caa a22 4500 445839953 CHVBK 20180317145340.0 cr unu---uuuuu 170323e20111201xx s 000 0 eng 10.1007/s00186-011-0373-2 doi (NATIONALLICENCE)springer-10.1007/s00186-011-0373-2 Postoptimality for mean-risk stochastic mixed-integer programs and its application [Elektronische Daten] [Zhiping Chen, Feng Zhang, Li Yang] The mean-risk stochastic mixed-integer programs can better model complex decision problems under uncertainty than usual stochastic (integer) programming models. In order to derive theoretical results in a numerically tractable way, the contamination technique is adopted in this paper for the postoptimality analysis of the mean-risk models with respect to changes in the scenario set, here the risk is measured by the lower partial moment. We first study the continuity of the objective function and the differentiability, with respect to the parameter contained in the contaminated distribution, of the optimal value function of the mean-risk model when the recourse cost vector, the technology matrix and the right-hand side vector in the second stage problem are all random. The postoptimality conclusions of the model are then established. The obtained results are applied to two-stage stochastic mixed-integer programs with risk objectives where the objective function is nonlinear with respect to the probability distribution. The current postoptimality results for stochastic programs are improved. Springer-Verlag, 2011 Stochastic programming nationallicence Mixed-integer programming nationallicence Postoptimality nationallicence Contamination technique nationallicence Mean-risk model nationallicence Chen Zhiping Department of Scientific Computing and Applied Software, Faculty of Science, Xi'an Jiaotong University, 710049, Xi'an, Shaanxi, People's Republic of China aut Zhang Feng Department of Scientific Computing and Applied Software, Faculty of Science, Xi'an Jiaotong University, 710049, Xi'an, Shaanxi, People's Republic of China aut Yang Li Department of Scientific Computing and Applied Software, Faculty of Science, Xi'an Jiaotong University, 710049, Xi'an, Shaanxi, People's Republic of China aut Mathematical Methods of Operations Research Springer-Verlag 74/3(2011-12-01), 445-465 1432-2994 74:3<445 2011 74 186 https://doi.org/10.1007/s00186-011-0373-2 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00186-011-0373-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Chen Zhiping Department of Scientific Computing and Applied Software, Faculty of Science, Xi'an Jiaotong University, 710049, Xi'an, Shaanxi, People's Republic of China aut NATIONALLICENCE 700 1- Zhang Feng Department of Scientific Computing and Applied Software, Faculty of Science, Xi'an Jiaotong University, 710049, Xi'an, Shaanxi, People's Republic of China aut NATIONALLICENCE 700 1- Yang Li Department of Scientific Computing and Applied Software, Faculty of Science, Xi'an Jiaotong University, 710049, Xi'an, Shaanxi, People's Republic of China aut NATIONALLICENCE 773 0- Mathematical Methods of Operations Research Springer-Verlag 74/3(2011-12-01), 445-465 1432-2994 74:3<445 2011 74 186 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer