209 results about "Job shop" patented technology

The invention relates to a multi-objective optimization scheduling method and system for a multi-AGV flexible job shop. The method comprises the steps of collecting job shop layout data, AGV operation parameters and production task data, and constructing a scheduling system basic model; performing recurrent neural network training and physical constraint model integration to obtain a hybrid scheduling prediction model; subtask decomposition and path search processing are carried out, and a dual-objective optimization model is obtained through constraint linearization conversion; performing particle swarm optimization solution on the dual-objective optimization model, and obtaining a non-dominated solution set through two-stage iterative calculation of an adaptive weight strategy and constraint conversion; performing multi-dimensional evaluation calculation on the non-dominated solution set to obtain an optimal scheduling execution scheme; and performing model feedback updating on the hybrid scheduling prediction model to obtain a target scheduling prediction model. According to the invention, multi-target balance of task completion time and energy consumption is realized, and the real-time response capability and operation stability of the system are improved.

The invention relates to the technical field of industrial scheduling, and discloses an excitation-driven demand response-oriented flexible job shop energy-saving scheduling method, which comprises the following steps of: representing an energy-saving scheduling state of a flexible job shop based on an improved heterogeneous disjunction graph, and constructing a graph reinforcement learning model; training the graph reinforcement learning model by adopting a near-end strategy optimization algorithm; and outputting a corresponding scheduling scheme according to the production task and the day-ahead peak clipping invitation based on the trained graph reinforcement learning model. The method effectively coordinates and optimizes the three targets of the completion period, the total energy consumption and the incentive return, provides technical support for power grid operation personnel to design incentive policies, and helps to encourage more industrial loads to participate in power system operation.

The invention relates to a production and logistics cooperative scheduling method for a dynamic flexible job shop, and the method comprises the following steps: obtaining the real-time information of the dynamic flexible job shop, inputting a production and logistics cooperative scheduling problem model for the dynamic flexible job shop, carrying out the solving through a deep reinforcement learning method, and obtaining a shop scheduling scheme; wherein the production and logistics collaborative scheduling problem model oriented to the dynamic flexible job shop sets respective optimization targets for production activities and logistics activities, fault scenes and corresponding processing strategies of logistics equipment are considered, and the solving process is as follows: S301, designing an adaptive multi-agent nested hierarchical framework; s302, designing Markov decision process key elements of each agent; and S303, performing cooperative training on each agent by using a multi-agent near-end strategy optimization algorithm. Compared with the prior art, a more reasonable and effective flexible job scheduling scheme can be obtained.

The invention relates to the technical field of cloud manufacturing, in particular to a real-time order receiving and scheduling method and system for a flexible job shop. Comprising the following steps: extracting related data from a production site, and constructing a flexible job shop environment model according to the related data; an agent decision model is constructed, a corresponding DQN algorithm model is constructed, and the DQN algorithm model comprises an order acceptance DQN and a scheduling DQN; on the basis of interaction with the intelligent agent, training an order acceptance DQN algorithm model and a scheduling DQN algorithm model, the training of the order acceptance DQN being completed on the basis of the scheduling DQN having a good effect; and deploying the trained order acceptance DQN and the scheduling DQN to make a decision. According to the method, comprehensive fine consideration of the external production capacity and the expected available production capacity of the actual production workshop is added, a high-customization order-oriented order acceptance decision and production logistics collaborative scheduling real-time comprehensive scheduling model is established, the deviation of real-time acceptance decision is reduced, and more efficient and scientific management and control of the production process are realized.

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 provides a predictive scheduling method and system for a dynamic flexible job shop, and the method comprises the steps: collecting the operation state of a machine in real time, and building a mathematical model of a shop environment; the fault probability of each machine is predicted by taking the machine as an object and combining historical data and data collected in real time; establishing a scheduling model composed of a workshop environment and a scheduling agent adopting a reinforcement learning algorithm, in the scheduling model, taking the machine fault probability and the machine operation state obtained in the workshop environment as input data of the reinforcement learning algorithm, and outputting a corresponding optimal scheduling scheme conforming to a scheduling rule; and executing and monitoring the optimal scheduling scheme, and rescheduling when a triggering condition is met. According to the method, the problems of machine faults and new operation insertion in the dynamic production environment can be effectively solved, the scheduling performance is optimized, workpiece delay is reduced, and the overall production efficiency is improved.

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 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 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 relates to a flexible job shop energy-saving batch flow scheduling optimization method considering machine tool deterioration and preventive maintenance, and belongs to the technical field of shop scheduling. Comprising the following steps: constructing a flexible job shop energy-saving batch flow scheduling mathematical model taking completion time, total energy consumption and total production cost as optimization targets and considering machine tool deterioration and preventive maintenance; a model solving method is put forward, a strategy is introduced based on population diversity of historical populations, and the coping capacity of the algorithm to environment changes is enhanced; developing a self-adaptive crossover mutation operator, and balancing the diversity and convergence of the algorithm; and proposing a neighborhood search strategy, and exploring a local optimal solution of the energy-saving batch flow scheduling problem. According to the method, the problem of flexible job shop energy-saving batch flow scheduling multi-objective optimization considering machine tool deterioration and preventive maintenance can be efficiently solved, under-maintenance or excessive maintenance of the machine tool is effectively avoided, production interruption and maintenance cost are reduced, and a scheduling scheme with excellent completion time, total energy consumption and total production cost is obtained.

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 invention belongs to the technical field of industrial scheduling, and discloses a flexible job shop lean production self-organization optimization method for a peak load shifting strategy, and the method comprises the steps: building a problem model, and determining an optimization target and a constraint condition; encoding and decoding chromosomes, and initializing a population; sorting the individuals, executing selection, crossover and variation, and forming a primary filial generation population by using an equipment efficiency cooperative operator and an energy efficiency cooperative operator; combining the parent population and the filial population, performing sorting, selection, crossover and variation again, forming a new filial population by using an equipment efficiency cooperative operator and an energy efficiency cooperative operator, and taking the previous filial population as a new parent population; and repeatedly executing the iteration step, and outputting an optimal scheduling scheme after a termination condition is reached. According to the method, the productivity and the energy efficiency can be effectively coordinated, the actual technical support is provided for the flexible job shop to realize peak clipping and valley filling and reduce the peak-valley electricity proportion, and the method system of the shop scheduling problem is enriched.

The invention discloses a scheduling method for a flexible job shop considering transportation time. The scheduling method comprises the following steps: constructing a corresponding heterogeneous graph model according to a current shop environment; feature extraction is carried out on the heterogeneous graph model based on the relation among processes, machines and AGVs, and node features and joint features are obtained; the node features are input into a multiple attention network for GAT coding, and new features are obtained; and combining the new features with the joint features, performing MLP decoding by using an Actor network for decision in a decision module with trained network parameters to obtain corresponding actions and probability distribution thereof, selecting and executing the action with the highest probability, updating the current workshop environment according to the executed action, and performing repeated iteration until all scheduling processes are completed.

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 an active fault-tolerant scheduling method for a flexible job shop based on multi-view transfer learning, and the method comprises the steps: collecting time series data of all equipment in the shop in real time, and carrying out the preprocessing for data cleaning and standardization; generating an industrial data interpolation model of an adversarial network through a weighted condition based on gradient penalty, and predicting and filling missing or abnormal equipment state data to complete acquired data; the model is used for carrying out data prediction on the state of a future system; identifying potential problems by using a task migration module based on unscented transformation in combination with a fault analysis technology; a state calculation module is used for calculating the specific state of the intelligent agent in the environment state space according to the complete time sequence data after interpolation; and establishing a flexible job shop dynamic scheduling model, and performing action selection according to the state characteristics of the current production environment based on a multi-view enhanced deep reinforcement learning scheduling module to generate a scheduling scheme. According to the invention, the scheduling efficiency and the fault-tolerant capability in the flexible production environment can be improved.

The invention provides a dynamic flexible job-shop multi-target scheduling method based on deep reinforcement learning. The dynamic flexible job-shop multi-target scheduling method comprises the following steps: converting a deterministic scheduling problem into a Markov decision model; based on a Markov decision model, constructing a deterministic scheduling algorithm based on MOHDQN, designing a training process, training the algorithm, and testing on a random example to form a deterministic scheduling model agent; constructing a Markov jump decision process, and converting the event-driven pre-reactive scheduling problem into the Markov jump decision process; based on an HDRQN algorithm, constructing a training process, and interactively training an event-driven pre-reactive scheduling agent with a simulation environment; and testing an interactive training result on a random example to form an event-driven pre-reactive scheduling agent. According to the method provided by the invention, the optimal balance between the solving precision and the solving speed is realized, and the complex multi-target scheduling problem with various uncertain events can be solved by the proposed method.

The present invention discloses a job shop adaptive scheduling method based on deep reinforcement learning. An optimized action policy and an asynchronous update mechanism are designed in the proximal policy optimization algorithm to form a proximal policy optimization algorithm for direct and efficient exploration and asynchronous update. Based on the proximal policy optimization algorithm for direct and efficient exploration and asynchronous update, a graph neural network is combined with the hierarchical non-linear refinement of the original state information to design an end-to-end reinforcement learning method. Based on this, an adaptive scheduling system is obtained. The proximal policy optimization algorithm for direct and efficient exploration and asynchronous update of the present invention has high robustness, the scheduling score is increased by 5.6% compared with the proximal policy optimization algorithm, and the minimum completion time is reduced by 8.9% compared with the deep Q-network algorithm. The experimental results prove the effectiveness and generality of the proposed adaptive scheduling strategy.

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 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 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 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 provides a multi-AGV flexible job shop active scheduling method based on an improved VNS-INSGA-II algorithm and a related device, and belongs to the technical field of shop active scheduling. According to the method, a multi-objective optimization model for active scheduling of the multi-AGV flexible job shop is constructed, and constraint conditions of the multi-objective optimization model are set; the VNS-INSGA-II algorithm is improved by adopting a multi-layer coding mode, a hybrid population initialization mode, a time-varying coefficient-based dual-strategy binary tournament selection strategy and a variable neighborhood search algorithm, and the improved VNS-INSGA-II algorithm is obtained; based on a set constraint condition of the multi-objective optimization model, an improved VNS-INSGA-II algorithm is adopted to solve decision variables in an objective function of the established multi-objective optimization model for active scheduling of the multi-AGV flexible job shop, and a Pareto frontier is obtained; and selecting a group of solutions from the Pareto frontier to obtain an active scheduling result, thereby carrying out active scheduling on the multi-AGV flexible job shop. According to the invention, the problems of low stability and low scheduling precision of the scheduling system are solved.

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 provides a distributed flexible workshop scheduling method and system based on a multi-target grey wolf algorithm, and belongs to the technical field of workshop scheduling. The method comprises the following steps: initializing algorithm parameters, and defining a coding and decoding mechanism by adopting three-layer coding including a process sequence, machine selection and factory selection; initializing the initial wolf pack by adopting a hybrid strategy based on three-layer coding; dividing non-dominated levels of the initial wolf pack through non-dominated sorting and sorting population crowding degree; determining the position of a wolf pack leader based on the number of non-dominated hierarchies and crowding degree sorting; performing crossover variation and wolf pack combination on the individual positions of the wolf pack, and updating the combined wolf pack to obtain a non-dominated elite solution set; setting a destruction operator and a repair operator, dynamically adjusting the selection probability of the destruction operator and the repair operator, introducing a simulated annealing algorithm to expand a search solution space, and optimizing an elite solution set to obtain a final solution. The scheduling efficiency of the distributed flexible job shop is improved, local optimum is avoided, and the production benefit and quality are guaranteed.

The invention relates to the technical field of workshop production and manufacturing, and discloses a joint scheduling method, system and equipment for flexible processing and assembling and a medium. The method comprises the following steps: constructing a joint scheduling model according to a plurality of processing procedures and a plurality of assembling procedures of a plurality of workpieces in a production and manufacturing process and a cooperative scheduling process between each processing procedure and each assembling procedure; representing a solution of the joint scheduling model by adopting batch coding, process coding and machine coding; solving the joint scheduling model by adopting a genetic algorithm by taking the minimum maximum completion time of all the workpieces as a target to obtain a target batch number of each workpiece, a target distribution sequence of each machining process and each assembly process of each workpiece and a target distribution machine of each machining process and each assembly process of each workpiece; the scheduling relation between processing and assembling in the flexible job shop is clearly described, a better joint scheduling scheme is generated, and the production and manufacturing efficiency is greatly improved.

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 present disclosure belongs to the field of flexible job shop scheduling, and relates to a dual-effect scheduling method for heterogeneous robots in a flexible job shop. In a case of strong coupling of processing and transferring, this method comprehensively considers such constraints on selection of flexible manufacturing cells (FMCs), transferring time by automatic guided vehicles (AGVs) and processing resource waste, and improves encoding schemes and genetic operators with order completion time and minimization of resource consumption as evaluation criteria. Additionally, this method can fully apply environmental characteristics of job shop to scheduling design, and automatically design more precise scheduling schemes, overcoming the deficiencies of slow response and prone to local optimal solutions existing in conventional scheduling schemes, and ensuring efficient and green operation.