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Athanassios N. Avramidis
Researcher at University of Southampton
Publications - 40
Citations - 1463
Athanassios N. Avramidis is an academic researcher from University of Southampton. The author has contributed to research in topics: Estimator & Monte Carlo methods for option pricing. The author has an hindex of 18, co-authored 38 publications receiving 1365 citations. Previous affiliations of Athanassios N. Avramidis include Purdue University & Université de Montréal.
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Journal ArticleDOI
Modeling Daily Arrivals to a Telephone Call Center
TL;DR: Stochastic models of time-dependent arrivals are developed, with focus on the application to call centers, including the essential features of the arrival process, the goodness of fit of the estimated models, and the sensitivity of various simulated performance measures of the call center to the choice of arrival process model.
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The application of forecasting techniques to modeling emergency medical system calls in Calgary, Alberta
TL;DR: The objective is to offer simple and effective models that could be used for realistic simulation of the system and for forecasting daily and hourly call volumes.
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Optimizing daily agent scheduling in a multiskill call center
TL;DR: This work examines and compares simulation-based algorithms for solving the agent scheduling problem in a multiskill call center and proposes a solution approach that combines simulation with integer or linear programming, with cut generation.
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Markov chain models of a telephone call center with call blending
Alexandre Deslauriers,Pierre L'Ecuyer,Juta Pichitlamken,Armann Ingolfsson,Athanassios N. Avramidis +4 more
TL;DR: This work presents a collection of continuous-time Markov chain (CTMC) models which capture many real-world characteristics while maintaining parsimony that results in fast computation and discusses and explores the tradeoffs between model fidelity and efficacy.
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Efficient Monte Carlo and Quasi--Monte Carlo Option Pricing Under the Variance Gamma Model
TL;DR: This work develops and study efficient Monte Carlo algorithms for pricing path-dependent options with the variance gamma model, and combines the gamma bridge sampling with randomized quasi--Monte Carlo to reduce the variance and thus further improve the efficiency.