An automated guided vehicle scheduling method and device, computer equipment and storage medium

Abstract

The application provides an automatic guided vehicle scheduling method and device, computer equipment and a storage medium, and belongs to the technical field of production scheduling. The method comprises the following steps: acquiring the workpiece quantity, the process quantity and the parallel machine quantity for processing the workpiece of the automatic guided vehicle; encoding the priority of the workpiece transportation and the parallel machine selection of each workpiece in each process according to the workpiece quantity, the process quantity and the parallel machine quantity, to obtain an initial scheduling scheme; for any workpiece, determining the transportation plan of the workpiece in the current process, the state of the corresponding parallel machine and the position of the automatic guided vehicle in the idle state, and determining the transportation plan of the workpiece in the next process; constructing a parallel multi-population hybrid genetic algorithm, iteratively optimizing the initial scheduling scheme according to the transportation plan of any workpiece in the next process, to obtain an optimal scheduling scheme; and scheduling the automatic guided vehicle according to the optimal scheduling scheme. In this way, the utilization efficiency of AGV scheduling can be effectively improved.

Description

Technical Field

[0001] This invention belongs to the field of production scheduling technology, and specifically relates to an automated guided vehicle (AGV) scheduling method, device, computer equipment, and storage medium. Background Technology

[0002] In many manufacturing enterprises today, hybrid assembly line production models are commonly used in production workshops. With the continuous improvement of industrial automation, unmanned workshops are gradually becoming a reality. Workpieces are transferred between machines in different processes using AGVs (Automated Guided Vehicles), achieving production transportation without human intervention. The introduction of this technology has greatly improved the efficiency of production transportation, but it has also brought more complex challenges to the scheduling of hybrid assembly line workshops.

[0003] With the widespread application of AGV technology, the scheduling of hybrid assembly line workshops faces new problems and challenges. Traditional workshop scheduling methods may struggle to effectively address the complex scheduling needs in multi-AGV scenarios, and the scheduling schemes for AGVs may fail to maximize AGV utilization efficiency. Local convergence and computational efficiency are two significant challenges in solving the scheduling problem. Traditional genetic algorithms often suffer from early convergence, where the population prematurely clusters at a local optimum, reducing its ability to explore the global solution and ultimately preventing the acquisition of an optimal scheduling scheme.

[0004] Therefore, the existing scheduling schemes for AGVs still have significant shortcomings and cannot maximize the utilization efficiency of AGVs. Summary of the Invention

[0005] To address the problem of low utilization efficiency in AGV scheduling, this invention provides an automated guided vehicle scheduling method, apparatus, computer equipment, and storage medium.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] Firstly, an automated guided vehicle (AGV) 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] Based on the number of workpieces, the number of processes, and the number of parallel machines, the priority of workpiece transportation and the selection of parallel machines for each workpiece in each process are encoded to obtain an initial scheduling scheme.

[0010] For any workpiece, the transportation plan for the workpiece in the current process is determined based on the initial scheduling scheme, and the state of the parallel machine corresponding to the transportation plan of the current process and the position of the automated guided vehicle in the idle state are determined, thereby determining the transportation plan for the workpiece in the next process.

[0011] A parallel multi-population hybrid genetic algorithm is constructed with the goal of minimizing the total time for all workpieces to complete processing. The initial scheduling scheme is iteratively optimized by considering the transportation plan of any workpiece in the next process to obtain the optimal scheduling scheme.

[0012] The automated guided vehicles are scheduled according to the optimal scheduling scheme.

[0013] Optionally, encoding the priority of workpiece transportation and the selection of a parallel machine for each workpiece in each process, based on 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 as follows:

[0015] 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. The priority encoding sets a priority for each workpiece.

[0016] The second layer of 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)×n-th gene bit of the encoding represents the machine selection for the i-th workpiece in the j-th process, where 1≤j≤m.

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

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

[0019] The workpiece number in the transportation plan for the next process is the same as the workpiece number in the transportation plan for the current process.

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

[0021] The workpiece transportation priority in the transportation plan of the next process is the same as the workpiece transportation priority in the transportation plan of the current process.

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

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

[0024] The earliest start 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 current process transportation plan.

[0025] Optionally, the earliest start 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 its current position and the transportation destination in the current process transportation plan, including:

[0026] The label is R v The idle automated guided vehicle arrives at the current location src old The time is:

[0027]

[0028] Among them, TT c,p' R represents v From the current machine M c Move the empty load to src old Corresponding machine M p' Time required, R v Arrive at the terminal machine location dst of the transport mission old The time is:

[0029]

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

[0031]

[0032] Among them, UT i Indicates the unloading time of the workpiece, MIT i,p Before starting machining of the current workpiece, machine tool M p The earliest free time, Indicates machine M p From the previous processed workpiece J i' Switch to processing the current workpiece Ji The required setup time, the completion time C of the j-th operation of the current workpiece. i,j for:

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

[0034] Among them, PT i,j Let st represent the processing time of the j-th operation on the current workpiece. Then, the earliest start time of the next operation's transport task is: st new =C i,j .

[0035] Secondly, an automated guided vehicle (AGV) dispatching device is also provided, the device comprising:

[0036] The acquisition module is used to acquire the number of workpieces transported by the automated guided vehicle, the number of processes, and the number of parallel machines processing the workpieces.

[0037] The encoding module is used to encode the priority of workpiece transportation and the selection of parallel machines for 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 scheme.

[0038] The determination module determines the transportation plan of the workpiece in the current process based on the initial scheduling scheme, and determines the state of the parallel machine corresponding to the transportation plan of the current process and the position of the automated guided vehicle in the idle state, and then determines the transportation plan of the workpiece in the next process.

[0039] The optimization module is used to construct a parallel multi-population hybrid genetic algorithm with the goal of minimizing the total time for all workpieces to complete processing. Based on the transportation plan of any workpiece in the next process, the initial scheduling scheme is iteratively optimized to obtain the optimal scheduling scheme.

[0040] The scheduling module is used to schedule the automated guided vehicle according to the optimal scheduling scheme.

[0041] Additionally, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the aforementioned automated guided vehicle scheduling method.

[0042] Finally, a computer device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the aforementioned automated guided vehicle scheduling method.

[0043] The automated guided vehicle (AGV) scheduling method provided by this invention has the following beneficial effects:

[0044] First, the priority of workpiece transportation and the selection of parallel machines for each workpiece in each process are encoded. This ensures that AGVs respond quickly to pending transportation tasks through priority, and separately encodes the priority and parallel machine selection, effectively expanding the space for information utilization and broadening the search range of the scheduling scheme, thereby increasing the possibility of finding the global optimum and further optimizing the transportation process. Second, the initial scheduling scheme is iteratively optimized using a parallel multi-population hybrid genetic algorithm, effectively improving the scheduling efficiency and flexibility of multi-AGV mixed production lines. It can provide intelligent and efficient solutions under complex scheduling requirements, thus having significant application value in improving production planning, optimizing resource utilization, shortening production cycles, and reducing production costs, which is conducive to maximizing production efficiency. Attached Figure Description

[0045] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a schematic flowchart of an automated guided vehicle (AGV) scheduling method according to an exemplary embodiment of the present invention.

[0047] Figure 2 This is a feasible decoding example provided by the present invention according to an exemplary embodiment.

[0048] Figure 3 This is a schematic diagram of transportation task allocation provided by the present invention according to an exemplary embodiment.

[0049] Figure 4 The flowchart shows the solution process of the parallel multi-population hybrid genetic algorithm provided by this invention.

[0050] Figure 5 This is a block diagram of an automated guided vehicle (AGV) scheduling device according to an exemplary embodiment of the present invention. Detailed Implementation

[0051] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0052] The technical solutions provided by the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0053] First, this invention provides an automated guided vehicle (AGV) scheduling method, specifically as follows: Figure 1 As shown, it includes the following steps:

[0054] S101. Obtain the number of workpieces, the number of processes, and the number of parallel machines processing the workpieces by the automated guided vehicle.

[0055] In this step, in order to improve the utilization rate of the AGV scheduling scheme, it is first necessary to clarify the task objectives of the AGV, namely the number of workpieces, the number of processes, and the number of parallel machines to be processed by the AGV.

[0056] S102. Based on the number of workpieces, the number of processes, and the number of parallel machines, the priority of workpiece transportation and the selection of parallel machines for each workpiece in each process are encoded to obtain the initial scheduling scheme.

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

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

[0059] The mixed flow shop scheduling problem can be described as follows: a set of jobs contains n jobs, each job has m operations, and for operation j, M... j 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 AGVs. The model satisfies the following assumptions:

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

[0061] Each machine can only process one step;

[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 must not be interrupted;

[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 AGV to load and unload workpieces is known and fixed.

[0067] Each machine needs to be set with a timer before processing, depending on the order of the workpiece to be processed and the previous workpiece.

[0068] All processing machines and AGVs had no downtime or maintenance.

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

[0070] Secondly, the encoding and decoding scheme is employed. A two-layer encoding method is used to represent the scheduling scheme. The first layer reflects the priority of workpiece transportation tasks, while the second layer determines the machine selection for each workpiece in each process step. This design not only enhances information representation capabilities but also ensures that the AGV can quickly respond to pending transportation tasks through priority queue decoding. More importantly, the two-layer encoding method effectively expands the solution space, making the search range of the scheduling scheme broader, thereby increasing the possibility of finding the globally optimal solution. Whenever the AGV is idle, the highest priority task is extracted from the priority queue and allocated, further optimizing the transportation process.

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

[0072] For example, such as Figure 2 The diagram shows a feasible solution encoding example for an HFSP (High-Frequency Automated Guided Vehicle) algorithm with 5 workpieces, 3 processes, and a parallel machine count of {2, 3, 2} for each process. The first-level encoding is [1, 4, 5, 3, 2], indicating that the transport task priorities for workpieces 1 to 5 are 1, 4, 5, 3, 2. The second-level encoding is [2, 2, 1, 1, 2, 4, 3, 3, 5, 5, 7, 6, 6, 7, 6], indicating that workpieces 1 to 5 are assigned to 2, 2, 1, 1, 2 processing machines in the first process, 4, 3, 3, 5, 5 processing machines in the second process, and 7, 6, 6, 7, 6 processing machines in the third process. Based on this encoding, an initial AGV scheduling scheme can be generated.

[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 idle automatic guided vehicles, and then determine the transportation plan of the workpiece for the next process.

[0074] Among them, the parameters of the workpiece transportation plan include the workpiece number job, the number of completed processes stage, the earliest start time st of the task, the workpiece transportation priority priority, the current position src of the workpiece, and the 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 pending transportation tasks. Each pending transportation task can be represented as a six-tuple (job, stage, st, priority, src, dst), which represent the workpiece number job, the number of completed processes stage, the earliest start time st of the task, the workpiece transportation priority priority, the current position src of the workpiece, and the transportation destination dst, respectively. At most one pending transportation task of 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. The smaller the priority value, the higher the priority. The value of priority for each workpiece is determined according to the first-layer coding. 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 an idle state, the task at the top of the heap is taken out from the priority queue for assignment. If there are multiple AGVs in an 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 < m - 1 in the task six-tuple, it indicates that there are still transportation tasks for this workpiece in the future. Therefore, create the transportation task of this workpiece for 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, based on the initial scheduling scheme, the transportation plan for the workpiece in the next process is determined, and then the transportation plan is executed step by step. This allows for the determination of the workpiece processing time based on the initial scheduling scheme. The specific process includes:

[0078] The workpiece number in the next process transportation plan is the same as 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 in the current process plus 1; the workpiece transportation priority in the next process transportation plan is the same as 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 automated guided vehicle and the distance between the current position and the transportation destination in the current process transportation plan.

[0079] The earliest start time for the task in the next process transportation plan can be determined through the following steps:

[0080] The label is R v The idle automated guided vehicle arrives at the current location src old The time is:

[0081]

[0082] Among them, TT c,p' R represents v From the current machine M c Move the empty load to src old Corresponding machine M p' Time required, R v Arrive at the terminal machine location dst of the transport mission old The time is:

[0083]

[0084] Among them, LT i TT represents the loading time of the workpiece. p',p R represents v From src old Corresponding machine M p' Move the load to dst old Corresponding machine M p The required time, and the start time of the next process j for the current workpiece:

[0085]

[0086] Among them, UT i Indicates the unloading time of the workpiece, MIT i,pBefore starting machining of the current workpiece, machine tool M p The earliest free time, Indicates machine M p From the previous processed workpiece J i' Switch to processing the current workpiece J i The required setup time, the completion time C of the j-th operation of the current workpiece. i,j for:

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

[0088] Among them, PT i,j Let st represent the processing time of the j-th operation on the current workpiece. Then, the earliest start time of the next operation's transport task is: st new =C i,j .

[0089] Taking the HFSP example again, the transportation task allocation process at a certain point in the middle is as follows: Figure 3 As shown. The current transport task at the top of the stack is (3,1,42,5,1,3). The SAT of AGV 2 is closest to the st of this task, so this transport task is assigned to AGV 2 for execution.

[0090] S104. Construct a parallel multi-population hybrid genetic algorithm with the goal of minimizing the total time for all workpieces to complete processing. Iteratively optimize the initial scheduling scheme by taking the transportation plan of any workpiece in the next process, and obtain the optimal scheduling scheme.

[0091] Initialize the algorithm parameters. The evolutionary computation for each population is handled by a different thread. It is recommended that the population size be close to the number of logical cores on the current computing platform to reduce performance overhead caused by frequent thread scheduling. The mutation probability of individuals in each population is set to the range [P]. m,low ,P m,high Random values ​​within [P], with crossover probabilities taking values ​​within the interval [P]. c,low ,P c,high The random values ​​within the population allow various populations to evolve under different survival environments. Each population has N individuals. The first layer of encoding is obtained by executing a stack shuffling algorithm on sequences 1 to n. In the second layer of encoding, the i+(j-1)×n gene position is 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 in descending order of fitness. The top 10% of individuals are marked as high-quality individuals, 10% to 30% are marked as low-quality individuals, and the rest are considered ordinary individuals. Ordinary individuals undergo crossover and mutation operations, while low-quality individuals undergo random local search, including the following four search operators:

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

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

[0095] Randomly select a process and assign a task to the machine with the shortest processing time in that process;

[0096] Randomly select a process and assign a task to the machine with the fewest processing tasks in that process.

[0097] High-quality individuals perform local search based on the critical path, including 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 priority.

[0102] Each time a local search is performed, a weighted round-robin algorithm is used to select the local search operator. During the population initialization phase, the interval [W] is randomly selected. low W high The integers within the range are used as weights for each operator, allowing different populations to have different preferences for local search operators, thus guiding them to evolve in different directions and enriching population diversity. The composition of the next generation of the population is determined based on a binary tournament and elite retention selection strategy.

[0103] Whenever a population locally converges, the best individual in the current population is added to the global elite set, and best individuals from other populations are obtained from the global elite set and added to the current population, thus enabling the sharing of high-quality genes among various populations. If the best individuals in the global elite set are continuously I gIf no improvement is made, the global convergence condition is reached, the algorithm terminates, and the optimal individual is the final solution.

[0104] S105. The automated guided vehicle is scheduled according to the optimal scheduling scheme.

[0105] Finally, the optimal scheduling scheme can be implemented to complete the scheduling of the automated guided vehicles.

[0106] The above method first encodes the priority of workpiece transportation and the selection of parallel machines for each workpiece in each process. This ensures that AGVs respond quickly to pending transportation tasks through priority, and separately encodes priority and parallel machine selection, effectively expanding the information utilization space and broadening the search range for AGV scheduling schemes. This increases the likelihood of finding the global optimum and further optimizes the transportation process. Secondly, a parallel multi-population hybrid genetic algorithm iteratively optimizes the initial scheduling scheme, effectively improving the scheduling efficiency and flexibility of multi-AGV mixed production lines. It provides intelligent solutions for complex scheduling needs, demonstrating significant application value in improving production planning, optimizing resource utilization, shortening production cycles, and reducing production costs, thus maximizing production efficiency.

[0107] Secondly, the present invention also provides an automated guided vehicle (AGV) scheduling device, such as... Figure 5 As shown, it includes:

[0108] The acquisition module 501 is used to acquire the number of workpieces transported by the automated 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 workpiece transportation and the selection of parallel machines for 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 scheme.

[0110] The determination module 503 determines the transportation plan of the workpiece in the current process based on the initial scheduling scheme, and determines the state of the parallel machine corresponding to the transportation plan of the current process and the position of the automated 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 for all workpieces to complete processing. Based on the transportation plan of any workpiece in the next process, the initial scheduling scheme is iteratively optimized to obtain the optimal scheduling scheme.

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

[0113] Using the aforementioned device, the priority of workpiece transportation and the selection of parallel machines for each workpiece in each process are first encoded. This ensures that the AGV responds quickly to pending transportation tasks through priority, and separately encodes the priority and parallel machine selection, effectively expanding the information utilization space and broadening the search range for AGV scheduling schemes. This increases the likelihood of finding the global optimum and further optimizes the transportation process. Secondly, a parallel multi-population hybrid genetic algorithm iteratively optimizes the initial scheduling scheme, effectively improving the scheduling efficiency and flexibility of multi-AGV mixed production lines. It provides intelligent solutions for complex scheduling needs, demonstrating significant application value in improving production planning, optimizing resource utilization, shortening production cycles, and reducing production costs, thus maximizing production efficiency.

[0114] The present invention also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 The steps of the provided automated guided vehicle (AGV) scheduling method.

[0115] This invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, memory, and non-volatile memory, and may also include other hardware required for various operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then executes it to achieve the above-mentioned functions. Figure 1 The steps of the provided automated guided vehicle (AGV) scheduling method.

[0116] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0118] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0119] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0120] It should be noted that the above-described specific embodiments enable those skilled in the art to more fully understand the present invention, but do not limit the present 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 modifications or equivalent substitutions can still be made to the present invention; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are covered within the protection scope of the patent of the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for scheduling automated guided vehicles, characterized in that, The method includes: Obtain the number of workpieces transported by the automated guided vehicle, the number of processes, and the number of parallel machines processing the workpieces; Based on the number of workpieces, the number of processes, and the number of parallel machines, the priority of workpiece transportation and the selection of parallel machines for each workpiece in each process are encoded to obtain an initial scheduling scheme. For any workpiece, the transportation plan for the workpiece in the current process is determined based on the initial scheduling scheme, and the state of the parallel machine corresponding to the transportation plan of the current process and the position of the automated guided vehicle in the idle state are determined, thereby determining the transportation plan for the workpiece in the next process. A parallel multi-population hybrid genetic algorithm is constructed with the goal of minimizing the total time for all workpieces to complete processing. The initial scheduling scheme is iteratively optimized by using the transportation plan of any workpiece in the next process to obtain the optimal scheduling scheme. The automated guided vehicles are scheduled according to the optimal scheduling scheme.

2. The automated guided vehicle scheduling method according to claim 1, characterized in that, Based on the number of workpieces, the number of processes, and the number of parallel machines, the priority of workpiece transportation and the selection of parallel machines for each workpiece in each process are encoded, including: 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 as follows: 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. The priority encoding sets a priority for each workpiece. The second layer of 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)×n-th gene bit of the encoding represents the machine selection for the i-th workpiece in the j-th process, where 1≤j≤m.

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

4. The automated guided vehicle scheduling method according to claim 3, characterized in that, The process of determining the transportation plan for the workpiece in the next process includes: The workpiece number in the transportation plan for the next process is the same as the workpiece number in the transportation plan for the current process. The number of completed processes in the transportation plan for the next process is the number of completed processes in the current process plus 1; The workpiece transportation priority in the transportation plan of the next process is the same as the workpiece transportation priority in the transportation plan of the current process. The current location of the workpiece in the transportation plan for the next process is the transportation destination in the transportation plan for the current process; The transportation destination in the transportation plan for the next process is determined by the second-level coding; The earliest start 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 current process transportation plan.

5. The automated guided vehicle scheduling method according to claim 4, characterized in that, The earliest start 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 label is R v The idle automated guided vehicle arrives at the current location src old The time is: Among them, TT c，p′ R represents v From the current machine M c Move the empty load to src old Corresponding machine M p′ Time required, R v Arrive at the terminal machine location dst of the transport mission old The time is: Among them, LT i TT represents the loading time of the workpiece. p′，p R represents v From src old Corresponding machine M p′ Move the load to dst old Corresponding machine M p The required time, and the start time of the next process j for the current workpiece: Among them, UT i Indicates the unloading time of the workpiece, MIT i，p Before starting machining of the current workpiece, machine tool M p The earliest free time, Indicates machine M p From the previous processed workpiece J i′ Switch to processing the current workpiece J i The required setup time, the completion time C of the j-th operation of the current workpiece. i，j for: C i,j =S i,j +PT i,j ; Among them, PT i,j Let st represent the processing time of the j-th operation on the current workpiece. Then, the earliest start time of the next operation's transport task is: st new =C i,j .

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

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method described in any one of claims 1 to 5.

8. A computer device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in any one of claims 1 to 5.

Images