41 results about "Job shop scheduling problem" 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 relates to the technical field of industrial manufacturing, and discloses a flexible workshop scheduling optimization method based on deep reinforcement learning, and the method comprises the following steps: constructing a graph neural network model, and carrying out the modeling of a workshop operation process of a flexible workshop scheduling problem; the flexible job-shop scheduling problem is converted into a Markov decision process, a deep reinforcement learning algorithm is used for dynamic decision making, and the Markov decision process comprises a state space, an action space, a reward function and a state transfer function; and restraining the action space of the deep reinforcement learning algorithm in combination with a heuristic scheduling rule. The flexible workshop scheduling is optimized through the deep reinforcement learning algorithm, and the scheduling strategy can be dynamically adjusted in real time according to the actual production state and task requirements of the workshop. Compared with a traditional scheduling method, the method does not need to preset a fixed scheduling rule, autonomously learns and decides through the intelligent agent, and adapts to a complex and changeable production environment.

The invention relates to a factory job shop scheduling control optimization method and device, equipment and a medium, and the method comprises the steps: classifying workpieces irrelevant to all MPBPO sets into a first workpiece set, classifying the workpieces corresponding to each MPBPO set into one set to determine a second workpiece set, independently generating job task sorting sub-chromosome segments, and generating a job task sorting sub-chromosome segment; corresponding operation machine numbers and operation time are respectively filled into corresponding gene positions of the operation machine sub-chromosomes and the operation time sub-chromosomes, and the operation task sorting sub-chromosomes, the operation machine sub-chromosomes and the operation time sub-chromosomes are combined to obtain initial chromosomes so as to generate initial population individuals; genetic manipulation is carried out on the initial population individuals, and population individuals with the high fitness and the number of the population individuals being the population scale are selected as a next-generation initial population; and repeatedly executing, and outputting the production scheduling scheme with the optimal earliest completion time. According to the method, algorithm premature convergence can be avoided in a generalized job-shop scheduling problem with a forced parallel batch processing procedure.

The invention belongs to the technical field of job shop scheduling, and particularly relates to a self-learning job shop scheduling method meeting waiting time constraint, which comprises the following steps: S0, constructing a job shop scheduling problem model with waiting time constraint; the method comprises the following steps: S1, acquiring job shop scheduling problem data, a configuration algorithm and operation parameters; s2, constructing chromosome individuals, generating chromosomes and initializing a population; s3, calculating the fitness value of each chromosome individual; s4, forming a new generation of population; s5, combining the fitness information of the current population, dynamically selecting a crossover rate Pc through a Q learning algorithm, and performing crossover operation on the population; s6, dynamically determining a mutation rate Pm in the same parameter combination space by using a Q learning algorithm, and performing mutation operation on the crossover progeny to generate an updated population; and S7, judging whether an iteration termination condition is met or not. According to the method, a feasible and near-optimal scheduling scheme can be efficiently generated on the premise of ensuring that the inter-process waiting time constraint is met.

The present application relates to the technical field of intelligent manufacturing production scheduling and combination optimization, in particular to a distributed flexible job shop scheduling method considering process dependency, comprising: initializing algorithm parameters, alternately using a heuristic method and a random method to generate an initial population; selecting a parent solution from the current population through an adaptive adjustment strategy, sequentially executing a crossover operator, a mutation operator and a local search on the parent solution, updating the population; calculating the current optimal solution of the current population, judging whether the termination time is reached, if yes, terminating evolution, outputting the current optimal solution and the maximum completion time, otherwise, continuing iteration. The present application solves the problems of existing solving methods, such as significant time consumption increase and search into local optimum when the scale of the distributed flexible job shop scheduling problem considering process dependency is expanded, and achieves the positive effects of reducing the maximum completion time and improving the search efficiency and solution quality of large-scale instances.

The present application relates to the technical field of intelligent manufacturing and industrial artificial intelligence, in particular to a man-machine cooperation flexible job shop scheduling method based on offline reinforcement learning, which firstly standardizes mathematical modeling of man-machine cooperation double-resource flexible job shop scheduling problem; constructs a heterogeneous scheduling graph containing three types of heterogeneous nodes of process, machine and worker; designs a multi-attention feature extractor; constructs a quantile alignment Actor-Critic offline reinforcement learning framework based on uncertainty control to complete robust offline policy training; and finally realizes end-to-end real-time scheduling decision through the trained model. The present application can complete model training without online environment interaction, has high solution accuracy, strong generalization ability, low deployment cost and high decision robustness, and is suitable for man-machine cooperation flexible production scene in the industrial 5.0 environment.

The invention proposes a multi-agent flexible job-shop scheduling method based on graph deep learning and evolutionary neural topology, and the method comprises the steps: firstly building a multi-agent flexible job-shop scheduling model, initializing a multi-agent flexible job-shop scheduling problem, then converting a mathematical model into a Markov decision process, constructing a heterogeneous graph model, and carrying out the optimization of the multi-agent flexible job-shop scheduling model. The multi-agent flexible job shop environment information is represented; secondly, state features of procedures and machines are extracted in a layered mode according to the information of the customer agency, wherein the state features comprise a node-level attention mechanism and a semantic-level attention mechanism; finally, constructing and training an actor-commentator model as a scheduling decision model; and solving the multi-agent flexible job shop scheduling problem by using the scheduling decision model to obtain a multi-agent flexible job shop scheduling scheme. According to the method, the limitation that an existing model only pays attention to global efficiency optimization is broken through, and the problems of high subjectivity, high trial and error cost, limited generalization ability and the like existing in artificial design of a neural network structure and hyper-parameters are solved.

The invention belongs to the technical field of intelligent manufacturing and production scheduling, and particularly relates to a construction method and a scheduling method of a flexible job shop scheduling model based on a heterogeneous graph neural network and deep reinforcement learning. Constructing heterogeneous graph representation of a flexible job shop scheduling problem; constructing a deep reinforcement learning scheduling framework based on the heterogeneous graph representation; extracting machine node features by adopting a graph attention mechanism, and extracting process node features by adopting a heterogeneous graph Transform; designing a scale sensing module, and fusing problem scale information into node features; training a scheduling strategy model by adopting a near-end strategy optimization algorithm; and on-line scheduling decision making is carried out based on the trained model. According to the method, through heterogeneous graph representation and a scale perception mechanism, scheduling of different scales and dynamic disturbance scenes can be adapted through one-time training, and the generalization ability, the solving quality and the real-time performance of a scheduling strategy are remarkably improved.

The invention provides an inductance workshop scheduling method capable of reentering an outsourcing process, and the method comprises the steps: S1, arranging a machinable machine for the process of each workpiece based on an inductance process flow and a machine condition; s2, formulating a mathematical model of a reentrant inductance job shop scheduling problem according to actual production, wherein the mathematical model comprises an optimization target and constraint conditions; s3, based on the reentrant inductance job shop scheduling problem mathematical model, constructing a PPO sudden change controller and an improved GNN critical path predictor; s4, generating an initial scheduling plan by adopting an enhanced genetic algorithm fusing a PPO sudden change controller and an improved GNN critical path predictor; s5, performing key path analysis and optimization on the initial scheduling plan by utilizing topological sorting; s6, according to an outsourcing process completion event and a machine fault event, marking state update of an associated process or a machine, identifying an affected subsequent process set, extracting uncompleted processes to construct a temporary scheduling problem model, and re-planning arrangement of the uncompleted processes by adopting an enhanced genetic algorithm; and S7, repeating the steps S5-S6, and after an outsourcing process completion event or a machine fault event is processed, carrying out key path optimization again until the last process, so as to realize continuous dynamic adjustment of the scheduling plan.

The invention relates to the technical field of production line scheduling control, and particularly discloses a flexible production line arrangement scheduling and communication method by using an improved simulated annealing algorithm, which comprises the following steps: carrying out mathematical modeling simulation on production scheduling of an assembly line, and converting a multi-target flexible job shop scheduling problem into a mixed integer programming model; solving the mixed integer programming model by using an improved simulated annealing algorithm; a Transform model of an encoder-decoder architecture is adopted, multi-modal feature fusion is achieved through position encoding, wavelet transformation and type embedding, and a production scheduling scheme is obtained and used for assembly simulation of the discrete mixed flow assembly line; designing a Chisel-based tunnel communication architecture, and combining TCP long connection with TLS bidirectional authentication; through deep integration of multi-target mathematical modeling, a feature-driven prediction model and a high-reliability communication system, breakthrough improvement of dynamic process scheduling efficiency and equipment task allocation capability is realized.

This invention discloses a flexible job shop scheduling method and system based on a hybrid sandcat optimization algorithm. The method includes the following steps: S1, reading information from a standard format flexible job shop scheduling problem example, and storing the machine information, machine processing time information, and workpiece information in a list for classification; S2, randomly generating multiple feasible process codes and machine codes based on a two-stage coding rule as an initial population; S3, using the obtained initial population, setting the sandcat sensitivity, iteratively optimizing the process codes using a hybrid sine and cosine algorithm of the sandcat optimization algorithm, and updating the machine codes using a uniform crossover operator; S4, recording the minimum maximum completion time in the population after each iteration, and outputting the Gantt chart of the optimal solution obtained by the algorithm after the iteration. This invention solves the problem of inaccurate manual scheduling calculations in industrial production, which requires a large amount of human resources.

The application belongs to the technical field of workshop scheduling, and particularly discloses a kind of inconsistent batch quantity flow flexible job shop scheduling method and system, it includes: for inconsistent batch quantity flow flexible job shop scheduling problem, with sub-batch quantity as one of decision variables to construct mixed integer linear programming model;Intelligent optimization algorithm is used to solve mixed integer linear programming model iteratively, in the iteration process, in the intermediate solution generated in each iteration, select part of elite solution, fix the machine allocation of each sub-batch in elite solution and the value of processing order variable of each sub-batch on machine, with the sub-batch quantity in elite solution as optimization variable, construct batch sub-problem model;Sub-problem model is solved, so that elite solution is optimized, and the intermediate solution after processing continues to the next round of iteration.The application can efficiently solve inconsistent batch quantity flow flexible job shop scheduling problem, and realize the improvement of processing efficiency and equipment utilization.

The application provides a flexible job shop scheduling method and system based on an evolutionary strategy and meta-reinforcement learning, comprising the following steps: a certain number of flexible job shop scheduling problem instances are randomly generated to form a training data set, and the training data set is replaced every fixed update round; a meta-reinforcement learning framework based on an evolutionary strategy is constructed to train a meta-model, the optimal parameters of the meta-model are determined by minimizing the total average completion time of a verification set, and the meta-model is used as an initialization model to adapt to new tasks in an inference process; the completion time of test data is obtained by using the trained meta-model, and the optimal result for each instance in the test data is obtained by fine-tuning each instance a limited number of times.

This invention provides a deep reinforcement learning-based job shop scheduling method based on multiple client agent bidding, addressing the problem of existing technologies being unable to schedule multi-agent job shops. The method includes: modeling the multi-agent job shop scheduling problem and obtaining the client agent agents corresponding to the client agents, the job shop agent agents corresponding to the job shops, and the scheduling status of the job shops; obtaining the feature representations of each process node and outputting the set of processes participating in bidding in each round of bidding; obtaining the feature representations of each process node and outputting the bidding decision order; the job shop agent agents collecting the bidding process sets of all client agents to make bidding decisions, continuously repeating the bidding process until a final scheduling scheme is generated. This invention, while protecting the private preferences of each client agent, enables multiple agents with personalized goals to participate in the scheduling decision-making process, forming a scheduling scheme acceptable to all parties.

The invention provides a flexible job shop scheduling optimization method based on a double-target empire competition algorithm. In order to improve the efficiency of the core equipment in the flexible job shop scheduling problem FJSP, a mathematical model is established, which takes the minimization of the maximum completion time and the maximization of the core equipment utilization as the target. An improved empire competition algorithm IICA is designed to solve the model. In the algorithm, a left shift decoding rule is proposed to solve the scheduling integration problem. Two heuristic search strategies are used for global search of the empire competition algorithm. On this basis, the adaptive number revolution algorithm is used to improve the convergence speed in the later stage to improve the local search efficiency. Simulation experiments verify the feasibility of the optimization model. The experimental results show that the algorithm has higher efficiency and effectiveness than the existing algorithm.

The invention discloses a dynamic multi-target flexible job shop scheduling method based on deep reinforcement learning. According to the method, a dynamic multi-target flexible job shop scheduling problem is modeled as a Markov decision process, and a lightweight deep reinforcement learning network named as A2DC-Net is provided for solving. The A2DC-Net dynamically focuses on key scheduling features by introducing a feature extraction module of an attention mechanism; an improved Actor-Critic framework comprising double independent Critic networks is adopted, values of different optimization targets are evaluated respectively, and multi-target strategy optimization is guided accurately; and a novel multi-target reward function is combined for training. According to the method, light weight is strived on the network structure, the calculation burden is remarkably reduced while the scheduling quality is ensured, and efficient real-time response to dynamic events is realized. Experiments show that the method is superior to a traditional scheduling rule, a meta-heuristic algorithm and other advanced deep reinforcement learning methods in convergence speed, multi-objective optimization performance and generalization ability.

The invention relates to a job-shop scheduling method based on deep reinforcement learning collaborative simulated annealing, and the method comprises the steps: setting problem parameters according to a job-shop scheduling problem instance; constructing a scheduling pre-optimization model according to the set problem parameters, and performing scheduling pre-optimization based on the scheduling pre-optimization model of deep reinforcement learning; and constructing a local optimization model, and performing local optimization based on the local optimization model of the enhanced simulated annealing. According to the method, end-to-end deep reinforcement learning is combined with the enhanced simulated annealing algorithm, a scheduling scheme rapidly generated by the deep reinforcement learning is used as'warm start 'input of the enhanced simulated annealing algorithm, and fine local search optimization is performed on the basis of the enhanced simulated annealing algorithm. The technical defects that a single deep reinforcement learning method is prone to falling into local optimum and a single simulated annealing algorithm is slow in convergence and unstable in solution quality are remarkably overcome, and the solving efficiency and quality of the job-shop scheduling problem can be greatly improved.

This invention relates to a job shop scheduling method based on heterogeneous graph neural networks and masked reinforcement learning, comprising: S1: reading instance data of the job shop scheduling problem; S2: constructing a heterogeneous graph data object containing process nodes and machine nodes in an event-driven simulation environment; S3: generating action mask vectors for the corresponding process set based on the completion status of the process's prerequisites and the idle state of the specified processing machine; S4: inputting the heterogeneous graph data object into the heterogeneous graph neural network model, outputting the action probability corresponding to each process and the value assessment scalar of the current state; S5: combining the action mask vectors and the action probabilities to perform action sampling; updating the network parameters of the heterogeneous graph neural network model using a near-end policy optimization algorithm to obtain a scheduling optimization model; S6: using the scheduling optimization model to reason about new scheduling instances, selecting the process with the highest action probability at each step to generate the optimal process.

To provide a technique capable of obtaining a solution of a job shop scheduling problem without requiring excessive solution time.SOLUTION: A method of the present disclosure includes (a) setting a processing condition including a job list defining set work times in a plurality of machines for each of a plurality of jobs, (b) creating a simplified job list by dividing each of the set work times in the job list by a time divisor, and obtaining a tentative solution of a work plan by solving a job shop scheduling problem regarding the simplified job list as a 0-1 integer programming problem, and (c) correcting a work time of each job in the tentative solution to the set work time to obtain a final solution of the work plan.SELECTED DRAWING: Figure 2

The embodiment of the invention provides a fuzzy flexible job shop multi-target scheduling method and device, a medium and a product, and relates to the technical field of shop scheduling. The method comprises the following steps: modeling a fuzzy flexible job shop scheduling problem, and constructing a scheduling model taking a preset scheduling performance index as an optimization target; the preset scheduling performance indexes comprise fuzzy completion time minimization, total energy consumption minimization and machine group operation efficiency maximization; the machine group operation efficiency is used for representing the average equipment operation rate of all started machines and the ratio of the number of started machines to the total number of machines; and based on a pre-constructed non-dominated sorting genetic algorithm, solving the scheduling model, and determining a target scheduling scheme. According to the scheme, the problem that the operation time uncertainty in industrial practice cannot be accurately reflected due to the fact that an existing scheduling model and a related scheduling method use deterministic parameters is solved.

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.

This invention discloses a job shop scheduling method based on heterogeneous graph neural networks and priority post-experience replay, relating to the field of dynamic job shop scheduling technology. By loading instances of the dynamic job shop scheduling problem and initializing deep reinforcement learning model parameters, state encoding, policy execution, environmental interaction, and experience storage operations are performed at decision points during training rounds. After each round, synthetic experience is generated through target relabeling, and a hybrid experience replay buffer containing both original and synthetic experience is constructed, with priorities assigned based on temporal difference errors. Experience samples are sampled according to priority, and network parameters are optimized using the PPO algorithm. The optimal model parameters are then loaded to perform real-time scheduling decisions for new instances. This invention improves sample utilization efficiency and policy convergence speed, exhibiting excellent scheduling optimization performance in both static and dynamic environments.

The invention discloses an optimization method for solving a flexible workshop scheduling problem. According to the method, the white whale optimization algorithm and the genetic algorithm are combined, and after the white whale algorithm completes whale falling operation and obtains a group of high-quality feasible solutions, crossover and mutation operation of the genetic algorithm is applied to the solutions for further optimization. Specifically, the crossover operation adopts a self-adaptive crossover mechanism to enhance the diversity of solutions; a directional search strategy is introduced in the variation step to guide the search process to avoid local optimum; finally, investigation behaviors of an artificial bee colony algorithm are fused, and a minimum completion time inverse search strategy is adopted to improve the quality of a solution. The method is used for solving the workshop scheduling problem and can effectively cope with workshop scheduling examples of different scales so as to obtain a better scheduling scheme.

The application provides a workshop scheduling optimization method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring current scheduling information of a workshop, extracting current local state information from the current scheduling information of the workshop; inputting the current local state information into a scheduling model corresponding to a current agent to obtain an optimal action set of the current agent; updating the current scheduling information of the workshop based on the optimal action set of the current agent to obtain next scheduling information of the workshop; inputting the next scheduling information of the workshop into a scheduling model corresponding to a next agent to obtain an optimal action set of the next agent; and determining a workshop scheduling scheme based on the optimal action sets corresponding to all agents of the workshop. The application solves the job shop scheduling problem through a multi-agent Markov decision process.

The application discloses a flexible job shop scheduling method based on an improved grey wolf algorithm, first constructs a flexible job shop scheduling problem model, and encodes workshop equipment and processes, creates external archives and sets parameters of the grey wolf algorithm. Secondly, an initial population is generated by combining a chaotic mapping and an extended GLR method of opposite learning, all individuals in the population are evaluated, decision layer individuals are determined, and the external archives are updated. Finally, it is judged whether the algorithm termination condition is met, if yes, the algorithm ends, flexible job shop equipment coding and corresponding process coding sorting are obtained, otherwise, it is judged whether the absolute value of a coefficient vector A calculated by a convergence factor is greater than or equal to 1, the position is updated until the algorithm ends. The application improves the performance on the flexible job shop, and solves the problems of slow convergence speed and easy falling into local optimum of the traditional swarm intelligence optimization algorithm.

The application belongs to the technical field of job shop scheduling, and discloses a flexible job shop robust scheduling method and system based on a key chain. The method comprises the following steps: for a flexible job shop scheduling problem containing remanufacturing jobs, a multi-objective optimization model with the objective functions of minimizing the completion time and minimizing the overlapping degree is constructed; the multi-objective optimization model is solved to obtain a group of Pareto non-dominated scheduling schemes; and the scheme with the lowest overlapping degree in the group of Pareto non-dominated scheduling schemes is selected as the final scheduling scheme. Through the application, the problems of non-robust scheduling scheme, large completion time fluctuation and low resource utilization efficiency caused by the uncertainty of remanufactured part working hours are solved.

The application belongs to the field of workshop scheduling, and particularly discloses a priority scheduling rule generation method and system for a distributed job workshop, comprising: constructing a scheduling rule generation model for decision-making of a distributed job workshop scheduling problem, wherein the distributed job workshop scheduling problem is represented as a disjunctive graph; each factory corresponds to a sub-disjunctive graph; the sub-disjunctive graphs of all factories are spliced to obtain a disjunctive graph capable of representing factory allocation and process sequencing within the factory, each node of the disjunctive graph comprising factory information allocated; the disjunctive graph is solved through a Markov decision model; in the decision-making process, the features of the disjunctive graph are extracted through a graph neural network, and action decision-making is performed through an actor network; the scheduling rule generation model is trained according to a pre-acquired data set, the parameters of the graph neural network and the actor network are updated, and a trained scheduling rule generation model is obtained. The application can realize priority scheduling rule generation for a distributed job workshop, and has good performance and generalization.

The invention discloses a multi-AGV green job shop integrated scheduling method and system based on an improved genetic algorithm, and the method comprises the steps: S1, constructing a multi-AGV green job shop scheduling problem model, wherein the model takes minimization of total energy consumption, maximum completion time and total cost as optimization targets, and comprises energy consumption constraints and charging constraints of the AGV in waiting, no-load and full-load states and energy consumption constraints of processing equipment; s2, solving the scheduling problem model by adopting an improved genetic algorithm; wherein chromosome coding of the improved genetic algorithm comprises process sequence coding, equipment allocation coding and AGV allocation coding; and S3, outputting an optimal scheduling scheme through the improved genetic algorithm, and scheduling the AGV and the processing equipment in the workshop according to the output optimal scheduling scheme.

A method of the present disclosure includes (a) setting a processing condition including a job list that defines set-up work times in a plurality of machines for each of a plurality of jobs, (b) obtaining a temporary solution for a work plan by dividing each of the set-up work times in the job list by a time divisor to create a simplified job list and solving a job shop scheduling problem relating to the simplified job list as a 0-1 integer programming problem, and (c) modifying work times of respective jobs in the temporary solution of the set-up work times and obtaining a final solution of the work plan.

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.