Seminar series in Probability and Statistics

We are happy to announce that our seminar series is back to its physical, on-campus format.

The Seminar Series in Probability and Statistics is a lecture series of invited speakers in the fields of probability, statistics, and related fields, organized every second week. The talks are in seminar format, aimed at a broad audience ranging from graduate students of probability and statistics to advanced researchers of these and related fields. The seminar usually lasts an hour with roughly 35-45 minutes of presentation and the remaining time spent on questions and discussion. This year we introduce an informal gathering immediately after the seminar, with some drinks and snacks/cookies triggering further discussion.

PhD students from EEMCS will receive 1 Graduate School credit from the Graduate School for attending 6 seminars of the series.

27 mei 2024 15:45 t/m 16:45

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

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.

When:

Mondays from 4pm to 5 pm
(unless otherwise specified)

Organizers