Production line resource scheduling method, scheduling apparatus, electronic device, and storage medium

Abstract

The present application discloses a production line resource scheduling method, a scheduling apparatus, an electronic device, and a storage medium. The method comprises: generating an initial scheduling population on the basis of production line resource information, the initial scheduling population comprising a plurality of pieces of first scheduling information; on the basis of the production line resource information, determining a first scheduling time of each piece of first scheduling information; performing a crossover operation on a plurality of pieces of target first scheduling information to obtain a crossover scheduling population, the crossover scheduling population comprising a plurality of pieces of second scheduling information; obtaining third scheduling information on the basis of the crossover scheduling population, a third scheduling time of the third scheduling information being less than a second scheduling time of the second scheduling information; and outputting fourth scheduling information on the basis of the first scheduling time and the third scheduling time. According to the method, in an automatic production process, the crossover operation is performed on the initial scheduling population that is generated on the basis of the production line resource information, so that a scheduling scheme of the production line resources is automatically generated, thereby improving the production efficiency of the automatic production process.

Description

Production line resource scheduling method, scheduling device, electronic equipment and storage medium

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on March 28, 2024, with application number 202410369764.1 and invention name “Scheduling method, scheduling device, electronic device and storage medium for production line resources”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application belongs to the technical field of production scheduling, and in particular relates to a scheduling method, a scheduling device, an electronic device and a storage medium for production line resources. Background Art

[0003] In the automated production process, various production line resource scheduling is involved, such as the scheduling of production equipment, automated guided vehicles (AGVs), and production tasks. The scheduling plan of production line resources has a crucial impact on production efficiency.

[0004] Currently, in automated production processes, production line resource scheduling plans are primarily manually developed by production personnel based on production line resource parameters. However, when there are many production line resource parameters, production personnel need to spend a considerable amount of time developing production line resource scheduling plans, resulting in reduced production efficiency in the automated production process.

[0005] Application Contents

[0006] In view of this, embodiments of the present application provide a scheduling method, a scheduling device, an electronic device, and a storage medium for production line resources to solve the above problems of the prior art. Technical Solutions

[0007] The technical solution adopted in the embodiment of this application is:

[0008] In a first aspect, an embodiment of the present application provides a method for scheduling production line resources, including:

[0009] generating an initial scheduling population according to the production line resource information, wherein the initial scheduling population includes a plurality of first scheduling information;

[0010] determining a first scheduling time for each first scheduling information according to the production line resource information;

[0011] Performing a cross operation on the plurality of target first scheduling information to obtain a cross scheduling population, where the cross scheduling population includes the plurality of second scheduling information;

[0012] Obtain third scheduling information according to the cross-scheduling population, where a third scheduling time of the third scheduling information is less than a second scheduling time of the second scheduling information;

[0013] Output fourth scheduling information according to the first scheduling time and the third scheduling time, where the fourth scheduling information is scheduling information corresponding to the minimum scheduling time between the first scheduling time and the third scheduling time.

[0014] In some optional embodiments, determining the first scheduling time of each first scheduling information according to the production line resource information includes:

[0015] Constructing a minimum scheduling time function for scheduling production line resources. The minimum scheduling time function is used to characterize the corresponding relationship between the scheduling path of the scheduling production line resources and the scheduling time of the scheduling production line resources.

[0016] determining a first scheduling path for each first scheduling information;

[0017] The first scheduling time of each first scheduling information is calculated according to the scheduling time minimum function and the first scheduling path.

[0018] In some optional embodiments, each first scheduling information includes a set of first production task information, first production equipment information, and first automated guided vehicle information. Determining a first scheduling path for each first scheduling information includes:

[0019] Encode each set of first production task information, first production equipment information, and first automated guided vehicle information based on a preset encoding rule to obtain a encoding table, the encoding table including multiple production task codes, multiple production equipment codes, and multiple automated guided vehicle codes;

[0020] Decoding the code table based on a preset decoding rule to obtain a dispatch path matrix, wherein the dispatch path matrix includes multiple dispatch paths, and matrix sorting information of each dispatch path is associated with an automated guided vehicle code;

[0021] According to the automatic guided vehicle code of each automatic guided vehicle information, the scheduling path matrix is ​​searched to obtain a first scheduling path.

[0022] In some optional embodiments, each set of first production task information, first production equipment information, and first automated guided vehicle information is encoded based on a preset encoding rule to obtain a coding table, including:

[0023] Encoding each set of first production task information according to the operation sequence of the production task corresponding to each set of first production task information to obtain a production task code;

[0024] Encoding each set of first production equipment information according to the equipment selection order of the production equipment corresponding to each set of first production equipment information to obtain a production equipment code;

[0025] encoding each set of first automated guided vehicle information according to a transportation sequence of the automated guided vehicles corresponding to each set of first automated guided vehicle information to obtain an automated guided vehicle code;

[0026] A code table is generated based on multiple production task codes, multiple production equipment codes, and multiple automatic guided vehicle codes.

[0027] In some optional embodiments, decoding the coding table based on a preset decoding rule to obtain a scheduling path matrix includes:

[0028] Constructing an automatic guided vehicle array according to multiple automatic guided vehicle codes;

[0029] Constructing a production task array according to multiple production task codes;

[0030] Construct a production equipment array based on multiple production equipment codes;

[0031] According to the code of each automated guided vehicle, the production task array and the production equipment array are traversed to generate a scheduling path matrix.

[0032] In some optional embodiments, the production task array and the production equipment array are traversed according to the code of each automated guided vehicle to generate a scheduling path matrix, including:

[0033] Determine the permutation index number of each automated guided vehicle code;

[0034] Each production task code in the production task array and each production equipment code in the production equipment array are traversed according to each arrangement index number to obtain a scheduling path matrix.

[0035] In some optional embodiments, calculating the first scheduling time of each first scheduling information according to the scheduling time minimum function and the first scheduling path includes:

[0036] Determine a corresponding target production task code according to each first scheduling path;

[0037] Determine the transportation time and production time for each target production task code. The transportation time is used to represent the time it takes for the automated guided vehicle to transport the target production task corresponding to the target production task code. The production time is used to represent the time it takes for the production equipment to produce the target production task.

[0038] The first scheduling time of each first scheduling information is calculated according to the scheduling time minimum function and the transportation time and production time of each target production task code.

[0039] In some optional embodiments, performing a cross operation on a plurality of target first scheduling information to obtain a cross scheduling population includes:

[0040] According to the preset selection algorithm and the initial scheduling population, a cross pool is constructed, where the cross pool includes a plurality of target first scheduling information;

[0041] Determine the intersection probability of each target first scheduling information;

[0042] A crossover operation is performed on the target first scheduling information whose crossover probability is greater than the crossover probability threshold to obtain a crossover scheduling population.

[0043] In some optional embodiments, each target first scheduling information includes a set of first production task information, first production equipment information, and first automated guided vehicle information. A crossover operation is performed on the target first scheduling information having a crossover probability greater than a crossover probability threshold to obtain a crossover scheduling population, including:

[0044] Selecting target first production task information, target first production equipment information, and target first automated guided vehicle information whose crossover probability is greater than a crossover probability threshold from multiple target first scheduling information;

[0045] constructing a first chromosome and a second chromosome according to target first production task information, target first production equipment information, and target first automated guided vehicle information;

[0046] The different target first production task information, target first production equipment information, and target first automated guided vehicle information included in the first chromosome and the second chromosome are crossed to obtain a cross scheduling population.

[0047] In some optional embodiments, each target first scheduling information includes a set of first production task information, first production equipment information, and first automated guided vehicle information. A crossover operation is performed on the target first scheduling information having a crossover probability greater than a crossover probability threshold to obtain a crossover scheduling population, including:

[0048] Selecting target first production task information, target first production equipment information, and target first automated guided vehicle information whose crossover probability is greater than a crossover probability threshold from multiple target first scheduling information;

[0049] constructing a third chromosome and a fourth chromosome according to target first production task information, target first production equipment information, and target first automated guided vehicle information;

[0050] Generate a random number for the third chromosome and get the fifth chromosome;

[0051] The different target first production task information, target first production equipment information, and target first automated guided vehicle information contained in the fifth chromosome and the fourth chromosome are crossed to obtain a crossover scheduling population.

[0052] In some optional embodiments, each second scheduling information includes a set of second production task information, second production equipment information, and second automated guided vehicle information, and the third scheduling information is obtained based on the cross-scheduling population, including:

[0053] Determine the critical scheduling path based on production line resource information;

[0054] Each set of second production task information, second production equipment information and second automatic guided vehicle information is searched according to the key scheduling path to obtain third scheduling information, which includes a set of third production task information, third production equipment information and third automatic guided vehicle information.

[0055] In some optional embodiments, the production line resource information includes at least a plurality of production tasks, and determining the critical scheduling path based on the production line resource information includes:

[0056] Determine multiple task paths according to the operation sequence of multiple production tasks, each task path includes the same starting production task and ending production task;

[0057] Determine the task path duration of each task path based on the task node duration of each production task;

[0058] The task path corresponding to the longest task path duration is selected from multiple task paths as the critical scheduling path.

[0059] In some optional embodiments, each set of second production task information, second production equipment information, and second automated guided vehicle information is searched according to the critical scheduling path to obtain third scheduling information, including:

[0060] Based on a preset search operator, searching each set of second production task information, second production equipment information, and second automated guided vehicle information according to a critical scheduling path to generate first neighborhood solution scheduling information, where the first neighborhood solution scheduling information includes a set of fourth production task information, fourth production equipment information, and fourth automated guided vehicle information;

[0061] Determining a first neighboring area descheduling time of the first neighboring area descheduling information;

[0062] If it is determined that the first neighborhood descheduling time is less than the second scheduling time, updating the corresponding second scheduling information to the first neighborhood descheduling information;

[0063] Repeating the steps of searching each set of second production task information, second production equipment information, and second automated guided vehicle information according to the critical scheduling path to generate first neighborhood de-scheduling information, until, if it is determined that the first neighborhood de-scheduling time is greater than or equal to the second scheduling time, the corresponding first neighborhood de-scheduling time is determined as the second neighborhood de-scheduling time;

[0064] The second neighboring area descheduling information corresponding to the second neighboring area descheduling time is determined as the third scheduling information.

[0065] In some optional embodiments, the preset search operator includes multiple sub-preset search operators, each sub-preset search operator is associated with a search count, and the second neighborhood descheduling information corresponding to the second neighborhood descheduling time is determined as the third scheduling information, including:

[0066] If it is determined that the second neighborhood descheduling time is greater than or equal to the second scheduling time, determining whether the current search number reaches a preset number;

[0067] If it is determined that the current search number has not reached the preset number, determining a target sub-preset search operator for the next search number;

[0068] Updating the preset search operator to the target sub-preset search operator, and updating the second scheduling information to the second neighborhood solution scheduling information;

[0069] Repeat the steps of searching each group of second production task information, second production equipment information and second automatic guided vehicle information based on the preset search operator according to the key scheduling path to generate the first neighborhood de-scheduling information, until it is determined that the current search number reaches the preset number, and the second neighborhood de-scheduling information corresponding to the current search number is determined as the third scheduling information.

[0070] In some optional embodiments, before obtaining the third scheduling information according to the cross-scheduling population, the scheduling method for production line resources further includes:

[0071] Perform mutation operation on the cross-scheduling population to obtain the mutated scheduling population;

[0072] The third scheduling information is obtained according to the cross-scheduling population, including:

[0073] The third scheduling information is obtained according to the mutated scheduling population.

[0074] In some optional embodiments, before outputting the fourth scheduling information based on the first scheduling time and the third scheduling time, the method for scheduling production line resources further includes:

[0075] Determine whether the iteration termination condition is met;

[0076] Outputting fourth scheduling information according to the first scheduling time and the third scheduling time includes:

[0077] When it is determined that the iteration termination condition is satisfied, fourth scheduling information is output according to the first scheduling time and the third scheduling time.

[0078] In some optional embodiments, the method for scheduling production line resources further includes:

[0079] When it is determined that the iteration termination condition is not met, the step of performing a cross operation on multiple target first scheduling information to obtain a cross scheduling population is repeated, until it is determined that the iteration termination condition is met, and the step of outputting the fourth scheduling information according to the first scheduling time and the third scheduling time is performed.

[0080] In a second aspect, an embodiment of the present application provides a scheduling device for production line resources, including:

[0081] A generating module, configured to generate an initial scheduling population according to the production line resource information, wherein the initial scheduling population includes a plurality of first scheduling information;

[0082] A time determination module, configured to determine a first scheduling time for each first scheduling information according to production line resource information;

[0083] A cross module, configured to perform a cross operation on a plurality of target first scheduling information to obtain a cross scheduling population, wherein the cross scheduling population includes a plurality of second scheduling information;

[0084] an obtaining module, configured to obtain third scheduling information according to the cross-scheduling population, wherein a third scheduling time of the third scheduling information is less than a second scheduling time of the second scheduling information;

[0085] The output module is used to output fourth scheduling information according to the first scheduling time and the third scheduling time, where the fourth scheduling information is scheduling information corresponding to the minimum scheduling time between the first scheduling time and the third scheduling time.

[0086] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0087] Memory;

[0088] one or more processors coupled to the memory;

[0089] One or more applications, wherein the one or more applications are stored in a memory and configured to be executed by one or more processors, and the one or more applications are configured to execute the production line resource scheduling method provided in the first aspect above.

[0090] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which program code is stored. The program code can be called by a processor to execute the production line resource scheduling method provided in the first aspect above.

[0091] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on a computer device, it enables the computer device to execute the production line resource scheduling method provided in the first aspect above. Beneficial effects

[0092] The first beneficial effect provided by the embodiment of the present application is that: in the automated production process, based on cross-processing of the initial scheduling population generated according to the production line resource information, a scheduling plan for the production line resources is automatically generated. Compared with the time spent on manually formulating a scheduling plan based on the production line resource information, the time spent on automatically generating a scheduling plan based on the production line resource information is shorter, thereby improving the production efficiency of the automated production process.

[0093] It can be understood that the beneficial effects of the second to fifth aspects of the present application can be found in the relevant description of the first aspect of the present application and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present application. The same reference numerals are used throughout the drawings to represent the same components. In the drawings:

[0095] FIG1 shows a flow chart of a method for scheduling production line resources according to an embodiment of the present application.

[0096] FIG2 shows a schematic diagram of a coding scenario in a method for scheduling production line resources provided in an embodiment of the present application.

[0097] FIG3 shows a flow chart of a decoding method in a method for scheduling production line resources provided in an embodiment of the present application.

[0098] FIG4 shows a flow chart of a POX cross-operation process in a method for scheduling production line resources provided in an embodiment of the present application.

[0099] FIG5 shows a schematic diagram of a scenario of a POX cross-operation process in a method for scheduling production line resources provided in an embodiment of the present application.

[0100] FIG6 shows a flow chart of the RPX cross-operation process in the production line resource scheduling method provided in an embodiment of the present application.

[0101] FIG7 shows a schematic diagram of a scenario of the RPX cross-operation process in the production line resource scheduling method provided in an embodiment of the present application.

[0102] FIG8 shows a flow chart of a method for determining a critical scheduling path in a method for scheduling production line resources provided in an embodiment of the present application.

[0103] FIG9 shows another flow chart of a method for scheduling production line resources provided in an embodiment of the present application.

[0104] FIG10 shows a flow chart of a scenario of a method for scheduling production line resources provided in an embodiment of the present application.

[0105] FIG11 shows a structural block diagram of a scheduling device for production line resources provided in an embodiment of the present application.

[0106] FIG12 shows a functional block diagram of an electronic device provided in an embodiment of the present application.

[0107] Figure 13 shows a computer-readable storage medium provided in an embodiment of the present application for storing or carrying program code for implementing the scheduling method for production line resources provided in an embodiment of the present application.

[0108] FIG14 shows a computer program product provided in an embodiment of the present application for storing or carrying program codes for implementing a method for scheduling production line resources provided in an embodiment of the present application. DETAILED DESCRIPTION

[0109] In order to make the purpose, features, and advantages of the invention of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described below are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0110] It will be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0111] It should also be understood that the terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0112] It should be further understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0113] In addition, in the description of the present application, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0114] In the automated production process, various production line resource scheduling is involved, such as the scheduling of production equipment, automated guided vehicles (AGVs), and production tasks. The scheduling plan of production line resources has a crucial impact on production efficiency.

[0115] Currently, in automated production processes, production line resource scheduling plans are primarily manually developed by production personnel based on production line resource parameters. However, when there are many production line resource parameters, production personnel need to spend a considerable amount of time developing production line resource scheduling plans, resulting in reduced production efficiency in the automated production process.

[0116] In response to the above problems, the scheduling method, scheduling device, electronic device and storage medium for production line resources provided in the embodiments of the present application generate an initial scheduling population based on the production line resource information, the initial scheduling population includes multiple first scheduling information, and determines the first scheduling time of each first scheduling information based on the production line resource information, and performs cross-operations on multiple target first scheduling information to obtain a cross-scheduling population, the cross-scheduling population includes multiple second scheduling information, and obtains third scheduling information based on the cross-scheduling population, the third scheduling time of the third scheduling information is less than the second scheduling time of the second scheduling information, and outputs fourth scheduling information based on the first scheduling time and the third scheduling time, and the fourth scheduling information is the scheduling information corresponding to the minimum scheduling time between the first scheduling time and the third scheduling time.

[0117] This application realizes the automatic generation of scheduling plans for production line resources based on cross-processing of the initial scheduling population generated according to production line resource information during the automated production process. Compared with the time spent on manually formulating scheduling plans based on production line resource information, the time spent on automatically generating scheduling plans based on production line resource information is shorter, thereby improving the production efficiency of the automated production process.

[0118] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application.

[0119] The scheduling method for production line resources provided in the embodiments of the present application can be applied to processing equipment, and the processing equipment can include but is not limited to any one of a server or a terminal device.

[0120] The server may include but is not limited to an independent physical server, a server cluster or distributed system consisting of multiple physical servers, and a cloud server.

[0121] Terminal devices may include but are not limited to mobile terminal devices (for example, mobile phones, personal digital assistants (PDAs), tablet personal computers (Tablet PCs), laptops, smart watches, smart bracelets, etc.) and fixed terminal devices (for example, desktop computers, smart panels, all-in-one computers, etc.).

[0122] Please refer to Figure 1, which shows a flow chart of a method for scheduling production line resources according to one embodiment of the present application. In a specific embodiment, the method for scheduling production line resources can be applied to processing equipment. The following will use processing equipment as an example to explain the process shown in Figure 1 in detail. The method for scheduling production line resources can include the following steps S110 to S150.

[0123] Step S110: Generate an initial scheduling population based on production line resource information.

[0124] In an embodiment of the present application, when production personnel need to schedule production line resources, they can send scheduling instructions to the processing device, the processing device receives and responds to the scheduling instructions, obtains production line resource information, and generates an initial scheduling population based on the production line resource information.

[0125] The production line resource information may include, but is not limited to, multiple pieces of first production equipment information, multiple pieces of first AGV information, and multiple pieces of first production task information. The initial scheduling population may include multiple pieces of first scheduling information, each piece of first scheduling information representing information for scheduling production equipment, AGVs, and production tasks. Each piece of first scheduling information is composed of a set of first production equipment information, first AGV information, and first production task information.

[0126] In some embodiments, when production personnel need to schedule production line resources, they can send scheduling instructions to the processing equipment, which receives and responds to the scheduling instructions, obtains multiple first production equipment information, multiple first AGV information, and multiple first production task information, and randomly combines the multiple first production equipment information, multiple first AGV information, and multiple first production task information into multiple first scheduling information to obtain an initial scheduling population.

[0127] In some embodiments, when production personnel need to schedule production line resources, they can send scheduling instructions to the processing device, the processing device receives and responds to the scheduling instructions, and sends an acquisition instruction to the service platform that pre-stores the production line resource information through the network. The service platform receives and responds to the acquisition instruction, and sends the production line resource information to the processing device through the network. The processing device receives the production line resource information returned by the service platform.

[0128] Among them, the network may include but is not limited to ZigBee network, Bluetooth (BT) network, Wireless Fidelity (Wi-Fi) network, Home Internet of Things Communication Protocol Technology (Thread) network, Long Range Radio (LoRa) network, Low-Power Wide-Area Network (LPWAN), infrared network, Narrow Band Internet of Things (NB-IoT), Controller Area Network (CAN), Digital Living Network Alliance (DLNA) network, Wide Area Network (WAN), Local Area Network (LAN), Metropolitan Area Network (MAN) or Wireless Personal Area Network (WPAN), etc.

[0129] The service platform may include but is not limited to an independent physical platform, a platform cluster or distributed system composed of multiple physical platforms, a cloud platform that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), big data or artificial intelligence platforms, etc.

[0130] In some embodiments, when production personnel need to schedule production line resources, they can send scheduling instructions to the processing device, which receives and responds to the scheduling instructions, generates prompt information, and receives production line resource information uploaded by the production personnel based on the prompt information.

[0131] Among them, the prompt information can be used to prompt production personnel to upload production line resource information to the processing equipment. The first prompt information can include but is not limited to at least any one of sound prompt information, light prompt information or text prompt information.

[0132] In some embodiments, the processing device may pre-store production line resource information. When production personnel need to schedule production line resources, they may send scheduling instructions to the processing device. The processing device receives and responds to the scheduling instructions and reads the pre-stored production line resource information.

[0133] In some embodiments, the processing device may be provided with an input panel. When production personnel need to schedule production line resources, they may input scheduling instructions on the input panel of the processing device. For example, they may input scheduling instructions by handwriting on the input panel, or they may input scheduling instructions by pressing buttons on the input panel. The processing device receives the scheduling instructions through the input panel.

[0134] In some embodiments, the processing device may be provided with a voice recognition module. When production personnel need to schedule production line resources, they may send voice information within the voice collection range of the voice recognition module. The voice recognition module collects the voice information sent by the production personnel, and performs voice recognition on the collected voice information. Based on the recognition results of the voice recognition, it is determined that the recognition results contain keywords for instructing the processing device to schedule production line resources. For example, the keyword is "production line resource scheduling", and for another example, the keywords are "production line resources" and "scheduling", etc., then it is determined that a scheduling instruction for scheduling production line resources has been received.

[0135] As an example, the voice message sent by the user is: scheduling production line resources, and the recognition result of the voice recognition contains the keywords "production line resources" and "scheduling", then it is determined that the scheduling instruction for scheduling production line resources has been received.

[0136] In some embodiments, when production personnel need to schedule production line resources, they can send scheduling instructions to a user client associated with a processing device. The user client receives and responds to the scheduling instructions, forwards the scheduling instructions to the processing device via the network, and the processing device receives the scheduling instructions forwarded by the user client.

[0137] Among them, the user client may include but is not limited to any one of a mobile client (for example, a mobile phone client, a PDA client, a Tablet PC client, a laptop client, a smart watch client, a smart bracelet client or a wearable client, etc.) or a fixed client (for example, a desktop computer client, a smart panel client, etc.).

[0138] Step S120: determining a first scheduling time for each first scheduling information according to the production line resource information.

[0139] In an embodiment of the present application, the processing device may determine the first scheduling time of each first scheduling information based on the production line resource information using a genetic algorithm, wherein the first scheduling time may be used to represent the fitness of the initial scheduling population.

[0140] Specifically, the processing device can construct a minimum scheduling time function for scheduling production line resources based on the genetic algorithm, determine the first scheduling path of each first scheduling information, and calculate the first scheduling time of each first scheduling information based on the minimum scheduling time function and the first scheduling path, thereby realizing the construction of the fitness function of the genetic algorithm based on the scheduling path and scheduling time of the scheduling production line resources, simplifying the fitness function construction process, and reducing the calculation complexity of the individual fitness of the genetic algorithm.

[0141] The scheduling time minimum function can be used to characterize the corresponding relationship between the scheduling path of the scheduling production line resources and the scheduling time of the scheduling production line resources.

[0142] The processing device can encode each group of first production task information, first production equipment information and first AGV information based on preset coding rules to obtain a coding table, and decode the coding table based on preset decoding rules to obtain a scheduling path matrix, and search the scheduling path matrix according to the AGV code of each AGV information to obtain the first scheduling path, thereby encoding the production line resource information into a coding table based on preset coding rules, and decoding the coding table based on preset decoding rules to obtain the first scheduling path, thereby simplifying the calculation process of the first scheduling path.

[0143] Among them, the coding table may include multiple production task codes, multiple production equipment codes and multiple AGV codes, the scheduling path matrix may include multiple scheduling paths, and the matrix sorting information of each scheduling path may be associated with an AGV code.

[0144] In one application scenario, production line resource information may include multiple production equipment, multiple AGVs, and multiple production tasks.

[0145] Variables can be predefined:

[0146] x ija Indicates whether the ath AGV is responsible for transporting from production task i to production task j, x ija =1 means the ath AGV is responsible for transporting from production task i to production task j, x ija =0 means that the ath AGV is not responsible for transporting from production task i to production task j, and production task j is the next task of production task i;

[0147] ylj Indicates whether production equipment l is assigned to production task j, y lj =1 means that production equipment l is assigned to production task j, y lj =0 means that production equipment l is not assigned to production task j;

[0148] t ija represents the time required for the a-th AGV to transport from production task i to production task j;

[0149] s lj It represents the time required for production equipment l to complete task j.

[0150] Construct the minimum scheduling time function as

[0151] The constraints are:

[0152] It means that for any production task i, only one AGV among all AGVs is assigned to production task j;

[0153] When the AGV is loaded with products, d ij represents the distance between task i and task j, v load represents the transport speed of the AGV carrying the product from task i to task j;

[0154] When the AGV is empty, d ij represents the distance between task i and task j, v unload represents the transport speed of the AGV when transporting an empty load from task i to task j;

[0155] It means that any production task j will only be assigned to one production device l.

[0156] Each first scheduling information may include a set of first production equipment information, first AGV information, and first production task information.

[0157] The preset coding rule may be a three-dimensional coding. Each set of first production task information may be encoded according to the operation sequence of the production task corresponding to each set of first production task information to obtain a production task code (Operate Sequence, OS code), each set of first production equipment information may be encoded according to the equipment selection sequence of the production equipment corresponding to each set of first production equipment information to obtain a production equipment code (Machine Sequence, MS code), each set of first AGV information may be encoded according to the transportation sequence of the AGV corresponding to each set of first AGV information to obtain an AGV code (Route Sequence, RS code), and a coding table may be generated based on multiple OS codes, multiple MS codes, and multiple RS codes.

[0158] As shown in FIG2 , it shows a scenario diagram of a coding table, in which the OS code, MS code and RS code in the coding table are read from left to right respectively.

[0159] The number I in the first line of the OS code appears for the Jth time. The number I represents the Ith production task, and J represents the Jth step of the Ith production task, which is recorded as O IJ For example, 12 Represents the second step of the first production task.

[0160] The first line of the MS code is number K, which represents the serial number of the production equipment, recorded as M K For example, M3 represents the third production equipment.

[0161] The first line of the RS code is numbered L, which is the AGV number and is recorded as A L For example, A1 represents the first AGV.

[0162] The permutation index numbers of the OS code, the permutation index numbers of the MS code, and the permutation index numbers of the RS code all correspond one to one. 12 , M3 corresponds to A1, indicating that the first AGV transports the product corresponding to the first production task to the third production equipment for the second production step.

[0163] As shown in FIG3 , the process of decoding the coding table based on the preset decoding rule may include steps S201 to S204 .

[0164] Step S201: Construct an AGV array Inx according to multiple RS codes.

[0165] Specifically, the RS code is performed from left to right, and the same AGV number is divided and the subscript (arrangement index number) is extracted to obtain the AGV array Inx.

[0166] Among them, Inx(r,:) (the rth row of the matrix Inx) represents the permutation index set of the rth AGV in the RS encoding.

[0167] get:

[0168] Step S202: Construct a production task array J according to multiple OS codes.

[0169] Specifically, according to multiple OS codes, a production task matrix J having the same size as the AGV array Inx is initialized.

[0170] The relationship between the production task array J and the AGV array Inx is: J(r, n)=OS(Inx(r, n)), where n is the sequence number of the element in the r-th row of the matrix J.

[0171] For example, J(1, 2) = OS(Inx(1, 2)) = OS(3), where the 3 in OS(3) indicates that the index number of the OS code is 3.

[0172] J(2,2)=OS(Inx(2,2))=OS(4), where 4 in OS(4) indicates that the index number of the OS code is 4.

[0173] Step S203: Construct a production equipment array RM according to the multiple MS codes.

[0174] Specifically, according to the multiple MS codes, the production equipment array RM having the same size as the AGV array Inx is initialized.

[0175] The relationship between the production equipment array RM and the AGV array Inx is: RM(r, n)=MS(Inx(r, n)).

[0176] For example, RM(1, 2) = MS(Inx(1, 2)) = MS(3), where the 3 in MS(3) indicates that the index number of the MS code is 3.

[0177] get:

[0178] Step S204: According to each RS code, the production task array J and the production equipment array RM are traversed to generate a scheduling path matrix Res.

[0179] Specifically, the permutation index number of each RS code can be determined, and each OS code in the production task array J and each MS code in the production equipment array RM can be traversed according to each permutation index number to obtain the scheduling path matrix Res.

[0180] As an example, initially set r=1, read RM(r,:) from left to right, and each time an element of RM(r,:) is read, insert the corresponding element of RM(r,n) into Res(r,:). At the same time, read a column of matrix J, and determine whether the number n in the column of matrix J has appeared. If it has appeared, record the position m, and insert the generation device number corresponding to the previous n at the position of Res(r,m-1), which represents the no-load path, until the rth row of RM is traversed.

[0181] For example, when the first AGV traverses J(1, 2), 3 has already appeared once. According to RM, we know that the previous step of the third production task is executed on the second production equipment, so the second production equipment number 2 is inserted.

[0182] When r is greater than the number of AGVs, the decoding is terminated and the Res matrix is ​​output; otherwise, r is updated to r+1.

[0183] get:

[0184] The number of rows in the matrix Res is r, and the elements of the matrix Res are the first scheduling path x ija .

[0185] In some embodiments, the processing device can determine a corresponding target production task code based on each first scheduling path, and determine the transportation time and production time of each target production task code, and calculate the first scheduling time of each first scheduling information based on the scheduling time minimum function and the transportation time and production time of each target production task code.

[0186] The transportation duration can be used to represent the duration for the AGV to transport the target production task corresponding to the target production task code, and the production duration can be used to represent the duration for the production equipment to produce the target production task.

[0187] Step S130: performing a cross operation on multiple target first scheduling information to obtain a cross scheduling population.

[0188] In an embodiment of the present application, the processing device may perform a cross operation on multiple target first scheduling information to obtain a cross scheduling population.

[0189] Specifically, multiple target first scheduling information is included in multiple first scheduling information. That is, each target first scheduling information can include a set of first production task information, first production equipment information, and first AGV information. The cross-scheduling population can include multiple second scheduling information. Each second scheduling information can be composed of a set of second production equipment information, second AGV information, and second production task information.

[0190] Specifically, the processing device can construct a crossover pool based on a preset selection algorithm and an initial scheduling population. The crossover pool can include multiple target first scheduling information, and determine the crossover probability of each target first scheduling information. Based on a preset crossover operator, a crossover operation is performed on the target first scheduling information whose crossover probability is greater than a crossover probability threshold to obtain a crossover scheduling population. This realizes the crossover of multiple target first scheduling information based on the preset crossover operator and the crossover probability threshold to obtain a crossover scheduling population, thereby improving the population diversity of the crossover scheduling population.

[0191] The preset selection algorithm may be a roulette wheel selection algorithm, and the crossover probability P of each target first scheduling information may be calculated according to the following formula: c .

[0192] Among them, α is the maximum crossover probability (constant), f1 is the fitness of the first scheduling information of each target, and f min is the fitness of the first scheduling information of the optimal target, f avg The average fitness of the first scheduling information of multiple targets.

[0193] At the crossover probability P c When the probability of the target scheduling information is greater than the crossover probability threshold, a crossover operation is performed on the target first scheduling information whose crossover probability is greater than the crossover probability threshold to obtain a crossover scheduling population.

[0194] As an example, the crossover probability threshold may be 0.5. c If the crossover probability P is greater than 0.5, c The corresponding target first scheduling information is cross-operated to obtain a cross-scheduling population.

[0195] The preset crossover operator may include but is not limited to a precedence operation crossover (POX) crossover operator and a random precedence operation (RPX) crossover operator.

[0196] In some embodiments, the preset crossover operator may be a POX crossover operator, and the processing device may construct a crossover pool including multiple target first scheduling information based on a preset selection algorithm and an initial scheduling population, and determine the crossover probability of each first scheduling information, and based on the POX crossover operator, perform a crossover operation on the target first scheduling information having a crossover probability greater than a crossover probability threshold to obtain a crossover scheduling population, thereby realizing the crossover of multiple target first scheduling information based on a priority operation crossover operator and a crossover probability threshold to obtain a crossover scheduling population, thereby improving the population diversity of the crossover scheduling population.

[0197] Specifically, the processing device can select target first production task information, target first production equipment information and target first AGV information with a crossover probability greater than a crossover probability threshold from multiple target first scheduling information, and construct the first chromosome and the second chromosome based on the target first production task information, the target first production equipment information and the target first AGV information, and cross the different target first production task information, target first production equipment information and target first AGV information contained in the first chromosome and the second chromosome to obtain a cross-scheduling population.

[0198] In an application scenario, as shown in FIG4 , the POX cross-operation process may include steps S301 to S303 .

[0199] Step S301: Select target first production task information, target first production equipment information, and target first AGV information whose crossing probability is greater than a crossing probability threshold from multiple target first scheduling information.

[0200] Step S302: Construct a first chromosome and a second chromosome according to the target first production task information and the target first AGV information.

[0201] Specifically, the target OS code is obtained according to the target first production task information, the target MS code is obtained according to the target first production equipment information, and the target RS code is obtained according to the target first AGV information. The target OS code and the target RS code are randomly divided into two non-empty subsets, which are respectively recorded as the first chromosome P1 and the second chromosome P2.

[0202] Step S303: Cross the different target first production task information and target first AGV information contained in the first chromosome and the second chromosome to obtain a cross scheduling population.

[0203] Specifically, the target OS coding portion in P1 containing the gene of J1, the device corresponding to the target MS coding, and the corresponding target RS coding portion of the gene is copied to P2 to obtain C1, and the target OS coding portion in P2 containing the gene of J2, the device corresponding to the target MS coding, and the corresponding target RS coding portion of the gene is copied to P1 to obtain C2, as shown in Figure 5.

[0204] It should be noted that during the POX crossing operation, the target first production task information and target first production equipment information with a crossing probability greater than the crossing probability threshold can be selected from multiple target first scheduling information for crossing, or the target first production equipment information and target first AGV information with a crossing probability greater than the crossing probability threshold can be selected from multiple target first scheduling information for crossing, or the target first production task information, target first production equipment information and target first AGV information with a crossing probability greater than the crossing probability threshold can be selected from multiple target first scheduling information for crossing, which is not limited here.

[0205] In some embodiments, the preset crossover operator can be a random priority operation crossover operator. The processing device can construct a crossover pool including multiple target first scheduling information based on the preset selection algorithm and the initial scheduling population, and determine the crossover probability of each first scheduling information. Based on the RPX crossover operator, a crossover operation is performed on the target first scheduling information whose crossover probability is greater than the crossover probability threshold to obtain a crossover scheduling population. This realizes the crossover of multiple target first scheduling information based on the random priority operation crossover operator and the crossover probability threshold to obtain a crossover scheduling population, thereby improving the population diversity of the crossover scheduling population.

[0206] Specifically, the processing device can select target first production task information, target first production equipment information and first AGV information with a crossover probability greater than a crossover probability threshold from multiple target first scheduling information, and construct a third chromosome and a fourth chromosome based on the target first production task information, the target first production equipment information and the first AGV information, and randomly generate a random number for the third chromosome to obtain the fifth chromosome, and cross the different target first production task information, target first production equipment information and target first AGV information contained in the fifth chromosome and the fourth chromosome to obtain a cross-scheduling population.

[0207] In an application scenario, as shown in FIG6 , the RPX cross-operation process may include steps S401 to S404 .

[0208] Step S401: Select target first production equipment information with a crossover probability greater than a crossover probability threshold from multiple target first scheduling information.

[0209] Step S402: Constructing a third chromosome and a fourth chromosome according to the target first production equipment information.

[0210] Specifically, the target MS code is obtained according to the target first production equipment information, and the target MS code is randomly divided into two non-empty subsets, which are respectively recorded as the third chromosome P3 and the fourth chromosome P4.

[0211] Step S403: randomly generate a random number for the third chromosome to obtain the fifth chromosome.

[0212] Specifically, the target MS code of the third chromosome P3 includes 8 MS codes. If the 8 MS codes are 1, 2, 3, 1, 1, 3, 2, and 3, a random number can be generated for each of the 8 MS codes, such as the random numbers are 0.3, 0.4, 0.6, 0.1, 0.5, 0.9, 0.3, and 0.7, to obtain the fifth chromosome P5.

[0213] Step S404: Cross the different target first production equipment information contained in the fifth chromosome and the fourth chromosome to obtain a cross scheduling population.

[0214] Specifically, the target MS coding portion of the gene containing J1 in P5 is copied to P4 to obtain C3, and the target MS coding portion of the gene containing J2 in P4 is copied to P5 to obtain C4, as shown in FIG7 .

[0215] It should be noted that during the RPX crossover operation, the target first production task information with a crossover probability greater than the crossover probability threshold can be selected from multiple target first scheduling information for crossover, the target first AGV information with a crossover probability greater than the crossover probability threshold can be selected from multiple target first scheduling information for crossover, and at least two of the target first production task information, target first production equipment information and target first AGV information with a crossover probability greater than the crossover probability threshold can be selected from multiple target first scheduling information for crossover, which is not limited here.

[0216] Step S140: Obtain third scheduling information according to the cross-scheduling population.

[0217] In an embodiment of the present application, the processing device may search the cross-scheduling population to obtain the third scheduling information.

[0218] Among them, the search method can be a variable neighborhood search method, and the third scheduling time of the third scheduling information can be less than the second scheduling time of the second scheduling information, indicating that the fitness of the third scheduling information is less than the fitness of the second scheduling information.

[0219] Specifically, the processing equipment can determine the critical scheduling path based on the production line resource information, and search each group of second production task information, second production equipment information and second AGV information according to the critical scheduling path to obtain the third scheduling information, thereby realizing the search based on the neighborhood solution of the critical scheduling path to obtain the third scheduling information, so that the third scheduling information has the global optimality on the critical scheduling path.

[0220] The third scheduling information may include a set of third production task information, third production equipment information and third AGV information.

[0221] The production line resource information may include at least multiple production tasks. The processing equipment may determine multiple task paths based on the operation sequence of the multiple production tasks, and determine the task path duration of each task path based on the task node duration of each production task, and select the task path corresponding to the longest task path duration from the multiple task paths as the critical scheduling path. Each task path may include the same starting production task and ending production task.

[0222] In an application scenario, as shown in FIG8 , the critical scheduling path determination method may include steps S501 to S504 .

[0223] Step S501: Determine the starting node 0 and the ending node 0' based on the production equipment information and the production task information.

[0224] Step S502: Decode the OS code and the MS code to obtain the processing task steps of each production device and the processing time corresponding to the task steps.

[0225] Step S503: For a production task node processed by the same production equipment, the preceding process and the succeeding process of the node are connected.

[0226] Specifically, according to the information of each production device, the operation sequence of the production task information processed by the same production device can be connected in sequence to obtain a directed graph.

[0227] Step S504: Find the scheduling path between 0 and 0' with the longest sum of node times, and record it as the key scheduling path.

[0228] In some embodiments, the processing device can determine the critical scheduling path based on the production line resource information, and based on a preset search operator, search each group of second production task information, second production equipment information and second AGV information according to the critical scheduling path to generate first neighborhood de-scheduling information, and determine the first neighborhood de-scheduling time of the first neighborhood de-scheduling information. When it is determined that the first neighborhood de-scheduling time is less than the second scheduling time, the first neighborhood de-scheduling information is updated with the corresponding second scheduling information, and the step of searching each group of second production task information, second production equipment information and second AGV information according to the critical scheduling path to generate the first neighborhood de-scheduling information is repeatedly performed until it is determined that the first neighborhood de-scheduling time is greater than or equal to the second scheduling time, the corresponding first neighborhood de-scheduling time is determined as the second neighborhood de-scheduling time, and the second neighborhood de-scheduling information corresponding to the second neighborhood de-scheduling time is determined as the third scheduling information. This achieves the situation where when it is determined that the first neighborhood de-scheduling time is less than the second scheduling time, the neighborhood solution of the critical scheduling path is repeatedly searched locally based on the preset search operator, further making the third scheduling information globally optimal on the critical scheduling path.

[0229] The preset search operator may be a variable neighborhood search operator, for example, a two-element inversion search operator, a two-element swap search operator, or a one-element insertion search operator. The first neighborhood solution dispatch information may include a set of fourth production task information, fourth production equipment information, and fourth AGV information.

[0230] In some implementations, the preset search operator may include multiple sub-preset search operators, and each sub-preset search operator is associated with a search count.

[0231] The processing equipment can determine the critical scheduling path according to the production line resource information, and based on the preset search operator, search each group of second production task information, second production equipment information and second AGV information according to the critical scheduling path to generate first neighborhood de-scheduling information, and determine the first neighborhood de-scheduling time of the first neighborhood de-scheduling information, and when it is determined that the first neighborhood de-scheduling time is less than the second scheduling time, update the corresponding second scheduling information to the first neighborhood de-scheduling information, and repeat the steps of searching each group of second production task information, second production equipment information and second AGV information according to the critical scheduling path based on the preset search operator to generate the first neighborhood de-scheduling information, until when it is determined that the first neighborhood de-scheduling time is greater than or equal to the second scheduling time, determine the corresponding first neighborhood de-scheduling time as the second neighborhood de-scheduling time, and when it is determined that the second neighborhood de-scheduling time is greater than or equal to the second scheduling time. When the current search times reaches or is equal to the second scheduling time, determine whether the current search times have reached the preset times, and when it is determined that the current search times have not reached the preset times, determine the target sub-preset search operator for the next search times, and update the preset search operator to the target sub-preset search operator, and update the second scheduling information to the second neighborhood solution scheduling information, and repeatedly execute the steps of searching each group of second production task information, second production equipment information and second AGV information according to the critical scheduling path based on the preset search operator to generate the first neighborhood solution scheduling information, until it is determined that the current search times have reached the preset times, determine the second neighborhood solution scheduling information corresponding to the current search times as the third scheduling information, and realize repeated local search for the neighborhood solution of the critical scheduling path based on multiple sub-preset search operators, so as to further make the third scheduling information globally optimal on the critical scheduling path.

[0232] Step S150: Output fourth scheduling information according to the first scheduling time and the third scheduling time.

[0233] In an embodiment of the present application, the processing device may output fourth scheduling information based on the first scheduling time and the third scheduling time, where the fourth scheduling information is scheduling information corresponding to the minimum scheduling time between the first scheduling time and the third scheduling time.

[0234] Specifically, the processing device may replace the worst individual in the initial scheduling population based on the third scheduling information to obtain an updated population, calculate the fitness of each individual in the updated population, and select the individual with the smallest fitness as the fourth scheduling information.

[0235] In some embodiments, the processing device can schedule the production equipment and AGV according to the fourth scheduling information, thereby realizing automatic scheduling of production line resources according to production line resource information based on genetic algorithms and variable neighborhood search algorithms during the automated production process, thereby improving the user's scheduling experience of scheduling production line resources.

[0236] The solution provided in the present application generates an initial scheduling population based on production line resource information, the initial scheduling population includes multiple first scheduling information, and determines the first scheduling time of each first scheduling information based on the production line resource information, and performs cross-operation on multiple target first scheduling information to obtain a cross-scheduling population, the cross-scheduling population includes multiple second scheduling information, and obtains third scheduling information based on the cross-scheduling population, the third scheduling time of the third scheduling information is less than the second scheduling time of the second scheduling information, and outputs fourth scheduling information based on the first scheduling time and the third scheduling time, the fourth scheduling information is the scheduling information corresponding to the minimum scheduling time between the first scheduling time and the third scheduling time, thereby realizing automatic generation of a scheduling plan for production line resources based on cross-processing of the initial scheduling population generated based on the production line resource information in the automated production process. Compared with the time spent on manually formulating a scheduling plan based on the production line resource information, the time spent on automatically generating a scheduling plan based on the production line resource information is shorter, thereby improving the production efficiency of the automated production process.

[0237] Furthermore, the genetic algorithm has the characteristic of short search time, and the variable neighborhood search algorithm can improve the global search capability of the algorithm. The optimal scheduling solution can be generated in a short time based on the genetic algorithm and the variable neighborhood search algorithm.

[0238] Please refer to Figure 9, which shows a flow chart of a method for scheduling production line resources according to another embodiment of the present application. In a specific embodiment, the method for scheduling production line resources can be applied to processing equipment. The following will use processing equipment as an example to explain the process shown in Figure 9 in detail. The method for scheduling production line resources can include the following steps S610 to S660.

[0239] Step S610: Generate an initial scheduling population based on production line resource information.

[0240] Step S620: Determine the first scheduling time of each first scheduling information according to the production line resource information.

[0241] Step S630: performing a cross operation on multiple target first scheduling information to obtain a cross scheduling population.

[0242] In this embodiment, step S610, step S620 and step S630 may refer to the contents of the corresponding steps in the aforementioned embodiment, and will not be repeated here.

[0243] Step S640: performing a mutation operation on the cross-scheduling population to obtain a mutated scheduling population.

[0244] In this embodiment, the processing device may perform a mutation operation on the cross-scheduling population to obtain a mutated scheduling population.

[0245] Specifically, the processing device may determine the mutation probability of each second scheduling information, and based on a preset mutation rule, perform a mutation operation on the second scheduling information whose mutation probability is greater than a mutation probability threshold to obtain a mutated scheduling population.

[0246] The crossover probability P of each second scheduling information can be calculated according to the following formula: m .

[0247] Among them, β is the maximum mutation probability (constant), Iter is the number of current iterations, and f2 is the fitness of each second scheduling information.

[0248] Step S650: Obtain third scheduling information according to the mutated scheduling population.

[0249] In this embodiment, the processing device can perform a variable neighborhood search on the variant scheduling population to obtain the third scheduling information, perform a mutation operation on the cross scheduling population to obtain the variant scheduling population, increase the search space of the variant scheduling population, avoid the variant scheduling population from falling into the local optimal solution, and perform a variable neighborhood search on the variant scheduling population so that the third scheduling information has the global optimality relative to the variant scheduling population.

[0250] In this embodiment, the process of the processing device performing a variable neighborhood search on the variant scheduling population can refer to the contents of the corresponding steps in the aforementioned embodiment, which will not be repeated here.

[0251] In some implementations, after performing a variable neighborhood search on the variant scheduling population and obtaining the third scheduling information, the processing device may determine whether an iteration termination condition is satisfied.

[0252] The iteration termination condition may include but is not limited to the number of iterations pre-set by the production personnel being greater than or equal to an iteration number threshold.

[0253] When the number of iterations is greater than or equal to the iteration number threshold, it is determined that the iteration termination condition is met; when the number of iterations is less than the iteration number threshold, it is determined that the iteration termination condition is not met.

[0254] Step S660: Output fourth scheduling information according to the first scheduling time and the third scheduling time.

[0255] In this embodiment, step S660 can refer to the contents of the corresponding steps in the above embodiments, which will not be repeated here.

[0256] In some embodiments, when the processing device determines that the iteration termination condition is met, it can output the fourth scheduling information based on the first scheduling time and the third scheduling time. This achieves the output of the fourth scheduling information when it is determined that the iteration termination condition is met, avoids the algorithm converging too early and causing the fourth scheduling information to be limited to the local optimum, and improves the generation quality of the global optimal solution for the production line resource scheduling plan.

[0257] In some embodiments, when the processing device determines that the iteration termination condition is not met, it can repeatedly perform the step of performing cross operations on multiple target first scheduling information to obtain a cross scheduling population, until it determines that the iteration termination condition is met, and outputs the fourth scheduling information according to the first scheduling time and the third scheduling time. This achieves the repeated iteration of the genetic algorithm and the variable neighborhood search algorithm when it is determined that the iteration termination condition is not met, further avoiding the algorithm from confining the fourth scheduling information to the local optimum due to premature convergence, and further improving the generation quality of the global optimal solution for the production line resource scheduling plan.

[0258] In an application scenario, as shown in FIG10 , the method for scheduling production line resources may include steps S701 to S710 .

[0259] Step S701: Initialize parameters.

[0260] Specifically, the population size Popsize, the cross pool size 0.25 Popsize, the maximum number of iterations MaxIter, the maximum crossover probability α, the maximum mutation probability β, and the current number of iterations Iter=0 are initially set.

[0261] Step S702: Initialize the population.

[0262] Specifically, an initial scheduling population of Popsize is generated according to random initialization.

[0263] Step S703: Calculate the individual fitness of the initial scheduling population and determine the current optimal individual.

[0264] Specifically, the first scheduling time of each first scheduling information in the initial scheduling population is calculated according to the scheduling time minimum function and the scheduling path, that is, the individual fitness of each first scheduling information.

[0265] Step S704: Select an operation to build a cross pool.

[0266] Specifically, according to the roulette wheel selection algorithm, 0.25 Popsize of the first scheduling information is selected to form a cross pool.

[0267] Step S705: crossover operation.

[0268] Specifically, the chromosome individuals that meet the crossover probability in the crossover pool are crossovered according to the crossover strategy, and the crossover probability P of each individual is updated. c and mutation probability P m .

[0269] Step S706: mutation operation.

[0270] Specifically, the individuals obtained by crossover that meet the mutation probability are mutated according to the mutation strategy.

[0271] Step S707: Determine the critical scheduling path of the variant individual.

[0272] Specifically, the critical scheduling path can be determined based on the production line resource information.

[0273] Step S708: Determine the current variable neighborhood search operator.

[0274] Specifically, there are three variable neighborhood search operators, which are randomly arranged, and the variable neighborhood search operator ranked first is determined as the first variable neighborhood search operator, and the first variable neighborhood search operator is determined as the current variable neighborhood search operator, and the variable neighborhood search operator ranked in the middle is determined as the second variable neighborhood search operator, and the variable neighborhood search operator ranked at the end is determined as the third variable neighborhood search operator.

[0275] Step S709: Generate neighborhood solution individuals.

[0276] Specifically, based on the current variable neighborhood search operator, a variable neighborhood search is performed on the mutated individuals according to the critical scheduling path to obtain the neighborhood solution individuals.

[0277] Step S710: Calculate the neighborhood solution individual fitness of the neighborhood solution individual.

[0278] Specifically, the neighborhood solution individual fitness of the neighborhood solution individual is calculated according to the scheduling time minimum function.

[0279] Step S711: Determine whether the neighborhood solution individual is improved compared to the mutated individual based on the fitness of the neighborhood solution individual.

[0280] Specifically, the fitness of the mutant individual is calculated according to the minimum scheduling time function, and whether the neighborhood solution individual is improved compared with the mutant individual is determined according to the fitness of the mutant individual and the fitness of the neighborhood solution individual.

[0281] If the fitness of the mutant individual is greater than or equal to the fitness of the neighborhood solution individual, it is determined that the neighborhood solution individual is improved compared to the mutant individual, and step S712 is executed;

[0282] When the fitness of the mutant individual is less than the fitness of the neighborhood solution individual, it is determined that the neighborhood solution individual is not improved compared to the mutant individual, and step S713 is executed.

[0283] Step S712: Update the neighborhood solution to the mutant individual and execute step S708.

[0284] Step S713: Determine whether the current variable neighborhood search times reaches a preset number.

[0285] Specifically, if it is determined that the number of current variable neighborhood search operations has not reached the preset number, the current variable neighborhood search operator is updated to the next variable neighborhood search operator, and step S709 is executed;

[0286] If it is determined that the number of current neighborhood searches reaches the preset number, step S714 is executed.

[0287] Step S714: Determine whether the termination condition is met.

[0288] If the termination condition is met, execute step S715;

[0289] If it is determined that the termination condition has not been met, step S704 is executed.

[0290] Step S715: Output the optimal individual in the population.

[0291] Specifically, the third scheduling information is determined based on the optimal individual obtained from the variable neighborhood search, and the optimal individual in the initial scheduling population is determined as the first scheduling information. According to the first scheduling time of the first scheduling information and the third scheduling time of the third scheduling information, the fourth scheduling information is output, which is the optimal individual in the population.

[0292] The solution provided in this embodiment generates an initial scheduling population based on production line resource information, determines the first scheduling time of each first scheduling information based on the production line resource information, performs a cross operation on multiple target first scheduling information to obtain a cross scheduling population, performs a mutation operation on the cross scheduling population to obtain a mutated scheduling population, obtains third scheduling information based on the mutated scheduling population, and outputs fourth scheduling information based on the first scheduling time and the third scheduling time. This realizes the automatic generation of a scheduling plan for production line resources based on cross processing of the initial scheduling population generated based on the production line resource information in the automated production process. Compared with the time spent on manually formulating a scheduling plan based on the production line resource information, the time spent on automatically generating a scheduling plan based on the production line resource information is shorter, thereby improving the production efficiency of the automated production process.

[0293] Furthermore, the genetic algorithm has the characteristic of short search time, and the variable neighborhood search algorithm can improve the global search capability of the algorithm. The optimal scheduling solution can be generated in a short time based on the genetic algorithm and the variable neighborhood search algorithm.

[0294] Furthermore, the processing device can perform a variable neighborhood search on the variant scheduling population to obtain the third scheduling information, perform a mutation operation on the cross scheduling population to obtain the variant scheduling population, increase the search space of the variant scheduling population, avoid the variant scheduling population from falling into the local optimal solution, and perform a variable neighborhood search on the variant scheduling population, so that the third scheduling information has the global optimality relative to the variant scheduling population.

[0295] Please refer to Figure 11, which shows a scheduling device 800 for production line resources provided by an embodiment of the present application. The scheduling device 800 for production line resources can be applied to processing equipment. The following will take the processing equipment as an example to explain in detail the scheduling device 800 for production line resources shown in Figure 11. The scheduling device 800 for production line resources can include a generation module 810, a time determination module 820, a cross module 830, an acquisition module 840 and an output module 850.

[0296] The generation module 810 can be used to generate an initial scheduling population based on the production line resource information, and the initial scheduling population can include multiple first scheduling information; the time determination module 820 can be used to determine the first scheduling time of each first scheduling information based on the production line resource information; the cross module 830 can be used to perform cross operations on multiple target first scheduling information to obtain a cross scheduling population, and the cross scheduling population can include multiple second scheduling information; the acquisition module 840 can be used to obtain third scheduling information based on the cross scheduling population, and the third scheduling time of the third scheduling information can be less than the second scheduling time of the second scheduling information; the output module 850 can be used to output fourth scheduling information based on the first scheduling time and the third scheduling time, and the fourth scheduling information can be the scheduling information corresponding to the minimum scheduling time between the first scheduling time and the third scheduling time.

[0297] In some implementations, the time determination module 820 may include a first construction submodule, a first determination submodule, and a calculation submodule.

[0298] The first construction submodule can be used to construct a minimum scheduling time function for scheduling production line resources, and the minimum scheduling time function can be used to characterize the correspondence between the scheduling path of scheduling production line resources and the scheduling time of scheduling production line resources; the first determination submodule can be used to determine the first scheduling path of each first scheduling information; the calculation submodule can be used to calculate the first scheduling time of each first scheduling information based on the minimum scheduling time function and the first scheduling path.

[0299] In some embodiments, each first scheduling information may include a set of first production task information, first production equipment information, and first automatic guided vehicle information, and the first determination submodule may include an encoding unit, a decoding unit, and a search unit.

[0300] The encoding unit can be used to encode each group of first production task information, first production equipment information and first automatic guided vehicle information based on preset encoding rules to obtain a coding table, which includes multiple production task codes, multiple production equipment codes and multiple automatic guided vehicle codes; the decoding unit can be used to decode the coding table based on preset decoding rules to obtain a scheduling path matrix, which can include multiple scheduling paths, and the matrix sorting information of each scheduling path can be associated with an automatic guided vehicle code; the search unit can be used to search the scheduling path matrix according to the automatic guided vehicle code of each automatic guided vehicle information to obtain the first scheduling path.

[0301] In some embodiments, the encoding unit may include a first encoding subunit, a second encoding subunit, a third encoding subunit, and a generating subunit.

[0302] The first encoding subunit can be used to encode each group of first production task information according to the operation sequence of the production tasks corresponding to each group of first production task information, and obtain a production task code; the second encoding subunit can be used to encode each group of first production equipment information according to the equipment selection sequence of the production equipment corresponding to each group of first production equipment information, and obtain a production equipment code; the third encoding subunit can be used to encode each group of first automatic guided vehicle information according to the transportation sequence of the automatic guided vehicles corresponding to each group of first automatic guided vehicle information, and obtain an automatic guided vehicle code; the generation subunit can be used to generate a coding table based on multiple production task codes, multiple production equipment codes and multiple automatic guided vehicle codes.

[0303] In some embodiments, the decoding unit may include a first construction subunit, a second construction subunit, a third construction subunit, and a traversal subunit.

[0304] The first construction subunit can be used to construct an automatic guided vehicle array based on multiple automatic guided vehicle codes; the second construction subunit can be used to construct a production task array based on multiple production task codes; the third construction subunit can be used to construct a production equipment array based on multiple production equipment codes; the traversal subunit can be used to traverse the production task array and the production equipment array according to each automatic guided vehicle code to generate a scheduling path matrix.

[0305] In some implementations, traversing the sub-unit may include determining a sub-sub-unit and traversing the sub-sub-unit.

[0306] The determination sub-unit can be used to determine the arrangement index number of each automated guided vehicle code; the traversal sub-unit can be used to traverse each production task code in the production task array and each production equipment code in the production equipment array according to each arrangement index number to obtain a scheduling path matrix.

[0307] In some implementations, the calculation submodule may include a first determination unit, a second determination unit, and a calculation unit.

[0308] The first determination unit can be used to determine a corresponding target production task code based on each first scheduling path; the second determination unit can be used to determine the transportation time and production time of each target production task code, the transportation time can be used to characterize the duration of the automatic guided vehicle transporting the target production task corresponding to the target production task code, and the production time can be used to characterize the duration of the production equipment producing the target production task; the calculation unit can be used to calculate the first scheduling time of each first scheduling information based on the minimum scheduling time function and the transportation time and production time of each target production task code.

[0309] In some implementations, the cross module 830 may include a second construction submodule, a second determination submodule, and a cross submodule.

[0310] The second construction submodule can be used to construct a crossover pool based on a preset selection algorithm and an initial scheduling population. The crossover pool may include multiple target first scheduling information. The second determination submodule can be used to determine the crossover probability of each target first scheduling information. The crossover submodule can be used to perform a crossover operation on the target first scheduling information whose crossover probability is greater than the crossover probability threshold to obtain a crossover scheduling population.

[0311] In some embodiments, each target first scheduling information may include a set of first production task information, first production equipment information and first automatic guided vehicle information, and the intersection submodule may include a first selection unit, a first construction unit and a first intersection unit.

[0312] The first selection unit can be used to select target first production task information, target first production equipment information and target first automatic guided vehicle information whose crossover probability is greater than a crossover probability threshold from multiple target first scheduling information; the first construction unit can be used to construct the first chromosome and the second chromosome based on the target first production task information, the target first production equipment information and the target first automatic guided vehicle information; the first crossing unit can be used to cross the different target first production task information, target first production equipment information and target first automatic guided vehicle information contained in the first chromosome and the second chromosome to obtain a crossover scheduling population.

[0313] In some embodiments, each target first scheduling information may include a set of first production task information, first production equipment information and first automatic guided vehicle information, and the intersection submodule may also include a second selection unit, a second construction unit, a generation unit and a second intersection unit.

[0314] The second selection unit can be used to select target first production task information, target first production equipment information and target first automatic guided vehicle information with a crossover probability greater than a crossover probability threshold from multiple target first scheduling information; the second construction unit can be used to construct the third chromosome and the fourth chromosome based on the target first production task information, the target first production equipment information and the target first automatic guided vehicle information; the generation unit can be used to randomly generate a random number for the third chromosome to obtain the fifth chromosome; the second crossover unit can be used to cross different target first production task information, target first production equipment information and target first automatic guided vehicle information contained in the fifth chromosome and the fourth chromosome to obtain a crossover scheduling population.

[0315] In some embodiments, each second scheduling information may include a set of second production task information, second production equipment information, and second automated guided vehicle information, and the obtaining module 840 may include a determining submodule and a searching submodule.

[0316] The determination submodule can be used to determine the critical scheduling path based on the production line resource information; the search submodule can be used to search each group of second production task information, second production equipment information and second automatic guided vehicle information according to the critical scheduling path to obtain third scheduling information, which may include a group of third production task information, third production equipment information and third automatic guided vehicle information.

[0317] In some embodiments, the production line resource information may include at least a plurality of production tasks, and the determination submodule may include a third determination unit, a fourth determination unit, and a third selection unit.

[0318] The third determination unit can be used to determine multiple task paths based on the operation sequence of multiple production tasks, and each task path can include the same starting production task and ending production task; the fourth determination unit can be used to determine the task path duration of each task path based on the task node duration of each production task; the third selection unit can be used to select the task path corresponding to the longest task path duration from multiple task paths as the critical scheduling path.

[0319] In some implementations, the search submodule may include a search unit, a fifth determination unit, an update unit, a repeat execution unit, and a sixth determination unit.

[0320] The search unit can be used to search each group of second production task information, second production equipment information and second automatic guided vehicle information according to the preset search operator and the critical scheduling path to generate first neighborhood de-scheduling information, and the first neighborhood de-scheduling information may include a group of fourth production task information, fourth production equipment information and fourth automatic guided vehicle information; the fifth determination unit can be used to determine the first neighborhood de-scheduling time of the first neighborhood de-scheduling information; the update unit can be used to update the corresponding second scheduling information to the first neighborhood de-scheduling information when it is determined that the first neighborhood de-scheduling time is less than the second scheduling time; the repeated execution unit can be used to repeatedly execute the step of searching each group of second production task information, second production equipment information and second automatic guided vehicle information according to the critical scheduling path to generate the first neighborhood de-scheduling information, until it is determined that the first neighborhood de-scheduling time is greater than or equal to the second scheduling time, and the corresponding first neighborhood de-scheduling time is determined as the second neighborhood de-scheduling time; the sixth determination unit can be used to determine the second neighborhood de-scheduling information corresponding to the second neighborhood de-scheduling time as the third scheduling information.

[0321] In some embodiments, the preset search operator may include multiple sub-preset search operators, each sub-preset search operator may be associated with a search count, and the sixth determination unit may include a first determination subunit, a second determination subunit, an update subunit, and a repeated execution subunit.

[0322] The first determination subunit can be used to determine whether the current search times have reached the preset times when it is determined that the second neighborhood descheduling time is greater than or equal to the second scheduling time; the second determination subunit can be used to determine the target sub-preset search operator for the next search times when it is determined that the current search times have not reached the preset times; the update subunit can be used to update the preset search operator to the target sub-preset search operator, and update the second scheduling information to the second neighborhood descheduling information; the repeated execution subunit can be used to repeatedly execute the steps of searching each group of second production task information, second production equipment information and second automatic guided vehicle information according to the critical scheduling path based on the preset search operator to generate the first neighborhood descheduling information, until it is determined that the current search times have reached the preset times, and the second neighborhood descheduling information corresponding to the current search times is determined as the third scheduling information.

[0323] In some embodiments, the production line resource scheduling device 800 may further include a variation module.

[0324] The mutation module may be configured to perform a mutation operation on the cross-scheduling population to obtain a mutated scheduling population before the obtaining module 840 obtains the third scheduling information based on the cross-scheduling population.

[0325] In some implementations, the obtaining module 840 may further include an obtaining submodule.

[0326] The obtaining submodule can be used to obtain third scheduling information according to the mutated scheduling population.

[0327] In some embodiments, the production line resource scheduling device 800 may further include a condition determination module.

[0328] The condition determination module may be used to determine whether an iteration termination condition is satisfied before the output module 850 outputs the fourth scheduling information according to the first scheduling time and the third scheduling time.

[0329] In some implementations, the output module 850 may include output sub-modules.

[0330] The output submodule may be configured to output fourth scheduling information according to the first scheduling time and the third scheduling time when it is determined that the iteration termination condition is satisfied.

[0331] In some embodiments, the production line resource scheduling device 800 may further include a repeated execution module.

[0332] The repeated execution module can be used to repeatedly execute the step of performing cross operations on multiple target first scheduling information to obtain a cross scheduling population when it is determined that the iteration termination condition is not met, until it is determined that the iteration termination condition is met, and output the fourth scheduling information according to the first scheduling time and the third scheduling time.

[0333] The solution provided in this embodiment generates an initial scheduling population based on production line resource information, the initial scheduling population includes multiple first scheduling information, and the first scheduling time of each first scheduling information is determined based on the production line resource information, and a cross-operation is performed on the multiple target first scheduling information to obtain a cross-scheduling population, the cross-scheduling population includes multiple second scheduling information, and third scheduling information is obtained based on the cross-scheduling population, the third scheduling time of the third scheduling information is less than the second scheduling time of the second scheduling information, and fourth scheduling information is output based on the first scheduling time and the third scheduling time, and the fourth scheduling information is the scheduling information corresponding to the minimum scheduling time between the first scheduling time and the third scheduling time. This realizes the automatic generation of a scheduling plan for production line resources based on cross-processing of the initial scheduling population generated based on the production line resource information in the automated production process. Compared with the time spent on manually formulating a scheduling plan based on the production line resource information, the time spent on automatically generating a scheduling plan based on the production line resource information is shorter, thereby improving the production efficiency of the automated production process.

[0334] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to in detail. For the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, refer to the partial description of the method embodiments. Any processing method described in the method embodiment can be implemented by the corresponding processing module in the device embodiment, and will not be repeated in detail in the device embodiment.

[0335] In addition, the functional modules in the various embodiments of the present application may be integrated into a processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The above-mentioned integrated modules may be implemented in the form of hardware or software functional modules.

[0336] Please refer to Figure 12, which shows a functional block diagram of an electronic device 900 provided by an embodiment of the present application. The electronic device 900 may include one or more of the following components: a memory 910, a processor 920, and one or more applications, wherein the one or more applications may be stored in the memory 910 and configured to be executed by the one or more processors 920, and the one or more applications are configured to execute the method described in the aforementioned method embodiment.

[0337] The memory 910 may include a random access memory (RAM) or a read-only memory (ROM). The memory 910 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 910 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as generating an initial scheduling population, determining a first scheduling time, crossover operation, obtaining a crossover scheduling population, obtaining third scheduling information, outputting fourth scheduling information, constructing a scheduling time minimum function, determining a first scheduling path, calculating a first scheduling time, encoding, obtaining a coding table, decoding, searching a scheduling path matrix, obtaining a first scheduling path, constructing a crossover pool, determining a crossover probability, determining a critical scheduling path, generating first neighborhood descheduling information, determining a first neighborhood descheduling time, determining that the first neighborhood descheduling time is less than a second scheduling time, updating second scheduling information, repeating execution, determining third scheduling information, determining that the first neighborhood descheduling time is greater than or equal to the second scheduling time, determining whether the current search number reaches a preset number, determining that the current search number does not reach a preset number, determining a target sub-preset search operator, updating a preset search operator, mutation operation, obtaining a mutated scheduling population, determining whether an iteration termination condition is satisfied, determining that an iteration termination condition is satisfied, and determining that an iteration termination condition is not satisfied, etc.), instructions for implementing the following various method embodiments, etc. The storage data area can also store data created by the electronic device 900 during use (such as production line resource information, initial scheduling population, first scheduling information, first scheduling time, second scheduling information, target first scheduling information, cross scheduling population, second scheduling information, third scheduling time, third scheduling time, second scheduling time, fourth scheduling information, minimum scheduling time, scheduling time minimum function, scheduling path, scheduling time, corresponding relationship, first scheduling path, preset encoding rule, encoding table, preset decoding rule, preset selection algorithm, cross pool, cross probability, cross probability threshold, critical scheduling path, preset search operator, first neighborhood descheduling information, first neighborhood descheduling time, second neighborhood descheduling time, second neighborhood descheduling information, sub-preset search operator, current search number, preset number, next search number, target sub-preset search operator, mutation scheduling population and iteration termination condition), etc.

[0338] The processor 920 may include one or more processing cores. The processor 920 utilizes various interfaces and circuits to connect various components within the electronic device 900. It executes instructions, programs, code sets, or instruction sets stored in the memory 910, and accesses data stored in the memory 910 to perform various functions and process data within the electronic device 900. Optionally, the processor 920 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 920 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing display content; and the modem handles wireless communications. It is understood that the modem may not be integrated into the processor 920 and may be implemented separately via a communication chip.

[0339] Please refer to Figure 13, which shows a block diagram of a computer-readable storage medium provided in an embodiment of the present application. The computer-readable storage medium 1000 stores program code 1010, which can be called by a processor to execute the method described in the above method embodiment.

[0340] The computer-readable storage medium 1000 can be an electronic memory such as a flash memory, an EEPROM (Electrically Erasable Programmable Read-Only Memory), an EPROM, a hard disk, or a ROM. Alternatively, the computer-readable storage medium 1000 includes a non-transitory computer-readable storage medium. The computer-readable storage medium 1000 has storage space for program code 1010 for executing any of the method steps described above. These program codes can be read from or written to one or more computer program products. The program code 1010 can be compressed, for example, in a suitable form.

[0341] Please refer to Figure 14, which shows a block diagram of a computer program product 1100 provided in an embodiment of the present application. Computer program product 1100 includes a computer program / instructions 1110, which is stored in a computer-readable storage medium of a computer device. When computer program product 1100 is executed on a computer device, a processor of the computer device reads computer program / instructions 1110 from the computer-readable storage medium and executes computer program / instructions 1110, causing the computer device to perform the method described in the above method embodiment.

[0342] The solution provided in this embodiment generates an initial scheduling population based on production line resource information, the initial scheduling population includes multiple first scheduling information, and the first scheduling time of each first scheduling information is determined based on the production line resource information, and a cross-operation is performed on the multiple target first scheduling information to obtain a cross-scheduling population, the cross-scheduling population includes multiple second scheduling information, and third scheduling information is obtained based on the cross-scheduling population, the third scheduling time of the third scheduling information is less than the second scheduling time of the second scheduling information, and fourth scheduling information is output based on the first scheduling time and the third scheduling time, and the fourth scheduling information is the scheduling information corresponding to the minimum scheduling time between the first scheduling time and the third scheduling time. This realizes the automatic generation of a scheduling plan for production line resources based on cross-processing of the initial scheduling population generated based on the production line resource information in the automated production process. Compared with the time spent on manually formulating a scheduling plan based on the production line resource information, the time spent on automatically generating a scheduling plan based on the production line resource information is shorter, thereby improving the production efficiency of the automated production process.

[0343] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

[0344] Finally, it should be noted that the embodiments of the present application can be manufactured or used industrially.

Claims

1. A method for scheduling production line resources, wherein: include: generating an initial scheduling population according to the production line resource information, wherein the initial scheduling population includes a plurality of first scheduling information; Determining a first scheduling time for each first scheduling information according to the production line resource information; Performing a cross operation on the plurality of target first scheduling information to obtain a cross scheduling population, wherein the cross scheduling population includes the plurality of second scheduling information; Obtain third scheduling information according to the cross-scheduling population, wherein a third scheduling time of the third scheduling information is less than a second scheduling time of the second scheduling information; Output fourth scheduling information according to the first scheduling time and the third scheduling time, where the fourth scheduling information is scheduling information corresponding to the minimum scheduling time between the first scheduling time and the third scheduling time.

2. The scheduling method according to claim 1, wherein: The determining the first scheduling time of each first scheduling information according to the production line resource information includes: Constructing a minimum scheduling time function for scheduling production line resources, wherein the minimum scheduling time function is used to represent a corresponding relationship between a scheduling path for scheduling the production line resources and a scheduling time for scheduling the production line resources; determining a first scheduling path for each first scheduling information; The first scheduling time of each first scheduling information is calculated according to the scheduling time minimum function and the first scheduling path.

3. The scheduling method according to claim 2, wherein: Each first scheduling information includes a set of first production task information, first production equipment information, and first automated guided vehicle information. Determining a first scheduling path for each first scheduling information includes: Encode each set of first production task information, first production equipment information, and first automated guided vehicle information based on a preset encoding rule to obtain a encoding table, wherein the encoding table includes multiple production task codes, multiple production equipment codes, and multiple automated guided vehicle codes; Decoding the coding table based on a preset decoding rule to obtain a scheduling path matrix, wherein the scheduling path matrix includes a plurality of scheduling paths, and matrix sorting information of each scheduling path is associated with an automated guided vehicle code; The scheduling path matrix is ​​searched according to the automatic guided vehicle code of each automatic guided vehicle information to obtain a first scheduling path.

4. The scheduling method according to claim 3, wherein: The first production task information, the first production equipment information, and the first automated guided vehicle information of each group are encoded based on the preset encoding rules to obtain a coding table, including: Encoding each set of first production task information according to the operation sequence of the production task corresponding to each set of first production task information to obtain a production task code; Encoding each set of first production equipment information according to the equipment selection order of the production equipment corresponding to each set of first production equipment information to obtain a production equipment code; encoding each set of first automated guided vehicle information according to a transportation sequence of the automated guided vehicles corresponding to each set of first automated guided vehicle information to obtain an automated guided vehicle code; The code table is generated based on a plurality of production task codes, a plurality of production equipment codes, and a plurality of automatic guided vehicle codes.

5. The scheduling method according to claim 3 or 4, wherein: The decoding of the coding table based on a preset decoding rule to obtain a scheduling path matrix includes: Constructing an automatic guided vehicle array according to multiple automatic guided vehicle codes; Constructing a production task array according to multiple production task codes; Construct a production equipment array based on multiple production equipment codes; According to the code of each automated guided vehicle, the production task array and the production equipment array are traversed to generate a scheduling path matrix.

6. The scheduling method according to claim 5, wherein: The step of traversing the production task array and the production equipment array according to each automated guided vehicle code to generate a scheduling path matrix includes: Determine the permutation index number of each automated guided vehicle code; Each production task code in the production task array and each production equipment code in the production equipment array are traversed according to each arrangement index number to obtain a scheduling path matrix.

7. The scheduling method according to any one of claims 3 to 6, wherein: The calculating, according to the scheduling time minimum function and the first scheduling path, the first scheduling time of each first scheduling information includes: Determine a corresponding target production task code according to each first scheduling path; Determine the transport duration and production duration for each target production task code, wherein the transport duration is used to represent the time it takes for an automated guided vehicle to transport the target production task corresponding to the target production task code, and the production duration is used to represent the time it takes for production equipment to produce the target production task; The first scheduling time of each first scheduling information is calculated according to the scheduling time minimum function and the transportation duration and the production duration coded for each target production task.

8. The scheduling method according to any one of claims 1 to 7, wherein: The cross-operation is performed on the plurality of target first scheduling information to obtain a cross-scheduling population, including: Constructing a cross pool according to a preset selection algorithm and the initial scheduling population, wherein the cross pool includes a plurality of target first scheduling information; Determine the intersection probability of each target first scheduling information; A crossover operation is performed on the target first scheduling information whose crossover probability is greater than a crossover probability threshold to obtain a crossover scheduling population.

9. The scheduling method according to claim 8, wherein: Each target first scheduling information includes a set of first production task information, first production equipment information, and first automated guided vehicle information. The crossover operation is performed on the target first scheduling information having a crossover probability greater than a crossover probability threshold to obtain a crossover scheduling population, including: Selecting target first production task information, target first production equipment information, and target first automated guided vehicle information whose crossover probability is greater than a crossover probability threshold from a plurality of target first scheduling information; Constructing a first chromosome and a second chromosome according to the target first production task information, the target first production equipment information, and the target first automated guided vehicle information; The different target first production task information, target first production equipment information, and target first automated guided vehicle information contained in the first chromosome and the second chromosome are crossed to obtain a crossover scheduling population.

10. The scheduling method according to claim 8, wherein: Each target first scheduling information includes a set of first production task information, first production equipment information, and first automated guided vehicle information. The crossover operation is performed on the target first scheduling information having a crossover probability greater than a crossover probability threshold to obtain a crossover scheduling population, including: Selecting target first production task information, target first production equipment information, and target first automated guided vehicle information whose crossover probability is greater than a crossover probability threshold from a plurality of target first scheduling information; constructing a third chromosome and a fourth chromosome according to the target first production task information, the target first production equipment information, and the target first automated guided vehicle information; randomly generating a random number for the third chromosome to obtain a fifth chromosome; The different target first production task information, target first production equipment information, and target first automated guided vehicle information contained in the fifth chromosome and the fourth chromosome are crossed to obtain a crossover scheduling population.

11. The scheduling method according to any one of claims 1 to 10, wherein: Each second scheduling information includes a set of second production task information, second production equipment information, and second automated guided vehicle information. The third scheduling information obtained according to the cross-scheduling population includes: Determine a critical scheduling path based on the production line resource information; According to the key scheduling path, each group of second production task information, second production equipment information and second automatic guided vehicle information is searched to obtain third scheduling information, which includes a group of third production task information, third production equipment information and third automatic guided vehicle information.

12. The scheduling method according to claim 11, wherein: The production line resource information includes at least a plurality of production tasks, and determining a critical scheduling path according to the production line resource information includes: Determine multiple task paths according to the operation sequence of multiple production tasks, each task path includes the same starting production task and ending production task; Determine the task path duration of each task path based on the task node duration of each production task; The task path corresponding to the longest task path duration is selected from multiple task paths as the critical scheduling path.

13. The scheduling method according to claim 11 or 12, wherein: The step of searching each set of second production task information, second production equipment information, and second automated guided vehicle information according to the key scheduling path to obtain third scheduling information includes: Based on a preset search operator, searching each set of second production task information, second production equipment information, and second automated guided vehicle information according to the critical scheduling path to generate first neighborhood solution scheduling information, the first neighborhood solution scheduling information including a set of fourth production task information, fourth production equipment information, and fourth automated guided vehicle information; Determining a first neighboring area descheduling time for the first neighboring area descheduling information; If it is determined that the first neighboring area descheduling time is less than the second scheduling time, updating the corresponding second scheduling information to the first neighboring area descheduling information; Repeating the step of searching each set of second production task information, second production equipment information, and second automated guided vehicle information according to the critical scheduling path to generate first neighborhood de-scheduling information, until, if it is determined that the first neighborhood de-scheduling time is greater than or equal to the second scheduling time, the corresponding first neighborhood de-scheduling time is determined as the second neighborhood de-scheduling time; The second neighboring area descheduling information corresponding to the second neighboring area descheduling time is determined as the third scheduling information.

14. The scheduling method according to claim 13, wherein: The preset search operator includes a plurality of sub-preset search operators, each sub-preset search operator is associated with a search count, and determining the second neighborhood descheduling information corresponding to the second neighborhood descheduling time as the third scheduling information includes: If it is determined that the second neighborhood descheduling time is greater than or equal to the second scheduling time, determining whether the current search number reaches a preset number; If it is determined that the current search number has not reached the preset number, determining a target sub-preset search operator for the next search number; Updating the preset search operator to the target sub-preset search operator, and updating the second scheduling information to the second neighborhood solution scheduling information; Repeat the preset search operator to search each group of second production task information, The second production equipment information and the second automatic guided vehicle information are searched to generate the first neighborhood dispatching information, until it is determined that the current search times reaches the preset times, and the second neighborhood dispatching information corresponding to the current search times is determined as the third dispatching information.

15. The scheduling method according to any one of claims 1 to 14, wherein: Before obtaining the third scheduling information according to the cross-scheduling population, the scheduling method further includes: Performing a mutation operation on the cross-scheduling population to obtain a mutated scheduling population; The obtaining third scheduling information according to the cross-scheduling population includes: The third scheduling information is obtained according to the variant scheduling population.

16. The scheduling method according to any one of claims 1 to 15, wherein: Before outputting the fourth scheduling information according to the first scheduling time and the third scheduling time, the scheduling method further includes: Determine whether the iteration termination condition is met; The outputting fourth scheduling information according to the first scheduling time and the third scheduling time includes: When it is determined that the iteration termination condition is met, the fourth scheduling information is output according to the first scheduling time and the third scheduling time.

17. The scheduling method according to claim 16, wherein: Also includes: When it is determined that the iteration termination condition is not met, the step of performing a cross operation on multiple target first scheduling information to obtain a cross scheduling population is repeated until the step of outputting the fourth scheduling information according to the first scheduling time and the third scheduling time is performed when it is determined that the iteration termination condition is met.

18. A scheduling device for production line resources, wherein: include: A generating module, configured to generate an initial scheduling population according to production line resource information, wherein the initial scheduling population includes a plurality of first scheduling information; a time determination module, configured to determine a first scheduling time for each first scheduling information according to the production line resource information; a cross module, configured to perform a cross operation on a plurality of target first scheduling information to obtain a cross scheduling population, wherein the cross scheduling population includes a plurality of second scheduling information; an obtaining module, configured to obtain third scheduling information according to the cross-scheduling population, wherein a third scheduling time of the third scheduling information is less than a second scheduling time of the second scheduling information; The output module is used to output fourth scheduling information according to the first scheduling time and the third scheduling time, where the fourth scheduling information is scheduling information corresponding to the minimum scheduling time between the first scheduling time and the third scheduling time.

19. An electronic device, wherein: include: Memory; one or more processors coupled to the memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by one or more processors, and the one or more applications are configured to execute the scheduling method according to any one of claims 1 to 17.

20. A computer-readable storage medium, wherein: The computer-readable storage medium stores program code, which can be called by a processor to execute the scheduling method according to any one of claims 1 to 17.

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