Material automatic handling task scheduling method and system based on cooperation of MES and AGV

Abstract

The present application relates to the technical field of automatic guided vehicle scheduling, and proposes a material automatic handling task scheduling method and system coordinated by MES and AGV, comprising: constructing a rolling horizon dynamic scheduling model of AGV with the latest start time as a hard constraint condition; at each rescheduling time, running the rolling horizon dynamic scheduling model, calculating the marginal cost of inserting an initial handling task into different positions of the current execution task sequence of AGV, and generating an insertion task allocation scheme of idle AGV; converting the insertion task allocation scheme into handling instructions and issuing the handling instructions to the corresponding AGV for execution, and synchronously feeding back the expected arrival time to MES; when a preset trigger condition is monitored, taking the current time as a new rescheduling time, re-running the rolling horizon dynamic scheduling model for the unexecuted initial handling task to generate an updated allocation scheme, converting the updated allocation scheme into updated handling instructions and issuing the updated handling instructions to the corresponding AGV, and feeding back the updated expected arrival time to MES. The present application can improve the timeliness of material distribution.

Description

Technical Field

[0001] This invention relates to a method and system for scheduling automated material handling tasks in collaboration between MES and AGV, belonging to the field of automated guided vehicle scheduling technology. Background Technology

[0002] Automated material handling (MES) in collaboration with AGVs refers to the Manufacturing Execution System (MES) automatically generating handling instructions based on production cycle time and work order requirements, directly driving AGVs to complete a closed-loop process of material retrieval, placement, and cross-workstation transfer. Its significance lies in breaking down information silos between production scheduling and underlying logistics execution, achieving precise matching of when materials are needed and when they are delivered. This avoids delivery delays or material accumulation at the line caused by traditional manual scheduling, and ensures that AGVs no longer run aimlessly, thereby significantly reducing material waiting time and warehouse space usage, maintaining the continuous and uniform operation of the production line.

[0003] Automated material handling in collaboration between MES and AGV typically relies on manual coordination based on MES work orders, followed by manual calls or triggering of AGVs to perform handling. This approach is static and passive, with AGVs acting merely as blind execution tools, unable to optimize tasks globally or dynamically insert orders based on their own power and real-time location. In the event of equipment malfunctions or emergency order insertions, the established task sequence cannot be reconstructed in time, easily leading to material shortages at the line, line stoppages, and vehicles running idly in clusters. Summary of the Invention

[0004] This invention provides a method and system for scheduling automated material handling tasks in collaboration between MES and AGV, the main purpose of which is to improve the timeliness of material delivery.

[0005] To achieve the above objectives, the present invention provides a method for scheduling automated material handling tasks in collaboration between MES and AGV, comprising: Based on the production work orders issued by MES, the initial handling task of the objects to be moved is generated. Obtain the real-time topology map of the workshop and the real-time status data of the AGVs, and construct the rolling time-domain dynamic scheduling model of the AGVs with the latest start time as a hard constraint. At each scheduling moment, the rolling time-domain dynamic scheduling model is run. Based on the real-time topology map of the workshop and the real-time status data, the marginal cost of inserting the initial handling task into different positions of the current execution task sequence of the AGV is calculated. Combined with the joint optimization objective, an insertion task allocation scheme for idle AGVs is generated. The inserted task allocation scheme is converted into a handling instruction and issued to the corresponding AGV for execution, and the estimated delivery time is simultaneously fed back to the MES. During the execution of the transport instruction, when a preset trigger condition is detected, the current time is taken as the new rescheduling time. The rolling time-domain dynamic scheduling model is rerun for the initial transport task that has not been executed to generate an updated allocation scheme. The updated allocation scheme is converted into an updated transport instruction and sent to the corresponding AGV. The updated estimated delivery time is fed back to the MES.

[0006] Optionally, using the latest start time as a hard constraint, a rolling time-domain dynamic scheduling model for the AGV is constructed, including: The latest start time is taken as an insurmountable hard constraint. Construct an objective function for the empty driving distance of the AGV from its current location to the material storage location. A task delay time objective function is constructed based on the difference between the estimated task completion time and the sum of the latest start time and the estimated handling time. The estimated handling time is determined based on the real-time topology map of the workshop and the AGV running speed. By combining the hard constraints, the empty driving distance objective function, and the task delay time objective function, a rolling time-domain dynamic scheduling model for the AGV is established.

[0007] Optionally, by combining the hard constraints, the empty driving distance objective function, and the task delay time objective function, a rolling time-domain dynamic scheduling model for the AGV is established, including: The objective function of empty driving distance and the objective function of task delay time are dimensionless to obtain normalized empty driving index and normalized delay index. Based on a preset weight allocation strategy, the normalized empty run index and the normalized delay index are weighted and summed to construct a joint optimization objective. The hard constraints are introduced as penalty terms into the joint optimization objective to establish the AGV rolling time-domain dynamic scheduling model.

[0008] Optionally, based on the production work order issued by MES, an initial handling task for the object to be handled is generated, including: Analyze the production work orders issued by MES to extract the material demand time window, material storage location and target workstation information of the materials to be transported in the production work orders; Based on the material demand time window and the preset logistics lead time, calculate the latest start time for the delivery of the materials to be transported. Based on the material storage location, target workstation information, and latest start time of the material to be moved, an initial moving task for the material to be moved is generated.

[0009] Optionally, based on the real-time topology map of the workshop and the real-time status data, the marginal cost of inserting the initial handling task into different positions of the current execution task sequence of the AGV is calculated, including: Traverse the available insertion positions of the idle AGVs in the currently executing task sequence; For the insertable position, the rolling time-domain dynamic scheduling model is used to calculate the empty driving distance increment and task delay time increment caused by inserting the initial handling task into the insertable position based on the workshop real-time topology map and the real-time status data. The weighted sum of the empty driving distance increment and the task delay time increment is determined as the marginal cost.

[0010] Optionally, by combining the joint optimization objective, an insert-type task allocation scheme for idle AGVs is generated, including: Based on the joint optimization objective, determine the empty driving distance weight coefficient and the task delay time weight coefficient in the weighted sum of the empty driving distance increment and the task delay time increment; Based on the empty driving distance weighting coefficient and the task delay time weighting coefficient, the comprehensive marginal cost of the idle AGV at the insertable position is calculated, and the insertable position with the minimum comprehensive marginal cost is determined as the target insertion position of the corresponding AGV. Among the idle AGVs, the AGV with the lowest overall marginal cost at the target insertion position is identified as the target AGV; The initial handling task is assigned to the target insertion position of the target AGV to generate an insertion task allocation scheme for the idle AGV.

[0011] Optionally, the insert-based task allocation scheme is converted into a handling instruction and issued to the corresponding AGV for execution, and the estimated delivery time is simultaneously fed back to the MES, including: The target sequence path of the corresponding target AGV in the inserted task allocation scheme is analyzed, and the node coordinates and action logic of the target sequence path are extracted to generate the transport instruction; The travel time of the target AGV on each road segment is calculated based on the real-time topology map of the workshop, so as to calculate the delivery time of the target AGV to the transport destination, which is used as the estimated delivery time. The transport command is sent to the onboard control system of the target AGV for execution, and the estimated delivery time is synchronously transmitted back to the MES through the workshop communication network.

[0012] Optionally, calculating the travel time of the target AGV on each road segment based on the real-time topology map of the workshop includes: determining the path length of each road segment traversed by the target AGV according to the real-time topology map of the workshop, obtaining the real-time congestion status and speed limit parameters of each road segment, and calculating the travel time of the target AGV on each road segment in combination with the current operating speed of the target AGV.

[0013] Optionally, the preset trigger conditions include any one of the following: the arrival of a new handling task, AGV malfunction, or workstation congestion.

[0014] To address the aforementioned problems, this invention also provides a material handling task scheduling system that integrates MES and AGV, the system comprising: The work order parsing module is used to generate the initial handling task for the object to be moved based on the production work order issued by MES. The scheduling model construction module is used to obtain the real-time topology map of the workshop and the real-time status data of the AGV, and construct the rolling time-domain dynamic scheduling model of the AGV with the latest start time as a hard constraint. The allocation scheme generation module is used to run the rolling time-domain dynamic scheduling model at each scheduling time, calculate the marginal cost of inserting the initial handling task into different positions of the current execution task sequence of the AGV based on the real-time topology map of the workshop and the real-time status data, and generate an insertion task allocation scheme for the idle AGV by combining the joint optimization objective. The instruction execution feedback module is used to convert the insert-type task allocation scheme into a handling instruction and issue it to the corresponding AGV for execution, and simultaneously feed back the estimated delivery time to the MES. The dynamic rescheduling module is used to, during the execution of the transport instruction, when a preset trigger condition is detected, take the current time as the new rescheduling time, rerun the rolling time-domain dynamic scheduling model to generate an updated allocation scheme for the initial transport task that has not been executed, convert the updated allocation scheme into an updated transport instruction and issue it to the corresponding AGV, and feed back the updated estimated delivery time to the MES.

[0015] This invention constructs a rolling time-domain dynamic scheduling model, combining a real-time workshop topology map with AGV status data. Using the latest start time as a hard constraint, it achieves precise and efficient allocation of production work order tasks, solving the problem that traditional static scheduling cannot adapt to dynamic environments. By calculating the marginal cost of inserting tasks into different positions and jointly optimizing them, it significantly reduces the total cost of multi-AGV collaborative operation and improves equipment utilization. At the same time, by triggering a real-time rescheduling mechanism through preset trigger conditions, it can quickly respond to sudden disturbances in the workshop, dynamically adjust unexecuted tasks, effectively ensure the timeliness of material delivery and the coordination of production rhythm, and improve the overall robustness of the intelligent factory logistics system. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a method for scheduling automated material handling tasks using MES and AGV collaboration, provided in an embodiment of the present invention. Figure 2 This is a system structure diagram of a material handling task scheduling system that integrates MES and AGV according to an embodiment of the present invention. Figure 3 This is a functional block diagram of a material handling task scheduling system that integrates MES and AGV according to an embodiment of the present invention. Figure 4 This is a schematic diagram of a computer device for a material handling task scheduling method that integrates MES and AGV in collaboration, according to an embodiment of the present invention. The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0018] This application provides a method for scheduling automated material handling tasks in collaboration between MES and AGV. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for scheduling automated material handling tasks in collaboration between MES and AGV can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0019] Reference Figure 1 The diagram shown is a flowchart illustrating a material handling task scheduling method integrating MES and AGV according to an embodiment of the present invention. In this embodiment, the material handling task scheduling method integrating MES and AGV includes: S1. Based on the production work order issued by MES, generate the initial handling task for the object to be moved.

[0020] This invention generates initial handling tasks for objects to be moved based on production work orders issued by MES. This ensures that the handling tasks are strictly controlled within the production cycle, preventing the objects to be moved from accumulating on the line side due to early arrival or causing line stoppages due to late arrival.

[0021] Specifically, the initial handling task for the object to be handled, generated from the production work order issued by MES, includes: Analyze the production work orders issued by MES to extract the material demand time window, material storage location and target workstation information of the materials to be transported in the production work orders; Based on the material demand time window and the preset logistics lead time, calculate the latest start time for the delivery of the materials to be transported. Based on the material storage location, target workstation information, and latest start time of the material to be moved, an initial moving task for the material to be moved is generated.

[0022] Wherein, MES refers to the Manufacturing Execution System responsible for production planning, scheduling, and underlying data integration; the materials to be transported refer to raw materials, work-in-process, or semi-finished products to be transferred on the production line; the production order refers to the production execution instruction issued by MES that includes specific production batches and material consumption requirements; the material demand time window refers to the start and end time range within which the target workstation is allowed to deliver materials; the material storage location refers to the starting point for picking up goods in the buffer zone or storage area within the workshop; the target workstation refers to the end point for material consumption on the production line; the preset logistics lead time is the lead time calculated based on the workshop's historical handling time and safety buffer margin; the latest start time refers to the critical moment for starting handling to ensure that materials are delivered no later than the demand time window; the initial handling task refers to a handling unit to be scheduled, converted from a production order, containing the identity of the materials to be transported, the start and end locations, and the latest start time, and which has not yet been assigned to an AGV for execution.

[0023] S2. Obtain the real-time topology map of the workshop and the real-time status data of the AGV, and construct the rolling time-domain dynamic scheduling model of the AGV with the latest start time as a hard constraint.

[0024] It should be explained that the real-time workshop topology map refers to a workshop road network map that includes the locations of workstations, storage points, and the connectivity of passageways; the AGV refers to an intelligent mobile carrier that performs material handling tasks; and the real-time status data refers to a set of dynamic parameters that characterize the current operating status of the AGV, including its current coordinate position, running speed, cargo status, and working mode.

[0025] This invention constructs a rolling time-domain dynamic scheduling model for the AGV by using the latest start time as a hard constraint to ensure production cycle time. Combined with rolling time-domain dynamic optimization of empty runs and delays, it achieves a synergistic improvement in task timeliness and handling efficiency.

[0026] Specifically, the step of constructing the rolling time-domain dynamic scheduling model for the AGV, using the latest start time as a hard constraint, includes: The latest start time is taken as an insurmountable hard constraint. Construct an objective function for the empty driving distance of the AGV from its current location to the material storage location. A task delay time objective function is constructed based on the difference between the estimated task completion time and the sum of the latest start time and the estimated handling time. The estimated handling time is determined based on the real-time topology map of the workshop and the AGV running speed. By combining the hard constraints, the empty driving distance objective function, and the task delay time objective function, a rolling time-domain dynamic scheduling model for the AGV is established.

[0027] Among them, the insurmountable hard constraint condition refers to the mandatory boundary that the task start time is strictly prohibited from being later than the latest start time; the estimated handling time refers to the time taken to travel from the pickup point to the target workstation with the goods loaded, calculated based on the map path and vehicle speed; the estimated task completion time refers to the time when the materials are delivered to the target workstation, calculated based on the AGV status and road conditions at the current scheduling moment; the task delay time objective function refers to the penalty function that quantifies the extent to which the estimated task completion time exceeds the allowable delivery deadline jointly defined by the latest start time and the handling time; and the empty driving distance objective function refers to the mathematical expression that quantifies the cost of the AGV traveling from its current idle position to the pickup point without a load.

[0028] Furthermore, the step of establishing a rolling time-domain dynamic scheduling model for the AGV by combining the hard constraints, the empty driving distance objective function, and the task delay time objective function includes: The objective function of empty driving distance and the objective function of task delay time are dimensionless to obtain normalized empty driving index and normalized delay index. Based on a preset weight allocation strategy, the normalized empty run index and the normalized delay index are weighted and summed to construct a joint optimization objective. The hard constraints are introduced as penalty terms into the joint optimization objective to establish the AGV rolling time-domain dynamic scheduling model.

[0029] The dimensionless processing refers to the normalization operation that eliminates the difference between the physical dimensions of distance and time; the weighted summation refers to the method of weighting and superimposing the normalized empty runs and delay indicators into a single evaluation value based on the production rhythm preference; the joint optimization objective refers to the comprehensive optimization direction that takes into account both reducing ineffective empty runs and strictly adhering to delivery timeliness; and the rolling time-domain dynamic scheduling model refers to the mathematical framework that integrates the forced time boundary and the comprehensive optimization direction, and iteratively solves the target task sequence within a continuous time window as execution progresses.

[0030] Furthermore, the objective function expression of the rolling time-domain dynamic scheduling model is:

[0031] in, This represents the objective function of the rolling time-domain dynamic scheduling model. Represents the function to be minimized. This represents the total number of tasks to be scheduled in the rolling time-domain dynamic scheduling model. Indicates the first One task to be scheduled Indicates the first The normalized empty running distance index for tasks to be scheduled. Indicates the first The normalized delay time metric for each task to be scheduled. Indicates the first Penalty terms for hard constraints of each task to be scheduled.

[0032] S3. At each scheduling moment, run the rolling time-domain dynamic scheduling model, calculate the marginal cost of inserting the initial handling task into different positions of the current execution task sequence of the AGV based on the real-time topology map of the workshop and the real-time status data, and generate an insertion task allocation scheme for idle AGVs by combining the joint optimization objective.

[0033] This invention achieves dynamic response to sudden tasks and coordinated optimization of global efficiency by running the rolling time-domain dynamic scheduling model at each scheduling moment, and calculating the marginal cost of inserting the initial handling task into different positions of the current execution task sequence of the AGV based on the real-time topology map of the workshop and the real-time status data.

[0034] In detail, the step of calculating the marginal cost of inserting the initial handling task into different positions of the AGV's current execution task sequence based on the real-time topology map of the workshop and the real-time status data includes: Traverse the available insertion positions of the idle AGVs in the currently executing task sequence; For the insertable position, the rolling time-domain dynamic scheduling model is used to calculate the empty driving distance increment and task delay time increment caused by inserting the initial handling task into the insertable position based on the workshop real-time topology map and the real-time status data. The weighted sum of the empty driving distance increment and the task delay time increment is determined as the marginal cost.

[0035] Wherein, the idle AGV refers to an automated guided vehicle currently in a standby state that can be assigned new tasks; the currently executing task sequence is the queue of tasks that the AGV has accepted and is executing in sequence; the insertable position is the insertion point in the sequence that does not violate scheduling constraints; the real-time topology map of the workshop contains dynamic geographical information such as workstations and passageways; the real-time status data reflects dynamic information such as the current coordinates and loading status of the AGV; the empty driving distance increment is the additional unloaded driving distance after inserting a task; the task delay time increment is the increase in the delay of task completion time after inserting a task; and the marginal cost is the weighted sum of these two types of increments, representing the impact of inserting a task on the total cost.

[0036] This invention generates an insert-type task allocation scheme for idle AGVs by combining joint optimization objectives, which can effectively balance task execution efficiency and overall system resource utilization, and achieve efficient scheduling in dynamic environments.

[0037] In detail, the method of generating an insertable task allocation scheme for idle AGVs by combining the joint optimization objective includes: Based on the joint optimization objective, determine the empty driving distance weight coefficient and the task delay time weight coefficient in the weighted sum of the empty driving distance increment and the task delay time increment; Based on the empty driving distance weighting coefficient and the task delay time weighting coefficient, the comprehensive marginal cost of the idle AGV at the insertable position is calculated, and the insertable position with the minimum comprehensive marginal cost is determined as the target insertion position of the corresponding AGV. Among the idle AGVs, the AGV with the lowest overall marginal cost at the target insertion position is identified as the target AGV; The initial handling task is assigned to the target insertion position of the target AGV to generate an insertion task allocation scheme for the idle AGV.

[0038] The empty-run distance weighting coefficient refers to the degree of preference for the empty-run distance increment in the joint optimization objective. A larger value indicates that the scheduling strategy focuses more on reducing the unloaded travel of AGVs. The task delay time weighting coefficient refers to the degree of preference for the task delay time increment in the joint optimization objective. A larger value indicates that the scheduling strategy focuses more on ensuring the timeliness of tasks. The comprehensive marginal cost refers to the total cost increase obtained by weighting and summing the empty-run distance increment and the task delay time increment using the above weighting coefficients. This is used to uniformly quantify the impact of inserting new tasks on the overall system performance. The target insertion position refers to the insertion position with the minimum comprehensive marginal cost in the current task sequence of a single AGV. The target AGV refers to the automated guided vehicle with the lowest comprehensive marginal cost at its target insertion position among all idle AGVs. The insertion-type task allocation scheme refers to the allocation result of the specific AGVs to be executed and their task insertion positions determined for the initial handling task.

[0039] S4. The inserted task allocation scheme is converted into a handling instruction and issued to the corresponding AGV for execution, and the estimated delivery time is simultaneously fed back to the MES.

[0040] This invention converts the insertable task allocation scheme into a handling instruction and issues it to the corresponding AGV for execution, while simultaneously feeding back the estimated delivery time to the MES.

[0041] Specifically, the step of converting the plug-in task allocation scheme into a handling instruction and issuing it to the corresponding AGV for execution, and simultaneously feeding back the estimated delivery time to the MES, includes: The target sequence path of the corresponding target AGV in the inserted task allocation scheme is analyzed, and the node coordinates and action logic of the target sequence path are extracted to generate the transport instruction; The travel time of the target AGV on each road segment is calculated based on the real-time topology map of the workshop, so as to calculate the delivery time of the target AGV to the transport destination, which is used as the estimated delivery time. The transport command is sent to the onboard control system of the target AGV for execution, and the estimated delivery time is synchronously transmitted back to the MES through the workshop communication network.

[0042] Wherein, the target sequence path refers to the complete task execution route after the target AGV inserts a new task; the node coordinates refer to the spatial location of each waypoint and stop point in the path; the action logic refers to the type of operation performed by the AGV at the corresponding node, such as picking up goods, placing goods, or waiting; the handling instruction refers to the control data packet containing node coordinates and action logic; the travel time of each segment refers to the travel time between adjacent nodes calculated based on the real-time topology map; the estimated delivery time refers to the arrival time at the destination obtained by combining the current time and the cumulative travel time; the vehicle control system refers to the action control unit of the AGV's underlying execution hardware; and the workshop communication network refers to the communication link between the scheduling system and the AGV and MES for transmitting data.

[0043] Furthermore, the step of calculating the travel time of the target AGV on each road segment based on the real-time topology map of the workshop includes: determining the path length of each road segment traversed by the target AGV according to the real-time topology map of the workshop, obtaining the real-time congestion status and speed limit parameters of each road segment, and calculating the travel time of the target AGV on each road segment in combination with the current operating speed of the target AGV.

[0044] The path length refers to the actual travel distance between adjacent nodes on the map, the real-time congestion status refers to the number of AGVs currently traveling in the road segment and the queuing situation, and the speed limit parameter refers to the maximum allowable travel speed of the road segment.

[0045] S5. During the execution of the transport instruction, when a preset trigger condition is detected, the current time is taken as the new rescheduling time. The rolling time-domain dynamic scheduling model is rerun for the initial transport task that has not been executed to generate an updated allocation scheme. The updated allocation scheme is converted into an updated transport instruction and sent to the corresponding AGV. The updated estimated delivery time is fed back to the MES.

[0046] This invention ensures the scheduling scheme's dynamic response capability to sudden disturbances in the workshop by re-running the rolling time-domain dynamic scheduling model to generate an updated allocation scheme for the unexecuted initial handling tasks when a preset trigger condition is detected during the execution of the handling instruction, using the current time as the new rescheduling time. This achieves global rolling optimization of unexecuted tasks to maintain efficient system operation.

[0047] Specifically, the preset triggering conditions include any one of the following: the arrival of a new handling task, AGV malfunction, or workstation congestion.

[0048] It should be explained that the updated allocation scheme refers to the task matching and sorting results generated after re-optimizing the unexecuted tasks based on the new rescheduling time and AGV; the updated estimated delivery time refers to the time to reach the transportation destination recalculated according to the updated allocation scheme.

[0049] See Figure 2 The diagram shown is a system architecture diagram of a material handling task scheduling system that integrates MES and AGV according to an embodiment of the present invention. The system architecture diagram illustrates the core architecture of the material handling task scheduling system that integrates MES and AGV. MES acts as the source of production execution instructions, generating initial handling tasks through the work order parsing module and inputting them into the rolling time-domain dynamic scheduling model. The rolling time-domain dynamic scheduling model simultaneously receives real-time workshop topology map and real-time AGV status data as scheduling basis, outputs an insert-type task allocation scheme, assigns it to the target AGV, and executes it through the vehicle control system. At the same time, it feeds back the estimated delivery time to MES. The dynamic rescheduling module monitors preset trigger conditions and triggers rescheduling during the execution of handling instructions, improving the system's dynamic response capability to sudden disturbances and maintaining global rolling optimization of unexecuted tasks.

[0050] like Figure 3 The diagram shown is a functional block diagram of the material handling task scheduling system that integrates MES and AGV in accordance with the present invention.

[0051] The MES and AGV collaborative material handling task scheduling system 300 of this invention can be installed in an electronic device. Depending on the functions implemented, the MES and AGV collaborative material handling task scheduling system includes a work order parsing module 301, a scheduling model construction module 302, an allocation scheme generation module 303, an instruction execution feedback module 304, and a dynamic rescheduling module 305. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0052] In this embodiment of the invention, the functions of each module / unit are as follows: The work order parsing module 301 is used to generate the initial handling task of the object to be handled based on the production work order issued by MES. The scheduling model construction module 302 is used to obtain the real-time topology map of the workshop and the real-time status data of the AGV, and construct the rolling time-domain dynamic scheduling model of the AGV with the latest start time as a hard constraint. The allocation scheme generation module 303 is used to run the rolling time-domain dynamic scheduling model at each scheduling moment, calculate the marginal cost of inserting the initial handling task into different positions of the current execution task sequence of the AGV based on the workshop real-time topology map and the real-time status data, and generate an insertion task allocation scheme for the idle AGV by combining the joint optimization objective. The instruction execution feedback module 304 is used to convert the insert-type task allocation scheme into a handling instruction and send it to the corresponding AGV for execution, and simultaneously feed back the estimated delivery time to the MES. The dynamic rescheduling module 305 is used to, during the execution of the transport instruction, when a preset trigger condition is detected, take the current time as the new rescheduling time, rerun the rolling time-domain dynamic scheduling model to generate an updated allocation scheme for the initial transport task that has not been executed, convert the updated allocation scheme into an updated transport instruction and send it to the corresponding AGV, and feed back the updated estimated delivery time to the MES.

[0053] In detail, the modules in the MES and AGV collaborative material handling task scheduling system 300 described in this embodiment of the invention adopt the same approach as described above. Figure 1 The method described above uses the same technical means as the MES and AGV collaborative material handling task scheduling method, and can produce the same technical effect, so it will not be repeated here.

[0054] In one embodiment, a computer device is provided, which may be a server or a client, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used for communication with external clients via a network connection. When the computer program is executed by the processor, it implements the functions or steps of the MES and AGV collaborative material handling task scheduling method on the server or client side.

[0055] In one embodiment, 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 executes the computer program to perform the following steps: The work order parsing module is used to generate the initial handling task for the object to be moved based on the production work order issued by MES. The scheduling model construction module is used to obtain the real-time topology map of the workshop and the real-time status data of the AGV, and construct the rolling time-domain dynamic scheduling model of the AGV with the latest start time as a hard constraint. The allocation scheme generation module is used to run the rolling time-domain dynamic scheduling model at each scheduling time, calculate the marginal cost of inserting the initial handling task into different positions of the current execution task sequence of the AGV based on the real-time topology map of the workshop and the real-time status data, and generate an insertion task allocation scheme for the idle AGV by combining the joint optimization objective. The instruction execution feedback module is used to convert the plug-in task allocation scheme into a handling instruction and send it to the corresponding AGV for execution, and simultaneously feed back the estimated delivery time to the MES. The dynamic rescheduling module is used to, during the execution of the transport instruction, when a preset trigger condition is detected, take the current time as the new rescheduling time, rerun the rolling time-domain dynamic scheduling model to generate an updated allocation scheme for the initial transport task that has not been executed, convert the updated allocation scheme into an updated transport instruction and issue it to the corresponding AGV, and feed back the updated estimated delivery time to the MES.

[0056] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: The work order parsing module is used to generate the initial handling task for the object to be moved based on the production work order issued by MES. The scheduling model construction module is used to obtain the real-time topology map of the workshop and the real-time status data of the AGV, and construct the rolling time-domain dynamic scheduling model of the AGV with the latest start time as a hard constraint. The allocation scheme generation module is used to run the rolling time-domain dynamic scheduling model at each scheduling time, calculate the marginal cost of inserting the initial handling task into different positions of the current execution task sequence of the AGV based on the real-time topology map of the workshop and the real-time status data, and generate an insertion task allocation scheme for the idle AGV by combining the joint optimization objective. The instruction execution feedback module is used to convert the plug-in task allocation scheme into a handling instruction and send it to the corresponding AGV for execution, and simultaneously feed back the estimated delivery time to the MES. The dynamic rescheduling module is used to, during the execution of the transport instruction, when a preset trigger condition is detected, take the current time as the new rescheduling time, rerun the rolling time-domain dynamic scheduling model to generate an updated allocation scheme for the initial transport task that has not been executed, convert the updated allocation scheme into an updated transport instruction and issue it to the corresponding AGV, and feed back the updated estimated delivery time to the MES.

[0057] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0058] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0059] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0060] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0061] Finally, it should be noted that in the above embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for scheduling automated material handling tasks in collaboration between MES and AGV, characterized in that, The method includes: Based on the production work orders issued by MES, the initial handling task of the objects to be moved is generated. Obtain the real-time topology map of the workshop and the real-time status data of the AGVs, and construct the rolling time-domain dynamic scheduling model of the AGVs with the latest start time as a hard constraint. At each scheduling moment, the rolling time-domain dynamic scheduling model is run. Based on the real-time topology map of the workshop and the real-time status data, the marginal cost of inserting the initial handling task into different positions of the current execution task sequence of the AGV is calculated. Combined with the joint optimization objective, an insertion task allocation scheme for idle AGVs is generated. The inserted task allocation scheme is converted into a handling instruction and issued to the corresponding AGV for execution, and the estimated delivery time is simultaneously fed back to the MES. During the execution of the transport instruction, when a preset trigger condition is detected, the current time is taken as the new rescheduling time. The rolling time-domain dynamic scheduling model is rerun for the initial transport task that has not been executed to generate an updated allocation scheme. The updated allocation scheme is converted into an updated transport instruction and sent to the corresponding AGV. The updated estimated delivery time is fed back to the MES.

2. The material handling task scheduling method of MES and AGV collaboration as described in claim 1, characterized in that, Using the latest start time as a hard constraint, a rolling time-domain dynamic scheduling model for the AGV is constructed, including: The latest start time is treated as an insurmountable hard constraint. Construct an objective function for the empty travel distance of the AGV from its current location to the material storage location. A task delay time objective function is constructed based on the difference between the estimated task completion time and the sum of the latest start time and the estimated handling time. The estimated handling time is determined based on the real-time topology map of the workshop and the AGV running speed. By combining the hard constraints, the empty driving distance objective function, and the task delay time objective function, a rolling time-domain dynamic scheduling model for the AGV is established.

3. The material handling task scheduling method of MES and AGV collaboration as described in claim 2, characterized in that, Combining the hard constraints, the empty driving distance objective function, and the task delay time objective function, a rolling time-domain dynamic scheduling model for the AGV is established, including: The objective function of empty driving distance and the objective function of task delay time are dimensionless to obtain normalized empty driving index and normalized delay index. Based on a preset weight allocation strategy, the normalized empty run index and the normalized delay index are weighted and summed to construct a joint optimization objective. The hard constraints are introduced as penalty terms into the joint optimization objective to establish the AGV rolling time-domain dynamic scheduling model.

4. The material handling task scheduling method of MES and AGV collaboration as described in claim 1, characterized in that, Based on the production work order issued by MES, the initial handling task for the objects to be handled is generated, including: Analyze the production work orders issued by MES to extract the material demand time window, material storage location and target workstation information of the materials to be transported in the production work orders; Based on the material demand time window and the preset logistics lead time, calculate the latest start time for the delivery of the materials to be transported. Based on the material storage location, target workstation information, and latest start time of the material to be transported, an initial transport task for the material to be transported is generated.

5. The material handling task scheduling method of MES and AGV collaboration as described in claim 1, characterized in that, Based on the real-time topology map of the workshop and the real-time status data, the marginal cost of inserting the initial handling task into different positions of the AGV's current execution task sequence is calculated, including: Traverse the available insertion positions of the idle AGVs in the currently executing task sequence; For the insertable position, the rolling time-domain dynamic scheduling model is used to calculate the empty driving distance increment and task delay time increment caused by inserting the initial handling task into the insertable position based on the workshop real-time topology map and the real-time status data. The weighted sum of the empty driving distance increment and the task delay time increment is determined as the marginal cost.

6. The material handling task scheduling method of MES and AGV collaboration as described in claim 1, characterized in that, Based on the joint optimization objective, an insert-type task allocation scheme for idle AGVs is generated, including: Based on the joint optimization objective, determine the empty driving distance weight coefficient and the task delay time weight coefficient in the weighted sum of the empty driving distance increment and the task delay time increment; Based on the empty driving distance weighting coefficient and the task delay time weighting coefficient, the comprehensive marginal cost of the idle AGV at the insertable position is calculated, and the insertable position with the minimum comprehensive marginal cost is determined as the target insertion position of the corresponding AGV. Among the idle AGVs, the AGV with the lowest overall marginal cost at the target insertion position is identified as the target AGV; The initial handling task is assigned to the target insertion position of the target AGV to generate an insertion-type task allocation scheme for the idle AGV.

7. The material handling task scheduling method of MES and AGV collaboration as described in claim 1, characterized in that, The inserted task allocation scheme is converted into a handling instruction and issued to the corresponding AGV for execution, and the estimated delivery time is simultaneously fed back to the MES, including: The target sequence path of the corresponding target AGV in the inserted task allocation scheme is analyzed, and the node coordinates and action logic of the target sequence path are extracted to generate the transport instruction; The travel time of the target AGV on each road segment is calculated based on the real-time topology map of the workshop, so as to calculate the delivery time of the target AGV to the transport destination, which is used as the estimated delivery time. The transport command is sent to the onboard control system of the target AGV for execution, and the estimated delivery time is synchronously transmitted back to the MES through the workshop communication network.

8. The material handling task scheduling method of MES and AGV collaboration as described in claim 7, characterized in that, The calculation of the travel time of the target AGV on each road segment based on the real-time topology map of the workshop includes: determining the path length of the target AGV through each road segment according to the real-time topology map of the workshop, obtaining the real-time congestion status and speed limit parameters of each road segment, and calculating the travel time of the target AGV on each road segment in combination with the current running speed of the target AGV.

9. The material handling task scheduling method of MES and AGV collaboration as described in claim 1, characterized in that, The preset trigger conditions include any one of the following: the arrival of a new handling task, AGV malfunction, or workstation congestion.

10. A material handling task scheduling system that integrates MES and AGV, characterized in that, The system is used to execute the material handling task scheduling method of MES and AGV collaboration as described in any one of claims 1-9, the system comprising: The work order parsing module is used to generate the initial handling task for the object to be moved based on the production work order issued by MES. The scheduling model construction module is used to obtain the real-time topology map of the workshop and the real-time status data of the AGV, and construct the rolling time-domain dynamic scheduling model of the AGV with the latest start time as a hard constraint. The allocation scheme generation module is used to run the rolling time-domain dynamic scheduling model at each scheduling time, calculate the marginal cost of inserting the initial handling task into different positions of the current execution task sequence of the AGV based on the real-time topology map of the workshop and the real-time status data, and generate an insertion task allocation scheme for the idle AGV by combining the joint optimization objective. The instruction execution feedback module is used to convert the plug-in task allocation scheme into a handling instruction and send it to the corresponding AGV for execution, and simultaneously feed back the estimated delivery time to the MES. The dynamic rescheduling module is used to, during the execution of the transport instruction, when a preset trigger condition is detected, take the current time as the new rescheduling time, rerun the rolling time-domain dynamic scheduling model to generate an updated allocation scheme for the initial transport task that has not been executed, convert the updated allocation scheme into an updated transport instruction and issue it to the corresponding AGV, and feed back the updated estimated delivery time to the MES.

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