Joint optimization of ordering and maintenance with condition monitoring data

Abstract

We study a single-unit deteriorating system under condition monitoring for which collected signals are only stochastically related to the actual level of degradation. Failure replacement is costlier than preventive replacement and there is a delay (lead time) between the initiation of the maintenance setup and the actual maintenance, which is closely related to the process of spare parts inventory and/or maintenance setup activities.We develop a dynamic control policy with a two-dimensional decision space, referred to as a warning-replacement policy, which jointly optimizes the replacement time and replacement setup initiation point (maintenance ordering time) using online condition monitoring data. The optimization criterion is the long-run expected average cost per unit of operation time. We develop the optimal structure of such a dynamic policy using a Partially Observable Semi-Markov Decision Process (POSMDP) and provide some important results with respect to optimality and monotone properties of the optimal policy. We also discuss how to find the optimal values of observation/inspection interval and lead time using historical condition monitoring data. Illustrative numerical examples are provided to show that our joint policy outperforms conventional suboptimal policies commonly used in the literature.

Joint optimization of bi‐level imperfect maintenance and spare parts inventory considering order quantity

“…Several detailed studies exist on the joint optimization of PM and spare inventory, including the joint optimization models that consider either age-based maintenance 10 or condition-based maintenance. [11][12][13] However, the current research methods rarely consider the improvement of spare inventories. We reviewed the research results by Pınar Bülbül et al 14 and others who studied inventory control policies and determined the most common policies employed were (T, s, S), (T, S), (T, Q), (s, Q), and (s, S).…”

Spare parts control strategies for offshore wind farms: A critical review and comparative study

“…Digiesi et al (2015) used various terms in the objective function to measure the environmental impact of SPM. Other researchers worked on stocking strategies (Botter and Fortuin, 2000), heterogeneous environment (Barabadi et al, 2021; Chang et al, 2015), heuristic approach (Bülbül et al, 2019), stochastic demand classification (Conceição et al, 2015), uncertain demand (Gu et al, 2015), hybrid approach (Muniz et al, 2021), multiple components (Olde Keizer et al, 2017), binary decision fault tree (Remenyte-Prescott and Andrews, 2008), equivalent equipment exchange (Khademi and Eksioglu, 2018), optimizing spare parts necessity (Eruguz et al, 2018), spare parts demand planning (Kian et al, 2019) based on shelf degradation and without knowing the degradation level (Ghasemi et al, 2007; Moghaddass and Ertekin, 2018).…”

Review of Post-Prognostics Decision-Making in Prognostics and Health Management

“…Moghaddass et al (Moghaddass & Ertekin, 2018) proposed a dynamic decision policy to jointly optimize ordering and replacement dates for a single-unit inventory system. The proposed method consists of periodically collecting and observing data related to the system operations and then determining whether to start the setup of a maintenance intervention on one level.…”