33 results about "Machine selection" patented technology

The invention discloses a reconfigurable flexible job shop scheduling optimization method with secondary clamping constraint, and relates to the technical field of intelligent manufacturing and production optimization. The method comprises the following steps of: 1) establishing a mixed integer linear programming model considering a reconfigurable flexible job shop scheduling problem of secondary clamping by taking minimization of maximum completion time and minimum number of chemical workers as targets; 2) designing a three-segment coding mode and a decoding mode corresponding to the mixed integer linear programming model based on process sorting, machine selection and worker selection; and 3) based on the three-segment coding mode and the decoding mode, adopting an improved multi-target genetic algorithm to solve an optimal scheduling scheme of the mixed integer linear programming model. According to the method, processing machine selection, auxiliary module selection, processing sequence sorting and secondary clamping worker selection of a manufacturing workshop can be considered at the same time, the workshop production efficiency is improved, and the method has the advantages of being good in model performance, small in result fluctuation and high in stability.

The invention belongs to the field of workshop scheduling, and particularly discloses a hybrid flow workshop dynamic scheduling method and system considering machine predictive maintenance, and the method comprises the steps: taking the minimization of total completion time, maintenance cost and processing cost as the target, building a hybrid flow workshop scheduling problem as a multi-target joint optimization model, and carrying out the optimization of the multi-target joint optimization model; setting intelligent agents with the same number as the processing stages, and constructing a Markov decision process; each agent has an independent scheduling network, including a workpiece scheduling network and a machine scheduling network; based on a Markov decision process, training an intelligent agent, maintaining a workshop at an operation and maintenance point, and respectively calling a workpiece scheduling network and a machine scheduling network at a scheduling point to select workpieces and machines; and after training is completed, workshop dynamic scheduling is realized based on the trained intelligent agent. According to the method, the problem of hybrid flow shop dynamic scheduling considering machine predictive maintenance is effectively solved through workshop scheduling integrating machine operation and maintenance, and the method has good dynamics and adaptivity.

The invention provides a whole-process production scheduling method considering processing and assembling, which comprises the following steps: constructing a two-stage flexible flow shop scheduling model, scheduling processing tasks of all parts of a plurality of products on a parallel machine in the first stage, and scheduling assembling of the product parts and processing tasks of semi-finished products in the second stage; minimizing the maximum completion time and the total energy consumption of the machine is taken as a double-optimization target; a multi-target swarm intelligence algorithm is adopted to solve the model, population individuals represent a scheduling scheme through two-segment coding, the first segment of coding defines a process execution sequence, and the second segment of coding defines a machine selection result; in an iteration process, alternately executing a Thompson sampling strategy and a dedirectional sampling and generating strategy according to a preset probability, adaptively selecting a bottom layer optimization operator to realize global search, and constructing a guide solution set to realize local mining; and outputting a non-dominated solution set after iteration is ended, and obtaining a corresponding whole-process scheduling scheme after decoding.

The invention provides a remanufacturing workshop scheduling method based on reinforcement learning and evolutionary algorithm fusion, relates to the technical field of production scheduling optimization, and solves the problem of remanufacturing system scheduling in three stages of disassembly, reprocessing and assembly. A four-dimensional decision model covering process sorting, factory distribution, machine selection and speed gear scheduling is constructed, and an initial population with diversity and high-quality characteristics is generated by adopting a multi-strategy hybrid initialization method. A reinforcement learning decision module based on Q-learning is introduced, an optimal combination is dynamically selected from various crossover operators, mutation operators and neighborhood search strategies, the crossover probability and the mutation probability are dynamically corrected according to the distribution characteristics of the Pareto leading edge, and a dual escape mechanism is integrated to enhance the capability of the algorithm to jump out of local optimum. According to the scheme, the group search advantage of the evolutionary algorithm is exerted, the dynamic optimization of the search process is realized, and an efficient and accurate scheduling solution is provided for a complex remanufacturing system.

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 discloses a hybrid learning-based flexible job shop energy-saving batch scheduling method, which relates to the technical field of shop scheduling and comprises the following steps of: establishing a flexible job shop energy-saving scheduling model by taking minimization of maximum completion time and total energy consumption of a machine as optimization objectives; the method comprises the following steps of: constructing a mapping relationship between an operation process and machine selection by adopting a three-layer coding mode, initializing a population through multiple initialization strategies, decoding a coding part, and generating an initial solution; constructing a neighborhood structure, performing population evolution through an adaptive crossover and mutation operator, and adjusting the crossover and mutation probability according to the population evolution degree; searching an optimal solution through a plurality of search strategies; traversing the population to perform non-dominated sorting, and adjusting the crossover mutation rate according to a non-dominated sorting result; and judging whether an iteration termination condition is met or not, if not, continuing iteration, and if so, outputting the optimal Pareto frontier solution. According to the invention, the energy consumption is reduced while the processing time is minimized.

The invention discloses a flexible job shop integrated scheduling optimization method, device and equipment considering AGV electric quantity constraint, through the algorithm, process processing sorting and machine selection of a population individual solution are completed through two-segment coding, in order to improve the quality of an initial population solution, a population is initialized in a manner of combining heuristic generation and random generation, and the quality of the initial population solution is improved. During decoding, scheduling of the AGV is completed by using a heuristic rule, the problems of power consumption and charging of the AGV are considered, generation of an unreasonable solution caused by pre-designation of the AGV in a coding link is effectively avoided, and the problem that a genetic algorithm is prone to falling into local optimum is solved by designing multiple neighborhood structures for local search.

A flexible job shop scheduling method based on improved HHO of co-evolution, steps include S1 data collection; S2 abstract FJSP as a mathematical model; S3 divide FJSP into machine selection sub-problem and process processing sequence sub-problem; S4 machine selection sub-problem processing: adopt DI coding method coding, map discrete machine selection sequence to continuous solution space of HHO algorithm; Each process selects the decision of the process in the machine set as a variable; After iteration solution by HHO algorithm, DI coding is mapped back to discrete machine selection sequence by decoding; S5 process processing sequence sub-problem processing: adopt OS coding method coding, and adopt genetic operator to co-evolution; S6 select one individual from DI population and OS population respectively, combine into DIOS code; Calculate the maximum completion time of DIOS code; S7 DI population and OS population co-evolution; Decode the optimal solution and output.

A construction management system includes an output unit that causes a display device to display a remotely operable work machine, a selection data acquisition unit that acquires machine selection data indicating specification of the work machine, and a remote operation permission unit that permits start of a remote operation of the work machine based on the machine selection data.

A drilling machine for selecting pin holes in strength training equipment is characterized by two transverse guide rails and a slide plate at the front of the worktable, with a lead screw and lead screw motor underneath the slide plate. Above the slide plate are drilling square columns and center hole square columns. Behind the drilling square columns are two vertical drilling guide rails, with a drilling head on each guide rail. In front of the drilling head are a vertical drilling lead screw and a drilling feed motor, and above the drilling head are a drilling gearbox and a drilling spindle motor. Behind the center hole square columns are two vertical center hole guide rails, with a center hole drilling head on each guide rail. In front of the center hole drilling head are a vertical center hole lead screw and a center hole feed motor, and above the center hole drilling head are a gearbox and a spindle motor. At the rear of the worktable, multiple rows of transverse positioning blocks are arranged longitudinally, with a clamping cylinder in the gap between each row of positioning blocks. This design solves the problems of low efficiency and poor precision in machining selection pin holes using ordinary drilling machines, making it suitable for use by fitness equipment manufacturers.

The invention discloses an intelligent task scheduling method and system based on labels and cue words. The method comprises the following steps of obtaining total resources and residual resources of each machine currently used for executing tasks; adding a plurality of initial labels and cue words for each task, and assigning a weight for each initial label and cue word; converting the cue word into an additional label by using a natural language analysis method; and according to the initial label, the additional label, the weight, and the total resource and the residual resource of each machine, utilizing a preset machine selection formula and a task execution sequence formula to calculate and obtain a machine where each task is executed and a task execution sequence when the same machine executes. The resource utilization rate is improved, the task waiting time is shortened, and the user experience is improved.

This invention provides a scheduling method for additive manufacturing workshops based on safety reinforcement learning, relating to the field of deep learning technology. The method includes: determining a workshop state vector S based on workshop simulation environment parameters; where T represents the current time, L represents the number of tasks being processed in the current batch, Tard represents the total delay time of all tasks, C represents the total completion time of completed tasks, and Index represents the task currently being scheduled; using the DQN algorithm, combined with the workshop state vector S and a reward function, determining the priority of tasks to be scheduled, and determining the batch of tasks to be scheduled based on the priority; using an improved A3C algorithm for machine selection, allocating the tasks to be scheduled in different batches to target machines; the improved A3C algorithm is an A3C algorithm incorporating a safety shield mechanism. By comprehensively considering task urgency, processing time, and machine load, the method dynamically optimizes production scheduling, improving scheduling efficiency and resource utilization, and achieving multi-objective balance.

The invention discloses a flexible job shop scheduling method based on a Harris eagle optimization algorithm, and the method comprises the steps: collecting basic data of a flexible job shop, and constructing a processing time length matrix of the flexible job shop and a flexible job shop scheduling mathematical model; performing standardization processing on the processing duration matrix, determining the probability that the machine selects to process the process in combination with the machinable mask of the process-machine, and generating an initial solution of a flexible job shop scheduling scheme; the generated initial solution serves as an eagle individual, a flexible job shop scheduling mathematical model serves as a fitness function, and the maximum number of iterations is set; calculating the fitness value of each eagle individual by using a fitness function, retaining the eagle individual with the minimum fitness value, calculating a nonlinear energy factor according to the current iteration number, and adjusting the optimization strategy of the eagle individuals according to the nonlinear energy factor until the maximum iteration number is reached, and taking the eagle individual with the minimum fitness function value as the optimal flexible job shop scheduling scheme.

To provide a technique for efficiently selecting Ising machines.SOLUTION: An Ising machine selection device includes: a feature quantity vector calculation unit configured to calculate a feature quantity vector of a graph corresponding to an Ising Hamiltonian to be selected; a first selection unit configured to select an Ising Hamiltonian Hm_0 corresponding to a feature quantity vector with the minimum distance to the feature quantity vector using a relationship R2 between an Ising Hamiltonian Hm and a feature quantity vector Xm of the graph corresponding to the Ising Hamiltonian Hm; and a second selection unit configured to select an allowable calculation time Tk_0 that is closest to an allowable time for calculating the Ising Hamiltonian to be selected and select a predetermined number of Ising machines corresponding to a value Vm_0,n,k_0 in order from the smallest one using a relationship among the Ising Hamiltonian Hm, an Ising machine In, an allowable calculation time Tk, and a value Vm,n,k of the Ising Hamiltonian Hm at the time when the allowable calculation time Tk has elapsed since the Ising machine In started calculation.SELECTED DRAWING: Figure 4

The present invention relates to a method for piloting an aircraft (1) comprising a head-up display (30) and an autopilot system (20) which includes a human-machine selection interface (20) for choosing an operating mode from among several predetermined operating modes including a disengaged mode and an engaged mode, the autopilot system (20) being configured in the engaged mode to control at least one actuator (25) in order to make a current value of a piloted parameter (PAR) tend towards a setpoint value.The method comprises: i) detection, using a control system (40), of a current mode applied from among several predetermined operating modes; ii) control and display on the head-up display (30) of a symbol bearing said current value according to a graphic charter specific to the current mode, a first graphic charter applied during the disengaged mode being different from a second graphic charter applied during the engaged mode. Abbreviated figure: Figure 1.

The invention belongs to the technical field of load rack device design, and particularly relates to an elevating machine layout design dynamic optimization method, which comprises the following steps: establishing a simple model of an elevating machine lifting rack; setting model parameters, and constructing a motion relation process of the elevating machine length and the rack angle; constructing an elevating machine elementary substance point kinetic model according to the rack kinetic parameters; the method comprises the following steps: selecting the layout position of an elevating machine as an optimization variable, parameterizing the whole motion process, setting constant-speed or smooth start-stop dynamic motion parameters, planning the motion state of the elevating machine by using an interpolation mode, constructing a minimum optimization target including acting, power, stroke and thrust, and optimizing the motion state of the elevating machine according to optimization constraints including distance, the stroke of the elevating machine and driving force. A nonlinear optimization model is constructed, an optimization problem is finally solved to obtain high-low machine layout parameters meeting the use working condition, and stroke, driving force and power data in the whole process are used for achieving model selection work. According to the method, the design period can be shortened, and the optimal elevating machine type selection and layout scheme can be quickly obtained.

The application provides an automatic guided vehicle scheduling method and device, computer equipment and a storage medium, and belongs to the technical field of production scheduling. The method comprises the following steps: acquiring the workpiece quantity, the process quantity and the parallel machine quantity for processing the workpiece of the automatic guided vehicle; encoding the priority of the workpiece transportation and the parallel machine selection of each workpiece in each process according to the workpiece quantity, the process quantity and the parallel machine quantity, to obtain an initial scheduling scheme; for any workpiece, determining the transportation plan of the workpiece in the current process, the state of the corresponding parallel machine and the position of the automatic guided vehicle in the idle state, and determining the transportation plan of the workpiece in the next process; constructing a parallel multi-population hybrid genetic algorithm, iteratively optimizing the initial scheduling scheme according to the transportation plan of any workpiece in the next process, to obtain an optimal scheduling scheme; and scheduling the automatic guided vehicle according to the optimal scheduling scheme. In this way, the utilization efficiency of AGV scheduling can be effectively improved.

The invention belongs to the related technical field of workshop scheduling, and discloses a two-stage batch flow flexible job workshop dynamic scheduling method and equipment, and the method comprises the steps: (1) extracting product processing technology features to construct a multi-dimensional batch feature set, carrying out the automatic selection and combination of batch features in the multi-dimensional batch feature set through employing a genetic programming algorithm, and carrying out the automatic selection and combination of the batch features in the multi-dimensional batch feature set; generating a plurality of batch scheduling rules through evolution operation, wherein the plurality of batch scheduling rules form an initial batch scheduling rule set; and (2) carrying out weight dynamic optimization on the initial batch scheduling rule set by adopting a deep reinforcement learning algorithm, and then determining a current optimal workpiece sorting and machine selection strategy so as to obtain a scheduling scheme. Through cooperative work of the genetic programming algorithm and the deep reinforcement learning algorithm, real-time response and adaptive scheduling of dynamic disturbance such as new workpiece arrival and machine faults in the production process are realized, the optimization efficiency is effectively improved, and the workshop production efficiency and the resource utilization rate are improved.

The application discloses a three-stage hybrid algorithm for solving a flexible job shop scheduling method, and solves a flexible job shop scheduling problem with the minimum maximum completion time as the target through a three-stage hybrid algorithm. In the search process, three stages are divided, and a population is divided into common individuals, elite individuals and alert individuals. In the first stage, a variable neighborhood breadth search algorithm is proposed to extensively search a solution space of process selection coding and update machine selection coding through a simplified Nopt1 neighborhood. When facing a small-scale scheduling problem, fast convergence to a global optimal effect can be realized. In the second stage, an adaptive elite individual number updating formula is proposed, and a crossover mutation operation is used to help the algorithm better exploit the elite individuals obtained in the previous stage. In the last stage, the alert individuals are updated to increase the ability of individuals in the population to escape from a local optimum. The method has the advantages of not being easily trapped in a local solution and high solution precision.

The invention relates to an aerospace component incidence relation mining method. The method comprises the following steps: preprocessing aerospace component selection list data; carrying out space navigation component selection list similarity analysis: constructing a selection list disorder tree and an electronic single machine selection component adjacent matrix, calculating selection list structure similarity and selection component specification similarity, and screening similar component lists; and for the screened similar component list, iteratively retrieving all component association selection frequent item sets which have the support degree greater than the minimum support degree threshold and possibly have association relationships, constructing a rule meeting the minimum confidence coefficient from the frequent item sets, and setting the support degree and the confidence coefficient value to obtain the component association selection frequent item sets. And adopting an Apriori algorithm to obtain a high-frequency association rule in the aerospace component selection process. According to the method, pre-analysis is performed from the two aspects of the component analysis range and the list similarity in a targeted manner, the problem of low mining efficiency is solved, manual subjective judgment is not needed, and the data mining accuracy is improved.

The application belongs to the field of workshop scheduling, and particularly discloses a method and system for inconsistent batched batch flow flexible job shop scheduling integrating mathematical model and reinforcement learning, and a storage medium, which comprises the following steps: firstly, a strict workshop state representation is introduced, which contains information such as decision-related processes, machines and batches, and a double attention network is used to extract state feature information; then, a scheduling decision framework based on a near segment strategy optimization algorithm is designed to solve the process sequencing sub-problem and obtain a batch division adaptive coefficient; finally, considering the processing preparation time, a process-level batch machine collaborative optimization mixed integer linear programming model is constructed to realize comprehensive optimization of the batch division sub-problem and the machine selection sub-problem. The scheduling method has significant advantages in solving the batch flow flexible job shop scheduling problem, and can quickly solve the inconsistent batched batch flow flexible job shop scheduling problem to obtain a high-quality workshop scheduling method.

The application provides a wafer box carrying method, an electronic device and a readable storage medium, the method comprising: obtaining current state information of each wafer box; determining a first target wafer box to be fetched according to the current state information of each wafer box and a first preset wafer box fetching rule; obtaining current state information of each machine in the area where the first target wafer box to be fetched is located; determining a target carrying machine according to the current state information of each machine and a preset machine selection rule; and controlling an automatic carrying system to carry the first target wafer box to be fetched to the target carrying machine according to current position information of the first target wafer box to be fetched and position information of the target carrying machine, and controlling the target carrying machine not to implement a locking action on the first target wafer box to be fetched. The application can realize automatic carrying of wafer boxes, effectively save manpower, improve carrying efficiency and reduce the possibility of human errors.

The invention discloses a rake-frame-free paste storage type thickener testing method and system, and belongs to the technical field of mine engineering. The rake-frame-free paste storage type thickener testing method comprises the steps that tailing mortar is prepared; preparing a flocculating agent; a flocculating settling experiment is carried out, and the specific experiment comprises a concentration time experiment, a mud layer height experiment and a sand discharge rate experiment; and discharging sand stably. In order to solve the problem that a traditional paste thickener type selection method is not suitable for a rake-free paste storage type thickener which completely depends on self-weight compression, the invention provides an experimental method focusing on compression characteristics of materials and final paste state characteristics by abandoning a machine selection method taking unit area treatment capacity as a judgment basis. And the underflow concentration, the critical mud layer height and the steady-state sand discharge rate are measured through the experimental method to obtain a real basis for design calculation, so that a reliable basis for selecting machine selection and operation parameters of the rake-free paste storage type thickener is provided.

The application discloses a flexible job shop AGV and machine combined scheduling method, and relates to the technical field of industrial automation. The method comprises the following steps: acquiring workshop scheduling basic data, and constructing combined scheduling input data; adopting a genetic algorithm to jointly optimize process sequencing, machine selection and AGV distribution, and utilizing a static path search algorithm to quickly estimate transportation time, and generating structured interface data; generating discrete reference path data satisfying space-time mutual exclusion constraints based on space-time discrete search, and generating continuous trajectory data satisfying AGV nonholonomic kinematic constraints through rolling horizon optimization; when continuous trajectory optimization fails to be solved, a soft kinematic constraint iterative relaxation strategy is executed; when multiple vehicle stagnation or interlocking is detected, a multi-scenario deadlock resolution mechanism is triggered. The application realizes unified coordination from discrete scheduling decision to continuous physical execution, and improves the robustness and engineering feasibility of flexible job shop multi-AGV collaborative scheduling.

The application discloses a kind of scheduling method of distributed manufacturing based on empire competition algorithm, comprising the following steps: establishing the objective function including minimizing maximum completion time, minimizing maximum machine load and minimizing total energy consumption, and setting the constraint condition of objective function according to the assembly process of product;Set coding and decoding rules, encode process sequencing and machine selection to obtain process sequencing vector OSV and machine selection vector MAV;Set the hybrid initialization rule of OSV and MAV, generate initial solution set;Calculate the objective function value of all initial solutions in initial solution set, select the better solution as the colony from it, the remaining initial solution is as the colony, adopt OX cross to realize the assimilation between colony, adopt POX cross to realize the assimilation between colony and colony, generate new solution set;Design the neighborhood of OSV and MAV, carry out variable neighborhood descent search to colony, obtain the optimal production scheduling scheme.

The application discloses a kind of forced same machine constraint generalized job shop scheduling encoding conversion method.The encoding conversion method steps of the present application include constructing the mathematical model of generalized job shop scheduling under forced same machine constraint;Using two-stage encoding mode, the process arrangement problem described in the mathematical model of generalized job shop scheduling under forced same machine constraint and machine selection problem are encoded;Construct process template, and the encoding of part of process is compressed;Encoding conversion is carried out, and the corresponding process encoding and machine encoding are obtained based on the scheduling scheme.The present application provides the encoding basis for the generalized job shop scheduling problem under forced same machine constraint.

The application belongs to the field of workshop scheduling, and particularly discloses a mixed flow shop dynamic scheduling method and system considering machine predictive maintenance, which comprises the following steps: taking minimizing total completion time, maintenance cost and processing cost as the target, establishing a mixed flow shop scheduling problem as a multi-objective joint optimization model, setting the same number of intelligent agents as the number of processing stages, and constructing a Markov decision process; each intelligent agent has an independent scheduling network, including a workpiece scheduling network and a machine scheduling network; based on the Markov decision process, the intelligent agents are trained, the workshop is maintained at the operation and maintenance point, and the workpiece and machine selection are respectively performed by calling the workpiece scheduling network and the machine scheduling network at the scheduling point; after the training is completed, the trained intelligent agents are used to realize the dynamic scheduling of the workshop. The application effectively overcomes the mixed flow shop dynamic scheduling problem considering machine predictive maintenance by integrating the workshop scheduling of machine operation and maintenance, and has good dynamic and adaptability.

The application discloses a kind of multi-agent multi-objective optimization workshop scheduling method based on man-machine cooperation.First, three objective functions including process sequencing, machine selection, speed selection and worker selection are constructed to optimize maximum completion time, total energy consumption and machine load balancing.Second, a distributed constraint processing mechanism based on local resource agent (LRA) is designed, and each LRA autonomously decides speed and worker allocation based on local resource state to ensure the feasibility of the scheduling scheme.Finally, a hierarchical multi-agent evolutionary algorithm is used to evolve collaboratively between global planning layer and field execution layer, and efficiently search for the Pareto optimal solution set through distributed evaluation, environment selection and adaptive policy adjustment.The application overcomes the problems of single resource constraint, rigid processing model and limited optimization objectives in traditional scheduling, achieving multi-objective collaborative optimization under double resource and multi-speed constraints, and significantly improving the greenness, balance and efficiency of production scheduling.

This invention discloses a flexible workshop production resource scheduling method based on multi-strategy deep reinforcement learning, relating to the field of intelligent manufacturing. The method constructs three strategy networks (jobs, machines, and AGVs) and three value networks. At each scheduling moment, the job selection strategy network selects scheduling rules based on the job status and maps them to specific operations; the AGV selection strategy network selects scheduling rules based on the AGV status and maps them to the first AGV for transport operations; the machine selection strategy network maps the machine status to the first machine for processing operations. Through multi-strategy collaboration, the operation is determined to be transported by AGV to the machine for processing. The start and end times of the operation are calculated, and the status, actions, and rewards are stored in the corresponding experience pools. The value networks sample data from the experience pools, calculate losses, and update network parameters. After the experience pools are cleared, the next iteration begins. This method can dynamically respond to changes in production demand and optimize resource allocation and scheduling decisions in real time.

The present application relates to the technical field of new energy system operation control, and discloses a new energy station group maximum available power calculation method, device and medium, the present application constructs the space-time characteristics of fusing time sequence law and the correlation between units, and carries out dynamic partition and template machine selection based on the space-time characteristics, and then calculates the maximum available power of the whole field, which can reflect the influence of environmental condition change on the output of the station group in real time, avoid the defects that fixed template machine or fixed power curve cannot adapt to environmental fluctuations and other actual working conditions, significantly improve the estimation accuracy of the maximum available power of large-scale new energy station group, provide more accurate available power boundary for station group dispatching control, and help to improve the power generation efficiency and operation economy of new energy station group.