Open Access
RAIRO-Oper. Res.
Volume 55, Number 4, July-August 2021
Page(s) 2567 - 2581
Published online 30 August 2021
  • M.A. Abo-Sinna and I.A. Baky, Fuzzy goal programming procedure to bilevel multiobjective linear fractional programming problems. Int. J. Math. Math. Sci. 2010 (2010). [Google Scholar]
  • A.Y. Adhami and F. Ahmad, Interactive pythagorean-hesitant fuzzy computational algorithm for multiobjective transportation problem under uncertainty. Int. J. Manag. Sci. Eng. Manag. 15 (2020) 1–10. [Google Scholar]
  • F. Ahmad, Interactive neutrosophic optimization technique for multiobjective programming problems: an application to pharmaceutical supply chain management. Ann. Oper. Res. (2021a) 1–35. [Google Scholar]
  • F. Ahmad, Robust neutrosophic programming approach for solving intuitionistic fuzzy multiobjective optimization problems. Complex Intell. Syst. (2021b) 1–20. [Google Scholar]
  • F. Ahmad and A.Y. Adhami, Neutrosophic programming approach to multiobjective nonlinear transportation problem with fuzzy parameters. Int. J. Manag. Sci. Eng. Manag. 14 (2019a) 218–229. [Google Scholar]
  • F. Ahmad and A.Y. Adhami, Total cost measures with probabilistic cost function under varying supply and demand in transportation problem. Opsearch 56 (2019b) 583–602. [Google Scholar]
  • F. Ahmad, A.Y. Adhami and F. Smarandache, Single Valued Neutrosophic Hesitant Fuzzy Computational Algorithm for Multiobjective Nonlinear Optimization Problem. Neutrosophic Sets Syst. 22 (2018) 76–86. [Google Scholar]
  • F. Ahmad, A.Y. Adhami and F. Smarandache, Neutrosophic optimization model and computational algorithm for optimal shale gas water management under uncertainty. Symmetry 11 (2019). [Google Scholar]
  • F. Ahmad, A.Y. Adhami and F. Smarandache, Modified neutrosophic fuzzy optimization model for optimal closed-loop supply chain management under uncertainty. In Optimization theory based on neutrosophic and plithogenic sets, Elsevier (2020) 343–403. [Google Scholar]
  • F. Ahmad, S. Ahmad and M. Zaindin, A sustainable production and waste management policies for covid-19 medical equipment under uncertainty: A case study analysis. Comput. Ind. Eng. 157 (2021) 107381. [PubMed] [Google Scholar]
  • S. Ahmad, F. Ahmad and M. Sharaf, Supplier selection problem with type-2 fuzzy parameters: A neutrosophic optimization approach. Int. J. Fuzzy Syst. 23 (2021) 755–775. [Google Scholar]
  • A.A.H. Ahmadini and F. Ahmad, A novel intuitionistic fuzzy preference relations for multiobjective goal programming problems. Int. J. Fuzzy Syst. 40 (2021a) 4761–4777. [Google Scholar]
  • A.A.H. Ahmadini and F. Ahmad, Solving intuitionistic fuzzy multiobjective linear programming problem under neutrosophic environment. AIMS Math. 6 (2021b) 4556–4580. [Google Scholar]
  • M. Arabani, Application of rough set theory as a new approach to simplify dams location. Sci. Iran. 13 (2006). [Google Scholar]
  • E. Dolan, The neos server 4.0 administrative guide. Tech. Technical report, Memorandum ANL/MCS-TM-250, Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, USA (2001) [Google Scholar]
  • A. Hamzehee, M.A. Yaghoobi and M. Mashinchi, Linear programming with rough interval coefficients. J. Intell. Fuzzy Syst. 26 (2014) 1179–1189. [Google Scholar]
  • M. Imran, M.H. Agha, W. Ahmed, B. Sarkar and M.B. Ramzan, Simultaneous customers and supplier’s prioritization: An ahp-based fuzzy inference decision support system (ahp-fidss). Int. J. Fuzzy Syst. 22 (2020) 2625–2651. [Google Scholar]
  • K. Lachhwani and M.P. Poonia, Mathematical solution of multilevel fractional programming problem with fuzzy goal programming approach. J. Ind. Eng. Int. 8 (2012) 16. [Google Scholar]
  • A.S. Mahapatra, B. Sarkar, M.S. Mahapatra, H.N. Soni and S.K. Mazumder, Development of a fuzzy economic order quantity model of deteriorating items with promotional effort and learning in fuzziness with a finite time horizon. Inventions 4 (2019) 36. [Google Scholar]
  • A.I. Malik and B. Sarkar, Coordinating supply-chain management under stochastic fuzzy environment and lead-time reduction. Mathematics 7 (2019) 480. [Google Scholar]
  • S. Mishra, Weighting method for bi-level linear fractional programming problems. Eur. J. Oper. Res. 183 (2007) 296–302. [Google Scholar]
  • S. Nayak and A. Ojha, On multi-level multi-objective linear fractional programming problem with interval parameters. RAIRO-Operations Research 53 (2019) 1601–1616. [Google Scholar]
  • M. Osman, O. Emam and M. El Sayed, Solving multi-level multi-objective fractional programming problems with fuzzy demands via fgp approach. Int. J. Appl. Comput. Math. 4 (2018) 41. [Google Scholar]
  • M.S. Osman, K.R. Raslan, O.E. Emam and F.A. Farahat, Solving multi-level multi-objective fractional programming problem with rough intervals in the objective functions. J. adv. math. Comput. Sci. (2017) 1–17. [Google Scholar]
  • Z. Pawlak, Rough sets. Int. J. Comput. Inf. Syst. 11 (1982) 341–356. [Google Scholar]
  • Z. Pawlak and A. Skowron, Rudiments of rough sets. Inf. Sci. 177 (2007) 3–27. [Google Scholar]
  • S. Pramanik and T.K. Roy, Fuzzy goal programming approach to multilevel programming problems. Eur. J. Oper. Res. 176 (2007) 1151–1166. [Google Scholar]
  • H. Rashmanlou, M. Pal, S. Raut, F. Mofidnakhaei and B. Sarkar, Novel concepts in intuitionistic fuzzy graphs with application. J. Intell. Fuzzy Syst. 37 (2019) 3743–3749. [Google Scholar]
  • R.M. Rizk-Allah, A.E. Hassanien and M. Elhoseny, A multi-objective transportation model under neutrosophic environment. Comput. Electr. Eng. 69 (2018) 705–719. [Google Scholar]
  • N. Server, State-of-the-Art Solvers for Numerical Optimization (2016). [Google Scholar]
  • F. Smarandache, A unifying field in logics: Neutrosophic logic. In Philosophy, American Research Press (1999) 1–141. [Google Scholar]

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