93 results about "Flow shop scheduling" patented technology

The invention relates to a clustering and entropy-guided reentrant hybrid flow shop scheduling method. The method comprises the following steps: step 1, establishing a problem model; step 2, setting algorithm operation parameters; 3, adopting an initialization strategy to generate an exploration population and a development population; 4, judging whether a first-stage termination condition is met or not, if not, executing a first-stage evolutionary strategy and an updating strategy on the exploration population and the development population, and otherwise, executing the step 5; 5, constructing an elite population; 6, judging whether a second-stage termination condition is met or not, and if not, executing a second-stage evolutionary strategy on the elite population; otherwise, outputting a Pareto solution set; and 7, updating the elite population. According to the method, dynamic balance of global exploration and local development is realized, and the solution distribution can be improved while the solution set convergence is ensured, so that the completion time and the total energy consumption are reduced, the production cost is reduced, and the workshop scheduling efficiency is improved.

The invention relates to the technical field of distributed flow shop scheduling, and belongs to a solving method for a distributed heterogeneous replacement flow shop scheduling problem. Comprising the following steps: determining to take minimization of total weighting completion time as a solving target, initializing a population, and optimizing the population to obtain an optimized population; selecting a solution from the initial population by using a ternary tournament method, and executing six local search operators in sequence, and executing each local search operator for five times; replacing the solution with the maximum target value in the optimized population with a new solution to obtain a new population, if no improvement is continuously carried out for five times, triggering a disturbance mechanism to update the new population, combining the new population after disturbance with the new population before disturbance, sorting each solution in the combined population from small to large according to the target value of the solution, and selecting part to continue iteration; and continuing to execute the next round of search until the limited maximum time is reached, and outputting the solution with the minimum target value of the solution. The method has the positive effects of improving the production efficiency and the stability of the production line.

The invention relates to the technical field of hybrid flow shop scheduling optimization, in particular to a hybrid flow shop scheduling optimization method and system based on equipment green degree sequencing, and the method comprises the steps: constructing a comprehensive green degree evaluation matrix of each equipment based on a comprehensive green degree evaluation index system; according to the comprehensive green degree evaluation matrix of each device, respectively calculating a relative pasting degree and a grey correlation degree; according to the relative pasting progress and the grey correlation degree, determining a comprehensive green degree evaluation value of each device; sorting according to a descending order of the comprehensive green degree evaluation values of the devices, and screening out an initial population from a sorted device set according to a set selection strategy; and taking the initial population as an input value of a differential evolution algorithm, executing the differential evolution algorithm to obtain an optimal solution, and taking the optimal solution as a hybrid flow shop scheduling optimization result. According to the method, the hybrid flow shop scheduling scheme is efficiently and reasonably generated.

The invention belongs to the technical field of production scheduling, and discloses a hybrid flow shop scheduling method based on a double-agent neighborhood search algorithm, and the method comprises the following specific steps: 1, researching a real production scene of a hybrid flow shop, mining constraint conditions, and analyzing a main scheduling problem which mainly affects a production cycle; the invention integrates the advantages of a deep Q network (DQN) and a meta-heuristic algorithm, provides a double-agent neighborhood search algorithm, can quickly respond to the change of a workshop environment and dynamically adjust a scheduling strategy, and aims to provide a new efficient solution scheme for hybrid flow shop scheduling and derivative problems thereof. Two intelligent agents capable of interacting with the workshop environment in real time are constructed, wherein the first intelligent agent can determine the machining sequence of workpieces in each stage, and a promising initial solution is rapidly generated; and the second agent can select an optimal neighborhood search strategy, and dynamically adjust the search direction to accelerate convergence of the algorithm.

The invention belongs to the technical field of flow shop scheduling, and particularly relates to a hybrid flow shop full-active batch scheduling method. On the basis of a construction process of full active scheduling, complete scheduling is generated, and a compact and feasible initial solution is obtained; a damage-repair process based on a track is introduced, and complete scheduling is enhanced by focusing key operation to gradually converge to a stable state. According to the method, for the first time, on the basis of a construction process of full-active scheduling, complete scheduling is generated, batch dispatching, batching and distribution rules are effectively integrated, a high-quality scheme is constructed by utilizing a weighted priority mechanism, a damage-repair process based on a track is introduced, and the complete scheduling is strengthened by focusing key operation and is gradually converged to a stable state. Cross-stage waiting can be reduced, the equipment utilization rate is improved, and the total completion time is shortened; a high-quality schedule is generated in a short calculation time, and the method is suitable for a real-time or quasi-real-time production environment.

The invention relates to a hybrid flow shop scheduling robust optimization method and device based on a hybrid genetic algorithm, which can realize the improvement of the robustness of a hybrid flow shop scheduling scheme, emphasizes the mean value performance while paying attention to the worst situation, and has relatively strong capabilities of global search and jumping out of a locally optimal solution. The method comprises the following steps: determining a hybrid flow shop scheduling problem mathematical model based on robust optimization; constructing a first optimization algorithm based on a genetic algorithm according to the constraint of the hybrid flow shop scheduling problem; constructing a second optimization algorithm based on a simulated annealing algorithm framework; constructing a scheduling scheme evaluation method based on the stochastic simulation algorithm and considering the worst situation and the average performance; and fusing the first optimization algorithm and the second optimization algorithm to obtain a target optimization algorithm, and performing scheduling scheme optimization based on the target optimization algorithm and the scheduling scheme evaluation method to obtain an optimal scheduling scheme.

The invention relates to the technical field of production scheduling, in particular to a hybrid flow shop self-learning scheduling method considering machine deterioration, and aims to solve the problem of hybrid flow shop scheduling considering machine deterioration, a self-learning artificial bee colony algorithm is adopted to optimize the maximum completion time. In the hired bee stage, populations are divided into P1, P2 and P3 groups according to fitness to realize diversified learning, in the bee observation stage, the food source selection probability is improved based on knowledge, multi-neighborhood search is combined, the investigation bee stage is improved, and a nectar source diversity enhancement strategy is added. The SLABC is superior to PPSOGA, DWSA and other algorithms under different workpiece numbers, stage numbers and deterioration rates, the influence of machine deterioration on the construction period can be effectively handled, the scheduling performance is remarkably improved, and the actual production requirements are met.

The invention discloses a distributed assembly replacement flow shop scheduling method based on deep reinforcement learning, and the method comprises the steps: 1) building an environment framework of a distributed assembly replacement flow shop, initializing environment parameters, setting a constraint condition of shop scheduling, and setting a scheduling target as the minimum total flow time; 2) defining a state space, an action space and reward mechanisms, and calculating the influence of different reward mechanisms on the total flow time; 3) updating related information of the operation and the machine; 4) operating a PPO algorithm for learning and training; 5) recording a scheduling result until a training result reaches a termination condition; and 6) drawing an iterative graph of the total flow time to obtain a workshop scheduling result of the lowest total flow time. According to the method, the PPO algorithm is used for solving the scheduling problem of the distributed assembly replacement flow shop, a new composite scheduling rule and a new reward mechanism are designed, the algorithm can be better learned, and the algorithm can find a better solution more quickly.

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

The invention discloses a distributed heterogeneous flexible flow shop batch processing scheduling method and system, relates to the technical field of distributed production scheduling in the manufacturing industry, and aims to solve the problems that an existing scheduling method is not comprehensive in constraint consideration, poor in energy consumption optimization and low in algorithm efficiency. According to the method, a mixed integer linear programming model containing multiple constraints such as release time and sequence-related preparation time is constructed, a learning-assisted dual-objective co-evolution framework is established, and the maximum completion time and the total energy consumption are synchronously optimized by combining mixed initialization, global-local search collaboration, decision reinforcement learning operator selection and a collaborative energy-saving strategy. The release time, the sequence-related preparation time, the inter-stage transportation time and the batch processing scheduling are simultaneously considered in the distributed heterogeneous flexible flow shop scheduling for the first time, the established mixed integer linear programming model better fits the actual production scene, and the method fits the actual production scene, is good in energy consumption optimization effect and can be adapted to the non-ferrous metal metallurgy aluminum production process.

The invention discloses a zero-latency flow shop scheduling method based on a discrete coding red mouth blue magpie optimization algorithm, and belongs to the technical field of flow shop scheduling. According to the method, on the basis of a discrete coding Red-billed Blue-Tapie optimization algorithm (DRBMO), the zero-latency flow shop scheduling problem with the goal of minimizing the maximum completion time is solved. The method mainly comprises the following steps: proposing a method based on INEH, random and reverse learning to generate a high-quality initial population; dividing the population into a large group and a small group according to the fitness value; discretizing a continuous position updating formula of the algorithm, and updating the position of the individual by using the discretized updating formula; variable neighborhood search is carried out, an adaptive parameter q and two neighborhood structures are given, the global search capability in the earlier stage of iteration is high, and the local search capability in the later stage is high; substitution operation: reserving one of the same individuals in the population, and randomly generating other repeated individuals; and checking a termination condition, if the termination condition is satisfied, stopping iteration, and outputting an optimal individual as an optimal scheduling scheme, otherwise, re-dividing the population.

The invention relates to a reinforcement learning-based production scheduling method for a reentrant hybrid flow shop, and the method comprises the steps: firstly analyzing the production demands of the reentrant hybrid flow shop, and determining the production information of a current product; secondly, determining optimization objectives, taking minimization of the maximum completion time and minimization of the total delay time as the optimization objectives, determining constraint conditions and parameters, determining constraints and related parameter variables in an optimization problem, and establishing a multi-objective reentrant hybrid flow shop scheduling problem model; performing optimization solution by utilizing reinforcement learning, describing and constructing a state space and an action space based on a Markov decision process model, and constructing a global external reward and an internal reward after completion of each stage of production by adopting a layered reward mode in order to overcome challenges caused by a reward sparse problem in reinforcement learning; a bidirectional long short-term memory network (BiLSTM) is used to construct a scheduling strategy, internal scheduling information is extracted, a model is trained by using near-end strategy optimization (PPO), and intelligent decision-making of action selection is realized.

The invention discloses a batch processor-containing aviation composite material manufacturing hybrid flow shop scheduling method, which comprises the following steps of: carrying out characteristic analysis on two key processes of laying and hot press molding in aviation composite material manufacturing, and determining related parameters; non-equal parallel processing equipment and a non-equal parallel batch processor are respectively arranged for the two working procedures; workpiece family compatibility, time window constraint and resource limitation are comprehensively considered, and a scheduling model with the purpose of minimizing completion time is constructed. And solving an optimal scheduling scheme by adopting an extended particle swarm optimization algorithm introducing a hybrid population initialization strategy, a self-adaptive parameter control mechanism and a local search strategy. The optimal scheduling scheme is applied to the actual manufacturing process, and the production efficiency and the scheduling level are improved. According to the method, the machining sequence and batch composition of the workpieces can be reasonably arranged, the manufacturing efficiency is improved while various constraint conditions are met, and powerful decision support is provided for production scheduling in an aviation composite material manufacturing system.

The invention provides a multi-target scheduling method and system for a distributed assembly flow shop, and relates to the technical field of production scheduling, and the method comprises the steps: collecting the technological parameters of the distributed assembly flow shop; based on the process parameters, determining scheduling constraint conditions by taking minimization of total energy consumption and completion time as targets; under the constraint of a scheduling constraint condition, generating an initial feasible scheduling scheme, calculating the maximum completion time and total energy consumption of the initial feasible scheduling scheme, and establishing a distributed assembly flow shop scheduling mixed integer linear optimization model comprising a workpiece processing sequence, machine start and stop, a machine processing rotating speed and a workpiece assembly sequence; in combination with a heuristic algorithm and a local search algorithm, optimizing the distributed assembly flow shop scheduling mixed integer linear optimization model, and outputting a Pareto optimal file representing an optimal scheduling scheme; and performing multi-target scheduling of the distributed assembly flow shop according to the Pareto optimal file.

The invention relates to the technical field of flow shop scheduling, in particular to a Chinese patent medicine production workshop scheduling problem-oriented solving method, which comprises the following steps of: determining to take minimization of maximum completion time as a problem solving target, and initializing parameters; constructing an initial solution by using an improved heuristic algorithm, performing damage reconstruction on the initial solution, and selecting the initial solution with the minimum target value as an optimal initial solution; scheduling the current optimal initial solution based on an active scheduling strategy to generate a complete scheduling scheme; searching a key path, and determining a key block which affects the maximum completion time and a key workpiece contained in the key block; key workpieces in the identified key blocks are adjusted, four neighborhood structures are used for searching, neighborhood structure tailoring is executed, and an optimal scheduling scheme with the maximum completion time smaller than the optimal initial solution is output. According to the method, production scheduling is more reasonable, and the positive effect of effectively improving the production efficiency and the stability of a production line is achieved.

The invention discloses an optimization method for a distributed heterogeneous hybrid workshop scheduling problem, and belongs to the technical field of distributed hybrid flow workshop scheduling. The method comprises the following steps: S1, establishing a mixed integer linear programming model of a distributed heterogeneous mixed workshop scheduling problem, and setting constraints for the model; s2, solving an optimization scheduling problem of a mixed integer linear programming model of a distributed heterogeneous mixed workshop scheduling problem by using a Q learning hyper-heuristic algorithm to obtain an optimization scheme; and S3, verifying the accuracy of the model. According to the method, the distributed heterogeneous hybrid workshop scheduling problem is optimized, the solving complexity is reduced, and meanwhile, the electricity price is reduced on the premise that the production efficiency is not influenced through a right shift strategy, so that effective guidance is provided for actual production.

The invention discloses a flow shop scheduling optimization method based on preventive maintenance and degradation, and the method comprises the steps: considering the interaction among scheduling, production, maintenance and degradation for a flow shop scheduling problem, and building a mathematical model with the minimum completion time and the minimum equipment average idle time as an optimization target; the influence of equipment degradation on actual machining time and preventive maintenance time is considered, and a workpiece machining time degradation model and an equipment preventive maintenance time degradation model are established; and a preventive maintenance strategy is researched and established by considering the relationship among faults, maintenance, preventive maintenance and progress. And finally, obtaining a reasonable scheduling scheme according to the characteristics of the problem. According to the scheduling optimization method provided by the invention, the relationship among production, equipment maintenance and equipment degradation can be effectively coordinated and balanced, so that the efficiency of a manufacturing system is improved, the production cost is saved, and optimal utilization of resources is realized.

The invention relates to the technical field of workshop production scheduling, in particular to a distributed hybrid flow workshop scheduling method oriented to rocket tank production, and aims to construct a distributed hybrid flow workshop scheduling model including an assembly process by taking the maximum completion time of a minimized product as an objective function. According to the method, a multi-layer coding genetic algorithm framework is designed, and an elite selection strategy and a neighborhood search operator are introduced into the framework. The elite selection strategy ensures that high-quality individuals are reserved in the iteration process, and the convergence speed of the algorithm is increased; a network diagram method is adopted to identify key paths, neighborhood search is carried out on key workpieces, the scheduling bottleneck is optimized, the quality of solutions is improved, and local optimum is avoided. The method not only can greatly improve the scheduling efficiency of the rocket tank, but also has good applicability and universality, and can be widely applied to other types of distributed hybrid flow shop scheduling environments.

The invention belongs to the technical field of flow shop scheduling, and discloses a replacement flow shop scheduling method and system based on a multi-view graph neural network, and the method comprises the steps: defining a problem, and constructing a target function and a constraint condition; the method comprises the steps of obtaining a time feature vector of a problem based on the same job node, constructing a node sparse graph, obtaining node matrix embedding based on the time feature vector and the node sparse graph, aggregating the node matrix embedding, obtaining global node embedding, and calculating graph embedding according to the global node embedding; then, node context embedding is constructed, node probability distribution is calculated according to the node context embedding, and jobs are selected according to the node probability distribution until all the jobs are completed; and optimizing model parameters to obtain a final scheduling scheme. According to the method, initial setting time is considered, a multi-feature representation method is provided, the feature information of the problem is extracted and embedded by using a multi-view graph neural network, and stability and high efficiency can be kept on problems of different scales.

The application belongs to the field of workshop scheduling, and aims at solving the problem that the existing production scheduling method is only applicable to the homogeneous factory model.The method comprises the following steps: S1, establishing a distributed heterogeneous flow shop scheduling model; S2, initializing the basic parameters of the meme algorithm, and generating an initial population according to a hybrid initialization strategy; S3, evaluating the fitness value of the population individuals, and performing a Pareto non-dominated sorting; S4, performing a crossover operation on the individuals in the population with a certain probability; S5, performing a collaborative search on the population by using a search operator; S6, merging the offspring population and the parent population, and selecting half of the individuals as local search targets; S7, performing a local search on the selected target individuals; S8, updating the population; S9, if the algorithm meets the stopping condition, ending the algorithm process, and outputting a non-dominated solution set; otherwise, the algorithm goes to S4 and continues iteration.

The invention relates to the technical field of workshop scheduling, in particular to a multi-modal multi-target reentrant hybrid flow workshop scheduling method, which is based on a multi-modal multi-target memetic method driven by hierarchical online learning and comprises the following steps of: 1, establishing a problem model; 2, setting operation parameters of the method; 3, generating a population by adopting an initialization strategy; 4, judging whether a first-stage termination condition is met or not, if not, executing a first-stage evolutionary strategy and an environment selection strategy on the population, and otherwise, executing the step 5; step 5, constructing a multi-modal file; 6, judging whether a second-stage termination condition is met or not; according to the method, the diversity of the decision space is maintained while the convergence of the target space is ensured, and a large number of multi-modal scheduling solutions with equivalent performance and different structures can be effectively mined.

The invention discloses a multi-stage hybrid flow shop scheduling optimization method and system based on an improved MPRVNS, and relates to the field of shop scheduling optimization, and the scheduling optimization method comprises the steps: initializing the input parameters of an algorithm; calculating the complexity of each order; orders are dynamically distributed to machines with light loads in all stages; an improved MPRVNS algorithm is used to optimize the manufacturing cycle of the order. According to the improved MPRVNS-based multi-stage hybrid flow shop scheduling optimization method and system, a machine load peak value is accurately predicted through a dynamic processing time model, an order complexity sorting rule is combined, high-complexity order delay accumulation is avoided, the length of a critical path is shortened, the number of iterations is reduced through adaptive neighborhood search of MPRVNS, and the optimization efficiency of the multi-stage hybrid flow shop scheduling optimization method and system is improved. Meanwhile, high-quality gene segments of parents and individuals are fused, invalid search is reduced, a machine allocation path of an order is fixed, the scheduling scheme adjustment frequency is reduced, and computational logic is simplified.

This invention belongs to the field of production scheduling technology and discloses a hybrid assembly line workshop scheduling method based on a dual-agent neighborhood search algorithm. The specific steps are as follows: Step 1: Study the real-world production scenario of the hybrid assembly line workshop, identify constraints, and analyze the main scheduling problems affecting the production cycle. This invention integrates the advantages of Deep Q-Network (DQN) and metaheuristic algorithms, and proposes a dual-agent neighborhood search algorithm. This algorithm can quickly respond to changes in the workshop environment and dynamically adjust the scheduling strategy, aiming to provide a new and efficient solution for hybrid assembly line workshop scheduling and its derivative problems. This invention constructs two agents that can interact with the workshop environment in real time: the first agent can determine the processing sequence of workpieces at each stage and quickly generate promising initial solutions; the second agent can select the optimal neighborhood search strategy and dynamically adjust the search direction to accelerate the convergence of the algorithm.

The invention discloses a two-stage assembly flow shop scheduling method oriented to mechanical arm performance degradation, and aims at minimizing the maximum completion time and the total energy consumption as optimization targets for the production-logistics collaborative scheduling problem in a two-stage assembly flow shop. First, the problem is converted into a novel mathematical model. In order to solve the problem, an accurate method and an approximation method are adopted at the same time. For an accurate method, a double-target model is converted into a single-target model through a constraint method, and then a GUROBI solver is called to complete solution; for an approximation method, a heuristic algorithm oriented to the problem is designed, and the algorithm can also generate a specified number of Pareto optimal solutions for a decision maker. According to the invention, a high-quality scheduling scheme can be obtained, and production-logistics collaborative scheduling is realized.

The present application relates to a kind of clustering and entropy guided reentrant hybrid flow shop scheduling method, the method includes the following steps: step 1: establishing problem model;Step 2: setting algorithm running parameter;Step 3: using initialization strategy to generate exploration population and development population;Step 4: judge whether it satisfies the first stage termination condition, if not satisfied then exploration population and development population are executed first stage evolution strategy and update strategy, otherwise step 5 is executed;Step 5: build elite population;Step 6: judge whether it satisfies the second stage termination condition, if not satisfied then elite population is executed second stage evolution strategy;Otherwise, output Pareto solution set;Step 7: update elite population.The present application realizes the dynamic balance of global exploration and local development, can guarantee the convergence of solution set while improving the distribution of solution, so as to reduce completion time and total energy consumption, reduce production cost, and improve workshop scheduling efficiency.

The invention provides a non-replacement flow shop scheduling and opportunity maintenance optimization method based on quality control, and the method comprises the steps: setting basic assumptions, parameter definitions and constraint conditions according to the characteristics of a non-replacement flow shop; constructing a non-replacement flow shop scheduling optimization model with the maximum completion time, the weighted advance / tardiness cost, the quality loss cost and the preventive maintenance cost being minimized as optimization objectives; an original optimization problem is decomposed into two sub-problems: a workpiece processing sequence arrangement sub-problem, and a maintenance opportunity prevention and opportunity maintenance grouping arrangement sub-problem, and an improved multi-target co-evolution artificial bee colony algorithm is used to solve a constructed non-permutation flow shop scheduling optimization model. And outputting an optimal workpiece scheduling and opportunity prevention maintenance scheme. According to the method, collaborative optimization of production efficiency, maintenance economy and product stability is realized, and a feasible solution is provided for a complex manufacturing system.

The invention discloses a BIM-based fabricated building construction information management system and method, and relates to the technical field of information management, and the method comprises the steps: obtaining a prefabricated part demand plan through a BIM model; according to the prefabricated part demand plan and the obtained construction progress plan, the production and transportation time of the prefabricated part is calculated, and whether the production and transportation time of the prefabricated part accords with the prefabricated part demand plan is judged; and based on a judgment result, sending the production and transportation time of the prefabricated part which does not conform to the demand plan of the prefabricated part to the BIM model, obtaining the internal opinions of the related party, calculating the internal opinions and a predefined weight matrix to obtain equilibrium opinions, and feeding back the equilibrium opinions to the BIM model. According to the method, the production and transportation time is calculated for the working procedures with different characteristics by using the flow shop scheduling method, the individual opinions of the related parties are balanced through the recursive relationship according to the predefined weight matrix, and the cross-organization cooperation efficiency and the management efficiency and resource utilization rate of the fabricated building are improved.

The invention provides a multi-target hybrid flow shop scheduling method and device, electronic equipment and a medium, and belongs to the technical field of workshop production, and the method comprises the steps: carrying out the constraint construction processing of pre-stored production process data, pre-stored equipment arrangement data and pre-stored manpower data in a multi-target hybrid flow shop, obtaining a process equipment constraint and a human resource limitation constraint; constructing a first optimization objective function taking the delivery cycle as a first production requirement, and constructing a second optimization objective function taking the energy consumption as a second production requirement; performing analysis processing on the preset production demand, and performing genetic algorithm coding processing to obtain a target solution; according to the pre-stored production process data, constructing a production bottleneck significance index taking the average waiting time between adjacent processes as a judgment standard; and performing secondary division processing according to the production bottleneck significance index and the target solution to obtain an optimized equipment scheduling result. The equipment utilization rate and the production efficiency can be improved.

The invention discloses a large-scale dynamic reentry flow shop scheduling method and system based on reinforcement learning, and is applied to the technical field of intelligent scheduling. Constructing an action space based on a three-layer composite priority scheduling framework; generating an instant reward and a cluster periodic reward corresponding to the action space, and superposing to form a mixed reward; generating a workshop scheduling scheme through a Markov decision process; and constructing an online bootstrap sample set updated in real time through an online bootstrap method, and updating the value function through the online bootstrap sample set. According to the method, the efficiency reduced by frequent product change, demand fluctuation and set time adjustment in a workshop is improved; responsive batch-machine allocation and adaptive policy fine tuning are realized, and the high efficiency, flexibility and adaptability of large-scale, dynamic and re-entry flow shop scheduling are improved.

The invention discloses a distributed non-replacement dynamic flow shop scheduling optimization method based on deep reinforcement learning, and belongs to the technical field of distributed green production scheduling. The method aims at solving the problem of multi-target dynamic scheduling in a distributed heterogeneous environment and aims at minimizing the maximum completion time, the total energy consumption and the tardiness cost. The method comprises the following steps: firstly, constructing a workshop state heterogeneous graph model, and extracting topological features of processes and machines by using a graph attention network; secondly, constructing a PPO decision model combined with an action mask mechanism, and outputting a scheduling action meeting physical constraints; and finally, mining the energy-saving potential of the non-critical process through a post-processing strategy based on the critical path. Dynamic disturbance such as emergency order insertion can be responded in real time, the production efficiency and the green energy-saving index are effectively balanced, and efficient utilization of workshop resources is achieved.