Results 131 to 140 of about 295,816 (187)

Integrated Design and Scheduling of Hydrogen Processes under Uncertainty: A Quantile Neural Network Approach. [PDF]

Ind Eng Chem Res
Ghilardi LMP, Patrón GD, Alcántara A, Tsay C.   +3 more
europepmc   +1 more source

Privacy-preserving data analysis using a memristor chip with colocated authentication and processing. [PDF]

Sci Adv
Liu Z, Wang Z, Ding C, Lin B, Tang J, Gao B, Wong N, Wu H.   +7 more
europepmc   +1 more source

Enhanced twin delayed DDPG with prioritized experience replay and Noisy Nets for regional economic dispatch. [PDF]

Sci Rep
Xu C, Hayashi N, Inuiguchi M, Raymond WJK, Mokhlis H, Illias HA.   +5 more
europepmc   +1 more source

Adaptive energy management in smart homes through fuzzy reinforcement learning and metaheuristic optimization algorithms to minimize costs. [PDF]

Sci Rep
Hamedani MMK, Jahangiri A, Mehri R, Shamim AG.   +3 more
europepmc   +1 more source

Optimizing Cold Food Supply Chains for Enhanced Food Availability Under Climate Variability. [PDF]

Foods
Hernandez-Cuellar D, Castillo-Villar KK, Castillo-Villar FR.   +2 more
europepmc   +1 more source

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Stochastic Offline Programming

2009 21st IEEE International Conference on Tools with Artificial Intelligence, 2009
We propose a framework which we call stochastic off-line programming (SOP). The idea is to embed the development of combinatorial algorithms in an off-line learning environment which helps the developer choose heuristic advisors that guide the search for satisfying or optimal solutions.
YURI MALITSKY, MEINOLF SELLMANN
openaire   +1 more source

Asymptotic Stochastic Programs

Mathematics of Operations Research, 1995
Consider a stochastic program with unique solution. By the notion of epi-convergence in distribution in local coordinates, we define the asymptotic stochastic program associated to it. Stochastic programs may be classified according to the type of the pertaining asymptotic program.
openaire   +1 more source

Stochastic Programming

Statistica Neerlandica, 1967
SummaryThis report presents an approach to stochastic programming. It treats mainly the difficulties arising in formulating the problem and the possibilities to derive a deterministic problem by which it can be replaced. In a sense the approach of this report unifies different viewpoints on stochastic programming problems as they have been published in
openaire   +1 more source

Discrete Stochastic Programming

Management Science, 1968
A method is presented for solving linear programming problems where (any number of) the functional, restraint, and input-output coefficients are subject to discrete; probability distributions. The objective function is formulated in terms of variance and/or expectation.
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Periodical Multistage Stochastic Programs

SIAM Journal on Optimization, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Alexander Shapiro, Lingquan Ding
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