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Five pitfalls of empirical scheduling research

  • J. Christopher Beck
  • Andrew J. Davenport
  • Mark S. Fox
Session 6
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1330)

Abstract

A number of pitfalls of empirical scheduling research are illustrated using real experimental data. These pitfalls, in general, serve to slow the progress of scheduling research by obsfucating results, blurring comparisons among scheduling algorithms and algorithm components, and complicating validation of work in the literature. In particular, we look at difficulties brought about by viewing algorithms in a monolithic fashion, by concentrating on CPU time as the only evaluation criteria, by failing to prepare for gathering of a variety of search statistics at the time of experimental design, by concentrating on benchmarks to the exclusion of other sources of experimental problems, and, more broadly, by a preoccupation with optimization of makespan as the sole goal of scheduling algorithms.

Keywords

Schedule Problem Schedule Algorithm Constraint Satisfaction Search Statistic Constraint Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • J. Christopher Beck
    • 1
  • Andrew J. Davenport
    • 2
  • Mark S. Fox
    • 1
    • 2
  1. 1.Department of Computer ScienceUniversity of TorontoTorontoCanada
  2. 2.Department of Industrial EngineeringUniversity of TorontoTorontoCanada

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