228 results about "Job shop scheduling" patented technology

The present invention relates to the technical field of job shop scheduling, and particularly relates to a shop scheduling method and system for a large-sized complex product, and a device and a medium. The method comprises: firstly, determining a scheduling decision problem of assemble, adjusting and debugging shops of a large-sized complex product, and a precondition hypothesis, so as to obtain a maximum workpiece completion time; then, taking the maximum workpiece completion time of the large-sized complex product being as small as possible as an optimization objective, and establishing an assemble, adjusting and debugging shop scheduling optimization model of the large-sized complex product; and finally, using a multi-objective genetic algorithm to perform optimization solving, so as to obtain a target scheduling decision and a target completion time of the assemble, adjusting and debugging shops of the large-sized complex product. Therefore, an assemble, adjusting and debugging process highly matching an actual production process is ensured, and the precision and convergence speed of model solving are improved.

The invention discloses an intelligent job shop scheduling method. The method comprises the following steps: S1, establishing a personalized improved NSGA-II algorithm module and a DQN-based adaptive decision algorithm module; s2, establishing a mathematical model taking the maximum completion time, the total load of equipment and the total energy consumption of a workshop as optimization targets; s3, in a personalized improved NSGA-II algorithm module, considering a workshop optimization target, adopting multiple strategies for initialization, and adding a user personalized demand to screen a target population; s4, in a DQN-based adaptive decision algorithm module, population features are extracted to construct an agent state space, and a deep Q network experience playback mechanism is utilized to train the relationship between agent capture scheduling information and a scheduling target to perform adaptive action decision; and S5, returning the optimal action predicted by the DQN model to the NSGA-II, adjusting the optimal parameter proportion according to the real-time state, and improving the multi-target solution quality.

A Flexible Job Shop scheduling method includes: obtaining a shop scheduling state including at least one of a sequential order dependency relationship between job tasks being processed, a sequential order dependency relationship between operation steps in each job task of the job tasks, a processing / being processed relationship between the job tasks and machines, or mutual constraint relationships between the machines; representing the shop scheduling state as a state hypergraph; extracting a hypergraph-based job feature from the state hypergraph using a hypergraph neural network; and determining an action configured to change the shop scheduling state, wherein the action is determined according to the hypergraph-based job feature using a policy network.

The invention relates to the technical field of flexible job shop scheduling in intelligent manufacturing, in particular to an optimization method for a flexible job shop scheduling problem considering adjustment time, and the method comprises the steps: initializing parameters of a collaborative variable neighborhood search algorithm; initializing a population; jittering operation is executed, and two new solutions are generated through the jittering operation; executing a variable neighborhood descent operation; updating the current algebra; judging whether a restart strategy is executed or not; and selecting a solution with shorter maximum completion time in the improved solutions generated by the collaborative variable neighborhood search algorithm as an initial solution of the constrained programming model, and further optimizing the quality of the solution through the constrained programming model. The invention provides an effective method for solving the problem of flexible job shop scheduling considering the adjustment time of sequence dependence, and makes substantial progress in reducing the maximum completion time of the shop.

The invention discloses a flexible job shop scheduling method based on deep reinforcement learning, and belongs to the field of intelligent manufacturing and intelligent scheduling, and the method comprises the steps: firstly, taking the minimization of the maximum job completion time and the maximization of the average machine utilization rate as optimization targets, and obtaining a target model; then representing a flexible job shop scheduling problem by using a disjunction graph model to obtain a scheduling state; according to the scheduling state and the target model, an end-to-end learning framework is constructed, and the framework is combined with a multi-attention network for deep feature extraction and a decision network for extensible decision, and a Markov decision process is utilized to obtain a job scheduling model; and finally, the multiple attention network constructs a production adaptability operation-AGV-machine scheme by using the complex relationship among the operation, the AGV and the machine to support the decision of the decision network, and obtains a scheduling schedule by using the job scheduling model to complete flexible job shop scheduling. According to the method, complex modes and relations in data can be better captured, and the performance and generalization ability of the model are improved.

The invention discloses a flexible job shop scheduling method based on a graph neural network and deep reinforcement learning, and relates to the technical field of flexible job shop scheduling. The method at least comprises the following steps: S1, firstly, carrying out Markov Decision Process (MDP) on a flexible job shop scheduling problem, namely, FJSP, and initializing a scheduling state; and S2, representing a complex relationship between a job and a machine by using a heterogeneity graph, and effectively mapping different entities (the job, the machine, the operation and the like) of the problem and the relationship between the different entities into a graph structure, wherein the different entities (the job, the machine, the operation and the like) of the problem and the relationship between the different entities (the job, the machine, the operation and the like) of the problem are represented by the heterogeneity graph. According to the method, the graph neural network based on the meta-relationships is provided, different graph convolution modes are innovatively adopted for different meta-relationships to extract features, original semantic information is reserved, the global information capturing capability is enhanced, and a reinforcement learning agent is more accurate when making a scheduling decision.

The embodiment of the invention provides a dynamic flexible workshop scheduling method and related equipment, and belongs to the technical field of industrial intelligent manufacturing and production scheduling. The method comprises the steps of obtaining current state information of a workshop in response to a scheduling event; inputting the state information into a pre-trained scheduling decision model for processing; the model outputs state feature embedding through a two-stage feature extraction network: in the first stage, feature extraction is performed on a heterogeneous disjunction graph by using a graph attention network, and in the second stage, expert output is dynamically fused through a hybrid expert model; and finally, outputting and executing a process-machine pairing decision by the actor network. Wherein the model is trained by adopting a near-end strategy optimization algorithm based on multiple commentators; and a meta-learning framework is integrated during training, so that the model obtains strong generalization ability. According to the method, the defects of an existing scheduling method in the aspects of state characterization, multi-target tradeoff and environmental adaptability are effectively overcome, and the efficiency, quality and robustness of dynamic flexible workshop scheduling are remarkably improved.

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 discloses a digital twinning system construction method and system based on flexible job shop scheduling, and relates to the technical field of digital twinning, and the method specifically comprises the steps: constructing a twinning model of a physical workshop according to the production data of the physical workshop; building a workshop scheduling discrete event simulation model based on the twinborn model of the physical workshop; based on an internal and external virtual-real interaction mechanism, virtual-real mapping interaction between the twin model of the physical workshop and the physical workshop and virtual-real mapping interaction between the twin model of the physical workshop and the workshop scheduling discrete event simulation model are realized, and a virtual workshop is generated; data generated in the interaction process is stored in a twin data pool in real time; a workshop service system is constructed based on a three-dimensional visual integration technology, so that a physical workshop and a virtual workshop carry out interactive feedback, and construction of a scheduling digital twin system is completed. According to the method, the self-adaptive capability of the system in the face of complex production tasks is enhanced, and flexible scheduling according to actual conditions is supported.

The invention relates to the technical field of intelligent workshop scheduling, and provides a job shop scheduling method and system based on a principal component heterogeneous graph neural network, and the method comprises the following steps: determining a constraint condition of a distributed flexible workshop, taking the minimization of the maximum completion time as a control target, and building a problem model according to the constraint condition; obtaining a current state of workshop operation, performing embedding based on a graph attention network to obtain an embedded representation of the state, and extracting an embedded representation of a key state based on principal component analysis after fusion; the obtained embedded representation of the current key state serves as input, the problem model is solved through a strategy network, and probability distribution, distributed to a specific machine tool, of each working procedure serves as an initial solution; and based on the obtained initial solution, performing iterative optimization by adopting an enhanced local search heuristic algorithm to obtain an optimal solution. According to the method, the high efficiency and accuracy of the final solution can be ensured, the defects of a traditional GNN-based method in a subsequent optimization mechanism are overcome, and the method is suitable for the scheduling problem of various types of distributed flexible workshops and has high expansibility and adaptability.

The invention relates to a flexible workshop scheduling method and system. Comprising the following steps: constructing a flexible job shop scheduling mathematical model according to a flexible job shop scheduling problem; the flexible job shop scheduling mathematical model is converted into a Markov decision process, a state space and an action space are set, and a reward function is obtained; constructing a heterogeneous graph network, defining types and numbers of nodes and edges, and defining a directed relationship between the edges; constructing a heterogeneous perception encoder, and carrying out feature embedding on the heterogeneous graph network to obtain an environment state feature vector; constructing an actor-commentator network model, and inputting an environment state feature vector; and the PPO algorithm optimizes the actor-commentator network model according to a reward function, updates parameters, obtains a flexible workshop scheduling model and completes FJSP solution. According to the method, the resource and constraint relationship in the scheduling environment can be accurately reflected, and the FJSP can be more accurately solved; and the scheduling problem of different scales can be solved, the generalization performance is good, and the method is excellent in large-scale application.

The invention discloses a dynamic scheduling optimization method based on deep reinforcement learning and a heterogeneous graph neural network. The method comprises the following steps: firstly, converting a flexible job shop scheduling problem into a Markov decision process, and setting a state, an action, a state transition and a reward function; then constructing a basic heterogeneous graph and an enhanced heterogeneous graph, and defining the types and the number of nodes and edges and a directed relationship between the edges; performing three-stage feature embedding by adopting a heterogeneous graph attention network to obtain machine node embedding, operation node embedding, distribution instance node embedding and global state features; and inputting an action state vector formed after each feature is processed, optimizing a decision network according to a reward function, updating parameters to obtain a flexible job shop scheduling model, and completing FJSP solving. The method can effectively consider the flexible job shop scheduling problem under transport time and machine reachability constraints, can also process scheduling problems of different scales, has good generalization ability, and shows excellent performance in large-scale application.

The invention discloses a DQN-based intelligent factory job shop scheduling method and system, and belongs to the technical field of production scheduling, and the method specifically comprises the following steps: obtaining real-time data generated by intelligent equipment, and extracting key shop system features; calculating a current workshop system state based on the key workshop system characteristics; based on the current workshop system state, using the DQN network to select and execute an optimal scheduling rule, and obtaining a scheduling result; and calculating and obtaining a reward function rt based on a scheduling result. And finally, updating the weight of the DQN network according to the workshop system state and the scheduling rule, obtaining the updated DQN network according to the updated weight of the DQN network, and selecting a better scheduling rule in future decisions by using the updated DQN network. According to the method provided by the invention, a large amount of data can be processed in real time, and the optimal scheduling rule is automatically explored and learned through deep reinforcement learning, so that the scheduling efficiency and the capability of coping with a complex environment are improved.

The invention discloses a multi-target flexible workshop scheduling method based on a double-attention network and deep reinforcement learning. The method comprises the following steps: 1, acquiring basic information in flexible job shop scheduling; 2, constructing a workshop scheduling mathematical model based on a target function and constraint conditions; 3, formalizing a flexible job shop scheduling problem into a Markov decision process, and defining state parameters, action parameters, state transition parameters and reward parameters in a deep reinforcement learning method; 4, constructing a double-attention network, and extracting feature information of procedures and machines; and 5, obtaining an optimal scheduling scheme by using a deep reinforcement learning algorithm. According to the invention, the scheduling scheme with shortest time consumption and minimum machine load can be obtained, direct learning from original data to decision can be realized, dependence on expert knowledge is reduced, production scheduling time is reduced, workshop generation efficiency is improved, and decision basis is provided for actual production scheduling.

The invention discloses a co-evolution method for distributed heterogeneous flexible job shop scheduling, which designs a three-section type coding and full-active decoding method based on three coupling sub-problems, and constructs a search space for the distributed heterogeneous flexible job shop scheduling problem. On the basis of a key path and key block theory, various neighborhood structures are designed for three-section type coding and serve as individual search directions; a deep reinforcement learning technology is utilized to perform feature extraction on a key factory, and a training algorithm is designed to train an intelligent agent, so that customized neighborhood structures can be selected for different individuals, and the diversity of a population is increased; a probability matrix is calculated in the training process, a high-quality population is generated in the co-evolution process, and convergence of the algorithm is accelerated through crossover and mutation operators among sub-populations. The method can also adapt to different scales and complexity problems through learning and optimization of the intelligent agent, and the overall performance and the resource utilization rate of a production system are remarkably improved.

The invention relates to a dual-resource constraint flexible job shop scheduling method for reducing worker load, which comprises the following steps: S1, construction of a worker load model: dividing the worker load model into four conditions of light work, moderate work, micro-severe work and rest according to daily work arrangement of workers, and setting the maximum working time length for each worker, the calculation module is used for calculating extra workloads; in the scheduling method provided by the invention, three different initialization strategies are combined, and the proportion of the initialization strategies in a population is set, so that the diversity and quality of an initial solution are ensured, specifically, the used initialization strategies comprise random initialization, initialization according to the process completion time and initialization according to the process remaining time; under the random initialization strategy, the initial solution of the population has great diversity, which is helpful for avoiding the trouble of a local optimal solution.

The invention relates to a multi-stage collaborative scheduling method for a ship block assembly workshop, and belongs to the technical field of assembly workshop scheduling. The ship block assembly job shop scheduling modeling is an assembly job shop collaborative scheduling problem model considering three stages of processing, transportation and assembly; and solving an assembly job shop collaborative scheduling problem model of three stages of processing, transportation and assembly based on an improved genetic algorithm. Taking minimization of completion time as an optimization target, and converting a part integrity constraint in a machining stage, a time difference constraint of a part in a transportation stage and a flowing water blockage constraint in an assembly stage into control variable constraints in a mathematical model; the strategy is improved on the basis of the genetic algorithm, and the solving capacity of the genetic algorithm for the ship block assembly job shop scheduling problem is improved; the method effectively solves the problem of ship block assembly workshop scheduling, and has solving precision and stability.

The invention relates to a fuzzy flexible job shop scheduling method, a fuzzy flexible job shop scheduling system, fuzzy flexible job shop scheduling equipment and a storage medium, which are applied to a scene in which scheduling is required when disturbance occurs in a job shop operation process. Parameters of the genetic algorithm are initialized according to the real-time resource state of the current production environment, wherein the parameters comprise the maximum number of iterations; an initial population is generated according to the parameters, a weight vector is initialized, an external file is created, the external file is used for storing an optimal solution or an optimal solution in the algorithm, and the optimal solution is a solution capable of increasing population diversity; selecting an operation individual in the population and the external archive; and performing iterative optimization on the population based on the operation individuals until the maximum number of iterations is reached, outputting a non-dominated solution set, and after each iterative optimization, updating the external archive and the weight vector based on the generated non-dominated solution so as to optimize the fuzzy flexible job shop scheduling problem under the urgent order insertion and improve the production efficiency and the response flexibility.

The invention discloses a job shop scheduling method under transportation constraint based on deep reinforcement learning, and the method comprises the following steps: obtaining workshop scheduling environment data and workshop scheduling order feature data, and constructing a training data set; constructing a deep reinforcement learning model based on the job shop scheduling state under the transportation constraint; the deep reinforcement learning model comprises a Markov process and a scheduler; according to the training data set, using a multi-agent near-end strategy optimization algorithm to train a deep reinforcement learning model; and inputting the workshop scheduling task into the trained deep reinforcement learning model, and outputting a scheduling result. According to the method, the optimal solution of the workshop scheduling problem can be quickly and effectively found, and the scheduling efficiency and quality are improved. The method can be widely applied to the technical field of industrial scheduling.

The invention relates to the technical field of flexible job shop scheduling in intelligent manufacturing and production scheduling, in particular to a flexible job shop scheduling optimization method for solving and adjusting resource constraints. Comprising the steps of initializing parameters and randomly generating an initial population; sequentially using a crossover operator and a mutation operator to evolve the current population; executing a hybrid decoding strategy on the current population; sorting the individuals in the current population from small to large according to the maximum completion time to form an elite population, and updating the elite population through question specific local search; and judging whether an evolution condition is met or not, if so, executing CP-based mathematical evolution, and outputting a final solution when the running time reaches the total running time. The method has the positive effects of reducing the resource waiting time, improving the machine utilization rate and improving the resource utilization efficiency and the scheduling performance of the whole workshop production.

The invention relates to the technical field of intelligent manufacturing, in particular to a distributed flexible job shop scheduling method and system based on double deep reinforcement learning and a multi-layer agent. A global complex problem is decomposed into a plurality of local problems by introducing a multi-level agent scheduling framework and through cooperative work and hierarchical decision making of a top layer agent, a middle layer agent and a bottom layer agent; besides, different scheduling tasks of the distributed flexible job shop are executed dispersedly through the intelligent agent of each level, each decision does not need to be managed by a central coordinator, and when the environment is locally changed, each level can quickly adapt and make an optimal decision according to the specific context of the environment, so that the scheduling efficiency of the distributed flexible job shop is improved. And meanwhile, the overall goal of the whole system is still served. The objective of the invention is to solve the problem of how to improve the scheduling precision of the distributed flexible job shop.

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 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 field of production scheduling and resource optimization, in particular to a flexible job shop scheduling method based on deep reinforcement learning, which is characterized by comprising the following steps: 1) constructing a flexible shop scheduling environment; (2) a flexible workshop scheduling environment is abstracted to be Disjunction Graph, and the flexible workshop scheduling environment is abstracted to be Disjunction Graph; 3) extracting state features; 4) coding the state features; 5) executing a decision based on the encoded features to obtain scheduling probability distribution; and 6) generating a scheduling plan according to the probability distribution. According to the invention, through multi-level feature extraction and fusion, accurate perception of a production environment is realized, and the accuracy and robustness of a scheduling decision are effectively improved; by utilizing a deep reinforcement learning framework, the dynamic change of the production environment can be adaptively learned, the scheduling strategy is continuously optimized, and the generalization ability is relatively high; and feature representation is enhanced through an attention mechanism, so that the model can still quickly find a global optimal solution when facing a large-scale and high-complexity FJSP problem.

The invention provides a distributed heterogeneous flexible job shop scheduling optimization method based on solution space conversion, which aims to solve the problem of energy-saving distributed heterogeneous flexible job shop scheduling, and comprises the following steps of: 1, preprocessing related data of factories and workpieces; 2, constructing a distributed heterogeneous flexible workshop scheduling problem model; 3, presetting value method related parameters; 4, the average machining time of each workpiece is calculated; step 5, constructing a workpiece group through a hierarchical clustering method of optimal leaf order constraint; step 6, constructing an initial scheme according to the workpiece group; and 7, solving the model through a distributed heterogeneous flexible job shop scheduling optimization method based on solution space conversion to obtain an optimal scheduling scheme. According to the method, the distributed heterogeneous flexible job shop scheduling problem can be quickly solved, a scheme of minimum maximum completion time and total energy consumption is provided for actual production scheduling, and the production efficiency and the resource utilization rate are improved.

A flexible job shop scheduling method is provided. The method includes: obtaining a shop scheduling state including at least one of a sequential order dependency relationship between a plurality of job tasks being processed, a sequential order dependency relationship between a plurality of operation steps in each job task of the plurality of job tasks, a processing / being processed relationship between the plurality of job tasks and a plurality of machines, and a mutual constraint relationship between the plurality of machines; representing the shop scheduling state as a state hypergraph using a hypergraph; extracting a hypergraph-based job feature from the state hypergraph using a hypergraph neural network; and determining an action to change the shop scheduling state according to the hypergraph-based job feature by using a policy network.

The invention discloses a supply chain process job scheduling method and system based on a hybrid expert model, and the method comprises the steps: obtaining a target job shop scheduling problem, and selecting a target gated network from an expert selection gated network based on static similarity and a gated network based on weight learning according to a preset threshold value; when the target gated network is the gated network based on weight learning, constructing a scheduling graph according to the target job shop scheduling problem, and processing the scheduling graph based on a plurality of preset expert networks and the trained gated network based on weight learning to obtain a first target strategy; and when the target gated network is an expert selection gated network based on static similarity, selecting a target expert network from a plurality of preset expert networks according to the target job shop scheduling problem, and processing the target job shop scheduling problem through the target expert network to obtain a second target strategy. According to the invention, the job shop scheduling problem can be accurately processed.

The invention provides a numerical control multi-objective optimization energy-saving method and system based on an improved wolf pack algorithm, and the method comprises the steps: constructing a mixed integer linear programming model of multi-objective flexible job shop scheduling, taking machine tool distribution, a machining sequence, tool distribution and a machining speed as decision variables, and taking total weighted tardiness and total energy consumption as optimization targets; the method comprises the following steps: constructing an MOGWO-ALNS algorithm for solving a mixed integer linear programming model based on an MOGWO algorithm and an adaptive large neighborhood search mechanism, constructing a three-stage collaborative optimization architecture by the MOGWO-ALNS algorithm through a dual-threshold trigger mechanism, solving the mixed integer linear programming model based on the MOGWO-ALNS algorithm, obtaining a Pareto optimal solution set of the mixed integer linear programming model, decoding the Pareto optimal solution set, and finally obtaining the mixed integer linear programming model. And generating a multi-target balanced energy-saving scheduling scheme. According to the method, collaborative optimization of the delivery date and the energy consumption in numerical control machining is achieved, the solving efficiency and the optimization effect are improved, and the method has good engineering application value.

The invention discloses a multi-modal multi-target flexible job shop scheduling optimization method. The multi-modal characteristic of an FJSP is verified through experiments; designing a multi-modal solution archiving mechanism, and storing and analyzing a multi-modal solution in an optimization process; a non-dominated sorting genetic algorithm is improved, a combined crossover variation strategy is introduced, and a multi-modal solution in the algorithm operation process is searched. According to the method, MOFJSP is solved through NSGA-MSPM, and the algorithm provides various different solutions for the workshop scheduling problem by storing a plurality of high-quality multi-modal solutions, so that the flexibility and adaptability of production scheduling are improved.

The invention relates to the technical field of intelligent manufacturing, in particular to an efficient and energy-saving flexible job shop scheduling method considering adjustment time. Comprising the steps of 1, initializing an initial population of an MTCP-SPEA algorithm; 2, executing a spatial awareness environment selection strategy; step 3, judging whether the current running time of the MTCP-SPEA algorithm reaches half of the total running time, if so, executing step 6, and otherwise, executing step 4; 4, a crossover operator and a mutation operator are adopted to evolve the current population, and current non-dominated individuals are selected to form a Pareto solution set; 5, executing local search operation and returning to the step 2; and step 6, executing a multi-thread-based constrained programming auxiliary optimization method to generate an improved elite set, screening out all non-dominated individuals from the improved elite set to form an optimal solution set, and outputting the optimal solution set. According to the method, the problem of efficient and energy-saving flexible job shop scheduling optimization considering time adjustment is effectively solved.