共查询到18条相似文献,搜索用时 515 毫秒
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基于Hopfield神经网络的作业车间生产调度方法 总被引:22,自引:2,他引:22
该文提出了基于Hopfield神经网络的作业车间生产调度的新方法.文中给出了作业车
间生产调度问题(JSP)的约束条件及其换位矩阵表示,提出了新的包括所有约束条件的计算能
量函数表达式,得到相应的作业车间调度问题的Hopfield神经网络结构与权值解析表达式,并
提出相应的Hopfield神经网络作业车间调度方法.为了避免Hopfield神经网络容易收敛到局部
极小,从而产生非法调度解的缺点,将模拟退火算法应用于Hopfield神经网络求解,使Hopfield
神经网络收敛到计算能量函数的最小值0,从而保证神经网络输出是一个可行调度方案.该文
改进了已有文献中提出的作业调度问题的Hopfield神经网络方法,与已有算法相比,能够保证
神经网络稳态输出为可行的作业车间调度方案. 相似文献
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提出一种基于深度强化学习的微电网在线优化调度策略.针对可再生能源的随机性及复杂的潮流约束对微电网经济安全运行带来的挑战,以成本最小为目标,考虑微电网运行状态及调度动作的约束,将微电网在线调度问题建模为一个约束马尔可夫决策过程.为避免求解复杂的非线性潮流优化、降低对高精度预测信息及系统模型的依赖,设计一个卷积神经网络结构学习最优的调度策略.所提出的神经网络结构可以从微电网原始观测数据中提取高质量的特征,并基于提取到的特征直接产生调度决策.为了确保该神经网络产生的调度决策能够满足复杂的网络潮流约束,结合拉格朗日乘子法与soft actor-critic,提出一种新的深度强化学习算法来训练该神经网络.最后,为验证所提出方法的有效性,利用真实的电力系统数据进行仿真.仿真结果表明,所提出的在线优化调度方法可以有效地从数据中学习到满足潮流约束且具有成本效益的调度策略,降低随机性对微电网运行的影响. 相似文献
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用遗传算法与自适应神经网络混合方法解Job-shop调度问题 总被引:2,自引:0,他引:2
提出一种用遗传算法结合基于约束满足的自适应神经网络进行Job—shop调度问题求解的混合方法。遗传算法被用来进行迭代寻优。当前代经交叉和变异后生成的染色体对应非可行解,由自适应神经网络运算后得到可行解,对应的染色体作为新一代染色体。仿真表明该算法是快速有效的 相似文献
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提出一种用遗传算法结合基于约束满足的自适应神经网络进行Job-shop调度问题求解的混合方法。遗传算法被用来进行迭代寻优。当前代经交叉和变异后生成的染色体对应非可行解,由自适应神经网络运算后得到可行解,对应的染色体作为新一代染色体。仿真表明该算法是快速有效的。 相似文献
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众所周知, 生产调度问题属组合优化问题, 一般来说不存在求得精确最优解的多项式算法. 因此, 对于大规
模调度问题, 人们应用启发式算法和元启发式算法以企求得满意解. 在实际的应用中, 许多工业过程需要满足严格
的工艺约束. 对于这类过程的调度问题, 很难应用启发式算法和元启发式算法, 因为这些方法难于保证所求得调度
的可行性. 为了解决这一问题, 本文以半导体芯片制造中组合设备的调度问题作为例子, 介绍了一种基于离散事件
系统控制理论的生产调度新方法. 利用Petri网建模, 任何违反约束的状态均被描述为非法状态, 而使非法状态出现
的调度则是不可行调度. 通过可行调度的存在性分析, 该方法获得可行解空间并将调度问题转化为连续优化问题,
从而可以有效求解. 并且指出, 该方法可以应用于其他应用领域. 相似文献
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提出一种基于约求满足的自适应神经网络方法求解车间作业调度问题。在该算法中,神经网络在运行过程中能够根据问题的约束类型、约束满足情况、启发式规则的选择来自适应调节神经元之间的连接权值,从而求得问题的可行解。仿真实验证明了算法的有效性。 相似文献
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针对钢铁生产中加热炉调度问题,考虑炉容受限的情况,以最小化板坯的Makespan和最小化总在炉加工时间为目标建立问题的多目标优化模型,将其归结为多旅行商问题。针对问题的NP-难特性,提出一种改进的修复式约束满足算法求解。松弛炉容约束得到初始调度,在检测冲突变量并构造冲突板坯的可替换加热炉集合的基础上,以开工时间偏移最小规则为冲突板坯重新指派加热炉,得到可行的调度方案。数据实验验证了模型和算法的可行性和有效性。 相似文献
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A constraint satisfaction neural network and heuristic combined approach for concurrent activities scheduling
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Scheduling activities in concurrent product development process is of great sig-nificance to shorten developements lead time and minimize the cost.Moreover,it can eliminate the unnecessary redesign periods and guarantee that serial activities can be executed as concurrently as possible,This paper presents a constraint satisfaction neural network and heuristic combined approach for concurrent activities scheduling.In the combined approack,the neural network is used to obtain a feasible starting time of all the activities based on sequence constraints ,the heuristic algorithm is used to obtain a feasible solution of the scheduling problem based on resource constrainsts.The feasible scheduling solution is obtained by a gradient optimization function .Sim-ulations have shown that the proposed combined approach is efficient and fasible with respect to concurrent activities scheduling. 相似文献
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A Constraint Satisfaction Neural Network and Heuristic Combined Approach for Concurrent Activities Scheduling
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Scheduling activities in concurrent product development process is of great significance to shorten development lead time and minimize the cost. Moreover, it can eliminate the unnecessary redesign periods and guarantee that serial activities can be executed as concurrently as possible. This paper presents a constraint satisfaction neural network and heuristic combined approach for concurrent activities scheduling. In the combined approach, the neural network is used to obtain a feasible starting time of all the activities based on sequence constraints, the heuristic algorithm is used to obtain a feasible solution of the scheduling problem based on resource constraints. The feasible scheduling solution is obtained by a gradient optimization function. Simulations have shown that the proposed combined approach is efficient and feasible with respect to concurrent activities scheduling. 相似文献
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用约束满足自适应神经网络和有效的启发式算法解Job-shop调度问题 总被引:6,自引:0,他引:6
提出一种用约束满足自适应神经网络结合有效的启发式算法求解Job-shop调度问题.在混合算法中,自适应神经网络具有在网络运行过程中神经元的偏置和连接权值自适应取值的特性,被用来求得调度问题的可行解,启发式算法分别被用来增强神经网络的性能、获得确定排序下最优解和提高可行解的质量.仿真表明了本文提出的混合算法的快速有效性. 相似文献
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Multiprocessor Task Assignment with Fuzzy Hopfield Neural Network Clustering Technique 总被引:1,自引:0,他引:1
Most scheduling applications have been demonstrated as NP-complete problems. A variety of schemes are introduced in solving
those scheduling applications, such as linear programming, neural networks, and fuzzy logic. In this paper, a new approach
of first analogising a scheduling problem to a clustering problem and then using a fuzzy Hopfield neural network clustering
technique to solve the scheduling problem is proposed. This fuzzy Hopfield neural network algorithm integrates fuzzy c-means
clustering strategies into a Hopfield neural network. This investigation utilises this new approach to demonstrate the feasibility
of resolving a multiprocessor scheduling problem with no process migration and constrained times (execution time and deadline).
Each process is regarded as a data sample, and every processor is taken as a cluster. Simulation results illustrate that imposing
the fuzzy Hopfield neural network onto the proposed energy function provides an appropriate approach to solving this class
of scheduling problem. 相似文献
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A novel neural network approach is proposed for solving linear bilevel programming problem. The proposed neural network is proved to be Lyapunov stable and capable of generating optimal solution to the linear bilevel programming problem. The numerical result shows that the neural network approach is feasible and efficient. 相似文献
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Constraint satisfaction adaptive neural network and heuristicscombined approaches for generalized job-shop scheduling 总被引:3,自引:0,他引:3
This paper presents a constraint satisfaction adaptive neural network, together with several heuristics, to solve the generalized job-shop scheduling problem, one of NP-complete constraint satisfaction problems. The proposed neural network can be easily constructed and can adaptively adjust its weights of connections and biases of units based on the sequence and resource constraints of the job-shop scheduling problem during its processing. Several heuristics that can be combined with the neural network are also presented. In the combined approaches, the neural network is used to obtain feasible solutions, the heuristic algorithms are used to improve the performance of the neural network and the quality of the obtained solutions. Simulations have shown that the proposed neural network and its combined approaches are efficient with respect to the quality of solutions and the solving speed. 相似文献
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Alireza Nazemi 《Engineering Applications of Artificial Intelligence》2013,26(2):685-696
In this paper, a neural network model is constructed on the basis of the duality theory, optimization theory, convex analysis theory, Lyapunov stability theory and LaSalle invariance principle to solve general convex nonlinear programming (GCNLP) problems. Based on the Saddle point theorem, the equilibrium point of the proposed neural network is proved to be equivalent to the optimal solution of the GCNLP problem. By employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the original problem. The simulation results also show that the proposed neural network is feasible and efficient. 相似文献
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基于神经网络模型的有约束的FMS资源调度 总被引:4,自引:0,他引:4
本文介绍了用神经网络求解FMS中有约束的资源调度问题的方法,有约束的资源调度问题首和无被分解成一系列多维背包模型并且为背包模型建立了一个等价的Hopfield神经网络,然后通过扩展Hopfield网络,给出了一种求解有约束的资源调度问题的方法。这咱方法可以避免通常神经网络所具有的不稳定性和容易陷入局部极小点的缺陷。 相似文献