A multi-workpiece parallel processing method and related device

Abstract

The application discloses a kind of multi-workpiece parallel processing method and related equipment, method includes: obtaining the process route data of multiple workpieces, process route data includes each the workpiece of each process, each process corresponding processing equipment;Obtain the running state, current load and processing capacity data of each process corresponding processing equipment;According to the running state, current load and processing capacity data of processing equipment, determine the available processing equipment set of each process;Obtain logistics equipment information;With minimizing final completion time and minimizing total delay time as optimization goal, based on the available processing equipment set of each process and logistics equipment information, iteration is solved by genetic algorithm, and the optimal solution of optimization goal is obtained;Based on optimal solution, obtain the processing optimization scheduling scheme of multiple workpieces.The application scheme can effectively deal with production emergency, improve production efficiency, avoid delivery delay, and can be widely applied to processing optimization scheduling technical field.

Description

Technical Field

[0001] This application relates to the field of processing optimization and scheduling technology, and in particular to a method and related equipment for parallel processing of multiple workpieces. Background Technology

[0002] As the manufacturing industry moves towards intelligence, flexibility, and efficiency, traditional rigid production systems can no longer meet the market demands for diversified, small-batch, and rapid production changeover. Especially in discrete manufacturing fields (such as automotive parts, aerospace, and electronic assembly industries), the same product or workpieces often need to be processed in the same steps during production. In order to increase capacity, shorten production cycles, and optimize resource utilization, more and more companies hope to achieve the ability to process multiple workpieces in the same step by multiple machines in parallel.

[0003] However, traditional manufacturing execution systems or shop floor scheduling systems typically use linear or serial processing logic for processing, meaning that a process is bound to only one piece of equipment or flows along a fixed path, lacking support for flexible resource allocation. When equipment bottlenecks, sudden failures, or a surge in orders occur, the system is difficult to adjust dynamically, leading to problems such as decreased production efficiency and delivery delays. Summary of the Invention

[0004] The main objective of this application is to propose a method and related equipment for parallel processing of multiple workpieces, which can effectively cope with unexpected production situations, improve production efficiency, and avoid delivery delays.

[0005] To achieve the above objectives, one aspect of this application proposes a method for parallel processing of multiple workpieces, including: Acquire process route data for multiple workpieces, wherein the process route data includes each process step of each workpiece and the processing equipment corresponding to each process step; Obtain the operating status, current load, and processing capacity data of the processing equipment corresponding to each of the aforementioned processes; Based on the operating status, current load, and processing capacity data of the processing equipment, determine the set of available processing equipment for each of the aforementioned processes; Obtain information on logistics equipment; With the optimization objectives of minimizing the final completion time and minimizing the total delay time, the optimal solution for the optimization objective is obtained by iteratively solving the problem using a genetic algorithm based on the set of available processing equipment for each process and the information of the logistics equipment. Based on the optimal solution, multiple processing optimization scheduling schemes for the workpieces are obtained, wherein the optimization scheduling schemes include processing equipment allocation schemes, processing timing schemes, and logistics equipment allocation schemes.

[0006] In some embodiments, the optimization objective of minimizing the final completion time and minimizing the total delay time is achieved by iteratively solving the problem using a genetic algorithm based on the available processing equipment set for each process and the logistics equipment information, to obtain the optimal solution for the optimization objective. This includes: Determine the processing sequence of each process for each workpiece; Based on the processing sequence of each process of the workpiece, the set of available processing equipment, and the logistics equipment information, an initial population for the genetic algorithm is generated, wherein the initial population includes multiple chromosomes; Based on the optimization objective, each chromosome is decoded to obtain the fitness of each chromosome; Based on the fitness, parent chromosomes are selected from the initial population, and crossover and mutation operations are simultaneously performed on the processing sequence, processing equipment allocation sequence, and logistics equipment allocation sequence of the parent chromosomes to generate a new population. The new population is used as the current population to return to the step of decoding each chromosome based on the optimization objective and continue to iterate until the preset termination condition is met. The maximum fitness corresponding to each chromosome during the iteration process is selected, and the optimal solution of the optimization objective is obtained based on the highest fitness corresponding to each chromosome.

[0007] In some embodiments, generating an initial population for a genetic algorithm based on the processing sequence of each step of the workpiece, the set of available processing equipment, and the logistics equipment information includes: A processing sequence is generated based on the processing order of each process for each workpiece; Based on the set of available processing equipment, processing equipment is randomly assigned to each step in the processing sequence to generate a processing equipment assignment sequence; Based on the logistics equipment information, logistics equipment is randomly assigned to each process in the processing sequence to generate a logistics equipment allocation sequence; Each of the processes in the processing sequence, each of the processing equipment in the processing equipment allocation sequence, and each of the logistics equipment in the logistics equipment allocation sequence are combined into a chromosome according to the same index position; wherein, each chromosome includes one of the processes, the processing equipment corresponding to the process, and the logistics equipment; Repeat the steps of combining chromosomes until the initial population is obtained according to the preset population size.

[0008] In some embodiments, decoding each chromosome based on the optimization objective to obtain the fitness of each chromosome includes: Obtain the processing time and preset delivery date for each process in each workpiece; According to the processing sequence, the completion time of each process is determined sequentially based on the processing time of each process. The completion time of the last process in each workpiece is determined as the actual completion time of each workpiece, and the maximum value of the actual completion times of all workpieces is determined as the final completion time. Calculate the difference between the actual completion time of each workpiece and the preset delivery date corresponding to each workpiece, and sum the differences for each workpiece to obtain the total delay time. Based on the optimization objective, the final completion time and the total delay time are weighted and summed to obtain the fitness.

[0009] In some embodiments, determining the completion time of each process sequentially based on the processing time of each of the processes includes: Determine the target processing equipment and target logistics equipment allocated to each process, and determine the transportation time for the target logistics equipment; Determine the completion time of the preceding process for each process; The candidate start time for each process is determined by summing the completion time of the previous process, the transportation time of the target logistics equipment, and the idle time while waiting for the target logistics equipment. Determine the idle available time of the target processing equipment; The maximum value between the available idle time and the candidate start time is taken as the target start time of the process; The completion time of each process is obtained by summing the target start time and the processing time.

[0010] In some embodiments, the weighted summation of the final completion time and the total delay time to obtain the fitness includes: Determine the first weight corresponding to the final completion time and the second weight corresponding to the total delay time; Multiply the final completion time by the first weight to obtain the first weighted component; Multiply the total delay time by the second weight to obtain the second weighted component; The fitness of the chromosome is obtained by adding the first weighted component to the second weighted component.

[0011] To achieve the above objectives, another aspect of this application provides a multi-workpiece parallel processing apparatus, the apparatus comprising: The process route data acquisition module is used to acquire process route data for multiple workpieces, wherein the process route data includes each process of each workpiece and the processing equipment corresponding to each process. The processing equipment data acquisition module is used to acquire the operating status, current load, and processing capacity data of the processing equipment corresponding to each of the aforementioned processes; The available equipment determination module is used to determine the set of available processing equipment for each of the processing processes based on the operating status, current load, and processing capacity data of the processing equipment. The logistics equipment information acquisition module is used to acquire logistics equipment information; The solution module is used to obtain the optimal solution for the optimization objective by using a genetic algorithm to iteratively solve the problem based on the set of available processing equipment for each process and the information of the logistics equipment, with the optimization objective being to minimize the final completion time and minimize the total delay time. The scheduling scheme generation module is used to obtain multiple optimized scheduling schemes for the processing of the workpieces based on the optimal solution, wherein the optimized scheduling schemes include a processing equipment allocation scheme, a processing timing scheme, and a logistics equipment allocation scheme.

[0012] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0013] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0014] This application also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned method.

[0015] The embodiments of this application include at least the following beneficial effects: This application provides a method for parallel processing of multiple workpieces. This method first acquires process route data for multiple workpieces, including each process step of each workpiece and the corresponding processing equipment for each process step; acquires the operating status, current load, and processing capacity data of the processing equipment corresponding to each process step; determines the set of available processing equipment for each process step based on the operating status, current load, and processing capacity data of the processing equipment; acquires logistics equipment information; with the optimization objective of minimizing the final completion time and minimizing the total delay time, iteratively solves the problem using a genetic algorithm based on the set of available processing equipment for each process step and the logistics equipment information to obtain the optimal solution for the optimization objective; based on the optimal solution, obtains a processing optimization scheduling scheme for multiple workpieces, wherein the optimization scheduling scheme includes a processing equipment allocation scheme, a processing timing scheme, and a logistics equipment allocation scheme. This application, based on the set of available processing equipment for each process step, logistics equipment information, and optimization objective, uses a genetic algorithm for iterative solution, enabling iterative processing of multiple workpieces... equipment By performing a global search on the combined optimization problem of logistics, etc., the optimal solution that meets the optimization objective can be found quickly. Even when there are equipment bottlenecks, sudden failures, or a surge in orders, it can dynamically adjust resource allocation to effectively deal with unexpected production situations, thereby improving production efficiency and avoiding delivery delays. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of an implementation environment provided in an embodiment of this application; Figure 2 This is a flowchart of a multi-workpiece parallel processing method provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a multi-workpiece parallel processing device provided in an embodiment of this application; Figure 4 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application.

[0018] It is understood that the terms "first," "second," "third," "fourth," etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0019] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0021] Traditional manufacturing execution systems or shop floor scheduling systems typically use linear or serial processing logic for processing, meaning that a process is bound to only one piece of equipment or flows along a fixed path. They lack support for flexible resource allocation. When equipment bottlenecks, sudden failures, or a surge in orders occur, the system is difficult to adjust dynamically, leading to problems such as decreased production efficiency and delivery delays.

[0022] In view of this, this application provides a method and related equipment for parallel processing of multiple workpieces. The method first acquires process route data for multiple workpieces, including each process step and the corresponding processing equipment for each step; it then acquires the operating status, current load, and processing capacity data of the processing equipment for each step; based on the operating status, current load, and processing capacity data, it determines the set of available processing equipment for each step; it acquires logistics equipment information; with the optimization objective of minimizing the final completion time and minimizing the total delay time, it iteratively solves the problem using a genetic algorithm based on the set of available processing equipment for each step and the logistics equipment information to obtain the optimal solution for the optimization objective; based on the optimal solution, it obtains a processing optimization scheduling scheme for multiple workpieces, including a processing equipment allocation scheme, a processing timing scheme, and a logistics equipment allocation scheme. This application, based on the set of available processing equipment for each step, logistics equipment information, and optimization objective, uses a genetic algorithm for iterative solving, enabling the processing of multiple workpieces... equipment By performing a global search on the combined optimization problem of logistics, etc., the optimal solution that meets the optimization objective can be found quickly. Even when there are equipment bottlenecks, sudden failures, or a surge in orders, it can dynamically adjust resource allocation to effectively deal with unexpected production situations, thereby improving production efficiency and avoiding delivery delays.

[0023] The multi-workpiece parallel processing method provided in this application relates to the field of processing optimization and scheduling technology. This multi-workpiece parallel processing method can be applied to a terminal, a server, or software running on a terminal or server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the multi-workpiece parallel processing method, but is not limited to the above forms.

[0024] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0025] like Figure 1 The diagram shown is a schematic representation of an implementation environment provided in an embodiment of this application. (Refer to...) Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected via a network, either wirelessly or via a wired connection, to complete data transmission and exchange.

[0026] Server 101 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0027] Additionally, server 101 can also be a node server in a blockchain network. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms.

[0028] Terminal 102 can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc. It can also be a vehicle-mounted terminal of the various device types described above, but is not limited to these. Terminal 102 and server 101 can be directly or indirectly connected via wired or wireless communication, and this embodiment does not impose any limitations.

[0029] For example, based on Figure 1The implementation environment shown in this application embodiment provides a method for parallel processing of multiple workpieces. The following description uses the application of this method in server 101 as an example. It can be understood that this method can also be applied in terminal 102.

[0030] Reference Figure 2 , Figure 2 The flowchart illustrates a multi-workpiece parallel processing method applied to a server, as provided in this application embodiment. The execution subject of this method can be any of the aforementioned computer devices (including servers or terminals). (Refer to...) Figure 2 The method may include the following steps: S100. Obtain process route data for multiple workpieces, wherein the process route data includes each process step of each workpiece and the processing equipment corresponding to each process step.

[0031] Specifically, the process route data includes all the processes required for each workpiece and their sequence, as well as the processing equipment corresponding to each process. A process refers to each independent processing stage of the workpiece. For example, for any workpiece, its processes include roughing, finishing, and quality inspection. The processing sequence of these processes can be represented as "roughing (OP1) → finishing (OP2) → quality inspection (OP3)". Each process is pre-configured with one or more processing equipment to perform that process. It should be noted that some processes in the acquired process route data are configured with multiple processing equipment, meaning that these processes are parallel processing processes.

[0032] S200: Obtain the operating status, current load, and processing capacity data of the processing equipment corresponding to each of the aforementioned processes.

[0033] The operating status includes whether the equipment is available or unavailable, such as whether the equipment is malfunctioning, under maintenance, occupied, or idle; the current load refers to the total processing time that the processing equipment has been occupied; the processing capacity data refers to the ability of the processing equipment to meet specific processing requirements, or the types of processes and parameter ranges supported by the processing equipment.

[0034] S300. Based on the operating status, current load, and processing capacity data of the processing equipment, determine the set of available processing equipment for each of the aforementioned processes; S400: Obtain information on logistics equipment.

[0035] For example, the logistics equipment information includes the logistics equipment and its transportation time; wherein, the transportation time refers to the time required for the logistics equipment to transport a workpiece from one location (e.g., the processing equipment of the previous process) to the processing equipment of the next process.

[0036] S500. With minimizing the final completion time and minimizing the total delay time as the optimization objectives, the optimal solution for the optimization objective is obtained by iteratively solving the problem using a genetic algorithm based on the set of available processing equipment for each process and the information of the logistics equipment.

[0037] Minimizing the final completion time can be understood as minimizing the actual completion time of all workpieces in order to shorten the overall production cycle.

[0038] For example, the steps of obtaining the optimal solution to the optimization objective by iteratively solving the problem using a genetic algorithm based on the set of available processing equipment, logistics equipment information, and optimization objective for each process include S510-S550: S510. Determine the processing sequence of each process for each workpiece.

[0039] Specifically, obtain the sequence of processes that each workpiece needs to go through and their order. For example, workpiece J1 needs to complete rough machining (OP1), then finish machining (OP2), and finally quality inspection (OP3).

[0040] S520. Based on the processing sequence of each process of the workpiece, the set of available processing equipment, and the logistics equipment information, generate an initial population for the genetic algorithm, wherein the initial population includes multiple chromosomes; For example, the steps for generating the initial population for the genetic algorithm include S521-S525: S521. Generate a processing sequence according to the processing order of each process of each workpiece; Specifically, according to the processing order of each step in each workpiece, all steps of all workpieces are arranged into a sequence. The length of this sequence is equal to the number of workpieces multiplied by the number of steps. Each element in the sequence can be represented by a workpiece identifier followed by a step identifier, and the steps of the same workpiece must appear in the correct process order (e.g., J1-OP1 must precede J1-OP2). The processing sequence of each chromosome in the initial population can be randomly generated, but it must satisfy the processing order constraint.

[0041] S522. Randomly allocate processing equipment to each process in the processing sequence according to the set of available processing equipment, and generate a processing equipment allocation sequence; For example, in the embodiments of this application, for each step in the processing sequence, a processing equipment number is randomly selected from the set of available processing equipment for each step, and the number is recorded in sequence to obtain the processing equipment allocation sequence.

[0042] S523. Randomly allocate logistics equipment to each process in the processing sequence according to the logistics equipment information, and generate a logistics equipment allocation sequence; For example, in this embodiment of the application, for each step in the processing sequence, a logistics equipment number (e.g., AGV1) is randomly selected from the available logistics equipment included in the logistics equipment information, and this number is recorded sequentially to obtain a logistics equipment allocation sequence. This logistics equipment is used to transport the workpiece from the processing equipment of the previous step to the processing equipment of the current step.

[0043] S524. The processes in the processing sequence, the processing equipment in the processing equipment allocation sequence, and the logistics equipment in the logistics equipment allocation sequence are combined into a chromosome according to the same index position; wherein, each chromosome includes one process, the processing equipment corresponding to the process, and the logistics equipment; S525. Repeat the steps of combining chromosomes until the initial population is obtained according to the preset population size.

[0044] Specifically, the above steps are repeated, generating a different chromosome each time (by randomly shuffling the sorted sequence and randomly assigning processing and logistics equipment) until the number of chromosomes generated reaches the preset population size (e.g., 50 or 100), thus obtaining the initial population.

[0045] S530. Based on the optimization objective, decode each chromosome to obtain the fitness of each chromosome.

[0046] For example, the steps of decoding each chromosome to obtain the fitness of each chromosome include S531-S535: S531. Obtain the processing time and preset delivery date for each process in each workpiece; S532. According to the processing sequence, the completion time of each process is determined sequentially based on the processing time of each process, the steps of which include S5321-S5326: S5321. Determine the target processing equipment and target logistics equipment allocated to each process, and determine the transportation time of the target logistics equipment; Specifically, based on the chromosome processing equipment allocation sequence and logistics equipment allocation sequence, the number of the target processing equipment and the number of the target logistics equipment corresponding to the current process are read, and the transportation time required for the target logistics equipment to transport the process to the target processing equipment is obtained from the logistics equipment information.

[0047] S5322. Determine the completion time of the preceding process for each process; It should be noted that for the first process of each workpiece, the completion time of the preceding process is 0.

[0048] S5323. Based on the completion time of the previous process, the transportation time of the target logistics equipment, and the idle time while waiting for the target logistics equipment, sum them up to determine the candidate start time for each process. Specifically, the candidate start time = completion time of the previous process + transportation time required for the target logistics equipment + idle time waiting for the target logistics equipment. Idle time refers to the length of time the target logistics equipment is currently occupied by other transportation tasks and needs to wait to become idle.

[0049] S5324. Determine the idle available time of the target processing equipment; S5325. The maximum value between the idle available time and the candidate start time is taken as the target start time of the process; In this embodiment, the start time of the process needs to meet the following condition: Internal constraint of the workpiece: The start time of the k-th process of a certain workpiece ≥ the start time of the k-th process of that workpiece. Completion time of one process step; Processing equipment constraint: Start time of the t-th task of a certain processing equipment ≥ start time of the t-th task of that processing equipment. The completion time of a task; the target logistics equipment must first transport the workpiece to the target processing equipment before the target processing equipment can start processing. Therefore, the target start time is selected as the maximum of the candidate start time and the idle available time of the processing equipment.

[0050] S5326. Sum the target start time and the processing time to obtain the completion time of each process.

[0051] S533. The completion time of the last process in each workpiece is determined as the actual completion time of each workpiece, and the maximum value among the actual completion times of all workpieces is determined as the final completion time. S534. Calculate the difference between the actual completion time of each workpiece and the preset delivery date corresponding to each workpiece, and sum the difference for each workpiece to obtain the total delay time. S535. Based on the optimization objective, the final completion time and the total delay time are weighted and summed to obtain the fitness optimization objective.

[0052] For example, the step of obtaining the fitness by weighted summation of the final completion time and the total delay time includes: Determine the first weight corresponding to the final completion time and the second weight corresponding to the total delay time; Multiply the final completion time by the first weight to obtain the first weighted component; Multiply the total delay time by the second weight to obtain the second weighted component; The fitness of the chromosome is obtained by adding the first weighted component and the second weighted component, and the calculation formula is as follows: F=α Cmax1+(1 α) T1; Where α∈[0,1] represents the first weight, which can be adjusted according to actual needs. α is the second weight, Cmax1 represents the final completion time, and T1 represents the total delay time.

[0053] S540. Based on the fitness, select parent chromosomes from the initial population, and simultaneously perform crossover and mutation operations on the processing sequence, processing equipment allocation sequence, and logistics equipment allocation sequence of the parent chromosomes to generate a new population.

[0054] For example, this embodiment uses a tournament to select parent chromosomes, that is, each time a number of individuals (e.g., 1 to 5) are randomly selected from the current population, and the fitness values ​​of the individuals are compared. The individual with the best (smallest) fitness is selected as the parent. This process is repeated until the number of parents selected is the same as the population size.

[0055] Crossover operation: For the selected parent chromosomes, crossover is performed with a preset crossover probability (e.g., 0.8). Specifically, this includes: performing crossover on the processing sequences: the middle part of offspring 1 inherits the middle part of parent 2, and the left and right parts of offspring 1 are taken from the processing sequence of parent 1 in the original order, and the process genes that did not appear in the middle part are sequentially filled into the empty positions of the left and right segments, thereby ensuring that the processing order of each process of the same workpiece is not disrupted. Specifically: assuming the processing sequence of parent 1 is [J1-OP1,J2-OP1,J1-OP2,J2-OP2], and the processing sequence of parent 2 is [J2-OP1,J1-OP1,J2-OP2,J1-OP2], the 2nd and 3rd positions are selected as crossover points. The middle part of offspring 1 inherits the 2nd and 3rd positions of parent 2, i.e. [J1-OP1, J2-OP2]; the left and right parts are obtained by taking genes from parent 1 that do not appear in the middle part: if J1-OP1 already exists in parent 1, it is skipped, J2-OP1 is taken and placed in the left segment, and then J1-OP2 is taken and placed in the right segment in turn, so that the processing sequence of offspring 1 is [J2-OP1, J1-OP1, J2-OP2, J1-OP2].

[0056] Crossover operations are performed on the processing equipment allocation sequences and logistics equipment allocation sequences: At the same two crossover points, the processing equipment allocation sequences and logistics equipment allocation sequences of the two parent chromosomes are exchanged, resulting in two corresponding sequences for the offspring. After the exchange, if a device number in the processing equipment allocation sequence does not belong to the set of available processing equipment corresponding to that process, then that number is replaced with another valid number from that set; if a logistics equipment number in the logistics equipment allocation sequence does not belong to the set of logistics equipment actually available in the workshop, then that number is replaced with another valid number from the logistics equipment information.

[0057] Mutation Operation: To prevent the algorithm from prematurely converging to a local optimum, the offspring chromosomes generated after crossover are mutated with a preset mutation probability (e.g., 0.01–0.05). Specifically, this includes: For processing sequences, randomly selecting the process gene exchange positions for two different workpieces (e.g., exchanging the order of J1-OP1 and J2-OP1) to ensure that the processing order is not violated. For processing equipment allocation sequences, randomly selecting a gene and replacing its processing equipment number with another valid equipment number for the corresponding process (e.g., changing processing equipment 1 in J1-OP1 to processing equipment 2, ensuring that processing equipment 2 exists in process OP1). For logistics equipment allocation sequences: randomly selecting a gene position and replacing the current logistics equipment number at that position with another valid number from the logistics equipment information.

[0058] For example, in the embodiments of this application, the crossover probability and mutation probability can be dynamically adjusted according to the iteration progress. In the early stage of iteration, the crossover probability can be set to 0.8 to 0.9 and the mutation probability can be set to 0.05 to 0.1 to enhance the global search capability. In the later stage of iteration, the crossover probability drops to 0.6 to 0.7 and the mutation probability drops to 0.01 to 0.03 to strengthen the local fine search and stabilize the obtained optimal solution.

[0059] S550. Using the new population as the current population, return to the step of decoding each chromosome based on the optimization objective and continue iteratively solving until the preset termination condition is met. Select the maximum fitness corresponding to each chromosome during the iteration process, and obtain the optimal solution of the optimization objective based on the highest fitness corresponding to each chromosome.

[0060] The termination conditions include: the number of iterations reaches a preset value (e.g., 100-500 generations, adjusted according to the complexity of the problem), the optimal fitness value does not improve for multiple consecutive generations (e.g., 20 generations), or the final completion time reaches a preset threshold.

[0061] It is understandable that a higher fitness indicates a better solution. Therefore, during the iteration process, the maximum fitness of each chromosome is recorded to obtain the optimal solution of the optimization objective. This optimal solution corresponds to a complete scheduling scheme, including process sequencing, processing equipment allocation, and logistics equipment allocation.

[0062] S600. Based on the optimal solution, a plurality of workpiece processing optimization scheduling schemes are obtained, wherein the optimization scheduling schemes include processing equipment allocation schemes, processing timing schemes, and logistics equipment allocation schemes.

[0063] To explain in detail the principles of the technical solution of this application, the overall process of this application will be described below with reference to some specific embodiments. It is easy to understand that the following is an explanation of the technical principles of this application and should not be regarded as a limitation of this application.

[0064] In one specific embodiment, the multi-workpiece parallel processing process of this application includes the following steps: S1. Obtain the required processes and their sequence for each workpiece, as well as the processing equipment corresponding to each process; S2. Based on the operating status, current load, and processing capacity data of the processing equipment, obtain the set of available processing equipment for each process; S3. Obtain the logistics equipment and its transportation time, where transportation time refers to the time required for the logistics equipment to transport the workpiece from one location (e.g., the processing equipment of the previous process) to the processing equipment of the next process; S4. With minimizing the final completion time and minimizing the total delay time as the optimization objectives, use a genetic algorithm to iteratively solve for the optimal solution to the optimization objective, thereby obtaining the processing equipment allocation scheme, processing sequence scheme, and logistics equipment allocation scheme for multiple workpieces. Specifically, the steps to obtain the optimal solution to the optimization objective may include: (1) Generate a processing sequence according to the processing order of each process of each workpiece; for each process in the processing sequence, randomly select a processing equipment number from the set of available processing equipment for each process to obtain a processing equipment allocation sequence; for each process in the processing sequence, randomly select a logistics equipment number from the available logistics equipment included in the logistics equipment information to obtain a logistics equipment allocation sequence.

[0065] (2) Combine each process in the processing sequence, each processing equipment in the processing equipment allocation sequence, and each logistics equipment in the logistics equipment allocation sequence into a chromosome according to the same index position. Repeat the step of combining chromosomes until the initial population is obtained according to the preset population size.

[0066] (3) Obtain the completion time of the previous process corresponding to each process, add the completion time of the previous process, the transportation time of the target logistics equipment, and the idle time of the target logistics equipment to calculate the candidate start time of each process; based on the workpiece internal constraints and processing equipment constraints, and the fact that the target logistics equipment must first transport the workpiece to the target processing equipment before the target processing equipment can start processing, the final start time is selected as the maximum of the candidate start time and the available idle time of the processing equipment. Workpiece internal constraints: The start time of the kth process of a certain workpiece ≥ the kth process of the workpiece Completion time of one process; Processing equipment constraint: Start time of the t-th task of a certain equipment ≥ The t-th task of the equipment The completion time of a task. The final start time is added to the processing time of each step to obtain the completion time of each step. The completion time of the last step in each workpiece is taken as the actual completion time of that workpiece, and the maximum value of the actual completion times of the workpieces is taken as the final completion time. The difference between the actual completion time of each workpiece and the corresponding delivery date is calculated to obtain the total delay time.

[0067] (4) Calculate fitness, the expression of which is: F=α Cmax1+(1 α) T1; Where α∈[0,1] represents the weight, which can be adjusted according to actual needs, Cmax1 represents the final completion time, and T1 represents the total delay time.

[0068] It should be noted that the processing method of this application is applicable to the following fields: Machining and metal products: Automotive parts: engine blocks, connecting rods, gears, etc., in milling, drilling, tapping and other processes; Molds: injection molds for mobile phone shells and home appliance shells, in the roughing and finishing processes of the mold core and mold frame.

[0069] Electronics Manufacturing: Printed Circuit Boards (PCBs): After surface mount technology (SMT), soldering processes (such as reflow soldering and wave soldering) can use multiple parallel soldering furnaces; Electronic Product Assembly: For the functional testing and software programming processes of mobile phones and computers, multiple parallel test stations or programming stations can be set up to match the fast pace of the preceding assembly lines.

[0070] Plastic and rubber products: Injection molded products: mobile phone cases, toy parts, etc. For large orders, multiple injection molding machines can be used to produce the same product simultaneously; Rubber parts: vulcanization molding process for O-rings and seals.

[0071] Textiles and Apparel: Garment Processing: The cutting process of the same shirt can be carried out by multiple automatic cutting beds in parallel; in the sewing process, the sewing of the same cuffs or collars can be completed by multiple workers operating the same sewing machine; Fabric Production: Dyeing and printing processes of greige fabric.

[0072] Food and pharmaceuticals: For example, the blister packaging (aluminum-plastic packaging) process for tablets can use multiple packaging machines in parallel; the filling process for beverages; and the baking process for biscuits.

[0073] In summary, this application provides a method and related equipment for parallel processing of multiple workpieces. The method first acquires process route data for multiple workpieces, including each process step and the corresponding processing equipment for each step. It then acquires the operating status, current load, and processing capacity data of the processing equipment for each step. Based on this data, it determines the set of available processing equipment for each step and acquires logistics equipment information. With the optimization objective of minimizing final completion time and total delay time, it iteratively solves the problem using a genetic algorithm based on the set of available processing equipment and logistics equipment information for each step to obtain the optimal solution. Based on the optimal solution, it obtains an optimized scheduling scheme for processing multiple workpieces, including a processing equipment allocation scheme, a processing timing scheme, and a logistics equipment allocation scheme. This application, based on the set of available processing equipment, logistics equipment information, and optimization objective for each step, uses a genetic algorithm for iterative solving, enabling efficient processing of multiple workpieces. equipment By performing a global search on the combined optimization problem of logistics, etc., the optimal solution that meets the optimization objective can be found quickly. Even when there are equipment bottlenecks, sudden failures, or a surge in orders, it can dynamically adjust resource allocation to effectively deal with unexpected production situations, thereby improving production efficiency and avoiding delivery delays.

[0074] like Figure 3 As shown in the diagram, this application also provides a schematic diagram of a multi-workpiece parallel processing device, which can implement the above-described method. The device may include: The process route data acquisition module 21 is used to acquire process route data for multiple workpieces, wherein the process route data includes each process of each workpiece and the processing equipment corresponding to each process. The processing equipment data acquisition module 22 is used to acquire the operating status, current load and processing capacity data of the processing equipment corresponding to each of the aforementioned processes; Available equipment determination module 23 is used to determine the set of available processing equipment for each process based on the operating status, current load and processing capacity data of the processing equipment; The logistics equipment information acquisition module 24 is used to acquire logistics equipment information; The solution module 25 is used to obtain the optimal solution for the optimization objective by using a genetic algorithm to iteratively solve the problem based on the set of available processing equipment for each process and the information of the logistics equipment, with the optimization objective being to minimize the final completion time and minimize the total delay time. The scheduling scheme generation module 26 is used to obtain multiple optimized scheduling schemes for the processing of the workpieces based on the optimal solution, wherein the optimized scheduling schemes include a processing equipment allocation scheme, a processing timing scheme, and a logistics equipment allocation scheme.

[0075] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0076] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described multi-workpiece parallel processing method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0077] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0078] Please see Figure 4 , Figure 4 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and called and executed by the processor 901 using the multi-workpiece parallel processing method of the embodiments of this application. The input / output interface 903 is used to implement information input and output; The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904); The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0079] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described multi-workpiece parallel processing method.

[0080] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0081] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0082] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0083] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0084] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0085] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0086] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0087] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0088] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0089] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0090] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for parallel processing of multiple workpieces, characterized in that, include: Acquire process route data for multiple workpieces, wherein the process route data includes each process step of each workpiece and the processing equipment corresponding to each process step; Obtain the operating status, current load, and processing capacity data of the processing equipment corresponding to each of the aforementioned processes; Based on the operating status, current load, and processing capacity data of the processing equipment, determine the set of available processing equipment for each of the aforementioned processes; Obtain information on logistics equipment; With the optimization objectives of minimizing the final completion time and minimizing the total delay time, the optimal solution for the optimization objective is obtained by iteratively solving the problem using a genetic algorithm based on the set of available processing equipment for each process and the information of the logistics equipment. Based on the optimal solution, multiple processing optimization scheduling schemes for the workpieces are obtained, wherein the optimization scheduling schemes include processing equipment allocation schemes, processing timing schemes, and logistics equipment allocation schemes.

2. The method for parallel processing of multiple workpieces according to claim 1, characterized in that, The optimization objective is to minimize the final completion time and the total delay time. Based on the available processing equipment set for each process and the logistics equipment information, a genetic algorithm is used to iteratively solve for the optimal solution to the optimization objective, including: Determine the processing sequence of each process for each workpiece; Based on the processing sequence of each process of the workpiece, the set of available processing equipment, and the logistics equipment information, an initial population for the genetic algorithm is generated, wherein the initial population includes multiple chromosomes; Based on the optimization objective, each chromosome is decoded to obtain the fitness of each chromosome; Based on the fitness, parent chromosomes are selected from the initial population, and crossover and mutation operations are simultaneously performed on the processing sequence, processing equipment allocation sequence, and logistics equipment allocation sequence of the parent chromosomes to generate a new population. The new population is used as the current population to return to the step of decoding each chromosome based on the optimization objective and continue to iterate until the preset termination condition is met. The maximum fitness corresponding to each chromosome during the iteration process is selected, and the optimal solution of the optimization objective is obtained based on the highest fitness corresponding to each chromosome.

3. The method for parallel processing of multiple workpieces according to claim 2, characterized in that, The step of generating an initial population for a genetic algorithm based on the processing sequence of each step of the workpiece, the set of available processing equipment, and the logistics equipment information includes: A processing sequence is generated based on the processing order of each process for each workpiece; Based on the set of available processing equipment, processing equipment is randomly assigned to each step in the processing sequence to generate a processing equipment assignment sequence; Based on the logistics equipment information, logistics equipment is randomly assigned to each process in the processing sequence to generate a logistics equipment allocation sequence; Each of the processes in the processing sequence, each of the processing equipment in the processing equipment allocation sequence, and each of the logistics equipment in the logistics equipment allocation sequence are combined into a chromosome according to the same index position; wherein, each chromosome includes one of the processes, the processing equipment corresponding to the process, and the logistics equipment; Repeat the steps of combining chromosomes until the initial population is obtained according to the preset population size.

4. The method for parallel processing of multiple workpieces according to claim 2, characterized in that, The process of decoding each chromosome based on the optimization objective to obtain the fitness of each chromosome includes: Obtain the processing time and preset delivery date for each process in each workpiece; According to the processing sequence, the completion time of each process is determined sequentially based on the processing time of each process. The completion time of the last process in each workpiece is determined as the actual completion time of each workpiece, and the maximum value of the actual completion times of all workpieces is determined as the final completion time. Calculate the difference between the actual completion time of each workpiece and the preset delivery date corresponding to each workpiece, and sum the differences for each workpiece to obtain the total delay time. Based on the optimization objective, the final completion time and the total delay time are weighted and summed to obtain the fitness.

5. The method for parallel processing of multiple workpieces according to claim 4, characterized in that, The method of determining the completion time of each process based on the processing time of each process includes: Determine the target processing equipment and target logistics equipment allocated to each process, and determine the transportation time for the target logistics equipment; Determine the completion time of the preceding process for each process; The candidate start time for each process is determined by summing the completion time of the previous process, the transportation time of the target logistics equipment, and the idle time while waiting for the target logistics equipment. Determine the idle available time of the target processing equipment; The maximum value between the available idle time and the candidate start time is taken as the target start time of the process; The completion time of each process is obtained by summing the target start time and the processing time.

6. The method for parallel processing of multiple workpieces according to claim 4, characterized in that, The step of weighted summing of the final completion time and the total delay time to obtain the fitness includes: Determine the first weight corresponding to the final completion time and the second weight corresponding to the total delay time; Multiply the final completion time by the first weight to obtain the first weighted component; Multiply the total delay time by the second weight to obtain the second weighted component; The fitness of the chromosome is obtained by adding the first weighted component to the second weighted component.

7. A multi-workpiece parallel processing device, characterized in that, The device includes: The process route data acquisition module is used to acquire process route data for multiple workpieces, wherein the process route data includes each process of each workpiece and the processing equipment corresponding to each process. The processing equipment data acquisition module is used to acquire the operating status, current load, and processing capacity data of the processing equipment corresponding to each of the aforementioned processes; The available equipment determination module is used to determine the set of available processing equipment for each of the processing processes based on the operating status, current load, and processing capacity data of the processing equipment. The logistics equipment information acquisition module is used to acquire logistics equipment information; The solution module is used to obtain the optimal solution for the optimization objective by using a genetic algorithm to iteratively solve the problem based on the set of available processing equipment for each process and the information of the logistics equipment, with the optimization objective being to minimize the final completion time and minimize the total delay time. The scheduling scheme generation module is used to obtain multiple optimized scheduling schemes for the processing of the workpieces based on the optimal solution, wherein the optimized scheduling schemes include a processing equipment allocation scheme, a processing timing scheme, and a logistics equipment allocation scheme.

8. An electronic device, characterized in that, Including the processor and memory; The memory is used to store programs; The processor executes the program to implement the multi-workpiece parallel machining method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the multi-workpiece parallel processing method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the multi-workpiece parallel processing method as described in any one of claims 1 to 6.

Images