arXiv subjects

Peter Verleijsdonk

Publications and source records attributed to Peter Verleijsdonk.

3 recordsLinked to original sources

arXiv2026

Maintenance Optimization for Asset Networks with Unknown Degradation Parameters

We consider the key practical challenge of multi-asset maintenance optimization in settings where degradation parameters are heterogeneous and unknown, and must be inferred from degradation data. To address this, we propose scalable methods suitable for complex asset networks. Degradation is modeled as a stochastic shock process, and real-time data are continuously incorporated into estimation of shock rates and magnitudes via a Bayesian framework. This constitutes a partially observable Markov decision process formulation, from which we analytically derive monotonic policy structures. Moreover, we propose an open-loop feedback approach that enables policies trained via deep reinforcement learning (DRL) in a simulation environment with access to the true parameters to remain effective when deployed with real-time Bayesian point estimates instead. Complementing this, we develop a Bayesian Markov decision process (BMDP) framework wherein the agent maintains and updates posterior distributions during deployment. This formulation captures the evolution of parameter uncertainty over time, thereby facilitating the training of scalable DRL-based policies that adapt as additional data become available. We validate our approach through experiments on synthetic asset networks and a real-world case involving interventional X-ray system filaments. We find that the proposed DRL methods consistently outperform traditional heuristics across various scenarios. The policies trained for the BMDP perform well even when priors must be estimated from historical data, and remain effective in networks with high asset heterogeneity. Knowledge of true degradation parameters yields only marginal cost benefits, underscoring the ability of our approach to make effective decisions under limited information on degradation processes.

math.OC↗

arXiv2024

Scalable Policies for the Dynamic Traveling Multi-Maintainer Problem with Alerts

Downtime of industrial assets such as wind turbines and medical imaging devices is costly. To avoid such downtime costs, companies seek to initiate maintenance just before failure, which is challenging because: (i) Asset failures are notoriously difficult to predict, even in the presence of real-time monitoring devices which signal degradation; and (ii) Limited resources are available to serve a network of geographically dispersed assets. In this work, we study the dynamic traveling multi-maintainer problem with alerts ($K$-DTMPA) under perfect condition information with the objective to devise scalable solution approaches to maintain large networks with $K$ maintenance engineers. Since such large-scale $K$-DTMPA instances are computationally intractable, we propose an iterative deep reinforcement learning (DRL) algorithm optimizing long-term discounted maintenance costs. The efficiency of the DRL approach is vastly improved by a reformulation of the action space (which relies on the Markov structure of the underlying problem) and by choosing a smart, suitable initial solution. The initial solution is created by extending existing heuristics with a dispatching mechanism. These extensions further serve as compelling benchmarks for tailored instances. We demonstrate through extensive numerical experiments that DRL can solve single maintainer instances up to optimality, regardless of the chosen initial solution. Experiments with hospital networks containing up to $35$ assets show that the proposed DRL algorithm is scalable. Lastly, the trained policies are shown to be robust against network modifications such as removing an asset or an engineer or yield a suitable initial solution for the DRL approach.

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arXiv2022

Policies for the Dynamic Traveling Maintainer Problem with Alerts

Downtime of industrial assets such as wind turbines and medical imaging devices comes at a sharp cost. To avoid such downtime costs, companies seek to initiate maintenance just before failure. Unfortunately, this is challenging for the following two reasons: On the one hand, because asset failures are notoriously difficult to predict, even in the presence of real-time monitoring devices which signal early degradation. On the other hand, because the available resources to serve a network of geographically dispersed assets are typically limited. In this paper, we propose a novel dynamic traveling maintainer problem with alerts model that incorporates these two challenges and we provide three solution approaches on how to dispatch the limited resources. Namely, we propose: (i) Greedy heuristic approaches that rank assets on urgency, proximity and economic risk; (ii) A novel traveling maintainer heuristic approach that optimizes short-term costs; and (iii) A deep reinforcement learning (DRL) approach that optimizes long-term costs. Each approach has different requirements concerning the available alert information. Experiments with small asset networks show that all methods can approximate the optimal policy when given access to complete condition information. For larger networks, the proposed methods yield competitive policies, with DRL consistently achieving the lowest costs.

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