Time series semi-Markov decision process with variable costs for maintenance planning

Dawid, Rafael; McMillan, David; Revie, Matthew

doi:10.1201/9781315374987-172

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…Shim et al [16] defined bridge maintenance benefits as an improvement in the NBI deck condition rating after the application of deck maintenance measures. In practical applications, some states in the United States have also provided relevant definitions of bridge maintenance benefits [17]. For example, Connecticut defines bridge maintenance benefits based on the improvement in bridge condition compared to no maintenance work, while New Mexico defines bridge maintenance benefits by measuring the ability to improve the NBI rating.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The purpose of this interaction is to maximize the improvement in bridge structural performance. Dawid et al [17] proposed a semi-Markov decision model that enables wind farm operators to automate the mid-term and short-term maintenance planning process for a batch or for all turbines rather than making decisions for individual turbines to determine their maintenance strategies. Wei et al [18] combined sequential decision making with deep learning to explore maintenance strategies for simply supported beam bridge deck elements and long-span cable-stayed bridge elements.…”

Section: Literature Reviewmentioning

confidence: 99%

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Modeling the Optimal Maintenance Strategy for Bridge Elements Based on Agent Sequential Decision Making

Xin,

Liang,

et al. 2023

Applied Sciences

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In addressing the issues of low efficiency in bridge maintenance decision making, the inaccurate estimation of maintenance costs, and the lack of specificity in decision making regarding maintenance measures for specific defects, this study utilizes data from regular bridge inspections. It employs a two-parameter Weibull distribution to model the duration variables of the states of bridge elements, thereby enabling the prediction of the duration time of bridge elements in various states. Referring to existing bridge maintenance and repair regulations, the estimation process of maintenance costs is streamlined. Taking into account the specific types and development state of bridge defects, as well as considering the adequacy of maintenance and the restorative effects of maintenance measures, an intelligent agent sequential decision-making model for bridge maintenance decisions is established. The model utilizes dynamic programming algorithms to determine the optimal maintenance and repair measures for elements in various states. The decision results are precise, all the way down to the specific bridge elements and maintenance measures for individual defects. In using the case of the regular inspection data of 222 bridges along a highway loop, this study further validates the effectiveness of the proposed research methods. By constructing an intelligent agent sequential decision-making model for bridge element maintenance, the optimal maintenance measures for 21 bridge elements in different states are obtained, thereby significantly enhancing the efficiency of actual bridge maintenance and the practicality of decision results.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Modeling the Optimal Maintenance Strategy for Bridge Elements Based on Agent Sequential Decision Making

Xin,

Liang,

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Alternatively, the relative value of maintaining all of the turbines depending on the type of action required and external factors such as weather can be calculated as shown in Dawid, McMillan & Revie [25].…”

Section: Risk Aversion Factor and Value Functionmentioning

confidence: 99%

Decision Support Tool for Offshore Wind Farm Vessel Routing under Uncertainty

2018

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This paper for the first time captures the impact of uncertain maintenance action times on vessel routing for realistic offshore wind farm problems. A novel methodology is presented to incorporate uncertainties, e.g., on the expected maintenance duration, into the decision-making process. Users specify the extent to which these unknown elements impact the suggested vessel routing strategy. If uncertainties are present, the tool outputs multiple vessel routing policies with varying likelihoods of success. To demonstrate the tool's capabilities, two case studies were presented. Firstly, simulations based on synthetic data illustrate that in a scenario with uncertainties, the cost-optimal solution is not necessarily the best choice for operators. Including uncertainties when calculating the vessel routing policy led to a 14% increase in the number of wind turbines maintained at the end of the day. Secondly, the tool was applied to a real-life scenario based on an offshore wind farm in collaboration with a United Kingdom (UK) operator. The results showed that the assignment of vessels to turbines generated by the tool matched the policy chosen by wind farm operators. By producing a range of policies for consideration, this tool provided operators with a structured and transparent method to assess trade-offs and justify decisions.

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Time series semi-Markov decision process with variable costs for maintenance planning

Cited by 2 publications

References 15 publications

Modeling the Optimal Maintenance Strategy for Bridge Elements Based on Agent Sequential Decision Making

Modeling the Optimal Maintenance Strategy for Bridge Elements Based on Agent Sequential Decision Making

Decision Support Tool for Offshore Wind Farm Vessel Routing under Uncertainty

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