Open Access
Issue
RAIRO-Oper. Res.
Volume 59, Number 2, March-April 2025
Page(s) 803 - 833
DOI https://doi.org/10.1051/ro/2025010
Published online 14 March 2025
  • M.A. Abo-sinna, Pareto optimality for bi-level programming problem with fuzzy parameters. Opsearch 38 (2001) 372–393. [CrossRef] [MathSciNet] [Google Scholar]
  • M.A. Abo-Sinna and I.A. Baky, Interactive balance space approach for solving multi-level multi-objective programming problems. Inf. Sci. 177 (2007) 3397–3410. [CrossRef] [Google Scholar]
  • F. Ahmad, S. Ahmad, A.T. Soliman and M. Abdollahian, Solving multi-level multiobjective fractional programming problem with rough interval parameter in neutrosophic environment. RAIRO-Oper. Res. 55 (2021) 2567–2581. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • N. Aydin and Z. Cetinkale, Analyses on ICU and non-ICU capacity of government hospitals during the COVID-19 outbreak via multi-objective linear programming: an evidence from Istanbul. Comput. Biol. Med. 146 (2022) 105562. [CrossRef] [Google Scholar]
  • E. Babaee Tirkolaee, H. Golpîra, A. Javanmardan and R. Maihami, A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: an interactive possibilistic programming approach for a real case study. Soc.-Econ. Planning Sci. 85 (2023) 101439. [CrossRef] [Google Scholar]
  • I.A. Baky, Fuzzy goal programming algorithm for solving decentralized bi-level multi-objective programming problems. Fuzzy Sets Syst. 160 (2009) 2701–2713. [CrossRef] [Google Scholar]
  • I.A. Baky, Solving multi-level multi-objective linear programming problems through fuzzy goal programming approach. Appl. Math. Modell. 34 (2010) 2377–2387. [CrossRef] [Google Scholar]
  • I.A. Baky, M.H. Eid and M.A. El Sayed, Bi-level multi-objective programming problem with fuzzy demands: a fuzzy goal programming algorithm. Opsearch 51 (2014) 280–296. [CrossRef] [MathSciNet] [Google Scholar]
  • O. Ben-Ayed, Bilevel linear programming. Comput. Oper. Res. 20 (1993) 485–501. [CrossRef] [MathSciNet] [Google Scholar]
  • J. Bracken and J.T. McGill, Mathematical programs with optimization problems in the constraints. Oper. Res. 21 (1973) 37–44. [CrossRef] [Google Scholar]
  • J. Bracken and J.T. McGill, Technical note – a method for solving mathematical programs with nonlinear programs in the constraints. Oper. Res. 22 (1974) 917–1133. [CrossRef] [Google Scholar]
  • B. Colson, P. Marcotte and G. Savard, Bilevel programming: a survey. 4OR 3 (2005) 87–107. [CrossRef] [MathSciNet] [Google Scholar]
  • R. Gabasov and F.M. Kirillova, Linear Programming Methods. House, Minsk (1978). [Google Scholar]
  • R. Gabasov and F.M. Kirillova, New linear programming methods and their application to optimal control. IFAC Proc. Vol. 12 (1979) 17–30. [CrossRef] [Google Scholar]
  • R. Gabasov, F.M. Kirillova and S.V. Prischepova, Optimal Feedback Control. Lecture Notes in Control and Information Sciences. Springer, Berlin, Heidelberg (1995). [Google Scholar]
  • M. Kaci and S. Radjef, The set of all the possible compromises of a multi-level multi-objective linear programming problem. Croatian Oper. Res. Rev. 13 (2022) 13–30. [CrossRef] [MathSciNet] [Google Scholar]
  • M. Kaci and S. Radjef, An adaptive method to solve multilevel multiobjective linear programming problems. Oper. Res. Decisions 33 (2023) 29–44. [Google Scholar]
  • V.V. Kalashnikov, S. Dempe, G.A. Pérez-Valdés, N.I. Kalashnykova and J.F. Camacho-Vallejo, Bilevel programming and applications. Math. Prob. Eng. 2015 (2015) 1–17. [Google Scholar]
  • K. Lachhwani, On solving multi-level multi objective linear programming problems through fuzzy goal programming approach. Opsearch 51 (2014) 624–637. [CrossRef] [MathSciNet] [Google Scholar]
  • K. Lachhwani, Solving the general fully neutrosophic multi-level multiobjective linear programming problems. Opsearch 58 (2021) 1192–1216. [CrossRef] [MathSciNet] [Google Scholar]
  • A. Miniguano-Trujillo, F. Salazar, R. Torres, P. Arias and K. Sotomayor, An integer programming model to assign patients based on mental health impact for tele-psychotherapy intervention during the COVID-19 emergency. Health Care Manag. Sci. 24 (2021) 286–304. [CrossRef] [PubMed] [Google Scholar]
  • D. Mollalign, A. Mushi and B. Guta, Solving multiobjective multilevel programming problems using two-phase intuitionistic fuzzy goal programming method. J. Comput. Sci. 63 (2022) 1877–7503. [Google Scholar]
  • S. Nayak and A. Ojha, On multi-level multi-objective linear fractional programming problem with interval parameters. RAIRO-Oper. Res. 53 (2019) 1601–1616. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • S. Pardeshi, S. Gawade and P. Hemant, Student learning time analysis during COVID-19 using linear programming – simplex method. Soc. Sci. Human. Open 5 (2022) 100266. [Google Scholar]
  • C.O. Pieume, P. Marcotte, L.P. Fotso and P. Siarry, Solving bilevel linear multiobjective programming problems. Am. J. Oper. Res. 01 (2011) 214–219. [Google Scholar]
  • M. Sakawa and I. Nishizaki, Interactive fuzzy programming for multi-level programming problems: a review. Int. J. Multicriteria Decis. Making 2 (2012) 241–266. [CrossRef] [Google Scholar]
  • S. Sinha, Fuzzy programming approach to multi-level programming problems. Fuzzy Sets Syst. 136 (2003) 189–202. [CrossRef] [Google Scholar]
  • S.B. Sinha and S. Sinha, A linear programming approach for linear multi-level programming problems. J. Oper. Res. Soc. 55 (2004) 312–316. [CrossRef] [Google Scholar]
  • M. Tavana, K. Govindan, A.K. Nasr, M.S. Heidary and H. Mina, A mathematical programming approach for equitable COVID-19 vaccine distribution in developing countries. Ann. Oper. Res. (2021). [Google Scholar]
  • L. Vicente and P. Calamai, Bilevel and multilevel programming: a bibliography review. J. Global Optim. 5 (1994) 291–306. [CrossRef] [MathSciNet] [Google Scholar]

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