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Optimal Maintenance Modeling for Systems with Multiple Non-Identical Units Using Extended DSSP Method
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作者 Xiaohong Zhang Jianchao Zeng 《American Journal of Operations Research》 2016年第4期275-295,共22页
In the optimal maintenance modeling, all possible maintenance activities and their corresponding probabilities play a key role in modeling. For a system with multiple non-identical units, its maintenance requirements ... In the optimal maintenance modeling, all possible maintenance activities and their corresponding probabilities play a key role in modeling. For a system with multiple non-identical units, its maintenance requirements are very complicated, and it is time-consuming, even omission may occur when enumerating them with various combinations of units and even with different maintenance actions for them. Deterioration state space partition (DSSP) method is an efficient approach to analyze all possible maintenance requirements at each maintenance decision point and deduce their corresponding probabilities for maintenance modeling of multi-unit systems. In this paper, an extended DSSP method is developed for systems with multiple non-identical units considering opportunistic, preventive and corrective maintenance activities for each unit. In this method, different maintenance types are distinguished in each maintenance requirement. A new representation of the possible maintenance requirements and their corresponding probabilities is derived according to the partition results based on the joint probability density function of the maintained system deterioration state. Furthermore, focusing on a two-unit system with a non-periodical inspected condition-based opportunistic preventive-maintenance strategy;a long-term average cost model is established using the proposed method to determine its optimal maintenance parameters jointly, in which “hard failure” and non-negligible maintenance time are considered. Numerical experiments indicate that the extended DSSP method is valid for opportunistic maintenance modeling of multi-unit systems. 展开更多
关键词 Extended Deterioration State Space Partition (dssp) Condition-Based Opportunistic Preventive-Maintenance Hard Failure Non-Negligible Maintenance Times Multi-Unit Systems
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基于双向信号子空间投影的高光谱图像虚拟维数估计 被引量:2
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作者 梅少辉 何明一 戴玉超 《西北工业大学学报》 EI CAS CSCD 北大核心 2012年第5期640-646,共7页
提出一种基于双向信号子空间投影的高光谱图像虚拟维数估计算法。该算法分别在高光谱图像的像元方向和波段图像方向进行信号子空间估计,虽然这两个方向上信号子空间的分布不同,但其维数均等于图像的虚拟维数。该方法不需要对信号子空间... 提出一种基于双向信号子空间投影的高光谱图像虚拟维数估计算法。该算法分别在高光谱图像的像元方向和波段图像方向进行信号子空间估计,虽然这两个方向上信号子空间的分布不同,但其维数均等于图像的虚拟维数。该方法不需要对信号子空间和噪声子空间进行区分,仅通过对不同方向上的信号子空间投影进行比较,获取图像的虚拟维数。仿真像元实验和实际高光谱图像实验均证明该算法改善了传统的基于单向投影的虚拟维数估计算法的性能,其性能优于常用的虚拟维数估计算法:Neyamn-Pearson检测算法和信号子空间估计算法。 展开更多
关键词 虚拟维数 本证维数 高光谱图像 混合像元分解
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库存与运输整合问题的多种算法比较
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作者 裴英梅 叶春明 +1 位作者 左翠红 刘立辉 《工业工程》 北大核心 2013年第1期105-109,共5页
通过循序渐进地应用拉格朗日乘数法、基于样本的DSSP(Dynamic Slope Scaling Procedure)启发法和基于拉格朗日松弛模型的DSSP启发法等几种算法,分别求解多对多配送系统中的库存与运输整合优化问题,逐渐找到了解决问题的更加有效的方法... 通过循序渐进地应用拉格朗日乘数法、基于样本的DSSP(Dynamic Slope Scaling Procedure)启发法和基于拉格朗日松弛模型的DSSP启发法等几种算法,分别求解多对多配送系统中的库存与运输整合优化问题,逐渐找到了解决问题的更加有效的方法———基于拉格朗日松弛模型的DSSP启发法。通过比较实验证明了此法在解决库存与运输整合优化问题时能在更少的计算时间里获得更优化的解。 展开更多
关键词 库存与运输 整合优化 基于拉格朗日松弛模型的dssp启发法
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