Method and device for optimizing task scheduling and running path of shuttle vehicle

Abstract

This invention discloses an optimization method and apparatus for combining task scheduling and operation path selection in shuttle operations. The method includes: acquiring warehouse information, establishing a rasterized map, and constructing a feasible path table; establishing an optimization model for task scheduling and path selection based on the shuttle operation path; taking the task scheduling and path selection process of a four-way shuttle system as the research object with the goal of minimizing the total operation time, and constructing a mathematical model for scheduling of operations and path selection based on the optimization model; using the mathematical model, optimizing the scheduling order and operation path of goods to be received and to be dispatched using a parallel local iterative search algorithm to determine the optimal solution for completing the scheduling. This invention establishes a rasterized model based on the shuttle operation path and uses a parallel local iterative search algorithm to improve the model search efficiency, effectively improving efficiency and stability of warehouse operations, and can be widely applied in the field of shuttle system scheduling and optimization.

Description

Technical Field

[0001] This invention relates to the field of shuttle system scheduling and optimization, and in particular to an optimization method and apparatus that combines task scheduling and operation path optimization for shuttle operations. Background Technology

[0002] Four-way shuttle warehousing systems integrate storage and picking, offering high storage density, flexibility, predictability, and adaptability. Driven by e-commerce, mass customization, and just-in-time (JIT) principles, these systems are continuously evolving. A four-way shuttle warehousing system primarily consists of four-way shuttles, inbound / outbound elevators, inbound / outbound temporary storage locations, longitudinal / lateral movement aisles, and storage locations. The shuttle warehousing scheduling system monitors the status of the four-way shuttles and storage locations within the warehousing system, and needs to perform functions such as order receiving, task allocation, and vehicle scheduling. As the main transportation equipment in this warehousing system, the four-way shuttles continuously move and transport goods along the complex aisles between rack locations and inbound / outbound elevators.

[0003] Current technologies primarily focus on the analysis and numerical modeling optimization of the scheduling process for four-way shuttle systems, or on path planning and conflict resolution under defined shuttle task scheduling. However, there is little research on simultaneously optimizing scheduling and path optimization for four-way shuttles. Furthermore, research on the scheduling of complex tasks involving four-way shuttles is insufficient; most studies assume a single outbound or inbound mode for the shuttle, which is inadequate for handling complex tasks. To ensure the predictability of shuttle tasks, shuttle warehousing systems need to provide precise execution plans for the entire order execution process, including scheduling optimization and operational paths. Summary of the Invention

[0004] In order to at least partially solve one of the technical problems existing in the prior art, the purpose of this invention is to provide an optimization method and apparatus for combining task scheduling and operation path of shuttle operation.

[0005] The technical solution adopted in this invention is:

[0006] An optimization method combining task scheduling and route planning for shuttle operations includes the following steps:

[0007] Obtain warehouse information, create a raster map, and construct a feasible route table;

[0008] An optimization model for task scheduling and path selection is established based on the shuttle's running path. The model features the generation of a task scheduling sequence and a path selection sequence for the shuttle. Tasks are assigned to the shuttle that completed the previous task earliest. The feasible path table is consulted according to the sequence, a feasible path is selected, and the actual running time is calculated using a time window algorithm.

[0009] With the goal of minimizing the total operation time, this study takes the task scheduling and path selection process of a four-way shuttle system as the research object, and constructs a mathematical model for the scheduling of operations and path selection based on the optimization model.

[0010] Using the established mathematical model, a parallel local iterative search algorithm is employed to optimize the scheduling order and running path of goods to be put into and taken out of the warehouse, thereby determining the optimal solution for completing the scheduling.

[0011] Furthermore, the operational tasks of the four-way shuttle system consist of several point-to-point movement operations;

[0012] For the inbound task at the target cargo point coordinates Ti, the four-way shuttle will start from the current position as the starting point S0, move to the inbound elevator buffer coordinates Gin to load the goods, and then transport the buffer cargo pallet to the storage location where the outbound goods are located as the endpoint Ti and unload the goods.

[0013] For an outbound task with a storage point coordinate of To, the four-way shuttle will move from its current position as the starting point S0, move to the shelf of goods to be outbound To, load the goods pallet and transport it to the outbound elevator buffer Gout as the destination, unload the goods pallet and complete the outbound transportation process.

[0014] Among them, the task points Ti, To, Gin, and Gout of the batch order are the relevant task points in the path lookup table.

[0015] Furthermore, the construction of the feasible path table includes:

[0016] The improved Dijkstra algorithm is used to calculate and save a table of multiple feasible paths between any two task points; the table of feasible paths is used to save multiple paths between any two task-related points, and path search is used as a preprocessing step for task scheduling.

[0017] Retrieve the current pending orders and set the relevant storage locations to "in stock" status to avoid the impact of orders on feasible paths.

[0018] Furthermore, the step of calculating and storing a table of multiple feasible paths between any two task points using the improved Dijkstra algorithm includes:

[0019] Based on the rasterized map of the current warehouse information, an improved Dijkstra algorithm that considers turning costs and records the location of obstacle-avoiding shelves is used to search for different valid paths on the warehouse path map. To avoid generating too many duplicate path searches during the task scheduling phase, when searching multiple paths, the main routes of frequently used road segments are heuristically combined with one-way traffic in different directions. At the same time, to avoid path deadlock in the time window algorithm, multiple locations that can temporarily provide path conflict avoidance and a table of specific avoidance operation paths are recorded when searching paths.

[0020] Furthermore, the shortest path job time of the task scheduling sequence and path selection sequence calculated using the time window algorithm is used as the objective function of the optimization model.

[0021] Furthermore, when calculating the task scheduling time using the time window algorithm, the algorithm simulates the running path selected by the shuttle car, taking into account turning time, waiting time for inbound / outbound elevators, loading / unloading time, and path conflict waiting time. The formula for the running time of a single task by the shuttle car is as follows:

[0022]

[0023] Assuming the total number of tasks to be scheduled is n and the number of shuttles is m, the objective function formula for calculating the total task completion time is:

[0024]

[0025] Among them, t u For the turning time, t o t represents the time for inbound and outbound interactions. s The loading and unloading time at the cargo location, t w For the additional time required to run obstacle avoidance, Δx is the lateral movement distance, Δy is the longitudinal movement distance, v is the movement speed, and c is the number of turns; This represents the running time of the i-th shuttle performing the j-th task.

[0026] Furthermore, the parallel local iterative search algorithm integrates the characteristics of local iterative search algorithms and parallel algorithms, allowing multiple threads to simultaneously perform search optimization solutions in multiple directions. The steps of the parallel local iterative search algorithm are as follows:

[0027] 1) Encoding and decoding methods;

[0028] 2) Initialization algorithm;

[0029] 3) Neighborhood construction;

[0030] 4) Establish disturbance strategies and acceptance criteria;

[0031] 5) Perform parallel local iterative search.

[0032] Furthermore, the optimization method also includes the following steps:

[0033] For large-scale orders, the orders are processed in batches, and distributed parallel computing is used to obtain results quickly.

[0034] Furthermore, for large-scale orders, the process involves batch processing the orders and using distributed parallel computing to quickly obtain results, including:

[0035] When the number of tasks assigned is large, the tasks are divided into multiple batches, and the first batch of tasks is searched.

[0036] If there are already completed search results in the previous batch, a new search process is randomly created to perform a new search. The shuttle's endpoint with the better result of the preceding task is used as the starting point for the current task to perform shuttle task scheduling and path iteration search.

[0037] Another technical solution adopted in this invention is:

[0038] An optimization device combining task scheduling and route planning for shuttle operations, comprising:

[0039] At least one processor;

[0040] At least one memory for storing at least one program;

[0041] When the at least one program is executed by the at least one processor, the at least one processor implements the method described above.

[0042] The beneficial effects of this invention are: based on the shuttle's running path, this invention establishes an optimization model for task scheduling and path selection, and uses a parallel local iterative search algorithm to improve the model search efficiency, thereby effectively improving the efficiency of inbound and outbound operations and the stability of warehouse operation. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following description is provided with accompanying drawings of the relevant technical solutions in the embodiments of the present invention or the prior art. It should be understood that the accompanying drawings described below are only for the purpose of clearly illustrating some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a main flowchart of an embodiment of the present invention;

[0045] Figure 2This is a schematic diagram of the layout of a four-way shuttle warehouse system according to an embodiment of the present invention;

[0046] Figure 3 This is a schematic diagram illustrating the implementation process of the improved Dijkstra algorithm in an embodiment of the present invention.

[0047] Figure 4 This is a schematic diagram of the improved Dijkstra algorithm raster map search process according to an embodiment of the present invention;

[0048] Figure 5 This is a schematic diagram of the search results for the four-way shuttle operation path according to an embodiment of the present invention;

[0049] Figure 6 This is a schematic diagram illustrating the encoding and decoding of the local iterative search algorithm in an embodiment of the present invention;

[0050] Figure 7 This is a schematic diagram illustrating the operation time calculation of the four-way shuttle time window algorithm according to an embodiment of the present invention;

[0051] Figure 8 This is a schematic diagram of the parallel local iterative algorithm for an example of an embodiment of the present invention. Detailed Implementation

[0052] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0053] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0054] In the description of this invention, "several" means one or more, "multiple" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. If "first" or "second" is used, it is only for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features. Furthermore, "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0055] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0056] To address the technical problems existing in the prior art, this invention, based on an analysis of the specific operational process and shuttle operating characteristics of a four-way shuttle system, focuses on the scheduling and path optimization problems of composite tasks in the four-way shuttle system. An optimization model for task scheduling and path selection is established considering the shuttle's operating path, and a parallel local iterative algorithm is employed to improve the model's search efficiency. The aim is to find a reasonable task scheduling scheme and feasible operating paths to improve system operational efficiency and ensure high predictability of shuttle storage.

[0057] like Figure 1 As shown, this embodiment provides an optimization method for shuttle operation that combines task scheduling and running path, including the following steps:

[0058] Step 1: Obtain warehouse information, create a raster map, and construct a feasible route table.

[0059] See Figure 2As shown, the layout and inventory information of the four-way shuttle storage system are obtained, the current pending orders are retrieved, the relevant storage locations are set to the "cargo-bearing" status, and a rasterized map of the current order's operational map is created. The four-way shuttle mainly consists of a four-way shuttle, inbound / outbound elevators and buffer positions, longitudinal / lateral movement channels, and storage locations. For inbound tasks, the four-way shuttle starts at its current position (S0), moves to the buffer position of the inbound elevator at the intermediate point (S1) to load goods, then transports the buffer pallet to the storage location of the outbound goods as its destination and unloads the goods (S2). For outbound tasks, the four-way shuttle starts at its current position (S0), moves to the shelf of goods to be shipped (S1) to load goods, transports them to the outbound elevator buffer position as its destination (S2), unloads the goods pallet, and completes the outbound transportation process. Therefore, the shuttle's operations in this case are all point-to-point movements; the previous task determines the shuttle's starting point, and the specific storage location determines the intermediate and destination points.

[0060] In some embodiments, for all starting and ending points appearing in the order, a modified Dijkstra algorithm is used to search and store multiple feasible paths obtained therefrom.

[0061] Among them, the improved Dijkstra algorithm, which considers turning costs and saving obstacle avoidance positions, virtualizes each node in two different directions during map search: vertical (PU) and horizontal (PL). Figure 3 The search is performed according to the process shown. The basic process starts from a vertex and progressively searches for the shortest path from that vertex to all other vertices in the graph. In each iteration, the vertex with the shortest distance among the currently unvisited vertices is selected, and the shortest distances to its adjacent vertices are updated, until all vertices have been visited. It's important to note that the adjacency search cost between barrier-free (thin solid lines) adjacency-friendly grids is per unit time. The additional search cost for the adjacency transformation of the PU and PL of the same point is the turn time (in this case, a value of 3). Finally, points in the results that are adjacent to grids with no cargo space are marked as cargo space obstacle avoidance points. The grid map search process is as follows... Figure 4 As shown, starting from the shuttle's mission location, the path is updated sequentially based on Dijkstra's algorithm. The top-left corner of the grid indicates the path time to the current grid in the vertical direction of the graph, and the bottom-right corner indicates the time to convert to the horizontal direction of the graph. Finally, the path to all target points under the current map is searched.

[0062] Different combinations of directional passages are applied to certain road segments in the raster map, and path searches are performed on each of these locally oriented raster maps to obtain multiple task execution paths running in different directions on paths with high conflict rates. The results of a specific shipping order from multiple implementation cases are shown below. Figure 5As shown, the improved Dijkstra search yielded multiple paths and retained three recommended feasible paths. It also obtained information on the turning points of the paths and the locations of shelves in path 3 that could be used to avoid conflicts. The improved Dijkstra algorithm primarily involves setting different travel directions for each horizontal and vertical passageway before searching, considering turning costs and the existence of obstacle avoidance locations, and then sorting and retaining the paths based on the shuttle's conflict-free execution time to obtain a hash table that can be used for querying.

[0063] Step 2: Based on the shuttle's running path, establish an optimization model for task scheduling and path selection. The model features the generation of a task scheduling sequence and a path selection sequence for the shuttle. Tasks are assigned to the shuttle that completed the previous task earliest. The feasible path table is consulted according to the sequence, a feasible path is selected, and the actual running time is calculated using a time window algorithm.

[0064] For the current pending orders, a shuttle task scheduling sequence and a path selection sequence are generated. To facilitate local iterative search, the encoding and decoding, as well as the decoding during the running iteration, participate... Figure 6 An integer sequence represents the order in which the shuttle's tasks are to be executed, and another integer sequence represents the preferred path for execution. If there are sequential constraints on order tasks, such as shipments from the same shelf, the same number is used during encoding, and the orders are remapped according to their order during decoding. For path selection codes in encoding, if a conflict occurs during execution, avoidance at the avoidance point is initiated first. If a deadlock conflict that prevents execution is detected, the path is reselected to obtain further executable decoding.

[0065] When calculating task scheduling time, the time window algorithm needs to simulate the running path chosen by the shuttle and obtain the path position at each time point. The formula for the running time considered by the shuttle task is as follows:

[0066]

[0067] The above formula takes into account turning time, waiting time for inbound / outbound elevators, loading / unloading time, and path conflict waiting time, which are respectively t u ,t o ,t s ,t w v represents the average running time of the shuttle over that displacement distance, which is summed sequentially in the time window algorithm according to the actual execution order.

[0068] The task is assigned sequentially to the shuttle that completed the previous task earliest. A feasible path is selected by searching the path table sequentially, and the actual running time is calculated using a time window algorithm. See also Figure 7 As shown, the initial execution time window for a given order represents the simulated location of the shuttle car at that time. At the outbound elevator buffer, the four-way shuttle A still needs 3 seconds to complete the execution path selected in the previous segment for the previous order. Therefore, the four-way shuttle A at cargo B is the next earliest shuttle to start. Assuming the path selection code for four-way shuttle B is 1, meaning it prioritizes path 1 to complete the current task, it will attempt to complete the task on path 1.

[0069] According to the time window algorithm model for the four-way shuttle operation, a conflict occurs in the longitudinal channel, causing the shuttle to revert to the previous path conflict avoidance point. Specifically, at time 5s, the four-way shuttle cannot request to advance into the longitudinal channel before time 12s. Therefore, the shuttle needs to wait until time 11s before the turn at time 5s. Alternatively, it can wait 4s inside the shelf before departing, reducing the time spent occupying the lateral channel. The specific actual operation time is determined by the task selection code and the time window algorithm simulation.

[0070] For this multi-shuttle operation task scheduling and route optimization method, the path code is iteratively searched in a locally parallel iterative search algorithm. Since the path selection code for the four-way shuttle B is 2, meaning path 2 is prioritized to complete the current task, the task is attempted to be completed on path 2. At this point, the shuttles run sequentially on the path and occupy path positions. Those without path conflicts reach the outbound elevator buffer position, but the order execution time is relatively long.

[0071] Step 3: With the goal of minimizing the total operation time, the task scheduling and path selection process of the four-way shuttle system is taken as the research object, and a mathematical model of task and path selection scheduling is constructed based on the optimization model.

[0072] With the goal of completing all assigned tasks as early as possible, a parallel local iterative search algorithm is used to optimize the task sequence code and path selection code of the four-way shuttle. In this implementation case, the detailed flowchart of the parallel local iterative search algorithm can be found here. Figure 8 The algorithm generates multiple initial solution codes using a completely random initialization method, and obtains the optimal runnable final initial solution equal to the number of threads created by trying out the codes of the initial solutions. Further, neighborhood construction is performed based on the warehouse layout and encoding method, using the following three neighborhood structures:

[0073] N1: Randomly swap task sequence codes. Randomly select two task sequence codes and swap them, and also swap the path selection code at that position.

[0074] N2: The path selection code is randomly selected. During the time window algorithm operation, the algorithm attempts to change the path selection code of the random task and continue the subsequent operation.

[0075] N3: Task combination optimization based on composite tasks. A lookup table for priority composite tasks is established for each task. During the time window algorithm, unexecuted orders that can be combined with currently completed tasks to form composite tasks are swapped to the next task and attempted to run.

[0076] Parallel local iterative search requires setting a perturbation strategy and acceptance criteria. In this embodiment, a random small perturbation strategy is adopted, that is, multiple N1 searches are attempted for the current solution and the result is directly accepted. For the acceptance method in the local iterative search, the acceptance criteria in the delayed acceptance hill-climbing algorithm are used. A target function memory of length h is used to record the optimal solution of the current position iteration. If the neighboring generated solution is smaller than the value in the current memory, it is accepted and the value in the current memory is reduced; otherwise, the new solution is accepted only if it is better than the current solution.

[0077] Step 4: Using the established mathematical model, a parallel local iterative search algorithm is employed to optimize the scheduling order and running path of goods to be put into and taken out of the warehouse, and to determine the optimal solution for completing the scheduling.

[0078] In some embodiments, for multiple optimal solutions obtained by multiple search threads, a random pairwise comparison method is used to select and retain the optimal solution. The current optimal solution library is sorted, and the solution with the optimal number of search threads is selected as the initial solution for the next iteration.

[0079] Specifically, an optimized example of random orders in the warehouse layout of this embodiment is compared. Turning time and loading / unloading time are set to 2 seconds, average speed is directly set to 1 m / s, shelf length and width are 1 / s, task size is 20 orders, and the number of shuttles is 3.

[0080] The comparison focuses on the execution of orders using a region-locking approach under a fixed random task order. The results show the optimization of this model using a classic genetic algorithm and the optimization of this model using a parallel local iterative search.

[0081] To eliminate the randomness and comparability of the experiment, the order was repeated 15 times and run on the same platform.

[0082] Table 1 shows the experimental results.

[0083]

[0084] The results of the comparative experiments show that the path selection of the job scheduling optimized by the optimization method outperforms the region-locking strategy executed under a fixed random task order. The optimization results of the method proposed in this invention are superior to those of the genetic algorithm; the average optimization result of the proposed method is approximately 4% higher than that of the genetic algorithm. Significant optimization is also achieved in terms of solution time. The method of this invention fully utilizes the advantages of multi-core processors and multi-threaded parallelism to achieve a short-time solution model, demonstrating strong practicality and better meeting the needs of online scheduling.

[0085] This embodiment also provides an optimization device that combines task scheduling and running path optimization for shuttle operations, including:

[0086] At least one processor;

[0087] At least one memory for storing at least one program;

[0088] When the at least one program is executed by the at least one processor, the at least one processor implements Figure 1 The method shown.

[0089] This embodiment provides an optimization device for combining task scheduling and running path of shuttle operations. It can execute the optimization method for combining task scheduling and running path of shuttle operations provided in the method embodiment of the present invention. It can execute any combination of implementation steps of the method embodiment and has the corresponding functions and beneficial effects of the method.

[0090] This application also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform... Figure 1 The method shown.

[0091] This embodiment also provides a storage medium storing instructions or programs that can execute the optimization method for combining task scheduling and running path of shuttle operation provided in the method embodiment of the present invention. When the instructions or programs are run, any combination of implementation steps of the method embodiment can be executed, and the method has the corresponding functions and beneficial effects.

[0092] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is altered and sub-operations described as part of a larger operation are executed independently.

[0093] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the described functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0094] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0095] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0096] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0097] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0098] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0099] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0100] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. An optimization method combining task scheduling and running path for shuttle operations, characterized in that, Includes the following steps: Obtain warehouse information, create a raster map, and construct a feasible route table; An optimization model for task scheduling and path selection is established based on the shuttle's running path. The model features the generation of a task scheduling sequence and a path selection sequence for the shuttle. Tasks are assigned to the shuttle that completed the previous task earliest. The feasible path table is consulted according to the path selection sequence. A feasible path is selected and the actual running time is calculated using a time window algorithm. The shortest path job time of the task scheduling sequence and path selection sequence calculated using the time window algorithm is used as the objective function of the optimization model. With the goal of minimizing the total operation time, this study takes the task scheduling and path selection process of a four-way shuttle system as the research object, and constructs a mathematical model for the scheduling of operations and path selection based on the optimization model. Using the established mathematical model, a parallel local iterative search algorithm is employed to optimize the scheduling order and running path of goods to be put into and taken out of the warehouse, thereby determining the optimal solution for completing the scheduling. The parallel local iterative search algorithm combines the characteristics of local iterative search algorithms and parallel algorithms, allowing multiple threads to perform search optimization solutions in multiple directions simultaneously.

2. The optimization method for combining task scheduling and running path of shuttle operation according to claim 1, characterized in that, The operation tasks of the four-way shuttle system consist of several point-to-point movement operations; For the inbound task at the target cargo point coordinates Ti, the four-way shuttle will start from the current position as the starting point S0, move to the inbound elevator buffer coordinates Gin to load the goods, and then transport the buffer cargo pallet to the storage location where the outbound goods are located as the endpoint Ti and unload the goods. For an outbound task with a storage point coordinate of To, the four-way shuttle will move from its current position as the starting point S0, move to the shelf of goods to be outbound To, load the goods pallet and transport it to the outbound elevator buffer Gout as the destination, unload the goods pallet and complete the outbound transportation process. Among them, the task points Ti, To, Gin, and Gout of the batch order are the relevant task points in the path lookup table.

3. The optimization method for combining task scheduling and running path of shuttle operation according to claim 1, characterized in that, The construction of the feasible path table includes: The improved Dijkstra algorithm is used to calculate and save a table of multiple feasible paths between any two task points; the table of feasible paths is used to save multiple paths between any two task-related points, and path search is used as a preprocessing step for task scheduling. Retrieve the current pending orders and set the relevant storage locations to "in stock" status to avoid the impact of orders on feasible paths.

4. The optimization method for combining task scheduling and running path of shuttle operation according to claim 3, characterized in that, The table of multiple feasible paths between any two task points, calculated and stored using the improved Dijkstra algorithm, includes: Based on the rasterized map of the current warehouse information, an improved Dijkstra algorithm that considers turning costs and records the location of obstacle-avoiding shelves is used to search for different valid paths on the warehouse path map. To avoid generating too many duplicate path searches during the task scheduling phase, when searching multiple paths, the main routes of frequently used road segments are heuristically combined with one-way traffic in different directions. At the same time, to avoid path deadlock in the time window algorithm, multiple locations that can temporarily provide path conflict avoidance and a table of specific avoidance operation paths are recorded when searching paths.

5. The optimization method for combining task scheduling and running path of shuttle operation according to claim 1, characterized in that, When calculating the task scheduling time using the time window algorithm, the algorithm simulates the running path selected by the shuttle car, taking into account turning time, waiting time for inbound / outbound elevators, loading / unloading time, and path conflict waiting time. The formula for the running time of a single task by the shuttle car is as follows: Assuming the total number of tasks to be scheduled is n and the number of shuttles is m, the formula for calculating the total objective function task time is: in, For the turning time, For inbound and outbound interaction time, This refers to the loading and unloading time at the cargo location. To allow for additional time for obstacle avoidance, For the lateral movement distance, For the longitudinal movement distance, For movement speed, Number of turns; Indicates the first i The shuttle car performed the first j The runtime of each task.

6. The optimization method for combining task scheduling and running path of shuttle operation according to claim 1, characterized in that, The optimization method further includes the following steps: For large-scale orders, the orders are processed in batches, and distributed parallel computing is used to obtain results quickly.

7. The optimization method for combining task scheduling and running path of shuttle operation according to claim 6, characterized in that, For large-scale orders, the process involves batch processing and using distributed parallel computing to quickly obtain results, including: When the number of tasks assigned is large, the tasks are divided into multiple batches, and the first batch of tasks is searched. If there are already completed search results in the previous batch, a new search process is randomly created to perform a new search. The shuttle's endpoint with the better result of the preceding task is used as the starting point for the current task to perform shuttle task scheduling and path iteration search.

8. An optimization device combining task scheduling and running path for shuttle operations, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method of any one of claims 1-7.

Images