Improving the performance of enumerative search methods-I. Exploiting structure and intelligence

Improving the performance of enumerative search methods-I. Exploiting structure and intelligence

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Article ID: iaor19951836
Country: United Kingdom
Volume: 22
Issue: 6
Start Page Number: 605
End Page Number: 613
Publication Date: Jul 1995
Journal: Computers and Operations Research
Authors: ,
Keywords: programming: branch and bound
Abstract:

Generally, branch and bound algorithms typically use mechanistic search strategies and generally do not fully exploit ‘local’ information inherent in problem structures; i.e. specific problem-domain knowledge. Incorporating intelligence in branch and bound algorithms has been suggested by Glover, but not studied in a rigorous experimental framework. The authors use the mean tardiness job sequencing problem to explore these issues. This paper is divided into two Parts. In Part I, the authors provide the intuitive motivation for this investigation and an experimental framework. In Part II, they present detailed computational results and statistical analysis. The results indicate that branch and bound algorithms can be enhanced significantly by exploiting local knowledge of problem structure and more judicious search strategies. Note: This is Part I of a two part article. The second part will appear in Volume 22 No. 9.

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