193 results about "Job shop scheduling" patented technology

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 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 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 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 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 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 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 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.

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 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.

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

The invention discloses a multi-target flexible job shop scheduling method based on a graph network and reinforcement learning, which can effectively characterize key features of a production model by using features of a double-graph attention network deep mining process and a machine. The depth characterization capability can not only improve the performance of the model, but also enhance the adaptability of the model to different production models, so that the model can flexibly cope with various unseen production scenes, and shows excellent generalization performance. Besides, in the field of multi-objective optimization, how to balance the relationship between different objectives is an important and challenging problem, so that the model can find an optimal balance point among the multiple objectives by distributing different weights for different optimization objectives, and the method not only can improve the solving efficiency, but also can improve the optimal balance point among the multiple objectives. And moreover, a high-quality Pareto frontier solution set can be generated, and an effective solution is provided for a multi-target flexible job shop scheduling problem.

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

The invention discloses a multi-dynamic flexible job shop scheduling optimization method based on a heterogeneous graph converter, which belongs to the technical field of shop scheduling optimization, and is characterized in that the heterogeneous graph converter is used as a basic framework of a strategy network, a dynamic scheduling environment is matched with strategy optimization, and a proper workpiece and machine combination is selected in a scheduling process. Firstly, in consideration of complexity of a scheduling scene, a heterogeneous graph converter is used for extracting and enhancing features of workpieces and machines, meanwhile, a corresponding reasonable combination of the workpieces and the machines is given, secondly, a reinforcement learning mechanism of a strategy optimization method is adopted to improve exploration capability and search efficiency, and then an efficient dynamic event processing method is provided. According to the multi-dynamic flexible job scheduling algorithm, the current dynamic event is processed in real time, so that the anti-disturbance capability of the scheduling algorithm on the dynamic event is enhanced, finally, the performance of the multi-dynamic flexible job scheduling algorithm is verified on a test set, and the robustness and effectiveness of the algorithm are verified by an experimental result.

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

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

The invention discloses a multi-cavity vulcanization constraint-containing flexible same-machine parallel batch optimization scheduling method for a rubber tire production workshop, and the method specifically comprises the steps: obtaining basic data of the rubber tire production workshop, and building a multi-cavity vulcanization constraint-containing flexible job workshop scheduling model; analyzing the customer order set to generate order-task-batch data; constructing a scheduling data structure, decoding each candidate scheduling scheme under multiple constraint conditions, generating a corresponding feasible scheduling scheme, evaluating the feasible scheduling scheme, executing iterative optimization, generating a new candidate scheduling scheme, and evaluating the new candidate scheduling scheme; and when a preset termination condition is met, taking the scheduling scheme with the optimal value as the optimal scheduling scheme. The method has the beneficial effects that the maximum completion time of a rubber tire production workshop can be remarkably reduced under the multi-cavity vulcanization constraint, the equipment utilization rate and the order delivery rate on schedule are improved, and good robustness and adaptability are kept when the order structure, the batch demand and the equipment load change.

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

The invention relates to the technical field of 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 low-carbon flexible job shop scheduling method based on deep reinforcement learning. The method comprises the following steps: S1, defining an LC-FJSP problem and constructing a disjunction graph model; s2, constructing a problem expression model based on a Markov decision process; s3, introducing a low-carbon map attention network, and integrating multiple attention modules; s4, improving the generalization ability of the model by adopting a graph pooling technology; s5, optimizing a solving process in combination with a Bayesian optimization method; s6, deep reinforcement learning training and verification tuning are carried out; and S7, performing job shop scheduling by using the trained model. According to the method, deep reinforcement learning and Bayesian optimization are combined, an efficient workshop scheduling decision in a low-carbon manufacturing scene is achieved, the constructed scheduling model has high environmental adaptability and energy consumption sensitivity, the low-carbon production scheduling requirement of a complex industrial scene can be effectively met, and intelligent decision support is provided for green manufacturing.

The invention discloses a dynamic distributed flexible job shop scheduling method based on hierarchical multi-agent deep reinforcement learning, and belongs to the technical field of distributed shop scheduling. Comprising the following steps: determining an initial state of a to-be-solved dynamic distributed flexible workshop scheduling problem; when a new workpiece arrives, constructing a global heterogeneous graph to represent a relationship between the new workpiece and the FMU; the upper-layer intelligent agent extracts features of a new workpiece and FMU through a process-level attention module, a workpiece-level attention module and FMU coding by using a hierarchical graph attention architecture, inputs the features into a decision network to obtain the probability of FMU action, and selects the FMU with the maximum probability; new workpieces are distributed to the designated FMU, and the original features of the FMU and the machine and process features in the FMU are updated; and constructing a bottom layer heterogeneous graph, predicting feasible actions by using a heterogeneous graph neural network, and selecting proper working procedures and machine combinations. According to the method, the problems of continuous arrival of dynamic workpieces, cooperative scheduling of multiple flexible manufacturing units and distributed workshop scheduling of real-time response can be efficiently solved.

The invention provides a hierarchical man-machine cooperation multi-target dynamic job shop scheduling method, and relates to the field of man-machine cooperation dynamic scheduling. On the basis of a hierarchical collaborative reinforcement learning framework, a high-dimensional global scheduling problem is decomposed into a plurality of low-dimensional sub-problems by dynamically partitioning a job set and allocating robot resources through a high-layer agent in a decision-making period, and the sub-problems are processed by the low-layer agent in parallel; two-tuple actions of a process selection rule and a processing mode selection rule of the process selection rule are quickly generated for the responsible stations in each time step and are verified, and the state space dimension needing to be processed by the reinforcement learning agent is remarkably compressed. Besides, a proposed target sharing type collaborative mechanism ensures that global multi-target weight parameters calculated by a high-level agent guide local decision of a low-level agent in real time, the deviation between a local optimization target and a global target is effectively reduced, the problem of multi-agent target splitting is solved, and the multi-target collaborative optimization capability is improved.

The invention discloses a flexible job shop scheduling method considering limited processing machine resources and multiple AGVs. The method comprises the following steps: S1, setting problem data and algorithm parameters; s2, performing three-section type coding to generate an initial population; s3, evaluating each chromosome by using an improved decoding strategy; s4, judging whether the maximum iteration number I of the algorithm is reached or not, and if so, skipping to S11; otherwise, if s = s + 1, skipping to S5; s5, calculating a fitness value and sorting the population; s6, selecting (m + 1) chromosomes through double strategies of elite retention and champion selection; s7, alternately selecting and grouping; s8, respectively selecting parent chromosomes P1 and P2 from the set X1 and the set X2 according to the crossover probability Pc; s9, selecting a parent chromosome P from the chromosome population according to the mutation probability Pm, and obtaining a filial generation C through random number optimization mutation and selection mutation; s10, updating the population, replacing a part in the initial population Xinit with the population obtained by variation, replacing the final (m + 1) chromosomes in population sorting with (m + 1) chromosomes selected by double strategies of elite retention and champion selection to obtain an evolved new population, and skipping to S3; and S11, outputting an optimal factory logistics scheduling scheme, calculating fitness values of all populations, and selecting the chromosome individual with the minimum fitness value for decoding to obtain the optimal factory logistics scheduling scheme.

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.