59 results about "Machine scheduling" patented technology

A scheduler for a machine includes a selector and a traverser. The selector selects the traverser and selects policies from a library based on a model of the machine. The traverser looks for a preferred itinerary. The scheduler includes a first module to choose an itinerary, a second module to calculate a combined weight, and logic to repeatedly operate the first and second modules to choose additional itineraries from the group of itineraries and calculate their combined weight until an end condition is reached. The end condition includes one or more of when a calculated combined weight for an itinerary exceeds a predetermined threshold, when a predetermined time has elapsed, when a combined weight for each of a predetermined number of itineraries has been calculated, and when a combined weight for each itinerary in the group of itineraries has been calculated. The first module chooses an itinerary from either a list of all of the itineraries or a sub-set of the itineraries. The second module includes an itinerary validator to compute an itinerary weight for the chosen itinerary for each of the policies and also includes a combiner to combine the computed itinerary weight for each of the policies into a combined weight for the chosen itinerary. The scheduler further includes a third module to choose an itinerary that has a combined weight calculated by the second module that is greater than a combined weight of any other itinerary calculated by the second module.

The invention discloses a method, a device and equipment for scheduling essential factors of an agricultural machine, and a readable storage medium. The method comprises the steps of dynamically generating agricultural machine scheduling subsystems based on an agricultural machine essential factor scheduling requirement and an object, wherein the agricultural machine scheduling subsystems comprises an operation information service subsystem, an agricultural machine terminal information dynamic accessing and management subsystem, an agricultural machine operation task management and schedulingsubsystem, an agricultural machine dynamic supervision subsystem and an agricultural machine operation auxiliary decision subsystem; acquiring and analyzing agricultural data information in a preset area, transmitting each piece of agricultural data information to the agricultural machine scheduling subsystem which corresponds an agricultural data information work attribute, thereby processing thereceived agricultural data information by the agricultural machine scheduling subsystem; and outputting and displaying the processing result of each agricultural machine scheduling subsystem to the agricultural data information. The method, the device, the equipment and the readable storage medium settles technical problems of low operation efficiency of an agricultural service organization and incapability of performing scientific matching and scheduling for aiming at a production requirement of the farmer.

The invention provides a hybrid heuristic shifting bottleneck procedure for solving parallel-machine job-shop scheduling. According to the procedure, a hybrid shifting bottleneck procedure is provided by combining a shifting bottleneck procedure and a heuristic tabu search method, and a GPU is further utilized to achieve a parallel tabu search algorithm. The hybrid shifting bottleneck procedure comprises the following solving processes of (1) decomposing a PDG of a parallel-machine job shop into a series of single-machine scheduling (SMS) sub-problems and parallel-machine scheduling (PMS) sub-problems; (2) solving each SMS sub-problem and PMS sub-problem; (3) determining and scheduling bottleneck machines; and (4) carrying out reordering and optimization on the current PDG during each iteration and rescheduling the scheduled bottleneck machines. According to the hybrid procedure, an unstable search result is avoided, an optimal solution can be found out and the calculation time of finding the optimal solution is shortened.

The present invention provides a farming machine cross-regional working scheduling method and system which are used for solving the farming machine scheduling problem. A farming machine cross-regional working scheduling platform determines average income per unit time between a farming machine and a farmland according to the information reported by farming machine terminals and farming terminals; and distributing a farmland to be worked having the highest average income per unit time with each farm machine to the farming machine, establishing the scheduling relation between the farm machine to be scheduled and the farmland to be worked, and sending the scheduling relation to the farming terminals corresponding to the farming machine identification and farmer terminals corresponding to the farmland identification in the scheduling relation. According to the technical scheme, the farming machine cross-regional working scheduling method and system can realize scientific and reasonable farming machine and farmland supply and demand scheduling and comprehensively consider various factors to realize benefit maximization of farm machines.

The invention discloses an aluminum profile turning and extruding workshop energy consumption optimized scheduling method based on the ant colony algorithm. According to the method, the aluminum profile extruding workshop production scheduling process is abstracted into a non-equal parallel machine scheduling problem with the time difference electricity price and the energy consumption of the scheduling process taken into consideration, the ant colony optimization algorithm based on the iterative calculation is provided on the basis of the analysis on this type of scheduling process, the scheduling of machines and workpieces is achieved through ants in the machine-workpiece-machine iterative mode, and the solve errors caused when the machines and the workpieces are independently scheduled in a traditional ant colony algorithm are reduced.

The invention discloses a method for optimizing energy consumption of an aluminum section workshop based on an ant colony algorithm and in view of time difference electricity price. According to the method, a production scheduling process of the aluminum section workshop is abstracted as a nonequivalent parallel machine scheduling problem in view of time difference electricity price and energy consumption; the ant colony optimization algorithm based on iterative computation is proposed on the basis of analysis on the scheduling problem, and ants realize scheduling of a machine and a workpiece through an iterative mode of machine-workpiece-machine, so that solution errors caused by independent scheduling of the machine and the workpiece in a conventional ant colony algorithm are reduced; in addition, a right-shift local search method is proposed according to the characteristics of time difference electricity price, so that the precision of a solution can be improved, the cycle frequency of ant colony computation can be greatly reduced, and the energy consumption of scheme solving is reduced.

The invention discloses a scheduling method for a porcelain calcination process based on ant colony optimization. As to a calcination link in the large-batch porcelain production process, a batch production scheme for porcelain production is obtained by adopting a coding / decoding mechanism and an optimization strategy of an ant colony algorithm, so that the production efficiency is greatly improved and the production cost is reduced. The method simultaneously gives a program development scheme under the JSP (Java Server Page) environment, so that the algorithm can be efficiently implemented for different practical problems, and then production schemes are obtained.

The invention relates to a software implementation method of a DC charging pile monitoring system. A main state machine is arranged for that monitoring system of the DC char pile to be dispatched. Some of the complex large working states in the master state machine nest small state machines, each working state under each small state machine is added to timeout process, some common working states of each large working state are found out as the common migration invocation state under the main state machine. When each communication function module is involved in the master state machine scheduling process, the master state machine scheduling adopts information exchange mechanism, sets different information exchange flags, and adopts non-blocking transceiver processing mode to realize the bottom communication driving module. The invention can meet the real-time requirements of complex logic control and multi-task parallel operation, can meet the real-time requirements of the program whenthe communication module is too many, the communication function module is easy to expand, the program logic readability is strong, and the program logic is easy to upgrade and maintain.

The invention, which belongs to the field of production scheduling and operations research, provides a production scheduling method based on a distributed set robust parallel machine scheduling model.According to the method, a distributed set robust optimization model DR-PMSP-RA having a risk aversion characteristic is constructed; on the basis of an objective function and a constraint conditionof the model, an expression of an initial model DR-PMSP-RA1 is obtained; the objective function of the DR-PMSP-RA model is transformed to obtain an estimation upper bound of the objective function, the initial model is transformed into an estimation model DR-PMSP-RA2, wherein the estimation model can be decomposed into two independent sub models, the two sub models are solved and the optimal sub model solution is an optimal solution of the model, and thus an optimal production scheduling plan is obtained. The model established based on the method conforms to the actual production situation well; and with information in a production environment, the decision-making risk is reduced under the circumstance that the system performance is guaranteed.

The invention provides an equipment state real-time monitoring method and system based on a distributed scheduling task. The method comprises the steps that monitoring information of a key fault partof equipment is received and the monitoring information is transmitted to a real-time database; the scheduling center realizes task allocation and scheduling, the job actuators are distributed in different machines, and the scheduling center allocates tasks and execution time executed by the job actuators on each machine according to a configured task allocation strategy; the actuator obtains dataneeded by equipment monitoring through the real-time database, then processes the data and calculates the current running state of the equipment. According to the invention, the concurrency rate of the scheduled tasks is improved by scheduling the timed tasks and splitting the scheduled tasks into the scheduling center and the executor, the execution efficiency of timed scheduling is improved, itis ensured that the state of the equipment can be monitored in real time, state change detection of the equipment is accurate to the second level, and the real-time performance and accuracy of equipment monitoring are further improved.

The invention discloses an RSTP state machine scheduling method. The method comprises the following steps: S1, dividing sub-state machines into a set A, a set B and a set C, wherein the set A is a sub-state machine which can directly generate influence when receiving the message, the set B is a sub-state machine which does not directly influence the received message, and the set C is a sub-state machine for sending a message; S2, operating the sub-state machines in the set A; S3, after the sub-state machines in the set A are operated, operating the sub-state machines in the set B; S4, after the sub-state machines in the set B are operated, operating the sub-state machines in the set C. According to the RSTP state machine scheduling method and system provided by the invention, the state machines of RSTP state machines are divided into three sets, and the operation sequence of the sets is set, so the operation disorder of the RSTP state machines in the operation process can be reduced, the state abnormality is avoided, and the convergence time is shortened.

The invention relates to an additive manufacturing identical parallel machine scheduling method for a selective laser melting process. The method comprises the following steps of S1, obtaining machining operation related parameters; S2, constructing a machining cost model according to the machining operation related parameters; S3, establishing an additive manufacturing identical parallel machinescheduling model considering the part construction direction by taking minimization of the unit volume machining cost of all operations as an optimization target according to the machining cost model;S4, constructing an improved firefly algorithm; and S5, based on the improved firefly algorithm, obtaining final optimization target values of all the operations and corresponding scheduling schemes.The additive manufacturing identical parallel machine scheduling for selective laser melting process is realized, and the parallel machine scheduling efficiency is improved while the part machining cost is reduced.

A device and method for scheduling a set of jobs for a plurality of machines are provided. The method for scheduling the set of jobs for the plurality of machines. Each job is defined by at least one feature which characterizes a processing time of the job. If any of the machines is free, a job from of the set of jobs is selected to be carried out by said machine and scheduled for said machine. The job is selected as follows: a Graph Neural Network receives as input the set of jobs and a current state of at least the machine which is free, the Graph Neural Network outputs a reward for the set of jobs if launched on the machines, which states are inputted into the Graph Neuronal Network, and the job for the free machine is selected depending on the Graph Neural Network output.

The invention relates to an uploading machine scheduling method and device, and the method comprises the steps: obtaining an uploading request transmitted by a client, and enabling the uploading request to carry the operation information of the client; selecting a set of alternative uploads for the client according to the operation information; and selecting an uploading machine for serving the uploading request according to the latest service weight value of each alternative uploading machine in the set. The method and the device are used for solving the problems that resources of each uppercomputer cannot be reasonably coordinated and optimal services are difficult to provide for users when scheduling is carried out according to the pre-configured weights of the upper computers.

The invention relates to a time constraint type parallel machine energy-saving scheduling method based on a collaborative algorithm, and relates to the technical field of production scheduling in themanufacturing industry. The time constraint type parallel machine energy-saving scheduling method based on the collaborative algorithm comprises the following steps: (1) performing formalized description on a time constraint type parallel machine energy-saving scheduling problem by using mathematical symbols and clearly considering performance indexes; (2) establishing a parallel machine scheduling model considering that the maximum completion time constraint is satisfied and the energy consumption is minimized; (3) designing a collaborative algorithm for the scheduling model to solve; and (4)generating a corresponding scheduling Gantt chart according to a scheduling scheme obtained after the algorithm is ended, and recording corresponding energy consumption and maximum completion time. Compared with the prior art, the method has the advantages that the maximum completion time requirement and the energy-saving target can be considered, and meanwhile, the designed collaborative algorithm has good search performance.

The invention provides an intelligent resource scheduling method based on vSphere, which is suitable for a complex environment of a multi-host cluster. The method comprises the following steps: setting the number of virtual machines and threshold values and weights of resource parameters; obtaining the resource utilization rate of each host in the cluster and the number of the virtual machines, checking whether the resource utilization rate of each host exceeds a threshold value or not, and adding the hosts exceeding the threshold value to a resource excess list; counting 24-hour performance usage reports of the hosts in each cluster, performing analyzing to obtain the least frequently-used time period of host resources, and setting the most frequently-used time period as virtual machine scheduling time; sorting the weight sequence of each host in each cluster according to a weight formula to obtain a resource weight ranking list of the hosts; obtaining the host with the threshold value, obtaining the weight ranking of the virtual machines through a weight formula, and storing the virtual machine with the highest ranking in a virtual machine list needing to be scheduled; and in thevirtual machine scheduling time, migrating to the host with the lowest host resource weight rank by using vMotion, or using cold migration resources after suspending the virtual machine.

The invention discloses a robot path planning method. The method comprises the following steps: acquiring a tracking path; controlling the robot to advance according to the tracking path; if an obstacle is detected on the tracking path in the advancing process of the robot, acquiring obstacle information of the obstacle; generating an obstacle avoidance path according to the obstacle information, wherein an end point of the obstacle avoidance path is arranged on the tracking path; and controlling the robot to advance from the current position to the tracking path according to the obstacle avoidance path. The invention further provides a robot path planning device, a management system and a computer readable storage medium. The robot can flexibly avoid obstacles, and the interference of obstacle avoidance of the robot on multi-machine scheduling is avoided.

The invention relates to the technical field of Internet of Things, in particular to a response simulation method, device and equipment and a computer readable storage medium. The method comprises thefollowing steps of: aiming at each subscription theme in a plurality of preset subscription themes, setting a response mode queue corresponding to the subscription theme, wherein the response mode queue comprises a plurality of response items; receiving target task information related to a target theme from a server, wherein the target task information is sent to the server by a scheduling device, and constructing a response load corresponding to the target task information based on a response mode queue corresponding to the target theme, and then sending the response load to the server, sothat the server sends the response load to the scheduling device to complete response simulation. According to the response simulation method, device and equipment and the computer readable storage medium provided by the embodiment of the invention, the test efficiency of the multi-machine scheduling system can be improved.

The invention discloses a multi-machine dispatching method for rail-mounted hanger rail robots, and relates to the technical field of rail-mounted robotics.The rail and a rail carrying robot are used for dispatching operation of a plurality of rail robots, and the rail comprises a closed-loop main rail, an auxiliary rail, a rail fixing rod and a rail fixing end; the rail carrying robot is installed on the main rail and controllably slides along the main rail, the rail robot is controllably hung on the rail carrying robot, and after the rail robot reaches one end of the auxiliary rail, the rail robot is controllably disengaged from the rail carrying robot and controllably hung on the auxiliary rail. And the guide rail can controllably slide along the auxiliary rail and span to different positions of the main rail, so that dispatching operation is completed.

A method for scheduling a set of jobs for a plurality of machines. Each job is defined by at least one feature which characterizes a processing time of the job. If any of the machines is free, a job from of the set of jobs is selected to be carrying out by said machine and scheduled for said machine. The job is selected as follows: a Graph Neural Network receives as input the set of jobs and a current state of at least the machine which is free, the Graph Neural Network outputs a reward for the set of jobs if launched on the machines, which states are inputted into the Graph Neuronal Network, and the job for the free machine is selected depending on the Graph Neural Network output.

The invention provides an uncorrelated parallel machine scheduling scheme, which deeply analyzes the complex mutual overlapping relationship between time windows and improves the linear relaxation upper bound of the model. A new preprocessing algorithm is designed, and more to-be-processed objects can be successfully scheduled. Specifically, firstly, a first variable representing whether an object to be processed is scheduled to a time window corresponding to an implementation subject and a second variable representing start time corresponding to the time window allocated to the object to be processed are introduced into a model, and then conflicts and relationships between the time windows are analyzed; corresponding scheduling schemes are obtained according to different conditions, and scheduling is carried out according to the scheduling schemes. A new improved mixed integer linear programming (MILP) model is obtained, introduction of a large M variable is avoided, and a tighter linear relaxation upper bound is achieved. The introduction of 0-1 variables among all the to-be-processed objects is avoided, and the solving efficiency of the scheduling scheme is improved.

The embodiment of the present invention provides a high-end equipment batch processor scheduling method and system for improving cuckoo search. First, assign workpieces to each machine according to the number of workpieces, the number of machines, and the first predetermined value, and determine the workpieces that need to be processed in each processing batch of each machine, then generate the initial population, and use the Levy flight in the cuckoo algorithm Operation, update the current population, calculate the improved individual fitness value, and compare it with the fitness value of the individual before improvement, retain the individual with a large fitness value; use random probability and cross mutation operation, discard the updated population Individuals, and finally, output the global optimal solution. The above technical solution determines that each workpiece will be assigned to a specific machine, the batching method of the workpieces on the machine, and the batch processing sequence can minimize the manufacturing span, improve production efficiency, and solve the problem of parallel machine batching under the learning effect. Scheduling problem.

The invention discloses a fast non-dominated sorting genetic algorithm for a dynamic equivalent parallel machine scheduling problem. The algorithm comprises the following steps: S1, generating a population by using a hybrid heuristic rule MODD, ATC, X-RM and a random method; s2, target value calculation is carried out on population individuals based on a dynamic programming decoding method; s3, judging whether a termination condition is met or not; if yes, ending; otherwise, performing rapid non-dominated sorting on the individuals in the population; s4, generating a filial generation population through a genetic operator; s5, combining the parent population and the child population into a population with the scale of 2N; s6, performing rapid non-dominated sorting on individuals in the population, and determining the population in combination with crowding degree calculation; and S7, performing neighborhood search on population individuals to generate a new solution to replace an original redundant solution. The method has the characteristics of improving the workshop machine utilization rate and meeting the customer delivery time.

The invention provides a single-machine minimized total tardiness scheduling method based on a distributed robust model and belongs to the technical field of production scheduling and internal and external production resource optimization. The method comprises the steps that a distributed robust optimization model for solving the single-machine scheduling problem is established firstly to obtain atarget function expression of the model; then, the distributed robust optimization model is converted into an integer second-order cone programming model; the converted model is solved, arrangement combinations of all workpiece machining sequences are expressed in a search tree in an enumeration way, the search tree is pruned through a branch-and-bound algorithm, and finally an optimal single-machine scheduling scheme of the minimized total tardiness is obtained. The method gives consideration to uncertain factors of a production environment, so that the model is a determined single-machine model more conforming to the actual production situation compared with an assumed production environment, and the obtained scheduling scheme can be better applied to actual production.

The invention relates to a single-machine scheduling method for additive manufacturing oriented to a selective laser melting process. This method proposes a single-objective mathematical model with the optimization goal of minimizing the production cost per unit volume of parts. On this basis, based on the excellent performance of the distribution estimation algorithm in combinatorial optimization problems such as production scheduling, combined with the neighborhood search strategy It is used to solve the single-machine scheduling problem of additive manufacturing considering the direction of part construction. In the algorithm, a real number encoding method including part sorting, job assignment and part construction direction is proposed. Based on this encoding, random sampling is used to generate On this basis, the probability model is updated by sampling the elite population. In order to ensure the diversity of the population and avoid local optimum, after each generation of the elite population is generated, the individuals in the elite population are locally adjusted through neighborhood search. Make the algorithm jump out of the local extremum and find a better solution.

The invention discloses a CDN node cross-machine scheduling method and system based on an eBPF technology, and the method comprises the steps: judging the load conditions of virtual network cards under a first server and a second server on a CDN node; when the first server does not have a virtual network card with a redundant bandwidth, guiding the access request to a virtual network card with the minimum load in a second server under the CDN node; embedding eBPF module codes in a TC layer of an operating system kernel in each machine, and installing a communication Agent module in each single machine, wherein the Agent module is mainly responsible for cross-machine communication (through an ARP broadcast function of a router) and data interaction (cross-machine network card load condition intercommunication, and UDP communication is adopted), and the eBPF module is mainly used for selecting a virtual network card with low bandwidth load from a machine cluster in a node to carry out data return, so that the problem of multiple 302 redirection of a client is solved. and the effect consumption of the request is reduced; besides, an endless loop is avoided during cross-machine scheduling in the node, so that the optimal scheduling is realized.

The invention discloses a Job Shop scheduling problem solving method and system based on a double-loop nested optimization strategy, and the method comprises the steps: dividing an actual order into processing tasks of the same product, the same delivery period and the same delivery period to form different virtual orders; constructing a double-loop nested optimization strategy to perform joint optimization of batch segmentation and job sorting on the virtual orders; performing batch segmentation scheme optimization on the virtual orders by the double-loop nested optimization strategy through a first quantum genetic algorithm on the outer layer; and disassembling the Job Shop scheduling problem into a plurality of same-type parallel machine scheduling problems on the inner layer based on a batch segmentation scheme, and performing job sorting optimization on each same-type parallel machine scheduling problem through a second quantum genetic algorithm. According to the method, an optimized operation plan scheme which meets the requirements of an actual manufacturing system and is free of tardiness or as little as possible in tardiness can be obtained through the double-loop nested optimization strategy, and the method is suitable for a large-scale batch type manufacturing system.

The invention provides a robust single machine scheduling method based on interval uncertainty, and belongs to the field of production scheduling and operations research. The method includes: establishing a robust optimization model RSMSP of single machine scheduling, wherein an optimization target is to search an optimal workpiece machining sequence to enable the maximum waiting time of the sequence to be minimum in a poorest scene; during solution, converting the model RSMSP to a mixed linear integer programming model P; and solving the model P by employing a two-stage heuristic solution algorithm to obtain the optimal machining sequence, namely an optimal scheme of robust single machine scheduling. According to the method, uncertain parameters are expressed by employing the mode of interval estimation, a method for recognizing finite possible poorest scenes in an infinite scene set, which is in more accordance with the production reality, is firstly proposed, and decision risks canbe reduced to the maximum and the system performance can be guaranteed in the condition of poor information.

A system with automatic on-off scheduling and its automatic on-off scheduling control method, in which multiple groups of automatic on-off time can be set by the user, and the system time can be checked by using the periodic system interruption generated by the periodic interruption of the chip . When the system time reaches or exceeds the set automatic shutdown time, set the next startup time, and shut down directly after the setting is completed. After the next startup, restart the periodic system interrupt generated by the periodic interrupt to check the system time, and compare it with the next set of power-on / off time set by the user. The cycle is repeated continuously until all the set automatic power-on and power-off times are executed. Here, the scheduling control of multiple automatic power-on and power-off of the system is achieved through the firmware program.

The invention aims to provide a multi-robot scheduling method and equipment, a server program of the multi-robot scheduling method mainly plays a role in sharing operation data, and is responsible for collecting and distributing operation data packets of shared robots to robot individuals; and the robot individuals can trigger multi-machine scheduling of the corresponding scheduling strategies only when running into the scheduling areas, the decision-making process of the multi-machine scheduling is not initiated by a server but independently completed by client programs carried by the robot individuals, and the scheduling strategies can be preset decision-making tree models of the corresponding scheduling areas according to the scheduling strategies. According to the invention, a lightweight data packet format is adopted between the robot and the server to carry out operation information exchange, so that the communication load caused by an overlarge data packet and a retransmission mechanism is greatly reduced. The server can be arranged in a local area network and does not need to be connected with an external network, so that the information safety in a user environment is ensured, and the interactive communication traffic between the server and the robot is small.