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AEGISi: Attribute Experimentation Guiding Improvement Searches Inline Framework

Author

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  • Michael Racer

    (Marketing & Supply Chain Management Department, University of Memphis, Memphis, TN, USA)

  • Robin Lovgren

    (Department of Mathematics and Computer Science, Belmont University, Nashville, TN, USA)

Abstract

The quality of a solution to an integer programming problem is a function of a number of elements. Lightly constrained problems are easier to solve than those with tighter constraints. Local search methods generally perform better than greedy methods. In the companion paper to this one, the authors investigated how peripheral information could be gathered and utilized to improve solving subsequent problems of the same type. In the current paper, they extend this to the dynamic environment – that is, utilizing such “peripheral” information as the solver is in progress, in order to determine how best to proceed.

Suggested Citation

  • Michael Racer & Robin Lovgren, 2016. "AEGISi: Attribute Experimentation Guiding Improvement Searches Inline Framework," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 7(2), pages 22-38, April.
    • Handle: RePEc:igg:joris0:v:7:y:2016:i:2:p:22-38
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