Relevant bibliographies by topics / Robust Combinatorial Optimization

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Robust Combinatorial Optimization'

Author: Grafiati
Published: 15 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Robust Combinatorial Optimization"

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Adjiashvili, David, Sebastian Stiller, and Rico Zenklusen. "Bulk-Robust combinatorial optimization." Mathematical Programming 149, no. 1-2 (2014): 361–90. http://dx.doi.org/10.1007/s10107-014-0760-6.

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Kawase, Yasushi, and Hanna Sumita. "Randomized Strategies for Robust Combinatorial Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7876–83. http://dx.doi.org/10.1609/aaai.v33i01.33017876.

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In this paper, we study the following robust optimization problem. Given an independence system and candidate objective functions, we choose an independent set, and then an adversary chooses one objective function, knowing our choice. The goal is to find a randomized strategy (i.e., a probability distribution over the independent sets) that maximizes the expected objective value in the worst case. This problem is fundamental in wide areas such as artificial intelligence, machine learning, game theory and optimization. To solve the problem, we propose two types of schemes for designing approxim
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Buchheim, Christoph, and Jannis Kurtz. "Min–max–min robust combinatorial optimization." Mathematical Programming 163, no. 1-2 (2016): 1–23. http://dx.doi.org/10.1007/s10107-016-1053-z.

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Poss, Michael. "Robust combinatorial optimization with knapsack uncertainty." Discrete Optimization 27 (February 2018): 88–102. http://dx.doi.org/10.1016/j.disopt.2017.09.004.

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Koster, Arie M. C. A., and Michael Poss. "Special issue on: robust combinatorial optimization." EURO Journal on Computational Optimization 6, no. 3 (2018): 207–9. http://dx.doi.org/10.1007/s13675-018-0102-1.

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Goerigk, Marc, and Stefan Lendl. "Robust Combinatorial Optimization with Locally Budgeted Uncertainty." Open Journal of Mathematical Optimization 2 (May 18, 2021): 1–18. http://dx.doi.org/10.5802/ojmo.5.

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Goerigk, Marc, and Stephen J. Maher. "Generating hard instances for robust combinatorial optimization." European Journal of Operational Research 280, no. 1 (2020): 34–45. http://dx.doi.org/10.1016/j.ejor.2019.07.036.

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Poss, Michael. "Robust combinatorial optimization with variable cost uncertainty." European Journal of Operational Research 237, no. 3 (2014): 836–45. http://dx.doi.org/10.1016/j.ejor.2014.02.060.

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Dokka, Trivikram, Marc Goerigk, and Rahul Roy. "Mixed uncertainty sets for robust combinatorial optimization." Optimization Letters 14, no. 6 (2019): 1323–37. http://dx.doi.org/10.1007/s11590-019-01456-3.

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Kurtz, Jannis. "Robust combinatorial optimization under budgeted–ellipsoidal uncertainty." EURO Journal on Computational Optimization 6, no. 4 (2018): 315–37. http://dx.doi.org/10.1007/s13675-018-0097-7.

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Dissertations / Theses on the topic "Robust Combinatorial Optimization"

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Udwani, Rajan. "Vignettes on robust combinatorial optimization." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120192.

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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 137-142).<br>In this thesis, we design and analyze algorithms for robust combinatorial optimization in various settings. First, we consider the problem of simultaneously maximizing multiple objectives, all monotone submodular, s
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Pass-Lanneau, Adèle. "Anchored solutions in robust combinatorial optimization." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS177.

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Si les données d'un problème d'optimisation combinatoire changent, une solution initiale peut devenir sous-optimale ou infaisable. Il est alors nécessaire de calculer une nouvelle solution, mais aussi souhaitable de maintenir les décisions prises dans la solution initiale. Dans cette thèse nous proposons le critère d'ancrage pour favoriser les décisions inchangées entre solutions. En réoptimisation, il s'agit de trouver une solution conservant un nombre maximal de décisions d'une solution initiale. En optimisation robuste à deux étapes, nous proposons l'approche robuste-ancrée, qui consiste à
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Hites, Romina. "Robustness and preferences in combinatorial optimization." Doctoral thesis, Universite Libre de Bruxelles, 2005. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210905.

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In this thesis, we study robust combinatorial problems with interval data. We introduce several new measures of robustness in response to the drawbacks of existing measures of robustness. The idea of these new measures is to ensure that the solutions are satisfactory for the decision maker in all scenarios, including the worst case scenario. Therefore, we have introduced a threshold over the worst case costs, in which above this threshold, solutions are no longer satisfactory for the decision maker. It is, however, important to consider other criteria than just the worst case.<p>Therefore, in
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Salazar-Neumann, Martha. "Advances in robust combinatorial optimization and linear programming." Doctoral thesis, Universite Libre de Bruxelles, 2010. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210192.

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La construction de modèles qui protègent contre les incertitudes dans les données, telles que la variabilité de l'information et l'imprécision est une des principales préoccupations en optimisation sous incertitude. L'incertitude peut affecter différentes domaines, comme le transport, les télécommunications, la finance, etc. ainsi que les différentes parts d'un problème d'optimisation, comme les coefficients de la fonction objectif et /ou les contraintes. De plus, l'ensemble des données incertaines peut être modélisé de différentes façons, comme sous ensembles compactes et convexes de l´espace
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Yu, Baosheng. "Robust Diversity-Driven Subset Selection in Combinatorial Optimization." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/19834.

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Subset selection is fundamental in combinatorial optimization with applications in biology, operations research, and computer science, especially machine learning and computer vision. However, subset selection has turned out to be NP-hard and polynomial-time solutions are usually not available. Therefore, it is of great importance to develop approximate algorithms with theoretical guarantee for subset selection in constrained settings. To select a diverse subset with an asymmetric objective function, we develop an asymmetric subset selection method, which is computationally efficient and has
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Hamaz, Idir. "Méthodes d'optimisation robuste pour les problèmes d'ordonnancement cyclique." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30205/document.

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Plusieurs problèmes d'ordonnancement cyclique ont été étudiés dans la littérature. Cependant, la plupart de ces travaux considèrent que les paramètres sont connus avec certitude et ne prennent pas en compte les différents aléas qui peuvent survenir. Par ailleurs, un ordonnancement optimal pour un problème déterministe peut très vite devenir le pire ordonnancement en présence d'incertitude. Parmi les incertitudes que nous pouvons rencontrer dans les problèmes d'ordonnancement, la variation des durées des tâches par rapport au valeurs estimées, pannes des machines, incorporation de nouvelles tâc
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Kurtz, Jannis [Verfasser], Christoph [Akademischer Betreuer] Buchheim, and Anita [Gutachter] Schöbel. "Min-max-min robust combinatorial optimization / Jannis Kurtz ; Gutachter: Anita Schöbel ; Betreuer: Christoph Buchheim." Dortmund : Universitätsbibliothek Dortmund, 2016. http://d-nb.info/1118847598/34.

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Hommelsheim, Felix [Verfasser], Christoph [Akademischer Betreuer] Buchheim, and Sebastian [Gutachter] Stiller. "Complexity of bulk-robust combinatorial optimization problems / Felix Hommelsheim ; Gutachter: Sebastian Stiller ; Betreuer: Christoph Buchheim." Dortmund : Universitätsbibliothek Dortmund, 2020. http://d-nb.info/122008073X/34.

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Solano, Charris Elyn Lizeth. "Optimization methods for the robust vehicle routing problem." Thesis, Troyes, 2015. http://www.theses.fr/2015TROY0026/document.

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Cette thèse aborde le problème de tournées de véhicules (VRP) adressant des incertitudes via l'optimisation robuste, en donnant le VRP Robuste (RVRP). D'abord, les incertitudes sont intégrées sur les temps de trajet. Ensuite, une version bi-objectif du RVRP (bi-RVRP) est considérée en prenant en compte les incertitudes sur les temps de trajet et les demandes. Pour résoudre le RVRP et le bi-RVRP, différentes méthodes sont proposées pour déterminer des solutions robustes en minimisant le pire cas. Un Programme Linéaire à Variables Mixtes Entières (MILP), six heuristiques constructives, un algori
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Gatto, Michael Joseph. "On the impact of uncertainty on some optimization problems : combinatorial aspects of delay management and robust online scheduling /." Zürich : ETH, 2007. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=17452.

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Books on the topic "Robust Combinatorial Optimization"

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Goerigk, Marc, and Michael Hartisch. An Introduction to Robust Combinatorial Optimization. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61261-9.

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Ahuja, Ravindra K. Robust and Online Large-Scale Optimization: Models and Techniques for Transportation Systems. Springer-Verlag Berlin Heidelberg, 2009.

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Hartisch, Michael, and Marc Goerigk. Introduction to Robust Combinatorial Optimization: Concepts, Models and Algorithms for Decision Making under Uncertainty. Springer, 2024.

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Liang, Sun. Robust-Optimizat1onFor Vehicle Routing Proplem: Vehicle Routing Problem Is an Important Research Content in the Field of Operations Research and Combinatorial Optimization, and It Has a Wide Range of Applications in Transportation, Logistics and Distribution. Hua Xia, 2024.

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Genetic algorithms and robotics: A heuristic strategy for optimization. World Scientific, 1991.

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Book chapters on the topic "Robust Combinatorial Optimization"

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Gabrel, Virginie, and Cécile Murat. "Robust Shortest Path Problems." In Paradigms of Combinatorial Optimization. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118600207.ch19.

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Gabrel, Virginie, and Cécile Murat. "Robust Shortest Path Problems." In Paradigms of Combinatorial Optimization. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781119005353.ch19.

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Eberle, Franziska, Ruben Hoeksma, Nicole Megow, Lukas Nölke, Kevin Schewior, and Bertrand Simon. "Speed-Robust Scheduling." In Integer Programming and Combinatorial Optimization. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73879-2_20.

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D’Angelo, Gianlorenzo, Gabriele Di Stefano, Alfredo Navarra, and Cristina M. Pinotti. "Recoverable Robust Timetables on Trees." In Combinatorial Optimization and Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02026-1_43.

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Kasperski, Adam, Adam Kurpisz, and Paweł Zieliński. "Recoverable Robust Combinatorial Optimization Problems." In Operations Research Proceedings. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00795-3_22.

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Genc, Begum, Mohamed Siala, Gilles Simonin, and Barry O’Sullivan. "On the Complexity of Robust Stable Marriage." In Combinatorial Optimization and Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71147-8_30.

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Orlin, James B., Andreas S. Schulz, and Rajan Udwani. "Robust Monotone Submodular Function Maximization." In Integer Programming and Combinatorial Optimization. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33461-5_26.

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Cicerone, Serafino, Gianlorenzo D’Angelo, Gabriele Di Stefano, Daniele Frigioni, and Alfredo Navarra. "Delay Management Problem: Complexity Results and Robust Algorithms." In Combinatorial Optimization and Applications. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85097-7_43.

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Gupta, Anupam, Viswanath Nagarajan, and Vijay V. Vazirani. "Thrifty Algorithms for Multistage Robust Optimization." In Integer Programming and Combinatorial Optimization. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36694-9_19.

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Ganapathy, Murali K. "Robust Mixing." In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11830924_33.

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Conference papers on the topic "Robust Combinatorial Optimization"

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Marich, Elizaveta, Andrea Galeazzi, Steven Sachio, Foteini Michalopoulou, and Maria M. Papathanasiou. "Design Space Exploration via Gaussian Process Regression and Alpha Shape Visualization." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.192990.

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This study introduces a novel methodology that combines Gaussian process regression (GPR) with alpha shape design space reconstruction to visualize multi-dimensional design spaces. The proposed GPR surrogate approach incorporates a kernel optimization step, employing a greedy tree search strategy to identify the optimal combinatorial kernel from a selection of base kernels. This approach efficiently evaluates design spaces around specific points of interest, enabling alpha shape reconstruction. The methodology's adaptability is demonstrated through its application to both lower-dimensional (2D
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Braniff, Austin, Fengqi You, and Yuhe Tian. "Enhanced Reinforcement Learning-driven Process Design via Quantum Machine Learning." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.149501.

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In this work, we introduce a quantum-enhanced reinforcement learning (RL) framework for process design synthesis. RL-driven methods for generating process designs have gained momentum due to their ability to intelligently identify optimal configurations without requiring pre-defined superstructures or flowsheet configurations. This eliminates reliance on prior expert knowledge, offering a comprehensive and robust design strategy. However, navigating the vast combinatorial design space poses computational challenges. To address this, a novel approach integrating RL with quantum machine learning
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Shao, Zhihui, Jianyi Yang, Cong Shen, and Shaolei Ren. "Learning for Robust Combinatorial Optimization: Algorithm and Application." In IEEE INFOCOM 2022 - IEEE Conference on Computer Communications. IEEE, 2022. http://dx.doi.org/10.1109/infocom48880.2022.9796715.

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Zhang, Xin, Yilin Fang, and Quan Liu. "Finding Robust Pareto-Optimal Solutions Over Time for Dynamic Disassembly Sequence Planning." In ASME 2022 17th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/msec2022-85358.

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Abstract Disassembly sequence planning plays a crucial role in the reuse and remanufacturing of end-of-life products, which is a combinatorial optimization problem and has been studied by many researchers. However, it is challenging to obtain optimal disassembly sequences due to great uncertainty owing to various unpredictable factors. We note that some of the uncertainties accompanying the products disassembly process are characterized by dynamic changes and can actually be regarded as dynamic disassembly sequence planning problem. Robust Pareto-optimal over time (RPOT) is a good approach to
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Kuroki, Kyo, Satoru Jimbo, Thiem Van Chu, Masato Motomura, and Kazushi Kawamura. "Classical Thermodynamics-based Parallel Annealing Algorithm for High-speed and Robust Combinatorial Optimization." In GECCO '24: Genetic and Evolutionary Computation Conference. ACM, 2024. http://dx.doi.org/10.1145/3638529.3654042.

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Zhang, J., W. Wu та M. Yagiura. "Worst case scenario lemma for Γ-robust combinatorial optimization problems under max-min criterion". У 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2017. http://dx.doi.org/10.1109/ieem.2017.8289850.

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Martin, Hugo, and Patrice Perny. "BiOWA for Preference Aggregation with Bipolar Scales: Application to Fair Optimization in Combinatorial Domains." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/252.

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We study the biOWA model for preference aggregation and multicriteria decision making from bipolar rating scales. A biOWA is an ordered doubly weighted averaging extending standard ordered weighted averaging (OWA) and allowing a finer control of the importance attached to positive and negative evaluations in the aggregation. After establishing some useful properties of biOWA to generate balanced Pareto-optimal solutions, we address fair biOWA-optimization problems in combinatorial domains. We first consider the use of biOWA in multi-winner elections for aggregating graded approval and disappro
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Tiwari, Santosh, Joshua Summers, and Georges Fadel. "A Genetic Algorithm Based Procedure for Extracting Optimal Solutions From a Morphological Chart." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35497.

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A novel approach using a genetic algorithm is presented for extracting globally satisfycing (Pareto optimal) solutions from a morphological chart where the evaluation and combination of “means to sub-functions” is modeled as a combinatorial multi-objective optimization problem. A fast and robust genetic algorithm is developed to solve the resulting optimization problem. Customized crossover and mutation operators specifically tailored to solve the combinatorial optimization problem are discussed. A proof-of-concept simulation on a practical design problem is presented. The described genetic al
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Jacobs, Tobias, Francesco Alesiani, and Gulcin Ermis. "Reinforcement Learning for Route Optimization with Robustness Guarantees." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/357.

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Application of deep learning to NP-hard combinatorial optimization problems is an emerging research trend, and a number of interesting approaches have been published over the last few years. In this work we address robust optimization, which is a more complex variant where a max-min problem is to be solved. We obtain robust solutions by solving the inner minimization problem exactly and apply Reinforcement Learning to learn a heuristic for the outer problem. The minimization term in the inner objective represents an obstacle to existing RL-based approaches, as its value depends on the full sol
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Koch, Patrick N., Janet K. Allen, Farrokh Mistree, and Dimitri Mavris. "The Problem of Size in Robust Design." In ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/dac-3983.

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Abstract To facilitate the effective solution of multidisciplinary, multiobjective complex design problems, a departure from the traditional parametric design analysis and single objective optimization approaches is necessary in the preliminary stages of design. A necessary tradeoff becomes one of efficiency vs. accuracy as approximate models are sought to allow fast analysis and effective exploration of a preliminary design space. In this paper we apply a general robust design approach for efficient and comprehensive preliminary design to a large complex system: a high speed civil transport (
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