Simulation-based scheduling optimization method and device, logistics equipment and medium

Abstract

The invention relates to the technical field of AGV scheduling, and discloses a scheduling optimization method and device based on simulation, logistics equipment and a medium, and the method comprises the steps: constructing a target simulation model according to a preset simulation demand, the target simulation model comprising scene layouts of different warehouses and driving routes of corresponding logistics equipment; creating a preset function list corresponding to the target simulation model, wherein the preset function list comprises an entry list, an equipment list, a task list and a path list; determining target logistics equipment and a target transportation task based on the entrance list, the equipment list and the task list, and generating a corresponding target transportation path based on the target transportation task; and controlling simulation operation of the target logistics equipment based on the target transportation path so as to execute the target transportation task. According to the invention, scheduling optimization is carried out on the task list to determine the current corresponding to-be-executed target transportation task in the idle logistics equipment, and operation simulation is carried out based on the task, so that the logistics equipment can be reasonably utilized, and resource waste is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of AGV scheduling, and in particular to a scheduling optimization method, device, logistics equipment and medium based on simulation. Background Art

[0002] In today's highly automated industrial production environment, Automated Guided Vehicles (AGVs) are important logistics equipment for material handling. Their scheduling efficiency and path planning directly affect the overall efficiency and flexibility of the production line. With the continuous expansion of production scale and the increasing complexity of production needs, how to efficiently manage and schedule AGVs has become a major challenge facing the manufacturing industry.

[0003] At present, AGV scheduling often relies on empirical judgment or simple algorithm models, such as optimal path, first-come-first-served, etc. It is difficult to fully consider various complex factors in the production environment, such as the collaborative operation of multiple AGVs, dynamically changing production tasks, the impact of real-time road conditions, etc. These factors make the formulation of scheduling strategies extremely difficult, and it is difficult to ensure the effectiveness and stability in practical applications. Based on such problems, simulation software is widely used in process optimization and performance evaluation in the fields of manufacturing and logistics. For example, Plant Simulation, a powerful discrete event simulation software, provides decision makers with a risk-free verification and optimization method by establishing a highly accurate simulation model to simulate various dynamic changes in the actual production environment.

[0004] However, for dynamically changing production tasks, most existing AGV scheduling performs scheduling simulation and verification in the order in which various tasks are generated. Specifically, when a long-distance task is first generated and received by the target AGV, if a new short-distance task is generated near the target AGV at this time, the target AGV will run a long distance to perform the long-distance task, thereby wasting a lot of task execution time. Therefore, there are problems such as unreasonable AGV scheduling and waste of resources, which seriously affects the utilization rate of AGV. Summary of the invention

[0005] In view of this, the present invention provides a simulation-based scheduling optimization method, device, logistics equipment and medium to solve the problem that existing logistics equipment is difficult to reasonably formulate scheduling strategies, which leads to a large waste of resources and seriously affects the utilization rate of AGVs.

[0006] In a first aspect, the present invention provides a simulation-based scheduling optimization method, which is applied to logistics equipment. The method includes:

[0007] Build a target simulation model based on preset simulation requirements. The target simulation model includes the scene layout of different warehouses and the driving routes of corresponding logistics equipment.

[0008] Create a preset function list corresponding to the target simulation model, which includes an entry list, an equipment list, a task list, and a path list; the entry list is used to store the three-dimensional warehouse entrances that are available in the warehouse, the equipment list is used to record the logistics equipment that is currently in an idle state, the task list is used to record all transportation tasks to be executed, and the path list is used to store the corresponding transportation paths dynamically generated by different transportation tasks;

[0009] Determine the target logistics equipment and target transportation task based on the entry list, equipment list and task list, and generate the corresponding target transportation path based on the target transportation task;

[0010] The target logistics equipment simulation operation is controlled based on the target transportation path to execute the target transportation task.

[0011] The simulation-based scheduling optimization method provided by the present invention constructs a target simulation model and its corresponding preset function list, determines the target logistics equipment and the target transportation task based on the entry list, the equipment list and the task list, and generates a target transportation path according to the task to make the target logistics equipment run the simulation and execute the corresponding target transportation task. It can take into account the optimal resource matching between the logistics equipment and each task to be executed in the task list, perform scheduling optimization on the task list to determine the current optimal target transportation task for the logistics equipment in the idle state, and perform operation simulation based on the task, so as to realize the reasonable formulation of scheduling strategies, which not only avoids the waste of equipment resources, but also saves the task execution time, and greatly improves the utilization rate of logistics equipment.

[0012] In an optional implementation, each transportation task to be executed in the task list is arranged in sequence based on the corresponding task generation time, and each transportation task to be executed includes a task number, a transportation starting point, a transportation end point, and a preset task priority; determining the target logistics equipment and the target transportation task based on the entry list, the equipment list, and the task list includes:

[0013] Read the first transport task from the task list;

[0014] Determine whether the first transport task has the highest task priority in the task list;

[0015] When the first transport task does not belong to the highest task priority, the task list is optimized based on each transport task to be executed and its corresponding preset task priority, and the step of reading the first transport task from the task list is returned again;

[0016] When the first transport task has the highest task priority, the first transport task is determined as the target transport task, and the nearest target logistics equipment is determined from the equipment list based on the transport starting point of the target transport task, wherein the transport starting point is dynamically determined based on the available stereoscopic warehouse entrance stored in the entrance list.

[0017] The present invention takes into account the importance and urgency of the tasks, and schedules and optimizes the task list through the preset task priority of each transport task to be executed in the task list, so as to ensure that each transport task to be executed in the task list can be executed in sequence according to the set task priority order, which meets the demand for efficient task execution to a certain extent, helps to optimize resource allocation, and thus greatly improves the transportation efficiency of logistics equipment.

[0018] In an optional implementation, each to-be-executed transport task in the task list further includes a cargo type and a preset cargo priority; before determining the first transport task as the target transport task, the simulation-based scheduling optimization method further includes:

[0019] Determine whether the first transport task has the highest cargo priority in the task list;

[0020] When the first transport task has the highest cargo priority, a process of determining the first transport task as a target transport task is executed;

[0021] When the first transport task does not belong to the cargo task priority, the task list is optimized based on each cargo type to be executed and its corresponding preset cargo priority, and the step of reading the first transport task from the task list is returned.

[0022] The present invention also takes into account the actual production needs and delivery needs, and schedules and optimizes the task list through the preset cargo priority of each transportation task to be executed in the task list, so as to ensure that each transportation task to be executed in the task list can be executed in sequence according to the set cargo priority order, which not only meets the efficient execution requirements of the tasks, but also meets the efficient transportation requirements of the goods, can reduce transportation costs, reduce resource waste, and optimize resource allocation to a certain extent.

[0023] In an optional implementation, before reading the first transport task from the task list, the simulation-based scheduling optimization method further includes:

[0024] Determine whether the device list and task list are empty respectively;

[0025] When both the equipment list and the task list are not empty, execute the process of reading the first transportation task from the task list;

[0026] When the device list and / or the task list is empty, return to the step of creating a preset function list corresponding to the target simulation model.

[0027] The present invention can ensure the rationality and effectiveness of logistics equipment scheduling strategy formulation by designing a process for determining whether the equipment list and the task list are not empty, thereby improving the utilization rate of logistics equipment to a certain extent.

[0028] In an optional implementation, after determining the closest target logistics equipment from the equipment list based on the transportation starting point of the target transportation task, the simulation-based scheduling optimization method further includes:

[0029] Get the current power of the target logistics equipment;

[0030] Determine whether the current power level is lower than a preset power threshold;

[0031] When the current power level is lower than the preset power threshold, the target logistics equipment is removed from the equipment list, the equipment list is updated, and the nearest target logistics equipment is re-determined from the updated equipment list based on the transportation starting point of the target transportation task.

[0032] The determination of the target logistics equipment in the present invention not only takes into account the demand for optimal allocation of resources closest to the transportation starting point of the target transportation task, but also takes into account the current performance of the target logistics equipment. That is, by designing the current power of the target logistics equipment and judging the size of the set power threshold, it is determined whether the currently scheduled target logistics equipment is suitable, which can greatly ensure the effectiveness and rationality of the target logistics equipment scheduling strategy.

[0033] In an optional implementation, the simulation-based scheduling optimization method further includes:

[0034] When the current power level is not lower than the preset power threshold, the historical failure times of the target logistics equipment are obtained. The historical failure times refer to the total number of failures that occurred when the target logistics equipment was performing other transportation tasks during the current simulation operation.

[0035] When the number of historical failures is not less than the preset number threshold, the target logistics equipment is removed from the equipment list, the equipment list is updated, and the nearest target logistics equipment is determined from the updated equipment list based on the transportation starting point of the target transportation task;

[0036] When the number of historical failures is less than a preset threshold, a simulation operation of the target logistics equipment is controlled based on the target transportation path to execute the process of the target transportation task.

[0037] The present invention not only takes into account the real-time power of the equipment, but also the historical failure conditions of the target logistics equipment when judging the current performance of the target logistics equipment. That is, by designing the total number of failures corresponding to the target logistics equipment performing other transportation tasks during the entire simulation operation and comparing it with the set failure occurrence threshold, it is determined whether the currently scheduled target logistics equipment is suitable, which helps to ensure the effectiveness and rationality of the target logistics equipment scheduling strategy.

[0038] In a second aspect, the present invention provides a simulation-based scheduling optimization device, which is applied to logistics equipment, and the device includes:

[0039] A model building module is used to build a target simulation model according to preset simulation requirements, wherein the target simulation model includes scene layouts of different warehouses and driving routes of corresponding logistics equipment;

[0040] A list creation module, used to create a preset function list corresponding to the target simulation model, wherein the preset function list includes an entry list, a device list, a task list and a path list; wherein the entry list is used to store the stereoscopic warehouse entrances in the warehouse that are in an available state, the device list is used to record the logistics equipment that is currently in an idle state, the task list is used to record all transportation tasks to be executed, and the path list is used to store the corresponding transportation paths dynamically generated by different transportation tasks;

[0041] A scheduling determination module, used to determine the target logistics equipment and the target transportation task based on the entry list, the equipment list and the task list, and generate a corresponding target transportation path based on the target transportation task;

[0042] The task execution module is used to control the simulation operation of the target logistics equipment based on the target transportation path to execute the target transportation task.

[0043] The simulation-based scheduling optimization device provided by the present invention constructs a target simulation model including the scene layout of different warehouses and the driving routes of corresponding logistics equipment, and creates a preset function list corresponding to the simulation model. The target logistics equipment and the target transportation task are determined through the function list, and the target transportation path corresponding to the target transportation task is generated to simulate the operation of the target logistics equipment. The optimal resource matching between the logistics equipment and each task to be executed in the task list can be considered. By optimizing the scheduling of the task list, the current optimal target transportation task of the logistics equipment in the idle state is determined, and the reasonable formulation of the scheduling strategy of the logistics equipment can be realized, which can not only avoid the waste of equipment resources, but also save the task execution time, thereby improving the utilization rate of the logistics equipment.

[0044] In a third aspect, the present invention provides a logistics equipment, which includes a controller, and the controller includes: a memory and a processor, the memory and the processor are communicatively connected to each other, computer instructions are stored in the memory, and the processor executes the simulation-based scheduling optimization method of the above-mentioned first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0045] In an optional embodiment, the logistics equipment is an AGV.

[0046] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the simulation-based scheduling optimization method of the above-mentioned first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 is a flow chart of a simulation-based scheduling optimization method according to an embodiment of the present invention;

[0049] Figure 2 is a flow chart of another simulation-based scheduling optimization method according to an embodiment of the present invention;

[0050] Figure 3 It is the CAD floor plan of the warehouse;

[0051] Figure 4 It is a simulation model layout diagram of the warehouse;

[0052] Figure 5 It is a simulation logic schematic diagram;

[0053] Figure 6 It is a logical diagram of AGV dynamic scheduling;

[0054] Figure 7 It is the simulation reset logic diagram;

[0055] Figure 8 is a structural block diagram of a scheduling optimization device based on simulation according to an embodiment of the present invention;

[0056] Fig. 9 Schematic diagram of the structure of a heating controller according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0058] According to an embodiment of the present invention, a simulation-based scheduling optimization method embodiment is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0059] In this embodiment, a simulation-based scheduling optimization method is provided, which is applied to logistics equipment. Figure 1 is a flow chart of a simulation-based scheduling optimization method according to an embodiment of the present invention, such as Figure 1 As shown, the process includes the following steps:

[0060] Step S101, constructing a target simulation model according to preset simulation requirements, wherein the target simulation model includes scene layouts of different warehouses and driving routes of corresponding logistics equipment.

[0061] It should be noted that the specific content of the preset simulation requirements and the specific types of logistics equipment in this embodiment are not limited here, and can be adaptively adjusted according to actual project requirements. For example, the simulation model is a production workshop including a production line; the logistics equipment is an automated guided vehicle (AGV), which is only used as an example.

[0062] Step S102, create a preset function list corresponding to the target simulation model, the preset function list includes an entry list, an equipment list, a task list and a path list; wherein the entry list is used to store the three-dimensional warehouse entrances that are in an available state in the warehouse, the equipment list is used to record the logistics equipment that is currently in an idle state, the task list is used to record all transportation tasks to be executed, and the path list is used to store the corresponding transportation paths dynamically generated by different transportation tasks.

[0063] It should be noted that the entrance to the three-dimensional warehouse in this embodiment refers to the passage or entrance area into the three-dimensional warehouse; among them, the three-dimensional warehouse usually refers to a warehouse that uses multi-layer shelves to store unit goods and uses corresponding material handling equipment to carry out goods warehousing and outbound operations.

[0064] Step S103, determining the target logistics equipment and the target transportation task based on the entry list, the equipment list and the task list, and generating the corresponding target transportation path based on the target transportation task.

[0065] In this embodiment, the target transportation task refers to the task that is executed first in the task list; the target logistics equipment refers to the most suitable equipment to execute the target transportation task, which is adaptively determined according to a certain scheduling strategy, such as the shortest distance, that is, the logistics equipment that is closest to the starting point of the task to be executed first and is in an idle state; the target transportation path is the optimal path dynamically generated according to the starting point and end point of the target transportation task, and the optimal path is obtained after dynamic planning based on the logistics equipment driving route set in advance in a warehouse. The specific path generation method is not limited here and is adaptively determined according to actual needs and conventional methods in the field.

[0066] Step S104, controlling the simulation operation of the target logistics equipment based on the target transportation path to execute the target transportation task.

[0067] In this embodiment, by simulating the target logistics equipment in the simulation software, the effectiveness of the scheduling strategy can be verified based on the equipment operation results.

[0068] The simulation-based scheduling optimization method of the embodiment of the present invention constructs a target simulation model and its corresponding preset function list, determines the target logistics equipment and the target transportation task based on the entry list, the equipment list and the task list, and generates a target transportation path according to the task to make the target logistics equipment run the simulation and execute the corresponding target transportation task. It can take into account the optimal resource matching between the logistics equipment and each task to be executed in the task list, perform scheduling optimization on the task list to determine the current optimal target transportation task for the logistics equipment in the idle state, and perform operation simulation based on the task, which can realize the reasonable formulation of scheduling strategies, which not only avoids the waste of equipment resources, but also saves task execution time, and greatly improves the utilization rate of logistics equipment.

[0069] In this embodiment, a simulation-based scheduling optimization method is provided, which is applied to logistics equipment. Figure 2 is a flow chart of another simulation-based scheduling optimization method according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0070] Step S201: construct a target simulation model according to preset simulation requirements. The target simulation model includes scene layouts of different warehouses and driving routes of corresponding logistics equipment. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0071] Step S202, create a preset function list corresponding to the target simulation model, the preset function list includes an entry list, a device list, a task list and a path list; wherein the entry list is used to store the three-dimensional warehouse entrances in the warehouse that are in an available state, the device list is used to record the logistics equipment that is currently in an idle state, the task list is used to record all the transportation tasks to be executed, and the path list is used to store the corresponding transportation paths dynamically generated by different transportation tasks. For details, please refer to Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0072] Step S203, determining the target logistics equipment and the target transportation task based on the entry list, the equipment list and the task list, and generating a corresponding target transportation path based on the target transportation task.

[0073] In this embodiment, each transportation task to be executed in the task list is arranged in sequence based on the generation time of the corresponding task, and each transportation task to be executed includes a task number, a transportation starting point, a transportation end point, and a preset task priority. It should be noted that the preset task priority indicates the execution order of the current transportation task, that is, the priority order, and its specific content is not limited here and can be determined according to actual needs. For example, for general tasks, urgent tasks, and important urgent tasks, the corresponding preset task priorities are: important urgent tasks>urgent tasks>general tasks, which is only an exemplary description.

[0074] In this embodiment, the above step S203 includes:

[0075] Step S2031, read the first transport task from the task list.

[0076] In actual scenarios, at a certain moment, there may be no logistics equipment in an idle state or no transportation tasks generated, so it is necessary to determine whether the equipment list and the task list are not empty to ensure the normal execution of the subsequent logistics equipment scheduling strategy. Therefore, before reading the first transportation task from the task list, the simulation-based scheduling optimization method of this embodiment also includes:

[0077] Step a1, determine whether the device list and task list are empty respectively.

[0078] Step a2: When both the equipment list and the task list are not empty, execute the process of reading the first transport task from the task list.

[0079] Step a3, when the device list and / or task list is empty, return to the step of creating a preset function list corresponding to the target simulation model.

[0080] In the embodiment of the present invention, by designing a process for determining whether the equipment list and the task list are not empty, the rationality and effectiveness of the formulation of the logistics equipment scheduling strategy can be ensured, and the utilization rate of the logistics equipment can be improved to a certain extent.

[0081] Step S2032, determining whether the first transport task has the highest task priority in the task list.

[0082] Step S2033, when the first transport task does not belong to the highest task priority, the task list is optimized based on each transport task to be executed and its corresponding preset task priority, and the process returns to the step of reading the first transport task from the task list.

[0083] In this embodiment, the task list is optimized, that is, the execution order of the corresponding tasks is adjusted accordingly according to the priority levels to ensure that the high-priority tasks are executed first.

[0084] Step S2034, when the first transport task belongs to the highest task priority, the first transport task is determined as the target transport task, and the nearest target logistics equipment is determined from the equipment list based on the transport starting point of the target transport task, wherein the transport starting point is dynamically determined based on the three-dimensional warehouse entrance in an available state stored in the entrance list.

[0085] In this embodiment, the importance and urgency of the tasks are taken into consideration, and the task list is scheduled and optimized by using the preset task priority of each transport task to be executed in the task list, so as to ensure that each transport task to be executed in the task list can be executed in sequence according to the set task priority order, which meets the requirements for efficient task execution to a certain extent, helps to optimize resource allocation, and thus greatly improves the transportation efficiency of logistics equipment.

[0086] In this embodiment, each transportation task to be executed in the task list also includes the cargo type and the preset cargo priority. It should be noted that the setting of the preset cargo priority can be adaptively adjusted according to the actual project needs, such as production needs, delivery needs, etc. In a specific embodiment, the cargo types include raw materials, semi-finished products and finished products; assuming that the preset cargo priority is set according to production needs, the corresponding preset cargo priority is: raw materials>semi-finished products>finished products; if the preset cargo priority is set according to delivery needs, the corresponding preset cargo priority is: finished products>semi-finished products>raw materials, which is only for exemplary purposes.

[0087] In practical applications, different types of transported goods will also affect the execution order of logistics equipment transportation tasks to a certain extent. For example, if the types of goods transported include finished products and raw materials, the corresponding task of transporting finished products must be executed first according to the delivery requirements. Therefore, this embodiment also takes into account the priority of the type of goods to optimize the scheduling of the task list. Specifically, before determining the first transport task as the target transport task, the simulation-based scheduling optimization method of this embodiment also includes:

[0088] Step b1, determining whether the first transport task has the highest cargo priority in the task list.

[0089] Step b2, when the first transport task has the highest cargo priority, executing a process of determining the first transport task as a target transport task.

[0090] Step b3, when the first transport task does not belong to the cargo task priority, the task list is optimized based on each cargo type to be executed and its corresponding preset cargo priority, and the step of reading the first transport task from the task list is returned.

[0091] The embodiment of the present invention also takes into account the actual production needs and delivery needs, and schedules and optimizes the task list through the preset cargo priority of each transportation task to be executed in the task list, so as to ensure that each transportation task to be executed in the task list can be executed in sequence according to the set cargo priority order, which not only meets the efficient execution requirements of the tasks, but also meets the efficient transportation requirements of the goods, can reduce transportation costs, reduce resource waste, and optimize resource allocation to a certain extent.

[0092] It should be noted that in this embodiment, after selecting appropriate logistics equipment according to the target transportation task, the performance of the current logistics equipment needs to be considered, such as whether the current power of the logistics equipment supports it to complete the task. Therefore, after determining the nearest target logistics equipment from the equipment list based on the transportation starting point of the target transportation task, the simulation-based scheduling optimization method of this embodiment also includes:

[0093] Step c1, obtaining the current power of the target logistics equipment.

[0094] Step c2, determining whether the current power level is lower than a preset power threshold.

[0095] In this embodiment, the preset power threshold is essentially the minimum power required to perform a task, and its specific value is not limited here and can be adaptively set according to actual needs, such as through a large number of offline experimental calibrations.

[0096] Step c3, when the current power level is lower than the preset power threshold, the target logistics equipment is removed from the equipment list, the equipment list is updated, and the nearest target logistics equipment is re-determined from the updated equipment list based on the transportation starting point of the target transportation task.

[0097] In the embodiment of the present invention, the determination of the target logistics equipment not only takes into account the resource optimization configuration requirements closest to the transportation starting point of the target transportation task, but also takes into account the current performance of the target logistics equipment. That is, by designing the current power of the target logistics equipment and judging the size of the set power threshold, it is determined whether the currently scheduled target logistics equipment is suitable, which can greatly ensure the effectiveness and rationality of the target logistics equipment scheduling strategy.

[0098] It should be noted that in addition to considering the current power of the logistics equipment, in order to ensure the safe and effective execution of the task, this embodiment also needs to consider the historical failure of the equipment during the entire simulation process to determine the most suitable logistics equipment for executing the current task. Therefore, when the current power is not lower than the preset power threshold, the simulation-based scheduling optimization method of this embodiment also includes:

[0099] Step d1, obtaining the historical failure times of the target logistics equipment, where the historical failure times refer to the total number of failures that occurred when the target logistics equipment was performing other transportation tasks during the current simulation operation.

[0100] In this embodiment, the specific method for obtaining the number of historical failures is not limited here and can be adaptively determined according to actual needs, such as based on the equipment maintenance time of the target logistics equipment when performing other transportation tasks, where the equipment maintenance time refers to the equipment maintenance time, that is, once the equipment fails, it needs to be repaired immediately; or determined by the fault signal generated by the target logistics equipment when a failure occurs, which is only an exemplary illustration and is not limited to this.

[0101] Step d2, when the number of historical failures is not less than the preset number threshold, the target logistics equipment is removed from the equipment list, the equipment list is updated, and the nearest target logistics equipment is re-determined from the updated equipment list based on the transportation starting point of the target transportation task.

[0102] In this embodiment, the specific value of the preset number threshold is adaptively adjusted according to actual needs.

[0103] Step d3, when the number of historical failures is less than the preset number threshold, execute the process of controlling the target logistics equipment simulation operation based on the target transportation path to execute the target transportation task.

[0104] In the embodiment of the present invention, the determination of the current performance of the target logistics equipment not only takes into account the real-time power of the equipment, but also takes into account the historical failure conditions of the target logistics equipment. That is, by designing the total number of failures corresponding to the target logistics equipment performing other transportation tasks during the entire simulation operation and comparing it with the set failure occurrence threshold, it is determined whether the currently scheduled target logistics equipment is suitable, which helps to ensure the effectiveness and rationality of the target logistics equipment scheduling strategy.

[0105] Step S2035: Generate a corresponding target transportation route based on the target transportation task.

[0106] In this embodiment, the optimal target transportation path is dynamically generated according to the transportation starting point and transportation end point of the target transportation task.

[0107] Step S204: Control the target logistics equipment to simulate and run based on the target transportation path to perform the target transportation task. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0108] In a specific embodiment, the Plant Simulation simulation software is used to perform scheduling optimization simulation on logistics equipment (i.e., AGV). It should be noted that Plant Simulation, as a powerful discrete event simulation software, is widely used in process optimization and performance evaluation in the fields of manufacturing and logistics. It provides decision makers with a risk-free verification and optimization method by establishing a highly accurate simulation model to simulate various dynamic changes in the actual production environment. Plant Simulation can simulate the operating status of AGV in different scenarios, including driving path, speed, load, power consumption, etc., by establishing a detailed simulation model, so as to achieve accurate verification and optimization of the scheduling strategy. During the simulation process, the parameter settings of the scheduling strategy can be flexibly adjusted to observe its impact on key indicators such as AGV operating efficiency, path conflict, and task completion time, and then find the optimal scheduling solution through iterative optimization.

[0109] In a specific embodiment, an intelligent AGV scheduling optimization solution based on Plant Simulation is proposed. By integrating advanced simulation technology with the aforementioned scheduling optimization method of this embodiment, a more scientific and efficient solution is provided for the AGV scheduling problem, which helps to promote the further development of the manufacturing industry towards intelligence and automation. Specifically, the solution mainly includes the following steps:

[0110] 1. Establish a simulation model: Use Plant Simulation to establish a simulation model of the AGV system, including detailed modeling of production lines, material conveyor lines, AGVs and other elements. Specifically, for a warehouse, its actual layout diagram can be found in Figure 3 The figure contains the specific factory area and its corresponding logistics information. The corresponding simulation model of the warehouse can be found in Figure 4 ,Right now Figure 4 It is based on the PlantSimulation software interface Figure 3 Constructed scene model.

[0111] 2. Set the scheduling strategy: According to the production demand, set a reasonable scheduling strategy and algorithm. In this embodiment, the task list is optimized (i.e., the execution order of the tasks is determined based on the task generation time, task priority and cargo priority), and the idle AGV at the nearest location is selected based on the starting point of the current task to be executed to execute the task, where the idle AGV is the optimal AGV in an available state after performance judgment.

[0112] 3. Simulation verification and optimization: Conduct multiple experiments in a simulation environment to verify the effectiveness and optimization potential of the scheduling strategy, that is, iteratively optimize the scheduling strategy through data analysis.

[0113] 4. Result analysis and decision support: Provide visual display of simulation results and data analysis reports to provide intuitive decision support for decision makers.

[0114] In a specific embodiment, a detailed simulation process of the above solution is provided, see Figure 5 , including the following steps:

[0115] 1. Model construction and logo placement.

[0116] In this embodiment, first, according to the specific planning documents of the project, a corresponding model is built in the simulation software, including warehouse layout, shelf location, aisle width, etc. Then, according to the planned AGV walking route, "markers" (such as virtual navigation points or path identifiers) are accurately placed in the model. These marks will serve as reference points for AGV path planning to ensure that the AGV can run efficiently according to the predetermined route.

[0117] 2. Initialization list and global variables.

[0118] In this embodiment, the creation of the corresponding lists and global variables is specifically as follows:

[0119] (1) Create an available entry list (i.e., entry list): This list is used to store all currently available stereo library entry information, including the entry number, status (i.e., whether it is available), etc.

[0120] (2) Create an idle AGV list (i.e., equipment list): used to record the AGV numbers or objects that are currently idle and waiting for tasks to be assigned.

[0121] (3) Create an AGV path list (i.e., path list): Although this list is initialized in this step, its specific content will be dynamically generated according to the task in the future, that is, it is used to store the path points that the AGV needs to pass through to perform the task.

[0122] (4) Create an AGV task list (i.e., task list): record all AGV tasks to be executed, including task ID, starting point, end point, priority, and other information.

[0123] (5) Setting global variables for AGV loading and unloading time: used to set the time taken by the AGV to load and unload goods in the simulation.

[0124] 3. Simulation initialization.

[0125] In this embodiment, a method is written to be automatically executed at the beginning of the simulation. The method first traverses all the entrances of the stereoscopic warehouse and pushes the entrances with the status of "available" into the "available list". Then, according to the number of AGVs configured in the system, a corresponding number of AGV instances are created and pushed into the "free AGV list". Finally, the "AGV scheduling method" is started to start the task allocation and scheduling process.

[0126] 4. AGV scheduling method.

[0127] In this embodiment, an "AGV scheduling method" is implemented, which periodically checks the "idle AGV list" and the "AGV task list". When both are not empty, the most suitable AGV is selected according to the character list optimization strategy of this embodiment, and the tasks with optimized execution order are assigned to the most suitable AGV from the "AGV task list". Figure 6 It is a logical diagram of AGV dynamic scheduling.

[0128] 5. The method by which AGV performs its tasks.

[0129] In this embodiment, the specific logic of the AGV design to execute the task is: when the AGV receives the task, it will first query the "AGV path table" and read the corresponding path information (in list form) in the table according to the starting point and end point of the task. It should be noted that the path information contains all the points that the AGV needs to pass through in sequence to ensure that the AGV can travel according to the predetermined route.

[0130] 6. Convert list to array.

[0131] In this embodiment, in order to optimize the reading efficiency of the AGV, a method is implemented to convert the list information in the "AGV path table" into an array form. Specifically, the converted array is more compact, which facilitates the AGV to quickly parse and execute the path.

[0132] 7. Fill in the AGV path table.

[0133] In this embodiment, detailed path information is manually or automatically filled in the "AGV path table" in a "list" format. It should be noted that this information is based on the actual layout of the warehouse and the adaptive setting of the AGV's walking rules to ensure that the AGV can accurately and safely reach the target location.

[0134] 8. Simulate the reset procedure.

[0135] In this embodiment, a program is also written to be executed when the simulation project is reset. The program is responsible for clearing all lists created in the second step (including the available entry list, the idle AGV list, the AGV path list, and the AGV task list) and resetting the global variables to ensure that each simulation test can start from a clean state and avoid the influence of historical data on the experimental results. Figure 7 is the simulation reset logic diagram.

[0136] Through the implementation of the above steps, this embodiment realizes an efficient and flexible AGV scheduling and optimization, which can significantly improve the automation level and operating efficiency of the logistics warehouse.

[0137] In summary, the beneficial effects of the AGV scheduling optimization solution based on Plant Simulation in this embodiment are mainly reflected in the following aspects:

[0138] 1. Significantly improve scheduling efficiency: Through Plant Simulation simulation verification, the scheduling strategy of AGV can be accurately simulated and optimized to ensure that path conflicts and task overlaps are reduced when multiple AGVs work together, significantly improving the transportation efficiency and task completion speed of AGVs. The efficient scheduling of this embodiment immediately reduces waiting time and idle time, allowing the production line to run more smoothly.

[0139] 2. Optimize resource allocation: Simulation analysis helps to accurately evaluate the resource requirements such as the number of AGVs, the location and capacity of charging stations under different configurations, thereby achieving optimal resource allocation. This embodiment can not only reduce unnecessary resource waste, such as charging delays caused by too many idle AGVs or insufficient charging station capacity, but also reduce overall operating costs.

[0140] 3. Enhance system adaptability and flexibility: Plant Simulation can simulate a variety of production scenarios and demand changes, allowing the AGV scheduling system to quickly adapt to these changes. That is, whether it is the adjustment of production plans, the insertion of emergency tasks, or the sudden occurrence of equipment failures, the system can respond quickly through simulation analysis and adjust the scheduling strategy to maintain efficient operation.

[0141] 4. Reduce risks and costs: Before actual deployment, scheduling strategies are verified and optimized through simulation, which greatly reduces the risks and costs caused by improper strategies, such as reducing the risk of collisions caused by path conflicts, task delays and increased production costs caused by improper scheduling, etc.

[0142] 5. Provide decision support: The detailed simulation data and visual reports provided by Plant Simulation provide decision makers with intuitive and comprehensive decision-making basis. That is, by comparing the performance indicators under different scheduling strategies, decision makers can more scientifically evaluate and select the optimal solution, further improving the decision-making level of production management.

[0143] In this embodiment, a simulation-based scheduling optimization device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" refers to a combination of software and / or hardware that can implement a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0144] The present invention provides a simulation-based scheduling optimization device, which is applied to logistics equipment, such as Figure 8 As shown, the device comprises:

[0145] The model building module 801 is used to build a target simulation model according to preset simulation requirements. The target simulation model includes scene layouts of different warehouses and driving routes of corresponding logistics equipment.

[0146] The list creation module 802 is used to create a preset function list corresponding to the target simulation model. The preset function list includes an entry list, an equipment list, a task list and a path list; wherein the entry list is used to store the three-dimensional warehouse entrances that are in an available state in the warehouse, the equipment list is used to record the logistics equipment that is currently in an idle state, the task list is used to record all transportation tasks to be executed, and the path list is used to store the corresponding transportation paths dynamically generated by different transportation tasks.

[0147] The scheduling determination module 803 is used to determine the target logistics equipment and the target transportation task based on the entry list, the equipment list and the task list, and to generate a corresponding target transportation path based on the target transportation task.

[0148] The task execution module 804 is used to control the simulation operation of the target logistics equipment based on the target transportation path to execute the target transportation task.

[0149] In some optional embodiments, the scheduling determination module 803 includes: a first scheduling submodule, a second scheduling submodule, a third scheduling submodule, a fourth scheduling submodule and a fifth scheduling submodule; wherein the first scheduling submodule is used to read the first transport task from the task list; the second scheduling submodule is used to determine whether the first transport task belongs to the highest task priority in the task list; the third scheduling submodule is used to optimize the task list based on each transport task to be executed and its corresponding preset task priority when the first transport task does not belong to the highest task priority, and return to the step of reading the first transport task from the task list; the fourth scheduling submodule is used to determine the first transport task as the target transport task when the first transport task belongs to the highest task priority, and determine the nearest target logistics equipment from the equipment list based on the transport starting point of the target transport task, wherein the transport starting point is dynamically determined according to the stereoscopic warehouse entrance in the available state stored in the entrance list; the fifth scheduling submodule is used to generate a corresponding target transport path based on the target transport task.

[0150] In some optional embodiments, the scheduling determination module 803 also includes: a non-empty judgment submodule, used to respectively judge whether the equipment list and the task list are empty; when both the equipment list and the task list are not empty, executing the process of reading the first transport task from the task list; when the equipment list and / or the task list is empty, returning to the step of creating a preset function list corresponding to the target simulation model.

[0151] In some optional implementations, the fourth scheduling submodule includes: a cargo priority determination unit, an equipment power determination unit, and an equipment failure determination unit; wherein the cargo priority determination unit is used to determine whether the first transport task belongs to the highest cargo priority in the task list; when the first transport task belongs to the highest cargo priority, execute the process of determining the first transport task as the target transport task; when the first transport task does not belong to the cargo task priority, optimize the task list based on each cargo type to be executed and its corresponding preset cargo priority, and return to the step of reading the first transport task from the task list; the equipment power determination unit is used to obtain the current power of the target logistics equipment; determine whether the current power is lower than the preset power threshold; at the current power When the power is lower than the preset power threshold, the target logistics equipment is removed from the equipment list, the equipment list is updated, and the nearest target logistics equipment is determined from the updated equipment list based on the transportation starting point of the target transportation task; the equipment failure judgment unit is used to obtain the historical failure times of the target logistics equipment when the current power is not lower than the preset power threshold; when the historical failure times are not less than the preset times threshold, the target logistics equipment is removed from the equipment list, the equipment list is updated, and the nearest target logistics equipment is determined from the updated equipment list based on the transportation starting point of the target transportation task; when the historical failure times are less than the preset times threshold, the simulation operation of the target logistics equipment is controlled based on the target transportation path to execute the process of the target transportation task.

[0152] The further functional description of each of the above modules is the same as that of the above corresponding embodiments and will not be repeated here.

[0153] The simulation-based scheduling optimization device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0154] The simulation-based scheduling optimization device of the embodiment of the present invention can consider the optimal resource matching between logistics equipment and each task to be executed in the task list, and determine the current optimal target transportation task for idle logistics equipment by scheduling optimization of the task list, which helps to ensure the reasonable formulation of logistics equipment scheduling strategies, effectively avoid serious waste of equipment resources, further save task execution time, and improve the utilization rate of logistics equipment.

[0155] The present invention also provides a logistics device, which includes a controller. Fig. 9 , Fig. 9 is a schematic diagram of the structure of the controller provided in an optional embodiment of the present invention, such as Fig. 9 As shown, the controller includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the controller, including instructions stored in or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple controllers can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig. 9 A processor 10 is taken as an example.

[0156] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0157] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0158] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required by at least one function; the data storage area may store data created according to the use of the controller, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the controller via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0159] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0160] The controller also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Fig. 9 The example of connecting through bus is taken in the following.

[0161] The input device 30 can receive input digital or character information, and generate signal input related to the user settings and function control of the thermal power unit operation control unit, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator rod, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device may be a touch screen.

[0162] In this embodiment, the logistics equipment is an AGV. Specifically, integrating the above-mentioned simulation-based scheduling optimization method of this embodiment on the logistics equipment can make such logistics equipment have a very stable and reliable simulation operation effect, can adaptively determine the optimal resource matching according to different task types, so that the logistics equipment currently in an idle state can perform the optimal transportation task, can effectively avoid the waste of equipment resources, greatly reduce the transportation cost of logistics equipment, meet the demand for efficient use of logistics equipment, and improve the utilization rate of logistics equipment to a certain extent.

[0163] A computer-readable storage medium is also provided in an embodiment of the present invention. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium and downloaded through a network, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor main control chip or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0164] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A simulation-based scheduling optimization method, applied to logistics equipment, characterized in that: The method comprises: Constructing a target simulation model according to preset simulation requirements, wherein the target simulation model includes scene layouts of different warehouses and driving routes of corresponding logistics equipment; Create a preset function list corresponding to the target simulation model, the preset function list includes an entry list, a device list, a task list and a path list; wherein the entry list is used to store the three-dimensional warehouse entrances in the warehouse that are in an available state, the device list is used to record the logistics equipment that is currently in an idle state, the task list is used to record all transportation tasks to be executed, and the path list is used to store the corresponding transportation paths dynamically generated by different transportation tasks; Determine the target logistics equipment and the target transportation task based on the entry list, the equipment list and the task list, and generate a corresponding target transportation path based on the target transportation task; The target logistics equipment simulation operation is controlled based on the target transportation path to execute the target transportation task.

2. The simulation-based scheduling optimization method according to claim 1, characterized in that: Each transport task to be executed in the task list is arranged in sequence based on the time when the corresponding task was generated, and each transport task to be executed includes a task number, a transport starting point, a transport end point, and a preset task priority; The determining of the target logistics equipment and the target transportation task based on the entry list, the equipment list and the task list includes: Read the first transport task from the task list; Determine whether the first transport task has the highest task priority in the task list; When the first transport task does not belong to the highest task priority, the task list is optimized based on each transport task to be executed and its corresponding preset task priority, and the step of reading the first transport task from the task list is returned again; When the first transport task belongs to the highest task priority, the first transport task is determined as the target transport task, and the nearest target logistics equipment is determined from the equipment list based on the transport starting point of the target transport task, wherein the transport starting point is dynamically determined based on the available stereoscopic warehouse entrance stored in the entrance list.

3. The simulation-based scheduling optimization method according to claim 2, characterized in that: Each transportation task to be executed in the task list also includes a cargo type and a preset cargo priority; Before determining the first transport task as the target transport task, the method further includes: Determine whether the first transport task has the highest cargo priority in the task list; When the first transport task has the highest cargo priority, executing the process of determining the first transport task as the target transport task; When the first transport task does not belong to the cargo task priority, the task list is optimized based on each cargo type to be executed and its corresponding preset cargo priority, and the step of reading the first transport task from the task list is returned.

4. The simulation-based scheduling optimization method according to claim 2, characterized in that: Before reading the first transport task from the task list, the method further includes: Determine whether the device list and task list are empty respectively; When both the equipment list and the task list are not empty, executing the process of reading the first transport task from the task list; When the device list and / or the task list is empty, return to the step of creating a preset function list corresponding to the target simulation model.

5. The simulation-based scheduling optimization method according to claim 2, characterized in that: After determining the closest target logistics equipment from the equipment list based on the transportation starting point of the target transportation task, the method further includes: Get the current power of the target logistics equipment; Determine whether the current power level is lower than a preset power level threshold; When the current power level is lower than a preset power threshold, the target logistics equipment is removed from the equipment list, the equipment list is updated, and the nearest target logistics equipment is re-determined from the updated equipment list based on the transportation starting point of the target transportation task.

6. The simulation-based scheduling optimization method according to claim 5, characterized in that: The method further comprises: When the current power level is not lower than the preset power level threshold, the number of historical failures of the target logistics equipment is obtained, where the number of historical failures refers to the total number of failures of the target logistics equipment corresponding to other transportation tasks performed during the current simulation operation; When the number of historical failures is not less than a preset number threshold, the target logistics equipment is removed from the equipment list, the equipment list is updated, and the nearest target logistics equipment is re-determined from the updated equipment list based on the transportation starting point of the target transportation task; When the number of historical failures is less than a preset number threshold, the process of controlling the target logistics equipment simulation operation based on the target transportation path to execute the target transportation task is executed.

7. A simulation-based scheduling optimization device, applied to logistics equipment, characterized in that: The device comprises: A model building module is used to build a target simulation model according to preset simulation requirements, wherein the target simulation model includes scene layouts of different warehouses and driving routes of corresponding logistics equipment; A list creation module, used to create a preset function list corresponding to the target simulation model, wherein the preset function list includes an entry list, a device list, a task list and a path list; wherein the entry list is used to store the stereoscopic warehouse entrances in the warehouse that are in an available state, the device list is used to record the logistics equipment that is currently in an idle state, the task list is used to record all transportation tasks to be executed, and the path list is used to store the corresponding transportation paths dynamically generated by different transportation tasks; A scheduling determination module, used to determine the target logistics equipment and the target transportation task based on the entry list, the equipment list and the task list, and generate a corresponding target transportation path based on the target transportation task; The task execution module is used to control the simulation operation of the target logistics equipment based on the target transportation path to execute the target transportation task.

8. A logistics equipment, characterized in that: The logistics equipment includes a controller, and the controller includes: a memory and a processor. The memory and the processor are communicatively connected to each other, and computer instructions are stored in the memory. The processor executes the simulation-based scheduling optimization method described in any one of claims 1 to 6 by executing the computer instructions.

9. The logistics equipment according to claim 8, characterized in that: The logistics equipment is AGV.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the simulation-based scheduling optimization method according to any one of claims 1 to 6.

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