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Scheduling under Uncertainty: Optimizing against a Randomizing Adversary

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Approximation Algorithms for Combinatorial Optimization (APPROX 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1913))

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Abstract

Deterministic models for project schedulingand control suf- fer from the fact that they ssume complete inform tion and neglect random influences that occur during project execution. A typical con- sequence is the underestimation of the expected project duration and cost frequently observed in practice. To cope with these phenomena, we consider schedulingmodels in which processingtimes are random but precedence and resource constraints are fixed. Scheduling is done by poli- cies which consist of an an online process of decisions that are based on the observed past and the a priori knowledge of the distribution of pro- cessing times. We give an informal survey on different classes of policies and show that suitable combinatorial properties of such policies give in- sights into optimality, comput tional methods, and their approximation behavior. In particular, we present recent constant-factor approximation algorithms for simple policies in machine scheduling that are based on suitable polyhedral relaxation of the performance space of policies.

Supported by Deutsche Forschungsgemeinschaft under grant Mo 346/3-3 and by German Israeli Foundation under grant I-564-246.06/97.

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Author information

Authors and Affiliations

  1. Technische Universität Berlin, 10623, Berlin, Germany

    Rolf H. Möhring

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  1. Rolf H. Möhring
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Editors and Affiliations

  1. Christian-Albrechts-Universität zu Kiel Institut für Informatik und praktische Mathematik Olshausenstr, 40, 24098, Kiel, Germany

    Klaus Jansen

  2. Computer Science Department, University of Maryland, A.V. Williams Bldg College Park, MD 20742, USA

    Samir Khuller

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Möhring, R.H. (2000). Scheduling under Uncertainty: Optimizing against a Randomizing Adversary. In: Jansen, K., Khuller, S. (eds) Approximation Algorithms for Combinatorial Optimization. APPROX 2000. Lecture Notes in Computer Science, vol 1913. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44436-X_3

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  • DOI: https://doi.org/10.1007/3-540-44436-X_3

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