| Description |
xiii, 182 pages : illustrations ; 24 cm |
| Series |
Operations research/computer science interfaces series, 1387-666X ; v. 51 |
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Operations research/computer science interfaces series. 1387-666X ; v. 51
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| Contents |
Contents note continued: 3.4.Limited Effectiveness of Perfect Anticipation -- 4.Synergies of Optimization and Data Mining -- 4.1.Preliminaries -- 4.1.1.Common Foundations -- 4.1.2.Data Mining -- 4.1.3.Integration of Optimization and Data Mining -- 4.2.Efficient Data Mining -- 4.2.1.Optimized Preprocessing -- 4.2.2.Optimized Information Extraction -- 4.3.Effective Optimization -- 4.3.1.Decision Model Substitution -- 4.3.2.Decision Model Approximation -- 5.Approximate Anticipation -- 5.1.Approximate Value Functions -- 5.1.1.State Space Aggregation -- 5.1.2.Predictive Modeling -- 5.2.Stochastic Gradient Updates -- 5.2.1.Steepest Descent -- 5.2.2.Stepsize Rules -- 5.3.The Generalized Actor-Critic Framework -- 5.3.1.Regression Models -- 5.3.2.General Information Structures -- 6.Dynamic Vehicle Routing -- 6.1.Foundations -- 6.1.1.Vehicle Routing Background -- 6.1.2.Dynamic Vehicle Routing Problems -- 6.2.State of the Art -- 6.2.1.Conventional Non-reactive Anticipation -- |
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Contents note continued: 6.2.2.Probabilisitic Non-reactive Anticipation -- 6.2.3.Implicit Anticipation -- 6.2.4.Approximate Anticipation -- 6.3.Dynamic Routing of a Service Vehicle -- 6.3.1.Problem Formulation -- 6.3.2.Case Study -- 7.Anticipatory Routing of a Service Vehicle -- 7.1.Perfect Anticipation -- 7.1.1.State Sampling -- 7.1.2.Solution Properties -- 7.1.3.Limited Effectiveness -- 7.2.Approximate Anticipation -- 7.2.1.Value Function Approximation -- 7.2.2.Decision Model Identification -- 7.2.3.Decision Model Approximation -- 7.2.4.The Full Scope of the Approach -- 7.3.Non-reactive Anticipation -- 7.3.1.Probabilistic Approaches -- 7.3.2.Conventional Approaches -- 8.Computational Study -- 8.1.Experimental Setup -- 8.1.1.Problem Instances -- 8.1.2.Actor-Critic Configuration -- 8.2.Non-reactive Anticipation -- 8.2.1.Conventional Approaches -- 8.2.2.Probabilistic Approaches -- 8.3.Elementary Value Function Approximation -- 8.3.1.Solution Properties -- 8.3.2.Results -- |
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Contents note continued: 8.4.Fine-grained Value Function Approximation -- 8.4.1.Results and Solution Properties -- 8.4.2.Variations -- 9.Managerial Impact of Anticipatory Optimization -- 9.1.Technological Preconditions -- 9.2.Selecting a Degree of Anticipation -- 10.Conclusions |
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Machine generated contents note: 1.Introduction -- 1.1.Recent Economic Developments -- 1.2.Examples of Emerging Problems -- 1.3.Problem Similarities and Implications -- 1.4.Outline of the Following Chapters -- 2.Basic Concepts and Definitions -- 2.1.Dynamic Decision Making -- 2.1.1.A Basic Dynamic Decision Process -- 2.1.2.Markov Decision Processes -- 2.2.Optimization -- 2.2.1.Optimization Problems -- 2.2.2.Optimization Techniques -- 2.3.Anticipation -- 2.3.1.Anticipatory Decisions -- 2.3.2.Degrees of Anticipation -- 3.Perfect Anticipation -- 3.1.Dynamic Programming -- 3.1.1.Value Iteration -- 3.1.2.Policy Iteration -- 3.1.3.Modified Policy Iteration -- 3.1.4.Linear Programming -- 3.2.Forward Dynamic Programming -- 3.2.1.Asynchronous State Sampling -- 3.2.2.Monte Carlo Updates -- 3.2.3.Stochastic Approximation -- 3.2.4.The Actor-Critic Framework -- 3.3.Model Free Dynamic Programming -- 3.3.1.Q-Learning -- 3.3.2.Post-decision States -- |
| Summary |
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 |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Decision making -- Data processing.
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Decision making -- Mathematical models.
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Management science.
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Dynamic programming.
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Mathematical optimization.
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| LC no. |
2011931714 |
| ISBN |
1461405041 (cased) |
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9781461405047 (cased) |
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(e-book.) |
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