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Stochastic programming

About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.


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Journal ArticleDOI
TL;DR: It turns out that a large VSS does not necessarily imply that the deterministic solution is useless for the stochastic setting, even when VSS is large, and very simple methods for studying structural similarities and differences are investigated.
Abstract: Stochastic programs are usually hard to solve when applied to real-world problems; a common approach is to consider the simpler deterministic program in which random parameters are replaced by their expected values, with a loss in terms of quality of the solution. The Value of the Stochastic Solution—VSS—is normally used to measure the importance of using a stochastic model. But what if VSS is large, or expected to be large, but we cannot solve the relevant stochastic program? Shall we just give up? In this paper we investigate very simple methods for studying structural similarities and differences between the stochastic solution and its deterministic counterpart. The aim of the methods is to find out, even when VSS is large, if the deterministic solution carries useful information for the stochastic case. It turns out that a large VSS does not necessarily imply that the deterministic solution is useless for the stochastic setting. Measures of the structure and upgradeability of the deterministic solution such as the loss using the skeleton solution and the loss of upgrading the deterministic solution will be introduced and basic inequalities in relation to the standard VSS are presented and tested on different cases.

97 citations

Journal ArticleDOI
TL;DR: Numerical experiments show that the proposed model is capable of efficiently and dynamically allocating berths and quay-cranes to calling containerships in real stochastic environments and reflects the risk preference of decision-maker.

97 citations

Journal ArticleDOI
TL;DR: In this paper, the minimization of stochastic functionals that are compositions of a nonsmooth convex function and a smooth function was studied, where the convex functions are composed of a convex and a stochastically weakly convex functal.
Abstract: We consider minimization of stochastic functionals that are compositions of a (potentially) nonsmooth convex function $h$ and smooth function $c$ and, more generally, stochastic weakly convex funct...

97 citations

Journal ArticleDOI
Yanpeng Cai, G.H. Huang, X.H. Nie, Y.P. Li, Q. Tan 
TL;DR: Highly uncertain information arising from simultaneous appearance of fuzziness and randomness for the lower and upper bounds of interval parameters can be effectively addressed through integrating chance constraint programming, interval linear programming, and fuzzy robust programming methods into a general optimization framework.
Abstract: A mixed interval parameter fuzzy-stochastic robust programming (MIFSRP) model is developed and applied to the planning of solid waste management systems under uncertainty The MIFSRP can explicitly address system uncertainties with multiple presentations It can be used as an extension of the existing interval-parameter fuzzy robust programming, interval-parameter linear programming, and chance constraint programming methods In this MIFSRP model, the hybrid uncertainties can be directly communicated into the optimization process and resulting solution through representing the uncertain parameters as interval numbers and fuzzy membership functions with random characteristics Highly uncertain information arising from simultaneous appearance of fuzziness and randomness for the lower and upper bounds of interval parameters can be effectively addressed through integrating chance constraint programming, interval linear programming, and fuzzy robust programming methods into a general optimization framework Th

97 citations

Book ChapterDOI
01 Apr 1989
TL;DR: Most systems that need to be controlled or analyzed involve some level of uncertainty about the value to assign to some of the parameters, if not about the actual layout of certain subcomponents of the system.
Abstract: Most systems that need to be controlled or analyzed involve some level of uncertainty about the value to assign to some of the parameters, if not about the actual layout of certain subcomponents of the system In many situations not much is lost by assigning “reasonable” values to these parameters or by choosing a particular design In other instances ignoring uncertainty may very well lead to totally misleading solutions that would invalidate any of the implications one may wish to draw from the analysis

97 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023175
2022423
2021526
2020598
2019578
2018532