TITLE: Predictive Modeling of Vehicle Failures with Hierarchical Bayesian Methods for Workforce Planning<\/p>\n
Speaker: Do\u011fu\u015f Berk Ko\u00e7ak<\/p>\n
Advisor: Prof. Sava\u015f Dayan\u0131k<\/p>\n
Date & Time: July 23, 2025, Wednesday at 10:00<\/p>\n
Place: EA409<\/p>\n
ABSTRACT:
\nVehicles that operate under demanding conditions need an understanding of failures to ensure reliability and take appropriate actions. To address this, a statistical framework is developed for modeling failure times using real-world operational data. The approach employs Bayesian Generalized Linear Mixed Models to capture unit and vehicle-level effects and intervention effects. A sequential simulation framework models temporal dependencies and generates multi-step failure predictions with full uncertainty quantification. The proposed model and simulation approach are evaluated to demonstrate both calibration and predictive performance. Additionally, the work shows how predictive outputs can inform decision-making by deriving new system-level metrics and assessing their reliability. Finally, the results are applied in a representative sequential decision-making problem on workforce planning for repair actions.<\/p>\n
BIO:
\nDo\u011fu\u015f Berk Ko\u00e7ak received his B.S. degree from the Department of Industrial Engineering at Middle East Technical University in June 2022. He is currently pursuing an M.S. degree in the Department of Industrial Engineering at Bilkent University under the supervision of Prof. Sava\u015f Dayan\u0131k. His main research interests include Bayesian statistics, stochastic processes, and sequential decision-making problems, with various applications in operations research.<\/p>\n","protected":false},"excerpt":{"rendered":"
TITLE: Predictive Modeling of Vehicle Failures with Hierarchical Bayesian Methods for Workforce Planning Speaker: Do\u011fu\u015f Berk Ko\u00e7ak Advisor: Prof. Sava\u015f Dayan\u0131k Date & Time: July 23, 2025, Wednesday at 10:00 Place: EA409 ABSTRACT: Vehicles that operate under demanding conditions need an understanding of failures to ensure reliability and take appropriate [\u2026] <\/p>\n