Open Access
Issue
RAIRO-Oper. Res.
Volume 59, Number 3, May-June 2025
Page(s) 1617 - 1644
DOI https://doi.org/10.1051/ro/2025058
Published online 20 June 2025
  • L.A. Zadeh, Fuzzy sets. Inf. Control 8 (1965) 338–353. [Google Scholar]
  • K.T. Atanassov, Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20 (1986) 87–96. [Google Scholar]
  • F. Smarandache, Neutrosophic set, a generalisation of the intuitionistic fuzzy sets. Int. J. Pure Appl. Math. 24 (2005) 287–297. [MathSciNet] [Google Scholar]
  • N.G. Arias, E.P. Calderón, L.R. Abarca and S.D.L. Rivas, Neutrosophic games applied to political situations. Neutrosophic Sets Syste. 37 (2020) 1. [Google Scholar]
  • İ. Deli, Linear optimization method on single valued neutrosophic set and its sensitivity analysis. TWMS J. Appl. Eng. Math. 10 (2020) 128–137. [Google Scholar]
  • S. Broumi, A. Bakali, M. Talea, F. Smarandache and L. Vladareanu, Shortest path problem under triangular fuzzy neutrosophic information, in December 2016, 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA). IEEE (2016) 169–174. [Google Scholar]
  • L. Abdullah, N. Zulkifli, N.A. Awang and H. Hashim, A combined decision-making analysis under single-valued neutrosophic set for selecting favorable knowledge management strategy. J. Uncertain Syst. 17 (2024) 2440006. [CrossRef] [Google Scholar]
  • S. Dhouib, A novel heuristic for the transportation problem: dhouib-matrix-TP1. Int. J. Recent Eng. Sci. 8 (2021) 1–5. [CrossRef] [Google Scholar]
  • S. Dhouib, Solving the single-valued trapezoidal neutrosophic transportation problems through the novel dhouibmatrix-TP1 heuristic. Math. Prob. Eng. 2021 (2021) 1–11. [CrossRef] [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]
  • S. Dhouib, Optimization of travelling salesman problem on single valued triangular neutrosophic number using dhouib-matrix-TSP1 Heuristic. Int. J. Eng. 34 (2021) 2642–2647. [Google Scholar]
  • S. Ghosh and S.K. Roy, Closed-loop multi-objective waste management through vehicle routing problem in neutrosophic hesitant fuzzy environment. Appl. Soft Comput. 148 (2023) 110854. [CrossRef] [Google Scholar]
  • S. Ghosh and S.K. Roy, Fuzzy-rough multi-objective product blending fixed-charge transportation problem with truck load constraints through transfer station. RAIRO-Oper. Res. 55 (2021) S2923–S2952. [CrossRef] [EDP Sciences] [Google Scholar]
  • B.K. Giri and S.K. Roy, Neutrosophic multi-objective green four-dimensional fixed-charge transportation problem. Int. J. Mach. Learn. Cybern. 13 (2022) 3089–3112. [CrossRef] [Google Scholar]
  • S. Ghosh, S.K. Roy and J.L. Verdegay, Fixed-charge solid transportation problem with budget constraints based on carbon emission in neutrosophic environment. Soft Comput. 26 (2022) 11611–11625. [CrossRef] [Google Scholar]
  • S. Ghosh, S.K. Roy and A. Fügenschuh, The multi-objective solid transportation problem with preservation technology using Pythagorean fuzzy sets. Int. J. Fuzzy Syst. 24 (2022) 2687–2704. [CrossRef] [Google Scholar]
  • S. Ghosh, S.K. Roy, A. Ebrahimnejad and J.L. Verdegay, Multi-objective fully intuitionistic fuzzy fixed-charge solid transportation problem. Complex Intell. Syst. 7 (2021) 1009–1023. [CrossRef] [Google Scholar]
  • Z. Ameri, S.S. Sana and R. Sheikh, Self-assessment of parallel network systems with intuitionistic fuzzy data: a case study. Soft Comput. 23 (2019) 12821–12832. [CrossRef] [Google Scholar]
  • R. Ghasemiyeh, R. Moghdani and S.S. Sana, A hybrid artificial neural network with metaheuristic algorithms for predicting stock price. Cybern. Syst. 48 (2017) 365–392. [CrossRef] [Google Scholar]
  • A.K. Birjandi, S. Dehmolaee, R. Sheikh and S.S. Sana, Analysis and classification of companies on Tehran stock exchange with incomplete information. RAIRO-Oper. Res. 55 (2021) S2709–S2726. [CrossRef] [EDP Sciences] [Google Scholar]
  • S.M.H. Hosseini, S.S. Sana and M. Rostami, Assembly flow shop scheduling problem considering machine eligibility restrictions and auxiliary resource constraints. Int. J. Syst. Sci. Oper. Logistics 9 (2022) 512–528. [Google Scholar]
  • A. Hasani, S.M.H. Hosseini and S.S. Sana, Scheduling in a flexible flow shop with unrelated parallel machines and machine-dependent process stages: trade-off between makespan and production costs. Sustain. Anal. Model. 2 (2022) 100010. [Google Scholar]
  • F. Behroozi, S.M.H. Hosseini and S.S. Sana, Teaching–learning-based genetic algorithm (TLBGA): an improved solution method for continuous optimization problems. Int. J. Syst. Assur. Eng. Manage. 12 (2021) 1362–1384. [CrossRef] [Google Scholar]
  • T. Bera and N.K. Mahapatra, Assignment problem with neutrosophic costs and its solution methodology. Asia Math. 3 (2018) 21–32. [Google Scholar]
  • L.S. Raj, S.J. Vinnarasi and A.T. Jeyaseeli, An approach to solve pentagonal fuzzy assignment problem using modified best candidate method. NVEO-Nat. Volatiles Essent. Oils 8 (2021) 9795–9808. [Google Scholar]
  • H.A.E.W. Khalifa and P. Kumar, A novel method for neutrosophic assignment problem by using interval-valued trapezoidal neutrosophic number. Neutrosophic Sets Syst. 36 (2020) 24–36. [Google Scholar]
  • A. Saha, I. Deli and S. Broumi, Hesitant triangular neutrosophic numbers and their applications to MADM. Neutrosophic Sets Syst. 35 (2020) 269–298. [Google Scholar]
  • S.A. Seyfi-Shishavan, F. Kutlu Gündoğdu, Y. Donyatalab, E. Farrokhizadeh and C. Kahraman, A novel spherical fuzzy bi-objective linear assignment method and its application to insurance options selection. Int. J. Inf. Technol. Decis. Making 20 (2021) 521–551. [CrossRef] [Google Scholar]
  • E. Jain, K. Dahiya and V. Verma, Branching technique for a bi-objective two-stage assignment problem. ANZIAM J. 64 (2022) 183–204. [MathSciNet] [Google Scholar]
  • G.S. Da¸s and F. Gzara, Column generation based solution for bi-objective gate assignment problems. Math. Methods Oper. Res. 100 (2024) 123–151. [CrossRef] [MathSciNet] [Google Scholar]
  • V.E. Sobana and D. Anuradha, Solving bi-objective interval assignment problem. Int. J. Emerg. Technol. 11 (2020) 219–222. [Google Scholar]
  • S. Sandhiya and D. Anuradha, Solving bi-objective assignment problem under neutrosophic environment. Reliab. Theory App. 17 (2022) 164–175. [Google Scholar]
  • A. Hamou and E.A. Mohamed, C. An exact method for the multi-objective assignment problem. Les Ann. RECITS 5 (2018) 31–36. [Google Scholar]
  • S. Tilva and J. Dhodiya, Multi-objective assignment problem solved by hybrid Jaya algorithm. J. Interdiscipl. Math. 25 (2022) 109–121. [CrossRef] [Google Scholar]
  • M. Yeola, A new approach using fuzzy programming to solve the multi-objective assignment problem. Adv. Fuzzy Sets Syst. 28 (2023) 65–76. [CrossRef] [Google Scholar]
  • N.T. Son, J. Jaafar, I.A. Aziz and B.N. Anh, A compromise programming for multi-objective task assignment problem. Computers 10 (2021) 15. [CrossRef] [Google Scholar]
  • F. Fouad, A.E.H. Kassam and S.S. Al-Zubaidi, A new heuristic method for solving unbalanced multi-objective assignment problem. Eng. Res. Express 6 (2024) 045429. [CrossRef] [Google Scholar]
  • S. Kar, A. Samanta and K. Basu, Solution of fuzzy multi objective generalised assignment problem. Int. J. Math. Oper. Res. 15 (2019) 33–54. [CrossRef] [MathSciNet] [Google Scholar]
  • S. Pramanik, Single-valued neutrosophic set: an overview. Transdisciplinarity (2022) 563–608. [CrossRef] [Google Scholar]
  • A.Y. Adhami, A. Melethil and F. Ahmad, Neutrosophic programming approach to multilevel decision-making model for supplier selection problem in a fuzzy situation. RAIRO-Oper. Res. 57 (2023) 1307–1328. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • M. Kamal, P. Kaur, I. Ali and A. Ahmed, A neutrosophic compromise programming technique to solve multi-objective assignment problem with T2TpFNs. Neutrosophic Sets Syst. 51 (2022) 12. [Google Scholar]
  • H. Khalifa, An approach to the optimization of multi-objective assignment problems with neutrosophic numbers. Int. J. Ind. Eng. Prod. Res. 31 (2020) 287–294. [Google Scholar]

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