261 results about "Robot scheduling" patented technology

The invention discloses a multi-task collaborative robot scheduling method and system oriented to an intelligent factory, and the method comprises the steps: receiving the input data of a plurality of tasks to be executed by the intelligent factory, the input data comprising the task type, the dependency relationship between the tasks, the task priority, the resource demand of each task, and the expected completion time of each task; determining an initial task execution sequence of the tasks according to the task types, the dependency relationships and the task priorities; obtaining current resource state data of all available robots in the intelligent factory; according to the resource demand of each task, the expected completion time of each task, the initial task execution sequence and the current resource state data of all available robots, a scheduling algorithm is adopted to generate a multi-task cooperative scheduling rule; and controlling the plurality of target robots to execute the plurality of tasks according to the multi-task cooperative scheduling rule. According to the method, the resource utilization rate of the robot in the intelligent factory can be effectively improved, and the task execution efficiency is optimized.

The invention discloses a multi-robot collaborative scheduling system in an automatic warehousing system, which relates to the technical field of robot collaborative scheduling and comprises a task management module, a path planning module, a communication collaborative module, an exception handling module, a warehousing space dynamic partition module and a task fusion scheduling module. The task management module comprises a task priority calculation unit, a task distribution unit and a dynamic energy consumption evaluation unit, the path planning module comprises a global path optimization unit, a local path adjustment unit and an obstacle avoidance path optimization unit, and by arranging the task management module, the dynamic task priority calculation and distribution function is achieved, and the dynamic energy consumption is evaluated. The problem of adaptation of dynamic task requirements and complex environments is solved, and the completion speed of task allocation and path planning is increased; by arranging the path planning module, the function of dynamically optimizing the path according to the real-time environment is realized, the problem of path conflict optimization in multi-robot scheduling is solved, and the path obstacle avoidance and execution efficiency is ensured.

The invention discloses a mobile storage and charging robot remote scheduling and path planning system based on the Internet of Things, and relates to the technical field of robot control. Comprising a path dependency modeling module, a path conflict analysis module, a resource dependency graph construction module, a decoupling rearrangement scheduling module, a time sequence offset evaluation module and a path weight regulation and control module, and obtaining a path node sequence and an access time period of a current to-be-executed task of each mobile storage and charging robot, and generating a task path pre-occupation graph. By constructing the path dependence model, the conflict prediction mechanism and the task decoupling rearrangement strategy, accurate identification and effective intervention of path conflicts and resource deadlocks in the multi-robot scheduling process are realized, the stability of the scheduling system and the task execution continuity are improved, efficient completion of energy supply is ensured, and the scheduling efficiency is improved. And the operation efficiency and safety of the system are obviously optimized.

The invention relates to the field of robots, and discloses a multi-robot scheduling method, device and equipment in warehouse management and a storage medium, and the method is used for realizing efficient conflict-free scheduling of multiple robots in warehouse management through fusion algorithm path planning and conflict prediction and processing. The method comprises the following steps: receiving an operation order, and sorting operation tasks in the operation order according to a priority and an overall path optimal principle to obtain an operation task sequence; assigning the operation tasks in the operation task sequence to the to-be-assigned robot according to the sequence, and generating a plurality of robot operation pairs; and a fusion algorithm formed by an improved artificial potential field method and a space-time A * algorithm of a model prediction mechanism is adopted to carry out path planning on the selected robot in each robot operation pair, a CBS algorithm is adopted to carry out conflict prediction, and when conflicts exist, robot operation paths are updated, and the selected robots are scheduled.

The invention relates to the technical field of robot scheduling, in particular to a distributed robot collaborative scheduling system and method in a dynamic environment, and the system comprises an environment sensing layer which is used for collecting environment dynamic data in real time; the distributed decision-making layer comprises local task scheduling modules of a plurality of robots; the cooperative communication layer is used for realizing task state synchronization and conflict detection among the robots based on a low-delay communication protocol; the dynamic weight calculation module is used for generating a real-time optimization weight according to the task emergency degree, the robot energy consumption and the path risk factor; according to the invention, by using a completely distributed collaborative scheduling architecture, through a decentralized task distribution mechanism and a distributed consensus protocol, a single-point fault risk existing in a traditional centralized scheduling system is thoroughly eliminated, and even if a part of robot nodes have faults or communication is interrupted, the system can work normally. And the system can still run continuously through autonomous negotiation of the remaining nodes, so that the reliability of the system in a complex environment is remarkably improved.

The invention relates to the technical field of industrial control, in particular to a robot scheduling method and system based on cooperative control, and the method comprises the steps: obtaining a task proportion and target node information in a task adjustment scheme, judging an optimal task distribution path and an optimal execution sequence according to a task priority order and a node adaptation rule, and carrying out the task scheduling according to the optimal task distribution path and the optimal execution sequence; determining an inspection task distribution blueprint; according to the routing inspection task distribution blueprint, the tasks are distributed in real time by adopting a low-delay scheduling framework, and a dynamic distribution map and real-time distribution update data of the tasks among the nodes are obtained according to node response delay and communication overhead among the nodes; the latest load state of each node is obtained through a dynamic distribution map and real-time distribution update data, and whether a potential calculation pressure overload risk exists or not is judged according to a distribution uniformity index and distribution anomaly detection. The invention aims to solve the problems of large performance difference, unbalanced load and low task scheduling efficiency in a multi-robot collaborative inspection process in a narrow space.

The invention discloses a building construction robot dynamic scheduling method and system based on artificial intelligence, and particularly relates to the technical field of building construction. Potential schedulable building construction robots are screened by analyzing process requirements and resource allocation of construction tasks; based on the robot task execution record, the operation stability is evaluated; the scheduling adaptability of the robot in different construction technologies is evaluated by analyzing task switching flexibility; and the real-time performance of the scheduling demand of the robot is judged by integrating the operation stability and the scheduling adaptation degree. And the flexibility demand intensity of robot scheduling is evaluated by analyzing the frequency change and the interference range of the path crossing area. Through comprehensive analysis of operation stability, scheduling adaptability and flexibility demand intensity, a robot scheduling strategy is dynamically adjusted, the method is suitable for a complex construction environment, scheduling efficiency can be optimized, construction conflicts can be reduced, and the overall performance of multi-robot cooperation can be improved.

The invention relates to the technical field of robot scheduling, and discloses a robot task management framework system based on asynchronous communication. The system comprises a task asynchronous analysis module for performing time sequence decoupling on an original task instruction stream of a heterogeneous robot terminal by means of a multi-channel buffer queue to separate out independent executable task units; the resource dynamic mapping module is used for establishing a variable granularity resource allocation mapping table according to cluster real-time load and task resource requirements, and recording various resource dynamic binding relationships; the task priority reconstruction module is used for constructing a directed acyclic graph scheduling topology based on task logic dependence and deadline constraint, and generating a weighted execution sequence through topological sorting; the exception isolation processing module captures exceptions during operation and triggers an isolation strategy; and the cross-node cooperation module guarantees the multi-robot cooperation task state consensus through a distributed consistency protocol. The system is suitable for heterogeneous robot cluster complex task scenes.

The invention relates to the technical field of intelligent logistics and path planning, and discloses a digital twinborn technology-based logistics robot path planning method, which comprises the steps of constructing a digital twinborn model consistent with a storage environment, identifying logistics path key nodes and obstacles, generating a plurality of candidate paths and carrying out simulation verification, and screening an optimal path according to simulation feedback, and finally issuing the path to the robot for execution. By establishing a virtual and physical two-way synchronous environment model, efficient simulation and prediction of a logistics path are realized, so that the intelligent level of robot scheduling and the stability of path advancing are improved, and the method is suitable for complex logistics scenes such as multi-robot cooperation and dynamic path optimization.

The invention discloses an AGV robot scheduling management method and system, and relates to the technical field of robot scheduling, and the method comprises the steps: calculating a task priority score Pi based on the task data of an AGV robot, generating a candidate path set Ri through employing a breadth-first search BFS in combination with an AGV robot operation environment topological graph G (V, E), carrying out the preliminary screening of the candidate path set Ri through employing a greedy algorithm, and carrying out the scheduling of the AGV robot. An initial path of the AGV is generated, an adaptive large neighborhood search ALNS is used, an objective function is set for iteration, and an optimal initial solution is output; and performing reverse annealing based on the optimal initial solution generated by the ALNS, and outputting an AGV robot scheduling management scheme in combination with neighborhood operation. According to the method, the candidate path set is preliminarily screened by using the greedy algorithm, the initial path of the AGV is generated, the ALNS is used for iterative optimization, the optimal initial solution is output, reverse annealing is combined, and an AGV scheduling management method is output, so that global optimal management of AGV robot scheduling is realized, and the intelligence and flexibility of AGV robot scheduling are improved.

The invention relates to the technical field of robot scheduling, and discloses an environmental sanitation robot scheduling method, device and equipment based on environmental sanitation task distribution, and the method comprises the steps: collecting the garbage accumulation information and people flow density data of a plurality of urban environmental sanitation regions, and carrying out the task distribution prediction through a space-time convolution network model, an environmental sanitation demand prediction result is obtained; according to the environmental sanitation demand prediction result, creating an urban region division scheme of a plurality of urban environmental sanitation regions and robot initial grouping configuration information; according to the urban area division scheme and the initial grouping configuration information of the robots, dynamic scheduling grouping of the sanitation robots is calculated in combination with the real-time garbage accumulation event data; the path planning and cleaning operation instruction of each environmental sanitation robot is output in groups based on dynamic scheduling of the environmental sanitation robots, intelligent planning of the paths of the environmental sanitation robots is realized, and the collaborative operation efficiency and stability of a multi-environmental sanitation robot system are ensured.

The invention discloses a train inspection robot scheduling control method and device, electronic equipment and a medium, and the method comprises the steps: constructing a road network map of a target operation region of a controlled inspection robot, the road network map comprising information of all path points passed by the robot, path information between the path points, and the weight of each path information; the weight is calculated according to the distance between two path points in the path and a preset cost value; generating an adjacent matrix according to all path point information and path information in the road network map and the weight on each path; when the controlled inspection robot executes a task, acquiring a starting point and an end point of the current task, and traversing the adjacent matrix according to the acquired starting point and end point to calculate an optimal path; and sending the generated optimal path information to the controlled inspection robot. The system can adapt to the complex environment of train inspection, and the efficiency, safety and reliability of train inspection are improved.

The invention provides a multi-agent cooperative task inference and robot scheduling system, which comprises an interaction unit used for acquiring a natural language instruction issued by a user, sending the natural language instruction to an intelligent processing unit and interacting with the intelligent processing unit, receiving an execution result of a task and body state information of a robot, and sending the received execution result to the intelligent processing unit; sending a task missing information supplementing request when the task information is missing; the intelligent processing unit is used for dynamically sensing an environment and a task state according to a user instruction, a body state and environment information fed back by the robot and stored memory, and performing dynamic planning and decision making based on a sensing result and an inverse result of a task execution result to realize task reasoning and robot scheduling; and the robot cluster executes tasks according to the action sequence and feeds back own body state information and environment information. According to the invention, through a multi-agent cooperation framework based on a large model, the robot is endowed with dynamic reasoning and active interaction capabilities, and the requirements of efficient task reasoning and cooperative execution in a dynamic environment are met.

The invention discloses a multi-robot cooperative scheduling system and method based on a distributed digital twinborn body, and relates to the technical field of intelligent control, and the system comprises a control module, the distributed digital twinborn body and a plurality of robots. The control module obtains the task request and determines an initial robot corresponding to the task request from the multiple robots based on the operation knowledge graph, the task object digital twinborn body and the robot digital twinborn body; obtaining state parameters of the initial robot based on the robot digital twin, and evaluating a robot cluster capable of executing the task request in the initial robot based on the state parameters; and determining a to-be-executed task of each target robot in the robot cluster based on a preset robot sorting scheduling algorithm. The robot scheduling system is used for solving the problem that in the prior art, an existing robot scheduling system cannot meet the requirements of multiple batches and multiple varieties of workpieces, and cooperative scheduling of multiple robots is achieved to meet the requirements of the multiple batches and multiple varieties of workpieces.

The invention relates to the technical field of robot scheduling, in particular to an airport terminal manned robot scheduling method and system, and the method comprises the steps: collecting the state information of a robot in real time, and receiving the real-time demands of a passenger, the real-time demands comprising the position information of the passenger and the target position information; a bipartite graph matching model is constructed, the bipartite graph matching model comprises a passenger demand set and an idle robot set which form two subsets of a bipartite graph respectively, and by constructing an OD matrix, the path length, the passenger waiting time and the robot energy consumption serve as optimization targets. A self-adaptive Hungary order dispatching algorithm is adopted, special environment characteristics of an airport terminal are combined, an airport terminal multi-robot dispatching scene is established, a bipartite graph order dispatching matching model is constructed, the path length, the passenger waiting time and the robot energy consumption serve as optimization targets, an OD matrix of a loading task is calculated, and the loading task is dispatched. And dynamic allocation and intelligent scheduling of passenger demands and robot resources are realized.

The invention discloses a cooperative scheduling method of an industrial robot scheduling system, and relates to the technical field of cooperative scheduling, and the method comprises the steps: recording each robot participating in a scheduling task as a target robot, and obtaining a path transmission quality imbalance coefficient, a queuing coefficient and a transmission signal attenuation coefficient between the target robot and other target robots; a communication delay coefficient is obtained according to the path transmission quality imbalance coefficient, the queuing coefficient and the transmission signal attenuation coefficient, and communication delays between the target robot and other target robots are divided into different grades by combining a preset threshold value; and different optimization measures are adopted according to different communication delay levels to assist the cooperative scheduling of the industrial robot scheduling system, so that the communication delay between the industrial robots can be detected and corrected, timely task coordination and no data distortion are ensured, the influence on the execution efficiency of the overall assembly task is reduced, and the efficiency of the whole assembly task is improved. The production line stability and the product quality are ensured.

The invention relates to the technical field of robot control, and discloses a robot scheduling method, device and system. Environment data sent by a robot is received, and the environment data is environment data of an area where the robot is located; according to the environment data, performing map model construction on the area to obtain map information; obtaining local map information, positioning information and a motion path according to the map information; and sending the local map information, the positioning information and the motion path to the robot to support autonomous movement and task execution of the robot in the current area. In this way, the overall operation efficiency and stability of the system are improved.

The invention relates to the technical field of AGV robot scheduling and control, and discloses an AGV robot scheduling method and system of an intelligent factory and a storage medium. The method comprises the steps of collecting bearing sensing data to identify a product height value according to a product carrying instruction and a transportation route, and if the value is higher than a reference value, generating an auxiliary scheduling instruction; next, nearby non-loaded AGVs are searched to serve as temporary robots, scheduling values are obtained in combination with task load and distance sorting of the temporary robots, and the optimal robot is selected to serve as a slave robot to be paired with the original AGV robot to jointly bear products; and finally, the master and slave robots respectively respond to the matched scheduling instructions. According to the method, through master-slave cooperation and accurate scheduling, the AGV transportation stability and efficiency are improved.

The invention provides an efficient transfer robot scheduling system for components in intelligent warehousing, and relates to the technical field of intelligent warehousing logistics, and the method comprises the steps: analyzing a transfer task instruction, extracting a task starting point coordinate, an end point coordinate and a path constraint condition, and collecting warehousing environment data in real time; the storage environment data comprises position offset state data, track boundary data and dynamic obstacle distance data; constructing a grid map model based on the position offset state data, the track boundary data and the dynamic obstacle distance data; and iteratively calculating a global final path node sequence on a magnetic stripe track by adopting a colistification algorithm fusing an oscillation factor disturbance mechanism and a self-adaptive weight strategy based on the grid map model. According to the invention, the problems of poor path flexibility and insufficient control precision in an intelligent warehousing component carrying system are solved.

The invention relates to the technical field of elevator control, and discloses an elevator task control method and system based on medical logistics robot scheduling. The method comprises the steps that according to a dispatching task of a medical logistics robot, the robot position and electric quantity information of an available medical logistics robot, a dispatching task to be executed, the floor where an elevator of an available elevator is located, the running direction, whether the elevator is fully loaded or not and the floor where the elevator needs to stop are recognized, and a distribution combination is constructed; an evaluation function is constructed according to the robot path length of each scheduling task in the distribution combination and the time of taking the corresponding available elevator, and a target medical logistics robot and a target available elevator are obtained through scheduling optimization based on the evaluation function; constructing a target control signal according to the target medical logistics robot and the target available elevator; and a scheduling path of the target medical logistics robot is generated, and elevator task control is conducted on the target available elevator according to the scheduling path and the target control signal. The accuracy of elevator task control can be improved.

The invention discloses a multi-task cooperative control robot scheduling method and system based on quantum optimization, and relates to the technical field of computers, and the method is characterized in that a structured data stream is generated according to an equipment operation parameter decision; a process constraint condition is generated through a digital twin engine; process constraint conditions are coded into feature vectors based on federated learning, and the feature vectors are mapped to a QUBO model; real-time production line topological structure hard constraints are injected, and an optimal scheduling scheme is solved through a quantum annealing algorithm; performing multi-physical field simulation verification; and dynamically updating model weight parameters of federated learning according to the deviation degree and an update gradient of a process constraint condition generated by a digital twin engine. According to the method, safe, efficient and elastic scheduling in a complex manufacturing scene is realized through a cooperative technology system of quantum optimization global solution, federated learning heterogeneous compatibility, digital twinning virtual-real verification and dynamic feedback continuous iteration.

The invention relates to the technical field of transportation scheduling, and particularly discloses an unmanned Internet of Things workshop cargo transportation robot scheduling method and system, and the method comprises the steps: receiving demand point locations inputted by an administrator, calculating the virtual distance between the demand point locations, and creating a point location distribution map; obtaining task parameters of each demand point location based on a preset matrix to obtain a task parameter table, and calculating a delivery level of each demand point location based on the task parameter table; selecting a distribution path in a point location distribution diagram according to the distribution level, and feeding back the distribution path to an administrator; and correcting the distribution path based on the feedback information of the administrator, and generating a scheduling instruction according to the corrected distribution path. According to the method, the task parameter table is obtained by counting the task parameters of each demand point location based on the matrix, the task parameter table is subjected to self-identification, the dynamic weight of each index is determined, and then the path selection process is adjusted, so that the integrating degree of the path and the actual situation is greatly improved.

A robot scheduling system, which relates to the technical field of logistics, and is used for solving the problem of the logistics efficiency of a warehousing system not being high. The system comprises a scheduling device and a rack (101), wherein the rack (101) corresponds to a plurality of transfer robots (102); the scheduling device is in communication connection with the plurality of transfer robots (102); and the scheduling device is configured to control, upon receiving an order instruction for instructing the transfer of goods in a target rack area, a transfer robot (102) corresponding to the target rack area to operate, the target rack area is one of a plurality of rack areas which are obtained by means of partitioning the rack (101) in the lengthwise direction and / or the depthwise direction of the rack (101) on the basis of attribute information of the rack (101) and / or a placement rule for goods on the rack (101), and one rack area at least corresponds to one transfer robot (102). Further provided are a robot scheduling method and a storage medium.

The invention provides a medical hospital guide robot scheduling method based on dynamic path planning. The method comprises the following steps: acquiring hospital guide information of a patient to be diagnosed; determining a task priority of the hospital guide information based on the hospital guide information; based on the task priority of the to-be-diagnosed patient, determining a target hospital guide robot, performing path planning processing through a preset reinforcement learning algorithm, and determining a walking path of the target hospital guide robot; and based on the walking route, controlling the target hospital guide robot to guide the to-be-diagnosed patient to the target consulting room. The hospital guide robot resource can be reasonably scheduled according to the task emergency degree, the surrounding environment information is sensed in real time through the reinforcement learning algorithm, the path is planned, the patient is guided to the destination, and the hospital guide efficiency is improved; the problems that according to an existing hospital guide robot, hospital guide robot resources cannot be reasonably dispatched according to the patient flow and the task emergency degree, surrounding environment information cannot be accurately sensed, and consequently path planning is inaccurate, and hospital guide efficiency is low are solved.

This invention relates to the field of robot scheduling technology, and more particularly to a multi-robot cloud scheduling and collaborative method and system. The method includes the following steps: acquiring building elevator group data, and dividing the building elevator service groups based on the building elevator group data to obtain building elevator service group data; acquiring robot target path data; segmenting the robot target path data and building elevator service group data into path resource segments to obtain split path data; acquiring current transfer floor data, and estimating transfer waiting time based on the current transfer floor data and split path data to obtain transfer waiting time estimation data; constructing a transfer map based on the transfer waiting time estimation data to obtain transfer map data; obtaining path conflict data based on the transfer map data; and obtaining robot scheduling data based on the path conflict data to perform multi-robot collaborative scheduling operations. This invention can improve task completion efficiency and scheduling intelligence in multi-robot cross-floor collaborative scenarios.

The invention provides a charging robot scheduling method and a storage medium. The charging robot scheduling method aims at a plurality of charging requests in an intelligent parking lot, performs intelligent scheduling on a charging robot according to priorities of the charging requests and prediction of newly added charging requests, and comprises the following steps: constructing a priority potential field of a parking space-charging system based on the plurality of charging requests of a user; based on the constructed priority potential field of the parking space-charging system, modeling a multi-target tracking and priority intelligent scheduling process as a Markov decision process; based on the modeled Markov decision process, a reinforcement learning network based on the model is designed, and the reinforcement learning network comprises a dynamic network, a value-strategy network and a target network; based on the constructed reinforcement learning network, parallel training of a plurality of networks is realized by designing an interaction mode between a model of a dynamic network and a model of a strategy-value network and a collected data set.

The invention discloses a storage robot cluster management system and method, and belongs to the technical field of robot scheduling. The system comprises a central management server and an autonomous mobile robot cluster. The central management server comprises a congestion prediction and global path planning module; the congestion prediction and global path planning module comprises a congestion prediction sub-module and an A * path planning sub-module with space-time cost; the autonomous mobile robot is internally provided with a multi-sensor sensing and communication module and a local decision module, and the off-line centralized training platform is used for optimizing the local decision module in each autonomous mobile robot through a centralized evaluation network with global information in a simulation environment. The invention provides a hybrid control architecture integrating global path planning, dynamic task allocation and local real-time decision based on multi-agent reinforcement learning, and efficient and smooth cluster collaborative operation can be realized.

The invention discloses a fire-fighting equipment availability monitoring and intelligent three-dimensional warehouse storage management system, and relates to the technical field of warehouse management, and the system comprises an intelligent three-dimensional warehouse storage module which is used for storing fire-fighting equipment and carrying out the access management of the fire-fighting equipment based on an automatic access system and a robot scheduling system. The intelligent three-dimensional warehouse storage module comprises a fire-fighting equipment pre-distribution mechanism based on dynamic storage optimization, and the fire-fighting equipment pre-distribution mechanism based on dynamic storage optimization is combined with fire risk analysis, historical fire-fighting equipment use data and fire-fighting equipment service life prediction data to dynamically adjust the storage position of fire-fighting equipment. The fire-fighting equipment availability monitoring module is designed, so that the functions of real-time monitoring and fault prediction of the fire-fighting equipment are realized, the problem of equipment failure caused by long-time non-use or equipment aging is solved, the availability and reliability of the equipment are improved, and fire emergency work is guaranteed.

The invention discloses a mobile charging robot return charging scheduling method, and particularly relates to the technical field of mobile robot scheduling management, and the method comprises the steps: collecting state information including the current position, residual electric quantity, task residual path length, battery performance and other contents; when the state meets a scheduling evaluation triggering condition, calculating a behavior urgency index and an energy competition index; inputting the two indexes into a trained learning model, and outputting a decision parameter group containing a scheduling trigger mark, a path sorting factor and a path tradeoff factor; weighing the candidate charging paths according to the decision parameter group, and selecting a comprehensive optimization result as a target path; according to the method, the scheduling quantitative judgment is realized by collecting the multi-dimensional state information and constructing the behavior urgency and energy competition index, the decision parameter group is generated based on the index result, target path screening is guided, a closed-loop mechanism from state perception and intelligent evaluation to path selection is formed, and the scheduling accuracy and the response efficiency are improved.

The invention discloses a multifunctional robot scheduling method, device and equipment and a medium. The method comprises the steps of querying a first robot in an idle state according to a to-be-executed task list, wherein the first robot can execute a plurality of different types of tasks; the first robot is scheduled to move to a target area to be paired with the second robot, and the second robot and the first robot can execute a cleaning task after being combined; and sending an instruction for executing the cleaning task to the successfully paired first robot according to the to-be-cleaned area. By adopting the embodiment of the invention, the first robot is scheduled to execute the cleaning task, so that the cleaning time and the task value of the first robot can be considered, coordination of a plurality of different types of tasks of the first robot is realized, and interruption and recovery of the cleaning task and notification of task abnormity are realized; the efficiency of the multifunctional robot is improved, and the cohesion of robot operation in a scene is conveniently improved based on the scheduling method.