B
Bahar Biller
Researcher at General Electric
Publications - 43
Citations - 680
Bahar Biller is an academic researcher from General Electric. The author has contributed to research in topics: Stochastic simulation & Independent and identically distributed random variables. The author has an hindex of 13, co-authored 41 publications receiving 628 citations. Previous affiliations of Bahar Biller include SAS Institute & Carnegie Mellon University.
Papers
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Proceedings ArticleDOI
Semiconductor manufacturing simulation design and analysis with limited data
Bahar Biller,Onur Dulgeroglu,Canan G. Corlu,Michael Hartig,Ronald J. Olson,Peter Micah Sandvik,Gerald Trant +6 more
TL;DR: Insight is provided on how an approach aimed to reflect learning from data can enable the authors' discrete-event stochastic simulation to accurately estimate the performance measures for SiC manufacturing at the PEMC facility.
Book ChapterDOI
Stochastic input model selection
Bahar Biller,Alp Akcay +1 more
TL;DR: This article addresses the key issues that arise in stochastic input modeling both in the presence and in the absence of historical data.
Journal ArticleDOI
Implementing Digital Twins That Learn: AI and Simulation Are at the Core
Bahar Biller,Stephan Biller +1 more
TL;DR: In this paper , the authors define process digital twins and their four foundational elements and discuss how key digital twin functions and enabling AI and simulation technologies integrate to describe, predict, and optimize supply chains for Industry 4.0 implementations.
Proceedings ArticleDOI
Inventory management under disruption risk
TL;DR: This work evaluates stocking decisions in the presence of operational disruptions caused by random events such as natural disasters or man-made disturbances, and applies data analytics to the simulation outputs to obtain insights to manage inventory under disruption risk.
Proceedings ArticleDOI
Demand fulfillment probability under parameter uncertainty
TL;DR: This work quantifies the variance of the demand fulfillment probability (i.e., the probability that all item demands will be satisfied from stock) that is due to demand parameter uncertainty using an asymptotic normality approximation and investigates the sensitivity of the variance to selected inventory model parameters.