The availability of today’s online information systems rapidly increases the relevance of dynamic decision making within a large number of operational contexts. Whenever a sequence of interdependent decisions occurs, making a single decision raises the need for anticipation of its future impact on the entire decision process. Anticipatory support is needed for a broad variety of dynamic and stochastic decision problems from different operational contexts such as finance, energy management, manufacturing and transportation. Example problems include asset allocation, feed-in of electricity produced by wind power as well as scheduling and routing. All these problems entail a sequence of decisions contributing to an overall goal and taking place in the course of a certain period of time. Each of the decisions is derived by solution of an optimization problem. As a consequence a stochastic and dynamic decision problem resolves into a series of optimization problems to be formulated and solved by anticipation of the remaining decision process. However, actually solving a dynamic decision problem by means of approximate dynamic programming still is a major scientific challenge. Most of the work done so far is devoted to problems allowing for formulation of the underlying optimization problems as linear programs. Problem domains like scheduling and routing, where linear programming typically does not produce a significant benefit for problem solving, have not been considered so far. Therefore, the industry demand for dynamic scheduling and routing is still predominantly satisfied by purely heuristic approaches to anticipatory decision making. Although this may work well for certain dynamic decision problems, these approaches lack transferability of findings to other, related problems. This book has serves two major purposes: ‐ It provides a comprehensive and unique view of anticipatory optimization for dynamic decision making. It fully integrates Markov decision processes, dynamic programming, data mining and optimization and introduces a new perspective on approximate dynamic programming. Moreover, the book identifies different degrees of anticipation, enabling an assessment of specific approaches to dynamic decision making. ‐ It shows for the first time how to successfully solve a dynamic vehicle routing problem by approximate dynamic programming. It elaborates on every building block required for this kind of approach to dynamic vehicle routing. Thereby the book has a pioneering character and is intended to provide a footing for the dynamic vehicle routing community.

In Dynamic Decision Making in Chess former World Championship Challenger Boris Gelfand continues his investigation into decision-making at the top level, discussing some of his best games as well as his worst slips, giving the reader a unique insight into the mind of a world-class grandmaster.

A comprehensive analysis of the macroeconomic and financial forces altering the economic landscape Financial decision-making requires one to anticipate how their decision will not only affect their business, but also the economic environment. Unfortunately, all too often, both private and public sector decision-makers view their decisions as one-off responses and fail to see their decisions within the context of an evolving decision-making framework. In Decision-Making in a Dynamic Economic Setting, John Silvia, Chief Economist of Wells Fargo and one of the top 5 economic forecasters according to Bloomberg News and USA Today, skillfully puts this discipline in perspective. Details realistic, decision-making approaches and applications under a broad set of economic scenarios Analyzes monetary policy and addresses the impact of financial regulations Examines business cycles and how to identify economic trends, how to deal with uncertainty and manage risk, the building blocks of growth, and strategies for innovation Decision-Making in a Dynamic Economic Setting details the real-world application of economic principles and financial strategy in making better business decisions.

The insights offered in this book are intended to guarantee the reader a more successful career. It is written especially for managers and executives whose jobs require managing people successfully, but it is also written for anyone who must make decisions that involve other people. The authors discuss the decision styles and habits that people form and how to change decision-making habits where necessary. The models and techniques for decision making presented here have been used throughout the world in all kinds of businesses and government agencies. Decision style concepts can benefit anyone, from a new management trainee or MBA student to the CEO of a large firm.

This volume collects research papers addressing topical issues in economics and management with a particular focus on dynamic models which allow to analyze and foster the decision making of firms in dynamic complex environments. The scope of the contributions ranges from daily operational challenges firms face to strategic choices in dynamic industry environments and the analysis of optimal growth paths. The volume also highlights recent methodological developments in the areas of dynamic optimization, dynamic games and meta-heuristics, which help to improve our understanding of (optimal) decision making in a fast evolving economy.

This book will help you understand how to integrate data-based decisions into the daily work of the school. It is a practical and relevant handbook for converting data into wise decision-making and planning. It will give you the skills to successfully make data-based decisions, measure student learning and program effectiveness, evaluate student progress, use data to improve instruction, integrate a "Dynamic Planning" process into the daily operation of your school.

This book presents TDF (Tactics Development Framework), a practical methodology for eliciting and engineering models of expert decision-making in dynamic domains. The authors apply the BDI (Beliefs, Desires, Intentions) paradigm to the elicitation and modelling of dynamic decision making expertise, including team behaviour, and map it to a diagrammatic representation that is intuitive to domain experts. The book will be of value to researchers and practitioners engaged in dynamic decision making.