Foundations and Trends® in Technology, Information and Operations Management > Vol 16 > Issue 1-2

Sequential Decision Analytics and Modeling: Modeling with Python - Part II

By Warren B. Powell, Professor Emeritus, Princeton University, USA, wbpowell328@gmail.com

 
Suggested Citation
Warren B. Powell (2022), "Sequential Decision Analytics and Modeling: Modeling with Python - Part II", Foundations and Trends® in Technology, Information and Operations Management: Vol. 16: No. 1-2, pp 1-176. http://dx.doi.org/10.1561/0200000103-II

Publication Date: 21 Nov 2022
© 2022 W. B. Powell
 
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In this article:
1. Applications, Revisited
2. Energy Storage I
3. Energy Storage II
4. Supply Chain Management I: The Two-agent Newsvendor Problem
5. Supply Chain Management II: The Beer Game
6. Ad-click Optimization
7. Blood Management Problem
8. Optimizing Clinical Trials
Acknowledgements
References

Abstract

Sequential decision problems arise in virtually every human process, spanning finance, energy, transportation, health, e-commerce and supply chains. They include pure learning problems as might arise in laboratory (or field) experiments. It even covers search algorithms to maximize uncertain functions. An important dimension of every problem setting is the need to make decisions in the presence of different forms of uncertainty, and evolving information processes. This book uses a teach-by-example style to illustrate a modeling framework that can represent any sequential decision problem. A major challenge is, then, designing methods (called policies) for making decisions. We describe four classes of policies that are universal, in that they span any method that might be used, whether from the academic literature or heuristics used in practice. While this does not mean that we can immediately solve any problem, the framework helps us avoid the tendency in the academic literature of focusing on narrow classes of methods.

DOI:10.1561/0200000103-II
ISBN: 978-1-63828-082-8
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ISBN: 978-1-63828-083-5
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Table of contents:
1. Applications, Revisited
2. Energy Storage I
3. Energy Storage II
4. Supply Chain Management I: The Two-agent Newsvendor Problem
5. Supply Chain Management II: The Beer Game
6. Ad-click Optimization
7. Blood Management Problem
8. Optimizing Clinical Trials
Acknowledgements
References

Sequential Decision Analytics and Modeling: Modeling with Python

Sequential decision problems arise in virtually every human process. They span finance, energy, transportation, health, e-commerce, and supply chains and include pure learning problems that arise in laboratory or field experiments. They even cover search algorithms to maximize uncertain functions. An important dimension of every problem setting is the need to make decisions in the presence of different forms of uncertainty and evolving information processes.

Warren B. Powell’s work in sequential decision problems started in the 1980s and spanned rail, energy, health, finance, e-commerce, supply chain management, and even learning for materials science. His work on a wide range of problems highlighted the importance of using a variety of methods. In the process, he came to realize that any sequential decision problem can be modeled using a single universal framework that involves searching over methods for making decisions.

The goal of this book is to enable readers to understand how to approach, model and solve a sequential decision problem. To that end, it uses a teach-by-example style to illustrate a modeling framework that can represent any sequential decision problem. It tackles the challenge of designing methods, called policies, for making decisions and describes four classes of policies that are universal in that they span any method that might be used; whether from the academic literature or heuristics used in practice. While this does not mean that every problem can be solved immediately, the framework helps avoid the tendency in the academic literature of focusing on narrow classes of methods.

 
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