14 results about "Flow shop scheduling" patented technology

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.

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 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.

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.

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.

The application belongs to the field of workshop scheduling, and aims at solving the problem of low efficiency of the existing scheduling method of heterogeneous factories. The application comprises the following steps: S1, constructing a distributed heterogeneous flow shop scheduling model; S2, determining the value of the basic parameters of the algorithm, and generating an initial population by using a random strategy according to the coding rules; S3, evaluating the initial population, and performing a Pareto non-dominated sorting according to the fitness value; S4, performing an EOX crossover operation on the population with two-layer coding; S5, searching the solution space by using multiple operators; S6, adopting an elite reservation strategy, merging the parent population and the child population, and selecting half of the individuals as the evolution target of the next stage; S7, performing an evolution operation of local search on the selected individuals; S8, updating the population; S9, if the algorithm reaches the stopping condition, the algorithm is ended and the result is output, otherwise, the algorithm jumps to step S4 and continues to execute.

The application provides a distributed blocking flow shop scheduling optimization system based on a co-evolution algorithm, and designs a knowledge-driven cross-region interactive learning mechanism assisted co-evolution algorithm to optimize the distributed blocking flow shop scheduling under the energy consumption constraint condition. A heuristic method based on the problem characteristics is designed to reduce the energy consumption and total delay in the production process. In order to improve the diversity and convergence rate of the population, the application designs a cross-region interactive learning mechanism guided by the optimal state value function of reinforcement learning, realizes comprehensive cooperation from the algorithm level, the parameter level and the individual level, and improves the accuracy and efficiency of the solution. In order to improve the solution quality, a poor solution repair strategy and an individual rebirth mechanism are provided. In addition, the energy consumption is reduced through the energy-saving operation on the critical path. Through comparison and verification on the continuous optimization problem test set and the examples composed of different numbers of workshops, workpieces and machines, the optimization effect of the optimization system designed by the application is better than that of other optimization systems.

The application discloses a distributed hybrid flow shop dynamic scheduling method based on a learnable iterative greedy algorithm, relates to the flow shop scheduling technical field, and aims at a dynamic workpiece arrival and machine fault scene, takes minimizing the maximum completion time as a core target, is based on three coupled subproblems of a distributed hybrid flow shop, adopts a two-dimensional coding strategy to represent a factory distribution and a workpiece sequencing scheme, combines a first available machine rule and a first come first served rule to construct a decoding mechanism, designs a multi-level neighborhood operation set as an action space of an agent, deals with emergent events of the dynamic workpiece arrival and the machine fault, extracts state features as input by using a long short-term memory network, trains the agent by using a proximal policy optimization algorithm and an experience replay mechanism, dynamically adjusts a decision strategy, makes the agent self-adaptively select a rescheduling, a destruction reconstruction and a local search strategy, balances global exploration and local development, and finally improves scheduling quality.

The present application relates to the technical field of workshop scheduling, in particular to a multi-modal multi-objective reentrant hybrid flow shop scheduling method, which is based on a multi-modal multi-objective meme method driven by hierarchical online learning, and comprises the following steps: step 1: establishing a problem model; step 2: setting method running parameters; step 3: generating a population by using an initialization strategy; step 4: judging whether a first-stage termination condition is met, if not, executing a first-stage evolution strategy and an environment selection strategy on the population, otherwise executing step 5; step 5: constructing a multi-modal archive; step 6: judging whether a second-stage termination condition is met; the present application can effectively mine a large number of performance-equivalent but structurally different multi-modal scheduling solutions while guaranteeing the convergence of the target space and maintaining the diversity of the decision space.

This invention belongs to the field of distributed replacement flow shop scheduling technology in intelligent manufacturing, and particularly relates to a distributed replacement flow shop scheduling optimization method with preventive maintenance. This invention divides the population into two co-evolutionary populations of explorers and developers. Through Q-learning, state evaluation and search action decisions are completed based on the population improvement rate and diversity. A multi-armed slot machine model is used to adaptively select eight neighborhood perturbation operators across three categories: factory layer, workpiece layer, and local layer. After iterative optimization through reproduction and diffusion, local search, competition elimination, and population migration, an elite pool is constructed to complete a deep search. Finally, the global optimal solution of a heuristic algorithm is used as the hot start point, and the final optimization is completed through a constrained programming (CP) model. This invention significantly improves the solution accuracy and efficiency of this type of scheduling problem, effectively balancing the algorithm's global exploration and local development capabilities.

This invention belongs to the technical field of intelligent manufacturing and workshop scheduling. It discloses a method and equipment for scheduling a dynamic hybrid assembly line workshop based on discrete event simulation and considering worker fatigue. The steps are as follows: (1) Constructing a discrete event simulation model that includes workpiece flow, worker resource scheduling, and worker work efficiency; the discrete event simulation model is used to update the processing time; (2) Providing a virtual interactive environment for the scheduling agent through the discrete event simulation model, and training the scheduling agent based on the measured data of the dynamic hybrid assembly line workshop using the near-end strategy optimization algorithm; during the training process, collecting decision trajectories and iteratively updating the policy network and value network of the near-end strategy optimization algorithm; (3) Inputting the real production data of the dynamic hybrid assembly line workshop to be scheduled into the scheduling agent, and the scheduling agent outputs a scheduling scheme adapted to the current state. This invention improves the practicality and adaptability of the scheduling scheme.

The present application relates to a flow shop scheduling method, comprising: a population initialization step, based on the number of multiple workpieces processed in the flow shop, the number of workbenches processing each workpiece in each process, the processing time of each workpiece processed by each workbench, randomly generating a set of N individual codes as a population; and a population evolution step, comprising: a population diversification step, using a genetic algorithm to expand the number of individual codes in the population to be greater than N in a manner that meets the pre-set limit conditions; a consistent coding step, removing duplicate individual codes in the population that can be decoded into the same job schedule; a decoding step, decoding each individual code in the population into a corresponding job schedule and calculating the value of the scheduling index; and a population screening step, sorting all individual codes in the population according to the size of the scheduling index value, and replacing the population with the set of the top N individual codes with better scheduling indexes.

This invention discloses a scheduling method for a flexible forging hybrid assembly line based on multiple orders, belonging to the field of intelligent manufacturing. It constructs a mathematical model incorporating multi-dimensional constraints such as furnace capacity, holding time, die replacement, and forging equipment availability. Addressing the characteristic that different forging orders require die replacement, it proposes an intelligent optimization algorithm suitable for scheduling flexible forging hybrid assembly lines with multiple orders, and designs a segmented three-layer real-number encoding. Considering the continuous processing characteristics of forging, a decoding and compilation strategy is designed. By controlling the holding time of the workpiece in the furnace during the decoding stage, the workpiece can be immediately put into forging after exiting the furnace. By combining the scheduling model with the intelligent algorithm to solve actual production problems, the effectiveness of this strategy in improving production efficiency and reducing energy consumption is verified, providing a solution for efficient scheduling of flexible forging hybrid assembly lines with multiple orders.