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Статті в журналах з теми "Chance Constraint Optimization"
Doerr, Benjamin, Carola Doerr, Aneta Neumann, Frank Neumann, and Andrew Sutton. "Optimization of Chance-Constrained Submodular Functions." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (April 3, 2020): 1460–67. http://dx.doi.org/10.1609/aaai.v34i02.5504.
Повний текст джерелаAtta Mills, Yu, and Zeng. "Satisfying Bank Capital Requirements: A Robustness Approach in a Modified Roy Safety-First Framework." Mathematics 7, no. 7 (July 1, 2019): 593. http://dx.doi.org/10.3390/math7070593.
Повний текст джерелаHäussling Löwgren, Bartolomeus, Joris Weigert, Erik Esche, and Jens-Uwe Repke. "Uncertainty Analysis for Data-Driven Chance-Constrained Optimization." Sustainability 12, no. 6 (March 20, 2020): 2450. http://dx.doi.org/10.3390/su12062450.
Повний текст джерелаLi, Hang, Zhe Zhang, Xianggen Yin, and Buhan Zhang. "Preventive Security-Constrained Optimal Power Flow with Probabilistic Guarantees." Energies 13, no. 9 (May 8, 2020): 2344. http://dx.doi.org/10.3390/en13092344.
Повний текст джерелаWu, Xinyu, Xilong Cheng, Meng Zhao, Chuntian Cheng, and Qilin Ying. "Multi-Level Dependent-Chance Model for Hydropower Reservoir Operations." Energies 15, no. 13 (July 4, 2022): 4899. http://dx.doi.org/10.3390/en15134899.
Повний текст джерелаAlshammari, G. A., F. A. Alshammari, T. Guesmi, B. M. Alshammari, A. S. Alshammari, and N. A. Alshammari. "A New Particle Swarm Optimization Based Strategy for the Economic Emission Dispatch Problem Including Wind Energy Sources." Engineering, Technology & Applied Science Research 11, no. 5 (October 12, 2021): 7585–90. http://dx.doi.org/10.48084/etasr.4279.
Повний текст джерелаMa, Litao, Jiqiang Chen, Sitian Qin, Lina Zhang, and Feng Zhang. "An Efficient Neurodynamic Approach to Fuzzy Chance-constrained Programming." International Journal on Artificial Intelligence Tools 30, no. 01 (January 29, 2021): 2140001. http://dx.doi.org/10.1142/s0218213021400017.
Повний текст джерелаLiu, Zhixin, Panpan Wang, Yuanqing Xia, Hongjiu Yang, and Xinping Guan. "Chance-constraint optimization of power control in cognitive radio networks." Peer-to-Peer Networking and Applications 9, no. 1 (December 18, 2014): 245–53. http://dx.doi.org/10.1007/s12083-014-0325-8.
Повний текст джерелаWei, Dongyuan, Yue Wang, Xinchao Li, and Shan Lu. "A Closed-Loop Assembly Network Optimization Based on Chance Constraint with Robust Approximation." Journal of Physics: Conference Series 2203, no. 1 (February 1, 2022): 012060. http://dx.doi.org/10.1088/1742-6596/2203/1/012060.
Повний текст джерелаKong, Xiangyu, Siqiong Zhang, Bowei Sun, Qun Yang, Shupeng Li, and Shijian Zhu. "Research on Home Energy Management Method for Demand Response Based on Chance-Constrained Programming." Energies 13, no. 11 (June 1, 2020): 2790. http://dx.doi.org/10.3390/en13112790.
Повний текст джерелаДисертації з теми "Chance Constraint Optimization"
Koker, Ezgi. "Chance Constrained Optimization Of Booster Disinfection In Water Distribution Networks." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613640/index.pdf.
Повний текст джерелаSassi, Achille. "Numerical methods for hybrid control and chance-constrained optimization problems." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLY005/document.
Повний текст джерелаThis thesis is devoted to the analysis of numerical methods in the field of optimal control, and it is composed of two parts. The first part is dedicated to new results on the subject of numerical methods for the optimal control of hybrid systems, controlled by measurable functions and discontinuous jumps in the state variable simultaneously. The second part focuses on a particular application of trajectory optimization problems for space launchers. Here we use some nonlinear optimization methods combined with non-parametric statistics techniques. This kind of problems belongs to the family of stochastic optimization problems and it features the minimization of a cost function in the presence of a constraint which needs to be satisfied within a desired probability threshold
Helmberg, Christoph, Sebastian Richter, and Dominic Schupke. "A Chance Constraint Model for Multi-Failure Resilience in Communication Networks." Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-175454.
Повний текст джерелаCalfa, Bruno Abreu. "Data Analytics Methods for Enterprise-wide Optimization Under Uncertainty." Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/575.
Повний текст джерелаLiu, Jianzhe. "On Control and Optimization of DC Microgrids." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1512049527948171.
Повний текст джерелаDai, Siyu S. M. Massachusetts Institute of Technology. "Probabilistic motion planning and optimization incorporating chance constraints." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120230.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (pages 201-208).
For high-dimensional robots, motion planning is still a challenging problem, especially for manipulators mounted to underwater vehicles or human support robots where uncertainties and risks of plan failure can have severe impact. However, existing risk-aware planners mostly focus on low-dimensional planning tasks, meanwhile planners that can account for uncertainties and react fast in high degree-of-freedom (DOF) robot planning tasks are lacking. In this thesis, a risk-aware motion planning and execution system called Probabilistic Chekov (p-Chekov) is introduced, which includes a deterministic stage and a risk-aware stage. A systematic set of experiments on existing motion planners as well as p-Chekov is also presented. The deterministic stage of p-Chekov leverages the recent advances in obstacle-aware trajectory optimization to improve the original tube-based-roadmap Chekov planner. Through experiments in 4 common application scenarios with 5000 test cases each, we show that using sampling-based planners alone on high DOF robots can not achieve a high enough reaction speed, whereas the popular trajectory optimizer TrajOpt with naive straight-line seed trajectories has very high collision rate despite its high planning speed. To the best of our knowledge, this is the first work that presents such a systematic and comprehensive evaluation of state-of-the-art motion planners, which are based on a significant amount of experiments. We then combine different stand-alone planners with trajectory optimization. The results show that the deterministic planning part of p-Chekov, which combines a roadmap approach that caches the all pair shortest paths solutions and an online obstacle-aware trajectory optimizer, provides superior performance over other standard sampling-based planners' combinations. Simulation results show that, in typical real-life applications, this "roadmap + TrajOpt" approach takes about 1 s to plan and the failure rate of its solutions is under 1%. The risk-aware stage of p-Chekov accounts for chance constraints through state probability distribution and collision probability estimation. Based on the deterministic Chekov planner, p-Chekov incorporates a linear-quadratic Gaussian motion planning (LQG-MP) approach into robot state probability distribution estimation, applies quadrature-sampling theories to collision risk estimation, and adapts risk allocation approaches for chance constraint satisfaction. It overcomes existing risk-aware planners' limitation in real-time motion planning tasks with high-DOF robots in 3- dimensional non-convex environments. The experimental results in this thesis show that this new risk-aware motion planning and execution system can effectively reduce collision risk and satisfy chance constraints in typical real-world planning scenarios for high-DOF robots. This thesis makes the following three main contributions: (1) a systematic evaluation of several state-of-the-art motion planners in realistic planning scenarios, including popular sampling-based motion planners and trajectory optimization type motion planners, (2) the establishment of a "roadmap + TrajOpt" deterministic motion planning system that shows superior performance in many practical planning tasks in terms of solution feasibility, optimality and reaction time, and (3) the development of a risk-aware motion planning and execution system that can handle high-DOF robotic planning tasks in 3-dimensional non-convex environments.
by Siyu Dai.
S.M.
Sun, Yufei. "Chance-constrained optimization & optimal control problems." Thesis, Curtin University, 2015. http://hdl.handle.net/20.500.11937/183.
Повний текст джерелаYang, Yi. "Sequential convex approximations of chance constrained programming /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?IELM%202008%20YANG.
Повний текст джерелаArellano-Garcia, Harvey. "Chance constrained optimization of process systems under uncertainty." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=982225652.
Повний текст джерелаLuedtke, James. "Integer Programming Approaches for Some Non-convex and Stochastic Optimization Problems." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19711.
Повний текст джерелаЧастини книг з теми "Chance Constraint Optimization"
Loucks, Daniel P. "Lagrangian Models." In International Series in Operations Research & Management Science, 135–41. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93986-1_11.
Повний текст джерелаZhao, Y. W., F. L. Huang, Z. F. Li, and Guo Xian Zhang. "Research in Method of Reliability Optimization Based-On Multi-Objective Fuzzy Matter-Element with Fuzzy Chance Constraint." In Advances in Machining & Manufacturing Technology VIII, 430–35. Stafa: Trans Tech Publications Ltd., 2006. http://dx.doi.org/10.4028/0-87849-999-7.430.
Повний текст джерелаAlanazi, Eisa. "Preference Constrained Optimization under Change." In Advances in Artificial Intelligence, 323–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38457-8_33.
Повний текст джерелаLoucks, Daniel P. "Chance Constrained and Monte Carlo Modeling." In International Series in Operations Research & Management Science, 177–85. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93986-1_14.
Повний текст джерелаChai, Runqi, Al Savvaris, Antonios Tsourdos, and Senchun Chai. "Stochastic Trajectory Optimization Problems with Chance Constraints." In Design of Trajectory Optimization Approach for Space Maneuver Vehicle Skip Entry Problems, 163–91. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9845-2_8.
Повний текст джерелаWets, Roger J.-B. "Stochastic Programs with Chance Constraints: Generalized Convexity and Approximation Issues." In Nonconvex Optimization and Its Applications, 61–74. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4613-3341-8_2.
Повний текст джерелаMayer, János. "On the Numerical Solution of Jointly Chance Constrained Problems." In Nonconvex Optimization and Its Applications, 220–35. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3150-7_12.
Повний текст джерелаXie, Weijun, and Shabbir Ahmed. "On the Quantile Cut Closure of Chance-Constrained Problems." In Integer Programming and Combinatorial Optimization, 398–409. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33461-5_33.
Повний текст джерелаDinh, Thai, Ricardo Fukasawa, and James Luedtke. "Exact Algorithms for the Chance-Constrained Vehicle Routing Problem." In Integer Programming and Combinatorial Optimization, 89–101. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33461-5_8.
Повний текст джерелаMedova, E. A., and J. E. Scott. "Management of Quality of Service through Chance-constraints in Multimedia Networks." In Nonconvex Optimization and Its Applications, 236–51. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3150-7_13.
Повний текст джерелаТези доповідей конференцій з теми "Chance Constraint Optimization"
Li, Yingxiong, and Xiangyang Li. "Chance Constraint Model for Unconventional Emergency Response." In 2011 Fourth International Joint Conference on Computational Sciences and Optimization (CSO). IEEE, 2011. http://dx.doi.org/10.1109/cso.2011.100.
Повний текст джерелаNeumann, Frank, and Carsten Witt. "Runtime Analysis of Single- and Multi-Objective Evolutionary Algorithms for Chance Constrained Optimization Problems with Normally Distributed Random Variables." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/665.
Повний текст джерелаNajafi, Soroush, Mohammad Mansour Lakouraj, Seyed Amin Sedgh, Hanif Livani, Mohammed Benidris, and M. S. Fadali. "Chance-Constraint Volt-VAR Optimization in PV-Penetrated Distribution Networks." In 2022 IEEE Kansas Power and Energy Conference (KPEC). IEEE, 2022. http://dx.doi.org/10.1109/kpec54747.2022.9814811.
Повний текст джерелаAlbey, Erinc, Reha Uzsoy, and Karl G. Kempf. "A chance constraint based multi-item production planning model using simulation optimization." In 2016 Winter Simulation Conference (WSC). IEEE, 2016. http://dx.doi.org/10.1109/wsc.2016.7822309.
Повний текст джерелаZhang, Peiyao, Ji Woong Kim, and Marin Kobilarov. "Towards Safer Retinal Surgery through Chance Constraint Optimization and Real-Time Geometry Estimation." In 2021 60th IEEE Conference on Decision and Control (CDC). IEEE, 2021. http://dx.doi.org/10.1109/cdc45484.2021.9683329.
Повний текст джерелаGong, Yanxue, Dao Huang, Enbo Wang, and Yigong Peng. "A Fuzzy Chance Constraint Programming Approach for Location-Allocation Problem under Uncertainty in a Closed-Loop Supply Chain." In 2009 International Joint Conference on Computational Sciences and Optimization, CSO. IEEE, 2009. http://dx.doi.org/10.1109/cso.2009.151.
Повний текст джерелаHalim, Nurfadhlina Abdul, Saiful Hafizah Jaaman, Noriszura Ismail, and Rokiah Ahmad. "Profit sharing ratio modeling for islamic hire-purchase contract: Robust optimization and chance constraint approach." In THE 5TH INTERNATIONAL CONFERENCE ON RESEARCH AND EDUCATION IN MATHEMATICS: ICREM5. AIP, 2012. http://dx.doi.org/10.1063/1.4724117.
Повний текст джерелаSirouspour, Shahin. "Optimal scheduling of a storage device in a grid-connected microgrid using stochastic chance-constraint optimization." In IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2016. http://dx.doi.org/10.1109/iecon.2016.7793476.
Повний текст джерелаCousin, A., J. Garnier, M. Guiton, and M. Zuniga. "Chance Constraint Optimization of a Complex System: Application to the Fatigue Design of a Floating Offshore Wind Turbine Mooring System." In 14th WCCM-ECCOMAS Congress. CIMNE, 2021. http://dx.doi.org/10.23967/wccm-eccomas.2020.082.
Повний текст джерелаDu, Xiaoping. "Reliability-Based Design Using Saddlepoint Approximation." In ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/detc2006-99077.
Повний текст джерелаЗвіти організацій з теми "Chance Constraint Optimization"
Singh, Bismark, and Jean-Paul Watson. Chance-Constrained Optimization for Critical Infrastructure Protection. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1474266.
Повний текст джерелаVincent, Charles, Saiful I. Ansari, and Mohammad Khodabakhshi. Joint chance-constrained reliability optimization with general form of distributions. CENTRUM Catolica Graduate Business School, January 2014. http://dx.doi.org/10.7835/ccwp-2014-01-0005.
Повний текст джерела