Markov decision processes in practice
Author(s)
Bibliographic Information
Markov decision processes in practice
（International series in operations research & management science, v. 248）
Springer, c2017
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Includes bibliographical references
Description and Table of Contents
Description
This book presents classical Markov Decision Processes (MDP) for real-life applications and optimization. MDP allows users to develop and formally support approximate and simple decision rules, and this book showcases state-of-the-art applications in which MDP was key to the solution approach. The book is divided into six parts. Part 1 is devoted to the state-of-the-art theoretical foundation of MDP, including approximate methods such as policy improvement, successive approximation and infinite state spaces as well as an instructive chapter on Approximate Dynamic Programming. It then continues with five parts of specific and non-exhaustive application areas. Part 2 covers MDP healthcare applications, which includes different screening procedures, appointment scheduling, ambulance scheduling and blood management. Part 3 explores MDP modeling within transportation. This ranges from public to private transportation, from airports and traffic lights to car parking or charging your electric car . Part 4 contains three chapters that illustrates the structure of approximate policies for production or manufacturing structures. In Part 5, communications is highlighted as an important application area for MDP. It includes Gittins indices, down-to-earth call centers and wireless sensor networks. Finally Part 6 is dedicated to financial modeling, offering an instructive review to account for financial portfolios and derivatives under proportional transactional costs. The MDP applications in this book illustrate a variety of both standard and non-standard aspects of MDP modeling and its practical use. This book should appeal to readers for practitioning, academic research and educational purposes, with a background in, among others, operations research, mathematics, computer science, and industrial engineering.
Table of Contents
One-Step Improvement Ideas And Computational Aspects.- Value Function Approximation In Complex Queueing Systems.- Approximate Dynamic Programming By Practical Examples.- Server Optimization Of Infinite Queueing Systems.- Structures Of Optimal Policies In Mdps With Unbounded Jumps: The State Of Our Art.- Markov Decision Processes For Screening And Treatment Of Chronic Diseases.- Stratified Breast Cancer Follow-Up Using A Partially Observable MDP.- Advance Patient Appointment Scheduling.- Optimal Ambulance Dispatching.- Blood Platelet Inventory Management.- Stochastic Dynamic Programming For Noise Load Management.- Allocation In A Vertical Rotary Car Park.- Dynamic Control Of Traffic Lights.- Smart Charging Of Electric Vehicles.- Analysis Of A Stochastic Lot Scheduling Problem With Strict Due-Dates.- Optimal Fishery Policies.- Near-Optimal Switching Strategies For A Tandem Queue.- Wireless Channel Selection With Restless Bandits.- Flexible Staffing For Call Centers With Non-Stationary Arrival Rates.- MDP For Query-Based Wireless Sensor Networks.- Optimal Portfolios And Pricing Of Financial Derivatives Under Proportional Transaction Costs.
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