94 results about "Job shop" patented technology

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

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

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

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

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

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

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

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

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

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

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

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

The invention discloses a hybrid learning-based flexible job shop energy-saving batch scheduling method, which relates to the technical field of shop scheduling and comprises the following steps of: establishing a flexible job shop energy-saving scheduling model by taking minimization of maximum completion time and total energy consumption of a machine as optimization objectives; the method comprises the following steps of: constructing a mapping relationship between an operation process and machine selection by adopting a three-layer coding mode, initializing a population through multiple initialization strategies, decoding a coding part, and generating an initial solution; constructing a neighborhood structure, performing population evolution through an adaptive crossover and mutation operator, and adjusting the crossover and mutation probability according to the population evolution degree; searching an optimal solution through a plurality of search strategies; traversing the population to perform non-dominated sorting, and adjusting the crossover mutation rate according to a non-dominated sorting result; and judging whether an iteration termination condition is met or not, if not, continuing iteration, and if so, outputting the optimal Pareto frontier solution. According to the invention, the energy consumption is reduced while the processing time is minimized.

The invention discloses a flexible job shop integrated scheduling optimization method, device and equipment considering AGV electric quantity constraint, through the algorithm, process processing sorting and machine selection of a population individual solution are completed through two-segment coding, in order to improve the quality of an initial population solution, a population is initialized in a manner of combining heuristic generation and random generation, and the quality of the initial population solution is improved. During decoding, scheduling of the AGV is completed by using a heuristic rule, the problems of power consumption and charging of the AGV are considered, generation of an unreasonable solution caused by pre-designation of the AGV in a coding link is effectively avoided, and the problem that a genetic algorithm is prone to falling into local optimum is solved by designing multiple neighborhood structures for local search.

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

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

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

This invention provides a dynamic flexible job shop scheduling method based on the Kepler optimization algorithm. Its main purpose is to solve the dynamic scheduling problem of flexible jobs shops, offering a solution to the uncertainty of events. The steps are as follows: 1. Modeling the job shop scheduling problem; 2. Presetting relevant parameters for the Kepler optimization algorithm; 3. Driving the Kepler optimization algorithm to solve for the scheduling scheme; 4. Dynamically rescheduling for sudden uncertain events to obtain a new production plan. This invention can fully utilize equipment, improve production efficiency, and provide a decision-making basis for actual production scheduling.

The application discloses a large-scale flexible job shop scheduling method and system, acquires job shop related information; creates job agents, machine agents and inventory agents according to the job shop related information based on a multi-agent ant colony algorithm, initializes multi-agent ant colony algorithm parameters and pheromone initial values; constructs a disjunctive graph in the form of a directed graph according to the job shop related information, sequentially traverses the disjunctive graph, and job agents, machine agents and inventory agents generate scheduling solutions according to an agent negotiation protocol and update pheromones; after multiple iterations, it is judged whether a new optimal solution is generated, if the iteration number exceeds the preset number of rounds, the current optimal solution is output, and the final job shop scheduling scheme is obtained. Advantage: the shortest completion time, machine utilization rate and calculation time required by the multi-agent ant colony algorithm have obvious advantages compared with the prior art, and are more suitable for large-scale job scheduling problems.

The application relates to a production and logistics collaborative scheduling method for a dynamic flexible job shop, comprising the following steps: obtaining real-time information of the dynamic flexible job shop, inputting a production and logistics collaborative scheduling problem model for the dynamic flexible job shop, solving by using a deep reinforcement learning method, and obtaining a workshop scheduling scheme; wherein the production and logistics collaborative scheduling problem model for the dynamic flexible job shop sets respective optimization targets for production activities and logistics activities, considers a fault scene of logistics equipment and a corresponding processing strategy, and the solving process is as follows: S301, designing a multi-agent nested hierarchical framework; S302, designing key elements of a Markov decision process of each agent; and S303, cooperatively training each agent by using a multi-agent proximal policy optimization algorithm. Compared with the prior art, the application can obtain a more reasonable and effective flexible job scheduling scheme.

The application discloses a multi-target dynamic production scheduling decision method and system, and belongs to the technical field of flexible job shop production scheduling. The method comprises the following steps: collecting order, equipment and material data, and constructing a flexible job shop production scheduling model with the maximum completion time, weighted delay and equipment utilization rate as targets; solving the model by using a double-file cooperative evolution non-dominated sorting genetic algorithm, embedding a robust buffer before a key path process during decoding; deciding a Pareto non-dominated solution set by using an entropy weight-approximate ideal solution sorting method; quantifying disturbance severity based on right shift propagation simulation of a disjunctive graph, matching right shift repair, local re-optimization or global rolling rearrangement of a critical path according to the disturbance severity, and returning disturbance samples to a knowledge base to update a threshold value and a work hour standard deviation. The system comprises data acquisition, optimization engine, decision, disturbance monitoring, rescheduling, knowledge base and visualization modules. The application improves the quality of a Pareto frontier, the scheme has disturbance absorption capacity, and rescheduling is adaptively executed according to the severity.

The invention provides an intelligent scheduling method for a job shop, and relates to the technical field of production scheduling, and the method comprises the following steps: constructing a resource availability matrix which is used for representing whether a processing procedure is allowed to use corresponding processing resources or not based on a job condition; constructing a resource strategy fingerprint used for representing a preset processing resource scheduling strategy for the processing procedure and the processing resource; according to a preset scheduling mode, synthesizing the resource availability matrix and the resource strategy fingerprint to generate an assignable parameter matrix; and inputting the assignable parameter matrix as a resource available set, constructing a scheduling optimization model and solving the scheduling optimization model in combination with a preset constraint condition and an optimization target of the job shop, and generating an intelligent scheduling scheme. According to the method, the resource availability matrix and the resource strategy fingerprints are independently constructed, the resource availability matrix and the resource strategy fingerprints are synthesized, an assignable parameter matrix method is obtained, decoupling of resource allocation and strategies is achieved, and therefore the flexibility, reproducibility and high efficiency of job shop scheduling are achieved.

The invention discloses a method, medium and device for solving flexible job shop scheduling based on deep reinforcement learning in the technical field of industrial intelligent simulation, and the method comprises the steps: obtaining the energy consumption data of a flexible job shop, and constructing a shop energy consumption model; constructing an integrated scheduling problem model according to the workshop energy consumption model; solving the integrated scheduling problem model; and scheduling the flexible job shop according to a solving result. According to the method for solving flexible job shop scheduling based on deep reinforcement learning, the medium and the device provided by the invention, multi-objective optimization of the completion time and the total energy consumption of the shop can be realized.

The application discloses a kind of component assembly unit finite equipment resource dynamic scheduling method considering delivery time limit constraint, solve the problem of unreasonable allocation of finite equipment resources and unable to effectively respond to fault disturbance under existing production scene.For the finite equipment resource scheduling problem of flexible job shop with sudden equipment failure disturbance, first, a model is established with the optimization goal of minimizing the maximum delay time, a three-layer coding and decoding method is designed, and an improved particle swarm algorithm with adaptive inertia factor is used to solve the initial scheduling scheme;Then, considering the product delay cost and equipment switching cost caused by equipment failure disturbance, a re-scheduling model is constructed to minimize the comprehensive cost;Finally, based on the event-driven re-scheduling mechanism, partial / complete re-scheduling strategy and right-shift re-scheduling strategy are used to generate two alternative schemes for comparison and optimization.

The application provides a flexible job shop multi-objective scheduling method with a limited number of AGVs, and belongs to the technical field of multi-objective scheduling.The method comprises the following steps: improving the oryx optimization algorithm based on the Pareto optimality theory, initializing the population through a hybrid chaotic strategy, and guiding the population update by using an elite group matrix;discretizing the population update mode according to the characteristics of the FJSP-AGV problem, including the discrete update operation in the development stage and the exploration stage, and the discrete update operation of affecting the population by using the success rate of the predator; adding a variable neighborhood local search strategy to improve the utilization efficiency of the population and the possibility of the algorithm jumping out of the local optimum, designing an improved oryx multi-objective optimization algorithm based on the variable neighborhood search, and realizing the multi-objective scheduling of the flexible job shop. The effectiveness of the algorithm provided by the application is verified through the design experiment, and it is shown that the algorithm has a remarkable effect on solving the multi-objective FJSP-AGV problem, and the convergence and solution quality of the algorithm are greatly improved.

The invention discloses a flexible job shop scheduling method based on a Harris eagle optimization algorithm, and the method comprises the steps: collecting basic data of a flexible job shop, and constructing a processing time length matrix of the flexible job shop and a flexible job shop scheduling mathematical model; performing standardization processing on the processing duration matrix, determining the probability that the machine selects to process the process in combination with the machinable mask of the process-machine, and generating an initial solution of a flexible job shop scheduling scheme; the generated initial solution serves as an eagle individual, a flexible job shop scheduling mathematical model serves as a fitness function, and the maximum number of iterations is set; calculating the fitness value of each eagle individual by using a fitness function, retaining the eagle individual with the minimum fitness value, calculating a nonlinear energy factor according to the current iteration number, and adjusting the optimization strategy of the eagle individuals according to the nonlinear energy factor until the maximum iteration number is reached, and taking the eagle individual with the minimum fitness function value as the optimal flexible job shop scheduling scheme.

The invention discloses a hybrid production flexible assembly job shop scheduling method considering multi-assembly sequence change based on a hybrid memetic algorithm, and the method comprises the following steps: S1, building a hybrid production flexible assembly job shop scheduling model with total production completion time, total inventory time and total labor cost as optimization targets under dual-resource constraint; a flexible assembly job shop scheduling mathematical model for multi-assembly sequence change mixed production is considered; s2, on the established mathematical model, providing a hybrid memetic algorithm to solve the provided problem, designing a four-layer segmented hybrid chromosome coding structure adapted to the problem and an updating method of two sub-generation chromosomes, and introducing a variable neighborhood search strategy and an elite retention strategy; s3, proving the superiority of the proposed scheduling method through case research; and S4, through comparison with a flower pollination algorithm and a rapid non-dominated genetic algorithm, verifying the effectiveness and robustness of the proposed algorithm. According to the hybrid production flexible assembly job shop scheduling method considering the multi-assembly sequence change based on the hybrid memetic algorithm, the processing sequence and the assembly sequence of product parts can be optimized at the same time in the multi-product hybrid production scheduling process, and machine and worker resources are reasonably distributed at the same time; and the production cycle of products is effectively shortened.

The present application relates to a kind of flexible job shop energy-saving batch flow scheduling multi-objective optimization method considering processing preparation resources, belong to workshop scheduling technical field.It includes steps: flexible job shop energy-saving batch flow scheduling multi-objective optimization mathematical model considering processing preparation resources;Model solution method based on knowledge-driven batching method and improved multi-objective evolutionary algorithm;Based on the tooling adjustment strategy of adjacent process.The optimization method proposed in the present application can efficiently solve the flexible job shop energy-saving batch flow scheduling multi-objective optimization problem considering processing preparation resources, realize the efficient collaborative optimization of workpiece batching, machine allocation, process sequencing, tooling allocation, and obtain the scheduling scheme with good completion time, total energy consumption and total processing cost.