Automatic guided vehicle scheduling method and device, computer equipment and storage medium

Abstract

The invention provides an automatic guided vehicle scheduling method and device, computer equipment and a storage medium, and belongs to the technical field of production scheduling, and the method comprises the steps: obtaining the number of workpieces transported by an automatic guided vehicle, the number of procedures and the number of parallel machines for machining the workpieces; according to the number of the workpieces, the number of the working procedures and the number of the parallel machines, the priority of workpiece transportation and the parallel machine selection of each workpiece in each working procedure are coded, and an initial scheduling scheme is obtained; for any workpiece, determining a transportation plan of the workpiece in the current working procedure, corresponding to the state of the parallel machine and the position of the automatic guided vehicle in the idle state, and determining a transportation plan of the workpiece in the next working procedure; constructing a parallel multi-population hybrid genetic algorithm, and carrying out iterative optimization on the initial scheduling scheme according to a transportation plan of any workpiece transportation in the next process to obtain an optimal scheduling scheme; and scheduling the automated guided vehicle according to the optimal scheduling scheme. Therefore, the utilization efficiency of AGV scheduling can be effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of production scheduling, and in particular relates to an automatic guided vehicle scheduling method, device, computer equipment and storage medium. Background Art

[0002] In many manufacturing companies today, production workshops generally adopt the production mode of hybrid flow workshops. With the continuous improvement of industrial automation level, unmanned workshops are gradually becoming a reality. Workpieces are transferred between machines in different processes through AGV (Automated Guided Vehicle), realizing production transportation without human intervention. The introduction of this technology has greatly improved the efficiency of production and transportation, but it has also brought more complex challenges to the scheduling of hybrid flow workshops.

[0003] With the widespread application of AGV technology, hybrid assembly line workshop scheduling faces new problems and challenges. Traditional workshop scheduling methods may not be able to effectively cope with the complex scheduling requirements under multi-AGV scenarios. The scheduling scheme for AGV cannot maximize the utilization efficiency of AGV. In solving scheduling problems, local convergence and computational efficiency are two important challenges. Traditional genetic algorithms often face the problem of early convergence, that is, the population gathers to a local optimal solution too early, resulting in a reduced ability to explore the global solution, which in turn leads to a scheduling scheme that cannot obtain the optimal solution.

[0004] Therefore, the scheduling scheme for AGV in the prior art still has major defects and cannot maximize the utilization efficiency of AGV. Summary of the invention

[0005] In order to solve the problem of low utilization efficiency of the above-mentioned AGV scheduling, the present invention provides an automatic guided vehicle scheduling method, device, computer equipment and storage medium.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] First, an automatic guided vehicle scheduling method is provided, the method comprising:

[0008] Obtain the number of workpieces transported by the automated guided vehicle, the number of processes, and the number of parallel machines processing the workpieces;

[0009] According to the number of workpieces, the number of processes and the number of parallel machines, the priority of workpiece transportation and the parallel machine selection of each workpiece in each process are encoded respectively to obtain an initial scheduling plan;

[0010] For any workpiece, determine the transportation plan of the workpiece in the current process based on the initial scheduling plan, determine the state of the parallel machine corresponding to the transportation plan of the current process and the position of the automatic guided vehicle in the idle state, and then determine the transportation plan of the workpiece in the next process;

[0011] Construct a parallel multi-population hybrid genetic algorithm with the goal of minimizing the total time to complete the processing of all workpieces. Through the transportation plan of any workpiece in the next process, the initial scheduling plan is iteratively optimized to obtain the optimal scheduling plan;

[0012] The automatic guided vehicle is dispatched according to the optimal dispatching plan.

[0013] Optionally, encoding the priority of workpiece transportation and the parallel machine selection of each workpiece in each process according to the number of workpieces, the number of processes and the number of parallel machines includes:

[0014] A two-layer coding method is used to encode the priority of workpiece transportation and the parallel machine selection of each workpiece in each process, specifically:

[0015] The first level of coding represents the priority of the workpiece transportation task, the sequence length is the number of workpieces n, and the i-th gene bit of the coding represents the transportation priority of the i-th workpiece; wherein the priority coding sets a priority for each workpiece;

[0016] The second-level encoding specifies the parallel machine selection for each workpiece in each process. The sequence length is the product of the number of workpieces n and the number of processes m. The i+(j-1)×nth gene bit of the encoding represents the machine selection of the i-th workpiece in the j-th process, where 1≤j≤m.

[0017] Optionally, the parameters of the transportation plan include the workpiece number job, the number of completed stages stage, the earliest start time of the task st, the workpiece transportation priority priority, the workpiece current position src and the transportation destination dst.

[0018] Optionally, determining a transportation plan for the workpiece in a next process includes:

[0019] The workpiece number in the transportation plan of the next process is the workpiece number in the transportation plan of the current process;

[0020] The number of completed processes in the transportation plan of the next process is the number of completed processes of the current process plus 1;

[0021] The workpiece transportation priority in the transportation plan of the next process is the workpiece transportation priority in the transportation plan of the current process;

[0022] The current position of the workpiece in the transportation plan of the next process is the transportation destination in the transportation plan of the current process;

[0023] The transportation destination in the transportation plan of the next process is determined by the second-level code;

[0024] The earliest starting time of the task in the transportation plan of the next process is determined by the position of the idle automated guided vehicle and the distance between the current position and the transportation destination in the transportation plan of the current process.

[0025] Optionally, the earliest starting time of the task in the next process transportation plan is determined by the position of the idle automatic guided vehicle and the distance between the current position and the transportation destination in the current process transportation plan, including:

[0026] The symbol is R v The idle automated guided vehicle reaches the current position src old The time is:

[0027]

[0028] Among them, TT c,p' Represents R v From the current machine M c No load moved to src old Compatible machine M p' Time required, R v Arrive at the destination machine location dst old The time is:

[0029]

[0030] Among them, LT i Indicates the loading time of the workpiece, TTp' ,p Represents R v From src old Compatible machine M p' Move to dst with load old Compatible machine M p The time required, the start time of the next process j of the current workpiece is:

[0031]

[0032] Among them, UT i Indicates the unloading time of the workpiece, MIT i,p The machine M before starting to process the current workpiece p The earliest free time, Indicates machine M p From the last processed workpiece J i' Switch to processing the current workpiece Ji The required setup time, the completion time of the jth process of the current workpiece C i,j for:

[0033] C i,j =S i,j +PT i,j ;

[0034] Among them, PT i,j represents the jth process processing time of the current workpiece, then the earliest starting time of the next process transportation task is: st new =C i,j .

[0035] Secondly, an automatic guided vehicle dispatching device is also provided, the device comprising:

[0036] An acquisition module, used to acquire the number of workpieces transported by the automatic guided vehicle, the number of processes, and the number of parallel machines processing the workpieces;

[0037] An encoding module, used for encoding the priority of workpiece transportation and the parallel machine selection of each workpiece in each process according to the number of workpieces, the number of processes and the number of parallel machines, to obtain an initial scheduling plan;

[0038] A determination module, which determines the transportation plan of the workpiece in the current process based on the initial scheduling plan, and determines the state of the parallel machine corresponding to the transportation plan of the current process and the position of the automatic guided vehicle in the idle state, and then determines the transportation plan of the workpiece in the next process;

[0039] An optimization module is used to construct a parallel multi-population hybrid genetic algorithm, with the goal of minimizing the total time to complete the processing of all workpieces, and iteratively optimize the initial scheduling plan according to the transportation plan of any workpiece in the next process to obtain the optimal scheduling plan;

[0040] A scheduling module is used to schedule the automatic guided vehicle according to the optimal scheduling plan.

[0041] In addition, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned automatic guided vehicle scheduling method is implemented.

[0042] Finally, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned automatic guided vehicle scheduling method when executing the program.

[0043] The automatic guided vehicle scheduling method provided by the present invention has the following beneficial effects:

[0044] First, the priority of workpiece transportation and the parallel machine selection of each workpiece in each process are encoded. On the one hand, the priority can ensure that the AGV responds quickly to the transportation tasks to be executed. On the other hand, the priority and the selection of parallel machines are encoded separately, which effectively expands the space of information utilization, making the search range of the scheduling plan wider, thereby increasing the possibility of finding the global optimal solution and further optimizing the transportation process. Secondly, the initial scheduling plan is iteratively optimized through a parallel multi-population hybrid genetic algorithm, which effectively improves the scheduling efficiency and flexibility of multi-AGV mixed flow workshops, and can provide intelligent and efficient solutions under complex scheduling requirements. It has significant application value in improving production plans, optimizing resource utilization, shortening production cycles, and reducing production costs, which is conducive to maximizing production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiment of the present invention and its design scheme, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 The present invention is a schematic flow chart of an automated guided vehicle scheduling method provided according to an exemplary embodiment.

[0047] Figure 2 A feasible decoding example is provided according to an exemplary embodiment of the present invention.

[0048] Figure 3 The present invention is a schematic diagram of transport task allocation according to an exemplary embodiment.

[0049] Figure 4 A solution flow chart of the parallel multi-population hybrid genetic algorithm provided by the present invention.

[0050] Figure 5 The present invention is a block diagram of an automatic guided vehicle scheduling device provided according to an exemplary embodiment. DETAILED DESCRIPTION

[0051] In order to enable those skilled in the art to better understand the technical solution of the present invention and implement it, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the scope of protection of the present invention.

[0052] The technical solutions provided by various embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0053] First, the present invention provides an automatic guided vehicle scheduling method, specifically as follows Figure 1 As shown, the following steps are included:

[0054] S101. Obtain the number of workpieces transported by the automatic guided vehicle, the number of working procedures, and the number of parallel machines processing the workpieces.

[0055] In this step, in order to improve the utilization rate of the AGV scheduling plan, it is first necessary to clarify the task objectives of the AGV, that is, the number of workpieces that the AGV needs to transport, the number of processes, and the number of parallel machines for processing.

[0056] S102: According to the number of workpieces, the number of processes and the number of parallel machines, the priority of workpiece transportation and the parallel machine selection of each workpiece in each process are encoded respectively to obtain an initial scheduling plan.

[0057] In one embodiment, a scheduling model may be constructed first. A hybrid flow shop scheduling model including multiple AGVs is constructed, and the basic elements of the scheduling problem are defined, including the number of workpieces, the number of processes, and the configuration of parallel machines. The model assumes that the workpieces are ready at the initial moment, and all machines and AGVs are in good condition. The goal is to minimize the maximum completion time.

[0058] For example, a multi-AGV hybrid flow shop scheduling model is constructed, which includes a hybrid flow shop scheduling problem description and model assumptions.

[0059] The hybrid flow shop scheduling problem can be expressed as: a set of jobs contains n workpieces, each workpiece has m processes, and for process j there are M j There are two identical parallel machines, and at least one process corresponds to M j ≥2, the transfer of workpieces between different processing machines is completed by AGV. The model meets the following assumptions:

[0060] Each machine can only process one workpiece at a time;

[0061] Each machine can only process one process;

[0062] Each workpiece can only be processed by one machine at a time;

[0063] All workpieces are ready to be moved at the initial moment;

[0064] The processing and transshipment process cannot be preempted;

[0065] The time it takes for an AGV to transfer a workpiece from one machine to another is known and fixed;

[0066] The time required for the AGV to load and unload workpieces is known and fixed;

[0067] Each machine needs setup time before processing, which depends on the sequence of the workpiece to be processed and the previous workpiece;

[0068] No breakdown or maintenance time for all processing machines and AGVs.

[0069] The goal of scheduling optimization is to minimize the maximum completion time, denoted by C i is the i-th workpiece J i The completion time is n, and the total number of workpieces is n, then the objective function is: minC max =min(max(C i )), where 1≤i≤n.

[0070] Secondly, the encoding and decoding scheme. The scheduling scheme is represented by two layers of encoding. The first layer of encoding reflects the priority of the workpiece transportation task, and the second layer of encoding determines the machine selection for each workpiece in each process. This design not only improves the ability to express information, but also ensures that the AGV can quickly respond to the transportation tasks to be executed through priority queue decoding. More importantly, the use of two-layer encoding effectively expands the solution space, making the search range of the scheduling scheme wider, thereby increasing the possibility of finding the global optimal solution. Whenever the AGV is idle, the highest priority task is extracted from the priority queue for allocation, further optimizing the transportation process.

[0071] For example, a two-layer encoding method can be used to encode the priority of workpiece transportation and the parallel machine selection of each workpiece in each process respectively. Specifically, the first layer of encoding represents the priority of the workpiece transportation task, the sequence length is the number of workpieces n, and the i-th gene bit of the encoding represents the transportation priority of the i-th workpiece; wherein the encoding of the priority sets a priority for each workpiece; the second layer of encoding specifies the parallel machine selection of each workpiece in each process, the sequence length is the product of the number of workpieces n and the number of processes m, and the i+(j-1)×n-th gene bit of the encoding represents the machine selection of the i-th workpiece in the j-th process, where 1≤j≤m.

[0072] For example, Figure 2 As shown in the figure, it is a feasible decoding example of the HFSP example with 5 workpieces, 3 processes, and the number of parallel machines in each process is {2,3,2}. The first layer of encoding is [1,4,5,3,2], which means that the transportation task priorities of workpieces 1 to 5 are 1, 4, 5, 3, 2. The second layer of encoding is [2,2,1,1,2,4,3,3,5,5,7,6,6,7,6], which means that the processing machines of workpieces 1 to 5 in the first process are allocated as 2, 2, 1, 1, 2, the processing machines in the second process are allocated as 4, 3, 3, 5, 5, and the processing machines in the third process are allocated as 7, 6, 6, 7, 6. In this way, the initial scheduling plan of AGV can be generated based on the above encoding.

[0073] S103. For any workpiece, determine the transportation plan of the workpiece for the current process based on the initial scheduling plan, determine the status of the parallel machines corresponding to the transportation plan for the current process and the positions of the automatically guided vehicles in the idle state, and then determine the transportation plan of the workpiece for the next process.

[0074] Among them, the parameters of the workpiece transportation plan include workpiece number job, number of completed processes stage, earliest start time of the task st, workpiece transportation priority priority, current position of the workpiece src, and transportation destination dst.

[0075] In this step, construct the transportation plan for the workpiece based on the initial scheduling plan as follows:

[0076] First, a priority queue needs to be established to store all tasks to be transported. Each task to be transported can be represented as a six-tuple (job, stage, st, priority, src, dst), which represent the workpiece number job, number of completed processes stage, earliest start time of the task st, workpiece transportation priority priority, current position of the workpiece src, and transportation destination dst in sequence. At most one task to be transported for each workpiece is located in the priority queue at the same time. In the priority queue, the transportation tasks are sorted according to st and priority. The smaller the st, the higher the priority of the transportation task. If the values of st are the same, then compare priority, and the smaller the priority value, the higher the priority. The value of priority for each workpiece is determined according to the first-layer encoding. Since the priority values of each workpiece are different, a unique sorting sequence of transportation tasks can be obtained in the priority queue. The priority queue is stored using the data structure of a min-heap, so efficient insertion and deletion operations can be performed with a time complexity of O(logn). Whenever an AGV is in the idle state, the task at the top of the heap is taken out from the priority queue for assignment. If multiple AGVs are in the idle state at a certain moment, the task is assigned to the AGV with the closest distance between the predicted arrival time SAT at the current position of the workpiece and the earliest start time st of the task. If stage in the task six-tuple < m - 1, it indicates that there are still transportation tasks for this workpiece in the future. Therefore, create the transportation task for this workpiece in the next process (job, stage new , st new , priority, src new , dst new ) and add it to the priority queue. Loop through this process until the priority queue is emptied, indicating that there are no more transportation tasks to be executed.

[0077] Secondly, the transportation plan of the workpiece in the next process is determined based on the initial scheduling plan, and then the transportation plan is gradually implemented to determine the processing time of the workpiece based on the initial scheduling plan. The specific process includes:

[0078] The workpiece number in the next process transportation plan is the workpiece number in the current process transportation plan; the number of completed processes in the next process transportation plan is the number of completed processes of the current process plus 1; the workpiece transportation priority in the next process transportation plan is the workpiece transportation priority in the current process transportation plan; the current position of the workpiece in the next process transportation plan is the transportation destination in the current process transportation plan; the transportation destination in the next process transportation plan is determined by the second-level coding; the earliest start time of the task in the next process transportation plan is determined by the position of the idle automatic guided vehicle and the distance between the current position and the transportation destination in the current process transportation plan.

[0079] Among them, the earliest starting time of the task in the transportation plan of the next process can be determined by the following steps:

[0080] The symbol is R v The idle automated guided vehicle reaches the current position src old The time is:

[0081]

[0082] Among them, TT c,p' Represents R v From the current machine M c No load moved to src old Compatible machine M p' Time required, R v Arrive at the destination machine location dst old The time is:

[0083]

[0084] Among them, LT i Indicates the loading time of the workpiece, TT p',p Represents R v From src old Compatible machine M p' Move to dst with load old Compatible machine M p The time required, the start time of the next process j of the current workpiece is:

[0085]

[0086] Among them, UT i Indicates the unloading time of the workpiece, MIT i,pThe machine M before starting to process the current workpiece p The earliest free time, Indicates machine M p From the last processed workpiece J i' Switch to processing the current workpiece J i The required setup time, the completion time of the jth process of the current workpiece C i,j for:

[0087] C i,j =S i,j +PT i,j .

[0088] Among them, PT i,j represents the jth process processing time of the current workpiece, then the earliest starting time of the next process transportation task is: st new =C i,j .

[0089] Still taking the above HFSP example, the transportation task allocation process at a certain moment is as follows: Figure 3 As shown. The current transport task at the top of the stack is (3,1,42,5,1,3), and the SAT of AGV No. 2 is closest to the st of this task, so the transport task is assigned to AGV No. 2 for execution.

[0090] S104. Construct a parallel multi-population hybrid genetic algorithm with the goal of minimizing the total time to complete the processing of all workpieces. Through the transportation plan of any workpiece in the next process, the initial scheduling plan is iteratively optimized to obtain the optimal scheduling plan.

[0091] Initialize the algorithm parameters. The evolutionary calculation of each population is assigned to different threads. The number of populations is recommended to be close to the number of logical cores of the current computing platform to reduce the performance loss caused by frequent thread scheduling. The mutation probability of individuals in each population is in the interval [P m,low ,P m,high ], the crossover probability takes the interval [P c,low ,P c,high ], so that various populations evolve in different living environments. Each population has N individuals. The first-level code is obtained by executing the stack shuffle algorithm from sequence 1 to n. The i+(j-1)×n gene bits in the second-level code are obtained by randomly selecting the optional processing machine of the i-th workpiece in the j-th process, thus ensuring that all randomly obtained initial solutions are feasible.

[0092] The algorithm solution process is as follows Figure 4 As shown, each population evolves independently. sGeneration. Calculate the fitness value of each individual in the population: fit = 1 / makespan, and sort them from large to small according to fitness. The top 10% of individuals are marked as high-quality individuals, 10% to 30% of individuals are marked as suboptimal individuals, and the rest are ordinary individuals. Ordinary individuals perform crossover and mutation operations, and suboptimal individuals perform random local search, which includes the following four search operators:

[0093] Randomly select a process and assign a processing task of the machine with the longest processing time under this process to another parallel machine;

[0094] Randomly select a process and assign a processing task of the machine with the most processing tasks under this process to another parallel machine;

[0095] Randomly select a process and pull a task to the machine with the shortest processing time under this process;

[0096] Randomly select a process and pull a task to the machine with the least processing tasks under this process.

[0097] The high-quality individuals perform a local search based on the critical path, which includes the following four search operators:

[0098] Randomly select a processing task on the critical path and assign it to another parallel machine;

[0099] Randomly select a processing task on the critical path and exchange it with a processing task on another parallel machine;

[0100] Randomly select a machine on the critical path and exchange all its processing tasks with another parallel machine;

[0101] Randomly select a workpiece on the critical path and increase its transportation task priority.

[0102] Each time a local search is performed, a weighted round-robin algorithm is used to select the local search operator. In the population initialization phase, a random interval [W low ,W high ] are used as the weights of each operator, so that each population has different preferences for the local search operator, which can guide the population to evolve in different directions and enrich the population diversity. The individual composition of the next generation of the population is determined based on the binary tournament and elite retention selection strategy.

[0103] Whenever the population converges locally, the best individual of the current population is added to the global elite set, and the best individuals of other populations are obtained from the global elite set and added to the own population, so that high-quality genes can be shared among various populations. gIf there is no improvement in the next generation, the global convergence condition is reached, the algorithm terminates, and the optimal individual is the final solution.

[0104] S105. Dispatching the automatic guided vehicle according to the optimal dispatching plan.

[0105] Finally, the optimal scheduling plan can be implemented to complete the scheduling of the automatic guided vehicle.

[0106] Using the above method, the priority of workpiece transportation and the parallel machine selection of each workpiece in each process are first encoded. In this way, on the one hand, the priority can ensure that the AGV responds quickly to the transportation task to be executed. On the other hand, the priority and the selection of parallel machines are encoded separately, which effectively expands the space of information utilization, making the search range of the AGV scheduling plan wider, thereby increasing the possibility of finding the global optimal solution and further optimizing the transportation process. Secondly, the initial scheduling plan is iteratively optimized through a parallel multi-population hybrid genetic algorithm, which effectively improves the scheduling efficiency and flexibility of multi-AGV mixed flow workshops, and can provide intelligent solutions under complex scheduling requirements, and thus has significant application value in improving production plans, optimizing resource utilization, shortening production cycles, and reducing production costs, which is conducive to maximizing production efficiency.

[0107] Secondly, the present invention also provides an automatic guided vehicle dispatching device, such as Figure 5 As shown, including:

[0108] The acquisition module 501 is used to acquire the number of workpieces transported by the automatic guided vehicle, the number of processes, and the number of parallel machines processing the workpieces.

[0109] The encoding module 502 is used to encode the priority of the workpiece transportation and the parallel machine selection of each workpiece in each process according to the number of workpieces, the number of processes and the number of parallel machines, so as to obtain an initial scheduling plan.

[0110] The determination module 503 determines the transportation plan of the workpiece in the current process based on the initial scheduling plan, and determines the state of the parallel machine corresponding to the transportation plan of the current process and the position of the automatic guided vehicle in the idle state, and then determines the transportation plan of the workpiece in the next process.

[0111] The optimization module 504 is used to construct a parallel multi-population hybrid genetic algorithm with the goal of minimizing the total time to complete the processing of all workpieces. According to the transportation plan of any workpiece in the next process, the initial scheduling plan is iteratively optimized to obtain the optimal scheduling plan.

[0112] The scheduling module 505 is used to schedule the automatic guided vehicle according to the optimal scheduling solution.

[0113] By using the above device, the priority of workpiece transportation and the parallel machine selection of each workpiece in each process are firstly encoded. In this way, on the one hand, the priority can ensure that the AGV responds quickly to the transportation task to be executed. On the other hand, the priority and the selection of parallel machines are respectively encoded, which effectively expands the space of information utilization, making the search range of the AGV scheduling plan wider, thereby increasing the possibility of finding the global optimal solution and further optimizing the transportation process. Secondly, the initial scheduling plan is iteratively optimized by a parallel multi-population hybrid genetic algorithm, which effectively improves the scheduling efficiency and flexibility of multi-AGV hybrid flow workshops, and can provide intelligent solutions under complex scheduling requirements, and thus has significant application value in improving production plans, optimizing resource utilization, shortening production cycles, and reducing production costs, which is conducive to maximizing production efficiency.

[0114] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 Steps of an automated guided vehicle scheduling method are provided.

[0115] The present invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 Steps of an automated guided vehicle scheduling method are provided.

[0116] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0117] The present invention is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0118] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0119] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0120] It should be noted that the above specific implementation method can enable those skilled in the art to understand the invention more comprehensively, but does not limit the invention in any way. Therefore, although the present invention has been described in detail in this specification, those skilled in the art should understand that the invention can still be modified or replaced by equivalents; and all technical solutions and improvements that do not deviate from the spirit and scope of the invention are included in the protection scope of the patent for the invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

Claims

1. An automated guided vehicle scheduling method, characterized in that: The method comprises: Obtain the number of workpieces transported by the automated guided vehicle, the number of processes, and the number of parallel machines processing the workpieces; According to the number of workpieces, the number of processes and the number of parallel machines, the priority of workpiece transportation and the parallel machine selection of each workpiece in each process are encoded respectively to obtain an initial scheduling plan; For any workpiece, determine the transportation plan of the workpiece in the current process based on the initial scheduling plan, determine the state of the parallel machine corresponding to the transportation plan of the current process and the position of the automatic guided vehicle in the idle state, and then determine the transportation plan of the workpiece in the next process; Construct a parallel multi-population hybrid genetic algorithm with the goal of minimizing the total time to complete the processing of all workpieces. Through the transportation plan of any workpiece in the next process, the initial scheduling plan is iteratively optimized to obtain the optimal scheduling plan; The automatic guided vehicle is dispatched according to the optimal dispatching plan.

2. The automatic guided vehicle scheduling method according to claim 1, characterized in that: According to the number of workpieces, the number of processes and the number of parallel machines, respectively encoding the priority of workpiece transportation and the parallel machine selection of each workpiece in each process includes: A two-layer coding method is used to encode the priority of workpiece transportation and the parallel machine selection of each workpiece in each process, specifically: The first level of coding represents the priority of the workpiece transportation task, the sequence length is the number of workpieces n, and the i-th gene bit of the coding represents the transportation priority of the i-th workpiece; wherein the priority coding sets a priority for each workpiece; The second-level encoding specifies the parallel machine selection for each workpiece in each process. The sequence length is the product of the number of workpieces n and the number of processes m. The i+(j-1)×nth gene bit of the encoding represents the machine selection of the i-th workpiece in the j-th process, where 1≤j≤m.

3. The automatic guided vehicle scheduling method according to claim 1, characterized in that: The parameters of the transportation plan include the workpiece number job, the number of completed stages stage, the earliest start time of the task st, the workpiece transportation priority priority, the workpiece current location src and the transportation destination dst.

4. The automatic guided vehicle dispatching method according to claim 3, characterized in that: Determining the transportation plan of the workpiece in the next process includes: The workpiece number in the transportation plan of the next process is the workpiece number in the transportation plan of the current process; The number of completed processes in the transportation plan of the next process is the number of completed processes of the current process plus 1; The workpiece transportation priority in the transportation plan of the next process is the workpiece transportation priority in the transportation plan of the current process; The current position of the workpiece in the transportation plan of the next process is the transportation destination in the transportation plan of the current process; The transportation destination in the transportation plan of the next process is determined by the second-level code; The earliest starting time of the task in the transportation plan of the next process is determined by the position of the idle automated guided vehicle and the distance between the current position and the transportation destination in the transportation plan of the current process.

5. The automatic guided vehicle dispatching method according to claim 4, characterized in that: The earliest starting time of the task in the next process transportation plan is determined by the position of the idle automated guided vehicle and the distance between the current position and the transportation destination in the current process transportation plan, including: The symbol is R v The idle automated guided vehicle reaches the current position src old The time is: Among them, TT c，p′ Represents R v From the current machine M c No load moved to src old Compatible machine M p′ Time required, R v Arrive at the destination machine location dst old The time is: Among them, LT i Indicates the loading time of the workpiece, TT p′，p Represents R v From src old Compatible machine M p′ Move to dst with load old Compatible machine M p The time required, the start time of the next process j of the current workpiece is: Among them, UT i Indicates the unloading time of the workpiece, MIT i，p The machine M before starting to process the current workpiece p The earliest free time, Indicates machine M p From the last processed workpiece J i′ Switch to processing the current workpiece J i The required setup time, the completion time of the jth process of the current workpiece C i，j for: C i,j =S i,j +PT i,j ; Among them, PT i,j represents the jth process processing time of the current workpiece, then the earliest starting time of the next process transportation task is: st new =C i,j .

6. An automatic guided vehicle dispatching device, characterized in that: The device comprises: An acquisition module, used to acquire the number of workpieces transported by the automatic guided vehicle, the number of processes, and the number of parallel machines processing the workpieces; An encoding module, used for encoding the priority of workpiece transportation and the parallel machine selection of each workpiece in each process according to the number of workpieces, the number of processes and the number of parallel machines, to obtain an initial scheduling plan; A determination module, which determines the transportation plan of the workpiece in the current process based on the initial scheduling plan, and determines the state of the parallel machine corresponding to the transportation plan of the current process and the position of the automatic guided vehicle in the idle state, and then determines the transportation plan of the workpiece in the next process; An optimization module is used to construct a parallel multi-population hybrid genetic algorithm, with the goal of minimizing the total time to complete the processing of all workpieces, and iteratively optimize the initial scheduling plan according to the transportation plan of any workpiece in the next process to obtain the optimal scheduling plan; A scheduling module is used to schedule the automatic guided vehicle according to the optimal scheduling plan.

7. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

8. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 5 when executing the program.

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