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

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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. We use the mean tardiness job sequencing problem to explore these issues. This paper is divided into two Parts. In Part I, we provide the intuitive motivation for this investigation and an experimental framework. In Part II, we 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.

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Adam Fadlalla holds an M.B.A. from Miami University (Ohio), an M.Sc. in computer science, and Ph.D. in Information Systems from the University of Cincinnati. He is currently an Assistant Professor of Computer and Information Systems at Cleveland State University. His research interests include optimization and applications of artificial intelligence. He is an active business consultant in the Middle East.

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