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Optimization of Temporal Networks under Uncertainty

  • Wolfram Wiesemann

Part of the Advances in Computational Management Science book series (AICM, volume 11)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Wolfram Wiesemann
    Pages 1-8
  3. Wolfram Wiesemann
    Pages 9-21
  4. Wolfram Wiesemann
    Pages 23-52
  5. Wolfram Wiesemann
    Pages 71-103
  6. Wolfram Wiesemann
    Pages 105-148
  7. Back Matter
    Pages 149-159

About this book

Introduction

Many decision problems in Operations Research are defined on temporal networks, that is, workflows of time-consuming tasks whose processing order is constrained by precedence relations. For example, temporal networks are used to model projects, computer applications, digital circuits and production processes.

Optimization problems arise in temporal networks when a decision maker wishes to determine a temporal arrangement of the tasks and/or a resource assignment that optimizes some network characteristic (e.g. the time required to complete all tasks). The parameters of these optimization problems (e.g. the task durations) are typically unknown at the time the decision problem arises.

This monograph investigates solution techniques for optimization problems in temporal networks that explicitly account for this parameter uncertainty. We study several formulations, each of which requires different information about the uncertain problem parameters.

Keywords

Decision-making under uncertainty Operations research and management science Temporal networks

Authors and affiliations

  • Wolfram Wiesemann
    • 1
  1. 1., Department of ComputingImperial College LondonLondonUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-23427-9
  • Copyright Information Springer-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Business and Economics
  • Print ISBN 978-3-642-23426-2
  • Online ISBN 978-3-642-23427-9
  • Series Print ISSN 1388-4301
  • Buy this book on publisher's site
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