Similar Search Results

Simulation algorithms for regenerative processes

Glynn, Peter W. - In: Simulation, (pp. 477-500). 2006

Persistent link: https://www.econbiz.de/10003588034

Limit Theory for Performance Modeling of Future Event Set Algorithms

Damerdji, Halim; Glynn, Peter W. - In: Management Science 44 (1998) 12-Part-1, pp. 1709-1722

In a discrete-event simulation, the information related to the events scheduled to occur in the future is kept in a data structure called the future event set (FES). In this paper, we study the interaction hold model, a popular stochastic model for FES performance analysis, corresponding to the...

Persistent link: https://www.econbiz.de/10009209109

Stochastic optimization by simulation : convergence proofs for the GI/G/1 queue in steady-state

L'Ecuyer, Pierre - In: Management science : journal of the Institute for … 40 (1994) 11, pp. 1562-1578

Persistent link: https://www.econbiz.de/10001176000

Asymptotic validity of batch means steady-state confidence intervals

Glynn, Peter W.; Lim, Eunji - In: Advancing the frontiers of simulation : a Festschrift …, (pp. 87-104). 2009

Persistent link: https://www.econbiz.de/10003918941

Zero-variance importance sampling estimators for Markov process expectations

Awad, Hernan P.; Glynn, Peter W.; Rubinstein, Reuven Y. - In: Mathematics of operations research 38 (2013) 2, pp. 358-388

Persistent link: https://www.econbiz.de/10009751518

Affine point processes : approximation and efficient simulation

Zhang, Xiaowei; Blanchet, Jose; Giesecke, Kay; Glynn, … - In: Mathematics of operations research 40 (2015) 4, pp. 797-819

Persistent link: https://www.econbiz.de/10011408901

Simulation-based prediction

Lim, Eunji; Glynn, Peter W. - In: Operations research 71 (2023) 1, pp. 47-60

Persistent link: https://www.econbiz.de/10014308246

Surgical scheduling via optimization and machine learning with long-tailed data

Shi, Yuan; Mahdian, Saied; Blanchet, Jose; Glynn, Peter W. - In: Health care management science : a new journal serving … 26 (2023) 4, pp. 692-718

Persistent link: https://www.econbiz.de/10015079823