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Avi Mandelbaum
Researcher at Technion – Israel Institute of Technology
Publications - 37
Citations - 3007
Avi Mandelbaum is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Queueing theory & Reflected Brownian motion. The author has an hindex of 22, co-authored 37 publications receiving 2901 citations. Previous affiliations of Avi Mandelbaum include Stanford University.
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From project to process management: an empirically-based framework for analyzing product development time
TL;DR: This study develops an empirically-based framework for analyzing development time in such contexts as a stochastic processing network in which engineering resources are "workstations" and projects are "jobs" that flow between the workstations.
Dimensioning large call centers
TL;DR: In this article, the authors develop a framework for asymptotic optimization of a queueing system, where a call center is modeled as an M/M/N queue, where the number of agents~$N$ is large.
Journal ArticleDOI
Dimensioning large call centers
TL;DR: A framework for asymptotic optimization of a queueing system based on the staffing problem of call centers with 100's of agents is developed, and the square-root safety staffing principle is revisited, which is a long-existing rule-of-thumb for staffing the M/M/N queue.
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Strong approximations for Markovian service networks
TL;DR: This work develops limit theorems for a large class of stochastic service network models where parameters like arrival and service rates, routing topologies for the network, and the number of servers at a given node are all functions of time as well as the current state of the system.
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Discrete flow networks: bottleneck analysis and fluid approximations
Hong Chen,Avi Mandelbaum +1 more
TL;DR: The analysis presupposes only the existence of long-run averages, and is based on a continuous fluid approximation to the network in terms of these averages, providing functional strong laws of large-numbers for stochastic Jackson queueing networks since they apply to their sample paths with probability one.