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- W2896059094 abstract "Input data in linear programming(LP) problems might be uncertain according to practical requirements. Robust linear optimization (RLO) processes uncertain data belong to some uncertain sets before modeling and searches for the optimal solution of the worst case. Besides, in traditional LP post-optimal analysis, basis invariancy sensitivity analysis determines some values of model parameters for which a given optimal basis remain optimal. In this paper, the basis invariancy sensitivity analysis for RLO is presented. Perturbations which happen in the cost coefficients, the right-hand-side (RHS) coefficients, and the technology matrix are both considered. The invariancy regions for RLO are given, and it is shown that if the worst case belongs to the invariancy regions, the optimal basis of robust solution will keep invariant to the primal optimal basis. On the contrary, the optimal basis of robust solution will be different from the primal optimal basis if the worst case does not belong to the invariancy regions. Finally, a numeric testing example is given to illustrate the result." @default.
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- W2896059094 date "2018-07-01" @default.
- W2896059094 modified "2023-09-23" @default.
- W2896059094 title "Basis Invariancy Sensitivity Analysis for Robust Linear Optimization" @default.
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- W2896059094 doi "https://doi.org/10.23919/chicc.2018.8482796" @default.
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