eBook Stochastic Simulation Optimization For Discrete Event Systems: Perturbation Analysis, Ordinal Optimization And Beyond, 1st Edition

  • Published By: World Scientific Publishing Company
  • ISBN-10: 9814513016
  • ISBN-13: 9789814513012
  • DDC: 003.83
  • Grade Level Range: College Freshman - College Senior
  • 276 Pages | eBook
  • Original Copyright 2013 | Published/Released January 2015
  • This publication's content originally published in print form: 2013
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Discrete event systems (DES) have become pervasive in our daily lives. Examples include (but are not restricted to) manufacturing and supply chains, transportation, healthcare, call centers, and financial engineering. However, due to their complexities that often involve millions or even billions of events with many variables and constraints, modeling these stochastic simulations has long been a hard nut to crack. The advance in available computer technology, especially of cluster and cloud computing, has paved the way for the realization of a number of stochastic simulation optimization for complex discrete event systems. This book will introduce two important techniques initially proposed and developed by Professor Y C Ho and his team; namely perturbation analysis and ordinal optimization for stochastic simulation optimization, and present the state-of-the-art technology, and their future research directions.

Table of Contents

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
1: Perturbation Analysis.
2: The IPA Calculus for Hybrid Systems.
3: Smoothed Perturbation Analysis: A Retrospective and Prospective Look.
4: Perturbation Analysis and Variance Reduction in Monte Carlo Simulation.
5: Adjoints and Averaging.
6: Infinitesimal Perturbation Analysis and Optimization Algorithms.
7: Simulation-based Optimization of Failure-prone Continuous Flow Lines.
8: Perturbation Analysis, Dynamic Programming, and Beyond.
9: Ordinal Optimization.
10: Fundamentals of Ordinal Optimization.
11: Optimal Computing Budget Allocation Framework.
12: Nested Partitions.
13: Applications of Ordinal Optimization.