Relevant bibliographies by topics / Robust Counterpart Optimization

Academic literature on the topic 'Robust Counterpart Optimization'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Robust Counterpart Optimization"

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Shang, Ke, Zuren Feng, Liangjun Ke, and Felix T. S. Chan. "Comprehensive Pareto Efficiency in robust counterpart optimization." Computers & Chemical Engineering 94 (November 2016): 75–91. http://dx.doi.org/10.1016/j.compchemeng.2016.07.022.

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Li, Zukui, Ran Ding, and Christodoulos A. Floudas. "A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: I. Robust Linear Optimization and Robust Mixed Integer Linear Optimization." Industrial & Engineering Chemistry Research 50, no. 18 (September 21, 2011): 10567–603. http://dx.doi.org/10.1021/ie200150p.

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Cipta, Hendra, Saib Suwilo, Sutarman Sutarman, and Herman Mawengkang. "Improved Benders decomposition approach to complete robust optimization in box-interval." Bulletin of Electrical Engineering and Informatics 11, no. 5 (October 1, 2022): 2949–57. http://dx.doi.org/10.11591/eei.v11i5.4394.

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Robust optimization is based on the assumption that uncertain data has a convex set as well as a finite set termed uncertainty. The discussion starts with determining the robust counterpart, which is accomplished by assuming the indeterminate data set is in the form of boxes, intervals, box-intervals, ellipses, or polyhedra. In this study, the robust counterpart is characterized by a box-interval uncertainty set. Robust counterpart formulation is also associated with master and subproblems. Robust Benders decomposition is applied to address problems with convex goals and quasiconvex constraints in robust optimization. For all data parameters, this method is used to determine the best resilient solution in the feasible region. A manual example of this problem's calculation is provided, and the process is continued using production and operations management–quantitative methods (POM-QM) software.
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Chaerani, D., E. Rusyaman, Mahrudinda, A. Marcia, and A. Fridayana. "Adjustable robust counterpart optimization model for internet shopping online problem." Journal of Physics: Conference Series 1722 (January 2021): 012074. http://dx.doi.org/10.1088/1742-6596/1722/1/012074.

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Chaerani, D., E. Rusyaman, Mahrudinda, A. Marcia, and A. Fridayana. "Adjustable robust counterpart optimization model for internet shopping online problem." Journal of Physics: Conference Series 1722 (January 2021): 012074. http://dx.doi.org/10.1088/1742-6596/1722/1/012074.

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Sichau, Adrian, and Stefan Ulbrich. "A Second Order Approximation Technique for Robust Shape Optimization." Applied Mechanics and Materials 104 (September 2011): 13–22. http://dx.doi.org/10.4028/www.scientific.net/amm.104.13.

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We present a second order approximation for the robust counterpart of general uncertain nonlinear programs with state equation given by a partial di erential equation.We show how the approximated worst-case functions, which are the essential part of the approximated robust counterpart, can be formulated as trust-region problems that can be solved effciently using adjoint techniques. Further, we describe how the gradients of the worst-case functions can be computed analytically combining a sensitivity and an adjoint approach. This methodis applied to shape optimization in structural mechanics in order to obtain optimal solutions that are robust with respect to uncertainty in acting forces. Numerical results are presented.
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Mahrudinda, Mahrudinda, Diah Chaerani, and Endang Rusyaman. "Systematic literature review on adjustable robust counterpart for internet shopping optimization problem." International Journal of Data and Network Science 6, no. 2 (2022): 581–94. http://dx.doi.org/10.5267/j.ijdns.2021.11.006.

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Internet Shopping Optimization Problem (ISOP) is the application of optimization to online shopping activities of all complexity. The ISOP is useful for consumers in minimizing the cost of purchasing goods. This paper presents a bibliometric analysis of peer-reviewed papers based on ISOP topics by utilizing the R application program in the mapping. Overall, 101 papers (233 authors) in the Scopus database have used ISOP topics with research growth of 11.61% annually. The researcher presents a network of citations from productive authors, the impact of research, trends in terms that have been used, and shows a collaborative network of citations. Finally, the researcher presents the thematic analysis of the papers that apply the ISOP as a research topic and shows how the research forms clusters based on analytical solutions and numerical simulations that generate suggestions in finding the latest topics in the ISOP study. Another target for this paper is to produce review analysis results through Preferred Reporting Items for Systematic reviews and Meta Analyses (PRISMA). Through bibliometric and PRISMA analysis, it was found that the latest method in completing ISOP optimization is the ARC method. The ARC method in the ISOP is still little published among researchers in the world.
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Wang, Lei, and Hong Luo. "Robust Linear Programming with Norm Uncertainty." Journal of Applied Mathematics 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/209239.

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We consider the linear programming problem with uncertainty set described byp,w-norm. We suggest that the robust counterpart of this problem is equivalent to a computationally convex optimization problem. We provide probabilistic guarantees on the feasibility of an optimal robust solution when the uncertain coefficients obey independent and identically distributed normal distributions.
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Kerdkaew, Jutamas, Rabian Wangkeeree, and Rattanaporn Wangkeeree. "Global optimality conditions and duality theorems for robust optimal solutions of optimization problems with data uncertainty, using underestimators." Numerical Algebra, Control & Optimization 12, no. 1 (2022): 93. http://dx.doi.org/10.3934/naco.2021053.

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<p style='text-indent:20px;'>In this paper, a robust optimization problem, which features a maximum function of continuously differentiable functions as its objective function, is investigated. Some new conditions for a robust KKT point, which is a robust feasible solution that satisfies the robust KKT condition, to be a global robust optimal solution of the uncertain optimization problem, which may have many local robust optimal solutions that are not global, are established. The obtained conditions make use of underestimators, which were first introduced by Jayakumar and Srisatkunarajah [<xref ref-type="bibr" rid="b1">1</xref>,<xref ref-type="bibr" rid="b2">2</xref>] of the Lagrangian associated with the problem at the robust KKT point. Furthermore, we also investigate the Wolfe type robust duality between the smooth uncertain optimization problem and its uncertain dual problem by proving the sufficient conditions for a weak duality and a strong duality between the deterministic robust counterpart of the primal model and the optimistic counterpart of its dual problem. The results on robust duality theorems are established in terms of underestimators. Additionally, to illustrate or support this study, some examples are presented.</p>
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Yang, Feng. "Two-Stage Robust Counterpart Model for Humanitarian Logistics Management." Discrete Dynamics in Nature and Society 2021 (February 15, 2021): 1–15. http://dx.doi.org/10.1155/2021/6669691.

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In the early stages of a major public emergency, decision-makers were troubled by the timely distribution of a large number of donations. In order to distribute caring materials reasonably and efficiently, considering the transportation cost and time delay cost, this paper takes the humanitarian logistics management as an example to study the scheduling problem. Based on the actual situation of insufficient supply during the humanitarian logistics management, this paper using optimization theory establishes a two-stage stochastic chance constrained (TS-SCC) model. In addition, due to the randomness of emergency occurrence and uncertainty of demand, the TS-SCC model is further transformed into the two-stage robust counterpart (TS-RC) model. At the same time, the validity of the model and the efficiency of the algorithm are verified by simulations. The result shows that the model and algorithm constructed are capable to obtain the distribution scheme of caring materials even in worst case. In the TS-BRC (with box set) model, the logistics service level increased from 89.83% to 93.21%, while in the TS-BPRC (with mixed box and polyhedron set) model, it increases from 90.32% to 94.96%. Besides, the model built in this paper can provide a more reasonable dispatching plan according to the actual situation of caring material supply.
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Book chapters on the topic "Robust Counterpart Optimization"

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"Chapter Two. Robust Counterpart Approximations of Scalar Chance Constraints." In Robust Optimization, 27–66. Princeton University Press, 2009. http://dx.doi.org/10.1515/9781400831050.27.

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"Chapter Eleven. Globalized Robust Counterparts of Uncertain Conic Problems." In Robust Optimization, 279–300. Princeton University Press, 2009. http://dx.doi.org/10.1515/9781400831050.279.

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"Chapter Three. Globalized Robust Counterparts of Uncertain LO Problems." In Robust Optimization, 67–80. Princeton University Press, 2009. http://dx.doi.org/10.1515/9781400831050.67.

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"Chapter One. Uncertain Linear Optimization Problems and their Robust Counterparts." In Robust Optimization, 3–26. Princeton University Press, 2009. http://dx.doi.org/10.1515/9781400831050.3.

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Huang, Ming Z. "Parametric Dimension Synthesis and Optimizations of Planar 5R Parallel Robots." In Robotic Systems, 340–54. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1754-3.ch017.

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The dimension synthesis problem for parallel robots in general is much more complex than their serial counterparts, due to the strong dependence of geometric parameters and their performances. In dimension synthesis for robots, typical performance characteristics that may be considered to evaluate the fitness of a design include workspace, manipulability, velocity, stiffness, and payload. A case study on optimal design for both workspace and manipulability had been presented previously for a class of planar parallel robots with 5R joints. This paper extends the design optimization study to include stiffness, velocity, and payload characteristics for the same class of 2-dof robots. A simple and effective parameter-variation-based, constrained optimization method will be demonstrated to obtain various optimal design solutions corresponding to those characteristics respectively. The optimal design solutions, obtained in scalable dimensionless forms, are global in nature and relative to a workspace constraint.
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Conference papers on the topic "Robust Counterpart Optimization"

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Yuliza, Evi, Fitri Maya Puspita, and Siti Suzlin Supadi. "Robust Optimization for the Counterpart Open Capacitated Vehicle Routing Problem With Time Windows." In 4th Sriwijaya University Learning and Education International Conference (SULE-IC 2020). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.201230.162.

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Chaerani, D., B. N. Ruchjana, S. P. Dewanto, A. S. Abdullah, J. Rejito, Y. Rosandi, and I. A. Dharmawan. "Determining the robust counterpart of uncertain spatial optimization model for water supply allocation problem." In APPLICATION OF MATHEMATICS IN INDUSTRY AND LIFE: Proceedings of the Third Conference on Industrial and Applied Mathematics (CIAM 2015). AIP Publishing LLC, 2016. http://dx.doi.org/10.1063/1.4942985.

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Zhou, Jianhua, Shuo Cheng, and Mian Li. "Sequential Quadratic Programming for Robust Optimization With Interval Uncertainty." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70139.

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Uncertainty plays a critical role in engineering design as even a small amount of uncertainty could make an optimal design solution infeasible. The goal of robust optimization is to find a solution that is both optimal and insensitive to uncertainty that may exist in parameters and design variables. In this paper, a novel approach, Sequential Quadratic Programing for Robust Optimization (SQP-RO), is proposed to solve single-objective continuous nonlinear optimization problems with interval uncertainty in parameters and design variables. This new SQP-RO is developed based on a classic SQP procedure with additional calculations for constraints on objective robustness, feasibility robustness, or both. The obtained solution is locally optimal and robust. Eight numerical and engineering examples with different levels of complexity are utilized to demonstrate the applicability and efficiency of the proposed SQP-RO with the comparison to its deterministic SQP counterpart and RO approaches using genetic algorithms. The objective and/or feasibility robustness are verified via Monte Carlo simulations.
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Pankaj Boindala, Sriman, G. Jaykrishnan, and Avi Ostfeld. "Source Treatment Level Optimization in Water Distribution Networks Considering Mixing Uncertainty at Cross Junctions: A Robust Counterpart Approach." In World Environmental and Water Resources Congress 2022. Reston, VA: American Society of Civil Engineers, 2022. http://dx.doi.org/10.1061/9780784484258.101.

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Chow, Yinlam, Brandon Cui, Moonkyung Ryu, and Mohammad Ghavamzadeh. "Variational Model-based Policy Optimization." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/316.

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Model-based reinforcement learning (RL) algorithms allow us to combine model-generated data with those collected from interaction with the real system in order to alleviate the data efficiency problem in RL. However, designing such algorithms is often challenging because the bias in simulated data may overshadow the ease of data generation. A potential solution to this challenge is to jointly learn and improve model and policy using a universal objective function. In this paper, we leverage the connection between RL and probabilistic inference, and formulate such an objective function as a variational lower-bound of a log-likelihood. This allows us to use expectation maximization (EM) and iteratively fix a baseline policy and learn a variational distribution, consisting of a model and a policy (E-step), followed by improving the baseline policy given the learned variational distribution (M-step). We propose model-based and model-free policy iteration (actor-critic) style algorithms for the E-step and show how the variational distribution learned by them can be used to optimize the M-step in a fully model-based fashion. Our experiments on a number of continuous control tasks show that our model-based (E-step) algorithm, called variational model-based policy optimization (VMBPO), is more sample-efficient and robust to hyper-parameter tuning than its model-free (E-step) counterpart. Using the same control tasks, we also compare VMBPO with several state-of-the-art model-based and model-free RL algorithms and show its sample efficiency and performance.
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Hu, Daijun, Yingchun Shan, Xiandong Liu, Weihao Chai, and Xiaoyin Wang. "Uncertainty Optimization Design of Vehicle Wheel Made of Long Glass Fiber Reinforced Thermoplastic." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-86769.

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The use of automobile lightweight is an effective measure to reduce energy consumption and vehicle emissions. The utilization of high-performance composite materials is an important way to achieve lightweight vehicles technically. The advantages of using thermoplastic composite wheels are: easy to form, high manufacturing efficiency, low cost and easy to recycle. This leads to broader application prospects. Taking composite anisotropy into consideration, the mechanical performance of a wheel made of long glass fiber reinforced thermoplastic (LGFT), is analysed using the finite element method (FEM). This is done by placing the wheel under a bending fatigue load simulation. According to the simulation results, the sample database is established by orthogonal experimental method on the Isight platform, and the approximate model is established by the Response Surface Methodology (RSM). Based on this model, uncertainty optimization analysis is then conducted on the wheel’s design using Sigma Principle whereby the optimization target is the mass minimization. The maximum deformation of the wheel and the stress on both sides of the spoke will serve as constraint conditions and the key dimension parameters of the wheel model will be taken as the design variables. The uncertainty optimization is based on the Sigma criterion, taking into consideration the wheel’s geometry and property-fluctuation materials. The feasibility of design schemes is then verified after comparison analysis between the optimization results and the simulation results obtained. The result shows that compared with deterministic optimization, though the weight of the wheel has slightly increased, the uncertainty optimization based on the Sigma criterion is much more robust and the reliabilities of the three constraints are all above 6 Sigma. The resulting optimized LGFT wheel weighs 5.28kg, which has a 5.5% more loss in weight than the initial target and is also 25.6% lighter than the counterpart wheel which is made of aluminum alloy. The desired design results is now achieved with this lightweight effect.
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Yan, Bicheng, Zhen Xu, Manojkumar Gudala, Zeeshan Tariq, and Thomas Finkbeiner. "Reservoir Modeling and Optimization Based on Deep Learning with Application to Enhanced Geothermal Systems." In SPE Reservoir Characterisation and Simulation Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212614-ms.

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Abstract With the energy demand arising globally, geothermal recovery by Enhanced Geothermal Systems (EGS) becomes a promising option to bring a sustainable energy supply and mitigate CO2 emission. However, reservoir management of EGS primarily relies on reservoir simulation, which is quite expensive due to the reservoir heterogeneity, the interaction of matrix and fractures, and the intrinsic multi-physics coupled nature. Therefore, an efficient optimization framework is critical for the management of EGS. We develop a general reservoir management framework with multiple optimization options. A robust forward surrogate model fl is developed based on a convolutional neural network, and it successfully learns the nonlinear relationship between input reservoir model parameters (e.g., fracture permeability field) and interested state variables (e.g., temperature field and produced fluid temperature). fl is trained using simulation data from EGS coupled thermal-hydro simulation model by sampling reservoir model parameters. As fl is accurate, efficient and fully differentiable, EGS thermal efficiency can be optimized following two schemes: (1) training a control network fc to map reservoir geological parameters to reservoir decision parameters by coupling it withfl ; (2) directly optimizing the reservoir decision parameters based on coupling the existing optimizers such as Adam withfl. The forward model fl performs accurate and stable predictions of evolving temperature fields (relative error1.27±0.89%) in EGS and the time series of produced fluid temperature (relative error0.26±0.46%), and its speedup to the counterpart high-fidelity simulator is 4564 times. When optimizing withfc, we achieve thermal recovery with a reasonable accuracy but significantly low CPU time during inference, 0.11 seconds/optimization. When optimizing with Adam optimizer, we achieve the objective perfectly with relatively high CPU time, 4.58 seconds/optimization. This is because the former optimization scheme requires a training stage of fc but its inference is non-iterative, while the latter scheme requires an iterative inference but no training stage. We also investigate the option to use fc inference as an initial guess for Adam optimization, which decreases Adam's CPU time, but with excellent achievement in the objective function. This is the highest recommended option among the three evaluated. Efficiency, scalability and accuracy observed in our reservoir management framework makes it highly applicable to near real-time reservoir management in EGS as well as other similar system management processes.
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Kubek, Daniel. "The impact of short term traffic forecasting on the effectiveness of vehicles routes planning in urban areas." In CIT2016. Congreso de Ingeniería del Transporte. Valencia: Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/cit2016.2016.3512.

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An impossibility to foresee in advance the accurate traffic parameters in face of dynamism phenomena in complex transportation system is a one of the major source of uncertainty. The paper presents an approach to robust optimization of logistics vehicle routes in urban areas on the basis of estimated short-term traffic time forecasts in a selected area of the urban road network. The forecast values of optimization parameters have been determined using the spectral analysis model, taking into account the forecast uncertainty degree. The robust counterparts approach of uncertain bi-criteria shortest path problem formulation is used to determining the robust routes for logistics vehicles in the urban network. The uncertainty set is created on the basis of forecast travel times in chosen sections, estimated by means of spectral analysis. The advantages and the characteristics are exemplified in the actual Krakow road network. The obtained data have been compared with classic approach wherein it is assumed that the optimization parameters are certain and accurate. The results obtained in the simulation example indicate that use of forecasting techniques with robust optimization models has a positive impact on the quality of final solutions.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3512
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Wang, Weijun, Stéphane Caro, Fouad Bennis, and Oscar Brito Augusto. "Toward the Use of Pareto Performance Solutions and Pareto Robustness Solutions for Multi-Objective Robust Optimization Problems." In ASME 2012 11th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/esda2012-82099.

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For Multi-Objective Robust Optimization Problem (MOROP), it is important to obtain design solutions that are both optimal and robust. To find these solutions, usually, the designer need to set a threshold of the variation of Performance Functions (PFs) before optimization, or add the effects of uncertainties on the original PFs to generate a new Pareto robust front. In this paper, we divide a MOROP into two Multi-Objective Optimization Problems (MOOPs). One is the original MOOP, another one is that we take the Robustness Functions (RFs), robust counterparts of the original PFs, as optimization objectives. After solving these two MOOPs separately, two sets of solutions come out, namely the Pareto Performance Solutions (PP) and the Pareto Robustness Solutions (PR). Make a further development on these two sets, we can get two types of solutions, namely the Pareto Robustness Solutions among the Pareto Performance Solutions (PR(PP)), and the Pareto Performance Solutions among the Pareto Robustness Solutions (PP(PR)). Further more, the intersection of PR(PP) and PP(PR) can represent the intersection of PR and PP well. Then the designer can choose good solutions by comparing the results of PR(PP) and PP(PR). Thanks to this method, we can find out the optimal and robust solutions without setting the threshold of the variation of PFs nor losing the initial Pareto front. Finally, an illustrative example highlights the contributions of the paper.
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Bing, Zhenshan, Christian Lemke, Zhuangyi Jiang, Kai Huang, and Alois Knoll. "Energy-Efficient Slithering Gait Exploration for a Snake-Like Robot Based on Reinforcement Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/785.

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Similar to their counterparts in nature, the flexible bodies of snake-like robots enhance their movement capability and adaptability in diverse environments. However, this flexibility corresponds to a complex control task involving highly redundant degrees of freedom, where traditional model-based methods usually fail to propel the robots energy-efficiently. In this work, we present a novel approach for designing an energy-efficient slithering gait for a snake-like robot using a model-free reinforcement learning (RL) algorithm. Specifically, we present an RL-based controller for generating locomotion gaits at a wide range of velocities, which is trained using the proximal policy optimization (PPO) algorithm. Meanwhile, a traditional parameterized gait controller is presented and the parameter sets are optimized using the grid search and Bayesian optimization algorithms for the purposes of reasonable comparisons. Based on the analysis of the simulation results, we demonstrate that this RL-based controller exhibits very natural and adaptive movements, which are also substantially more energy-efficient than the gaits generated by the parameterized controller. Videos are shown at https://videoviewsite.wixsite.com/rlsnake .
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