Stochastic Modeling and Optimization With Applications in Queues, Finance, and Supply Chains

Stochastic Modeling and Optimization With Applications in Queues, Finance, and Supply Chains / edited by David D. Yao, Hanqin Zhang, Xun Yu Zhou.

The objective of this volume is to highlight through a collection of chap­ ters some of the recent research works in applied prob ability, specifically stochastic modeling and optimization. The volume is organized loosely into four parts. The first part is a col­ lection of several basic methodologi...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Yao, David D. (Editor), Zhang, Hanqin (Editor), Zhou, Xun Yu (Editor)
Format: eBook
Language:English
Published: New York, NY : Springer New York : Imprint: Springer, 2003.
Edition:1st ed. 2003.
Series:Springer eBook Collection.
Subjects:
Operations research.
Management science.
Decision making.
Economics, Mathematical .
Probabilities.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 1 Discrete-time Singularly Perturbed Markov Chains
  • 1.1 Singularly Perturbed Markov Chains
  • 1.2 Asymptotic Expansions
  • 1.3 Occupation Measures
  • 1.4 Nonstationary Markov Chains and Applications
  • 1.5 Notes and Remarks
  • 1.6 References
  • 2 Nearly Optimal Controls of Markovian Systems
  • 2.1 Singularly Perturbed MDP
  • 2.2 Hybrid LQG Control
  • 2.3 Conclusions
  • 2.4 References
  • 3 Stochastic Approximation, with Applications
  • 3.1 SA Algorithms
  • 3.2 General Convergence Theorems by TS Method
  • 3.3 Convergence Theorems Under State-Independent Conditions
  • 3.4 Applications
  • 3.5 Notes
  • 3.6 References
  • 4 Performance Potential Based Optimization and MDPs
  • 4.1 Sensitivity Analysis and Performance Potentials
  • 4.2 Markov Decision Processes
  • 4.3 Problems with Discounted Performance Criteria
  • 4.4 Single Sample Path Based Implementations
  • 4.5 Time Aggregation
  • 4.6 Connections to Perturbation Analysis
  • 4.7 Application Examples
  • 4.8 Notes
  • 4.9 References
  • 5 An Interior-Point Approach to Multi-Stage Stochastic Programming
  • 5.1 Two-Stage Stochastic Linear Programming
  • 5.2 A Case Study
  • 5.3 Multiple Stage Stochastic Programming
  • 5.4 An Interior Point Method
  • 5.5 Finding Search Directions
  • 5.6 Model Diagnosis
  • 5.7 Notes
  • 5.8 References
  • 6 A Brownian Model of Stochastic Processing Networks
  • 6.1 Preliminaries
  • 6.2 Stochastic Processing Network Model
  • 6.3 Examples of Stochastic Processing Networks
  • 6.4 Brownian Model for Stochastic Processing Network
  • 6.5 Brownian Approximation via Strong Approximation
  • 6.6 Notes
  • 6.7 Appendix: Strong Approximation vs. Heavy Traffic Approximation
  • 6.8 References
  • 7 Stability of General Processing Networks
  • 7.1 Motivating Simulations
  • 7.2 Open Processing Networks
  • 7.3 Network and Fluid Model Equations
  • 7.4 Connection between Artificial and Standard Fluid Models
  • 7.5 Examples of Stable Policies
  • 7.6 Extensions
  • 7.7 Appendix
  • 7.8 Notes
  • 7.9 References
  • 8 Large Deviations, Long-Range Dependence, and Queues
  • 8.1 Fractional Brownian Motion and a Related Filter
  • 8.2 Moderate Deviations for Sample-Path Processes
  • 8.3 MDP for the Filtered Process
  • 8.4 Queueing Applications: The Workload Process
  • 8.5 Verifying the Key Assumptions
  • 8.6 Notes
  • 8.7 References
  • 9 Markowitz’s World in Continuous Time, and Beyond
  • 9.1 The Mean-Variance Portfolio Selection Model
  • 9.2 A Stochastic LQ Control Approach
  • 9.3 Efficient Frontier: Deterministic Market Parameters
  • 9.4 Efficient Frontier: Random Adaptive Market Parameters
  • 9.5 Efficient Frontier: Markov-Modulated Market Parameters
  • 9.6 Efficient Frontier: No Short Selling
  • 9.7 Mean-Variance Hedging
  • 9.8 Notes
  • 9.9 References
  • 10 Variance Minimization in Stochastic Systems
  • 10.1 Variance Minimization Problem
  • 10.2 General Variance Minimization Problem
  • 10.3 Variance Minimization in Dynamic Portfolio Selection
  • 10.4 Variance Minimization in Dual Control
  • 10.5 Notes
  • 10.6 References
  • 11 A Markov Chain Method for Pricing Contingent Claims
  • 11.1 The Markov Chain Pricing Method
  • 11.2 The Black-Scholes (1973) Pricing Model
  • 11.3 The GARCH Pricing Model
  • 11.4 Valuing Exotic Options
  • 11.5 Appendix: The Conditional Expected Value of hT* and hT*2
  • 11.6 References
  • 12 Stochastic Network Models and Optimization of a Hospital System
  • 12.1 A Multi-Site Service Network Model
  • 12.2 Patient Flow Management
  • 12.3 Capacity Design
  • 12.4 Switching Costs and Quality of Service
  • 12.5 Insights and Future Research Directions
  • 12.6 Notes
  • 12.7 References
  • 13 Optimal Airline Booking Control with Cancellations
  • 13.1 Preliminaries
  • 13.2 The Minimum Acceptable Fare and Threshold Control
  • 13.3 Extensions of the Basic Model
  • 13.4 Numerical Experiments
  • 13.5 Notes
  • 13.6 References
  • 14 Information Revision and Decision Making in Supply Chain Management
  • 14.1 Industrial Examples
  • 14.2 A Multi-Period, Two-Decision Model
  • 14.3 A One-Period, Multi-Information Revision Model
  • 14.4 Applications
  • 14.5 Notes
  • 14.6 References
  • About the Contributors.

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