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On Accuracy, Robustness and Tolerances in Vector Boolean Optimization

Nikulin Yury, Karelkina Olga, Mäkelä Marko, On Accuracy, Robustness and Tolerances in Vector Boolean Optimization. European Journal of Operational Research 224, 449–457, 2013.

Abstract:

A Boolean programming problem with a finite number of alternatives where initial coefficients (costs) of linear payoff functions are subject to perturbations is considered. We define robust solution as a feasible solution which for a given set of realizations of uncertain parameters guarantees the minimum value of the worst-case relative regret among all feasible solutions. For the Pareto optimality principle, an appropriate definition of the worst-case relative regret is specified. It is shown that this definition is closely related to the concept of accuracy function being recently intensively studied in the literature. We also present the concept of robustness tolerances of a single cost vector. The tolerance is defined as the maximum level of perturbation of the cost vector which does not destroy the solution robustness. We present formulae allowing the calculation of the robustness tolerance obtained for some initial costs. The results are illustrated with several numerical examples.

BibTeX entry:

@ARTICLE{jNiKaMx13a,
  title = {On Accuracy, Robustness and Tolerances in Vector Boolean Optimization},
  author = {Nikulin Yury and Karelkina Olga and Mäkelä Marko},
  journal = {European Journal of Operational Research},
  volume = {224},
  pages = {449–457},
  year = {2013},
}

Belongs to TUCS Research Unit(s): Turku Optimization Group (TOpGroup)

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