Foundations and Methods of Stochastic Simulation: A First Course

Author:   Barry L. Nelson ,  Linda Pei
Publisher:   Springer Nature Switzerland AG
Edition:   2nd ed. 2021
Volume:   316
ISBN:  

9783030861964


Pages:   313
Publication Date:   12 November 2022
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Foundations and Methods of Stochastic Simulation: A First Course


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Author:   Barry L. Nelson ,  Linda Pei
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   2nd ed. 2021
Volume:   316
Weight:   0.510kg
ISBN:  

9783030861964


ISBN 10:   3030861961
Pages:   313
Publication Date:   12 November 2022
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Chapter 1: Why Do We Simulate.- Chapter 2: Simulation Programming: Quick Start.- Chapter 3: Examples.- Chapter 4: Simulation Programming with PythonSim.- Chapter 5: Three Views of Simulation.- Chapter 6: Simulation Input.- Chapter 7: Simulation Output.- Chapter 8: Experiment Design and Analysis.- Chapter 9: Simulation Optimization and Sensitivity.- Chapter 10: Simulation for Research.- References.- Index.

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Author Information

Barry L. Nelson is the Walter P. Murphy Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University, US. His research expertise is in the design and analysis of computer simulation experiments on models of stochastic systems, focusing particularly on statistical efficiency and simulation optimization. His application domains include computer-performance modelling, manufacturing systems, financial engineering and transportation. He is a Fellow of INFORMS and IISE.  Linda Pei is a senior Ph.D. student in the Department of Industrial Engineering and Management Sciences at Northwestern University, US. Her research interests are simulation optimization and data science. She designed and developed Python programs for large-scale parallel simulation optimization and was named the Outstanding Teaching Assistant in the department.

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