140 results about "Job shop" patented technology

The embodiment of the invention discloses a flexible job shop scheduling method based on preference-driven graph reinforcement learning, and relates to the field of shop dynamic scheduling in an intelligent manufacturing technology. According to the method, by constructing a multi-objective optimization model, multiple objectives of the job shop can be optimized at the same time, including the minimum completion time, the total delay and the total cost. Wherein an imperfect maintenance model is constructed, and the maintenance demand and the maintenance opportunity of each machine are dynamically determined. And capturing a complex relationship between the operation and the machine by using an improved graph neural network. In combination with a preference-driven mechanism, a maintenance plan and workshop scheduling are adjusted in real time through a graph reinforcement learning method, and efficient priority scheduling rules under different preferences are learned, so that an integrated decision of machine allocation, an operation sequence and maintenance arrangement in dynamic scheduling is realized.

The invention belongs to the technical field of intelligent manufacturing and production scheduling, discloses a flexible job shop dynamic scheduling method and system considering machine aging, and designs a hierarchical environmental response strategy which can firstly evaluate the severity of environmental change. When the change is not violent, only a lightweight local optimization strategy is adopted for fine adjustment; and when the change is relatively violent, a global reconstruction strategy combining knowledge migration, reinitialization and directional repair is started. Therefore, the algorithm can intelligently allocate computing resources according to the intensity of environment change, blind global search is avoided, and the response speed and the operation efficiency of the algorithm are greatly improved. The design effectively balances the exploration and utilization capabilities of the algorithm, and maintains the diversity of the population while ensuring rapid convergence, thereby obtaining a group of Pareto optimal solution sets with good convergence and wider distribution.

The invention discloses a multi-rotating-speed flexible job shop scheduling method applied to double-resource constraint, and the method comprises the steps: taking the maximum completion time and minimum total energy consumption of a minimum machine as target functions, and constructing a flexible job shop scheduling model considering the rotating speed energy consumption of the machine and the production demands of a fine process; a machine speed gear constraint, a fine process constraint, a process sequence constraint, a completion time constraint, a machine processing constraint and a worker operation constraint are established as constraint conditions of the model; the flexible job shop scheduling problem is solved by adopting an improved artificial bee colony algorithm, bee colony search guided by excellent genes is adopted in bee learning operation in the improved artificial bee colony algorithm, and nectar source optimization is carried out based on the searched excellent genes; the following bee operation adopts a neighborhood structure which considers machine speed change and balances the working time of workers to carry out dynamic neighborhood search so as to optimize a nectar source. The effectiveness of the improved strategy is verified through experiments, and the superiority is verified through comparison of different algorithms on expansion standard examples.

The invention belongs to the technical field of workshop scheduling and maintenance combination, and particularly relates to a flexible job workshop production scheduling and machine maintenance combination optimization method and system. The method comprises the following steps: S1, acquiring basic information of flexible job shop production scheduling and machine maintenance joint optimization; s2, taking minimization of the total cost of a production system as a target function, and constructing a flexible job shop production scheduling and machine maintenance joint optimization model; s3, converting the flexible job shop production scheduling and machine maintenance joint optimization model into a Markov decision process, and respectively defining states, actions, state transition and rewards of production scheduling and machine maintenance; s4, designing a joint optimization method based on a digital twinning and double-layer reinforcement learning algorithm based on the Markov decision process, and obtaining a trained agent through training; and S5, applying the trained intelligent agent to an actual flexible job shop production scheduling and machine maintenance joint optimization problem.

The invention provides a flexible job shop scheduling method based on an improved bacterial foraging algorithm, and relates to the technical field of intelligent manufacturing. The method specifically comprises the following steps: acquiring original data of a flexible job shop, and constructing a flexible job shop scheduling optimization model; the method comprises the following steps: taking a flexible job shop scheduling scheme as an individual based on a flexible job shop scheduling optimization model, initializing parameters, and generating an initial population by adopting Logistic-Circle hybrid mapping; optimizing the initial population by adopting an improved bacterial foraging algorithm to obtain an optimal individual; and obtaining an individual position vector of the optimal individual, and converting the individual position vector of the optimal individual into a flexible job shop scheduling optimal scheme by using a conversion mechanism between the individual position vector and the scheduling scheme. According to the method, the production cycle of a manufacturing enterprise can be effectively shortened by optimizing flexible job shop scheduling.

The present invention discloses a job shop adaptive scheduling method based on deep reinforcement learning. An optimized action policy and an asynchronous update mechanism are designed in the proximal policy optimization algorithm to form a proximal policy optimization algorithm for direct and efficient exploration and asynchronous update. Based on the proximal policy optimization algorithm for direct and efficient exploration and asynchronous update, a graph neural network is combined with the hierarchical non-linear refinement of the original state information to design an end-to-end reinforcement learning method. Based on this, an adaptive scheduling system is obtained. The proximal policy optimization algorithm for direct and efficient exploration and asynchronous update of the present invention has high robustness, the scheduling score is increased by 5.6% compared with the proximal policy optimization algorithm, and the minimum completion time is reduced by 8.9% compared with the deep Q-network algorithm. The experimental results prove the effectiveness and generality of the proposed adaptive scheduling strategy.

The present invention relates to an optimization method for integrated joint scheduling of production and logistics in a flexible job shop (FJSP) having a plurality of different types of automated guided vehicles (AGVs). The invention belongs to the field of assembly workshop production scheduling. Comprising the following steps: 1) according to a special assembly workshop machine and AGV combined scheduling process, Tent chaotic mapping is adopted to initialize a scheduling scheme and encode the scheduling scheme; 2) performing iterative optimization adjustment on the scheduling scheme through an improved multi-target artificial bee colony algorithm; and 3) carrying out production scheduling by using the optimized scheduling scheme. According to the method, the maximum completion time and the total energy consumption are optimized at the same time, the production efficiency is concerned, the requirements of green manufacturing and sustainable development are also considered, and enterprises are helped to achieve cost reduction and efficiency improvement, especially in the production process sensitive to energy consumption.

The invention relates to a flexible job shop scheduling method considering grouping characteristics, belongs to the technical field of flexible job shop production scheduling, solves the problem of poor comparison effect between a scheduling scheme and an actual processing situation in the prior art, and comprises the following steps: S1, determining a job switching type and executing grouping classification to obtain a grouping classification result; s2, determining decision variables and basic parameters considering grouping characteristics according to the obtained job switching types and grouping classification results; s3, constructing an objective function considering grouping characteristics; s4, establishing a flexible job-shop scheduling problem constraint condition considering the grouping characteristics, and obtaining a flexible job-shop scheduling mathematical model considering the grouping characteristics; s5, solving the flexible job shop scheduling mathematical model considering the grouping characteristics by using an improved genetic algorithm to obtain a scheduling scheme with the optimal fitness; and S6, outputting the obtained scheduling scheme with the optimal fitness as an optimal scheme.

The invention relates to the technical field of intelligent production and manufacturing, in particular to an energy-saving fuzzy cascade scheduling method and system for regional gathering cooperative production, and aims to solve the composite problems of supply chain cascade scheduling heterogeneous factory resource allocation, multi-stage time accumulation effect, uncertainty interference and the like in regional cooperative transformation in the manufacturing industry. According to the method, a double-layer collaborative optimization framework is assisted through integrated learning, the uncertainty of quintuple interval fuzzy quantization processing, transportation and assembly time is adopted, an initial Q value matrix is generated through a pre-training layer, self-adaptive operator selection is achieved in combination with a dynamic decision-making layer, and local search, damage recombination and genetic operation are executed by multiple sub-groups. According to the method, the energy-saving second-class fuzzy distributed flow shop and multi-flexible job shop cascade scheduling problem model is effectively defined, three-segment coding and full-process energy consumption calculation are supported, and the overall scheduling efficiency and the energy efficiency balance capability of the regional aggregation industry are remarkably improved.

The invention belongs to the related technical field of flexible job shop scheduling, and discloses a flexible job shop scheduling method and system based on a genetic algorithm. The method comprises the following steps of: establishing a coding vector by using workpiece batching, process sorting, machine assignment and transportation equipment selection information of the flexible job shop; establishing a compliance function and a comprehensive constraint function as constraint conditions based on the sequential arrangement of processes, the selection of machines and the loading capacity and path of transportation equipment; and constructing an initial population by taking the minimum completion time as an objective function and taking the coding vector as an individual, and solving the objective function by adopting a genetic algorithm to obtain an optimal individual corresponding to the minimum completion time so as to realize the scheduling of the flexible job shop. According to the method and the device, the technical problems of difficulty in workpiece batching, complexity in coding, poor decoding quality and low algorithm solving efficiency in batch scheduling of the flexible job shop under limited transportation resources are solved.

The invention discloses a flexible job shop scheduling method and system based on an improved genetic algorithm, and the method comprises the steps: 1, problem modeling: defining parameters and constraint conditions of a flexible job shop scheduling problem, the parameters comprising a machine set, a workpiece set, process information, processing time and decision variables, 2, improved genetic algorithm design, and step 3, executing the scheduling scheme. According to the method, the initial population generation strategy and the multi-target fitness function of the genetic algorithm are improved, so that the global search capability and the convergence speed are improved, the maximum completion time is shortened, the target is optimized in combination with machine load balancing and cost, the resource utilization rate is improved, and the production cost is reduced; the system has flexibility and expansibility, can adapt to flexible workshops of different scales, and realizes real-time adjustment of a scheduling scheme through dynamic monitoring.

The invention provides a multi-AGV flexible job shop active scheduling method based on an improved VNS-INSGA-II algorithm and a related device, and belongs to the technical field of shop active scheduling. According to the method, a multi-objective optimization model for active scheduling of the multi-AGV flexible job shop is constructed, and constraint conditions of the multi-objective optimization model are set; the VNS-INSGA-II algorithm is improved by adopting a multi-layer coding mode, a hybrid population initialization mode, a time-varying coefficient-based dual-strategy binary tournament selection strategy and a variable neighborhood search algorithm, and the improved VNS-INSGA-II algorithm is obtained; based on a set constraint condition of the multi-objective optimization model, an improved VNS-INSGA-II algorithm is adopted to solve decision variables in an objective function of the established multi-objective optimization model for active scheduling of the multi-AGV flexible job shop, and a Pareto frontier is obtained; and selecting a group of solutions from the Pareto frontier to obtain an active scheduling result, thereby carrying out active scheduling on the multi-AGV flexible job shop. According to the invention, the problems of low stability and low scheduling precision of the scheduling system are solved.

The invention provides a discrete mixed job production line scheduling method based on an improved genetic algorithm, and relates to the technical field of industrial automation and production scheduling, and the method comprises the steps: S1, carrying out the modeling and data input of a production line scheduling problem, constructing a flexible job shop scheduling model, defining a decision variable, a constraint condition and a target function, and inputting basic data; s2, generating an initial population by adopting a hybrid initialization strategy, wherein the initial population comprises randomly generated individuals and individuals generated based on a heuristic rule; s3, chromosome coding is carried out on the scheduling scheme in a two-segment coding mode, wherein a process sorting segment and a machine distribution segment are included; s4, the fitness is calculated, and individual selection is carried out by adopting a tournament selection method; s5, executing improved genetic operations including adaptive crossover and mutation operations; s6, carrying out local search on the elite individuals, wherein the local search comprises key path identification and neighborhood disturbance; and S7, judging a termination condition, if the termination condition is met, outputting an optimal scheduling scheme, otherwise, returning to the step S4.

The invention relates to the technical field of workshop production and manufacturing, and discloses a joint scheduling method, system and equipment for flexible processing and assembling and a medium. The method comprises the following steps: constructing a joint scheduling model according to a plurality of processing procedures and a plurality of assembling procedures of a plurality of workpieces in a production and manufacturing process and a cooperative scheduling process between each processing procedure and each assembling procedure; representing a solution of the joint scheduling model by adopting batch coding, process coding and machine coding; solving the joint scheduling model by adopting a genetic algorithm by taking the minimum maximum completion time of all the workpieces as a target to obtain a target batch number of each workpiece, a target distribution sequence of each machining process and each assembly process of each workpiece and a target distribution machine of each machining process and each assembly process of each workpiece; the scheduling relation between processing and assembling in the flexible job shop is clearly described, a better joint scheduling scheme is generated, and the production and manufacturing efficiency is greatly improved.

The invention belongs to the technical field of workshop production scheduling, and discloses a flexible job workshop multi-target scheduling method and system based on preference driving, and the method comprises the steps: defining a target function and a constraint condition based on obtained information, and constructing a workshop scheduling model; converting a workshop scheduling problem into a Markov decision problem, designing a reward function, and creating a preference pool; selecting a preference vector from the preference pool, and respectively inputting the preference vector and the system state into a strategy network and a value network for iterative training to obtain a trained strategy network and a trained value network; and obtaining current preferences for different dispatches and a current state of the system, inputting the current preferences and the current state of the system into the trained strategy network, obtaining action probability distribution in a given state, and selecting a dispatching action with the maximum probability to obtain a corresponding optimal dispatching scheme. According to the method, the requirements of tire enterprises in different situations are met, high flexibility is achieved, the nonlinear target utility function is designed, and a tire workshop scheduling scheme with higher quality can be found out easily.

The invention discloses an adaptive multi-group interaction PSO flexible workshop AGVs integrated scheduling optimization method, which comprises the steps of dividing three functional populations, namely elite, exploration and development, and realizing global exploration and local mining of a solution space through multi-group interaction. A task matrix and a resource matrix are constructed to represent the correlation essence of the position of a task processing machine and AGVs transportation time, and a matrix updating mechanism based on local feature reconstruction and global trajectory avoidance is proposed and used for extracting an excellent solution gene mode and guiding search of a foreground area. The proposed method is verified on two standard data sets of FJSP and EX, and the result shows that the proposed method can effectively improve the scheduling solution quality, has relatively strong convergence capability and adaptability, has wide application potential in solving the AGVs scheduling problem of the complex flexible job shop, and has a wide application prospect. And efficient and feasible technical support can be provided for scheduling optimization of the intelligent manufacturing workshop.

The invention relates to an improved DQN-based flexible job shop dynamic batch flow scheduling optimization method considering emergency order insertion and reworking, and belongs to the technical field of shop scheduling. The method comprises the following steps of: considering a flexible job shop dynamic batch flow scheduling multi-objective optimization mathematical model of emergency order insertion and reworking; based on a knowledge-driven batching method and a multi-agent DQN model solving method, a local optimal solution of a flexible job shop dynamic batch flow scheduling model problem considering workpiece batching is further explored. The problem that a traditional optimization method is poor in applicability and efficiency under dynamic event interference is solved, and flexible job shop dynamic batch flow optimization scheduling is achieved. According to the optimization method, the problem of flexible job shop dynamic batch flow scheduling optimization under the order insertion and workpiece reworking dynamic event can be efficiently solved, waste of resources such as energy, time and cost is effectively avoided, production interruption and maintenance cost are reduced, and a scheduling scheme with good completion time and order insertion workpiece delay time is obtained.

The invention discloses a flexible job shop scheduling method, and particularly relates to a complex flexible job shop scheduling task planning method and device and electronic equipment, and the method comprises the steps: obtaining job shop associated data, a scheduling target and a plurality of tasks, each task corresponding to a plurality of transport windows; constraint conditions are constructed based on the job-shop associated data, a target function is constructed based on the scheduling target, and the constraint conditions comprise inventory constraint, shop point location occupation constraint, distance constraint, speed constraint and time constraint; the target function is solved under the constraint condition, an optimal conveying window combination is determined, the conveying window combination comprises a target conveying window corresponding to each task, and the target conveying window is any conveying window in a plurality of conveying windows corresponding to the task; and reversely calculating the time arrangement of each task at different time nodes based on the optimal transportation window combination to obtain a planning scheme. According to the invention, automation and intelligentization of flexible job shop scheduling scheme making can be realized.

The invention discloses a flexible job shop energy-saving scheduling optimization method considering a light storage condition and a load characteristic, and relates to the technical field of industrial scheduling, and the method comprises the steps: building a flexible job shop energy-saving scheduling problem model based on mixed integer programming, and setting an optimization target and a constraint condition; an ant colony algorithm is improved, an ant colony is divided into dynamic multi-level search, and a pheromone matrix is optimized; the updating effect of pheromones in the ant colony algorithm in the iteration process is optimized; optimizing the optimal solution of each generation of the ant colony algorithm by adopting a graph neural network off-line learning neighborhood search method; and stopping iteration when a preset termination condition is met, and outputting an optimal scheduling scheme. According to the method, a more efficient and flexible energy-saving scheduling strategy is developed to effectively coordinate the productivity and the energy efficiency, energy optimization and cost reduction in the production process are achieved, actual technical support is provided for energy-saving scheduling of the flexible job shop, and a method system for the workshop scheduling problem is enriched.

The present application relates to the field of intelligent manufacturing technology, and in particular to a distributed flexible job shop scheduling method and system based on dual-deep reinforcement learning and multi-layer intelligent agents. By introducing a multi-level intelligent agent scheduling framework, the top-level, middle-level and bottom-level intelligent agents work together and make hierarchical decisions to decompose global complex problems into multiple local problems; in addition, different scheduling tasks of the distributed flexible job shop are decentralizedly executed by intelligent agents at each level, without the need for a central coordinator to manage each decision. When the environment changes locally, each level can adapt quickly and make the best decision based on its specific context, while still serving the overall goal of the entire system. It aims to solve the problem of how to improve the scheduling accuracy of distributed flexible job shops.

The invention relates to a multi-process-route-oriented production scheduling integrated optimization method, and relates to the technical field of flexible workshop multi-target scheduling. The method comprises the steps that firstly, a production line process planning and production scheduling integrated optimization model is constructed, the model adapts to a machining sequence flexible scene, and optimal process route determination and procedure machining equipment distribution are synchronously achieved; secondly, double optimization targets are set, namely, the maximum completion time is minimized, and the equipment utilization rate is maximized; thirdly, designing an optimization solution process based on a genetic algorithm framework, generating a chromosome population containing a process route selection machine allocation process sequence, and fusing double targets through an adaptive fitness evaluation mechanism; and finally, developing a visual interaction system in a matched manner, and dynamically outputting an optimal process route scheme and an equipment scheduling plan. The method can effectively solve the problems of long processing task completion time and unbalanced equipment utilization rate in a multi-process route scene, is suitable for a scheduling scene of a flexible job shop, and improves the overall operation efficiency of a production line.

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.

The application aims at providing a multi-stage multi-level assembly job shop energy-saving scheduling multi-objective optimization method, relates to the technical field of assembly shop scheduling, and establishes a mathematical model of the problem in stages, and designs a problem-driven energy-saving strategy triggering mechanism according to the individual state, so that the quality and search efficiency of the solution are improved. Secondly, two heuristic rules and a random generation method are used to construct an initialization population that takes into account high quality and diversity, and a dynamic self-adaptive adjustment strategy for the assimilation operator parameters is realized through Q-learning, so that the convergence speed is improved while the population diversity is ensured, so that the exploration and mining ability of the algorithm is better balanced. A revolutionary operation guided by a hyper-heuristic variable neighborhood search is designed, and a high-quality colony found is searched in detail. The joint empire invasion operation is used to replace the competition, realizes the cooperative evolution and information interaction sharing of multiple empires, and thus finds non-dominated solutions with more uniform distribution.

This invention discloses a method for solving the job shop scheduling problem based on the red deer algorithm, belonging to the field of shop scheduling. The method decodes the job shop schedule using a random key, explores and utilizes a balancing algorithm of roaring, fighting, and pairing operations, and uses the Euclidean distance to measure the distance between male and female deer to solve the job shop scheduling problem. This invention is the first to attempt to apply the red deer algorithm to the job shop scheduling problem. Compared with traditional mathematical programming methods, this method can obtain satisfactory scheduling solutions for large-scale scheduling problems in polynomial time, while maintaining low computational complexity and high robustness.

The application discloses a scheduling method and device for a distributed flexible job shop with an automatic guided vehicle, and the method comprises the following steps: establishing encoding and decoding rules by taking the maximum completion time and energy consumption of a scheduling sequence as optimization targets; randomly obtaining two solutions from a feature space, performing a crossover-mutation strategy on the obtained two solutions, and generating two new solutions; selecting a solution and using a double deep neural network to decide a local search operator used; executing the selected local search operator, obtaining a new scheduling scheme, and updating the feature space; judging whether a termination condition is met, and if yes, outputting a current best scheduling sequence and two target values; and if not, returning to execute the step of randomly obtaining two solutions from the feature space. The application solves the scheduling problem of the distributed flexible job shop with the automatic guided vehicle, shortens the completion time of the scheduling sequence in workshop production, and reduces the energy consumption in processing.

The present invention discloses a method for active fault-tolerant scheduling of flexible job shops based on multi-perspective transfer learning. The method collects time series data of each device in the shop in real time, performs data cleaning and standardization through pre-processing; predicts and fills missing or abnormal device status data through an industrial data interpolation model based on gradient penalty weighted conditions to achieve data completion; and uses the model to perform data prediction on the future state of the system; utilizes a task migration module based on traceless transformation combined with fault analysis technology to identify potential problems; utilizes a state calculation module to calculate the specific state of the intelligent agent in the environment state space based on the complete time series data after interpolation; establishes a dynamic scheduling model for the flexible job shop, and based on a multi-perspective enhanced deep reinforcement learning scheduling module, selects actions according to the state characteristics of the current production environment and generates a scheduling plan. The present invention can improve scheduling efficiency and fault tolerance in flexible production environments.

The invention relates to the technical field of industrial scheduling, and discloses a flexible job shop self-organizing scheduling-oriented multi-target dual hyper-heuristic method, which comprises the following steps of: generating a process selection rule and an interval selection rule by utilizing a genetic programming rule, and generating a rule set; based on a deep reinforcement learning method, a dynamic decision strategy is constructed in combination with a rule set, and multi-step action sequence optimization is carried out when self-organizing scheduling is triggered; and generating an instance through random combination of parameter indexes of a predetermined dynamic event, and calculating a performance index according to the generated instance to realize feasibility verification. Through a dynamic collaborative optimization mechanism of self-organizing scheduling, an autonomous decision closed loop can be realized under multiple disturbances such as equipment failure, processing fluctuation, new order insertion and the like, and the self-healing capability, response speed and anti-interference toughness of a production system in a dynamic environment are remarkably improved; and an intelligent decision-making scheme with an autonomous evolution capability is provided for a complex scheduling problem in an intelligent manufacturing scene.

This invention proposes a predictive scheduling method and system for dynamic flexible job shops, comprising: real-time acquisition of machine operating status to establish a mathematical model of the job shop environment; predicting the failure probability of each machine by combining historical data and real-time acquired data; establishing a scheduling model composed of the job shop environment and a scheduling agent employing a reinforcement learning algorithm, wherein the machine failure probability and machine operating status obtained in the job shop environment are used as input data for the reinforcement learning algorithm, and the algorithm outputs an optimal scheduling scheme that conforms to the scheduling rules; executing the optimal scheduling scheme and monitoring it, and rescheduling when trigger conditions are met. This invention can effectively address machine failures and new job insertion problems in dynamic production environments, optimize scheduling performance, reduce workpiece delays, and improve overall production efficiency.