in the merged population have the corresponding level, where N is a positive integer variable.

[0189] Optionally, the computing submodule is specifically used for:

[0190] According to the formula Calculate the congestion degree, where w dist This represents the crowding level value of the i-th individual. Let represent the objective function value of the d-dimensional objective of the two individuals closest to the i-th individual when the i-th individuals are on the same Pareto front.

[0191] like Figure 7 As shown, this application embodiment also provides a manufacturing resource configuration device, including a transceiver 710, a memory 720, a processor 700, and a computer program stored on the memory 720 and running on the processor 700. When the processor 700 executes the computer program, it implements the various processes of the manufacturing resource configuration method embodiment described above and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0192] The transceiver 710 is used to receive and send data under the control of the processor 700.

[0193] Among them, Figure 7In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits of one or more processors represented by processor 700 and memory represented by memory 720 together. The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 710 can be multiple elements, including transmitters and receivers, providing a unit for communicating with various other devices over a transmission medium. For different devices, the user interface 730 can also be an interface capable of connecting external or internal devices, including but not limited to keypads, displays, speakers, microphones, joysticks, etc.

[0194] The processor 700 is responsible for managing the bus architecture and general processing, while the memory 720 can store the data used by the processor 700 during operation.

[0195] This application also provides a readable storage medium storing a program. When executed by a processor, this program implements the various processes of the above-described manufacturing resource configuration method embodiment and achieves the same technical effect. To avoid repetition, it will not be described again here. The readable storage medium may be, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0196] Furthermore, it should be noted that in the apparatus and method of this application, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent solutions of this application. Moreover, the steps performing the above series of processes can naturally be executed in the order described or in chronological order, but are not necessarily required to be executed in chronological order; some steps can be executed in parallel or independently of each other. Those skilled in the art will understand that all or any step or component of the method and apparatus of this application can be implemented in any computing device (including processors, storage media, etc.) or network of computing devices, in hardware, firmware, software, or a combination thereof. This is something that those skilled in the art can achieve by using their basic programming skills after reading the description of this application.

[0197] Therefore, the object of this application can also be achieved by running a program or a set of programs on any computing device. The computing device can be a known general-purpose device. Therefore, the object of this application can also be achieved simply by providing a program product containing program code implementing the method or apparatus. That is, such a program product also constitutes this application, and a storage medium storing such a program product also constitutes this application. Obviously, the storage medium can be any known storage medium or any storage medium developed in the future.

[0198] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0199] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principles described in this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for allocating manufacturing resources, characterized in that, include: Obtain the set of pending production processes, the set of manufacturing resources, the information on the current combination of manufacturing resources and units, and the set of current manufacturing resource units within the flexible operation workshop; Based on the manufacturing resource set, the currently existing manufacturing resource-unit combination information, and the currently existing manufacturing resource unit set, a population is initialized for the set of processes to be scheduled using a priority allocation rule. The initialized population includes the manufacturing resources allocated to each process in the set of processes to be scheduled and the units to which the allocated manufacturing resources belong. The initialized population includes workpiece-equipment chromosomes and unit-equipment chromosomes. With the optimization objectives of minimizing the maximum completion time and the minimum number of cross-cell steps, and constrained by a pre-established dynamic multi-objective cell construction model oriented towards manufacturing resources, a non-dominated genetic algorithm is executed on the initialized population to obtain the optimal resource allocation scheme corresponding to the set of production processes to be scheduled; including: The optimal resource allocation scheme is obtained by iteratively optimizing the workpiece-device chromosome and unit-device chromosome of the initialized population using the non-dominated genetic algorithm, including: Based on the workpiece-equipment chromosome, determine the manufacturing resource type corresponding to each process in the set of processes to be scheduled for production; Based on the unit-equipment chromosome, determine the unit set to which the manufacturing resource type corresponding to each process belongs; The optimal decoding scheme is obtained by establishing the number of permutations of the unit set to which the manufacturing resource type corresponding to each process belongs; wherein, the optimal decoding scheme is the scheme with the most consecutive processes within the same unit; the optimal decoding scheme is used to calculate the maximum completion time and the minimum number of cross-unit operations.

2. The method according to claim 1, characterized in that, The population is initialized for the set of processes to be scheduled using priority assignment rules, including: Based on the manufacturing resource set and the priority allocation rule, initialize the workpiece-equipment chromosome for each process in the set of processes to be scheduled; The unit-device chromosome is initialized based on the manufacturing resource set, the currently existing manufacturing resource-unit combination information, the currently existing manufacturing resource unit set, and the priority allocation rule.

3. The method according to claim 2, characterized in that, Based on the manufacturing resource set and the priority allocation rule, initialize the workpiece-equipment chromosome for each process in the set of processes to be scheduled, including: Using the earliest start time (AT) rule and the minimum work order slack time (SL) rule, select the processes that need to be scheduled from the set of processes to be scheduled. From the set of manufacturing resources associated with the processes that need to be scheduled, the types of resources that need to be scheduled are selected using the AT rule and the Shortest Processing Time (SPT) rule. A workpiece-equipment gene is formed based on the required processes and the required resource types; Based on the required processes and the required resource types, update the set of processes to be scheduled and the set of manufacturing resources, and return to the step of selecting the processes to be scheduled in the set of processes to be scheduled using AT rules and SL rules, until all processes to be scheduled have been assigned manufacturing resources; Based on the formed workpiece-equipment gene, the workpiece-equipment chromosome is generated.

4. The method according to claim 2, characterized in that, Based on the manufacturing resource set, the currently existing manufacturing resource-unit combination information, the currently existing manufacturing resource unit set, and the priority allocation rule, initialize the unit-device chromosome, including: Based on the set of manufacturing resources, the set of currently existing manufacturing resource units, and the information on the combination of currently existing manufacturing resources and units, determine the probability of each type of manufacturing resource appearing in each unit; Update the currently existing manufacturing resource-unit combination based on the probability; The unit-device chromosome is generated based on the updated set of manufacturing resource units.

5. The method according to claim 1, characterized in that, The optimal resource allocation scheme is obtained by iteratively optimizing the workpiece-device chromosome and unit-device chromosome of the initialized population using the non-dominated genetic algorithm, including: By performing the first operation, a child population is formed, wherein the first operation includes: performing multi-point crossover on the parent population, performing single-point mutation on the workpiece-equipment chromosome, and performing single-column mutation on the unit-equipment chromosome, wherein the parent population is the initialized population; In the merged population of the offspring population and the parent population, a fast non-dominated sort is performed to obtain the non-dominated level of each individual in the merged population. Calculate the crowding level of individuals in each non-dominant hierarchy; Based on the non-dominance level and the crowding degree, the parent population is updated using an elite retention strategy, and the process returns to the step of forming the offspring population by performing the first operation. When the number of iterations reaches the preset number of iterations, the optimal resource allocation is determined based on the current merged population.

6. The method according to claim 5, characterized in that, In the merged population of the offspring and the parent population, a fast non-dominated sort is performed to obtain the non-dominated level of each individual in the merged population, including: Decode the workpiece-equipment chromosome and unit-equipment chromosome in the merged population to obtain the optimal decoding scheme; The maximum completion time and minimum number of cell crossings are calculated using the optimal decoding scheme. Based on the maximum completion time and the minimum number of cross-units, a fast non-dominated sort is performed on the individuals in the merged population.

7. The method according to claim 6, characterized in that, Calculating the maximum completion time and minimum number of cross-unit operations using the optimal decoding scheme includes: According to the formula Calculate the maximum completion time, where C max ET represents the maximum completion time. ij =ST ij +T ij ×N ij ET ij O ij End time, O ij T represents the j-th process of workpiece i. ij O ij Processing time, N ij O ij The number of ST ij O ij The start time; According to the formula Calculate the minimum number of cross-cell steps; where CE min Let R represent the minimum number of steps across units, C represent the number of existing manufacturing resource units, I represent the number of jobs, n(i) represent the number of operations for job i, and R represent the minimum number of steps across units. ijc A value of 1 indicates O ij It was assigned to unit C for processing.

8. The method according to claim 6, characterized in that, Based on the maximum completion time and the minimum number of cross-units, a fast non-dominated sort is performed on the individuals in the merged population, including: Calculate all non-dominant individuals in the merged population and define them as the Nth rank; In the remaining population after removing all non-dominant individuals of level N, all non-dominant individuals are calculated and defined as level N+1, until all individuals in the merged population have the corresponding level, where N is a positive integer variable.

9. The method according to claim 5, characterized in that, Calculate the crowding level of individuals in each non-dominant hierarchy, including: According to the formula Calculate the congestion degree, where w dist This represents the crowding level value of the i-th individual. Let represent the objective function value of the d-dimensional objective of the two individuals closest to the i-th individual when the i-th individuals are on the same Pareto front.

10. A device for configuring manufacturing resources, characterized in that, include: The acquisition module is used to acquire the set of production processes to be scheduled, the set of manufacturing resources, the information on the current manufacturing resource-unit combinations, and the set of current manufacturing resource units in the flexible operation workshop; An initialization module is used to initialize a population for the set of processes to be scheduled for production based on the set of manufacturing resources, the currently existing manufacturing resource-unit combination information, and the currently existing set of manufacturing resource units, using priority allocation rules. The initialized population includes the manufacturing resources allocated to each process in the set of processes to be scheduled for production and the units to which the allocated manufacturing resources belong. The initialized population includes workpiece-equipment chromosomes and unit-equipment chromosomes. The execution module is used to perform a non-dominated genetic algorithm on the initialized population with the optimization objectives of minimizing the maximum completion time and the minimum number of cross-units, and with the constraints of a pre-established dynamic multi-objective unit construction model oriented towards manufacturing resources, to obtain the optimal resource allocation scheme corresponding to the set of production processes to be scheduled. The execution module is used to iteratively optimize the workpiece-device chromosomes and unit-device chromosomes of the initialized population using the non-dominated genetic algorithm to obtain the optimal resource allocation scheme; specifically, the execution module is used to: Based on the workpiece-equipment chromosome, determine the manufacturing resource type corresponding to each process in the set of processes to be scheduled for production; Based on the unit-equipment chromosome, determine the unit set to which the manufacturing resource type corresponding to each process belongs; The optimal decoding scheme is obtained by establishing the number of permutations of the unit set to which the manufacturing resource type corresponding to each process belongs; wherein, the optimal decoding scheme is the scheme with the most consecutive processes within the same unit; the optimal decoding scheme is used to calculate the maximum completion time and the minimum number of cross-unit operations.

11. A configuration device for manufacturing resources, comprising a transceiver, a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the method for configuring manufacturing resources as described in any one of claims 1 to 9.

12. A readable storage medium having a program or instructions stored thereon, characterized in that, When the program or instructions are executed by the processor, they implement the method for configuring manufacturing resources as described in any one of claims 1 to 9.

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