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Simulation Modeling and Analysis

TLDR
The text is designed for a one-term or two-quarter course in simulation offered in departments of industrial engineering, business, computer science and operations research.
Abstract
From the Publisher: This second edition of Simulation Modeling and Analysis includes a chapter on "Simulation in Manufacturing Systems" and examples. The text is designed for a one-term or two-quarter course in simulation offered in departments of industrial engineering,business,computer science and operations research.

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Journal ArticleDOI

Two Issues in Setting Call Centre Staffing Levels

TL;DR: Motivated by a problem facing the Police Communication Centre in Auckland, New Zealand, the setting of staffing levels in a call centre with priority customers is considered, and a method for detecting and addressing such a problem is described.
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Scheduling with priority dispatching rules and drum-buffer-rope in a recoverable manufacturing system

TL;DR: It is found that simple priority dispatching rules, such as first-in, first-out rule or earliest due-date, work best to support drum-buffer-rope, but that other factors must be considered when using drum- buffers for scheduling in a remanufacturing environment with changing demand rates.
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A case study for assessing uncertainty in local-scale regulatory air quality modeling applications

TL;DR: In this paper, the authors used Monte Carlo statistical techniques to estimate variability and uncertainty in predicted hexavalent chromium concentrations generated by welding operations at a shipbuilding and repair facility in California.
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Non-Gaussian effects in microcontact

TL;DR: The two parameter Weibull distribution is proposed as a model for asperity heights in this paper, which is capable of accounting for skewness, either positive or negative, in the Asperity height distribution.
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Modeling patient arrivals in community clinics

TL;DR: Improved methods for modeling and simulating the streams of patients arriving at a community clinic are developed and a nonhomogeneous Poisson process is proposed to model the random patient arrivals.