Open Access
Issue
RAIRO-Oper. Res.
Volume 59, Number 5, September-October 2025
Page(s) 3375 - 3401
DOI https://doi.org/10.1051/ro/2025129
Published online 04 November 2025
  • J.D. Schaffer, Some experiments in machine learning using vector evaluated genetic algorithms. Technical report, Vanderbilt Univ., Nashville, TN (USA) (1985). [Google Scholar]
  • C. Fonseca and P. Fleming, Multiobjective genetic algorithms in IEE Colloquium on Genetic Algorithms for Control Systems Engineering. Digest (1993). [Google Scholar]
  • P. Hajela, E. Lee and C.-Y. Lin, Genetic algorithms in structural topology optimization, in Topology Design Of Structures. Springer Netherlands, Dordrecht (1993) 117–133. [Google Scholar]
  • N. Srinivas and K. Deb, Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2 (1994) 221–248. [Google Scholar]
  • K. Deb, S. Agrawal, A. Pratap and T. Meyarivan, A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II, in International Conference on Parallel Problem Solving from Nature (2000) 849–858. [Google Scholar]
  • A. Barman, A.K. Chakraborty, A. Goswami, P. Banerjee and P.K. De, Pricing and inventory decision in a two-layer supply chain under the weibull distribution product deterioration: an application of NSGA-II. RAIRO-Oper. Res. 57 (2023) 2279–2300. [Google Scholar]
  • S.S. Moghadam, A. Aghsami and M. Rabbani, A hybrid NSGA-II algorithm for the closed-loop supply chain network design in e-commerce. RAIRO-Oper. Res. 55 (2021) 1643–1674. [Google Scholar]
  • S. Majumder, M.B. Kar, S. Kar and T. Pal, Uncertain programming models for multi-objective shortest path problem with uncertain parameters. Soft Comput. 24 (2020) 8975–8996. [Google Scholar]
  • K. Deb and H. Jain, An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans. Evol. Comput. 18 (2013) 577–601. [Google Scholar]
  • R.H. Bhesdadiya, I.N. Trivedi, P. Jangir, N. Jangir and A. Kumar, An NSGA-III algorithm for solving multi-objective economic/environmental dispatch problem. Cogent Eng. 3 (2016) 1269383. [Google Scholar]
  • K. Ji, W. Chen, X. Wu, H. Pang, J. Hu, S. Liu, F. Cheng and G. Tang, High frequency stability constraints based mmc controller design applying NSGA-III algorithm. CSEE J. Power Energy Syst. 9 (2021) 623–633. [Google Scholar]
  • S. Agnihotri and J.M. Dhodiya, Non-dominated sorting genetic algorithm III with stochastic matrix-based population to solve multi-objective solid transportation problem. Soft Comput. 27 (2023) 5641–5662. [Google Scholar]
  • A. Todkar and J.M. Dhodiya, Aspiration level-based non-dominated sorting genetic algorithm-II and III for multi-objective shortest path problem in a trapezoidal environment. Int. J. Math. Oper. Res. 27 (2024) 223–253. [Google Scholar]
  • A.S. Todkar and J.M. Dhodiya, Aspiration level-based non-dominated sorting genetic algorithm II & III to solve fuzzy multi-objective shortest path problem. Yugoslav J. Oper. Res. 35 (2025) 135–162. [Google Scholar]
  • A. Todkar and J. Dhodiya, Uncertain multi-objective multi-route shortest path problem by robust enhanced non-dominated sorting genetic algorithms: application to emergency medical services. J. Ind. Manage. Optim. 20 (2024) 3453–3485. [Google Scholar]
  • E.W. Dijkstra, A note on two problems in connexion with graphs. Numer. Math. 1 (1959) 269–271. [Google Scholar]
  • R. Bellman, On a routing problem. Q. Appl. Math. 16 (1958) 87–90. [Google Scholar]
  • R.W. Floyd, Algorithm 97: shortest path. Commun. ACM 5 (1962) 345. [Google Scholar]
  • S.E. Dreyfus, An appraisal of some shortest-path algorithms. Oper. Res. 17 (1969) 395–412. [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]
  • 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 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]
  • B.K. Giri and S.K. Roy, Neutrosophic multi-objective green four-dimensional fixed-charge transportation problem. Int. J. Mach. Learning Cybern. 13 (2022) 3089–3112. [Google Scholar]
  • V. Kakran and J. Dhodiya, Four-dimensional uncertain multi-objective multi-item transportation problem. Oper. Res. Decis. 32 (2022). [Google Scholar]
  • P. Hansen, Bicriterion path problems, in Multiple Criteria Decision Making Theory and Application: Proceedings of the Third Conference Hagen/Königswinter, West Germany, August 20–24, 1979 (1980) 109–127. [Google Scholar]
  • C.H. Papadimitriou and M. Yannakakis, On the approximability of trade-offs and optimal access of web sources, in Proceedings 41st Annual Symposium on Foundations of Computer Science. IEEE (2000) 86–92. [Google Scholar]
  • X. Gandibleux, F. Beugnies and S. Randriamasy, Martins’ algorithm revisited for multi-objective shortest path problems with a maxmin cost function. 4OR 4 (2006) 47–59. [Google Scholar]
  • A. Sedeno-Noda and A. Raith, A Dijkstra-like method computing all extreme supported non-dominated solutions of the biobjective shortest path problem. Comput. Oper. Res. 57 (2015) 83–94. [Google Scholar]
  • D.J. Dubois, Fuzzy Sets and Systems: Theory and Applications. Vol. 144. Academic Press (1980). [Google Scholar]
  • S. Mukherjee, Fuzzy programming technique for solving the shortest path problem on networks under triangular and trapezoidal fuzzy environment. Int. J. Math. Oper. Res. 7 (2015) 576–594. [Google Scholar]
  • A. Ebrahimnejad, Z. Karimnejad and H. Alrezaamiri, Particle swarm optimisation algorithm for solving shortest path problems with mixed fuzzy arc weights. Int. J. Appl. Decis. Sci. 8 (2015) 203–222. [Google Scholar]
  • A. Ebrahimnejad, M. Tavana and H. Alrezaamiri, A novel artificial bee colony algorithm for shortest path problems with fuzzy arc weights. Measurement 93 (2016) 48–56. [Google Scholar]
  • L. Lin, C. Wu and L. Ma, A genetic algorithm for the fuzzy shortest path problem in a fuzzy network. Complex Intell. Syst. 7 (2021) 225–234. [Google Scholar]
  • D. Di Caprio, A. Ebrahimnejad, H. Alrezaamiri and F.J. Santos-Arteaga, A novel ant colony algorithm for solving shortest path problems with fuzzy arc weights. Alexandria Eng. J. 61 (2022) 3403–3415. [Google Scholar]
  • G.V. Rani and B. Reddy, Multi-objective fuzzy shortest path selection for green routing and scheduling problems. Int. J. Adv. Res. Comput. Sci. 8 (2017) 47–475. [Google Scholar]
  • M. Bagheri, A. Ebrahimnejad, S. Razavyan, F.H. Lotfi and N. Malekmohammadi, Solving fuzzy multi-objective shortest path problem based on data envelopment analysis approach. Complex Intell. Syst. 7 (2021) 725–740. [Google Scholar]
  • A.S. Todkar and J.M. Dhodiya, Enhanced non-dominated sorting genetic algorithms for uncertain multi-objective shortest path problem: application to fire prevention services. Int. J. Uncertainty Fuzziness Knowl.-Based Syst. 32 (2024) 1215–1244. [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) 2923–2952. [Google Scholar]
  • A. Sedeno-Noda and M. Colebrook, A biobjective Dijkstra algorithm. Eur. J. Oper. Res. 276 (2019) 106–118. [CrossRef] [Google Scholar]
  • P.M. Casas, A. Sedeño-Noda and R. Borndörfer, An improved multiobjective shortest path algorithm. Comput. Oper. Res. 135 (2021) 105424. [Google Scholar]
  • A. Ebrahimnejad, M. Enayattabr, H. Motameni and H. Garg, Modified artificial bee colony algorithm for solving mixed interval-valued fuzzy shortest path problem. Complex Intell. Syst. 7 (2021) 1527–1545. [Google Scholar]
  • R.-C. Wang and T.-F. Liang, Applying possibilistic linear programming to aggregate production planning. Int. J. Prod. Econ. 98 (2005) 328–341. [Google Scholar]
  • P. Gupta and M.K. Mehlawat, A new possibilistic programming approach for solving fuzzy multiobjective assignment problem. IEEE Trans. Fuzzy Syst. 22 (2013) 16–34. [Google Scholar]
  • L.A. Zadeh, Fuzzy sets, in Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A Zadeh. World Scientific (1996) 394–432. [Google Scholar]
  • A.R. Tailor and J.M. Dhodiya, Genetic algorithm based hybrid approach to solve optimistic, most-likely and pessimistic scenarios of fuzzy multi-objective assignment problem using exponential membership function. J. Adv. Math. Comput. Sci. 17 (2016) 1–19. [Google Scholar]
  • R.V. Rao, Jaya: An Advanced Optimization Algorithm and its Engineering Applications. Springer (2019). [Google Scholar]
  • S.K. Maiti and S.K. Roy, Analysing interval and multi-choice bi-level programming for Stackelberg game using intuitionistic fuzzy programming. Int. J. Math. Oper. Res. 16 (2020) 354–375. [CrossRef] [MathSciNet] [Google Scholar]
  • S.K. Roy and S.K. Maiti, Reduction methods of type-2 fuzzy variables and their applications to Stackelberg game. Appl. Intell. 50 (2020) 1398–1415. [CrossRef] [Google Scholar]
  • S.K. Maiti, S.K. Roy and G.W. Weber, Gaussian type-2 fuzzy cooperative game based on reduction method: an application to multi-drug resistance problem. J. Dyn. Games 12 (2025) 215–242. [Google Scholar]
  • S.K. Das, F.Y. Vincent, S.K. Roy and G.W. Weber, Location–allocation problem for green efficient two-stage vehicle-based logistics system: a type-2 neutrosophic multi-objective modeling approach. Expert Syst. App. 238 (2024) 122174. [Google Scholar]

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