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Robustness in combinatorial optimization and scheduling theory: An extended annotated bibliography. (2006). Nikulin, Yury .
In: Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel.
RePEc:zbw:cauman:606.

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  1. Robust decision trees for the multi-mode project scheduling problem with a resource investment objective and uncertain activity duration. (2024). Guillaume, Romain ; Artigues, Christian ; Portoleau, Tom.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:312:y:2024:i:2:p:525-540.

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  2. Flight gate assignment and recovery strategies with stochastic arrival and departure times. (2017). Dorndorf, Ulrich ; Pesch, Erwin ; Jaehn, Florian.
    In: OR Spectrum: Quantitative Approaches in Management.
    RePEc:spr:orspec:v:39:y:2017:i:1:d:10.1007_s00291-016-0443-1.

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  3. An Analysis of Robustness Approaches for the Airport Baggage Sorting Station Assignment Problem. (2016). Asco, Amadeo.
    In: Journal of Optimization.
    RePEc:hin:jjopti:1213949.

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  4. Robustness Testing of Model Based Multiple Criteria Decisions: Fundamentals and Applications. (2015). Wierzbicki, Andrzej P ; Granat, Janusz ; Makowski, Marek.
    In: International Journal of Information Technology & Decision Making (IJITDM).
    RePEc:wsi:ijitdm:v:14:y:2015:i:05:n:s0219622015500157.

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  5. Flight gate scheduling with respect to a reference schedule. (2012). Pesch, Erwin ; Dorndorf, Ulrich ; Jaehn, Florian.
    In: Annals of Operations Research.
    RePEc:spr:annopr:v:194:y:2012:i:1:p:177-187:10.1007/s10479-010-0809-8.

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  6. Modelling Robust Flight-Gate Scheduling as a Clique Partitioning Problem. (2008). Dorndorf, Ulrich ; Pesch, Erwin ; Jaehn, Florian.
    In: Transportation Science.
    RePEc:inm:ortrsc:v:42:y:2008:i:3:p:292-301.

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References

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  2. [14] Bertsimas D. and Sim M. (2002). Robust discrete optimization. Working Paper. Operations Research Center, MIT. A robust version (counterpart) of integer programming problems is proposed for the case when both the cost coefficients and the data constraints are subject to uncertainty. When only the cost coefficients are subject to uncertainty and the problem is 0 — 1 discrete optimization problem on n variables the procedure of solving the robust counterpart by solving n + 1 instances of the original problem is described. As the consequence a very interesting fact stated: if t he original problem is polynomially solvable, than the robust counterpart problem also remains polynomially solvable. It means that robust versions of such well-known problems as matching, spanning tree, shortest path, matroid intersection etc. are polynomially solvable. Some results concerning the a-approximation of 0 —1 discrete optimization problems are given.
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  3. [15] Bertsimas D. and Sim M. (2003). Robust discrete optimization and network flows. Mathematical Programming 98, 49-71. Additionally to the results of [14], the authors propose an algorithm for robust network flows that solves the robust counterpart by solving a polynomial number of nominal minimum cost flow problems in a modified network.
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  5. [18] Bertsimas D. and Sim M. (2004). Robust discrete optimization under ellipsoidal uncertainty sets. Working Paper. Operations Research Center, MIT. It is probably the first attempt to investigate robust discrete optimization under ellipsoidal uncertainty sets. It is shown that the robust counterpart of a discrete optimization problem with correlated objective function data is NP-hard even though the original problem is polynomially solvable. For uncorrelated and identically distributed data, it is proved that the robust counterpart retains the complexity of the original problem. A generalization of the robust discrete optimization approach proposed earlier is given which presents the tradeoff between robustness and optimality.
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  6. [19] Bertsimas D., Pachamanova D. and Sim M. (2004). Robust linear optimization under general norms. Operations Research Letters 32, 510 - 516. The explicit characterization of the robust counterpart of a linear programming problem with uncertainty set is described by an arbitrary norm. This approach encompasses several approaches from the literature and provides guarantees for constraint violation under probabilistic models that allow arbitrary dependencies in the distribution of the uncertain coefficients.
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  15. [28] Kouvelis P. and Sayin S. (2005). Algorithm robust for the bicriteria discrete optimization problem. Annais of Operations Research. The authors study various definitions of robustness in a discrete scenario discrete optimization setting. It was shown that a generalized definition of robustness into which scenario weights are introduced can be used to identify the efficient solutions of multiple objective discrete optimization problems. It is proven that the Solution of a pair of optimization problems, with the first of them being a robust optimization one, is always an efficient Solution. Moreover, any efficient Solution can be obtained as an optimal Solution to a pair of such problems.
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  20. [32] Montemanni R. and Gambardella L. (2004). An exact algorithm for the robust shortest path problem with interval data. Computers and Operations Research 31, 1667 - 1680.
    Paper not yet in RePEc: Add citation now
  21. [33] Montemanni R., Gambardella L. and Donati A.V. (2004). A branch and bound algorithm for the robust shortest path problem with interval data. OR Letters 32, 225 -232. Montemanni et al. proposed a branch and bound algorithm for the robust spanning tree and the robust shortest path problem in [31] and [32], [33] respectively. The method embeds the extension of some result previously known in literature and some new original elements. It is claimed that the technique proposed is up to 210 faster then methods recently appeared in literature.
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  22. [34] Montemanni R. (2005). A Benders decomposition approach for the robust spanning tree problem with interval data. European Journal of Operational Research (to appear).
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  23. [35] Montemanni R. and Gambardella L. (2005). The robust shortest path problem with interval data via Benders decomposition. 40R 3, 315 - 328 . Montemanni and Gambardella propose a new exact algorithm, based on Benders decomposition, for the robust spanning tree and the robust shortest path problem in [34] and [35] respectively. Computational results highlight the efficiency of the new method. It was shown that the technique is very fast on all the benchmarks considered, especially on those that were harder to solve for the methods previously known.
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  24. [36] Montemanni R., Barta j. and Gambardella L. (2005). The robust traveling salesman problem with interval data. Technical Report IDSIA-20-05. The authors present a new extension to the basic problem, where travel times are specified as a ränge of possible values. The robust deviation criterion is applied to drive optimization over the interval data problem so obtained. Some interesting theoretical properties of the new optimization problems are identified and presented, together with a new mathematical formulation and some exact algorithms. The exact methods are compared from an experimental point of view. The methodology proposed can be used to attack the robust counterpart of other NP-hard combinatorial optimization problems too.
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  25. [38] Y. Nikulin. (2005). The robust shortest path problem with interval data: a probabilistic metaheuristic, Manuskripte aus den Instituten für Betriebswirtschaftslehre No. 597, Christian-Albrechts-Universität zu Kiel. The metaheuristic appraoch of [37] is applied to the robust shortest path problem with interval data.
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  26. [39] Pinar M. (2004). A note on robust 0-1 optimization with uncertain cost coefficients. 40R 2, 309 - 316. Based on the approach of Bertsimas and Sim [15], [18] to robust optimization in the presence of data uncertainty, an easily computable bound on the probability that robust Solution gives and objective function value worse than the robust objective function value, under the assumption that only cost coefficients are subject to uncertainty.
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  27. [4] Ben-Tal A., El Ghaoui L. and Nemirovski A. (2000). Robust semidefinite programming. In Saigal R., Vandenberghe L. and Wolkowicz H. editors, Handbook of Semidefinite programming and applications, Kluwer Academic Publishers, Waterloo.
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  29. [42] Yaman H., Karasan O. and Pinar M. (2004). Restricted robust optimization for maximization over uniform matroid with interval data uncertainty. BUkent University. For the problem of selecting p items with uncertain (interval) objective function coefficients so as to maximize total profit (maximization over uniform matroid) the authors introduce the r-restricted robust deviation criterion and seek solutions that minimize it. This new criterion increases the modeling power of the robust deviation (minmax regret) criterion by reducing the level of conservativeness of the robust Solution. It is shown that r-restricted robust deviation solutions can be computed efficiently.
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  31. [44] Yu G. and Yang J. (1997). On the robust shortest path problem. Comput&. Oper. Res. 25, 457 - 468. In this paper a scenario approach is adopted to characterize uncertainties. Two robustness criteria are specifed: the absolute robust criterion and the robust deviation criterion. It is shown that under both criteria the robust shortest path problem is NP-complete even for much more restricted layered networks of width 2, and with only 2 scenarios. A pseudo-polynomial algorithm is devised to solve the robust shortest path problem in general networks under bounded number of scenarios.
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  32. [47] Ballestin F. and Leus R. (2006). Metaheuristics for stable scheduling on a Single machine. Working Paper. A Single machine scheduling problem is considered. Two metaheuristics for solving an approximate formulation of the model that assumes that exactly one job is disrupted during schedule execution are proposed. Uncertainty is modelled for job duration and primal objective is to minimize deviation between planned and actual job starting times.
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  33. [48] Davenport A. and Beck J. (2000). A survey of techniques for scheduling with uncertainty. Working Paper, Toronto. (http://www.eil.utoronto.ca/profiles/chris/chris.papers.html) This paper surveys some robust scheduling techniques that have been appeared during the last decade since the well-known survey by McKay et al. about the state-of-the-art in job-shop scheduling was written. Many new approaches are discussed. redundancybased techniques (a reservation of extra time and resources for unexpected events), probabilistic techniques (they do not construct a robust schedule, but we have the possibility to measure the probability of uncertainty and, moreover, to construct an optimal schedule so to maximize), different on-line and off-line approaches as well as rescheduling techniques.
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  34. [49] Jensen M. (2001). Robust and flexible scheduling with evolutionary computation. Ph.D. thesis, University of Aarhus, Department of Computer Science. This thesis presents two fundamentally different approaches for scheduling Job shops facing machine breakdowns. The first method is called neighbourhood based robustness and is based on an idea of minimizing the cost of a neighbourhood of schedules. The scheduling algorithm attempts to find a small set of schedules with an acceptable level of Performance. The other method for stochastic scheduling uses the idea of co-evolution to create schedules with a guaranteed worst-case Performance for a known set of scenarios. The method is demonstrated to improve worst-case Performance of the schedules when compared to ordinary scheduling; it substantially reduces running time when compared to a more Standard approach explicitly considering all scenarios.
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  36. [50] Kouvelis P., Daniels R. and Vairaktarakis G. (2000). Robust scheduling of a two-machine flow shop with uncertain processing times. HE Transactions 36, 667-682. This paper is one of the first attempts to introduce the concept of robustness for scheduling problems. The authors suggest a robust schedule when processing times are uncertain, but they compute this robust schedule based on maximum absolute deviation between the robust Solution and all the possible scenarios, but this requires knowledge of all possible scenarios. Moreover, the optimal Solution of each scenario is supposed to be known a priori.
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  37. [51] Lambrechts O., Demeulemeester E. and Herroelen W. (2006). Proactive and reactive strategies for resource-constrained project scheduling with uncertain resource availability. Working Paper. The goal of the paper is to build a robust schedule that meets the project due date and minimizes the schedule instability cost, defined as the expected weighted sum of the absolute deviations between the planned and actually realized activity starting times during project execution. The authors describe how stochastic resource breakdowns can be modeled, which reaction is recommended when a resource infeasibility occurs due to a breakdown and how one can protect the initial schedule from the adverse effects of Potential breakdowns.
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  47. [62] Hebrard E, Hnich B. and Walsh T. (2004). Super solutions in constraint program ming. In Proceedings of CP-AI-OR 2004, 157- 172.
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  65. [8] El Ghaoui L., Oustry F. and Lebret H. (1998). Robust solutions to uncertain semidefinite programs. SIAM Journal Optimization 9, 33 - 52. El Ghaoui et al. derived in [7] - [8] results similar to [1] - [4], In particular, they deal with robust reformulation of optimization model by adapting robust control techniques under the assumption that the coefficient matrix data may vary inside ellipsoidal uncertainty set. The robust counterpart of some important problems are either exactly or approximately tractable problems that are efficiently solvable with interior point methods. However, the difficulty of the robust problems increases.

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  73. A comparison between multicriteria and robustness frameworks, IS-MG 2003/16, Universite Libre de Bruxelles. A parallelism between multicriteria optimization and robustness concepts is established. New problems like multicriteria multiscenarios problem and multicriteria evaluation of robustness are discussed.
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  74. A robust approach to solving linear optimization problems with uncertain data was proposed in the early 1970s and has recently been extensively studied and extended. The authors propose another approach which is different from previous ones in order to control the level of conservatism in the Solution. This approach has the advantage that it leads to a linear optimization model and it can be directly applied to discrete optimization problems (it was done later in [14]).
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  75. • Averbakh I. (2003). Complexity of robust single-facility location problems on networks with uncertain lengths of edges. Discrete Applied Mathematics 127, 505 -522.
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  76. • Averbakh I. (2004). Minmax regret linear resource allocation problems. Opera tions Research Letters 32, 174 - 180.
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  77. • Averbakh I. and German O. (2000). Algorithms for the robust 1-center problem on a tree. European Journal of OperationsI Research 123, 292 - 302.

  78. • Averbakh I. and German O. (2000). Minmax regret median location on a network under uncertainty. INFORMS Journal on Computing 12, 104 - 110.
    Paper not yet in RePEc: Add citation now
  79. • Averbakh I. and German O. (2003). An improved algorithm for the minmax regret median problem on a tree. Networks 41, 97 - 103.
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  80. • Averbakh I. and Lebedev V. (2005). On the complexity of minmax regret linear programming. European Journal of Operational Research 160, 227 - 231. • Lebedev V. and Averbakh I. Complexity of minimizing the total flow time with interval data and minmax regret criterion. (to appear in Discrete Applied Mathematics). • Averbakh I. The minmax relative regret median problem on networks. (to appear in INFORMS Journal on Computing). • Averbakh I. The minmax regret permutation flow shop problem with two Jobs, (to appear in European Journal of Operational Research).

  81. Kluwer Academic Publishers, Norwell, M.A. A comprehensive treatment of the State of the art (up to 1997) in robust discrete optimization and extensive references are presented in this work. However, there still are more open problems than solved ones. Most of the known results correspond to scenario-represented models of uncertainty, i.e. where there exists a finite number of possible scenarios each of which is given explicitly by listing the corresponding values of parameters. It is shown that most classical polynomially solvable combinatorial optimization problems loose this nice property and become NP-hard in a robust version with scenario-represented uncertainty (to appear).
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  82. The following paper are closely related to Averbakh's approach: • Averbakh I. (2000). Minmax regret solutions for minimax optimization problems with uncertainty. Operations Research Letters 27, 57 - 65.

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    RePEc:spr:joptap:v:177:y:2018:i:3:d:10.1007_s10957-018-1256-y.

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  3. A Utility Theory Based Interactive Approach to Robustness in Linear Optimization. (2018). Tunel, Levent ; Moazeni, Somayeh ; Karimi, Mehdi.
    In: Journal of Global Optimization.
    RePEc:spr:jglopt:v:70:y:2018:i:4:d:10.1007_s10898-017-0581-2.

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  4. Portfolio selection problems with Markowitz’s mean–variance framework: a review of literature. (2018). Guo, Sini ; Li, Xiang ; Zhang, Yuanyuan.
    In: Fuzzy Optimization and Decision Making.
    RePEc:spr:fuzodm:v:17:y:2018:i:2:d:10.1007_s10700-017-9266-z.

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  5. Conditions under which adjustability lowers the cost of a robust linear program. (2018). Ryan, Sarah M ; Haddad-Sisakht, Ali.
    In: Annals of Operations Research.
    RePEc:spr:annopr:v:269:y:2018:i:1:d:10.1007_s10479-018-2954-4.

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  6. The insertion of biogas in the sugarcane mill product portfolio: A study using the robust optimization approach. (2018). de Moraes, Raphael ; Rego, Erik Eduardo ; Mutran, Victoria Morgado ; de Oliveira, Celma.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:91:y:2018:i:c:p:729-740.

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  7. Optimizing make-to-stock policies through a robust lot-sizing model. (2018). Agra, Agostinho ; Santos, Micael ; Poss, Michael.
    In: International Journal of Production Economics.
    RePEc:eee:proeco:v:200:y:2018:i:c:p:302-310.

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  8. Agribusiness supply chain risk management: A review of quantitative decision models. (2018). Behzadi, Golnar ; Zhang, Abraham ; Olsen, Tava Lennon ; Osullivan, Michael Justin.
    In: Omega.
    RePEc:eee:jomega:v:79:y:2018:i:c:p:21-42.

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  9. Towards more resilient integrated power grid capacity expansion: A robust optimization approach with operational flexibility. (2018). Caunhye, Aakil M ; Cardin, Michel-Alexandre.
    In: Energy Economics.
    RePEc:eee:eneeco:v:72:y:2018:i:c:p:20-34.

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  10. Robust VaR and CVaR optimization under joint ambiguity in distributions, means, and covariances. (2018). Zenios, Stavros ; Lotfi, Somayyeh.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:269:y:2018:i:2:p:556-576.

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  11. Compromise solutions for robust combinatorial optimization with variable-sized uncertainty. (2018). Chassein, Andre ; Goerigk, Marc.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:269:y:2018:i:2:p:544-555.

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  12. The design of a reliable and robust hierarchical health service network using an accelerated Benders decomposition algorithm. (2018). Zarrinpoor, Naeme ; Pishvaee, Mir Saman ; Fallahnezhad, Mohammad Saber .
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:265:y:2018:i:3:p:1013-1032.

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  13. On nonsmooth robust multiobjective optimization under generalized convexity with applications to portfolio optimization. (2018). Zafarani, Jafar ; Fakhar, Majid ; Mahyarinia, Mohammad Reza.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:265:y:2018:i:1:p:39-48.

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  14. Ranking robustness and its application to evacuation planning. (2018). Hamacher, Horst W ; Goerigk, Marc ; Kinscherff, Anika.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:264:y:2018:i:3:p:837-846.

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  15. Robust games: theory and application to a Cournot duopoly model. (2017). Radi, Davide ; Rocca, Matteo ; Crespi, Giovanni Paolo.
    In: Decisions in Economics and Finance.
    RePEc:spr:decfin:v:40:y:2017:i:1:d:10.1007_s10203-017-0199-3.

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  16. Quasiconvexity of set-valued maps assures well-posedness of robust vector optimization. (2017). Rocca, Matteo ; Kuroiwa, Daishi ; Crespi, Giovanni P.
    In: Annals of Operations Research.
    RePEc:spr:annopr:v:251:y:2017:i:1:d:10.1007_s10479-015-1813-9.

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  17. Managing ambiguity in asset allocation. (2017). Kaya, Hakan.
    In: Journal of Asset Management.
    RePEc:pal:assmgt:v:18:y:2017:i:3:d:10.1057_s41260-016-0029-0.

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  18. A robust approach to airport gate assignment with a solution-dependent uncertainty budget. (2017). Wang, Fan ; Xu, Liang ; Xiao, Feng ; Zhang, Chao.
    In: Transportation Research Part B: Methodological.
    RePEc:eee:transb:v:105:y:2017:i:c:p:458-478.

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  19. Staffing and scheduling flexible call centers by two-stage robust optimization. (2017). Mattia, Sara ; Smriglio, Stefano ; Servilio, Mara ; Rossi, Fabrizio.
    In: Omega.
    RePEc:eee:jomega:v:72:y:2017:i:c:p:25-37.

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  20. Necessary and sufficient conditions for Pareto efficiency in robust multiobjective optimization. (2017). Bokrantz, Rasmus ; Fredriksson, Albin .
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:262:y:2017:i:2:p:682-692.

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  21. Robust goal programming using different robustness echelons via norm-based and ellipsoidal uncertainty sets. (2017). Hanks, Robert W ; Lunday, Brian J ; Weir, Jeffery D.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:262:y:2017:i:2:p:636-646.

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  22. Two-stage robust optimization approach to elective surgery and downstream capacity planning. (2017). Neyshabouri, Saba ; Berg, Bjorn P.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:260:y:2017:i:1:p:21-40.

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  23. Convergent hierarchy of SDP relaxations for a class of semi-infinite convex polynomial programs and applications. (2017). Chuong, T D ; Jeyakumar, V.
    In: Applied Mathematics and Computation.
    RePEc:eee:apmaco:v:315:y:2017:i:c:p:381-399.

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  24. Calibration of Distributionally Robust Empirical Optimization Models. (2017). , Andrew ; Kim, Michael Jong ; Gotoh, Jun-Ya.
    In: Papers.
    RePEc:arx:papers:1711.06565.

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  25. Robust Optimization of Schedules Affected by Uncertain Events. (2016). Morari, Manfred ; Goulart, Paul ; Vujanic, Robin.
    In: Journal of Optimization Theory and Applications.
    RePEc:spr:joptap:v:171:y:2016:i:3:d:10.1007_s10957-016-0920-3.

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  26. Robust optimization of supply chain network design in fuzzy decision system. (2016). Liu, Yankui ; Bai, Xuejie.
    In: Journal of Intelligent Manufacturing.
    RePEc:spr:joinma:v:27:y:2016:i:6:d:10.1007_s10845-014-0939-y.

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  27. Australian electricity market and price volatility. (2016). Nazari, A ; Mohammadian, G ; Filar, J A ; Boland, J.
    In: Annals of Operations Research.
    RePEc:spr:annopr:v:241:y:2016:i:1:d:10.1007_s10479-011-1033-x.

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  28. On Minimal Valid Inequalities for Mixed Integer Conic Programs. (2016). Kilin-Karzan, Fatma .
    In: Mathematics of Operations Research.
    RePEc:inm:ormoor:v:41:y:2016:i:2:p:477-510.

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  29. Derivative-Free Robust Optimization for Circuit Design. (2015). Lucidi, Stefano ; Liuzzi, Giampaolo ; Latorre, Vittorio ; Ciccazzo, Angelo ; Rinaldi, Francesco.
    In: Journal of Optimization Theory and Applications.
    RePEc:spr:joptap:v:164:y:2015:i:3:d:10.1007_s10957-013-0441-2.

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  30. Recent Developments in Robust Portfolios with a Worst-Case Approach. (2014). Fabozzi, Frank ; Kim, Woo Chang ; Ho, Jang.
    In: Journal of Optimization Theory and Applications.
    RePEc:spr:joptap:v:161:y:2014:i:1:d:10.1007_s10957-013-0329-1.

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  31. Robust Management and Pricing of Liquefied Natural Gas Contracts with Cancelation Options. (2014). Zubelli, J P ; Sagastizabal, C ; Guigues, V.
    In: Journal of Optimization Theory and Applications.
    RePEc:spr:joptap:v:161:y:2014:i:1:d:10.1007_s10957-013-0309-5.

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  32. Robust Multiple Objective Game Theory. (2013). Liu, H M ; Yu, H.
    In: Journal of Optimization Theory and Applications.
    RePEc:spr:joptap:v:159:y:2013:i:1:d:10.1007_s10957-012-0234-z.

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  33. The Whole Random Optimization with Application. (2013). Hu, Chuan-Gan.
    In: Journal of Optimization Theory and Applications.
    RePEc:spr:joptap:v:156:y:2013:i:3:d:10.1007_s10957-012-0117-3.

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  34. Infrastructure Planning for Electric Vehicles with Battery Swapping. (2013). Shen, Zuo-Jun Max ; Mak, Ho-Yin ; Rong, Ying.
    In: Management Science.
    RePEc:inm:ormnsc:v:59:y:2013:i:7:p:1557-1575.

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  35. Total Cost Control in Project Management via Satisficing. (2013). Goh, Joel ; Hall, Nicholas G.
    In: Management Science.
    RePEc:inm:ormnsc:v:59:y:2013:i:6:p:1354-1372.

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  36. Worst-Case Violation of Sampled Convex Programs for Optimization with Uncertainty. (2012). Takeda, Akiko ; Kanamori, Takafumi .
    In: Journal of Optimization Theory and Applications.
    RePEc:spr:joptap:v:152:y:2012:i:1:d:10.1007_s10957-011-9923-2.

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  37. A Geometric Characterization of the Power of Finite Adaptability in Multistage Stochastic and Adaptive Optimization. (2011). Bertsimas, Dimitris ; Andy, XU ; Goyal, Vineet .
    In: Mathematics of Operations Research.
    RePEc:inm:ormoor:v:36:y:2011:i:1:p:24-54.

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  38. The Robust Network Loading Problem Under Hose Demand Uncertainty: Formulation, Polyhedral Analysis, and Computations. (2011). Altin, Ayegul ; Pinar, Mustafa ; Yaman, Hande.
    In: INFORMS Journal on Computing.
    RePEc:inm:orijoc:v:23:y:2011:i:1:p:75-89.

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  39. Nonconvex Robust Optimization for Problems with Constraints. (2010). Bertsimas, Dimitris ; Teo, Kwong Meng ; Nohadani, Omid .
    In: INFORMS Journal on Computing.
    RePEc:inm:orijoc:v:22:y:2010:i:1:p:44-58.

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  40. Convexity Properties Associated with Nonconvex Quadratic Matrix Functions and Applications to Quadratic Programming. (2009). Beck, A.
    In: Journal of Optimization Theory and Applications.
    RePEc:spr:joptap:v:142:y:2009:i:1:d:10.1007_s10957-009-9539-y.

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  41. Reduced Vertex Set Result for Interval Semidefinite Optimization Problems. (2008). Dabbene, F ; Calafiore, G.
    In: Journal of Optimization Theory and Applications.
    RePEc:spr:joptap:v:139:y:2008:i:1:d:10.1007_s10957-008-9423-1.

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  42. Min-Max Regret Robust Optimization Approach on Interval Data Uncertainty. (2008). Ammons, J C ; Realff, M J ; Assavapokee, T.
    In: Journal of Optimization Theory and Applications.
    RePEc:spr:joptap:v:137:y:2008:i:2:d:10.1007_s10957-007-9334-6.

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  43. Adjustable Robust Optimization Models for a Nonlinear Two-Period System. (2008). Tutuncu, R H ; Taguchi, S ; Takeda, A.
    In: Journal of Optimization Theory and Applications.
    RePEc:spr:joptap:v:136:y:2008:i:2:d:10.1007_s10957-007-9288-8.

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  44. Robust Dynamic Programming. (2005). Iyengar, Garud N.
    In: Mathematics of Operations Research.
    RePEc:inm:ormoor:v:30:y:2005:i:2:p:257-280.

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  45. The Minmax Relative Regret Median Problem on Networks. (2005). Averbakh, Igor.
    In: INFORMS Journal on Computing.
    RePEc:inm:orijoc:v:17:y:2005:i:4:p:451-461.

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  46. Extended Matrix Cube Theorems with Applications to (mu)-Theory in Control. (2003). Ben-Tal, Aharon ; Roos, Cornelis ; Nemirovski, Arkadi.
    In: Mathematics of Operations Research.
    RePEc:inm:ormoor:v:28:y:2003:i:3:p:497-523.

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  47. On Cones of Nonnegative Quadratic Functions. (2003). Zhang, Shuzhong ; Sturm, Jos F.
    In: Mathematics of Operations Research.
    RePEc:inm:ormoor:v:28:y:2003:i:2:p:246-267.

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  48. Robust Portfolio Selection Problems. (2003). Goldfarb, D ; Iyengar, G.
    In: Mathematics of Operations Research.
    RePEc:inm:ormoor:v:28:y:2003:i:1:p:1-38.

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  49. LMI Approximations for the Radius of the Intersection of Ellipsoids: Survey. (2001). Arzelier, D ; Tarbouriech, S ; Henrion, D.
    In: Journal of Optimization Theory and Applications.
    RePEc:spr:joptap:v:108:y:2001:i:1:d:10.1023_a:1026454804250.

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  50. On Polyhedral Approximations of the Second-Order Cone. (2001). Ben-Tal, Aharon ; Nemirovski, Arkadi.
    In: Mathematics of Operations Research.
    RePEc:inm:ormoor:v:26:y:2001:i:2:p:193-205.

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