Suchergebnisse - Computational method stochastic programming

A novel multi-objective quantum particle swarm algorithm for suspension optimization von Grotti, Ewerton, Mizushima, Douglas Makoto, Backes, Artur Dieguez, de Freitas Awruch, Marcos Daniel, Gomes, Herbert Martins

“… In this paper, a novel multi-objective archive-based Quantum Particle Optimizer (MOQPSO) is proposed for solving suspension optimization problems. The …”
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Risk neutral and risk averse Stochastic Dual Dynamic Programming method von Shapiro, Alexander, Tekaya, Wajdi, da Costa, Joari Paulo, Soares, Murilo Pereira

“… ► Generic description of the Stochastic Dual Dynamic Programming (SDDP) method is given. ► Case studies related to operation planning of the Brazilian interconnected power system are presented …”
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Decomposition methods for multi-horizon stochastic programming von Zhang, Hongyu, Grossmann, Ignacio E., Tomasgard, Asgeir

“… Multi-horizon stochastic programming includes short-term and long-term uncertainty in investment planning problems more efficiently than traditional multi-stage stochastic programming …”
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Improving operating policies of large‐scale surface‐groundwater systems through stochastic programming von Macian‐Sorribes, H., Tilmant, A., Pulido‐Velazquez, M.

“… (with perfect foreknowledge of future inflows, which hinders their applicability to real‐life operations) or stochastic programming …”
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Quality evaluation of scenario-tree generation methods for solving stochastic programming problems von Keutchayan, Julien, Gendreau, Michel, Saucier, Antoine

“… This paper addresses the generation of scenario trees to solve stochastic programming problems that have a large number of possible values for the random parameters …”
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On level regularization with normal solutions in decomposition methods for multistage stochastic programming problems von van Ackooij, Wim, de Oliveira, Welington, Song, Yongjia

“… We consider well-known decomposition techniques for multistage stochastic programming and a new scheme based on normal solutions for stabilizing iterates during the solution process …”
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Sequential Monte Carlo optimization and statistical inference von Duan, Jin‐Chuan, Li, Shuping, Xu, Yaxian

“… Sequential Monte Carlo (SMC) is a powerful technique originally developed for particle filtering and Bayesian inference. As a generic optimizer for statistical …”
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Multicut Benders decomposition algorithm for process supply chain planning under uncertainty von You, Fengqi, Grossmann, Ignacio E.

“… In this paper, we present a multicut version of the Benders decomposition method for solving two-stage stochastic linear programming problems, including stochastic mixed-integer programs with only continuous recourse (two-stage) variables …”
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Mathematical programming methods for microgrid design and operations: a survey on deterministic and stochastic approaches von Wang, Guanglei, Hijazi, Hassan

“… Planning and operating a power grid is a nontrivial exercise due to conflicting objectives, nonlinear constraints and uncertainties at multiple decision …”
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Stochastic OD demand estimation using stochastic programming von Sun, Ran, Fan, Yueyue

“… The proposed two-stage stochastic programming-based method is flexible to incorporate various design principles and risk preferences and domain knowledge regarding travel behavioral and physical rules …”
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A multi-stage stochastic programming approach in master production scheduling von Körpeoğlu, Ersin, Yaman, Hande, Selim Aktürk, M.

“… We use a multi-stage stochastic programming approach in order to come up with the maximum expected profit given the demand scenarios …”
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Integral Reinforcement Learning-Based Stochastic Guaranteed Cost Control for Time-Varying Systems with Asymmetric Saturation Actuators von Liang, Yuling, Xie, Mengjia, Zhang, Juan, Ming, Zhongyang, Gao, Zhiyun

“… The purpose of introducing the MPCM is to simplify the computational complexity of stochastic dynamic programming (SDP) methods …”
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A scenario-based stochastic programming approach for the product configuration problem under uncertainties and carbon emission regulations von Li, Xiaohong, Yang, Dong, Hu, Mengqi

“… •Benders decomposition is proposed to solve stochastic mixed-integer programming model.•Computational results demonstrate the efficiency of suggested method …”
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Accelerated sample average approximation method for two-stage stochastic programming with binary first-stage variables von Mohammadi Bidhandi, Hadi, Patrick, Jonathan

“… This paper proposes an accelerated solution method to solve two-stage stochastic programming problems with binary variables in the first stage and continuous variables in the second stage …”
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Optimal Long-term Operation of Reservoir-river Systems under Hydrologic Uncertainties: Application of Interval Programming von Nikoo, Mohammad Reza, Karimi, Akbar, Kerachian, Reza

“… It is due to non-convex functions in water quality modeling and a large number of computational iterations required by most of stochastic programming methods …”
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Capacity planning in logistics corridors: Deep reinforcement learning for the dynamic stochastic temporal bin packing problem von Farahani, Amirreza, Genga, Laura, Schrotenboer, Albert H., Dijkman, Remco

“… In this context, we introduce the dynamic stochastic temporal bin packing problem. It models the assignment of individual containers to carriers …”
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A single cut proximal bundle method for stochastic convex composite optimization von Liang, Jiaming, Guigues, Vincent, Monteiro, Renato D. C.

“… To the best of our knowledge, this is the first proximal bundle method for stochastic programming able to deal with continuous distributions …”
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Multi-Period Active Distribution Network Planning Using Multi-Stage Stochastic Programming and Nested Decomposition by SDDIP von Ding, Tao, Qu, Ming, Huang, Can, Wang, Zekai, Du, Pengwei, Shahidehpour, Mohammad

“… A nested decomposition method is proposed which applies the stochastic dual dynamic integer programming (SDDIP …”
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Efficient operator‐splitting minimax algorithm for robust optimization von Liu, Jiulong, Zhu, Ya‐Nan, Zhang, Xiaoqun, Gao, Hao

“… Purpose This work will develop an efficient minimax robust optimization algorithm for improving plan quality and computational efficiency …”
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A Synthesizing Effect-Based Solution Method for Stochastic Rough Multi-objective Programming Problems von Zhou, Lei, Zhang, Guoshan, Li, Fachao

“… A new solution model, called stochastic rough multi-objective synthesis effect (MOSE) model, is developed to deal with a class of multiobjective programming problems with random rough coefficients …”
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