Stochastic Simulation Optimization For Discrete Event Systems: Perturbation Analysis, Ordinal Optimization And Beyond
Perturbation Analysis, Ordinal Optimization, and Beyond
Chun-Hung Chen, Qing-Shan Jia, Loo Hay Lee
- 276 Seiten
- English
- ePUB (handyfreundlich)
- Über iOS und Android verfügbar
Stochastic Simulation Optimization For Discrete Event Systems: Perturbation Analysis, Ordinal Optimization And Beyond
Perturbation Analysis, Ordinal Optimization, and Beyond
Chun-Hung Chen, Qing-Shan Jia, Loo Hay Lee
Über dieses Buch
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.
Contents:
- Part I: Perturbation Analysis:
- The IPA Calculus for Hybrid Systems
- Smoothed Perturbation Analysis: A Retrospective and Prospective Look
- Perturbation Analysis and Variance Reduction in Monte Carlo Simulation
- Adjoints and Averaging
- Infinitesimal Perturbation Analysis and Optimization Algorithms
- Simulation-based Optimization of Failure-prone Continuous Flow Lines
- Perturbation Analysis, Dynamic Programming, and Beyond
- Part II: Ordinal Optimization:
- Fundamentals of Ordinal Optimization
- Optimal Computing Budget Allocation Framework
- Nested Partitions
- Applications of Ordinal Optimization
Readership: Professionals in industrial and systems engineering, graduate reference for probability & statistics, stochastic analysis and general computer science, and research.