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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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Evaluating the design of a family practice healthcare clinic using discrete-event simulation.

TL;DR: A clinic effectiveness measure is introduced that captures several objectives within a health care clinic, including profitability and patient satisfaction, and recommendations are made for an appropriate staffing and facility size for a two physician family practice healthcare clinic.

Verification and Validation of Agent-based Scientific Simulation Models

TL;DR: This work designs, develops, adapt, and applies various verification and validation techniques to an agent-based scientific model, and investigates the sufficiency of these techniques for the validation of agent- based models.
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Batch Construction Heuristics and Storage Assignment Strategies for Walk/Rideand Pick Systems

TL;DR: The results indicate that the methods used for constructing batches of orders and for assigning storage space to individual items can significantly impact order retrieval efforts in warehouses of this type.
Journal ArticleDOI

Computer simulation to determine how rapid anesthetic recovery protocols to decrease the time for emergence or increase the phase I postanesthesia care unit bypass rate affect staffing of an ambulatory surgery center.

TL;DR: The results show potential decreases in ASCs' labor costs from technologies to decrease emergence times or increase the phase I bypass rates.
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Characterizations of generalized hyperexponential distribution functions

TL;DR: The generalized hyperexponential (GH) probability distribution as discussed by the authors is a generalization of the exponential probability distribution with the objective of preserving the computationally attractive feature of "memorylessness" possessed by the exponential distribution.