[STAT/AP] Collin Drent: Condition-Based Production for Stochastically Deteriorating Systems: Optimal Policies and Learning

27 mei 2024 15:45 t/m 16:45 - Locatie: Lecture Hall G | Zet in mijn agenda

Production systems used in the manufacturing industry degrade due to production and may eventually break down, resulting in high maintenance costs at scheduled maintenance moments. This degradation behavior, and hence the system's reliability, is affected by the system's production rate. While producing at a higher rate generates more revenue, the system's reliability may also decrease. Production should thus be controlled dynamically to trade-off reliability and revenue accumulation in between maintenance moments. We study this dynamic trade-off for (i) systems where the relation between production and degradation is known as well as (ii) systems where this relation is not known and needs to be learned on-the-fly from condition data. For systems with a known production-degradation relation, we cast the decision problem as a continuous-time Markov decision process and prove that the optimal policy has intuitive monotonic properties. We also present sufficient conditions for the optimality of bang-bang policies and we characterize the structure of the optimal interval between scheduled maintenance moments. For systems with an a-priori unknown production-degradation relation, we propose a Bayesian procedure to learn the unknown degradation rate under any production policy from real-time condition data. Numerical studies indicate that on average across a wide range of practical settings (i) condition-based production increases profits by 50% compared to static production, (ii) integrating condition-based production and maintenance interval selection increases profits by 21% compared to a state-of-the-art approach, and (iii) our Bayesian approach performs close, especially in the bang-bang regime, to an Oracle policy that knows each system's production-degradation relation.