2011
DOI: 10.1007/978-1-4419-7572-0_11
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Stochastic Analysis in Location Research

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“…From the modeling point of view, the P pCP falls into the stochastic programming paradigm, where uncertain values are described through probability distributions (see, for instance, Albareda-Sambola et al, 2011;Huang et al, 2010) as opposite to the robust optimization approach, which attempts to optimize the worst-case system performance when uncertain data is only described using data ranges (e.g., Kouvelis and Yu, 1997;Puerto and Rodríguez-Chía, 2003;Espejo et al, 2015;Lu, 2013;Lu and Sheu, 2013). The P pCP also differs from other analyzed location problems where the centers are not restricted to be nodes of a network (see Berman et al, 2011).…”
Section: Introductionmentioning
confidence: 99%
“…From the modeling point of view, the P pCP falls into the stochastic programming paradigm, where uncertain values are described through probability distributions (see, for instance, Albareda-Sambola et al, 2011;Huang et al, 2010) as opposite to the robust optimization approach, which attempts to optimize the worst-case system performance when uncertain data is only described using data ranges (e.g., Kouvelis and Yu, 1997;Puerto and Rodríguez-Chía, 2003;Espejo et al, 2015;Lu, 2013;Lu and Sheu, 2013). The P pCP also differs from other analyzed location problems where the centers are not restricted to be nodes of a network (see Berman et al, 2011).…”
Section: Introductionmentioning
confidence: 99%