Open Access
Issue |
RAIRO-Oper. Res.
Volume 57, Number 2, March-April 2023
|
|
---|---|---|
Page(s) | 857 - 880 | |
DOI | https://doi.org/10.1051/ro/2023034 | |
Published online | 28 April 2023 |
- P. Agarwal, M. Sharir and E. Welzl, The discrete 2-center problem. Discrete Comput. Geom. 20 (1998) 287–305. [CrossRef] [MathSciNet] [Google Scholar]
- S. Ağca, B. Eksioglu and J. Ghosh, Lagrangian solution of maximum dispersion problems. Nav. Res. Logist. 47 (2000) 97–114. [CrossRef] [Google Scholar]
- A. Auger, J. Bader, D. Brockhoff and E. Zitzler, Investigating and exploiting the bias of the weighted hypervolume to articulate user preferences, in Proceedings of GECCO 2009, ACM (2009) 563–570. [Google Scholar]
- C. Bazgan, F. Jamain and D. Vanderpooten, Discrete representation of the non-dominated set for multi-objective optimization problems using kernels. Eur. J. Oper. Res. 260 (2017) 814–827. [CrossRef] [Google Scholar]
- S. Borzsony, D. Kossmann and K. Stocker, The skyline operator, in Proceedings 17th International Conference on Data Engineering, IEEE (2001) 421–430. [Google Scholar]
- K. Bringmann, S. Cabello and M. Emmerich, Maximum volume subset selection for anchored boxes. Preprint arXiv:1803.00849 (2018). [Google Scholar]
- K. Bringmann, T. Friedrich and P. Klitzke, Two-dimensional subset selection for hypervolume and epsilon-indicator, in Annual Conference on Genetic and Evolutionary Computation, ACM (2014) 589–596. [Google Scholar]
- S. Cabello, Faster distance-based representative skyline and k-center along Pareto Front in the plane. J. Glob. Optim. (2023) 1–26 https://doi.org/10.1007/s10898-023-01280-1. [Google Scholar]
- M. Celebi, H. Kingravi and P. Vela, A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst. Appl. 40 (2013) 200–210. [CrossRef] [Google Scholar]
- R. Chandrasekaran and A. Daughety, Location on tree networks: p-centre and n-dispersion problems. Math. Oper. Res. 6 (1981) 50–57. [CrossRef] [MathSciNet] [Google Scholar]
- J. Choi, S. Cabello and H.-K. Ahn, Maximizing dominance in the plane and its applications. Algorithmica 83 (2021) 3491–3513. [CrossRef] [MathSciNet] [Google Scholar]
- A. Denstad, E. Ulsund, M. Christiansen, L. Hvattum and G. Tirado, Multi-objective optimization for a strategic ATM network redesign problem. Ann. Oper. Res. 296 (2019) 7–33. [Google Scholar]
- S. Doddi, M. Marathe, S. Ravi, D. Taylor and P. Widmayer, Approximation algorithms for clustering to minimize the sum of diameters. Nord. J. Comput. 7 (2000) 185–203. [Google Scholar]
- N. Dupin and E.-G. Talbi, Parallel matheuristics for the discrete unit commitment problem with min-stop ramping constraints. Int. Trans. Oper. Res. 27 (2020) 219–244. [CrossRef] [MathSciNet] [Google Scholar]
- N. Dupin and E.-G. Talbi, Matheuristics to optimize refueling and maintenance planning of nuclear power plants. J. Heuristics 27 (2021) 63–105. [CrossRef] [Google Scholar]
- N. Dupin, F. Nielsen and E.-G. Talbi, k-Medoids and p-median clustering are solvable in polynomial time for a 2d Pareto Front. Preprint arXiv:1806.02098 (2018). [Google Scholar]
- N. Dupin, F. Nielsen and E.-G. Talbi, k-Medoids clustering is solvable in polynomial time for a 2d Pareto Front, in World Congress on Global Optimization, Springer (2019) 790–799. [Google Scholar]
- N. Dupin, F. Nielsen and E.-G. Talbi, Clustering a 2d Pareto Front: p-center problems are solvable in polynomial time. in International Conference on Optimization and Learning, Springer (2020) 179–191. [CrossRef] [Google Scholar]
- N. Dupin, F. Nielsen and E.-G. Talbi, Unified polynomial dynamic programming algorithms for p-center variants in a 2D Pareto Front. Mathematics 9 (2021) 453. [CrossRef] [Google Scholar]
- M. Ehrgott and X. Gandibleux, Multiobjective combinatorial optimization – theory, methodology, and applications, in Multiple Criteria Optimization: State of the Art Annotated Bibliographic Surveys, Springer (2003) 369–444. [CrossRef] [Google Scholar]
- J. Erickson, Advanced Dynamic Programming (2020). http://jeffe.cs.illinois.edu/teaching/algorithms/notes/D-faster-dynprog.pdf (accessed 28 sept 2022) [Google Scholar]
- E. Erkut, The discrete p-dispersion problem. Eur. J. Oper. Res. 46 (1990) 48–60. [CrossRef] [Google Scholar]
- E. Erkut and S. Neuman, Comparison of four models for dispersing facilities. INFOR: Inf. Syst. Oper. Res. 29 (1991) 68–86. [Google Scholar]
- J. Falcón-Cardona and C. Coello, Indicator-based multi-objective evolutionary algorithms: a comprehensive survey. ACM Comput. Surv. (CSUR) 53 (2020) 1–35. [Google Scholar]
- G. Frederickson, Parametric search and locating supply centers in trees, in Workshop on Algorithms and Data Structures, Springer (1991) 299–319. [CrossRef] [Google Scholar]
- M. Gibson, G. Kanade, E. Krohn, I.A. Pirwani and K. Varadarajan, On metric clustering to minimize the sum of radii. Algorithmica 57 (2010) 484–498. [CrossRef] [MathSciNet] [Google Scholar]
- R. Graham, Bounds on multiprocessing timing anomalies. SIAM J. Appl. Math. 17 (1969) 416–429. [CrossRef] [MathSciNet] [Google Scholar]
- A. Grønlund, K. Larsen, A. Mathiasen, J. Nielsen, S. Schneider and M. Song, Fast exact k-means, k-medians and Bregman divergence clustering in 1D. Preprint arXiv:1701.07204 (2017). [Google Scholar]
- A. Guerreiro, C. Fonseca and L. Paquete, The hypervolume indicator: problems and algorithms. Preprint arXiv:2005.00515 (2010). [Google Scholar]
- P. Hansen and I Moon, Dispersing facilities on a network, Cahiers du GERAD (1995). [Google Scholar]
- R. Hassin and A. Tamir, Improved complexity bounds for location problems on the real line. Oper. Res. Lett. 10 (1991) 395–402. [CrossRef] [MathSciNet] [Google Scholar]
- J. Huang, Z. Chen and N. Dupin, Comparing local search initialization for k-means and k-medoids clustering in a planar Pareto Front, a computational study, in International Conference on Optimization and Learning, Springer (2021) 14–28. [CrossRef] [Google Scholar]
- M. Kuby, Programming models for facility dispersion: the p-dispersion and maxisum dispersion problems. Geograph. Anal. 19 (1987) 315–329. [Google Scholar]
- T. Kuhn, C. Fonseca, L. Paquete, S. Ruzika, M. Duarte and J. Figueira, Hypervolume subset selection in two dimensions: formulations and algorithms. Evol. Comput. 24 (2016) 411–425. [CrossRef] [PubMed] [Google Scholar]
- T. Lei and R. Church, On the unified dispersion problem: efficient formulations and exact algorithms. Eur. J. Oper. Res. 241 (2015) 622–630. [CrossRef] [Google Scholar]
- X. Lin, Y. Yuan, Q. Zhang and Y. Zhang, Selecting stars: the k most representative skyline operator, in 2007 IEEE 23rd International Conference on Data Engineering, IEEE (2007) 86–95. [Google Scholar]
- M. Magnani, I. Assent and M. Mortensen, Taking the big picture: representative skylines based on significance and diversity. VLDB J. 23 (2014) 795–815. [CrossRef] [Google Scholar]
- M. Mahajan, P. Nimbhorkar and K. Varadarajan, The planar k-means problem is NP-hard. Theor. Comput. Sci. 442 (2012) 13–21. [CrossRef] [Google Scholar]
- N. Megiddo, Linear-time algorithms for linear programming in R3 and related problems. SIAM J. Comput. 12 (1983) 759–776. [CrossRef] [MathSciNet] [Google Scholar]
- N. Megiddo and K. Supowit, On the complexity of some common geometric location problems. SIAM J. Comput. 13 (1984) 182–196. [CrossRef] [MathSciNet] [Google Scholar]
- N. Megiddo and A. Tamir, New results on the complexity of p-centre problems. SIAM J. Comput. 12 (1983) 751–758. [CrossRef] [MathSciNet] [Google Scholar]
- F. Nielsen, Output-sensitive peeling of convex and maximal layers. Inf. Process. Lett. 59 (1996) 255–259. [CrossRef] [Google Scholar]
- T. Peugeot, N. Dupin, M.-J. Sembely and C. Dubecq, MBSE, PLM, MIP and robust optimization for system of systems management, application to SCCOA French air defense program, in Complex Systems Design & Management, Springer (2017) 29–40. [CrossRef] [Google Scholar]
- D. Pisinger, Upper bounds and exact algorithms for p-dispersion problems. Comput. Oper. Res. 332006 (2006) 1380–1398. [CrossRef] [Google Scholar]
- S. Ravi, D. Rosenkrantz and G. Tayi, Heuristic and special case algorithms for dispersion problems. Oper. Res. 42 (1994) 299–310. [CrossRef] [Google Scholar]
- S. Sayn, Measuring the quality of discrete representations of efficient sets in multiple objective mathematical programming. Math. Prog. 87 (2000) 543–560. [CrossRef] [Google Scholar]
- D. Sayah and S. Irnich, A new compact formulation for the discrete p-dispersion problem. Eur. J. Oper. Res. 256 (2017) 62–67. [CrossRef] [Google Scholar]
- E. Schubert and P. Rousseeuw, Faster k-medoids clustering: improving the PAM, CLARA, and CLARANS algorithms. Preprint arXiv:1810.05691 (2018). [Google Scholar]
- O. Schuetze, C. Hernandez, E. Talbi, J. Sun, Y. Naranjani and F. Xiong, Archivers for the representation of the set of approximate solutions for MOPs. J. Heuristics 25 (2019) 71–105. [CrossRef] [Google Scholar]
- D. Shier, A min-max theorem for p-center problems on a tree. Transp. Sci. 11 (1977) 243–252. [CrossRef] [Google Scholar]
- E. Sintorn and U. Assarsson, Fast parallel GPU-sorting using a hybrid algorithm. J. Parallel Distrib. Comput. 68 (2008) 1381–1388. [CrossRef] [Google Scholar]
- E. Talbi, Metaheuristics: From Design to Implementation. Vol. 74, Wiley (2009). [Google Scholar]
- A. Tamir, Obnoxious facility location on graphs. SIAM J. Discrete Math. 4 (1991) 550–567. [CrossRef] [MathSciNet] [Google Scholar]
- A. Tamir, Comments on the paper: “Heuristic and special case algorithms for dispersion problems” by SS Ravi, DJ Rosenkrantz, and GK Tayi. Oper. Res. 46 (1998) 157–158. [CrossRef] [Google Scholar]
- G. Valkanas, A.N. Papadopoulos and D. Gunopulos, Skydiver: a framework for skyline diversification, in Proceedings of the 16th International Conference on Extending Database Technology (2013) 406–417. [Google Scholar]
- D. Wang and Y. Kuo, A study on two geometric location problems. Inf. Process. Lett. 28 (1988) 281–286. [Google Scholar]
- E. Zio and R. Bazzo, A clustering procedure for reducing the number of representative solutions in the Pareto Front of multiobjective optimization problems. Eur. J. Oper. Res. 210 (2011) 624–634. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.