65 results about "Machine scheduling" patented technology

The invention belongs to the technical field of industrial intelligence, and relates to a deep reinforcement learning-based non-correlation parallel machine scheduling method, which comprises the following steps of: constructing a mathematical model suitable for non-correlation parallel machine scheduling; the machining time of each workpiece on each heterogeneous machine is collected, and normalization processing is carried out; initializing a genetic algorithm scheduling population and a depth Q network; constructing a deep reinforcement learning training framework; expressing a state vector by using the average fitness, the optimal fitness and the optimal individual code; operating parameters of the genetic algorithm are controlled by using the action space, and parameters of the deep Q network are updated by using a reward function; and dynamically controlling operator selection in a genetic algorithm iteration process by using the trained deep reinforcement learning model to obtain an optimal scheduling solution and realize scheduling of the non-correlation parallel machine. According to the method, a deep reinforcement learning algorithm is provided for solving similar problems in the manufacturing industry production scheduling field by analyzing a non-correlation parallel machine scheduling problem data model, and the production efficiency is improved.

The invention discloses a task scheduling method, a quantum computer, a storage medium and a system, and the task scheduling method comprises the steps: inputting task operation parameters of a to-be-scheduled task into a quantum circuit model to obtain a task quantum circuit, carrying out the iteration of the parameters of the task quantum circuit until a preset optimization condition is satisfied, and obtaining a target quantum circuit; the target quantum circuit is operated, a target bit string is obtained based on an output result of the target quantum circuit, classic bits in the target bit string represent to-be-scheduled machines correspondingly distributed by to-be-processed jobs in the to-be-scheduled tasks, and values of the classic bits represent whether the to-be-processed jobs are operated on the correspondingly distributed to-be-scheduled machines or not; and controlling to execute the to-be-scheduled task based on the target bit string. According to the method, the isomorphic parallel machine scheduling task is converted into the quantum circuit, the task allocation is optimized through quantum computing global search, local optimum is avoided, the resource allocation reasonability is improved, and the production resource utilization rate and the use efficiency are improved.

The invention relates to the technical field of numerical control machining production control, in particular to a numerical control machining dynamic scheduling method oriented to multi-machine collaboration, which comprises the following steps of: establishing a directed graph according to real-time production state data, and then obtaining an adjustment priority score of each machining device based on a preset graph neural network according to the directed graph; the plurality of processing devices are scheduled based on the directed graph and the adjustment priority score. Compared with the prior art, the dynamic state of the multi-machine collaborative processing system can be more comprehensively and accurately modeled by constructing the directed graph containing the processing equipment node features and the process relationship, an adjustment priority score calculation mechanism based on the graph neural network is introduced, the adjustment suitability of each equipment in an emergency can be intelligently evaluated, and the processing efficiency of the multi-machine collaborative processing system is improved. The quick response capability of the scheduling scheme to abnormal events is effectively improved, better scheduling performance and higher production efficiency are achieved, and the problem that in the prior art, a multi-machine-tool scheduling method is poor in flexibility is solved.

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 heterogeneous multi-agent autonomous collaborative optimization method for production and maintenance integrated scheduling of a mixed flow manufacturing workshop. The heterogeneous multi-agent autonomous collaborative optimization method comprises the steps of limited buffer region constraint, feature screening, heterogeneous multi-agent and the like. The method is optimized on the basis of a TD3 algorithm, and comprises the following steps: firstly, constructing a heterogeneous multi-agent according to the characteristics of each processing site; secondly, in order to improve effective characterization of feature extraction, a feature screening mechanism based on NSGAIII is designed; and finally, in combination with the actual scheduling condition of the mixed-flow manufacturing workshop and aiming at the actual situation that the performance of the scheduling rule of the first-selected workpiece and the second-selected machine is poor, a composite scheduling rule based on the first-selected machine and the second-selected workpiece is adopted. According to the method provided by the invention, the effectiveness and superiority of the NSGAII-MATD3 algorithm are respectively demonstrated by comparing a traditional single scheduling rule, a feature-free screening MATD3 algorithm and five advanced deep reinforcement learning algorithms.

The invention discloses a green robust independent parallel locomotive scheduling method with uncertain processing time. The method comprises the following steps: acquiring a to-be-scheduled parameter set; constructing an irrelevant parallel machine scheduling model taking worst scene completion time WC and scene average energy consumption MTEC as double targets based on the parameters; a scene-driven double-population discrete artificial bee colony algorithm is adopted for solving, and the method comprises the steps of population initialization, employed bee global search based on ternary championics and two-point crossing, division into two sub-populations according to MTEC, MN local search based on a mean value scene, WN local search based on a worst scene, LN observation bee self-adaptive neighborhood search based on Q-learning and scout bee disturbance. And finally, outputting a robust scheduling solution set with both robustness and low-carbon property according to a Pareto criterion. And a plurality of scheduling schemes considering robustness and energy consumption optimization are provided for decision makers.

The invention relates to an intelligent decision task scheduling method for a steel grabbing machine in a scrap steel stock yard, and belongs to the technical field of steel grabbing machine scheduling, and the method comprises the following steps: S1, carrying out scene initialization according to input processed gridding data and material pile information, and generating a scene structural body; s2, updating grid data of material piles and truck warehouses in the scene, and selecting a grabbing point and a placing point of the round; s3, the steel grabbing machine carries out automatic operation according to the grabbing point and the placing point selected in the round; and S4, the steps S2-S3 are repeated till the truck warehouse is full, and steel grabbing is finished. The operation efficiency, the grabbing stability and the automation degree of the steel grabbing machine are remarkably improved, and the technical effect higher than that of a manual processing mode in the prior art is achieved.

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 an aviation composite material laying workshop man-machine scheduling method and system considering a collaborative learning effect, and the method comprises the steps: taking the minimum maximum completion time, the minimum maximum team task load, and the maximum worker proficiency degree improvement total amount as objective functions; a dual-resource constraint flexible job shop scheduling model considering a worker collaborative learning effect is constructed, and the scheduling model is solved through a multi-target whale optimization algorithm driven by domain knowledge; according to the method, the individual learning effect and the collaborative learning effect of workers are considered, a corresponding skill proficiency improvement model is constructed, and an optimal scheduling scheme is solved by designing a hybrid initialization method, a neighborhood search operator, an adaptive leader whale division strategy and an adaptive similarity threshold strategy and predation and search operators based on domain knowledge. And a scheduling scheme of worker and equipment resources can be generated more accurately, so that the processing efficiency is improved, and a resource allocation mechanism is optimized.

The present invention discloses an intelligent scheduling method for workshop production tasks based on an MES system, and relates to the technical field of scheduling management. The method comprises: decomposing workshop production tasks, obtaining a list of production subtasks, extracting the task features of each subtask for clustering identification, outputting the first, second and third production subtask lists, and connecting the personnel scheduling management system and the machine scheduling management system by the MES system to obtain resource information, generating a personnel scheduling strategy, a machine scheduling strategy and a human-machine collaborative scheduling strategy, and performing intelligent scheduling of workshop production tasks. The present invention solves the technical problems in the prior art that resource conflicts, production efficiency and product quality are affected due to the complicated trend of production processes and the limited and shared nature of production resources, and achieves the technical effect of intelligent scheduling of production resources, optimizing resource utilization, and improving production efficiency and product quality through task decomposition and subtask feature analysis.

The invention discloses an intelligent production scheduling method for a copper coil pipe coiling and drawing process. The intelligent production scheduling method comprises the following steps: step 1, converting a monthly order into a daily production order; 2, translating order specifications to obtain an order set; and step 3, scheduling production by using an intelligent algorithm, carrying out frame material division, batch distribution and machine scheduling, and arranging dynamic shutdown to reduce the power cost. According to the intelligent production scheduling method for the copper coil pipe coil drawing process, frame material process similarity clustering and multi-target optimization scheduling are introduced, automatic production scheduling of the copper coil pipe coil drawing process is achieved on the basis of order data, process parameters and equipment information, and therefore the production efficiency of the copper coil pipe coil drawing process is improved while it is guaranteed that an order production task is completed. The working efficiency of the disc drawing equipment is improved, and energy conservation and cost reduction are achieved while production scheduling is optimized.

The invention discloses a parallel machine scheduling mixed integer programming model considering model change and a dynamic programming algorithm thereof. The method comprises the following steps: 1) determining a target function and constraints; (2) the machining sequence rule of workpieces machined on each parallel machine is determined; 3) determining a symbol, an initial condition, a recursive expression and a target of dynamic planning; and 4) determining a conversion process from a dynamic planning result to a scheduling scheme. According to the method, the parallel machine scheduling problem considering die change is constructed into the mathematical programming model, the scheduling scheme is generated by using the dynamic programming algorithm, the machine sequence is set as the stage sequence of dynamic programming, and the number of workpieces processed and distributed by each group of dies is set as the stage variable for solving, so that the problem can be efficiently solved, and the method has relatively high technical and economic values.

The invention discloses an intelligent scheduling method for wharf and port resources, and belongs to the technical field of port logistics optimization. An improved CSA algorithm is adopted, the problem of efficiency bottleneck of a traditional scheduling mode in a complex scene is solved, and the method comprises the steps of collecting a data set, setting constraint conditions, establishing the data set and a distribution matrix, establishing a multi-target fitness function, establishing a door machine scheduling rule, establishing a berth-door machine cooperative scheduling model and solving to obtain a scheduling scheme. The method mainly aims at minimizing the total waiting time of the ship and the equipment balance utilization rate, and meanwhile optimizes the berth utilization rate and the portal crane utilization rate. By constructing a multi-target scheduling model, a parking space allocation and portal crane scheduling scheme is dynamically planned, and efficient utilization of resources is ensured. The method has the advantages of optimizing the resource utilization rate and the like, and can be widely applied to the field of modern port logistics and management.

The invention provides an uncorrelated parallel machine scheduling method, system and device, a medium and a product. The method comprises the steps of obtaining a main problem and a sub-problem corresponding to a scheduling task; obtaining a binary representation model of the main question; the scheduling quantum computer analyzes the binary representation model of the main problem to obtain a plurality of optional scheduling strategies; analyzing the sub-problem based on the plurality of selectable scheduling strategies to obtain a plurality of candidate sub-problem feedback information; screening the plurality of candidate sub-problem feedback information to obtain target sub-problem feedback information, and performing a next iterative analysis process based on the target sub-problem feedback information to obtain a plurality of new selectable scheduling strategies; and obtaining a target scheduling strategy until an iteration ending condition is met. Through the quantum and classical hybrid computing system, the quality and generation efficiency of the scheduling strategy are improved.

The invention discloses a modular scheduling system for warehouse logistics elevators, and the system comprises a scheduling controller which comprises a multi-machine centralized scheduling module, a plurality of single-machine scheduling modules and a mode switching module, the single-machine scheduling modules are connected with the multi-machine centralized scheduling module, the single-machine scheduling modules are used for single-machine floor task scheduling of one elevator, and the mode switching module is connected with the multi-machine centralized scheduling module; the single-machine scheduling module is used for sorting operation tasks, and the mode switching module is used for switching a single-machine scheduling mode and a multi-machine scheduling mode of the scheduling controller; and the enable signal output of the elevator controller is connected to the enable input end of the dispatching controller. The invention further discloses a modular scheduling method for the warehouse logistics elevator. The modular scheduling method comprises the following steps. Compared with the prior art, the problems that in an existing warehouse logistics elevator, multi-unit networking centralized dispatching cannot be achieved, and emergency operation tasks cannot be executed preferentially are solved.

This invention discloses a flexible scheduling method, system, and application for UPMS (Upright Manufacturing System) workshops based on an improved differential evolution algorithm. The method includes: establishing an independent parallel machine scheduling model with sequentially dependent mold-changing time as the objective of minimizing the maximum completion time; using real-valued vector encoding and decoding it into a scheduling scheme through the maximum position value rule; performing mutation and crossover operations of the differential evolution algorithm on the generated test vectors, with optimizations including load balancing optimization based on destruction and reconstruction and mold-changing optimization based on sequential smoothing; finally, updating the population and outputting the optimal scheduling scheme. This invention significantly reduces sequentially dependent mold-changing time, achieves dual optimization of load balancing and mold-changing cost, and improves solution accuracy and efficiency, making it particularly suitable for discrete manufacturing workshops such as textile printing and dyeing, and injection molding.

The invention provides a semiconductor manufacturing equipment scheduling method and device, electronic equipment and a storage medium, and relates to the technical field of semiconductors. The method comprises the following steps: acquiring a machine scheduling request; determining a next process and a plurality of available candidate machines meeting the requirements of the next process according to the wafer batch identifier and the current process identifier; according to the machine identifier of the current process, multi-dimensional scheduling information of each available candidate machine is obtained, and the multi-dimensional scheduling information at least comprises distance information, state information and load information; and according to the multi-dimensional scheduling information of each candidate machine, determining a target machine in the plurality of available candidate machines, and executing the next process through the target machine. According to the method, the scheduling score of each candidate machine is comprehensively evaluated by fusing three key dimensions including distance, state and load, accurate modeling and quick response to a complex dynamic environment in a semiconductor production line are realized, the production cycle is remarkably shortened, and the equipment utilization rate is improved.

This invention discloses a green scheduling method for unrelated parallel machines using dual factors of electricity and carbon. The method includes the following steps: S1, collecting parameters related to production conditions, and constructing an unrelated parallel machine scheduling model with the goal of maximizing completion time and minimizing electricity and carbon costs; S2, constructing a distributed evolutionary algorithm with adaptive and neighborhood search mechanisms, inputting the parameters related to production conditions, solving the unrelated parallel machine scheduling model, and obtaining an optimal scheduling method. When solving the unrelated parallel machine scheduling model, the algorithm randomly initializes the population, employs a multi-objective non-dominated sorting method and crowding distance calculation to improve individual diversity, and dynamically adjusts the learning rate and mutation rate through an information entropy adaptive mechanism. Simulation results show that the ANEDA algorithm performs significantly better in optimizing maximum completion time and total electricity and carbon costs, verifying the algorithm's effectiveness and practical value.

The application belongs to the technical field of intelligent manufacturing and production scheduling optimization, and particularly relates to a buffer workpiece group batch transfer and order allocation collaborative optimization method and system. The method firstly performs real-time group batch clustering on the to-be-transferred workpieces in the buffer based on attribute correlation to generate a transfer batch, and issues a transfer instruction under a double-threshold control pulse trigger mechanism. Then, a deep reinforcement learning model is constructed. At each pulse trigger time, a global state vector is constructed based on the transfer batches of all issued transfer instructions and the order types in the system, an order allocation decision is output based on the global state vector, and finally a batch-transfer equipment-path combination instruction is generated based on the order allocation decision. The application realizes global optimization of production efficiency and energy efficiency through collaborative optimization of machining scheduling and material transfer scheduling and deep integration of dynamic response of orders.

The present invention belongs to the field of industrial intelligence technology and is a non-correlated parallel machine scheduling method based on deep reinforcement learning. The method comprises the following steps: constructing a mathematical model suitable for non-correlated parallel machine scheduling; collecting and normalizing the processing time of each workpiece on each heterogeneous machine; initializing a genetic algorithm scheduling population and a deep Q network; constructing a deep reinforcement learning training framework; using average fitness, optimal fitness, and optimal individual encoding to represent the state vector; using action space to control the operating parameters of the genetic algorithm and using a reward function to update the parameters of the deep Q network; and using the trained deep reinforcement learning model to dynamically control operator selection during the genetic algorithm iteration process to obtain the optimal scheduling solution and implement the scheduling of non-correlated parallel machines. By analyzing the data model of the non-correlated parallel machine scheduling problem, the present invention provides a deep reinforcement learning algorithm for solving similar problems in the field of manufacturing production scheduling, thereby improving production efficiency.

The application provides a serial batch processing scheduling method based on a hybrid particle swarm algorithm in a fuzzy environment, and relates to the field of production scheduling.The method comprises the following steps: encoding and analyzing workpieces and machines based on machine indexes; in the case of parallel machine scheduling, initializing algorithm parameters based on the hybrid particle swarm algorithm, initializing a population by a heuristic algorithm, analyzing fitness, and updating the speed and position of particles; determining the minimization of the maximum completion time as an objective, executing a variable neighborhood descent local search strategy, and updating the local optimum and the global optimum; and in the case that an iteration index exceeds an iteration threshold, determining the end of the algorithm and outputting a target result to represent the allocation and scheduling information of the workpieces and machines.The application considers fuzzy processing time, learning effect and deterioration effect, and considers the scheduling problems of single machines and non-single machines in a fuzzy environment based on actual production conditions, which is helpful for iron and steel plants to formulate production strategies and reasonably arrange the allocation and scheduling of workpieces and machines.

The invention provides an improved double-depth Q network algorithm for production scheduling of a packaging test system, which is optimized on the basis of an original DDQN (Double Data Quality Network) and comprises the following steps of: firstly, providing an exponential self-adaptive action selection strategy related to the total process number in order to improve the learning speed of an intelligent agent and the quality of a scheduling solution; secondly, in order to realize the adaptivity of the IDDQN algorithm, an adaptive network parameter updating strategy closely related to the problem scale is designed; meanwhile, in consideration of the influence degree of the online network on the target network, a weighted target network parameter # imgabs0 # updating strategy is designed, the target network parameter # imgabs1 # is gradually changed towards a better parameter direction, and the influence on the target network parameter # imgabs2 # when the online network parameter theta is relatively poor is reduced. And finally, in combination with the actual scheduling condition of the packaging test workshop, a composite scheduling rule based on first selection of the machine and then selection of the workpiece is provided for the actual situation that the performance of the scheduling rule of first selection of the workpiece and then selection of the machine is poor.

The invention provides a serial batch processing scheduling method based on a hybrid particle swarm algorithm in a fuzzy environment, and relates to the field of production scheduling, and the method comprises the steps: carrying out the coding analysis of a workpiece and a machine based on a machine index; under the condition that the scheduling type is parallel machine scheduling, based on a hybrid particle swarm algorithm, initializing algorithm parameters, initializing a population through a heuristic algorithm, analyzing fitness, and updating the speed and position of particles; determining the minimum maximum completion time as a target, executing a variable neighborhood descent local search strategy, and updating local optimum and global optimum; and under the condition that the iteration index exceeds an iteration threshold value, determining that the algorithm is ended, and outputting a target result to represent distribution scheduling information of the workpiece and the machine. According to the method, fuzzy processing time, a learning effect and a deterioration effect are considered, single-machine and non-single-machine scheduling problems in a fuzzy environment are considered based on actual production conditions, and an iron and steel plant is helped to formulate a production strategy and reasonably arrange distribution scheduling of workpieces and machines.

The invention provides an additive manufacturing SLM single-machine scheduling method considering part nesting. The method comprises the steps that production data are acquired; constructing an integrated scheduling optimization model: taking predefined nested combination adopted by the parts, construction direction selection of the parts, operations formed by grouping the parts and sorting of all the operations as decision variables of collaborative optimization, and aiming at minimizing the unit volume production cost of all the operation parts, constructing the integrated scheduling optimization model; the constraints that the total projection area does not exceed the area of the processing platform and the completion time does not exceed the delivery time are met; solving the integrated scheduling optimization model by adopting an improved sparrow search algorithm, namely coding a processing sequence of associated parts through three reforms, constructing direction selection and operation grouping, measuring the distance between individuals in an algorithm population by adopting a Hamming distance, dynamically adjusting step length control parameters, and keeping the influence of the optimal solution of the current population on the next generation; the output scheduling scheme comprises the construction direction of each part, the work to which the part belongs, the adopted nested combination and the machining sequence of all the work.

The invention belongs to the technical field of computer software testing, and particularly relates to a Linux kernel vulnerability mining method based on diversity guidance. According to the method, firstly, collected PoCs are expressed by using a customized abstract syntax tree, clustering is carried out on the PoCs based on a Louvain community discovery algorithm, an initial diversity seed bank is constructed, and seeds are divided into a plurality of communities with different functions; in order to quantify seed diversity, a community prevalence rate index (CPR) is introduced; designing a double-layer multi-arm tiger machine scheduling framework based on the CPR, wherein the framework is used for efficiently allocating variable resources between communities and in the communities; a CPR-guided seed variation strategy is adopted to preferentially carry out rapid variation and expansion on high-diversity seeds, so that the coverage speed and efficiency of vulnerability triggering are improved. Experimental results show that compared with a current most advanced kernel fuzzy test tool, the method has the advantages that the code coverage rate is averagely increased by 17.4%, and the vulnerability discovery number is averagely increased by 9.1 times.

The multi-target single-machine scheduling algorithm with controllable production time in the MTO mode is characterized by comprising the following steps: a) setting printing conditions; b) constructing a mathematical model; c) carrying out discrete coding and decoding; d) performing population initialization; e) constructing a solution algorithm; f) carrying out social grade layering; g) updating operation; h), a cost reduction strategy; firstly, order printing conditions are set, then a mathematical model is constructed, discrete coding and decoding are carried out, then population initialization is carried out, a solving algorithm is constructed, social grade layering and updating operation is carried out in the solving process, and finally, a low-cost strategy is executed. According to the multi-target single-machine scheduling algorithm, the processing sequence of the printing order is finally obtained, so that the management task of production operation becomes simpler, the production plan and production scheduling are very reasonable, and the production management level and the economic benefit of an enterprise are improved.

The invention relates to a single-machine scheduling method based on reinforcement learning enhanced genetic evolution. The method comprises the following steps: setting problem parameters of single-machine total advance scheduling; constructing and optimizing a workpiece assignment probability model based on deep reinforcement learning, and inputting problem parameters of single machine total advance scheduling to obtain an initial solution set; and optimizing the initial solution set by using a genetic evolutionary algorithm to obtain an optimal single-machine scheduling scheme. Compared with a traditional method, the searching pressure of the optimization method can be relieved, the searching capability of the optimization method can be improved, and the solving efficiency and quality of the single-machine scheduling problem can be improved. Meanwhile, population hybrid initialization and population hybrid updating operation based on a workpiece assignment probability model are adopted, and through the hybrid population initialization operation, the population diversity can be guaranteed, and the initial population quality can be improved; and population mixed population updating operation is adopted, so that population evolution can be ensured, and gene diversity can be increased. Compared with a traditional method, it can be ensured that the algorithm considers both local search and global search capabilities.

The present invention discloses a multi-AGV distributed architecture scheduling method, which is composed of a number of AGVs, wherein each AGV is provided with a server, a client, a wireless network module, a controller, a traffic control module, a path planning module and a navigation positioning module; specifically, after the map information of the use scene is divided into regions, the first AGV entering each region is used as a server, and the subsequent AGVs entering each region are used as clients; through the real-time information transmission between the server AGV and the client AGV, the path planning and task execution of each AGV are completed. Compared with the prior art, the beneficial effect of the present invention is: the distributed structure of multiple AGVs is adopted, and the system architecture in which each AGV can be used as a server and a client is matched, so as to solve the problem that in the process of the traditional industrial computer scheduling method, once the industrial computer has a system failure or restarts, the operation cannot be performed, thereby greatly ensuring the operation efficiency of the AGV and improving the fluency of the entire production line flow operation.

The present invention discloses a fast non-dominated sorting genetic algorithm for the dynamic equivalent parallel machine scheduling problem, which is carried out according to the following steps: S1: Generate a population by mixing heuristic rules MODD, ATC, X-RM and a random method; S2: Calculate the objective values of the individuals in the population by a decoding method based on dynamic programming; S3: Determine whether the termination condition is satisfied; if so, end; otherwise, perform fast non-dominated sorting on the individuals in the population; S4: Generate an offspring population through genetic operators; S5: Combine the parent population and the offspring population to form a population of size 2N; S6: Perform fast non-dominated sorting on the individuals in the population, and determine the population by combining crowding degree calculation; S7: Perform neighborhood search on the individuals in the population to generate new solutions to replace the original redundant solutions. The present invention has the characteristics of improving the utilization rate of workshop machines and meeting the customer delivery date.

The present application relates to the technical field of wool textile machine table scheduling, and particularly relates to a production scheduling and machine table scheduling system for a wool textile workshop, comprising a data acquisition module, a data processing and analysis module, a production scheduling planning module, a machine table scheduling module, a monitoring and feedback module, a conveying unit, a user interface module and a process knowledge base module; the data acquisition module is responsible for acquiring various types of production data such as order information, equipment state information, process parameter information and production progress information of the wool textile workshop, and the data acquisition mode is realized through a combination of multiple modes such as sensors, equipment interfaces and manual input; the data processing and analysis module is used for cleaning, arranging and storing the collected production data; it is convenient to fully utilize the workshop equipment resources, avoid equipment idling or overloading, reduce the waiting time and downtime in the production process, thereby improving the overall production efficiency of the workshop, timely adjusting the production scheduling plan and the machine table scheduling scheme, and enhancing the production flexibility of the workshop.