46 results about "Flow shop scheduling" patented technology

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The invention provides a mixed flow workshop scheduling method based on a hybrid genetic variable neighborhood two-stage algorithm. The method comprises the following steps: S1, describing a mixed flow workshop scheduling problem, and giving a corresponding constraint condition and a corresponding optimization objective function; s2, setting related parameters, and generating an initialized population in an integer type two-segment coding mode and an IGLR mode; s3, calculating a target function value corresponding to each chromosome in the population, generating a new population according to a 50% IGLR and 50% binary tournament selection mode, and setting an elite library to store a population optimal solution; s4, a genetic algorithm is adopted for optimizing the population in combination with a multi-crossover mutation operator according to the probability parameters, and an elite library is updated through a process optimal solution; s5, selecting a current optimal solution of the elite library in combination with a multi-neighborhood search structure to perform two-stage neighborhood search optimization; and S6, judging whether an algorithm termination condition is met or not, taking the set maximum number of iterations as a termination condition, and if the algorithm termination condition is met, ending optimization and outputting the current optimal solution in the elite library.

This invention proposes a distributed assembly line workshop scheduling optimization system. A distributed estimation algorithm driven by Kalman filtering and a history learning mechanism is designed to optimize the system. Prediction, observation, first repair, and second repair are applied to the workpiece processing stage. Furthermore, enhanced repair information is fed back to the probabilistic model through the history learning mechanism to improve model accuracy. Additionally, an adaptive adjustment strategy is employed to balance the exploration and development capabilities of the optimization system. Experiments comparing the system with several classic optimization systems on a test set demonstrate the competitiveness of the proposed system. Tests on instances with varying numbers of workpieces, machines, factories, and products show that the proposed system can minimize the assembly completion time in the distributed assembly line workshop, thereby improving production efficiency.

This invention relates to a multi-objective distributed hybrid flow shop scheduling method, belonging to the technical field of hybrid flow shop scheduling. The invention establishes a corresponding objective function with the simultaneous minimization of maximum completion time and maximum processing time in distributed hybrid flow shop scheduling. First, a vector-based evaluation genetic algorithm and a fitness function based on Pareto dominance and non-dominance relationships are used to divide the population into three meme groups. Then, PSO is used to perform a global search on each meme group, exploring solutions in multiple directions of the Pareto front to accelerate the convergence speed of the upper and lower edges and the central region of the Pareto front. Second, critical-factory insert and critical-factory swap multi-neighborhood search operators are used to perform local searches on individuals, enhancing the quality of solutions in the meme groups. Third, a Q-Learning variable neighborhood search strategy is used to further enhance the algorithm's search capability on the three meme groups, thereby improving the quality and diversity of solutions and preventing the algorithm from converging prematurely and failing to find better solutions.

The invention provides an energy-saving hybrid flow shop scheduling method and system with worker constraint, and the method comprises the steps: obtaining workshop production information; based on the workshop production information, establishing a mixed integer programming model of an energy-saving hybrid flow workshop scheduling problem with worker constraints; a scheduling scheme solution meeting the mixed integer programming model is obtained through an improved genetic algorithm, and the improved genetic algorithm introduces speed level-based crossover operation on the basis of a traditional genetic algorithm. According to the scheduling method and the scheduling system, the worker constraint is introduced, so that the scheduling scheme better meets the actual production requirement, the feasibility of the scheme in actual application is ensured, and meanwhile, the scheduling method ensures the energy-saving effect and remarkably improves the production efficiency by optimizing the machining sequence of the workpieces and the distribution of machines and workers.

The application discloses a mixed flow shop scheduling method based on a historical information ant colony algorithm, and relates to the technical field of glass workpiece processing. The application discloses a kind of based on historical information ant colony algorithm's mixed flow shop scheduling method, comprising:1 constructs the total time of glass workpiece processing and energy consumption model;2 set corresponding constraint condition, construct double objective mixed flow shop scheduling model;3 according to glass workpiece and machine relevant information, and using historical information ant colony algorithm to the double objective mixed flow shop scheduling model is solved, obtains production processing scheme, to carry out production processing to glass workpiece.The application can obtain the optimal production processing scheme combined with time consumption and energy consumption, so as to improve the production processing efficiency of glass workpiece.

The invention discloses a scheduling method of a distributed assembly wait-free flow shop based on Q learning. The scheduling method comprises the following steps: 1, constructing an objective function of a distributed assembly wait-free flow shop model; 2, constructing constraint conditions of a distributed assembly wait-free flow shop scheduling model; and 3, solving the distributed assembly wait-free flow shop scheduling model by using a dual-population co-evolution algorithm based on Q learning, and generating a distributed assembly wait-free flow shop scheduling scheme. According to the method, the optimal production and processing scheme with short time consumption and low carbon emission can be obtained in a distributed assembly wait-free flow workshop scene, so that the production and processing efficiency is improved, and the carbon emission is reduced.

This invention discloses a reentrant hybrid flow shop scheduling method based on the IMOEA / D algorithm. Specifically, it first establishes a BPM-RHFSP multi-objective optimization mathematical model with the optimization objectives of minimizing maximum completion time, total delay time, and total equipment energy cost. This transforms the actual production job scheduling problem into a combinatorial optimization mathematical model problem. Then, based on IMOEA / D, it solves the problem model by designing a variable threshold batching strategy for batch processing job decoding; multi-region global search and random variable neighborhood search; and reinforcement search for individuals within the elite solution set. This invention can effectively coordinate the processing of multiple equipment, improve the rationality of resource allocation, shorten the workshop manufacturing cycle and product delay time, and reduce energy costs.

This invention relates to a flexible flow shop scheduling method, system, and computer-readable storage medium based on an environment model, belonging to the field of flexible flow shop scheduling and control technology. This invention decouples the construction of the environment model and the training of the agent. First, the environment model is trained independently using historical scheduling data, enabling it to accurately predict the shop state transitions under given states and actions. Then, the parameters in the environment model are fixed, and the environment model is used as a real interactive environment to interact with the agent. This method not only overcomes the dependence of model-free reinforcement learning on a high-fidelity simulation environment but also solves the convergence difficulties and computational complexity problems caused by the coupling training of the model and policy in traditional model-based reinforcement learning. It combines high sample efficiency with policy flexibility, significantly improving scheduling efficiency and system adaptability, and providing a feasible technical solution for intelligent scheduling of flexible flow shops.

The application belongs to the field of distributed production scheduling in manufacturing industry, and particularly relates to a distributed blocked flow shop scheduling optimization system based on reinforcement learning, which comprises a scheduling sequence diversification initialization module, an improved module based on Q-learning and a local search module based on neighborhood reconstruction; the scheduling sequence diversification initialization module designs a diversified initial population generation strategy, the improved module based on Q-learning designs a global search mechanism based on a reinforcement learning mechanism, and a search operator is adaptively selected according to a search state and historical experience of the operator; and the local search module based on neighborhood reconstruction comprises a deep local search strategy based on neighborhood reconstruction and an improved strategy based on path reconnection. The application has simple logic, is easy to implement and easy to expand, and can expand the optimizer to most scheduling problems in the current intelligent manufacturing production field.

The invention provides a flow shop scheduling dual-objective optimization method based on a bacterial foraging optimization algorithm, and relates to the technical field of production and manufacturing management. The method specifically comprises the steps of obtaining original data of production scheduling in an industrial assembly line, and constructing a coupling coding scheme based on a task sequence and a delay period matrix under the background of time-of-use electricity price; an initial population is generated by adopting a hybrid heuristic initialization strategy, wherein each individual represents a complete scheduling scheme through a coupling coding strategy based on a task sequence and a delay matrix. According to the method, global optimization is carried out on a solution space based on an improved multi-target bacterial foraging optimization algorithm, and finally, an optimal individual is output and serves as an optimal scheduling scheme of a flow shop. According to the method, the problem of double-target optimization faced by original equipment manufacturers in flow shop scheduling under a time-of-use electricity price strategy is effectively solved, and meanwhile, the production efficiency is improved and the electric charge expenditure is reduced.

The invention discloses a distributed flow shop scheduling method based on a large model aided multi-objective optimization algorithm, and particularly aims to solve the problems of insufficient sequence correlation setting time, insufficient wait-free constraint processing and low multi-objective optimization efficiency in existing distributed heterogeneous factory scheduling. And proposing a non-dominated sorting genetic algorithm based on large language model assistance. The method comprises the following steps: constructing a dual-objective optimization model, adopting a one-dimensional integer array coding solution structure and separating a factory operation sequence through '-1'; in combination with a greedy algorithm idea, selecting a greedy initialization algorithm based on maximum completion time or a greedy initialization algorithm based on sequence-dependent setting time to initialize a population; designing a large model cue word including problem definition, solution example, evolution instruction and legality verification, and executing parent selection, crossover and mutation operation by a large language model; and updating the population through non-dominated sorting, and finally outputting a Pareto frontier solution.

The application discloses a kind of distributed mixed flow shop scheduling method based on multi-agent deep reinforcement learning, belongs to the technical field of scheduling production workshop in manufacturing, to solve the problem that the technical generally exists slow response, the problem of low solving efficiency of distributed mixed flow shop scheduling.This application is for the optimization goal of minimum maximum completion time and minimum total energy consumption for distributed mixed flow shop scheduling problem, the method first constructs multi-agent neural network model by regarding each machine as agent, then uses the model to calculate and solve a large number of distributed mixed flow production examples, and uses experience database to save the action, reward and state change during training, then the experience database is randomly sampled to train each neural network, the model is tested using the verification example set during training, finally the trained model is used to solve the distributed mixed flow shop scheduling problem.

The application provides a multi-target mixed flow shop scheduling method and system, and belongs to the technical field of artificial intelligence. The application adopts a configurable MOEA framework, uses a CART enhanced I / F-Race method to automatically configure an optimal algorithm, and is used for solving a multi-target mixed flow shop scheduling problem. In subsequent F-Race iterations, before a new configuration is generated by using an F-Race learning model, a previously evaluated configuration in the F-Race is first used as training data to construct a CART model, and the CART model is subsequently used to predict the performance of the new configuration. Only the configuration predicted to be potential can enter the next F-Race iteration. The batch segmentation of each batch is considered in the application, and the maximum completion time and the total number of sub-batches are simultaneously optimized.

The invention discloses a single crystal silicon rod hybrid flow shop scheduling method with sequence correlation setting time, and belongs to the technical field of intelligent manufacturing and hybrid flow shop scheduling. The method comprises the following steps: firstly, establishing a mathematical model of the scheduling problem of the silicon single crystal rod hybrid flow shop with sequence correlation setting time; secondly, designing states, actions and reward functions in a Q learning algorithm in combination with the characteristics of the problem, further combining the five designed low-level heuristic operators, dynamically scheduling the use of the operators by Q learning, enabling the operators to play roles in a targeted manner in different search stages, and under the synergistic effect, solving the problem in the prior art. The method is superior to a traditional method depending on random exploration in the aspects of convergence rate optimization, solution quality and search stability, and the scheduling problem of the single crystal silicon rod hybrid flow shop with sequence correlation setting time can be more efficiently solved.

The application belongs to the technical field of workshop production scheduling, and discloses a distributed mixed flow water workshop scheduling method and system considering worker fatigue, which comprises the following steps: defining a problem, and constructing an objective function and constraint conditions; evenly distributing workpieces to each factory, each machine and each worker to obtain a workpiece scheduling sequence, a machine distribution vector and a worker distribution vector; performing a crossover operation on the workpiece scheduling sequence to obtain a first workpiece scheduling sequence, and performing a mutation operation on the machine distribution vector and the worker distribution vector to obtain a first machine distribution vector and a first worker distribution vector; performing an operation on the first workpiece scheduling sequence of a key factory to obtain a second workpiece scheduling sequence, calculating the value of the objective function based on the second workpiece scheduling sequence, and outputting a final workpiece scheduling sequence. The application establishes a mathematical model considering the constraint of worker fatigue, adopts a multi-objective evolutionary algorithm based on Q learning to solve the problem, and can simultaneously minimize the maximum completion time and total energy consumption.

This invention belongs to the field of hybrid flow shop scheduling technology in intelligent manufacturing, and particularly relates to a hybrid flow shop scheduling optimization method considering periodic preventive maintenance. The method constructs a DCABC-CP hybrid algorithm that integrates dual-population cooperative artificial bee colony and constraint programming. Through parameter initialization, dual-population hybrid encoding initialization, iterative optimization using hired bees, observer bees, and scout bees, adaptive population cooperation based on Thompson sampling multi-armed slot machine, forward and reverse decoding re-evaluation, and problem-specific local search, when the algorithm reaches 40% of its total execution time, the current optimal solution is imported into the CP model for precise optimization. Finally, it outputs a scheduling scheme that minimizes the maximum completion time while satisfying the periodic preventive maintenance constraint. This invention effectively balances global search and local optimization, enhances the ability to handle maintenance constraints, and significantly outperforms traditional algorithms in terms of solution efficiency and quality, making it suitable for large-scale hybrid flow shop scheduling scenarios.