A design method of a slab yard trolley dispatching simulation system

Abstract

This invention discloses a design method for a slab warehouse overhead crane scheduling simulation system, relating to the field of overhead crane scheduling technology. The method includes constructing a slab warehouse overhead crane scheduling system architecture and simulation environment; acquiring slab warehouse configuration parameters and scheduling task data; generating basic operational information; constructing multiple types of task allocation algorithms; allocating scheduling tasks to overhead cranes based on the basic operational information using the task allocation algorithms; decomposing the scheduling tasks to obtain multiple sub-task sequences; and mapping the sub-task sequences to the overhead crane's task scheduling list. This invention can select the corresponding algorithm to complete the overhead crane task allocation and decomposition according to the working conditions, adapting to various slab warehouse operating conditions, effectively ensuring the stable operation of overhead cranes. The simulation system can effectively verify the scheduling performance of various algorithms under different working conditions, facilitating the selection of the optimal scheduling algorithm and improving the adaptability of scheduling schemes in various warehouse operation scenarios.

Description

Technical Field

[0001] This invention relates to the field of overhead crane scheduling technology, and in particular to a design method for a slab warehouse overhead crane scheduling simulation system. Background Technology

[0002] In the steel industry's slab transfer process, the slab warehouse is a crucial transit hub connecting the continuous casting and hot rolling processes. The scheduling and operational efficiency of the overhead cranes within the warehouse directly impacts slab turnover speed, production line integration, and overall logistics costs. As steel production gradually shifts towards a high-density, highly collaborative model, the market is placing new demands on the dynamic adaptability of slab warehouse overhead crane scheduling simulation systems.

[0003] In the existing technology, the scheduling of overhead cranes in slab warehouses has applied a variety of scheduling algorithms. Most of the related systems adopt a fixed operating architecture, setting up various solution algorithms independently. The overall operating mode is relatively fixed, and it can only complete the daily task allocation according to a single preset algorithm. It relies on basic programs to realize the automated scheduling of slab transfer and meet the operational needs under normal production scenarios.

[0004] Existing technologies mostly employ single, fixed solution algorithms, with multiple task allocation algorithms operating in isolation, failing to construct a unified scheduling framework that integrates multiple algorithms and allows for flexible switching. Since the production conditions and workload of slab warehouses are constantly changing, fixed algorithms struggle to adapt to diverse operational scenarios and cannot adjust scheduling strategies based on working conditions, thus hindering the full realization of the algorithms' practical value. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a design method for a slab warehouse overhead crane scheduling simulation system, which solves the technical problems of existing technologies, such as the inability to flexibly select scheduling algorithms and insufficient scenario adaptability.

[0006] The technical means employed in this invention are as follows: This invention provides a design method for a slab warehouse overhead crane scheduling simulation system, comprising: Construct the architecture and simulation environment of the slab warehouse overhead crane scheduling system, obtain slab warehouse configuration parameters and scheduling task data, and generate basic operation information; Construct multi-type task allocation algorithms; Based on the basic operational information, the scheduling tasks are allocated to the overhead crane using the task allocation algorithm; the scheduling tasks are decomposed to obtain multiple sub-task sequences, and the sub-task sequences are mapped to the overhead crane's task scheduling list. Based on the subtask sequence and the basic operational information, a priority-based anti-collision mechanism for the same-span overhead crane is constructed. Based on the task allocation algorithm, the subtask sequence, and the anti-collision mechanism for the same-span overhead crane, a dynamic task management mechanism is constructed. Obtain the job data generated by the dynamic task management mechanism and construct a scheduling effect evaluation index system; Based on the slab warehouse crane scheduling system architecture, the task allocation algorithm, the anti-collision mechanism for cranes spanning the same distance, the dynamic task management mechanism, and the scheduling effect evaluation index system, a slab warehouse crane scheduling simulation system is constructed.

[0007] Furthermore, the architecture of the slab warehouse overhead crane scheduling system includes a presentation layer, a business logic layer, and a data layer, with the business logic layer encapsulating a simulation operation interface.

[0008] Furthermore, the construction of multiple types of task allocation algorithms includes: constructing task allocation algorithms including the least number of tasks allocation algorithm, the closest allocation algorithm to the starting point, the closest allocation algorithm to the ending point, the most tasks allocation algorithm, the furthest allocation algorithm from the starting point, the furthest allocation algorithm from the ending point, an adaptive dynamic rule allocation algorithm, a random overhead crane allocation algorithm, and a random selection rule allocation algorithm.

[0009] Furthermore, the calculation formula for the task minimum allocation algorithm is as follows:

[0010]

[0011] in, This represents the number of remaining tasks for the i-th overhead crane. This represents the status information of the i-th overhead crane. This represents the task scheduling list for the i-th overhead crane. This indicates the length of the task scheduling list. This represents the current task index of the i-th overhead crane. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen.

[0012] The calculation formula for the nearest assignment algorithm to the starting point is:

[0013]

[0014] in, This represents the distance from the starting point to the current position of the i-th overhead crane. This represents the real-time position coordinates of the i-th overhead crane at time t. This represents the starting position coordinates of the j-th task. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen.

[0015] The calculation formula for the nearest assignment algorithm to the endpoint is as follows:

[0016]

[0017] in, This represents the distance from the current position of the i-th overhead crane to the destination. This represents the real-time position coordinates of the i-th overhead crane at time t. This represents the coordinates of the endpoint of the j-th task. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen.

[0018] The calculation formula for the algorithm that maximizes the number of tasks is as follows:

[0019]

[0020] in, This represents the number of remaining tasks for the i-th overhead crane. This represents the status information of the i-th overhead crane. This represents the task scheduling list for the i-th overhead crane. This indicates the length of the task scheduling list. This represents the current task index of the i-th overhead crane. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen.

[0021] The calculation formula for the algorithm that assigns the furthest point from the starting point is as follows:

[0022]

[0023] in, This represents the distance from the starting point to the current position of the i-th overhead crane. This represents the real-time position coordinates of the i-th overhead crane at time t. This represents the starting position coordinates of the j-th task. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen.

[0024] The calculation formula for the algorithm that assigns the furthest point from the destination is as follows:

[0025]

[0026] in, This represents the distance from the current position of the i-th overhead crane to the destination. This represents the real-time position coordinates of the i-th overhead crane at time t. This represents the coordinates of the endpoint of the j-th task. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen.

[0027] The calculation formula for the adaptive dynamic rule allocation algorithm is as follows:

[0028]

[0029] in, This represents the normalized number of tasks for the i-th crane. This represents the number of remaining tasks for the i-th overhead crane. This indicates the maximum number of tasks threshold. This represents the normalized distance from the starting point to the current position of the i-th overhead crane. This represents the distance from the starting point to the current position of the i-th overhead crane. This represents the total length of the track across the k-th span. This represents the normalized task execution distance of the j-th task. This represents the coordinates of the starting position of the j-th task. This represents the coordinates of the endpoint of the j-th task. Let represent the adaptive dynamic rule score of the i-th overhead crane. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen.

[0030] The calculation formula for the random crane allocation algorithm is as follows:

[0031] in, Indicates from set The crane selection is carried out randomly. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen.

[0032] The calculation formula for the random selection rule allocation algorithm is as follows:

[0033]

[0034] in, Indicates from set The rules are selected randomly during the process. This represents the set of task allocation rules. This indicates the final task allocation algorithm selected. This indicates that the task allocation algorithm is used. Assign overhead cranes to the j-th task. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen.

[0035] Furthermore, the multiple sub-task sequences include: an unloaded movement sub-task sequence, a lifting sub-task sequence, a heavy-load movement sub-task sequence, and a placement sub-task sequence.

[0036] Furthermore, the priority expression for the priority-based anti-collision mechanism for cranes crossing the same level is:

[0037] in, This represents the overall priority value of the i-th overhead crane. This represents the real-time position coordinates of the i-th overhead crane at time t. Indicates the first The target location coordinates of each sub-task This represents the total length of the track across the k-th span. This indicates the current operating status of the i-th overhead crane. This indicates that the overhead crane is in an idle state with no scheduled tasks. This indicates the crane's unloaded movement status. This indicates the movement state of the overhead crane carrying the slab load. This indicates the crane's operation status of lifting or placing slabs. 1, 2, 3, and 4 represent the priority values ​​corresponding to different operating states of the crane.

[0038] Furthermore, the dynamic task management mechanism includes: retrieving the unreleased task queue within each simulation step, and transferring the scheduled tasks whose task dispatch time is no greater than the current simulation time to the allocable task queue; retrieving the execution task queue within each simulation step, and storing the tasks whose sub-task sequences are all completed into the task completion queue; retrieving the task completion queue within each simulation step, and ending the simulation when all tasks are stored in the task completion queue.

[0039] Furthermore, the step of acquiring the job data generated by the dynamic task management mechanism and constructing a scheduling effect evaluation index system includes: acquiring the job data generated by the dynamic task management mechanism and constructing a scheduling effect evaluation index system. A scheduling performance evaluation index system consisting of mean absolute time deviation, mean delay time, mean lead time, standard deviation of absolute time deviation, standard deviation of delay time, standard deviation of lead time, on-time rate, delay rate, and lead rate.

[0040] Furthermore, the expression for the mean absolute time deviation is:

[0041] in, Represents a set of tasks The mean absolute time deviation of all tasks in the process. Represents a set of tasks The total number of all tasks in the game. This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task.

[0042] The expression for the average delay time is:

[0043] in, Represents a set of tasks China satisfies Average latency of the task Represents a set of tasks China satisfies Total number of tasks This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task.

[0044] The expression for the average lead time is:

[0045] in, Represents a set of tasks China satisfies Average lead time of the task Represents a set of tasks China satisfies Total number of tasks This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task.

[0046] The expression for the standard deviation of the absolute time deviation is:

[0047] in, Represents a set of tasks The standard deviation of the absolute time deviation of all tasks. Represents a set of tasks The total number of all tasks in the game. This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. Represents a set of tasks The average absolute time deviation of all tasks.

[0048] The expression for the standard deviation of the delay time is:

[0049] in, Represents a set of tasks China satisfies The standard deviation of the task delay time, Represents a set of tasks China satisfies Total number of tasks This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. Represents a set of tasks China satisfies The average delay time of the task.

[0050] The expression for the standard deviation of the lead time is:

[0051] in, Represents a set of tasks China satisfies The standard deviation of the task lead time, Represents a set of tasks China satisfies Total number of tasks This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. Represents a set of tasks China satisfies Average lead time of a task.

[0052] The expression for the on-time rate is:

[0053] in, Indicates Task set under time-based threshold On-time performance of tasks Represents a set of tasks The total number of all tasks in the game. This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. This represents the time threshold for judgment, and num represents the total number of tasks that meet the requirements.

[0054] The expression for the latency rate is:

[0055] in, Indicates Task set under time-based threshold Task latency rate Represents a set of tasks The total number of all tasks in the game. This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. This represents the time threshold for judgment, and num represents the total number of tasks that meet the requirements.

[0056] The expression for the advance rate is:

[0057] in, Indicates Task set under time-based threshold Task lead time Represents a set of tasks The total number of all tasks in the game. This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. This represents the time threshold for judgment, and num represents the total number of tasks that meet the requirements.

[0058] Compared with the prior art, the present invention has the following advantages: This invention constructs a system architecture and simulation environment, and sets up multiple types of task allocation algorithms. It can select the corresponding algorithm to complete the allocation and decomposition of overhead crane tasks according to the working conditions, and can adapt to various slab warehouse operating conditions. It sets up a priority-based anti-collision mechanism for overhead cranes spanning the same distance and a dynamic task management mechanism, which can effectively ensure the stable operation of overhead cranes. By collecting operation data and establishing a scheduling effect evaluation index system, the scheduling performance can be quantified. This simulation system can effectively verify the scheduling performance of various algorithms under different working conditions, which facilitates the selection of the best scheduling algorithm and improves the adaptability of scheduling schemes in various warehouse operation scenarios.

[0059] Based on the above reasons, this invention can be widely applied in fields such as overhead crane scheduling. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 A flowchart illustrating the design method of a slab warehouse overhead crane scheduling simulation system; Figure 2 This is a diagram showing the overall architecture of the overhead crane scheduling system for the slab warehouse. Figure 3 A schematic diagram of the simulation environment layout for a multi-span, multi-crane slab warehouse; Figure 4 A schematic diagram of the decision-making process for various task allocation algorithms; Figure 5 This is a flowchart illustrating the task breakdown process; Figure 6 This is a schematic diagram of the anti-collision mechanism for overhead cranes spanning the same distance. Figure 7 This is a schematic diagram of a dynamic task management mechanism; Figure 8 A structural diagram of the scheduling performance evaluation index system; Figure 9 This is a schematic diagram of the functional modules of the overhead crane scheduling simulation system. Detailed Implementation

[0062] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0063] It should be noted that the terms "comprising" and "having" and any variations thereof in this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0064] The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0065] Please see Figure 1 , Figure 1 This is a flowchart illustrating the design method of a slab warehouse overhead crane scheduling simulation system.

[0066] This application provides a design method for a slab warehouse overhead crane scheduling simulation system, including the following steps: Step 101: Construct the slab warehouse overhead crane scheduling system architecture and simulation environment, obtain slab warehouse configuration parameters and scheduling task data, and generate basic operational information. Through the constructed slab warehouse overhead crane scheduling system architecture and simulation environment, and the basic operational information generated from the slab warehouse configuration parameters and scheduling task data, the system can simulate and analyze the crane's operating path, operating sequence allocation, and warehouse location access matching status within the slab warehouse. It can analyze the crane's operating status under different scheduling tasks, the load fluctuation amplitude of a single crane, and the differences in slab turnover time.

[0067] Please see Figure 2 and Figure 3 , Figure 2 This is the overall architecture diagram of the overhead crane scheduling system for the slab warehouse. Figure 3 A schematic diagram of the layout for a multi-span, multi-crane simulation environment of a slab warehouse.

[0068] In some embodiments, the architecture of the slab warehouse overhead crane scheduling system includes a presentation layer, a business logic layer, and a data layer, with the business logic layer encapsulating a simulation operation interface.

[0069] The slab warehouse overhead crane scheduling simulation system architecture is based on a presentation layer, a business logic layer, and a data layer. The business logic layer encapsulates a simulation execution interface as the solution unit. Specifically, it adopts a three-tier B / S architecture consisting of a Vue3 frontend, a FastAPI backend, and MySQL+YAML data storage, respectively handling human-computer interaction, business logic processing, and data storage configuration. The frontend can have eight interactive function pages, while the backend encapsulates the simulation execution interface, supporting the switching and execution of multiple task allocation algorithms, thus decoupling the scheduling algorithm from the business interface. Schedulers can complete all operations, including algorithm selection, data loading, simulation startup, and result viewing, through the frontend. The system initializes the simulation environment based on a YAML configuration file, accurately defining core parameters such as the overhead crane and warehouse layout to adapt to multiple parallel cross-area scenarios in the slab warehouse.

[0070] The simulation environment is based on multiple parallel, independent spans, with multiple overhead cranes configured in each span. The cranes run along horizontal tracks within the same span while maintaining a safe distance. Simultaneously, a rasterized spatial model is defined in the simulation environment, mapping the physical space of the slab warehouse to a standardized coordinate system. The crane movement is constrained by maximum travel speed, safe distance, and intra-span movement boundary constraints.

[0071] The configuration parameters for the slab warehouse can include: the crane's unloaded and loaded moving speeds, the safe distance between cranes in the same span, the maximum coordinates of a single track, the time required for lifting, placing, and fixing, and the maximum task quantity threshold. Operating constraints on the slab warehouse cranes can be defined to facilitate the statistical analysis of the moving time of a single slab warehouse crane, the lifting and placing time, and the task queuing scale.

[0072] During the initial configuration phase of the slab warehouse overhead crane scheduling system architecture and simulation environment startup, the system will create a unique status object for each overhead crane in the slab warehouse during initialization. The initial attributes, including unique identifier, span, coordinates, speed, load status, task status, and running status, will be uniformly configured. The initial priority of all overhead cranes will be uniformly set to 1, and a corresponding idle and no-task baseline status will be set.

[0073] The scheduling task data includes: task number, start and end coordinates, dispatch and completion time, and the area to which it belongs. This clearly defines the location, time, and area affiliation of each scheduling task, facilitating the matching of the corresponding slab warehouse overhead crane with the appropriate transfer operation.

[0074] Please see Figure 4 , Figure 4 A schematic diagram of the decision-making process for various task assignment algorithms.

[0075] Step 102: Construct multiple types of task allocation algorithms. These algorithms can adapt to different scheduling requirements of the slab warehouse. The corresponding task allocation algorithm decision logic is selected to pair the overhead cranes in the slab warehouse with the scheduled tasks.

[0076] In some embodiments, multiple types of task allocation algorithms are constructed, including: a task allocation algorithm with the fewest tasks, a closest allocation to the starting point, a closest allocation to the ending point, a task allocation algorithm with the most tasks, a furthest allocation to the starting point, a furthest allocation to the ending point, an adaptive dynamic rule allocation algorithm, a random overhead crane allocation algorithm, and a randomly selected rule allocation algorithm. This allows for the selection of an appropriate allocation algorithm to suit various operational scenarios in the slab warehouse, arranging the overhead cranes to undertake and schedule tasks.

[0077] In some embodiments, the task minimum allocation algorithm selects the crane with the fewest remaining tasks in the span k of task j. The calculation formula for the task minimum allocation algorithm is as follows:

[0078]

[0079] in, This represents the number of remaining tasks for the i-th overhead crane. This represents the status information of the i-th overhead crane. This represents the task scheduling list for the i-th overhead crane. This indicates the length of the task scheduling list. This represents the current task index of the i-th overhead crane. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen.

[0080] The algorithm for assigning tasks to the nearest crane from the starting point selects the crane located at the k-th span that is closest to the starting point of task j. The calculation formula for the algorithm is as follows:

[0081]

[0082] in, This represents the distance from the starting point to the current position of the i-th overhead crane. This represents the real-time position coordinates of the i-th overhead crane at time t. This represents the coordinates of the starting point (lifting) position of the j-th task. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen.

[0083] The algorithm for assigning tasks to the nearest destination selects the crane located at the nearest point in k from the destination of task j. The formula for calculating the algorithm for assigning tasks to the nearest destination is as follows:

[0084]

[0085] in, This represents the distance from the current position of the i-th overhead crane to the destination. This represents the real-time position coordinates of the i-th overhead crane at time t. This represents the coordinates of the endpoint (drop-off) position of the j-th task. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen.

[0086] The algorithm for maximizing task allocation selects the crane with the most remaining tasks in the span k of task j. The formula for this algorithm is as follows:

[0087]

[0088] in, This represents the number of remaining tasks for the i-th overhead crane. This represents the status information of the i-th overhead crane. This represents the task scheduling list for the i-th overhead crane. This represents the current task index of the i-th overhead crane. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen.

[0089] The algorithm for assigning tasks farthest from the starting point selects the crane located at the k-th span that is furthest from the starting point of task j. The formula for calculating the algorithm for assigning tasks farthest from the starting point is as follows:

[0090]

[0091] in, This represents the distance from the starting point to the current position of the i-th overhead crane. This represents the real-time position coordinates of the i-th overhead crane at time t. This represents the coordinates of the starting point (lifting) position of the j-th task. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen.

[0092] The algorithm for assigning tasks farthest from the destination selects the crane located at the k-th span that is furthest from the destination of task j. The formula for calculating the algorithm for assigning tasks farthest from the destination is as follows:

[0093]

[0094] in, This represents the distance from the current position of the i-th overhead crane to the destination. This represents the real-time position coordinates of the i-th overhead crane at time t. This represents the coordinates of the endpoint (drop-off) position of the j-th task. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen.

[0095] The adaptive dynamic rule allocation algorithm dynamically selects parameter weights based on the length of the task's transportation distance, calculates the score of the k-span crane containing the task based on these parameter weights, and then selects the appropriate crane. The calculation formula for the adaptive dynamic rule allocation algorithm is as follows:

[0096]

[0097] in, This represents the normalized number of tasks for the i-th crane. This represents the number of remaining tasks for the i-th overhead crane. This indicates the maximum number of tasks threshold. This represents the normalized distance from the starting point to the current position of the i-th overhead crane. This represents the distance from the starting point to the current position of the i-th overhead crane. This represents the total length of the track across the k-th span. This represents the normalized task execution distance of the j-th task. This represents the coordinates of the starting point (lifting) position of the j-th task. This represents the coordinates of the endpoint (drop-off) position of the j-th task. Let represent the adaptive dynamic rule score of the i-th overhead crane. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen.

[0098] The random crane allocation algorithm randomly selects a crane for the span k containing the task. The calculation formula for the random crane allocation algorithm is as follows:

[0099] in, Indicates from set The crane selection is carried out randomly. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen.

[0100] The random selection rule allocation algorithm selects one of the following algorithms for the crane: least number of tasks, closest to the starting point, closest to the ending point, most tasks, farthest from the starting point, farthest from the ending point, or an adaptive dynamic rule algorithm. The calculation formula for the random selection rule allocation algorithm is as follows:

[0101]

[0102] in, Indicates from set The rules are selected randomly during the process. This represents a set of task allocation rules (algorithm for assigning the fewest number of tasks, algorithm for assigning the task closest to the starting point, algorithm for assigning the task closest to the ending point, algorithm for assigning the task with the most tasks, algorithm for assigning the task furthest from the starting point, algorithm for assigning the task furthest from the ending point, and adaptive dynamic rule allocation algorithm). This indicates the final task allocation algorithm selected. This indicates that the task allocation algorithm is used. Assign overhead cranes to the j-th task. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen.

[0103] Please see Figure 5 , Figure 5 This is a schematic diagram of the task decomposition process.

[0104] Step 103: Based on the basic operational information, the scheduling tasks are allocated to the overhead crane using the task allocation algorithm; the scheduling tasks are decomposed into multiple sub-task sequences, and these sub-task sequences are mapped to the overhead crane's task scheduling list. This allows for the refinement of the transfer operations of the slab warehouse overhead crane in step-by-step operation, and the acquisition of the action sequence and operating parameters corresponding to each segment of the operation.

[0105] It is worth noting that the task allocation algorithm dispatches the task to the crane so that the crane can execute the task; the task decomposition breaks down the execution process of a scheduled task into multiple sub-tasks (e.g., four sub-tasks) so that the crane can execute them sequentially.

[0106] In some embodiments, the plurality of subtask sequences include: an empty-load movement subtask sequence, a lifting subtask sequence, a heavy-load movement subtask sequence, and a placement subtask sequence.

[0107] Specifically, after the task allocation algorithm in step 102 selects the target crane, the scheduling task is decomposed into a four-step sub-task sequence: empty movement (move_empty), picking up (pickup), heavy movement (move_loaded), and drop (drop), which are then mapped to the target crane's task scheduling list. The empty movement takes the task starting point as the target and its running speed is set to v; the picking up process is defined as the time taken to execute at the task starting point. Lifting operations; heavy-load movement with the task endpoint as the target, and the running speed set to... ; The execution time at the end of the task is set. Lowering and hoisting operation.

[0108] The simulation stepping process distinguishes between the movement subtask and the lifting and placing of the hoisting crane. The calculation formulas for the stepping variables corresponding to different subtasks are as follows:

[0109] in, This represents the real-time position coordinates of the i-th overhead crane at time t. This indicates whether the i-th overhead crane is loaded; a load status is 1. Indicates the step time interval. This indicates the real-time position coordinates of the i-th crane at time t, pointing towards the i-th crane. The direction of movement of the target position coordinates for each subtask, with the positive direction of the coordinate axis being positive. Indicates the progress of the lifting and lowering operation. This indicates the fixed time threshold for lifting and lowering operations.

[0110] All the four-step sub-task sequences obtained from the decomposition are bound to the corresponding original scheduled tasks, which facilitates the retention and storage of the completion information of the original tasks after all four-step sub-task sequences have been executed. The four-step sub-tasks of empty movement, lifting, heavy-load movement, and placement are mapped to the end of the task scheduling list of the corresponding slab warehouse crane according to the execution order. If the slab warehouse crane is currently in an idle baseline state, the sub-task execution will be automatically activated and started. If the slab warehouse crane is currently in a busy running state, the preceding tasks in the task scheduling list will continue to be executed in the original order. After all the preceding tasks have been completed, the sub-tasks in this group will be executed in sequence. If the slab warehouse crane has completed the current task scheduling list, it will stop and wait for a new sub-task sequence to be mapped to the task scheduling list.

[0111] Please see Figure 6 , Figure 6 This is a schematic diagram of the anti-collision mechanism for overhead cranes spanning the same distance.

[0112] Step 104: Based on the subtask sequence and basic operational information, construct a priority-based anti-collision mechanism for cranes operating in the same span. By establishing a priority-based anti-collision mechanism for cranes operating in the same span of the slab warehouse, the passage order of cranes in the same span area can be distinguished, the operating permissions of cranes can be coordinated and interfered with, and the risk of collisions between cranes operating in the same span can be reduced.

[0113] Specifically, the collision avoidance mechanism includes a pre-simulation phase and a collision avoidance phase. In the pre-simulation phase, the motion trajectory of the slab storage cranes is simulated under ideal conditions where there is no positional contact, and the operating parameters of each slab storage crane are stored in the simulation state. Simulation step calculations are performed on each slab storage crane, including motion task stepping and lifting / lowering task stepping, based on whether the lifting / lowering task progress is complete or the current position. The target location coordinates of the subtask The task is considered complete if the distance is below the reach threshold. The corresponding formula is as follows:

[0114] in, This represents the real-time position coordinates of the i-th overhead crane at time t. Indicates the first The target location coordinates of each sub-task This indicates the distance threshold reached.

[0115] The collision avoidance phase must satisfy the following conditions: overhead cranes in the same span area must not overstep their designated positions, and boundary constraints must be met. The constraint formulas are as follows:

[0116]

[0117]

[0118] in, This represents the left boundary of the current k-th cross-region. This represents the real-time position coordinates of the i-th overhead crane at time t. This represents the right boundary of the current k-th cross-region. This represents the real-time position coordinates of the z-th overhead crane at time t. This represents the absolute value of the difference between the workstation numbers of the i-th and z-th overhead cranes within the k-th span. This indicates the required safe distance between two adjacent overhead cranes. This represents the set of overhead cranes in the k-th cross-region.

[0119] For slab warehouse overhead cranes within the same span, they are sorted according to the priority value corresponding to the scheduling task, while maintaining a list of processed cranes. All slab warehouse overhead cranes are traversed one by one after priority sorting. Each crane first matches its fixed boundary constraint range within its span, then combines this with the real-time coordinates of the cranes in the processed crane list with higher priority values ​​to define its own passable left and right dynamic boundaries. The resulting dynamic boundary constraints determine the real-time coordinates of its next simulation step. After completing the position constraint determination, the simulated slab warehouse overhead crane state data, corrected by dynamic boundary constraints, is updated to the real slab warehouse overhead crane state before the step calculation is completed. This includes updating the corrected current crane position coordinates, actual load status, and task progress. Furthermore, if a crane completes a sub-task, its new priority is calculated.

[0120] The expression for the priority-based collision avoidance mechanism for cranes crossing the same level is:

[0121] in, This represents the overall priority value of the i-th overhead crane. This represents the real-time position coordinates of the i-th overhead crane at time t. Indicates the first The target location coordinates of each sub-task This represents the total length of the track across the k-th span. This indicates the current operating status of the i-th overhead crane. This indicates that the overhead crane is in an idle state with no scheduled tasks. This indicates the crane's unloaded movement status. This indicates the movement state of the overhead crane carrying the slab load. This indicates the crane's operation status of lifting or placing slabs. 1, 2, 3, and 4 represent the priority values ​​corresponding to different operating states of the crane.

[0122] Please see Figure 7 , Figure 7 This is a schematic diagram of a dynamic task management mechanism.

[0123] Step 105: Construct a dynamic task management mechanism based on task allocation algorithms, sub-task sequences, and anti-collision mechanisms for cranes spanning the same span. This dynamic task management mechanism is built by combining task allocation algorithms, sub-task sequences, and anti-collision mechanisms for cranes spanning the same span to adapt to the real-time operating status of the slab warehouse cranes, adjust the timing of sub-task issuance, smoothly distribute the workload of each slab warehouse crane, statistically analyze the completion status of each task, and manage the simulation operation process.

[0124] In some embodiments, the dynamic task management mechanism includes: retrieving the unreleased task queue within each simulation step, and transferring the scheduled tasks whose task dispatch time is no greater than the current simulation time to the allocable task queue; retrieving the execution task queue within each simulation step, and storing the tasks for which all four sub-task sequences have been completed into the task completion queue; retrieving the task completion queue within each simulation step, and ending the simulation when all tasks have been stored into the task completion queue.

[0125] Within each simulation step period, the slab warehouse crane scheduling simulation system searches the queue of unreleased scheduling tasks and filters the task dispatch times. For scheduling tasks less than or equal to the current simulation time, the selected scheduling tasks are moved to the allocable scheduling task queue; the completion information of scheduling tasks whose four-step sub-task sequences are all completed is stored in the task completion queue; the task completion queue is retrieved, and the single simulation ends when all scheduling tasks are completed. This operation mode allows scheduling tasks to enter the scheduling and processing flow according to the time constraints of the production plan, restoring the operation process of scheduling tasks arriving in sequence according to the production rhythm in actual production conditions.

[0126] Within each simulation step period, the slab warehouse overhead crane scheduling simulation system sequentially performs task allocation judgment operations on all scheduling tasks within the allocatable scheduling task queue; selects one of the nine task allocation algorithms recorded in step 102 as the task allocation algorithm, and matches and assigns the corresponding slab warehouse overhead crane to undertake the scheduling task; after the assignment is completed, sequentially executes the scheduling task decomposition operation corresponding to step 103 and the anti-collision mechanism operation of the same span overhead crane corresponding to step 104; after all scheduling tasks are stored in the task completion queue, the single slab warehouse overhead crane scheduling simulation process terminates.

[0127] Please see Figure 8 , Figure 8 This is a system structure diagram for evaluating scheduling effectiveness indicators.

[0128] Step 106: Obtain the operation data generated by the dynamic task management mechanism and construct a scheduling effect evaluation index system. By collecting the operation data produced by the dynamic task management mechanism, a scheduling effect evaluation index system is built to quantitatively compare the operation performance of the slab warehouse crane under different task allocation algorithms and intuitively reflect the overall scheduling completion level of each group of scheduling tasks.

[0129] In some embodiments, the process of acquiring job data generated by the operation of the dynamic task management mechanism and constructing a scheduling effect evaluation index system includes: acquiring job data generated by the operation of the dynamic task management mechanism and constructing a scheduling effect evaluation index system including: average absolute time deviation, average delay time, average lead time, standard deviation of absolute time deviation, standard deviation of delay time, standard deviation of lead time, on-time rate, delay rate and lead rate.

[0130] Specifically, once all scheduled tasks are stored in the task completion queue, i.e., all tasks have been executed, task subsets are divided according to statistical requirements. The slab warehouse overhead crane scheduling simulation system is based on the task set. The system calculates scheduling performance evaluation metrics based on the actual completion time and expected completion time of scheduled tasks. It supports setting 30-second, 60-second, and custom time thresholds, and calculates task on-time rate, lead time, and delay rate based on these thresholds. Simultaneously, it independently calculates the average absolute time deviation, average delay time, average lead time, standard deviation of absolute time deviation, standard deviation of delay time, and standard deviation of lead time, all unaffected by thresholds. This allows for adaptation to scheduling evaluation needs corresponding to different industrial production precision levels, forming a quantifiable scheduling evaluation benchmark across multiple operating scenarios.

[0131] The scheduling evaluation process can perform overall statistical analysis on all scheduling tasks, as well as calculate refined indicators for each span and individual slab warehouse crane. This evaluation system can quantify scheduling operation quality from multiple dimensions, including global scheduling performance, single-span operation level, and single crane execution effect. It accurately identifies local anomalies such as single crane operation deviations and inter-span scheduling load imbalances, adapting to complex slab warehouse scheduling scenarios involving multiple spans and multiple cranes working collaboratively. The system groups scheduling tasks based on the span to which the task belongs and the assigned crane, constructing three task sets: global, single-span, and single-crane, and further dividing the tasks into subsets. The following formula must be satisfied:

[0132] in, Represents the set of all tasks. This represents the set of tasks belonging to the k-th cross-region. Let K represent the set of tasks belonging to the i-th crane, K represent the global cross-area set, k represent the k-th cross, C represent the global crane set, and i represent the i-th crane.

[0133] After grouping, for the task set The task-related information is used to calculate all evaluation metrics, and the final output is a set of tasks. The report covers a comprehensive performance evaluation of slab warehouse scheduling, which includes nine core indicators.

[0134] Taking the 60s time threshold and the global evaluation dimension of the task set as an example: The slab warehouse crane scheduling simulation system traverses all scheduled tasks in the task completion queue and calculates the time deviation between the actual completion time and the expected completion time of each scheduled task. A positive time deviation indicates that the actual completion time is later than the expected completion time, and a negative time deviation indicates that the actual completion time is earlier than the expected completion time. For a single scheduled task, if the absolute value of the time deviation is less than or equal to the 60s time threshold, the task is considered completed on time; if the time deviation is less than the negative time threshold, the task is considered completed ahead of schedule; and if the time deviation is greater than the time threshold, the task is considered delayed. Based on the above judgment rules, the on-time rate, early completion rate, and delay rate can be statistically obtained. In addition, other evaluation indicators unrelated to this threshold are calculated simultaneously in the evaluation index system.

[0135] In some embodiments, the mean absolute time deviation (MAD) index is used to calculate the average absolute time deviation of all task completion times. It comprehensively reflects the overall deviation between the actual completion time and the expected completion time of all tasks; a smaller value indicates higher overall scheduling accuracy. The expression for MAD is:

[0136] in, Represents a set of tasks The mean absolute time deviation of all tasks in the process. Represents a set of tasks The total number of all tasks in the game. This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task.

[0137] The average latency metric applies only to those that meet the following criteria. The average task delay accurately characterizes the average delay level of task timeouts in the scheduling system, intuitively reflecting the severity of scheduling delay problems. The expression for the average delay time is:

[0138] in, Represents a set of tasks China satisfies Average latency of the task Represents a set of tasks China satisfies Total number of tasks This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task.

[0139] Average lead time metric statistics for all those who meet the criteria The average lead time of a task is used to measure the pre-completion redundancy of the scheduled task and reflects the operational efficiency margin of the overhead crane scheduling. The expression for the average lead time is:

[0140] in, Represents a set of tasks China satisfies Average lead time of the task Represents a set of tasks China satisfies Total number of tasks This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task.

[0141] The standard deviation of absolute time deviation, the standard deviation of delay time, and the standard deviation of advance time are used to measure the dispersion of global task time deviation, characterizing the fluctuation difference of delayed task delay and the fluctuation amplitude of advance task duration.

[0142] The expression for the standard deviation of absolute time deviation is:

[0143] in, Represents a set of tasks The standard deviation of the absolute time deviation of all tasks. Represents a set of tasks The total number of all tasks in the game. This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. Represents a set of tasks The average absolute time deviation of all tasks.

[0144] The expression for the standard deviation of the delay time is:

[0145] in, Represents a set of tasks China satisfies The standard deviation of the task delay time, Represents a set of tasks China satisfies Total number of tasks This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. Represents a set of tasks China satisfies The average delay time of the task.

[0146] The expression for the standard deviation of lead time is:

[0147] in, Represents a set of tasks China satisfies The standard deviation of the task lead time, Represents a set of tasks China satisfies Total number of tasks This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. Represents a set of tasks China satisfies Average lead time of a task.

[0148] On-time rate, delay rate, and lead time rate are used to reflect the job completion capability of a scheduling scheme within a certain time threshold: On-time rate reflects the ability of a task to be completed on time; delay rate quantifies the proportion of time-out tasks in the scheduling system and measures scheduling reliability; lead time rate is used to count the proportion of tasks that are completed too early and helps to avoid various problems caused by tasks being completed ahead of schedule.

[0149] The expression for on-time rate is:

[0150] in, Indicates Task set under time-based threshold On-time performance of tasks Represents a set of tasks The total number of all tasks in the game. This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. This represents the time threshold for judgment, and num represents the total number of tasks that meet the requirements.

[0151] The expression for latency rate is:

[0152] in, Indicates Task set under time-based threshold Task latency rate Represents a set of tasks The total number of all tasks in the game. This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. This represents the time threshold for judgment, and num represents the total number of tasks that meet the requirements.

[0153] The expression for advance rate is:

[0154] in, Indicated by Task set under time-based threshold Task lead time Represents a set of tasks The total number of all tasks in the game. This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. This represents the time threshold for judgment, and num represents the total number of tasks that meet the requirements.

[0155] The above nine indicators construct a complete crane scheduling timing evaluation system from multiple dimensions, including overall deviation, lead time, delay level, scheduling stability, and task compliance rate. This system can be adapted to production scheduling assessment needs of different precision and achieve a comprehensive and quantifiable evaluation of the scheduling algorithm performance.

[0156] Please see Figure 2 and Figure 9 , Figure 2 This is a diagram of the overall architecture of the overhead crane dispatching system. Figure 9 This is a schematic diagram of the functional modules of the overhead crane scheduling simulation system.

[0157] Step 107: Based on the slab warehouse crane scheduling system architecture, multi-type task allocation algorithms, anti-collision mechanism for cranes spanning the same distance, dynamic task management mechanism, and scheduling effect evaluation index system, construct the slab warehouse crane scheduling system.

[0158] The scheduling system platform adopts a B / S architecture with a front-end and back-end separation design, and is divided into a three-layer structure: presentation layer, business logic layer, and data layer.

[0159] The presentation layer is built on the Vue3 framework, combined with the ElementPlus component library for page layout and functional component development, and relies on the ECharts visualization library for data chart display. The front-end pages cover core functional pages such as login and registration, basic data maintenance, task configuration, real-time simulation operation, 3D spatiotemporal trajectory display, single-track spatiotemporal trajectory display, scheduling Gantt chart, and scheduling data analysis, comprehensively covering the entire process of system use and scheduling analysis. The business logic layer is based on the Python FastAPI framework, following the RESTful API interface design specifications to provide standardized interface services. The back-end is mainly divided into three functional interface modules: user authentication, configuration management, and simulation scheduling, which respectively undertake the responsibilities of system configuration scheduling and security permission control. The data layer adopts a diversified storage solution, persistently storing core data such as user information through a MySQL relational database, and saving basic equipment parameters such as crane and storage location in YAML configuration files, effectively avoiding the problem of frequent database operations, facilitating version iteration and cross-environment migration of parameter configurations, while storing original task information in JSON format to ensure the integrity of task data and efficient reading.

[0160] This invention constructs a system architecture and simulation environment, and sets up multiple types of task allocation algorithms. It can select the corresponding algorithm to complete the allocation and decomposition of overhead crane tasks according to the working conditions, and can adapt to various slab warehouse operating conditions. It sets up a priority-based anti-collision mechanism for overhead cranes spanning the same distance and a dynamic task management mechanism, which can effectively ensure the stable operation of overhead cranes. By collecting operation data and establishing a scheduling effect evaluation index system, the scheduling performance can be quantified. This simulation system can effectively verify the scheduling performance of various algorithms under different working conditions, which facilitates the selection of the best scheduling algorithm and improves the adaptability of scheduling schemes in various warehouse operation scenarios.

[0161] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A design method for a slab warehouse overhead crane scheduling simulation system, characterized in that, include: Construct the architecture and simulation environment of the slab warehouse overhead crane scheduling system, obtain slab warehouse configuration parameters and scheduling task data, and generate basic operation information; Construct multi-type task allocation algorithms; Based on the basic operational information, the scheduling tasks are allocated to the overhead crane using the task allocation algorithm; the scheduling tasks are decomposed to obtain multiple sub-task sequences, and the sub-task sequences are mapped to the overhead crane's task scheduling list. Based on the subtask sequence and the basic operational information, a priority-based anti-collision mechanism for the same-span overhead crane is constructed. Based on the task allocation algorithm, the subtask sequence, and the anti-collision mechanism for the same-span overhead crane, a dynamic task management mechanism is constructed. Obtain the job data generated by the dynamic task management mechanism and construct a scheduling effect evaluation index system; Based on the slab warehouse crane scheduling system architecture, the task allocation algorithm, the anti-collision mechanism for cranes spanning the same distance, the dynamic task management mechanism, and the scheduling effect evaluation index system, a slab warehouse crane scheduling simulation system is constructed.

2. The design method of a slab warehouse overhead crane scheduling simulation system according to claim 1, characterized in that, The architecture of the slab warehouse overhead crane scheduling system includes a presentation layer, a business logic layer, and a data layer. The business logic layer encapsulates a simulation operation interface.

3. The design method of a slab warehouse overhead crane scheduling simulation system according to claim 1, characterized in that, The construction of multiple types of task allocation algorithms includes: constructing task allocation algorithms with the fewest number of tasks, the closest allocation algorithm to the starting point, the closest allocation algorithm to the ending point, the most tasks, the furthest allocation algorithm from the starting point, the furthest allocation algorithm from the ending point, an adaptive dynamic rule allocation algorithm, a random overhead crane allocation algorithm, and a random rule selection allocation algorithm.

4. The design method of a slab warehouse overhead crane scheduling simulation system according to claim 3, characterized in that, The calculation formula for the algorithm that minimizes the number of tasks is as follows: in, This represents the number of remaining tasks for the i-th overhead crane. This represents the status information of the i-th overhead crane. This represents the task scheduling list for the i-th overhead crane. This indicates the length of the task scheduling list. This represents the current task index of the i-th overhead crane. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen. The calculation formula for the nearest assignment algorithm to the starting point is: in, This represents the distance from the starting point to the current position of the i-th overhead crane. This represents the real-time position coordinates of the i-th overhead crane at time t. This represents the starting position coordinates of the j-th task. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen. The calculation formula for the nearest assignment algorithm to the endpoint is as follows: in, This represents the distance from the current position of the i-th overhead crane to the destination. This represents the real-time position coordinates of the i-th overhead crane at time t. This represents the coordinates of the endpoint of the j-th task. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen. The calculation formula for the algorithm that maximizes the number of tasks is as follows: in, This represents the number of remaining tasks for the i-th overhead crane. This represents the status information of the i-th overhead crane. This represents the task scheduling list for the i-th overhead crane. This indicates the length of the task scheduling list. This represents the current task index of the i-th overhead crane. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen. The calculation formula for the algorithm that assigns the furthest point from the starting point is as follows: in, This represents the distance from the starting point to the current position of the i-th overhead crane. This represents the real-time position coordinates of the i-th overhead crane at time t. This represents the starting position coordinates of the j-th task. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen. The calculation formula for the algorithm that assigns the furthest point from the destination is as follows: in, This represents the distance from the current position of the i-th overhead crane to the destination. This represents the real-time position coordinates of the i-th overhead crane at time t. This represents the coordinates of the endpoint of the j-th task. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen. The calculation formula for the adaptive dynamic rule allocation algorithm is as follows: in, This represents the normalized number of tasks for the i-th crane. This represents the number of remaining tasks for the i-th overhead crane. This indicates the maximum number of tasks threshold. This represents the normalized distance from the starting point to the current position of the i-th overhead crane. This represents the distance from the starting point to the current position of the i-th overhead crane. This represents the total length of the track across the k-th span. This represents the normalized task execution distance of the j-th task. This represents the coordinates of the starting position of the j-th task. This represents the coordinates of the endpoint of the j-th task. Let represent the adaptive dynamic rule score of the i-th overhead crane. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen. The calculation formula for the random crane allocation algorithm is as follows: in, Indicates from set The crane selection is carried out randomly. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen. The calculation formula for the random selection rule allocation algorithm is as follows: in, Indicates from set The rules are selected randomly during the process. This represents the set of task allocation rules. This indicates the final task allocation algorithm selected. This indicates that the task allocation algorithm is used. Assign overhead cranes to the j-th task. This represents the set of overhead cranes in the k-th cross-region where task j to be assigned is located. This indicates that the overhead crane was ultimately chosen.

5. The design method of a slab warehouse overhead crane scheduling simulation system according to claim 1, characterized in that, The multiple sub-task sequences include: an unloaded movement sub-task sequence, a lifting sub-task sequence, a heavy-load movement sub-task sequence, and a placement sub-task sequence.

6. The design method of a slab warehouse overhead crane scheduling simulation system according to claim 1, characterized in that, The priority expression for the priority-based anti-collision mechanism for cranes crossing the same level is: in, This represents the overall priority value of the i-th overhead crane. This represents the real-time position coordinates of the i-th overhead crane at time t. Indicates the first The target location coordinates of each sub-task This represents the total length of the track across the k-th span. This indicates the current operating status of the i-th overhead crane. This indicates that the overhead crane is in an idle state with no scheduled tasks. This indicates the crane's unloaded movement status. This indicates the movement state of the overhead crane carrying the slab load. This indicates the crane's operation status of lifting or placing slabs. 1, 2, 3, and 4 represent the priority values ​​corresponding to different operating states of the crane.

7. The design method of a slab warehouse overhead crane scheduling simulation system according to claim 1, characterized in that, The dynamic task management mechanism includes: retrieving the unreleased task queue within each simulation step and transferring the scheduled tasks whose task dispatch time is no greater than the current simulation time to the allocable task queue; retrieving the execution task queue within each simulation step and storing the tasks whose sub-task sequences are all completed into the task completion queue; and retrieving the task completion queue within each simulation step and ending the simulation when all tasks are stored in the task completion queue.

8. The design method of a slab warehouse overhead crane scheduling simulation system according to claim 1, characterized in that, The step of acquiring the job data generated by the dynamic task management mechanism and constructing a scheduling effect evaluation index system includes: acquiring the job data generated by the dynamic task management mechanism and constructing a scheduling effect evaluation index system. A scheduling performance evaluation index system consisting of mean absolute time deviation, mean delay time, mean lead time, standard deviation of absolute time deviation, standard deviation of delay time, standard deviation of lead time, on-time rate, delay rate, and lead rate.

9. The design method of a slab warehouse overhead crane scheduling simulation system according to claim 8, characterized in that, The expression for the mean absolute time deviation is: in, Represents a set of tasks The mean absolute time deviation of all tasks in the process. Represents a set of tasks The total number of all tasks in the game. This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. The expression for the average delay time is: in, Represents a set of tasks China satisfies Average latency of the task Represents a set of tasks China satisfies Total number of tasks This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. The expression for the average lead time is: in, Represents a set of tasks China satisfies Average lead time of the task Represents a set of tasks China satisfies Total number of tasks This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. The expression for the standard deviation of the absolute time deviation is: in, Represents a set of tasks The standard deviation of the absolute time deviation of all tasks. Represents a set of tasks The total number of all tasks in the game. This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. Represents a set of tasks The average absolute time deviation of all tasks. The expression for the standard deviation of the delay time is: in, Represents a set of tasks China satisfies The standard deviation of the task delay time, Represents a set of tasks China satisfies Total number of tasks This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. Represents a set of tasks China satisfies The average delay time of the task. The expression for the standard deviation of the lead time is: in, Represents a set of tasks China satisfies The standard deviation of the task lead time, Represents a set of tasks China satisfies Total number of tasks This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. Represents a set of tasks China satisfies Average lead time of a task. The expression for the on-time rate is: in, Indicates Task set under time-based threshold On-time performance of tasks Represents a set of tasks The total number of all tasks in the game. This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. This represents the time threshold for judgment, and num represents the total number of tasks that meet the requirements. The expression for the latency rate is: in, Indicates Task set under time-based threshold Task latency rate Represents a set of tasks The total number of all tasks in the game. This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. This represents the time threshold for judgment, and num represents the total number of tasks that meet the requirements. The expression for the advance rate is: in, Indicates Task set under time-based threshold Task lead time Represents a set of tasks The total number of all tasks in the game. This represents the actual completion time of the j-th task. This represents the estimated completion time of the j-th task. This represents the time threshold for judgment, and num represents the total number of tasks that meet the requirements.

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