Integrated optimization of design, storage sizing, and maintenance policy as a Markov decision process considering varying failure rates

Integrated optimization of design, storage sizing, and maintenance policy as a Markov decision process considering varying failure rates
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设计、存储规模和维护策略的集成优化作为考虑不同故障率的马尔可夫决策过程

DOI:
10.1016/j.compchemeng.2020.107052
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发表时间:
2020
影响因子:
4.3
通讯作者:
Ramaswamy, Sivaraman
Ramaswamy, Sivaraman
中科院分区:
工程技术2区
文献类型:
--
作者:
Ye, Yixin;Grossmann, Ignacio E.;Pinto, Jose M.;Ramaswamy, Sivaraman

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可以采用多种策略以一定的成本来提高可靠性,包括设备冗余、产品存储和维护,这就产生了在策略之间优化分配可靠性提高成本并平衡它们与由于不可用而造成的潜在损失的问题。受空分装置可靠性问题的启发,我们使用马尔可夫决策过程对基于状态的维护的随机动态决策过程进行建模,假设单个装置的浴缸形故障率曲线,然后将其嵌入到考虑所有决策之间权衡的非凸MINLP(DMP)中。使用多个全局求解器直接求解中型问题的 MINLP (DMP) 的初步尝试揭示了严重的计算困难。作为回应,我们提出了一种定制的两阶段算法,可以大大减少所需的计算工作量。该算法还针对围绕 4 个处理阶段的原始示例和较大规模的问题随机生成的问题表现出一致的性能。
Various strategies can be applied to improve reliability at certain costs, including equipment redundancy, product storage, and maintenance, which gives rise to the problem of optimally allocating the reliability improvement costs among the strategies and balancing them against the potential loss due to unavailabilities. Motivated by the reliability concerns of air separation units, we use Markov Decision Process to model the stochastic dynamic decision making process of condition-based maintenance assuming bathtub shaped failure rate curves of single units, which is then embedded into a non-convex MINLP (DMP) that considers the trade-off among all the decisions. An initial attempt to directly solve the MINLP (DMP) for a mid-sized problem with several global solvers reveals severe computational difficulties. In response, we propose a custom two-phase algorithm that greatly reduces the required computation effort. The algorithm also shows consistent performance over randomly generated problems around the original example of 4 processing stages and problems of larger sizes.
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