Free Access
RAIRO-Oper. Res.
Volume 55, 2021
Regular articles published in advance of the transition of the journal to Subscribe to Open (S2O). Free supplement sponsored by the Fonds National pour la Science Ouverte
Page(s) S1051 - S1067
Published online 02 March 2021
  • F.J. André and C. Romero, Computing compromise solutions: on the connections between compromise programming and composite programming. Appl. Math. Comput. 195 (2008) 1–10. [Google Scholar]
  • R.J. Barro, Human Capital and Growth. Am. Econ. Rev. 91 (2001) 12–17. [Google Scholar]
  • J. Calero and O. Escardíbul, El rendimiento del alumnado de origen inmigrante en PISA 2012, edited by INEE. In: Vol. II of Análisis secundario PISA 2012: Programa para la evaluación internacional de los alumnos. Informe español. Ministerio de Educación, Cultura y Deporte, Instituto Nacional de Evaluación Educativa, Madrid (2013) 4–31. [Google Scholar]
  • J.F. Chizmar and T.A. Zak, Source modeling multiple outputs in educational production functions. Am. Econ. Rev. 73 (1983) 18–22. [Google Scholar]
  • K. Deb and K. Miettinen, Nadir point estimation using evolutionary approaches: better accuracy and computational speed through focused search, edited by M. Ehrgott, B. Naujoks, T.J. Stewart and J. Wallenius. In: Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems. Springer-Verlag, Berlin (2010) 339–354. [Google Scholar]
  • K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, A fast and elistic multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6 (2002) 182–197. [Google Scholar]
  • K. Deb, K. Miettinen and S. Chaudhuri, Towards an estimation of nadir objective vector using a hybrid of evolutionary and local search approaches. IEEE Trans. Evol. Comput. 14 (2010) 821–841. [Google Scholar]
  • J.J. Durillo and A.J. Nebro, jMetal: A java framework of multi-objective optimization. Adv. Eng. Softw. 42 (2011) 760–771. [Google Scholar]
  • M. Ehrgott and D. Tenfelde-Podehl, Computation of ideal and nadir values and implications for their use in MCDM methods. Eur. J. Oper. Res. 151 (2003) 119–139. [Google Scholar]
  • S. Gibbons and O. Silva, School quality, child wellbeing and parents’ satisfaction. Econ. Edu. Rev. 30 (2011) 312–331. [Google Scholar]
  • E. Hanushek and L. Woessmann, The economics of international differences in educational achievement, edited by E. Hanushek, S. Machin and L. Woessmann. In: Vol. 3 of Handbook of the Economics of Education. Elsevier, New York (2011) 89–200. [Google Scholar]
  • I. Kaliszewski, Quantitative Pareto Analysis by Cone Separation Technique. Kluwer Academic Publishers, Dordrecht (1994). [Google Scholar]
  • M. Luque, O.D. Marcenaro and L.A. Lopez-Agudo, On the potential balance among compulsory education outcomes through econometric and multiobjetive programming analysis. Eur. J. Oper. Res. 241 (2015) 527–540. [Google Scholar]
  • M. Luque, A.B. Ruiz, R. Saborido and O.D. Marcenaro, On the use of the Lp distance in reference point-based approaches for multiobjective optimization. Ann. Oper. Res. 235 (2015) 559–579. [Google Scholar]
  • O.D. Marcenaro, M. Luque and L.A. Lopez-Agudo, Balancing teachers’ math satisfaction and other indicators of the education system’s performance. Soc. Indic. Res. 129 (2016) 1319–1348. [Google Scholar]
  • K. Miettinen, Nonlinear Multiobjective Optimization. Kluwer Academic Publishers, Boston (1999). [Google Scholar]
  • G.K. Natvig, G. Albrektsen and U. Qvarnstrom, Associations between psychosocial factors and happiness among school adolescents. Int. J. Nursing Pract. 9 (2003) 166–175. [Google Scholar]
  • OECD, PISA 2015 Results in Focus, PISA, OECD Publishing, Paris. (2018). [Google Scholar]
  • Y. Qi, X. Ma, F. Liu, L. Jiao, J. Sun and J. Wu, MOEA/d with adaptive weight adjustment. Evol. Comput. 22 (2014) 231–264. [PubMed] [Google Scholar]
  • R.W. Rumberger and G.J. Palardy, Test scores, dropout rates, and transfer rates as alternative indicators of high school performance. Am. Edu. Res. J. 42 (2005) 3–42. [Google Scholar]
  • R. Saborido, A.B. Ruiz and M. Luque, Global WASF-GA: An evolutionary algorithm in multiobjective optimization to approximate the whole pareto optimal front. Evol. Comput. 25 (2017) 309–349. [PubMed] [Google Scholar]
  • S. Suldo, E. Shaffer and K. Riley, A social-cognitive-behavioral model of academic predictors of adolescents’ life satisfaction. School Psychol. Q. 23 (2008) 56–69. [Google Scholar]
  • S.M. Suldo, A. Thalji-Raitano, M. Hasemeyer, C. Gelley and B. Hoy, Understanding middle school students life satisfaction: Does school climate matter? Appl. Res. Q. Life 8 (2013) 169–182. [Google Scholar]
  • C. Uline and M. Tschannen-Moran, The walls speak: the interplay of quality facilities, school climate and student achievement. J. Edu. Admin. 46 (2008) 55–73. [Google Scholar]
  • R. Wang, Q. Zhang and T. Zhang, Descomposition – Based algorithms using pareto adaptive scalarazing methods. IEEE Trans. Evol. Comput. 20 (2016) 821–837. [Google Scholar]
  • A.P. Wierzbicki, The use of reference objectives in multiobjective optimization, edited by G. Fandel and T. Gal. In: Multiple Criteria Decision Making. Theory and Applications. Springer-Verlag, Berlin (1980) 468–486. [Google Scholar]
  • P.L. Yu, A class of solutions for group decision problems. Manage. Sci. 19 (1973) 936–946. [Google Scholar]
  • M. Zeleny, A concept of compromise solutions and the method of the displaced ideal. Comput. Oper. Res. 1 (1974) 479–496. [Google Scholar]
  • Q. Zhang and H. Li, MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 1 (2007) 712–731. [Google Scholar]

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